commit ed07c8a3d19b521a9b0c7b6b14aead4a4de36921 Author: ML Engineer Date: Sat Apr 25 17:18:43 2026 +0200 Initial commit: existing turf_saas codebase Co-Authored-By: Paperclip diff --git a/.gitignore b/.gitignore new file mode 100755 index 0000000..045d335 --- /dev/null +++ b/.gitignore @@ -0,0 +1,80 @@ +# Base de données +turf.db +turf.db.old +*.db +*.db.bak* + +# Backups auto +*.backup_* +*.bak +*.bak2 +*.broken +*.tmp +*.original +*.fixed + +# Données JSON volumineuses +v3_*.json +v4_*.json +v5_*.json +pmu_*.json +scoring_*.json +perf_*.json +agent_chat.json + +# Logs et rapports Telegram +*.log +telegram_*.txt +api_log.txt + +# Modèles ML entraînés +*.pkl +feature_importance_*.csv + +# Archives et binaires +awscliv2.zip +*.zip +*.gz + +# Python +__pycache__/ +*.pyc +*.pyo +venv/ +.env + +# Exports +exports/ + +# Fichiers dashboard temporaires / doublons +dashboard.html.bak* +dashboard_backup*.html +dashboard_new.html +dashboard_secured.py +dashboard_system.html +dashboard_api.py.broken +dashboard_api_fixed.py +dashboard_api_original.py +portal_server.py.original +portail.html.broken* +portail_new.html +portal.html + +# Fichiers vitesse doublons +vitesse_api_complete.py +vitesse_api_fixed.py + +# Multi-scraper anciennes versions +multi_scraper_v2.py +multi_scraper_v3.py +multi_scraper_v4.py + +# Fichiers temporaires de fix +tmp_fix.py +fix_*.py +repair_*.py +patch_*.py + +# Données scraping brutes +v3_*.json +v4_*.json diff --git a/ARCHITECTURE_SERVICES.html b/ARCHITECTURE_SERVICES.html new file mode 100755 index 0000000..a1cf279 --- /dev/null +++ b/ARCHITECTURE_SERVICES.html @@ -0,0 +1,282 @@ + + + + + Architecture Services H3R7 + + +

+ + +🏠Accueil + +

🏗️ Architecture des Services H3R7

+ +

📋 Vue d'Ensemble des Services

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Service	URL	Port	Description
Portail Central	/	8768	Point d'entrée unique
Turf Dashboard	/turf/	8765	Prédictions hippiques
Boîte à Idées	/turf/idees/	8765	Gestion idées business
Admin Menu	http://178.18.250.53:8766/	8766	Gestion menu restaurant
Réservation Client	http://178.18.250.53:8767/	8767	Réservation tables
Manager Tables	http://178.18.250.53:9090/manager.html	9090	Gestion tables (admin)
Templates Site	http://178.18.250.53:9090/	9090	Templates professionnels

+ +

🏇 TURF - Prédictions Hippiques

+ +

Dashboard (Port 8765)

URL: /turf/

Fonctionnalités:

📊 Vue d'ensemble des courses du jour
🏇 Liste des favoris par course
📈 Cotes en temps réel (PMU, ZEturf, Genybet)
🔄 Scraping automatique multi-sources
💾 Sauvegarde en base SQLite

+ +

API Turf

Endpoints:

GET /api/today → Courses du jour
GET /api → Toutes les données

+ +

💡 BOÎTE À IDÉES

+ +

Interface (Port 8765)

URL: /turf/idees/

Fonctionnalités:

➕ Ajouter une idée
✏️ Modifier une idée
🗑️ Supprimer une idée
🔍 Filtrer par catégorie
📊 Indicateur de potentiel

+ +

API Ideas

GET /api/ideas → Liste toutes les idées
GET /api/ideas/<id> → Récupère une idée
POST /api/ideas → Crée une idée
PUT /api/ideas/<id> → Met à jour
DELETE /api/ideas/<id> → Supprime

Auth: admin:turf2026

+ +

🍽️ TEMPLATES RESTAURANT

+ + + + + + + + + + + + + + + + + + + + + + +

Template	URL
Page d'accueil	http://178.18.250.53:9090/
Restaurant JSON	http://178.18.250.53:9090/template_restaurant_json.html
Boulangerie	http://178.18.250.53:9090/template_boulangerie_final.html
Artisan	http://178.18.250.53:9090/template_artisan_final.html

+ +

📅 SYSTÈME DE RÉSERVATIONS

+ +

Interface Client (Port 8767)

URL: http://178.18.250.53:8767/

📅 Sélection date
👥 Nombre de personnes
🕐 Choix du créneau
✅ Confirmation instantanée

+ +

Interface Manager (Port 9090)

URL: http://178.18.250.53:9090/manager.html

🖥️ Tableau de bord tables
🟢/🔴 Indicateurs Libre/Occupée
👤 Info client
➕/✅ Réserver/Libérer table

+ +

API Réservations

GET /api/available?date=...&people=...
POST /api/reserve
GET /api/tables
POST /api/tables/<id>/occupy
POST /api/tables/<id>/free

+ +

🔧 Configuration Technique

+ + + + + + + + + + + + + + + + + + + + + + + + + + +

Paramètre	Valeur
VPS IP	178.18.250.53
Authentification	admin:turf2026
Base de données	turf.db (SQLite)
Fichier idées	idees.json
Fichier réservations	reservations.json

+ +

📊 Matrice des Responsabilités

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Service	Port	Type	Données
Portal	8768	Web	-
Turf	8765	Web/API	turf.db
Ideas	8765	Web	idees.json
Admin Menu	8766	Web	config_restaurant.json
Réservation	8767	Web	reservations.json
Templates	9090	Web	-

+ +

+Document généré le 25/02/2026 - H3R7 +

+ + + diff --git a/ARCHITECTURE_SERVICES.md b/ARCHITECTURE_SERVICES.md new file mode 100755 index 0000000..a86ccb8 --- /dev/null +++ b/ARCHITECTURE_SERVICES.md @@ -0,0 +1,183 @@ +# 🏗️ Architecture des Services H3R7 + +## Vue d'Ensemble + +``` + ┌─────────────────────────────────────────────────────┐ + │ VPS H3R7 │ + │ 178.18.250.53 │ + └─────────────────────────────────────────────────────┘ + │ + ┌─────────────────────────────────┼─────────────────────────────────┐ + │ │ │ + ▼ ▼ ▼ +┌─────────────────────┐ ┌─────────────────────┐ ┌─────────────────────┐ +│ PORTAIL CENTRAL │ │ SERVICES API │ │ TEMPLATES SITE │ +│ Port 8768 │ │ Ports 8765-8767 │ │ Port 9090 │ +│ │ │ │ │ │ +│ ┌──────────────┐ │ │ ┌──────────────┐ │ │ ┌──────────────┐ │ +│ │ 🏇 Turf │ │ │ │ 💡 Idées │ │ │ │ 🍽️ Restaurant│ │ +│ │ (8765) │ │ │ │ (8765/idees)│ │ │ │ (JSON) │ │ +│ └──────────────┘ │ │ └──────────────┘ │ │ └──────────────┘ │ +│ ┌──────────────┐ │ │ ┌──────────────┐ │ │ ┌──────────────┐ │ +│ │ 💡 Idées │ │ │ │ 📅 Réservation│ │ │ │ 🥖 Boulangerie│ │ +│ │ (8765/idees) │ │ │ │ (8767) │ │ │ └──────────────┘ │ +│ └──────────────┘ │ │ └──────────────┘ │ │ ┌──────────────┐ │ +│ ┌──────────────┐ │ │ ┌──────────────┐ │ │ │ 🔨 Artisan │ │ +│ │ ⚙️ Admin Menu │ │ │ │ ⚙️ Admin Menu │ │ │ └──────────────┘ │ +│ │ (8766) │ │ │ │ (8766) │ │ │ ┌──────────────┐ │ +│ └──────────────┘ │ │ └──────────────┘ │ │ │ 🖥️ Manager │ │ +│ ┌──────────────┐ │ │ ┌──────────────┐ │ │ │ (tables) │ │ +│ │ 📅 Réservation│ │ │ │ 🎨 Templates │ │ │ └──────────────┘ │ +│ │ (8767) │ │ │ │ (9090) │ │ │ │ +│ └──────────────┘ │ │ └──────────────┘ │ │ │ +│ ┌──────────────┐ │ │ │ │ │ │ +│ │ 🎨 Templates │ │ │ │ │ │ │ +│ │ (9090) │ │ │ │ │ │ │ +│ └──────────────┘ │ │ │ │ │ │ +└─────────────────────┘ └─────────────────────┘ └─────────────────────┘ +``` + +--- + +## 📋 Liste des Services + +### 🏇 TURF - Prédictions Hippiques + +| Service | URL | Description | +|---------|-----|-------------| +| Dashboard | http://178.18.250.53:8765/ | Prédictions turf combinées | +| API Prédictions | http://178.18.250.53:8765/api/today | Données en temps réel | + +**Fonctionnalités :** +- Vue d'ensemble des courses du jour +- Liste des favoris par course +- Cotes en temps réel (PMU, ZEturf, Genybet, etc.) +- Scraping automatique multi-sources +- Sauvegarde en base SQLite + +--- + +### 💡 BOÎTE À IDÉES + +| Service | URL | Description | +|---------|-----|-------------| +| Interface | http://178.18.250.53:8765/idees/ | CRUD complet | +| API | http://178.18.250.53:8765/api/ideas | Backend JSON | + +**Fonctionnalités :** +- ➕ Ajouter une idée +- ✏️ Modifier une idée +- 🗑️ Supprimer une idée +- 🔍 Filtrer par catégorie +- 📊 Indicateur de potentiel + +--- + +### 🍽️ TEMPLATES RESTAURANT + +| Service | URL | Description | +|---------|-----|-------------| +| Templates | http://178.18.250.53:9090/ | Page d'accueil | +| Restaurant JSON | http://178.18.250.53:9090/template_restaurant_json.html | Menu configurable | +| Boulangerie | http://178.18.250.53:9090/template_boulangerie_final.html | Site boulangerie | +| Artisan | http://178.18.250.53:9090/template_artisan_final.html | Site artisan | + +--- + +### 📅 SYSTÈME DE RÉSERVATIONS + +| Service | URL | Description | +|---------|-----|-------------| +| Client | http://178.18.250.53:8767/ | Réservation tables | +| Manager | http://178.18.250.53:9090/manager.html | Gestion tables | + +**Fonctionnalités :** +- Réservation en ligne +- Tableau de bord tables +- Indicateurs Libre/Occupée + +--- + +### ⚙️ ADMIN + +| Service | Port | Description | +|---------|------|-------------| +| Menu Admin | 8766 | Gestion menu restaurant | + +--- + +## 🔧 Accès Technique + +- **VPS :** 178.18.250.53 +- **Auth :** admin:turf2026 +- **Ports :** 8765, 8766, 8767, 8768, 9090 + +--- + +## 📊 Matrice + +| Service | Port | Type | Data | +|---------|------|------|------| +| Portal | 8768 | Web | - | +| Turf | 8765 | Web/API | turf.db | +| Ideas | 8765/idees | Web | idees.json | +| Admin Menu | 8766 | Web | config_restaurant.json | +| Réservation | 8767 | Web | reservations.json | +| Templates | 9090 | Web | - | + +--- + +*Document généré le 25/02/2026* + +--- + +## 📧 EMAIL — Resend API + +| Service | URL | Description | +|---------|-----|-------------| +| Endpoint | `POST http://localhost:8765/api/send-email` | Envoi d'email via Resend | +| Alias | `POST http://localhost:8765/turf/api/send-email` | | + +**Champs requis :** `to`, `subject`, + `html` ou `text` +**Champ optionnel :** `from` (défaut: `H3R7Tech `) +**Clé env :** `RESEND_API` (injectée via systemd) + +**Exemple :** +```json +POST /api/send-email +{ + "to": "user@example.com", + "subject": "Alerte ROI", + "html": "

ROI exceptionnel détecté !

" +} +``` + +**Intégrations :** +- `metrics_alerts.py --email` : envoie le rapport par email +- `turf_scheduler.py` : alerte automatique quotidienne à 21h30 si ROI > 1.0€ + +--- + +## 🔍 BRAVE SEARCH + +| Service | URL | Description | +|---------|-----|-------------| +| Endpoint | `GET http://localhost:8765/api/brave-search?q=...&count=N` | Recherche web | +| Alias | `GET http://localhost:8765/turf/api/brave-search` | | + +**Paramètres :** `q` (requis), `count` (défaut: 10, max: 20), `offset`, `type` (`web` ou `news`) +**Clé env :** `BRAVE_SEARCH_API` (injectée via systemd) + +**Exemple :** +``` +GET /api/brave-search?q=courses+PMU&count=5&type=news +``` + +**Intégrations :** +- `portail.html` : zone de recherche Brave Search intégrée dans le portail H3R7Tech +- Zone de recherche avec filtre Web/Actualités, résultats affichés inline + +--- + +*Mis à jour le 25/04/2026 — HRT-18* diff --git a/BUSINESS_PLAN.md b/BUSINESS_PLAN.md new file mode 100755 index 0000000..651f429 --- /dev/null +++ b/BUSINESS_PLAN.md @@ -0,0 +1,272 @@ +# 🐾 H3R7Tech - Business Plan & Services + +_Document stratégique: Comment monétiser les outils créés_ + +--- + +## 🎯 NOS OUTILS ACTUELS (À VENDRE) + +| Outil | Description | Potentiel | +|--------|-------------|-----------| +| **Dépenses Trello** | Gestion personnelle + Trello | Particuliers, micro-entrepreneurs | +| **CRM Simplifié** | Gestion prospects | Artisans, PME locales | +| **Turf Predictions** | Paris hippiques IA | Parieurs, tipsters | +| **Dashboard Turf** | Suivi courses temps réel | Passionnés turf | + +--- + +## 💼 OFFRE DE SERVICES (À PARTIR DE NOVEMBRE) + +### Pack "Digital Local" +- **Prix:** 99-199€/mois +- **Client cible:** Artisans, restaurants, commerces locaux +- **Inclus:** + - Site web vitrine (1 page) + - Gestion reservations en ligne + - Page Facebook/Google optimisée + - Support email + +### Pack "Visibilité Pro" +- **Prix:** 299-499€/mois +- **Client cible:** PME, cabinets +- **Inclus:** + - Site web complet (5 pages) + - SEO local + - CRM intégré + - Dashboard analytics + +### Pack "Agence Digitale" +- **Prix:** Sur devis (500-2000€) +- **Client cible:** Restaurants, hotels, salons +- **Inclus:** + - Site sur-mesure + - Photos/produits + - Réservations complexes + - Maintenance + +--- + +## 🎯 NICHE #1: PRINT ON DEMAND + +### Concept +Vendre des produits personnalisés avec vos designs (t-shirts, mugs, stickers, posters) + +### Comment ça marche +1. **Tu crées un design** (toi, photos, textes) +2. **Client commande** sur ta boutique +3. **Printful/Teepublic imprime et expédie** +4. **TuTouches la différence** (marge 15-40%) + +### Outils nécessaires + +| Outil | Rôle | Prix | +|-------|-------|------| +| **Canva** | Créer designs | Gratuit/12€/mois | +| **Printful** | Impression/livraison | Gratuit (marge) | +| **Redbubble** | Marketplace clé en main | Gratuit | +| **Teespring** | T-shirts/hoodies | Gratuit | +| **Etsy** | Boutique directe | 0.20€/article | + +### Niche POD rentables en 2026 +- 🎨 **Artistes locaux** (villes françaises) +- 🏃 **Sports locaux** (clubs, marathon) +- 🍺 **Hobbies** (brasseries artisanales) +- 🐕 **Animals lovers** +- 👨‍💻 **Dev/tech** (t-shirts geeks) +- 🎮 **Gamers** (esports, streaming) +- 🏠 **Villes/regions** (souvenirs) + +### Implementation (Travail à faire) +- [ ] Créer compte Printful +- [ ] Designer 10-20 premiers produits +- [ ] Tester sur Redbubble (gratuit) +- [ ] Lancer boutique Etsy + +--- + +## 🎯 NICHE #2: SERVICES AUX ARTISANS + +### Concept +Aider les artisans (plombiers, électriciens, carreleurs) à avoir une présence web + +### Problème +- 80% des artisans n'ont pas de site web +- Ils perdent des clients vers les gros acteurs +- Pas le temps de s'en occuper + +### Solution (Ce qu'on peut offrir) + +| Service | Prix vente | Coût | +|---------|-------------|------| +| Site 1 page | 199€ | 0€ (outil existant) | +| Gestion Google Business | 49€/mois | 0€ | +| Photos produits | 99€ | 50€ (stagiaire) | +| Formation réseaux | 149€ | 0€ | + +### Outils à développer +- [ ] Template site artisan (automatisé) +- [ ] Générateur Google Business +- [ ] Dashboard reservations +- [ ] CRM simplifier pour artisans + +--- + +## 🎯 NICHE #3: TURF BETTING SERVICE + +### Concept +Vendre des pronostics turf automatisés + +### Monetisation possible + +| Offre | Prix | Contenu | +|-------|------|---------| +| **Gratuit** | 0€ | 1 prédiction/jour (email) | +| **Essentiel** | 9.90€/mois | 3 prédictions + analyse | +| **Premium** | 29.90€/mois | 5 prédictions + money management | +| **VIP** | 99€/mois | Picks exclusifs + suivi personnalisé | + +### Implementation +- [ ] Automatiser提取 des cotes (API PMU) +- [ ] Créer système d'envoi emails (Mailchimp) +- [ ] Page de vente (landing page) +- [ ] Suivi des performances (ROI tracker) + +--- + +## 🎯 NICHE #4: AUTOMATISATION PME + +### Concept +Automatiser les tâches repetitives des petites entreprises + +### Idées de services + +| Service | Prix | Description | +|---------|------|-------------| +| **Scraping pros** | 49-149€ | Extraire annuaires, créer base prospects | +| **Automatisation** | 99-299€ | Zaps, make.com pour TPE | +| **Formation IA** | 149€/jour | Apprendre à utiliser ChatGPT/Claude | + +### Croissance +- 5 premiers clients → 10 → 25 → 50 +- Prix augmente avec実績 (case studies) +- Recurring income avec maintenance + +--- + +## 🎯 NICHE #5: CRÉATEUR DE CONTENU + +### Concept +Aider les influencers/créateurs à gérer leur activité + +### Services possibles + +| Service | Prix | Description | +|---------|------|-------------| +| **Gestion réseaux** | 299-499€/mois | Posts, stories, community | +| **Edition photos** | 49-99€/mois | Retouche, filters | +| **Montage vidéo** | 99-199€/vidéo | Shorts, Reels | +| **Merch** | 199-499€ | Création produits dérivés | + +--- + +## 📊 PRIORITÉS 2026 + +### Phase 1: Maintenant → Juin 2026 +1. ✅ **Print on Demand** (tester, valider niche) +2. 📋 CRM pour artisans (améliorer, vendre) +3. 📋 Turf predictions (automatiser, monétiser) + +### Phase 2: Juillet → Novembre 2026 +1. 🚀 Lancer service digital local +2. 🚀 Acquérir premiers clients (5-10) +3. 🚀 Boucler système recurring + +### Phase 3: Novembre 2026+ +1. 🏢 Officialiser structure (EI ou SARL) +2. 🏢 Embauche 1-2 personnes +3. 🏢 Scale services + +--- + +## 🛠️ OUTILS À CRÉER POUR NOTRE BUSINESS + +| Outil | Niche | Status | +|-------|-------|--------| +| **Template POD** | Print on Demand | À créer | +| **CRM Artisan** | Services pros | Existe (améliorer) | +| **Email tracker** | Turf | À créer | +| **Landing page gen** |通用 | À créer | +| **Lead scraper** | Services | Existe | +| **Dashboard stats** |通用 | À créer | + +--- + +## 💰 OBJECTIFS CHIFFRÉS + +### 2026 +| Mois | Revenu objectif | +|------|-----------------| +| Mars | 100€ | +| Avril | 300€ | +| Mai | 500€ | +| Juin | 800€ | +| Juillet | 1000€ | +| Novembre | 2500€ | + +### 2027 +- Objectif: 5000€/mois +- 2-3 employés + +--- + +## 📈 PAGE WEB BUSINESS + +À créer: http://178.18.250.53:8765/h3r7tech-business.html + +Contenu: +- Services proposés +- Tarifs +- Portfolio/案例 +- Contact +- Blog (partage d'astuces) + +--- + +## 🎯 ACTIONS IMMÉDIATES + +### Cette semaine +- [ ] Finaliser page business H3R7Tech +- [ ] Créer 5 designs POD test +- [ ] Améliorer CRM (stats dashboard) + +### Ce mois +- [ ] Trouver 1 premier client service digital +- [ ] Lancer 10 produits POD +- [ ] Automatiser predictions turf + +### Ce trimestre +- [ ] Valider modèle économique +- [ ] Préparer结构 juridique +- [ ] Recruter premier freelance + +--- + +## 📚 RESSOURCES + +### POD +- r/printondemand (Reddit) +- Printful blog +- Redbubble academy + +### Services digitaux +- Fiverr, 1Work, Malt (trouver clients) +- Airtable (CRM simple) +- Notion (gestion projet) + +### Turf +- Zone-Turf API (scraping) +- Tipsters Facebook groups + +--- + +_Document généré le 27/02/2026 - H3R7Tech 🐾_ diff --git a/CARTOGRAPHIE_H3R7TECH.md b/CARTOGRAPHIE_H3R7TECH.md new file mode 100755 index 0000000..79f74f3 --- /dev/null +++ b/CARTOGRAPHIE_H3R7TECH.md @@ -0,0 +1,200 @@ +# 🐾 H3R7Tech - Cartographie des Services & Versions + +_Dernière mise à jour: 26 février 2026_ + +--- + +## 📋 Contexte du Projet + +**Objectif:** Créer une entreprise H3R7Tech pour services B2B (démarchage, marketing, scraping, CRM) + système de paris hippiques. + +**Infrastructure:** +- VPS: 178.18.250.53 (Contabo) +- User: h3r7 +- Ports clés: 8765-8773 +- Git: Gitea (port 3000) + +--- + +## 🏇 Module Turf - Paris Hippiques + +### v1.0 (2026-02-21) +- **Dashboard principal** - Port 8765 +- API combinée PMU, Boturfers, Zone-Turf, Canalturf +- Prédictions automatiques (3 favoris par course) +- Budget: 6€/course +- Statistiques ROI + +### v1.1 (2026-02-22) +- Ajout analyses jockeys/entraineurs +- Comparaison prédictions vs résultats +- Tracking performance + +### v1.2 (2026-02-23) +- Interface dashboard redesign +- Exports résultats +- Script automatisation cron + +### v1.3 (2026-02-24) +- Intégration cron jobs (10h-13h30) +- Résultats automatiques via API + +### v1.4 (2026-02-25) +- Amélioration scraping (Apify fallback) +- Nouvelles sources de cotes + +--- + +## 📊 CRM - Gestion Prospects + +### v1.0 (2026-02-25) +- **Port 8770** - CRM Production +- Import 123 prospects (Apify - Artisans Lille) +- Catégories: Restaurant, Boulangerie, Garage +- Statuts: Nouveau, Contacté, RDV, Proposition, Gagné, Perdu + +### v1.1 (2026-02-26) +- **Corrections JS** - render(), onclick handlers +- Filtre catégorie ajouté +- Liens Google Maps (onclick) + +### v2.0 (2026-02-26) +- **Port 8773** - CRM Candidatures (Kanban) +- Vue Pipeline: À Postuler → En Attente → Entretien 1 → Entretien 2 → Offre → Refus +- Historique contacts +- Rappels automatiques +- Transform prospects → candidatures + +--- + +## 🔧 Services Techniques + +### Scraping +| Service | Port | Description | +|---------|------|-------------| +| turf_api | 8765 | API Turf combinée | +| admin_api | 8766 | Interface admin | +| reservations | 8767 | Système réservations | +| portal | 8768 | Portail client | +| depenses | 8769 | Dépenses Trello | +| crm_api | 8770 | API CRM | +| agent_chat | 8771 | Chat IA | + +### Scripts +- `multi_scraper_v5.py` - Scraping courses +- `dashboard_api.py` - API dashboard +- `crm_api.py` - API CRM +- `backup.sh` - Backup automatique (3h/jour) + +--- + +## 🌐 Interfaces Web + +### Portails +| URL | Description | +|-----|-------------| +| http://178.18.250.53:8765 | Dashboard Turf | +| http://178.18.250.53:8768 | Portail (Tout, Turf, Perso...) | +| http://178.18.250.53:8769 | Dépenses Trello | +| http://178.18.250.53:8770 | CRM Prospects | +| http://178.18.250.53:8772 | CRM Dev | +| http://178.18.250.53:8773 | CRM Candidatures | +| http://178.18.250.53:3000 | Gitea | + +### Démarchage +- `SCRIPTS_DEMARCHAGE.html` - Scripts appels +- `liste_appels.md` - 123 prospects qualifiés + +--- + +## 🧑💻 Outils Perso + +### Dépenses Trello (v1.0 - 2026-02-27) +- **Port:** 8769 +- **URL:** http://178.18.250.53:8769/ +- **Description:** Gestion dépenses personnelles avec envoi automatique vers Trello +- **Fonctionnalités:** + - Ajout dépenses avec prénom, date, libellé, montant + - Status: "En attente" → "Envoyé ✅" + - 1 carte Trello par dépense (liste CP FEV 26) + - Format customizable {prenom} - {date} - {libelle} - {montant}€ + - Filtre Prénoms configurables +- **Stack +:** Flask Vanilla JS (mobile compatible) +- **Repo:** http://178.18.250.53:3000/admin/Perso + +--- + +## 📁 Fichiers Clés + +### Configuration +``` +/home/h3r7/turf_scraper/ +├── crm_prospects.json # 123 prospects +├── turf.db # Base données courses +├── crm_dashboard.html # CRM prod (v1.1) +├── crm_candidatures.html # CRM kandan (v2.0) +└── backup.sh # Script backup + +/home/h3r7/turf_scraper_dev/ +├── crm_dashboard.html # CRM dev +└── H3R7Tech_logo.svg # Logo + +/home/h3r7/depenses_trello/ +├── app.py # API Flask +├── config.json # Configuration +└── templates/index.html # UI +``` + +### Git +- **Repo:** http://178.18.250.53:3000/admin/h3r7tech +- **Repo Perso:** http://178.18.250.53:3000/admin/Perso +- **master:** Production (stable) +- **dev:** Développement + +--- + +## 📈 Métriques + +### CRM +| Métrique | Valeur | +|----------|--------| +| Total prospects | 123 | +| Restaurants | 94 (76%) | +| Boulangeries | 26 (21%) | +| Garages | 3 (2%) | +| Note moyenne | 4.4/5 | + +### Turf +| Métrique | Valeur | +|----------|--------| +| Analyses/jour | 5 | +| Budget/course | 6€ | +| Favoris/course | 3 | + +--- + +## 🔜 Prochaines Versions + +### v2.1 (Prévu) +- [ ] Améliorer filtrage CRM +- [ ] Export prospects → CSV +- [ ] Intégration email +- [ ] Automatisation rappels + +### v2.2 (Prévu) +- [ ] Dashboard stats prospection +- [ ] Scripts personnalisés par catégorie +- [ ] Intégration Google Calendar + +--- + +## 📝 Notes + +- **2026-02-21:** Lancement projet +- **2026-02-25:** Import 123 prospects via Apify +- **2026-02-26:** Création CRM Candidatures + corrections +- **2026-02-27:** Création app Dépenses Trello (port 8769), push Gitea + +--- + +_Cartographie générée automatiquement - H3R7Tech 🐾_ diff --git a/DOCUMENTATION.md b/DOCUMENTATION.md new file mode 100644 index 0000000..bcecd72 --- /dev/null +++ b/DOCUMENTATION.md @@ -0,0 +1,157 @@ +# 💸 Dépenses Trello - Application de Gestion des Dépenses + +## 📝 Description + +**Dépenses Trello** est une application web de gestion des dépenses personnelles avec synchronisation automatique vers Trello. Créée pour simplifier le suivi des dépenses au quotidien. + +--- + +## ✨ Fonctionnalités + +### Gestion des Dépenses +- ➕ **Ajout rapide** de dépenses (prénom, date, libellé, montant, catégorie) +- ✏️ **Modification** des dépenses existantes +- 🗑️ **Suppression** de dépenses +- 📤 **Export CSV** pour Excel/comptabilité +- 📥 **Import CSV** pour récupérer des données +- 📄 **Export PDF** pour les relevés + +### Catégories Automatiques +- 🚗 **Transport** (essence, gazole, parking) +- 🛒 **Courses** (Carrefour, Lidl, Auchan) +- 🎰 **Loisirs** (loto, bar, café, cinéma) +- 🏠 **Maison** (lumière, loyer, brico) +- ❤️ **Santé** (pharmacie, contrôle technique) +- 📦 **Autre** (divers) + +### Graphiques & Statistiques +- 📊 **Bar chart** - visualisation classique +- 🥧 **Camembert** - répartition par catégorie +- 👤 **Par personne** - suivi par membre du foyer +- 📅 **Par mois** - évolution dans le temps + +### Budget +- 💰 **Budget mensuel** configurable +- ⚠️ **Alerte** automatique quand le budget est dépassé + +### Dépenses Récurrentes +- 🔄 **Loyer**, EDF, téléphone - automatiquement listés + +### Synchronisation Trello +- 🟦 **Envoi automatique** des dépenses vers une liste Trello +- ✅ **Suivi du statut** (En attente / Envoyé) + +--- + +## 🛠️ Stack Technique + +| Composant | Technologie | +|-----------|--------------| +| **Backend** | Python Flask | +| **Base de données** | SQLite | +| **Frontend** | HTML5, Vanilla JavaScript | +| **Graphiques** | Chart.js | +| **PDF** | jsPDF | +| **Hébergement** | VPS (Linux) | + +--- + +## 📋 Spécifications + +- **Port**: 8769 +- **URL**: http://178.18.250.53:8769/ +- **API REST**: /api/depenses, /api/config, /api/budget, /api/recurring +- **Données**: 21+ dépenses en base (extensible) +- **Utilisateurs**: Multi-utilisateurs (via prénom) + +--- + +## 🚀 Installation + +```bash +# Cloner le projet +git clone http://178.18.250.53:3000/admin/Perso.git + +# Installer les dépendances +pip install flask requests + +# Lancer l'application +python app.py +``` + +--- + +## 💼 Potentiel Commercial + +### Cible +- 👨‍👩‍👧 **Particuliers** gestion budget familial +- 💼 **Auto-entrepreneurs** frais professionnels +- 🏢 **Petites entreprises** suivi dépenses + +### Arguments de Vente +1. ✅ **Simple** - interface épurée, pas de formation +2. ✅ **Complet** - catégories, graphiques, budget, PDF +3. ✅ **Automatisé** - synchronisation Trello +4. ✅ **Pas d'abonnement** - hébergement propre +5. ✅ **Open Source** - customisable + +### Prix Recommandés +| Offre | Prix | +|-------|------| +| Usage personnel | 19€ | +| Usage pro | 49€ | +| Installation + config | 29€ | +| Support mensuel | 9€/mois | + +--- + +## 📱 Captures d'Écran + +### Page Saisie +- Formulaire rapide avec catégories auto +- Liste des dernières dépenses +- Boutons Envoyer tout / Export + +### Page Dashboard +- Graphiques Bar / Camembert +- Filtres Mois / Personne / Catégorie +- Total en temps réel + +### Page Config +- Gestion des prénoms +- Personnalisation des catégories +- Configuration Trello (API Key, Token, List ID) +- Budget mensuel + +--- + +## 🔧 Configuration Trello + +1. Créer un Power-Up sur https://trello.com/power-ups/admin +2. Générer une API Key +3. Générer un Token (avec permissions write) +4. Copier le List ID cible +5. Coller dans Config + +--- + +## 📝 Roadmap Future + +- [ ] Application mobile (PWA) +- [ ] Mode hors-ligne +- [ ] Catégories personnalisées illimitées +- [ ] Rapports mensuels par email +- [ ] Intégration Slack/Discord +- [ ] Multi-devises + +--- + +## 📞 Contact + +**Développé par**: H3R7Tech +**Date**: Mars 2026 +**Version**: 1.6 + +--- + +*Document généré automatiquement - Dépenses Trello* diff --git a/EVOLUTION.md b/EVOLUTION.md new file mode 100755 index 0000000..d838f17 --- /dev/null +++ b/EVOLUTION.md @@ -0,0 +1,171 @@ +# 📈 Étude d'Évolution - H3R7Tech + +_Document de prospective fonctionnalité pour chaque application_ + +--- + +## 1. 💸 Dépenses Trello + +### État Actuel +- Port: 8769 +- Stack: Flask + Vanilla JS +- Fonctionnalités: Ajout dépenses, envoi Trello (1 carte/ligne), status (En attente/Envoyé) + +### Évolutions Possibles + +| Priorité | Feature | Description | Difficulté | +|----------|---------|-------------|-------------| +| 🔴 Haute | Export CSV | Exporter les dépenses en CSV | Facile | +| 🔴 Haute | Filtres dates | Filtrer par période (mois, semaine) | Facile | +| 🟡 Moyenne | Catégories | Catégories personnalisées (Courses, Essence, Loisirs...) | Moyenne | +| 🟡 Moyenne | Multiple List Trello | Choisir destination Trello par catégorie | Moyenne | +| 🟢 Basse | Rapports mensuels | Synthèse automatique mensuelle | Facile | +| 🟢 Basse | Widget Android | App native Android (Kotlin/Compose) | Difficile | +| 🟢 Basse | Intégration Google Sheets | Sync automatique Sheets | Moyenne | + +### Idées Innovantes +- **IA categorization**: Classifier automatiquement les dépenses par libellé +- **Récurrence**: Détecter les dépenses récurrentes (abonnements) +- **Budget alerts**: Alertes si dépasse budget mensuel + +--- + +## 2. 📊 CRM (Port 8770) + +### État Actuel +- 126 prospects (Restaurants, Boulangeries, Garages) +- Filtres: Catégorie, Statut +- Lien Google Maps + +### Évolutions Possibles + +| Priorité | Feature | Description | Difficulté | +|----------|---------|-------------|-------------| +| 🔴 Haute | Pipeline visual | Vue Kanban complète (drag & drop) | Moyenne | +| 🔴 Haute | Historique contacts | Timeline complète interactions | Moyenne | +| 🔴 Haute | Statistiques | Dashboard KPIs (conversion, taux réponse) | Facile | +| 🟡 Moyenne | Import CSV/Excel | Import prospects via fichier | Facile | +| 🟡 Moyenne | Export contacts | Export vCard pour téléphone | Facile | +| 🟡 Moyenne | Scoring auto | Score basé sur critères (taille, activité) | Moyenne | +| 🟢 Basse | AI recommandations | Suggestions prospects à appeler | Difficile | +| 🟢 Basse | Integration WhatsApp | Messages automatisés | Difficile | + +### Idées Innovantes +- **Lead scoring ML**: Score prédit bas� sur données publiques +- **Auto-enrichissement**: Compléter automatiquement emails/téléphones via API +- **Voice assistant**: Appeler prospects via API téléphone + +--- + +## 3. 🏇 Turf Dashboard (Port 8765) + +### État Actuel +- Scraping multi-sources (PMU, Zone-Turf, Canalturf...) +- Prédictions 3 favoris/course +- BDD SQLite historique + +### Évolutions Possibles + +| Priorité | Feature | Description | Difficulté | +|----------|---------|-------------|-------------| +| 🔴 Haute | Résultats auto | Scrap résultats automatiquement | Moyenne | +| 🔴 Haute | ROI Tracker | Suivi gains/pertes quotidien | Facile | +| 🔴 Haute | Alertes cotes | Notification quand cote évolue | Moyenne | +| 🟡 Moyenne | Comparatif IA | Comparer prédictions vs résultats | Moyenne | +| 🟡 Moyenne | Paris groupés | Générer paris combinés | Facile | +| 🟢 Basse | Telegram betting | Parier via bot Telegram | Difficile | +| 🟢 Basse | Prévisions météo | Météo hippodrome → impact | Facile | +| 🟢 Basse | Historique jockeys | Stats détaillées jockeys/entraineurs | Moyenne | + +### Idées Innovantes +- **ML predictions**: Modèle ML entraîné sur données historiques +- **Value betting**: Détecter les cotes "value" vs probabilité réelle +- **Arbitrage**: Détecter opportunités'arbitrage entre bookmakers + +--- + +## 4. 🌐 Portail (Port 8768) + +### État Actuel +- Grille d'applications +- Filtres: Turf, Restaurant, Perso, Admin... + +### Évolutions Possibles + +| Priorité | Feature | Description | Difficulté | +|----------|---------|-------------|-------------| +| 🔴 Haute | Recherche | Barre recherche applications | Facile | +| 🟡 Moyenne | Favoris | Marquer apps favorites | Facile | +| 🟡 Moyenne | Categories personalisées | Créer ses propres catégories | Moyenne | +| 🟢 Basse | Dark/Light mode | Toggle thème | Facile | +| 🟢 Basse | Widget dashboard | Résumé stats sur page d'accueil | Moyenne | +| 🟢 Basse | PWA | Installation comme app native | Moyenne | + +### Idées Innovantes +- **Unified search**: Rechercher across toutes les apps (CRM, Turf, Dépenses) +- **Command palette**: Ctrl+K pour lancer快速的 actions +- **Dashboard custom**: Widgets drag & drop + +--- + +## 5. 📬 Agent Chat (Port 8771) + +### État Actuel +- Interface chat avec IA + +### Évolutions Possibles + +| Priorité | Feature | Description | Difficulté | +|----------|---------|-------------|-------------| +| 🔴 Haute | Context CRM | Chat avec contexte prospects | Moyenne | +| 🔴 Haute | Templates | Réponses toutes faites | Facile | +| 🟡 Moyenne | Voice input | Entrée vocale | Moyenne | +| 🟢 Basse | Multi-langues | Support EN/ES/DE | Facile | + +--- + +## 6. 🎯 Priorités Définies + +### Court Terme (Cette semaine) +1. 📊 CRM - Dashboard statistiques +2. 💸 Dépenses - Filtres dates +3. 🏇 Turf - Résultats auto + +### Moyen Terme (Ce mois) +1. 📊 CRM - Pipeline Kanban complet +2. 💸 Dépenses - Catégories +3. 🌐 Portail - Recherche + +### Long Terme (Ce trimestre) +1. 🏇 Turf - ML predictions +2. 📊 CRM - Auto-enrichissement +3. 💸 Dépenses - App native Android + +--- + +## 📋 Matrice Effort/Bénéfice + +| App | Feature | Effort | Bénéfice | Score | +|-----|---------|--------|----------|-------| +| CRM | Stats dashboard | 2 | 5 | 10 | +| Dépenses | Filtres dates | 2 | 4 | 8 | +| Turf | ROI Tracker | 2 | 4 | 8 | +| CRM | Pipeline Kanban | 4 | 5 | 20 | +| Portail | Recherche | 2 | 3 | 6 | +| Turf | Alertes cotes | 3 | 4 | 12 | + +--- + +## 🔧 Tech Stack Recommandé + +| Besoin | Solution | +|--------|----------| +| Frontend mobile | React Native / Flutter | +| Backend API | FastAPI (Python) | +| Base de données | SQLite (local) + PostgreSQL (prod) | +| ML | scikit-learn / TensorFlow Lite | +| Hosting | VPS actuel + Docker | + +--- + +_Document généré le 27/02/2026 par H3R7Tech 🐾_ diff --git a/EVOLUTION_PREDICTIF.md b/EVOLUTION_PREDICTIF.md new file mode 100644 index 0000000..ca83a6d --- /dev/null +++ b/EVOLUTION_PREDICTIF.md @@ -0,0 +1,694 @@ +# Évolution du Système Prédictif Turf + +## Historique des modifications - 25 Mars 2026 (suite) + +### 25 Mars 2026 - Correction du titre du banner ✅ + +**Problème**: Le banner de la course affichait uniquement "🏇 MARSEILLE Quinté+" au lieu du nom complet de la course. + +**Solution**: +- Modification de `combined_api.py` pour utiliser `get_full_race_name()` qui retourne le format complet: "R1 BORELY - 13:55 GRAND NATIONAL DU TROT" +- Mise à jour du dashboard pour afficher le nom complet dans le banner +- Ajustement CSS pour permettre l'affichage du nom complet sans troncature +- Ajout du favicon avec le logo H3R7Tech + +**Résultat**: Le banner affiche maintenant "R1 BORELY - 13:55 GRAND NATIONAL DU TROT" avec le type de course "🏇 Quinté+" + +**Fichiers modifiés**: +- `combined_api.py` - Utilisation de `get_full_race_name()` dans l'API +- `dashboard.html` - Affichage du nom complet dans le banner + +### 25 Mars 2026 - Résultats groupés par course ✅ + +**Problème**: L'onglet Résultats affichait les positions 1-5 pour chaque course sans séparation, ce qui était incohérent. + +**Solution**: +- Modification de l'API pour inclure `race_label` (ex: "R1 BORELY") et `race_type` (ex: "GRAND NATIONAL DU TROT") dans les résultats +- Mise à jour du dashboard pour grouper les résultats par course et afficher un en-tête pour chaque course + +**Résultat**: Les résultats affichent maintenant chaque course séparément: +- R1 BORELY - GRAND NATIONAL DU TROT +- R2 CAEN - PRIX D'ACQUEVILLE + +**Fichiers modifiés**: +- `combined_api.py` - Ajout des infos de course dans les résultats +- `dashboard.html` - Groupement et affichage par course + +--- + +## Historique des modifications - 25 Mars 2026 + +--- + +## 1. Analyse de la Base de Données + +### Tables principales et volumes + +| Table | Lignes | Description | +|-------|--------|-------------| +| `historical_data` | 5 536 | Données historiques des courses | +| `pmu_courses` | 3 146 | Courses PMU | +| `pmu_reunions` | 480 | Réunions hippiques | +| `predictions` | 739 | Prédictions effectuées | +| `scoring` | 123 | Scores calculés par cheval | +| `results` | 68 | Résultats des courses | +| `performance` | 96 | Performance prédictions vs réels | +| `recommendations` | 32 | Recommandations de paris | +| `weather` | 4 | Données météo | + +### Métriques de performance (avant ML) + +| Métrique | Valeur | +|----------|--------| +| Taux de réussite global | 20.83% | +| Précision favori (rang 1) | 42.86% | +| Précision top 3 | 50% | +| Résultats remplis (recos) | 0/32 | + +--- + +## 2. Améliorations Effectuées + +### 2.1 Court Terme ✅ + +#### ✅ Script de mise à jour des résultats +- **Fichier**: `update_recommendation_results.py` +- **Fonction**: Compare les recommandations avec les résultats réels +- **Résultat**: 20 recommandations mises à jour (6 GAGNE, 14 PERDU) +- **Taux de réussite**: 30% + +#### ✅ Analyse du rang 0 +- **Cause**: `canalturf_partants` (356 prédictions) et `canalturf_selections` (165) ont rank 0 par défaut +- **Solution**: Seuls les ranks 1/2/3 constituent les vraies prédictions + +### 2.2 Moyen Terme ✅ + +#### ✅ Entraînement XGBoost +- **Fichier**: `train_xgboost.py` +- **Données**: 4 902 lignes de `historical_data` +- **Features**: 35 features (cote, forme, performances, etc.) + +| Modèle | CV AUC | Amélioration vs random | +|--------|--------|----------------------| +| Top 1 (gagnant) | **0.697** | +19.7% | +| Top 3 (placé) | **0.715** | +21.5% | + +**Top Features (par importance):** +1. `cote_directe` - La cote est le predictor le plus important +2. `rang_cote` - Classement par cote +3. `implied_prob` - Probabilité implicite (1/cote) +4. `ratio_cote_field` - Ratio cote vs moyenne du field +5. `tx_victoire` - Taux de victoire historique +6. `forme_recente` - Forme récente du cheval + +**Fichiers générés:** +- `xgboost_models.pkl` - Modèles entraînés +- `feature_importance_top1.csv` +- `feature_importance_top3.csv` + +### 2.3 Intégration Dashboard ✅ + +#### ✅ Nouvel onglet "Prédictions ML" +- **Fichier**: `dashboard.html` +- **Endpoint**: `/turf/api/ml_predictions` +- **Données affichées**: + - Probabilité Top 1 (%) + - Probabilité Top 3 (%) + - Score ML composite + - Conseil (TOP1 / TOP3 / PASS) + +#### ✅ Intégration vitesse +- **Fichier**: `combined_api.py` +- **Endpoint**: `/turf/api/vitesse` +- **Calcul**: Moyenne du `temps_obtenu` dans historical_data +- **Affichage**: Format mm:ss avec nombre de courses + +--- + +## 3. Architecture Actuelle + +``` +┌─────────────────────────────────────────────────────────┐ +│ PORTAL DUCKDNS │ +│ https://portal-kolifee.duckdns.org │ +│ (Auth: h3r7 / ****) │ +└─────────────────────┬───────────────────────────────────┘ + │ + ┌─────────────┴─────────────┐ + │ │ + Port 8765 Port 9999 + │ │ +┌───────▼────────┐ ┌───────▼────────┐ +│ combined_api │ │ vitesse_api │ +│ - /turf/api │ │ - /api/vitesse │ +│ - ML预测 │ │ (obsolète) │ +│ - Vitesse │ └──────────────────┘ +│ - Scoring │ +└───────────────┘ + │ + ▼ +┌───────────────────────────────────┐ +│ turf.db │ +│ - predictions │ +│ - historical_data (4 902 lignes) │ +│ - scoring │ +│ - results │ +│ - recommendations │ +└───────────────────────────────────┘ +``` + +--- + +## 4. Améliorations à Venir + +### 4.1 Priorité Haute + +#### 🔴 Comparaison Prédictions vs Résultats Réels +- **Problème**: Seulement 96 prédictions evaluées avec résultats +- **Solution**: + - Automatiser le remplissage des résultats dans `performance` table + - Créer script `backtest.py` qui compare prédictions ML vs réels + - Calculer ROI par type de pari (simple gagnant, place, couplé) +- **Métriques à suivre**: + - Précision par rang (base 1, chance 2, outsider 3) + - ROI par type de pari + - Evolution de la précision dans le temps + +#### 🔴 Ajouter plus de données historiques +- **Problème**: 4 902 lignes, seulement 364 jours +- **Solution**: Améliorer `improve_historical_data.py` pour charger plus de dates +- **Objectif**: 20 000+ lignes pour meilleur entraînement +- **API à utiliser**: PMU API (https://turfinfo.api.pmu.fr) + +#### 🔴 Intégration météo +- **Problème**: Table `weather` n'a que 4 lignes +- **Solution**: Scraping météo quotidien + impact sur prédictions +- **Impact**: Ajustement selon conditions (pluie, vent, température) +- **Sources**: weather.com, openweathermap.org + +#### 🔴 Système de "Value Bets" +- **Problème**: Pas de comparaison cote PMU vs cote "juste" +- **Solution**: Estimer cote juste avec le modèle ML, parier si valeur > seuil +- **Formule**: `value = (cote_PMU - cote_juste) / cote_juste` +- **Seuil**: Parier si value > 10% + +### 4.2 Priorité Moyenne + +#### 🟡 Scraping de Sources Hippiques Externes +- **Problèmes identifiés**: + - Canalturf: prédictions basiques + - Pas d'avis externes intégrés +- **Sources à scraper**: + - **Equidia**: Pronostics experts, commentaires + - **Paris-Turf**: Analyses courses, tiércé + - **Zeturf**: Pronostics communautaire + - **LeTrot**: Spécifique trot attelé +- **Données à récupérer**: + - Avis des experts (auteur, confiance, commentaire) + - Cotes de référence (moyenne de plusieurs sources) + - Statistiques jockeys/entraineurs + +#### 🟡 Backtest complet +- **Problème**: Seulement 96 prédictions evaluées +- **Solution**: Automatiser le backtest sur toutes les recommandations +- **Objectif**: ROI réel du modèle +- **Métriques**: + ``` + - ROI = (gains - mises) / mises + - Yield = gains_esperés / mises + - Hit Rate = paris_gagnés / total_paris + - CLV = Cote_Moyenne - Cote_Fermee + ``` + +#### 🟡 Amélioration du scoring +- **Problème**: Formule actuelle pondérée manuellement +- **Solution**: Utiliser les weights XGBoost pour pondérer le scoring +- **Impact**: Précisions bases: 42.86% → 50%+ + +#### 🟡 Modèles multi-cibles +- **Solution**: Entraîner modèles séparés pour: + - `model_quinte` - top5 (pour couplés, tiercé, quinté) + - `model_turf` - Discipline spécifique (trot vs plat) + - `model_distance` - Performance par catégorie de distance + - `model_hippodrome` - Performance par hippodrome + +#### 🟡 Intégration des performances jockeys/entraineurs +- **Données manquantes**: + - Taux de victoire du jockey + - Taux de réussite de l'entraineur + - Combinaison cheval/jockey historique +- **Solution**: Ajouter tables `jockeys` et `entraineurs` +- **Impact**: +5-10% de précision + +### 4.3 Priorité Basse + +#### 🟢 Interface admin +- **Solution**: Page pour gérer les prédictions, voir stats +- **Fonction**: + - Activer/désactiver scrapers + - Voir ROI en temps réel + - Historique des prédictions + - Configuration des seuils + +#### 🟢 Alertes Telegram/Discord +- **Solution**: Notification quand prediction forte (prob > 50%) +- **Impact**: Réactivité augmentée +- **Format**: + - Cheval recommandé + - Cote actuelle + - Probabilité ML + - Mise suggérée + +#### 🟢 Dashboards analytiques +- **Solution**: Graphiques d'évolution +- **Métriques**: + - Précision vs temps + - ROI vs temps + - Meilleurs/wpires chevaux + - Meilleurs/wpires jockeys + +--- + +## 5. Scraping de Sources Externes + +### 5.1 Sites à Intégrer + +| Site | Données | Difficulté | Priorité | +|------|---------|------------|----------| +| Equidia | Pronostics experts | Moyenne | Haute | +| Paris-Turf | Analyses courses | Moyenne | Haute | +| LeTrot | Spécifique trot | Facile | Moyenne | +| Zeturf | Pronostics communauté | Difficile | Basse | + +### 5.2 Données à Récupérer + +```python +# Exemple de structure pour données externes +external_data = { + "source": "equidia", + "horse_name": "...", + "expert_name": "...", + "expert_confidence": 0-100, + "comment": "Texte de l'analyse", + "cote_recommandee": 0.0, + "date": "YYYY-MM-DD" +} +``` + +### 5.3 Intégration dans le modèle + +1. **NLP sur les commentaires**: + - Sentiment: POSITIF / NEUTRE / NEGATIF + - Mots-clés: формы,腿部, qualité, etc. + - Score basé sur le texte + +2. **Fusion des sources**: + ``` + score_final = + 0.4 * score_ML + + 0.3 * score_Canalturf + + 0.2 * score_Equidia + + 0.1 * score_cote + ``` + +--- + +## 6. Métriques de Performance à Suivre + +### 6.1 Métriques Quotidiennes + +| Métrique | Description | Cible | +|----------|-------------|-------| +| `precision_bases` | % bases correctes | > 45% | +| `precision_chances` | % chances correctes | > 30% | +| `precision_outsiders` | % outsiders corrects | > 15% | +| `nb_predictions` | Nombre de prédictions | > 20 | + +### 6.2 Métriques Hebdomadaires + +| Métrique | Description | Cible | +|----------|-------------|-------| +| `roi_simple_gagnant` | ROI paris gagnant | > 5% | +| `roi_simple_place` | ROI paris placé | > 10% | +| `avg_cote` | Cote moyenne recommandée | - | +| `value_bets` | % de value bets trouvés | > 20% | + +### 6.3 Métriques Mensuelles + +| Métrique | Description | Cible | +|----------|-------------|-------| +| `roi_global` | ROI global | > 10% | +| `evolution` | Evolution vs mois précédent | positive | +| `meilleur_cheval` | Cheval le plus profitable | - | +| `pire_cheval` | Cheval le moins profitable | - | + +--- + +## 7. Structure de Données Suggérée + +### 7.1 Nouvelle Table: `expert_predictions` + +```sql +CREATE TABLE expert_predictions ( + id INTEGER PRIMARY KEY, + date TEXT NOT NULL, + source TEXT NOT NULL, -- 'equidia', 'paris_turf', etc. + expert_name TEXT, + horse_name TEXT NOT NULL, + horse_number INTEGER, + race_name TEXT, + confidence INTEGER, -- 0-100 + sentiment TEXT, -- 'POSITIF', 'NEUTRE', 'NEGATIF' + comment TEXT, + recommended_odds REAL, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP +); +``` + +### 7.2 Nouvelle Table: `backtest_results` + +```sql +CREATE TABLE backtest_results ( + id INTEGER PRIMARY KEY, + date TEXT NOT NULL, + race_name TEXT, + horse_name TEXT, + type_pari TEXT, -- 'simple_gagnant', 'simple_place', 'couple' + mise REAL, + Cote REAL, + Resultat TEXT, -- 'GAGNE', 'PERDU' + Gain REAL, + prob_predite REAL, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP +); +``` + +### 7.3 Nouvelle Table: `jockeys` + +```sql +CREATE TABLE jockeys ( + id INTEGER PRIMARY KEY, + name TEXT UNIQUE NOT NULL, + total_races INTEGER, + victories INTEGER, + places INTEGER, + taux_victoire REAL, + taux_place REAL, + last_updated TIMESTAMP +); +``` + +--- + +## 8. Plan d'Action Détaillé + +### Phase 1: Amélioration Données (Semaine 1-2) +- [ ] Augmenter historical_data à 10 000+ lignes +- [ ] Remplir quotidiennement les résultats +- [ ] Créer script backtest automatique +- [ ] Tableau de bord métriques + +### Phase 2: Intégration Sources (Semaine 3-4) +- [ ] Intégrer Equidia API/scraping +- [ ] Ajouter analyse sentiment des commentaires +- [ ] Fusionner scores multi-sources + +### Phase 3: Optimisation ML (Semaine 5-6) +- [ ] Réentraîner avec nouvelles features +- [ ] Modèles par discipline +- [ ] Système de value bets + +### Phase 4: Production (Semaine 7-8) +- [ ] Déploiement nouveau modèle +- [ ] Alertes Telegram +- [ ] Interface admin + +--- + +## 4.4 Implémenté ✅ + +#### ✅ Tables de données analytiques (25 Mars 2026) + +**Tables créées dans `turf.db`:** + +| Table | Description | Colonnes clés | +|-------|-------------|---------------| +| `bet_results` | Historique des paris | date, race_name, type_pari, horse_name, cote, mise, resultat, gain | +| `daily_stats` | Stats quotidiennes | date, total_bets, bets_gagne, mise_totale, gain_total, precision_pct, roi_pct | +| `stats_by_type` | Stats par type de pari | date, type_pari, total_bets, gagne, mise, gain, roi | +| `expert_predictions` | Prédictions externes | date, source, expert_name, horse_name, confidence, sentiment, comment | + +**Scripts:** +- `populate_analytics.py` - Remplit les tables depuis recommendations +- `update_recommendation_results.py` - Met à jour les résultats des paris + +**API endpoints:** +- `/api/backtest` - Lit depuis `bet_results` et `stats_by_type` +- `/api/stats` - Lit depuis `daily_stats` + +--- + +## 5. Commandes Utiles + +```bash +cd /home/h3r7/turf_scraper + +# ===== DONNÉES & PRÉDICTIONS ===== + +# Mettre à jour les résultats des recommandations +python3 update_recommendation_results.py + +# Réentraîner le modèle ML +python3 train_xgboost.py + +# ===== ANALYTICS ===== + +# Remplir les tables analytiques (à exécuter après mise à jour résultats) +python3 populate_analytics.py + +# ===== API ===== + +# Redémarrer l'API +pkill -f combined_api +python3 combined_api.py + +# Vérifier les logs +tail -f /tmp/combined.log + +# ===== BASE DE DONNÉES ===== + +# Voir les tables analytiques +sqlite3 turf.db "SELECT * FROM daily_stats LIMIT 5;" +sqlite3 turf.db "SELECT * FROM bet_results LIMIT 5;" +sqlite3 turf.db "SELECT * FROM stats_by_type;" + +# Statistiques globales +sqlite3 turf.db "SELECT SUM(total_bets) as total, SUM(gain_total) as gains, SUM(mise_totale) as mises FROM daily_stats;" +``` + +--- + +## 6. Fichiers Clés + +| Fichier | Description | +|---------|-------------| +| `combined_api.py` | API principale (port 8765) | +| `dashboard.html` | Interface frontend avec onglets | +| `train_xgboost.py` | Entraînement ML XGBoost | +| `update_recommendation_results.py` | Mise à jour résultats paris | +| `populate_analytics.py` | Remplit tables analytiques | +| `backtest.py` | Script de calcul du backtest | +| `fetch_results.py` | Récupération résultats 1h après course | +| `xgboost_models.pkl` | Modèles ML entraînés | +| `PREDICTION_MODEL_ANALYSIS.md` | Analyse détaillée du modèle | +| `EVOLUTION_PREDICTIF.md` | Ce document | + +--- + +## 9. Résultats Automatiques + +### 9.1 Source des données + +Les résultats sont récupérés depuis l'API PMU (`pmu_results.py`) et stockés dans: +- `pmu_partants` - données des partants avec ordre d'arrivée +- `pmu_courses` - informations des courses + +### 9.2 Récupération automatique + +Le script `fetch_results.py` récupère les résultats: +- Via `pmu_results.py` qui interroge l'API PMU +- 自动每小时 ou à 14h (après les courses) +- Mode scheduler: `python fetch_results.py` +- Mode unique: `python fetch_results.py --once` + +### 9.3 Affichage dashboard + +L'API affiche automatiquement les résultats: +```python +# Query: top 5 de chaque course française (course 1) +SELECT pp.nom, pp.ordre_arrivee, pp.cote_direct +FROM pmu_partants pp +JOIN pmu_reunions pr ON ... +WHERE pays_code='FRA' AND num_course=1 +``` + +--- + +## 13. Stratégie de Paris ZE5 (Focus B4/B3) + +### 13.1 Objectif + +Suite à l'analyse des rapports, le pari le plus rentable est le **ZE5 avec objectif B4/B3** (trouver 4 ou 5 premiers). + +### 13.2 Stratégies recommandées + +| Stratégie | Description | Mise conseillée | Risque | +|-----------|-------------|------------------|--------| +| **ZE5 B4/B3** | Bases + Chances + 1 Outsider | 3€ | Moyen | +| **ZE5 Conservateur** | Top Favori + 2 Chances | 3€ | Faible | +| **ZE5 Audacieux** | Base + 2 Outsiders | 1€ | Élevé | + +### 13.3 Logique + +- **B5** (5/5) : ZE5 Ordre/Désordre - Jackpot! +- **B4** (4/5) : Rapport ~20-30€ pour 2€ (fréquent) +- **B3** (3/5) : Rapport ~5-10€ pour 2€ (très fréquent) + +### 13.4 Implémentation API + +```python +def generate_ze5_recommendations(conn, today): + # Récupérer prédictions: Bases + Chances + Outsiders + # Générer combos optimisés pour B4/B3 + return { + 'ze5_b4b3': {...}, + 'ze5_conservateur': {...}, + 'ze5_audacieux': {...} + } +``` + +### 13.5 Résultats du 25 Mars 2026 + +| Pari | Mise | Résultat | Gain | +|------|------|----------|------| +| ZE5 B4/B3 | 3€ | B3 (3/5) | 12€ | +| ZE5 Conservateur | 3€ | B3 (3/5) | 12€ | +| ZE5 Audacieux | 1€ | PERDU | 0€ | + +**ROI: +242.9%** 🎯 + +--- + +## 14. Suivi des Performances + +### 14.1 Données sauvegardées + +Les informations suivantes sont enregistrées dans la table `recommendations`: + +| Champ | Description | +|--------|-------------| +| `date` | Date de la course | +| `race_name` | Nom de la course | +| `type_pari` | Type de pari (simple_gagnant, ze5_b4b3, etc.) | +| `cheval1` | Cheval(x) sélectionné(s) | +| `numero1` | Numéro du cheval | +| `cote` | Cote du pari | +| `mise` | Mise en euros | +| `gain_potentiel` | Gain potentiel estimé | +| `confiance` | Niveau de confiance (FORTE, BONNE, etc.) | +| `justification` | Explication du choix | +| `resultat` | Résultat (GAGNE, PERDU, B3, B4, B5) | + +### 14.2 Script de mise à jour + +Le script `update_recommendation_results.py`: +- Récupère les résultats depuis `pmu_partants` +- Évalue chaque pari (ZE5: compte les matches dans top 5) +- Met à jour le champ `resultat` + +### 14.3 Calcul du ROI + +```sql +SELECT + SUM(mise) as total_mise, + SUM(CASE WHEN resultat='GAGNE' THEN mise * Cote + WHEN resultat='B3' THEN mise * 4 + WHEN resultat='B4' THEN mise * 10 + ELSE 0 END) as total_gain +FROM recommendations +WHERE date = '2026-03-25'; +``` + +--- + +## 15. Règles de Sélection des Courses + +### 15.1 Critères d'inclusion + +| Critère | Description | +|---------|-------------| +| **Pays** | France uniquement (FR) | +| **Données suffisantes** | Minimum 10 partants | +| **Type de course** | Quinté, Trot, Plat, Toutes | + +### 15.2 Implémentation API + +```python +# Filtre France uniquement +france_condition = """ + EXISTS ( + SELECT 1 FROM pmu_reunions r + WHERE r.pays_code='FRA' + AND r.date_programme=date + AND (r.hippodrome_long LIKE '%' || race_hippodrome || '%' + OR r.hippodrome_court LIKE '%' || race_hippodrome || '%' + OR r.hippodrome_code LIKE '%' || race_hippodrome || '%') + ) +""" + +# Minimum 10 partants +min_partants_condition = """ + (SELECT COUNT(*) FROM predictions p2 + WHERE p2.race_name=predictions.race_name + AND p2.date=predictions.date + AND p2.source='canalturf_partants') >= 10 +""" +``` + +--- + +## 12. Récupération des Résultats + +### 12.1 Règle + +Les résultats sont récupérés **1 heure après l'heure de départ** de chaque course. + +### 12.2 Données collectées + +| Donnée | Description | +|--------|-------------| +| Position | Rang du cheval à l'arrivée | +| Cotes finales | Rapports PMU officiels | +| Gains | Gains potentiels par type de pari | + +### 12.3 Script fetch_results.py + +```python +import schedule +import time +from datetime import datetime, timedelta + +def fetch_daily_results(): + """Récupère les résultats du jour à 14h (1h après dernière course)""" + run_result_fetch() + +schedule.every().day.at("14:00").do(fetch_daily_results) + +while True: + schedule.run_pending() + time.sleep(60) +``` + +--- + +*Document généré le 25 Mars 2026* diff --git a/H3R7Tech_logo.png b/H3R7Tech_logo.png new file mode 100755 index 0000000..766a3e2 Binary files /dev/null and b/H3R7Tech_logo.png differ diff --git a/H3R7Tech_logo.svg b/H3R7Tech_logo.svg new file mode 100755 index 0000000..2735920 --- /dev/null +++ b/H3R7Tech_logo.svg @@ -0,0 +1,44 @@ + diff --git a/PLANNING_QUOTIDIEN.md b/PLANNING_QUOTIDIEN.md new file mode 100755 index 0000000..2408bbd --- /dev/null +++ b/PLANNING_QUOTIDIEN.md @@ -0,0 +1,85 @@ +# 📅 Planning Quotidien - H3R7Tech + +## Acteurs + +| Agent | Rôle | +|-------|------| +| **main** (moi) | Réponses directes, gestion humaine | +| **sub-agents** | Tâches background (analyses, scrap, CRM) | +| **VPS** | Stockage, scraping Python, base de données | + +--- + +## 🏇 HORAIRE TURF + +| Heure | Job | Action | +|-------|-----|--------| +| **09h00** | Turf Morning v5 | Scrape cotes matinales, sauvegardes en BDD | +| **10h00** | turf-matin-10h | Analyse + prédictions (3 favoris) | +| **11h00** | turf-matin-11h | 2e analyse, evolution cotes | +| **12h00** | turf-matin-12h | 3e analyse | +| **13h00** | turf-matin-13h / Turf Afternoon | Analyse aprèm +scrape final | +| **13h45** | turf-cotes-13h45 | Cotes finales Quinté+ | +| **14h00** | turf-apres-midi / Sales Appels | Analyses + appels prospects | +| **14h30** | turf-matin-14h30 | Analyse finale, paris finalisés | +| **18h00** | turf-resultats-soir | Vérif résultats courses jour | +| **19h00** | Turf Results Evening | Scrap + comparatif prédictions | +| **21h00** | turf-resultats-final | Bilan complet journée | + +--- + +## 📊 HORAIRE CRM + +| Heure | Job | Action | +|-------|-----|--------| +| **08h00** | Mailing - Rapport quotidien | Rapport emails envoyés | +| **09h00** | Scraper - Nouveaux prospects | Scrappe annuaires en ligne | +| **10h00** | Sales - Relances prospects | Relance prospects >7 jours | +| **14h00** | Sales - Appels sortants | Appelle top prospects (score 4-5★) | +| **18h00** | Analyst - Bilan quotidien | KPIs, conversions, recommandations | + +--- + +## 🎯 FLUX DE DONNÉES + +``` +1. Cron Trigger (heure) + ↓ +2. Sub-agent wakes up (isolated session) + ↓ +3. SSH → VPS ou données locales + ↓ +4. Traitement (scrape/analyse/CRM) + ↓ +5. Résultat → Telegram (delivery) +``` + +--- + +## ⚠️ NOTES + +- **web_search/browser**: Non activés pour sub-agents = utilise données locales VPS +- **SSH**: Peut échouer si clé SSH pas configurée +- **Telegram**: Delivery parfois échoue (thread not found) → recharger conversation + +--- + +## 🔧 COMMANDES UTILES + +```bash +# Voir jobs cron +cron list + +# Lancer un job manuellement +cron run + +# Voir sessions sub-agents +sessions_list + +# Historique d'un sub-agent +sessions_history +``` + +--- + +_Mis à jour: 27/02/2026_ diff --git a/POD/README.md b/POD/README.md new file mode 100644 index 0000000..c2e2590 --- /dev/null +++ b/POD/README.md @@ -0,0 +1,86 @@ +# 🛒 H3R7Tech POD Project + +Print on Demand automation project. + +## 📋 Status + +- [ ] Research +- [ ] Designs +- [ ] Platform setup +- [ ] First sale + +## 🎯 Goals + +1. Automate product creation +2. Scale to 100+ designs +3. Generate 100€/month + +## 📦 Platforms + +- **Redbubble** - Art, stickers, mugs +- **TeeSpring** - T-shirts, hoodies +- **Printify** (optional) - Premium POD + +## 🚀 Quick Start + +```bash +# Install dependencies +pip install requests + +# Run uploader +python3 pod_uploader.py +``` + +## 📁 Structure + +``` +pod_project/ +├── README.md # This file +├── pod_uploader.py # Main automation script +├── designs/ # Your designs (.png, .jpg) +├── mockup_prompts.json # AI prompts for mockups +└── pod_upload_data.json # Generated upload data +``` + +## 💰 Pricing Formula + +``` +Selling Price = Cost × 2 (minimum) +Margin = Selling Price - Cost +``` + +## 🔧 Configuration + +Edit `pod_uploader.py` to change: +- Platform (redbubble, teespring) +- Products and costs +- Design folder +- Tags + +## 📊 Products + +| Product | Cost | Price (2x) | Margin | +|---------|------|-------------|--------| +| T-shirt | 12€ | 25€ | 13€ | +| Mug | 5€ | 15€ | 10€ | +| Poster | 4€ | 20€ | 16€ | +| Sticker | 1.5€ | 8€ | 6.5€ | +| Hoodie | 20€ | 45€ | 25€ | + +## 🤖 Automation + +The script generates: +1. Product data with pricing +2. Titles and descriptions +3. Tags for SEO +4. AI prompts for mockups + +## 📝 Next Steps + +1. Add designs to `designs/` folder +2. Run `python3 pod_uploader.py` +3. Review generated JSON +4. Upload manually to platform + +--- +*Generated by H3R7Tech - $(date)* diff --git a/POD/niches_business.html b/POD/niches_business.html new file mode 100644 index 0000000..bf135e9 --- /dev/null +++ b/POD/niches_business.html @@ -0,0 +1,46 @@ + + + + + + 💼 Business - Leads Discord + + + + + +🏠Accueil +

💼 Business - Discord Leads

🔗 Connexion Leads Discord

Le dashboard des leads est accessible ci-dessous. Pour une connexion temps réel, démarrez le bot Discord et le dashboard Flask sur les ports configurés.

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+ + diff --git a/POD/pod_manager.html b/POD/pod_manager.html new file mode 100644 index 0000000..1d0a8cd --- /dev/null +++ b/POD/pod_manager.html @@ -0,0 +1,81 @@ + + + + + + POD Manager - H3R7Tech + + + +🏠Accueil +

📦 POD Manager

Gestion Print on Demand - Designs, produits et ventes

🎨 Designs

Gestion des designs et assets graphiques

12
Actifs

3
Brouillons

👕 Produits

Catalogue des produits POD configures

45
T-shirts

23
Mugs

💰 Ventes

Suivi des ventes et revenues

127
Ce mois

2.4k
Revenue

📊 Analytics

Statistiques et performances

8.2%
Conversion

4.5
Rating

+ + diff --git a/POD/pod_uploader.py b/POD/pod_uploader.py new file mode 100755 index 0000000..e387073 --- /dev/null +++ b/POD/pod_uploader.py @@ -0,0 +1,179 @@ +#!/usr/bin/env python3 +""" +H3R7Tech - POD Automation Script +Upload designs to Redbubble, TeeSpring, or Spreadshop +""" + +import os +import json +import time +import random +from datetime import datetime + +# Configuration +CONFIG = { + "platform": "redbubble", # redbubble, teespring, spreadshop + "products": [ + {"name": "T-shirt", "base_price": 25.00, "cost": 12.00}, + {"name": "Mug", "base_price": 15.00, "cost": 5.00}, + {"name": "Poster", "base_price": 20.00, "cost": 4.00}, + {"name": "Sticker", "base_price": 8.00, "cost": 1.50}, + {"name": "Hoodie", "base_price": 45.00, "cost": 20.00}, + ], + "design_folder": "./designs", + "tags": ["h3r7tech", "tech", "coding", "developer", "python"], + "title_prefix": "H3R7Tech - ", + "description": "Official H3R7Tech merchandise. High quality products.", +} + +def load_designs(folder): + """Load all design files from folder""" + designs = [] + if not os.path.exists(folder): + os.makedirs(folder) + print(f"⚠️ Created folder: {folder}") + print(f" Add your designs (.png, .jpg) and run again") + return designs + + for f in os.listdir(folder): + if f.lower().endswith(('.png', '.jpg', '.jpeg', '.svg')): + designs.append({ + "filename": f, + "path": os.path.join(folder, f), + "name": f.replace('.png', '').replace('.jpg', '').replace('.jpeg', '').replace('_', ' ').replace('-', ' ').title() + }) + + return designs + +def calculate_prices(product, markup=2.0): + """Calculate selling price based on cost""" + cost = product["cost"] + price = round(cost * markup, 2) + margin = round(price - cost, 2) + return { + "product": product["name"], + "cost": cost, + "price": price, + "margin": margin, + "margin_pct": round((margin / price) * 100, 1) + } + +def generate_product_data(design, product): + """Generate product data for upload""" + title = f"{CONFIG['title_prefix']}{design['name']} - {product['name']}" + tags = CONFIG['tags'] + [design['name'].lower().replace(' ', ''), product['name'].lower()] + + return { + "title": title, + "description": f"{CONFIG['description']}\n\nDesign: {design['name']}\nProduct: {product['name']}\n\nTags: {', '.join(tags)}", + "tags": tags[:15], # Most platforms limit tags + "price": calculate_prices(product), + "design": design, + "product_type": product["name"] + } + +def create_upload_summary(designs, products): + """Create summary of what would be uploaded""" + print("\n" + "="*50) + print("📦 POD UPLOAD SUMMARY") + print("="*50) + print(f"Platform: {CONFIG['platform'].upper()}") + print(f"Designs: {len(designs)}") + print(f"Products: {len(products)}") + print(f"Total items to create: {len(designs) * len(products)}") + print() + + total_potential_revenue = 0 + for design in designs: + print(f"📝 {design['name']}") + for product in products: + pricing = calculate_prices(product) + print(f" → {product['name']}: {pricing['price']}€ (marge: {pricing['margin']}€)") + total_potential_revenue += pricing['price'] + + print() + print(f"💰 Revenu potentiel total: {total_potential_revenue}€") + print("="*50) + + return { + "designs": len(designs), + "products": len(products), + "total_items": len(designs) * len(products), + "potential_revenue": total_potential_revenue + } + +def export_for_upload(designs, products, output_file="pod_upload_data.json"): + """Export data for manual upload or automation""" + data = { + "generated": datetime.now().isoformat(), + "platform": CONFIG["platform"], + "items": [] + } + + for design in designs: + for product in products: + item = generate_product_data(design, product) + data["items"].append(item) + + with open(output_file, 'w', encoding='utf-8') as f: + json.dump(data, f, indent=2, ensure_ascii=False) + + print(f"\n✅ Data exported to: {output_file}") + print(f" Ready for upload or manual process automation") + +def create_mockup_description(): + """Generate mockup prompts for AI image generation""" + prompts = [] + designs = load_designs(CONFIG["design_folder"]) + + for design in designs: + for product in CONFIG["products"]: + prompt = f"""Create a product mockup for {product['name']}: +- Design: {design['name']} +- Product: {product['name']} +- Style: Professional, clean, on white background +- Include: Front view of {product['name']} showing the design centered +- Quality: High resolution, photorealistic""" + prompts.append({ + "design": design["name"], + "product": product["name"], + "prompt": prompt + }) + + with open("mockup_prompts.json", "w") as f: + json.dump(prompts, f, indent=2) + + print(f"\n✅ Created {len(prompts)} mockup prompts in mockup_prompts.json") + print(" Use these with Midjourney, DALL-E, or Stable Diffusion") + +def main(): + print("🎨 H3R7Tech POD Automation") + print("="*40) + + # Load designs + designs = load_designs(CONFIG["design_folder"]) + + if not designs: + print("❌ No designs found!") + print(f" Add designs to: {CONFIG['design_folder']}/") + return + + print(f"✅ Found {len(designs)} designs") + + # Generate summary + summary = create_upload_summary(designs, CONFIG["products"]) + + # Export data + export_for_upload(designs, CONFIG["products"]) + + # Generate mockup prompts + create_mockup_description() + + print("\n📋 NEXT STEPS:") + print("1. Review pod_upload_data.json") + print("2. Generate mockups using AI (mockup_prompts.json)") + print("3. Upload to platform manually OR use browser automation") + print("4. Monitor sales and adjust pricing") + +if __name__ == "__main__": + main() diff --git a/PREDICTION_MODEL_ANALYSIS.md b/PREDICTION_MODEL_ANALYSIS.md new file mode 100644 index 0000000..be59f58 --- /dev/null +++ b/PREDICTION_MODEL_ANALYSIS.md @@ -0,0 +1,360 @@ +# Analyse du Modèle Prédictif Turf - Documentation Technique + +## 1. Vue d'Ensemble du Système + +### 1.1 Architecture Actuelle + +Le système de prédiction turf repose sur plusieurs composants: + +- **Collecte de données**: Scraping PMU (courses, cotes, résultats) +- **Calcul de scoring**: Agrégation de multiples features par cheval +- **Génération de recommandations**: Types de paris recommandés avec confiance +- **Suivi de performance**: Comparaison prédictions vs résultats réels + +### 1.2 Sources de Données + +| Source | Tables | Description | +|--------|--------|-------------| +| Canalturf | `predictions`, `odds_history` | Cotes et prédictions externes | +| PMU | `pmu_courses`, `pmu_reunions` | Courses et réunions officielles | +| Interne | `scoring`, `recommendations` | Scores calculés et recommandations | + +--- + +## 2. Analyse des Performances Actuelles + +### 2.1 Métriques Globales + +| Métrique | Valeur | Interprétation | +|----------|--------|-----------------| +| Total prédictions evaluées | 96 | Échantillon encore réduit | +| Prédictions réussies (hit) | 20 | - | +| **Taux de réussite global** | **20.83%** | En dessous du aléatoire (25% pour top 4) | +| 1er prédit = 1er réel | 1 | 42.86% de précision pour le favori | +| Chevaux dans le top 3 | 14 | 14.58% dans le top 3 | + +### 2.2 Analyse par Rang Prédit + +| Rang prédit | Total | Hits | Taux de réussite | +|-------------|-------|------|-------------------| +| 0 (non classé) | 69 | 9 | 13.04% | +| 1 (favori) | 7 | 3 | **42.86%** | +| 2 | 10 | 3 | 30.00% | +| 3 | 10 | 5 | **50.00%** | + +**Observations:** +- Le rang 3 a le meilleur taux (50%) - possible sur-optimisation +- Les favoris (rang 1) ont 42.86% de réussite +- La catégorie "non classé" (rang 0) représente 71.8% des prédictions + +### 2.3 Problemes Identifiés + +1. **Volume insuffisant**: Seulement 96 prédictions evaluées +2. **Catégorie 0 trop large**: 69 prédictions non classifiées +3. **Pas de suivi des résultats**: Table `recommendations` n'avait pas de résultats (0/32) → **CORRIGÉ** +4. **Déséquilibre des données**: Majorité de prédictions en rang 0 + +### 2.4 Analyse du Rang 0 (Résolu) + +**Cause racine identifié:** +- `canalturf_partants`: 356 prédictions (tous les partants, rank 0 par défaut) +- `canalturf_selections`: 165 prédictions (sélections supplémentaires, rank 0) +- `canalturf_prono_*`: 198 prédictions avec rank 1/2/3 (vraies prédictions) + +**Solution:** Les prédictions avec rank 0 ne doivent PAS être incluses dans l'évaluation de performance. +Seuls les ranks 1 (bases), 2 (chances), 3 (outsiders) constituent les vraie prédictions. + +### 2.5 Résultats des Recommandations (Mise à jour) + +Après mise à jour automatique: +| Résultat | Count | +|----------|-------| +| GAGNE | 6 | +| PERDU | 14 | + +**Taux de réussite: 30%** +**ROI: -70%** (à affiner avec analyse des cotes) + +### 2.6 Modèle XGBoost - Résultats + +Après entraînement sur 4 902 lignes de données historiques: + +| Modèle | CV AUC | Amélioration vs random | +|--------|--------|----------------------| +| Top 1 (gagnant) | **0.697** | +19.7% | +| Top 3 (placé) | **0.715** | +21.5% | + +**Top Features (par importance):** +1. `cote_directe` - La cote est le predictor le plus important +2. `rang_cote` - Classement par cote +3. `implied_prob` - Probabilité implicite (1/cote) +4. `ratio_cote_field` - Ratio cote vs moyenne du field +5. `tx_victoire` - Taux de victoire historique +6. `forme_recente` - Forme récente du cheval + +**Fichiers générés:** +- `xgboost_models.pkl` - Modèles entraînés +- `feature_importance_top1.csv` - Importance des features pour top1 +- `feature_importance_top3.csv` - Importance des features pour top3 + +--- + +## 3. Structure des Données + +### 3.1 Tables Principales + +#### `predictions` (739 lignes) +```sql +-- Prédictions brutes avant scoring +date, race_name, race_hippodrome, race_time, +horse_number, horse_name, odds, prediction_rank, source +``` + +#### `scoring` (123 lignes) +```sql +-- Score composite calculé par cheval +score = f(score_cote, score_forme, score_victoire, score_place, + score_rk, score_tendance, score_avis) + +-- Features individuelles: +score_cote -- Score basé sur la cote +score_forme -- Score basé sur la forme récente +score_victoire -- Taux de victoire historique +score_place -- Taux de placé historique +score_rk -- Classement rank +score_tendance -- Tendance de la cote +score_avis -- Avis de l'entraîneur +``` + +#### `historical_data` (5 536 lignes) +```sql +-- Données historiques enrichies +date, hippodrome, distance, discipline, allocation +horse_name, horse_number, driver, age, sexe +musique, nb_courses, nb_victoires, nb_places +gains_carriere, gains_annee, reduction_km +avis_entraineur, oeilleres, deferre +cote_directe, cote_reference, est_favori +tx_victoire, tx_place, forme_recente, tendance_forme +ordre_arrivee, top1, top3, top5 -- Variables cibles +``` + +#### `recommendations` (32 lignes) +```sql +-- Recommandations de paris +type_pari: simple_gagnant, simple_place, couple_gagnant, couple_place +cheval1, numero1, cheval2, numero2 +cote, mise, gain_potentiel, confiance, justification +``` + +### 3.2 Couverture des Données + +| Métrique | Valeur | +|----------|--------| +| Période couverte | 2025-03-19 à 2026-03-18 | +| Nombre de jours de courses | 364 | +| Hippodromes différents | 45 | +| Disciplines | 5 (PLAT, TROT, MONTE, HAIES, STEEPLE) | + +--- + +## 4. Modèle de Scoring Actuel + +### 4.1 Formule de Scoring + +Le score composite est calculé selon: + +``` +score = score_cote + score_forme + score_victoire + score_place + + score_rk + score_tendance + score_avis +``` + +**Features utilisées:** +- `score_cote`: Pondération inverse de la cote (plus petit = plus haut) +- `score_forme`: Basé sur `forme_recente` (0-25 scale) +- `score_victoire`: `tx_victoire` du cheval +- `score_place`: `tx_place` du cheval +- `score_rk`: Classement relatif dans la course +- `score_tendance`: Variation de la cote +- `score_avis`: Valeur textuelle convertie (POSITIF/NEUTRE/NEGATIF) + +### 4.2 Limites du Modèle Actuel + +1. **Pas depondération apprentissage**: Les poids sont-ils optimaux? +2. **Features statiques**: Pas de features dynamiques (météo, forme vs竞争对手) +3. **Pas de cross-validation**: Pas de validation robuste +4. **Ignorer les correlations**: Interactions entre features non capturées +5. **Pas de feature engineering**: Variables dérivées limitées + +--- + +## 5. Améliorations Proposées + +### 5.1 Court Terme (Quick Wins) + +#### 5.1.1 Améliorer le Suivi des Résultats +```python +# Ajouter le remplissage automatique des résultats +def update_recommendation_results(): + """Récupérer les résultats officiels et mettre à jour recommendations""" + # 1. Scraping des résultats PMU + # 2. Matching avec recommandations + # 3. Mise à jour du champ resultat +``` + +#### 5.1.2 Réduire la Catégorie "Non Classé" +- Analyser pourquoi 71.8% des prédictions ont `predicted_rank = 0` +- Implémenter un seuil minimum de confiance + +#### 5.1.3 Ajouter Plus de Prédictions +- Automatiser le scraping quotidien +- Cible: minimum 500 prédictions avant analyse + +### 5.2 Moyen Terme (Amélioration du Modèle) + +#### 5.2.1 Feature Engineering + +**Nouvelles features à créer:** + +```python +# Features basées sur l'historique +- forme_vs_favori = forme_recente / forme_favori_moyen +- performance_par_distance = tx_victoire_par_distance[dist] +- performance_par_discipline = tx_victoire_par_discipline[disc] +- performance_par_hippodrome = tx_victoire_par_hippodrome[hippo] +- forme_tendance = forme_3_der courses vs forme_10_dernières + +# Features dynamiques +- ecart_cote = cote_directe - cote_reference +- momentum = indicateur_tendance * volume_paris +- valeur = (cote - cote_juste_estimee) / cote_juste_estimee + +# Features d'interaction +- forme_x_cote = forme_recente * (1/cote) +- age_x_performance = age * tx_victoire +``` + +#### 5.2.2 Modèle de Machine Learning + +**Approche recommandée: XGBoost avec validation croisée** + +```python +# Structure de données pour ML +features_ml = [ + 'tx_victoire', 'tx_place', 'forme_recente', + 'age', 'nb_courses', 'cote_directe', + 'distance', 'nb_partants', 'est_favori', + 'reduction_km', 'gains_annee' +] + +target_top1 = 'top1' # 1 si cheval gagné +target_top3 = 'top3' # 1 si cheval placé top 3 + +# Pipeline ML +1. Séparation train/test (80/20) +2. Cross-validation 5-fold +3. Hyperparameter tuning +4. Feature importance analysis +5. Backtest sur données historiques +``` + +#### 5.2.3 pondérations Optimales + +**Analyse des poids actuels vs optimaux:** + +| Feature | Poids actuel | Importance suggèree | +|---------|-------------|---------------------| +| score_cote | 14.7 | Haute (mais non-linéaire) | +| score_forme | 25.0 | Très haute | +| score_victoire | 15.0 | Haute | +| score_place | 15.0 | Moyenne | +| score_rk | 7.0 | Basse | +| score_tendance | 9.3 | Moyenne | +| score_avis | 6.5 | Variable | + +### 5.3 Long Terme (Système Avancé) + +#### 5.3.1 Modèles Multi-Cibles + +| Modèle | Cible | Type de pari | +|--------|-------|--------------| +| `model_top1` | top1 | Simple Gagnant | +| `model_top3` | top3 | Simple Placé | +| `model_quinte` | top5 | Couplé/Quinté | + +#### 5.3.2 Système de Cotes Justes + +```python +# Estimer la cote "juste" vs cote PMU +cote_juste = f(tx_victoire_model, tx_place_model, dispersion) + +# Identifier les "value bets" +value = cote_PMU - cote_juste +if value > seuil: + recommander_pari() +``` + +#### 5.3.3 Intégration Météo (Non Implémentée) + +La table `weather` existe mais contient seulement 4 lignes. + +**À implémenter:** +- Scraping météo quotidien +- Impact sur performance (par course) +- Ajustement des prédictions selon conditions + +--- + +## 6. Plan d'Action + +### Phase 1: Collecte de Données (Semaine 1-2) +- [ ] Automatiser le scraping quotidien +- [ ] Compléter le remplissage des résultats +- [ ] Atteindre 500+ prédictions évaluées + +### Phase 2: Analyse Exploratoire (Semaine 3) +- [ ] Analyse univariée des features +- [ ] Corrélations entre features +- [ ] Identification des patterns + +### Phase 3: Modèle ML (Semaine 4-6) +- [ ] Feature engineering +- [ ] Entraînement XGBoost +- [ ] Validation croisée +- [ ] Comparaison avec modèle actuel + +### Phase 4: Production (Semaine 7-8) +- [ ] Déploiement nouveau modèle +- [ ] Monitoring des performances +- [ ] Ajustements itératifs + +--- + +## 7. KPIs à Suivre + +| KPI | Cible Court Terme | Cible Long Terme | +|-----|-------------------|-------------------| +| Taux hit global | 25% | 35% | +| Précision favori | 45% | 50% | +| Précision top 3 | 40% | 55% | +| ROI recommandations | 0% | 10%+ | +| Volume predictions | 500+ | 2000+ | + +--- + +## 8. Conclusion + +Le système actuel montre des bases solides mais nécessite: +1. **Plus de données** pour une analyse statistique robuste +2. **Un suivi rigoureux** des résultats pour mesure de performance +3. **Une évolution vers le ML** pour optimiser les pondérations + +Les recommandations immédiates: +1. Compléter le remplissage des résultats (0/32 actuellement) +2. Augmenter le volume de prédictions +3. Passer à un modèle XGBoost avec features enrichies + +--- + +*Document généré le 2026-03-25* +*Source: Analyse de turf.db - turf_scraper* diff --git a/PROJET_TURF.md b/PROJET_TURF.md new file mode 100755 index 0000000..371e783 --- /dev/null +++ b/PROJET_TURF.md @@ -0,0 +1,223 @@ +# 🐾 PROJET TURF AUTOMATISÉ - DOCUMENTATION + +## 📅 Date: 23 Février 2026 +## Auteur: Claw (AI Assistant) + +--- + +## 🎯 OBJECTIF + +Système automatisé d'analyse et de prédiction des courses hippiques françaises pour générer des revenus passifs. + +--- + +## 📊 ÉVOLUTIONS DU JOUR (23/02/2026) + +### Améliorations majeures : + +1. **Scraper Multi-Sites v4** + - 7 sites scrapés en parallèle + - Temps d'exécution: ~2 secondes + - Sources: Equidia, ZETurf, Canalturf, Boturfers, Zone-Turf, Genybet, RuedesJoueurs + +2. **Base de données SQLite** + - Chemin: `/home/h3r7/turf_scraper/turf.db` + - Tables: predictions, results, performance + +3. **Sauvegarde temps réel** + - Les prédictions sont sauvegardées instantanément dans la BDD + +4. **Performance Tracker (REX)** + - Calcul automatique du hit rate + - Calcul du ROI + - Exemple aujourd'hui: ROI +270% + +5. **Cron Jobs Automatisés** + - 09h00: Scrap + Prédictions + - 13h00: Scrap final + Cotes + - 19h00: Résultats + REX + +--- + +## 🏗️ ARCHITECTURE DU SYSTÈME + +### Composants : + +``` +┌─────────────────────────────────────────────┐ +│ SCRAPERS (VPS) │ +├─────────────────────────────────────────────┤ +│ • multi_scraper_v5.py (7 sites) │ +│ • horse_detail_scraper.py (fiches chevaux) │ +│ • turf_db.py (gestion BDD) │ +│ • performance_tracker.py (REX) │ +└─────────────────────────────────────────────┘ + ↓ +┌─────────────────────────────────────────────┐ +│ BASE DE DONNÉES SQLite │ +│ /home/h3r7/turf_scraper/turf.db │ +├─────────────────────────────────────────────┤ +│ • predictions (prédictions) │ +│ • results (résultats courses) │ +│ • performance (tracking REX) │ +└─────────────────────────────────────────────┘ + ↓ +┌─────────────────────────────────────────────┐ +│ ANALYSE RUNTIME V4 │ +│ /home/h3r7/Projet turf/Tutf RUNTIME V4.0 │ +├─────────────────────────────────────────────┤ +│ • 25 Features ML │ +│ • 7 Red Flags │ +│ • 11 Boosts │ +│ • 10 Malus │ +└─────────────────────────────────────────────┘ +``` + +--- + +## 📁 FICHIERS CRÉÉS + +### Sur VPS (`/home/h3r7/turf_scraper/`) + +| Fichier | Description | +|---------|-------------| +| `multi_scraper_v5.py` | Scraper principal 7 sites | +| `horse_detail_scraper.py` | Fiches détaillées chevaux | +| `turf_db.py` | Gestion Base de données | +| `performance_tracker.py` | Calcul REX et ROI | +| `turf.db` | Base SQLite | +| `v5_*.json` | Exports JSON | + +### Sur le projet RUNTIME V4 (`/home/h3r7/Projet turf/Tutf RUNTIME V4.0/`) + +| Fichier | Description | +|---------|-------------| +| `src/ml/features.py` | Feature engineering (14 features) | +| `src/ml/predictor.py` | Modèles ML (RF + XGBoost) | +| `src/core/workflow.py` | Workflow 5 phases | + +--- + +## 🔧 FONCTIONNALITÉS + +### 1. Scraping Automatique + +**Sources disponibles (7 sites) :** +- ✅ Equidia +- ✅ ZETurf +- ✅ Canalturf +- ✅ Boturfers +- ✅ Zone-Turf +- ✅ Genybet +- ✅ RuedesJoueurs +- ❌ PMU (bloqué) +- ❌ Geny.com (bloqué) +- ❌ Turfomania (bloqué) + +**Données récupérées :** +- Cotes +- Pronostics presse +- Performances chevaux +- Résultats +- Pédigrée +- Entraineurs/Jockeys + +### 2. Base de données + +**Tables :** + +```sql +-- Prédictions +CREATE TABLE predictions ( + id, date, race_name, race_hippodrome, race_time, + horse_number, horse_name, odds, prediction_rank, source +); + +-- Résultats +CREATE TABLE results ( + id, date, race_name, race_hippodrome, + position, horse_name, odds +); + +-- Performance +CREATE TABLE performance ( + id, prediction_date, race_date, horse_name, + predicted_rank, actual_position, hit +); +``` + +### 3. Analyse (RUNTIME V4) + +**Architecture en 5 phases :** + +| Phase | Description | +|-------|-------------| +| 1. Collecte | Scraping multi-sources | +| 2. Analyse | Forme, Conditions, Humain, H2H | +| 3. Modélisation | ML Scoring + Value | +| 4. Décision | Sélection + Paris | +| 5. Suivi | ROI + Amélioration | + +**Features (14) :** +- Cote, Forme, Jockey win rate, Entraineur win rate +- Distance avg, Victoires, Courses, Taux victoire + +**Red Flags (7) :** +- Absence musique +- Distance jamais courue +- Hippodrome inconnu +- Ferrure Da/Dm +- Jockey débutant +- Entraineur faible +- Longue absence + +### 4. Automatisation (Cron Jobs) + +| Job | Heure | Action | +|-----|-------|--------| +| Turf Morning v5 | 09:00 | Scrap + Prédictions | +| Turf Afternoon v5 | 13:00 | Scrap final | +| Turf Results | 19:00 | Résultats + REX | + +--- + +## 📈 RÉSULTATS + +### Exemple du 23/02/2026 (Prix Rose Laurel) + +**Prédictions :** +1. EMSILORD (rank 1) +2. PASSIONATA (rank 2) +3. GABISON (rank 3) + +**Résultats :** +- PASSIONATA: 1er ✅ +- GABISON: 2e ✅ +- EMSILORD: 5e + +**ROI: +270%** 🎉 + +--- + +## 🔄 PROCHAINES ÉTAPES + +1. ✅ Scrap 7 sites +2. ✅ BDD SQLite +3. ✅ Cron jobs +4. ⏳ Connecter scraper → RUNTIME V4 ML +5. ⏳ Automatiser les paris +6. ⏳ Améliorer parsing prédictions + +--- + +## 💰 COÛTS + +| Service | Prix | +|---------|------| +| minimax-free | 0€ | +| VPS Contabo | 13.4€/mois | +| **Total** | **13.4€/mois** | + +--- + +*Document généré le 23/02/2026 par Claw* diff --git a/PROJET_TURF_COMPLET.md b/PROJET_TURF_COMPLET.md new file mode 100755 index 0000000..589feb6 --- /dev/null +++ b/PROJET_TURF_COMPLET.md @@ -0,0 +1,473 @@ +# 🐾 PROJET TURF AUTOMATISÉ - DOCUMENTATION COMPLÈTE + +## 📅 Date: 23 Février 2026 +## Version: 1.0 +## Auteur: Claw (AI Assistant) + +--- + +## 🎯 OBJECTIF DU PROJET + +Système automatisé d'analyse et de prédiction des courses hippiques françaises utilisant: +- Scraping multi-sources +- Intelligence Artificielle (RUNTIME V4) +- Base de données pour historique +- Calcul automatique de performance (ROI) + +**But final:** Générer des revenus passifs pour payer les abonnements (VPS + LLM) + +--- + +## 📊 ÉVOLutions DU JOUR + +### 23/02/2026 - Session de travail + +| # | Amélioration | Status | +|---|--------------|--------| +| 1 | Scrap 7 sites (Equidia, ZETurf, Canalturf...) | ✅ | +| 2 | Base SQLite pour historique | ✅ | +| 3 | Sauvegarde temps réel prédictions | ✅ | +| 4 | Performance tracker (REX) | ✅ | +| 5 | Documentation complète | ✅ | + +--- + +## 🏗️ ARCHITECTURE TECHNIQUE + +### Vue d'ensemble + +``` +┌──────────────────────────────────────────────────────────────┐ +│ SERVEUR VPS │ +│ 178.18.250.53 │ +├──────────────────────────────────────────────────────────────┤ +│ │ +│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ +│ │ SCRAPERS │ │ BDD │ │ RUNTIME │ │ +│ │ Python │ │ SQLite │ │ V4 │ │ +│ │ (Parallel)│ │ │ │ (ML) │ │ +│ └──────┬──────┘ └──────┬──────┘ └──────┬──────┘ │ +│ │ │ │ │ +│ └───────────────────┼───────────────────┘ │ +│ ↓ │ +│ ┌────────────────┐ │ +│ │ CRON JOBS │ │ +│ │ (Auto) │ │ +│ └────────────────┘ │ +│ ↓ │ +│ ┌────────────────┐ │ +│ │ TELEGRAM │ │ +│ │ (Alerts) │ │ +│ └────────────────┘ │ +│ │ +└──────────────────────────────────────────────────────────────┘ + ↓ + ┌────────────────┐ + │ OPENCLAW │ + │ (Main Agent) │ + └────────────────┘ +``` + +--- + +## 🔧 COMPOSANTS DÉTAILLÉS + +### 1. SCRAPERS + +#### 1.1 multi_scraper_v5.py + +**Fonction:** Scrape 7 sites en parallèle + +**Sites supportés:** +```python +SITES = { + 'equidia': ['https://www.equidia.fr/courses', ...], + 'zeturf': ['https://www.zeturf.fr/...'], + 'canalturf': ['https://www.canalturf.com/...'], + 'boturfers': ['https://www.boturfers.fr/...'], + 'zone-turf': ['https://www.zone-turf.fr/...'], + 'genybet': ['https://www.genybet.fr/...'], + 'ruedesjoueurs': ['https://www.ruedesjoueurs.com/...'] +} +``` + +**Technique:** +- ThreadPoolExecutor (10 workers) +- Timeout: 12 secondes +- User-Agent aléatoire + +**Sortie:** +- JSON: `/home/h3r7/turf_scraper/v5_*.json` +- SQLite: Table `predictions` + +--- + +#### 1.2 horse_detail_scraper.py + +**Fonction:** Récupère les détails de chaque cheval + +**Données collectées:** +- Nom, âge, sexe +- Père, Mère (pédigrée) +- Entraineur +- Côte +- Performances récentes +- Gains +- Forme (music) + +**Exemple de données:** +```json +{ + "horse_name": "PASSIONATA", + "sex_age": "F4", + "father": "GREAT PRETENDER", + "mother": "ELLEN DES MOTTES", + "trainer": "D.BRESSOU", + "cote": 8.1, + "recent_performances": [ + "2ème 07/01/2026 - 5/1 PAU", + "2ème 06/12/2025 - 9.8/1 ANGERS" + ], + "wins": 0, + "placed": 6, + "total_races": 7, + "earnings": "29745€" +} +``` + +--- + +### 2. BASE DE DONNÉES + +#### 2.1 Emplacement +`/home/h3r7/turf_scraper/turf.db` + +#### 2.2 Schéma + +```sql +-- Table des prédictions +CREATE TABLE predictions ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + date TEXT NOT NULL, -- Date course (YYYY-MM-DD) + race_name TEXT, -- Nom course (ex: "Prix Rose Laurel") + race_hippodrome TEXT, -- Hippodrome (ex: "Auteuil") + race_time TEXT, -- Heure départ (ex: "13:55") + horse_number INTEGER, -- Numéro cheval + horse_name TEXT, -- Nom cheval + odds REAL, -- Côte PMU + prediction_rank INTEGER, -- Rang prédiction (1, 2, 3) + source TEXT, -- Source (ex: "Canalturf") + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP +); + +-- Table des résultats +CREATE TABLE results ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + date TEXT NOT NULL, + race_name TEXT, + race_hippodrome TEXT, + position INTEGER, -- Position arrivée (1-5) + horse_name TEXT, + odds REAL, -- Côte rapport + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP +); + +-- Table de performance (REX) +CREATE TABLE performance ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + prediction_date TEXT, -- Date prédiction + race_date TEXT, -- Date course + horse_name TEXT, + predicted_rank INTEGER, -- Prédiction (1-3) + actual_position INTEGER, -- Résultat réel + hit BOOLEAN, -- TRUE si exact + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP +); +``` + +#### 2.3 Requêtes utiles + +```python +# Voir prédictions du jour +SELECT * FROM predictions WHERE date = '2026-02-23'; + +# Voir résultats +SELECT * FROM results WHERE date = '2026-02-23' ORDER BY position; + +# Calculer hit rate +SELECT + COUNT(*) as total, + SUM(CASE WHEN hit = 1 THEN 1 ELSE 0 END) as hits, + ROUND(CAST(SUM(CASE WHEN hit = 1 THEN 1 ELSE 0 END) AS FLOAT) / COUNT(*) * 100, 1) as hit_rate +FROM performance; + +# Calculer ROI +SELECT SUM(stake) as total_stake, SUM(return) as total_return +FROM performance; +``` + +--- + +### 3. PERFORMANCE TRACKER + +#### 3.1 Fonction + +Le tracker calcule: +- Hit rate (% de bonnes prédictions) +- ROI (Return On Investment) +- Statistiques détaillées + +#### 3.2 Exemple de calcul + +``` +Stake: 2€ par pari × 3 paris = 6€ + +Résultats: +- PASSIONATA (rank 2): 1er → Return 16.2€ (gagnant 8.1/1) +- GABISON (rank 3): 2e → Return 6€ (placé) +- EMSILORD (rank 1): 5e → Perdu + +Calcul: +- Stake: 6€ +- Return: 16.20€ +- Profit: +10.20€ +- ROI: +170% +``` + +--- + +### 4. RUNTIME V4 (ML) + +#### 4.1 Architecture ML + +**Source:** `/home/h3r7/Projet turf/Tutf RUNTIME V4.0/` + +**Modèles utilisés:** +- Random Forest +- XGBoost +- LSTM (optionnel) + +#### 4.2 Features (14) + +| Feature | Description | +|---------|-------------| +| `cote` | Côte PMU (normalisée: 1/(1+odds)) | +| `music_score` | Score de forme basé sur les performances | +| `jockey_win_rate` | Taux de victoire jockey (0-1) | +| `trainer_win_rate` | Taux de victoire entraineur (0-1) | +| `weighted_win_rate` | (victoires + 0.5×places) / courses | +| `days_since_last_race` | Jours depuis dernière course | +| `races_at_distance` | Nb courses à cette distance | +| `races_at_hippodrome` | Nb courses à cet hippodrome | +| `form_trend` | Tendance forme (amélioration/stable/dégradation) | +| `horse_age` | Age cheval | +| `weight_carried` | Poids porté | +| `draw` | Numéro de départ | +| `prize_money` | Gains totaux | +| `consistency` | Régularité | + +#### 4.3 Red Flags (7) + +| # | Condition | Impact | +|---|----------|--------| +| RF1 | `len(music) < 3` | Confiance × 0.85 | +| RF2 | `races_at_distance == 0` | Confiance × 0.90 | +| RF3 | `races_at_hippodrome == 0` | Confiance × 0.92 | +| RF4 | `ferrure in ['Da','Dm']` + montagne | Confiance × 0.92 | +| RF5 | `jockey_win_rate < 0.05` | Confiance × 0.88 | +| RF6 | `trainer_win_rate < 0.08` | Confiance × 0.90 | +| RF7 | `days_since_last_race > 180` | Confiance × 0.85 | + +#### 4.4 Boosts (11) + +| Boost | Condition | Bonus | +|-------|-----------|-------| +| B1 | Côte < 5/1 | +10% | +| B2 | 3 dernières courses 1er/2e | +15% | +| B3 | Gagnant à cet hippodrome | +10% | +| B4 | Jockey top 10 | +8% | +| B5 | Entraineur top 10 | +8% | +| B6 | Récent winner (7 jours) | +12% | +| B7 | Distance favorite | +10% | +| B8 | Absent mais bien classé avant | +5% | +| B9 | Déjanté dans les 3 premiers | +10% | +| B10 | Ferrure avantageuse | +5% | +| B11 | Valeur détectée (cote > réelle) | +20% | + +#### 4.5 Malus (10) + +| Malus | Condition | Impact | +|-------|-----------|--------| +| M1 | Côte > 30/1 | -15% | +| M2 | Plus de 3 courses sans podium | -10% | +| M3 | Jamais gagné à cet hippodrome | -8% | +| M4 | Jockey debutant (<5 courses) | -10% | +| M5 | Mauvaise distance | -12% | +| M6 | Longue absence (>90 jours) | -15% | +| M7 | Chevaux engagés récemment | -5% | +| M8 | Mauvaise musique recente | -10% | +| M9 | Mauvais terrain | -8% | +| M10 | Charge lourde (>70kg) | -10% | + +#### 4.6 Workflow (5 Phases) + +``` +┌─────────────────────────────────────────────────────────────┐ +│ PHASE 1: COLLECTE │ +│ • Scraping PMU, Geny, CanalTurf │ +│ • Validation données │ +└──────────────────────────┬──────────────────────────────────┘ + ↓ +┌──────────────────────────┴──────────────────────────────────┐ +│ PHASE 2: ANALYSE │ +│ • Analyse forme (FormAnalyst) │ +│ • Analyse conditions (ConditionsAnalyst) │ +│ • Facteur humain (HumanFactorAnalyst) │ +│ • Tête-à-tête (H2HAnalyst) │ +└──────────────────────────┬──────────────────────────────────┘ + ↓ +┌──────────────────────────┴──────────────────────────────────┐ +│ PHASE 3: MODÉLISATION │ +│ • ML Scoring (RF + XGBoost) │ +│ • Value Detection │ +└──────────────────────────┬──────────────────────────────────┘ + ↓ +┌──────────────────────────┴──────────────────────────────────┐ +│ PHASE 4: DÉCISION │ +│ • Construction sélection │ +│ • Optimisation paris │ +└──────────────────────────┬──────────────────────────────────┘ + ↓ +┌──────────────────────────┴──────────────────────────────────┐ +│ PHASE 5: SUIVI │ +│ • Tracking résultats │ +│ • Calcul ROI │ +│ • Amélioration modèle │ +└─────────────────────────────────────────────────────────────┘ +``` + +--- + +### 5. CRON JOBS + +| Job ID | Nom | Heure | Action | +|--------|-----|-------|--------| +| 1 | Turf Morning v5 | 09:00 | Scrap 7 sites → BDD → 3 picks | +| 2 | Turf Afternoon v5 | 13:00 | Scrap final → BDD → cotes | +| 3 | Turf Results | 19:00 | Scrape résultats → REX → ROI | + +--- + +## 📁 STRUCTURE DES FICHIERS + +### Sur VPS (`/home/h3r7/`) + +``` +/home/h3r7/ +├── turf_scraper/ +│ ├── multi_scraper_v5.py # Scraper principal +│ ├── horse_detail_scraper.py # Fiches chevaux +│ ├── turf_db.py # Gestion BDD +│ ├── performance_tracker.py # Calcul REX +│ ├── turf.db # Base SQLite +│ ├── PROJET_TURF.md # Documentation +│ ├── v5_*.json # Exports JSON +│ └── horses_*.json # Fiches chevaux +│ +└── Projet turf/ + └── Tutf RUNTIME V4.0/ + ├── src/ + │ ├── ml/ + │ │ ├── features.py # Feature engineering + │ │ ├── predictor.py # Modèles ML + │ │ └── trainer.py # Entraînement + │ ├── core/ + │ │ ├── workflow.py # Workflow 5 phases + │ │ ├── orchestrator.py # Orchestration + │ │ └── config.py # Configuration + │ └── scraper/ + │ └── ... + └── config.yaml +``` + +--- + +## 💰 COÛTS + +| Service | Prix | Fréquence | +|---------|------|-----------| +| minimax-free | 0€ | Illimité | +| VPS Contabo | 13.4€/mois | Mensuel | +| **Total** | **13.4€/mois** | - | + +--- + +## 🔄 UTILISATION + +### Lancer le scraper manuellement + +```bash +ssh h3r7@178.18.250.53 +cd /home/h3r7/turf_scraper +python3 multi_scraper_v5.py +``` + +### Voir les prédictions + +```bash +sqlite3 turf.db "SELECT * FROM predictions;" +``` + +### Calculer le REX + +```bash +python3 performance_tracker.py +``` + +--- + +## 📈 RÉSULTATS OBTENUS + +### 23/02/2026 - Prix Rose Laurel (Auteuil) + +| Prédiction | Résultat | Côte | Status | +|------------|----------|------|--------| +| EMSILORD (1) | 5e | 5/1 | ❌ | +| PASSIONATA (2) | 1er | 8.1/1 | ✅ | +| GABISON (3) | 2e | 12/1 | ✅ | + +**Hit Rate:** 66% (2/3 dans le top 3) +**ROI:** +270% + +--- + +## 🚀 PROCHAINES ÉTAPES + +1. **Court terme:** + - [ ] Améliorer le parsing des prédictions + - [ ] Connecter scraper → RUNTIME V4 ML + - [ ] Automatiser extraction des cotes + +2. **Moyen terme:** + - [ ] Automatiser les paris (API PMU) + - [ ] Améliorer les features ML + - [ ] Ajouter plus de sources + +3. **Long terme:** + - [ ] Système de paris automatique + - [ ] Apprentissage automatique (feedback loop) + - [ ] Scaling multi-comptes + +--- + +## 📞 SUPPORT + +Pour toute question, consulter: +- Documentation: `/home/h3r7/turf_scraper/PROJET_TURF.md` +- Code source: `/home/h3r7/turf_scraper/` +- RUNTIME V4: `/home/h3r7/Projet turf/Tutf RUNTIME V4.0/` + +--- + +*Document généré automatiquement le 23/02/2026 par Claw* +*Version: 1.0* diff --git a/SCRIPTS_DEMARCHAGE.html b/SCRIPTS_DEMARCHAGE.html new file mode 100755 index 0000000..8985f67 --- /dev/null +++ b/SCRIPTS_DEMARCHAGE.html @@ -0,0 +1,160 @@ + + + + + Scripts de Démarchage - H3R7 + + +

+ + +🏠Accueil + +

📞 Scripts de Démarchage - H3R7Tech Pro

+ +

🎯 Principes de Base

+ +

La Règle des 3 "C"

Clarté : Message simple et direct
Concis : En 30 secondes maximum
Convaincant : Proposer une solution

+ +

Structure d'un Appel Réussi

1 Accroche (5 sec) : Qui je suis + pourquoi j'appelle
2 Question découverte (10 sec) : Comprendre le besoin
3 Proposition (10 sec) : La solution + bénéfices
4 Closing (5 sec) : Proposer un rendez-vous

+ +

🏪 Script - Artisans

+ +

🎬 Accroche

"Bonjour [Prénom], je m'appelle [MonNom] de H3R7Tech. J'ai un petit moment ?"

+ +

🔍 Découverte

"Je vous appelle car nous aidons les artisans comme vous à trouver de nouveaux clients sur votre secteur. Actuellement, comment trouvez-vous vos nouveaux chantiers ?"

+ +

💡 Proposition

"Nous proposons un service de incontournabilité digitale : site web professionnel, prospection ciblée, et gestion de votre présence en ligne. Vous recevez en moyenne [X] appels par mois de clients qui vous cherchent sur Google ?"

+ +

🔚 Closing

"Would you be interested in a quick 15-minute call this week to see how we could help you get more customers? Je peux vous proposer [jour] ou [jour], quel moment vous convient ?"

+ +

🥖 Script - Boulangers / Commerces

+ +

🎬 Accroche

"Bonjour [Prénom], de la part de H3R7Tech. Comment allez-vous aujourd'hui ?"

+ +

🔍 Découverte

"Je vous appelle car nous aidons les boulangeries et commerces de proximité à se faire connaître sur internet. Avez-vous un site web actuellement ?"

+ +

💡 Proposition

"Nous proposons un site vitrine élégant pour présenter vos produits, vos horaires, et surtout permettre aux clients de vous trouver sur Google. Avec notre formule, vous n'avez rien à gérer, on s'occupe de tout."

+ +

🔚 Closing

"Puis-je vous envoyer un exemple par email ? Comme ça vous voyez ce que ça donnerait pour votre boulangerie. Quelle est votre meilleure adresse email ?"

+ +

📧 Scripts - Emails de Prospection

+ +

Email #1 - Premier Contact

Objet: Quick question for [Company] + +Bonjour [Prénom], + +Je m'appelle [MonNom] et je travaille avec H3R7Tech. + +Je regardais votre site web [URL] et je voulais vous poser une question rapide : + +Avez-vous du mal à trouver de nouveaux clients qualifiés dans votre secteur ? + +Si oui, je pense pouvoir vous aider. Nous aidons les professionnels comme vous à être plus visibles en ligne et à automatiser leur prospection. + +Avez-vous 2 minutes pour en discuter ? + +Cordialement, +[MonNom] +H3R7Tech +Tel: [MonTel]

+ +

Email #2 - Suite

Objet: Following up on my previous email + +Bonjour [Prénom], + +Je reviens vers vous suite à mon dernier email car je sais que les artisans/entrepreneurs sont très occupés. + +En quelques mots, nous aidons les professionnels à : +✓ Avoir un site web professionnel +✓ Être trouvé sur Google +✓ Automatiser la prospection + +Si ça vous intéresse, je suis disponible pour un appel rapide. + +Cordialement, +[MonNom]

+ +

📞 Réponses aux Objections

+ + + + + + + +

Objection	Réponse
"Je n'ai pas le temps"	"Je comprends totalement, c'est pourquoi je vous propose un appel de seulement 10 minutes, pendant lequel je ne vous venderai rien."
"C'est trop cher"	"Notre formule starts à [prix] par mois, et c'est beaucoup moins cher qu'un salarié à temps plein."
"Je ne suis pas interesado"	"Je respecte ça. Et si je vous disais que c'est gratuit et sans engagement ?"
"J'ai déjà un prestataire"	"Excellent, ça veut dire que vous êtes conscient de l'importance du digital. Ce que je propose, c'est une seconde opinion gratuite."

+ +

📋 Checklist - Avant Appel

+ +

[ ] Rechercher l'entreprise sur Google
[ ] Visiter leur site web
[ ] Regarder leurs réseaux sociaux
[ ] Préparer 3 points clés à mentionner
[ ] Avoir le CRM ouvert pour prendre des notes
[ ] Sourire avant d'appeler (ça s'entend !)

+ +

📊 Suivi des Appels

+ + + + + + +

Date	Entreprise	Contact	Score	Actions Suivantes
25/02	Boulangerie Dupont	Jean Dupont	⭐⭐⭐	Rappeler semaine pro
25/02	EcoServices	Marie Martin	⭐⭐	Envoyé email suivi
25/02	Artisans Rhône	-	⭐	Pas répondu

+ +

Document généré le 25/02/2026 - H3R7Tech Pro

+ + + diff --git a/SCRIPTS_DEMARCHAGE.md b/SCRIPTS_DEMARCHAGE.md new file mode 100755 index 0000000..4106793 --- /dev/null +++ b/SCRIPTS_DEMARCHAGE.md @@ -0,0 +1,178 @@ +# 📞 Scripts de Démarchage - H3R7Tech Pro + +## 🎯 Principes de Base + +### La Règle des 3 "C" +1. **Clarté** : Message simple et direct +2. **Concis** : En 30 secondes maximum +3. **Convaincant** : Proposer une solution + +### Structure d'un Appel Réussi +``` +1. Accroche (5 sec) : Qui je suis + pourquoi j'appelle +2. Question découverte (10 sec) : Comprendre le besoin +3. Proposition (10 sec) : La solution + bénéfices +4. Closing (5 sec) : Proposer un rendez-vous +``` + +--- + +## 🏪 Script - Artisans (Plombier, Électricien, Macon) + +### 🎬 Accroche +> "Bonjour [Prénom], je m'appelle [MonNom] de H3R7Tech. J'ai unpettit moment ?" + +### 🔍 Découverte +> "Je vous appelle car nous aidons les artisans comme vous à trouver de nouveaux clients sur votre secteur. Actuellement, comment trouvez-vous vos nouveaux chantiers ?" + +### 💡 Proposition +> "Nous proposons un service de incontournabilité digitale : site web professionnel, prospection ciblée, et gestion de votre présence en ligne. Vous recevez en moyenne [X] appels par mois de clients qui vous cherchent sur Google ?" + +### 🔚 Closing +> "Would you be interested in a quick 15-minute call this week to see how we could help you get more customers? Je peux vous proposer [jour] ou [jour], quel moment vous convient ?" + +--- + +## 🥖 Script - Boulangers / Commerces Alimentaires + +### 🎬 Accroche +> "Bonjour [Prénom], de la part de H3R7Tech. Comment allez-vous aujourd'hui ?" + +### 🔍 Découverte +> "Je vous appelle car nous aidons les boulangeries et commerces de proximité à se faire connaître sur internet. Avez-vous un site web actuellement ?" + +### 💡 Proposition +> "Nous proposons un site vitrine élégant pour présenter vos produits, vos horaires, et surtout permettre aux clients de vous trouver sur Google. Avec notre formule, vous n'avez rien à gérer, on s'occupe de tout." + +### 🔚 Closing +> "Puis-je vous envoyer un exemple par email ? Comme ça vous voyez ce que ça donnerait pour votre boulangerie. Quelle est votre meilleure adresse email ?" + +--- + +## 📊 Script - Professionnels du Marketing + +### 🎬 Accroche +> "Bonjour [Prénom], j'ai vu que vous proposiez des services de marketing. Nous, c'est H3R7, on a une expertise en scraping et collecte de données." + +### 🔍 Découverte +> "Currently, how do you source your leads and prospect data? Est-ce que vous avez des besoins de veille concurrentielle ou de données qualifiés ?" + +### 💡 Proposition +> "Nous avons des outils de scraping puissants qui peuvent extraire des annuaires professionnels, des bases de données sectorielles, ou même la veille tarifaire de vos concurrents. Ça vous permettrait d'avoir des leads qualifiés automatiquement." + +### 🔚 Closing +> "Je peux vous faire une démo gratuite de 15 minutes pour vous montrer ce qu'on peut extraire pour votre secteur. On schedul ça pour la semaine prochaine ?" + +--- + +## 📧 Scripts - Emails de Prospection + +### Email #1 - Premier Contact (Objet: Quick question for [Company]) + +``` +Objet: Quick question for [Company Name] + +Bonjour [Prénom], + +Je m'appelle [MonNom] et je travaille avec H3R7Tech. + +Je regardais votre site web [URL] et je voulais vous poser une question rapide : + +Avez-vous du mal à trouver de nouveaux clients qualifiés dans votre secteur ? + +Si oui, je pense pouvoir vous aider. Nous aidons les professionnels comme vous à être plus visibles en ligne et à automatiser leur prospection. + +Avez-vous 2 minutes pour en discuter ? + +Cordialement, +[MonNom] +H3R7Tech +Tel: [MonTel] +``` + +### Email #2 - Suite (Objet: Following up on my previous email) + +``` +Objet: Following up - [Company Name] + +Bonjour [Prenom], + +Je reviens vers vous suite à mon dernier email car je sais que les artisans/entrepreneurs sont très occupés. + +En quelques mots, nous aidons les professionnels à : +✓ Avoir un site web professionnel +✓ Être trouvé sur Google +✓ Automatiser la prospection + +Si ça vous intéresse, je suis disponible pour un appel rapide. + +Sinon, n'hésitez pas à me répondre pour me dire si ce n'est pas d'actualité. + +Cordialement, +[MonNom] +``` + +### Email #3 - Demande de Rendez-vous (Objet: Call to discuss your marketing) + +``` +Objet: 15 min call to discuss [Company] growth? + +Bonjour [Prénom], + +Pour faire simple : je pense pouvoir vous aider à obtenir plus de clients. + +Nous avons aidé [X] professionnels cette année à : +- Augmenter leur visibilité en ligne +- Automatiser leur prospection +- Gagner du temps sur leur administratif + +Est-ce que 15 minutes cette semaine seraient possibles pour en discuter ? +- [Jour1] à [Heure1] +- [Jour2] à [Heure2] + +Sinon, cliquez ici pour réserver votre créneau : [Lien Calendly] + +À bientôt, +[MonNom] +``` + +--- + +## 📞 Scripts - Réponses aux Objections + +### Objection: "Je n'ai pas le temps" +> "Je comprends totalement, c'est pourquoi je vous propose un appel de seulement 10 minutes, pendant lequel je ne vous venderai rien. Je vous pose juste quelques questions pour voir si je peux vous aider. Ça vous va ?" + +### Objection: "C'est trop cher" +> "Je comprends. Justement, notre formule starts à [prix] par mois, et c'est beaucoup moins cher qu'un salarié à temps plein. Et surtout, ça vous apporte des clients directement." + +### Objection: "Je ne suis pas intéressé" +> "Je respecte ça. Et si je vous disais que c'est gratuit et sans engagement, vous seriez curieux de voir ce qu'on peut faire pour vous ?" + +### Objection: "J'ai déjà un prestataire" +> "Excellent, ça veut dire que vous êtes conscient de l'importance du digital. Ce que je vous propose, c'est une seconde opinion gratuite. Comme ça vous avez toutes les cartes en main pour comparer." + +--- + +## 📋 Checklist - Avant Appel + +- [ ] Rechercher l'entreprise sur Google +- [ ] Visiter leur site web +- [ ] Regarder leurs réseaux sociaux +- [ ] Préparer 3 points clés à mentionner +- [ ] Avoir le CRM ouvert pour prendre des notes +- [ ] Sourire avant d'appeler (ça s'entend !) + +--- + +## 📊 Suivi des Appels + +| Date | Entreprise | Contact | Score | Actions Suivantes | +|------|------------|---------|-------|-------------------| +| 25/02 | Boulangerie Dupont | Jean Dupont | ⭐⭐⭐ | Rappeler semaine pro | +| 25/02 | EcoServices | Marie Martin | ⭐⭐ | Envoyé email suivi | +| 25/02 | Artisans Rhône | - | ⭐ | Pas répondu | + +--- + +*Document généré le 25/02/2026 - H3R7Tech Pro* diff --git a/SPECS_TECHNIQUES_FONCTIONNELLES.docx b/SPECS_TECHNIQUES_FONCTIONNELLES.docx new file mode 100644 index 0000000..0c01577 Binary files /dev/null and b/SPECS_TECHNIQUES_FONCTIONNELLES.docx differ diff --git a/SUB_AGENTS.md b/SUB_AGENTS.md new file mode 100755 index 0000000..3582cf2 --- /dev/null +++ b/SUB_AGENTS.md @@ -0,0 +1,172 @@ +# 🤖 Sub-Agents H3R7Tech - Documentation + +## Vue d'Ensemble + +Les sub-agents sont des sessions automatisées qui peuvent exécuter des tâches en arrière-plan. Voici l'organisation proposée : + +``` + ┌─────────────────────┐ + │ 🌟 H3R7Tech │ + │ (Main Session) │ + └──────────┬──────────┘ + │ + ┌──────────────────────┼──────────────────────┐ + │ │ │ + ▼ ▼ ▼ +┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ +│ 🤖 AgentScraper │ │ 🤖 AgentSales │ │ 🤖 AgentMailing │ +│ (Sessions) │ │ (Sessions) │ │ (Sessions) │ +└─────────────────┘ └─────────────────┘ └─────────────────┘ +``` + +--- + +## 🎭 Rôles des Sub-Agents + +### 1. 🤖 AgentScraper +**Purpose:** Collecte automatique de données prospects + +**Sessions spawnables :** +- `scraper_annuaires` - Extraction PagesJaunes, Mistersuite +- `scraper_concurrents` - Veille tarifaire +- `scraper_social` - Données réseaux sociaux + +**Commandes :** +```bash +# Lancer un scraping +sessions_spawn(task="Scraper les plombiers sur Paris", agentId="scraper") + +# Via cron +cron add --schedule "0 9 * * *" --task "Scraper annuaires" --session scraper +``` + +--- + +### 2. 🤖 AgentSales +**Purpose:** Démarchage automatique et suivi + +**Sessions spawnables :** +- `sales_outbound` - Appels sortants +- `sales_follow` - Relances +- `sales_closing` - Fermeture des ventes + +**Commandes :** +```bash +# Lancer prospection +sessions_spawn(task="Appeler les 10 premiers prospects du CRM", agentId="sales") + +# Follow-up automatique +cron add --schedule "0 10 * * *" --task "Relance prospects" --session sales +``` + +--- + +### 3. 🤖 AgentMailing +**Purpose:** Campagnes email automatisées + +**Sessions spawnables :** +- `mailing_sequence` - Séquences de prospection +- `mailing_follow` - Relances email +- `mailing_report` - Rapports Campaign + +**Commandes :** +```bash +# Envoyer séquence +sessions_spawn(task="Envoyer séquence welcome aux nouveaux prospects", agentId="mailing") + +# Rapport quotidien +cron add --schedule "0 8 * * *" --task "Rapport emailing" --session mailing +``` + +--- + +### 4. 🤖 AgentSupport +**Purpose:** Relation client et SAV + +**Sessions spawnables :** +- `support_responses` - Réponses automatiques +- `support_tickets` - Gestion incidents + +--- + +### 5. 🤖 AgentAnalyst +**Purpose:** Analyses et rapports + +**Sessions spawnables :** +- `analyst_stats` - KPIs quotidiens +- `analyst_forecast` - Prédictions business + +--- + +## ⚙️ Configuration des Agents + +### Fichier de config agents (agents.json) : + +```json +{ + "agents": { + "scraper": { + "description": "Collecte de données prospects", + "model": "minimax-m2.1-free", + "system_prompt": "Tu es un expert en scraping et collecte de données. Ta mission est de trouver des prospects qualifiés pour H3R7Tech." + }, + "sales": { + "description": "Démarchage et prospection", + "model": "minimax-m2.1-free", + "system_prompt": "Tu es un expert en vente B2B. Tu sais persuasive et clos des deals. Tu travailles pour H3R7Tech." + }, + "mailing": { + "description": "Campagnes email", + "model": "minimax-m2.1-free", + "system_prompt": "Tu es un expert en email marketing. Tu sais écrire des emails qui convertissent. Tu travailles pour H3R7Tech." + } + } +} +``` + +--- + +## 📅 Planification (Cron Jobs) + +| Heure | Agent | Tâche | Fréquence | +|-------|-------|-------|-----------| +| 08:00 | Mailing | Rapport quotidien | Quotidien | +| 09:00 | Scraper | Nouveaux prospects | Quotidien | +| 10:00 | Sales | Relances | Quotidien | +| 14:00 | Sales | Appels sortants | Quotidien | +| 18:00 | Analyst | Bilan quotidien | Quotidien | + +--- + +## 🚀 Utilisation + +### Lancer un agent manuellement : +```python +sessions_spawn( + agentId="scraper", + task="Scraper les artisans boulangeries sur Lyon", + label="scraper_lyon" +) +``` + +### Suivre les sessions : +```bash +sessions_list() +``` + +### Historique : +```bash +sessions_history(sessionKey="scraper_lyon") +``` + +--- + +## 🔐 Sécurité + +- **Ne jamais** donner accès aux données clients outside +- **Toujours** valider avant envoi externe +- **Journaliser** toutes les actions + +--- + +*Document généré le 25/02/2026 - H3R7Tech* diff --git a/TUTORIEL_APIFY.md b/TUTORIEL_APIFY.md new file mode 100755 index 0000000..eb1f360 --- /dev/null +++ b/TUTORIEL_APIFY.md @@ -0,0 +1,127 @@ +# 🎬 Tutoriel Apify - Extraction Artisans + +## Qu'est-ce qu'Apify ? + +**Apify** est une plateforme cloud de scraping qui: +- ✅ Contourne les blocages (Cloudflare, anti-bot) +- ✅ Navigateurs headless intégrés +- ✅ Proxy automatique +- ✅ 5$/mois gratuit + +--- + +## Étape 1 : Inscription + +1. Aller sur **https://apify.com** +2. Cliquer **"Sign up free"** +3. S'inscrire avec **Google** ou **Email** +4. Valider l'email + +--- + +## Étape 2 : Rechercher un Actor + +1. Dans le dashboard, aller sur **"Store"** +2. Rechercher : `google-maps` ou `pagesjaunes` +3. Choisir un actor gratuit + +**Recommandés :** +- `quick-reviews-scraper` - Avis Google +- `google-places-scraper` - Places Google +- `web-scraper` - Général + +--- + +## Étape 3 : Configurer le Scraper + +Exemple avec **Google Maps Scraper** : + +``` +URL à scrapper : +https://www.google.com/maps/search/cordonnier/@50.6276,3.0535,13z/data=!3m1!4b1 + +Paramètres : +- maxResults: 50 +- language: fr +- country: fr +``` + +--- + +## Étape 4 : Lancer le Scraping + +1. Cliquer **"Run"** +2. Patienter (1-5 min) +3. Voir la progression dans **"Run"** + +--- + +## Étape 5 : Exporter les Données + +1. Aller dans **"Dataset"** +2. Cliquer **"Export"** +3. Choisir **CSV** ou **JSON** +4. Télécharger + +--- + +## Alternative : Créer son propre Actor + +### Code minimal pour Google Maps : + +```javascript +// actor.js +const { Actor } = require('apify'); +const Apify = require('apify'); + +Apify.main(async () => { + const input = await Apify.getInput(); + const { search, maxResults } = input; + + const browser = await Apify.launchPuppeteer(); + const page = await browser.newPage(); + + const url = `https://www.google.com/maps/search/${search}`; + await page.goto(url, { waitUntil: 'networkidle0' }); + + // Extraire les données + const results = await page.evaluate(() => { + // ... selecteurs CSS + }); + + await Apify.pushData(results); + await browser.close(); +}); +``` + +--- + +## Intégration CRM + +Une fois les données exportées : + +1. **Télécharger** le CSV depuis Apify +2. **Aller** sur http://178.18.250.53:8770/ +3. **Importer** les données manuellement + +--- + +## 💰 Coûts + +| Action | Coût | +|--------|-------| +| Inscription | Gratuit | +| 5$/mois | 1000 requêtes | +| Actor personnalisé | Gratuit | + +--- + +## 🔗 Liens Utiles + +- **Dashboard** : https://console.apify.com +- **Store** : https://console.apify.com/store +- **Docs** : https://docs.apify.com + +--- + +*Document généré le 25/02/2026 - H3R7Tech* diff --git a/WORKFLOW_SCRAPING.md b/WORKFLOW_SCRAPING.md new file mode 100755 index 0000000..638b282 --- /dev/null +++ b/WORKFLOW_SCRAPING.md @@ -0,0 +1,188 @@ +# 🏢 H3R7Tech - Workflow Extraction Artisans + +## 📋 Résumé + +Ce workflow permet d'extraire les coordonnées des artisans d'une ville précise depuis les annuaires professionnels et de les intégrer dans un CRM. + +--- + +## 🏗️ Architecture de la Solution + +``` +┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ +│ EXTRACTION │────▶│ STOCKAGE │────▶│ CRM │ +│ │ │ │ │ │ +│ • Pages Jaunes │ │ • Fichier JSON │ │ • Suivi statut │ +│ • Google Maps │ │ • Export CSV │ │ • Scoring │ +│ • Google Search │ │ • Google Sheets │ │ • Relances │ +└─────────────────┘ └─────────────────┘ └─────────────────┘ +``` + +--- + +## 🔧 Méthodes d'Extraction (Gratuites) + +### Option 1: Script Python (Ce projet) +```bash +python3 scraper_artisans.py --profession "cordonnier" --ville "lille" --cp "59000" +``` + +### Option 2: Extension Chrome "Web Scraper" +1. Installer l'extension "Web Scraper" sur Chrome +2. Créer un sitemap pour PagesJaunes +3. Exporter les données en CSV +4. Importer dans le CRM + +### Option 3: Google Sheets + ImportXML +```excel +=IMPORTXML("https://www.pagesjaunes.fr/annuaire/lille-59000/cordonnier", "//div[@class='bi-thu__ItemSearchResult']") +``` + +--- + +## 📊 Données Extraites + +| Champ | Description | Exemple | +|-------|-------------|---------| +| Nom | Nom de l'artisan | Dupont Philippe | +| Entreprise | Nom de l'entreprise | Dupont Cordonnerie | +| Activité | Métier | Cordonnier | +| Adresse | Adresse complète | 45 Rue Nationale, Lille 59000 | +| Téléphone | Numéro | 0320123456 | +| Site web | URL | www.dupont-cordonnerie.fr | +| Note | Note Google | 4.5 | +| Avis | Nombre d'avis | 120 | + +--- + +## 💾 Stockage + +### Format JSON (CRM local) +```json +{ + "prospects": [ + { + "id": 1, + "nom": "Dupont Philippe", + "entreprise": "Dupont Cordonnerie", + "tel": "0320123456", + "secteur": "Cordonnier", + "statut": "nouveau", + "score": 4, + "notes": "Adresse: 45 Rue Nationale...", + "source": "Scraping", + "created": "2026-02-25T20:42:00" + } + ] +} +``` + +### Export CSV (pour Google Sheets) +Le script génère un fichier CSV compatible Google Sheets: +- `exports/prospects_20260225_204200.csv` + +--- + +## 📊 CRM - Suivi des Contacts + +### Statuts de suivi + +| Statut | Description | Action | +|--------|-------------|--------| +| 🔵 Nouveau | Prospect nouvellement ajouté | À appeler | +| 🟡 Qualifié | Besoin identifié | Proposition | +| 🟣 Proposition | Devis envoyé | En attente | +| 🟢 Gagné | Client acquis | Suivi SAV | +| 🔴 Perdu | Pas de réponse | À relancer | + +### Score de qualification (1-5) + +| Score | Critères | +|-------|----------| +| ⭐ | Nom uniquement | +| ⭐⭐ | + Téléphone | +| ⭐⭐⭐ | + Note/avis | +| ⭐⭐⭐⭐ | + Site web | +| ⭐⭐⭐⭐⭐ | Complete | + +--- + +## 🚀 Utilisation + +### 1. Lancer le scraper +```bash +python3 scraper_artisans.py --profession "cordonnier" --ville "lille" --cp "59000" +``` + +### 2. Ajouter manuellement +Via l'interface: http://178.18.250.53:8765/scraper_pro.html + +### 3. Exporter vers Google Sheets +1.Exporter en CSV depuis le CRM +2.Importer dans Google Sheets +3.Configurer les filtres + +--- + +## 📱 Intégrations Gratuites Recommandées + +### CRM Gratuits +| Service | Limite gratuite | Features | +|---------|-----------------|----------| +| HubSpot Free | 1 user, 1M contacts | Complet | +| Airtable | 1,000 rows | Base + Views | +| Notion | Illimité | Database | +| Google Sheets | Illimité | + ImportXML | + +### Google Sheets - Configuration +1. Créer un nouveau Sheet +2. Importer le CSV exporté +3. Créer des vues filtrées par statut +4. Ajouter des validations de données + +--- + +## ⚠️ Limitations Techniques + +### PagesJaunes / Google Maps +- **Bloquent** les requêtes automatisées +- **Cloudflare** active des CAPTCHA +- **Solution**: Utiliser un navigateur headless (Playwright) avec stealth mode + +### Alternatives +- **APIfy**: Service payants avec proxy rotatif +- **ScraperAPI**: Crédits gratuits disponibles +- **Manual**: Ajout manuel via l'interface + +--- + +## 📁 Fichiers du Projet + +``` +/home/h3r7/turf_scraper/ +├── scraper_artisans.py # Script principal +├── crm_prospects.json # Base CRM +├── exports/ # Exports CSV +│ └── prospects_*.csv +└── scraper_pro.html # Interface web +``` + +--- + +## 🎯 Checklist de Qualification + +- [ ] Téléphone disponible → Appeler +- [ ] Note > 4.0 → Priorité haute +- [ ] Avis > 50 → Qualité confirmée +- [ ] Site web → Demander un devis en ligne +- [ ] Dans la zone → Visite commerciale + +--- + +## 📞 Scripts de Prospection + +Voir le document: SCRIPTS_DEMARCHAGE.html + +--- + +*Document généré le 25/02/2026 - H3R7Tech* diff --git a/add_equidia.py b/add_equidia.py new file mode 100755 index 0000000..5d3c5cd --- /dev/null +++ b/add_equidia.py @@ -0,0 +1,38 @@ +#!/usr/bin/env python3 +"""Add Equidia prediction""" +import sqlite3 +from datetime import datetime + +DB_PATH = "/home/h3r7/turf_scraper/turf.db" +conn = sqlite3.connect(DB_PATH) +c = conn.cursor() + +# Add Equidia prediction for today +# From email: "I'M A BELIEVER (7) peut mettre tout le monde d'accord" +# Let's add the top 3 from Equidia analysis + +today = "2026-02-24" +race = "Quinte Cagnes-sur-Mer" + +# Based on the email, we know I'M A BELIEVER is the favorite +# This is a placeholder - we'd need to parse the full email for complete picks +equidia_picks = [ + (today, "equidia", race, "I'M A BELIEVER", 0, 1, 80), # 80% confidence +] + +for date, source, race_name, horse, odds, rank, conf in equidia_picks: + c.execute(""" + INSERT INTO external_predictions + (date, source, race_name, horse_name, odds, rank, confidence) + VALUES (?, ?, ?, ?, ?, ?, ?) + """, (date, source, race_name, horse, odds, rank, conf)) + +conn.commit() +print("Equidia prediction added!") + +# Verify +c.execute("SELECT * FROM external_predictions WHERE source = 'equidia'") +for row in c.fetchall(): + print(row) + +conn.close() diff --git a/add_grok.py b/add_grok.py new file mode 100755 index 0000000..98d2241 --- /dev/null +++ b/add_grok.py @@ -0,0 +1,27 @@ +#!/usr/bin/env python3 +import sqlite3 + +DB_PATH = "/home/h3r7/turf_scraper/turf.db" +conn = sqlite3.connect(DB_PATH) +c = conn.cursor() + +# Add yesterday's Grok picks (from email) +picks = [ + ("2026-02-23", "Quinte Auteuil", "PASSIONATA", 8.1, 1), + ("2026-02-23", "Quinte Auteuil", "GABISON", 7.5, 2), + ("2026-02-23", "Quinte Auteuil", "EMSILORD", 5.2, 3), +] + +for date, race, horse, odds, rank in picks: + c.execute("INSERT INTO grok_predictions (date, race_name, horse_name, odds, rank) VALUES (?, ?, ?, ?, ?)", + (date, race, horse, odds, rank)) + +conn.commit() +print("OK - Grok predictions added") + +# Verify +c.execute("SELECT * FROM grok_predictions") +for row in c.fetchall(): + print(row) + +conn.close() diff --git a/add_grok_today.py b/add_grok_today.py new file mode 100755 index 0000000..b07b72c --- /dev/null +++ b/add_grok_today.py @@ -0,0 +1,29 @@ +import sqlite3 +conn = sqlite3.connect('/home/h3r7/turf_scraper/turf.db') +c = conn.cursor() + +today = '2026-02-24' + +# Delete old Grok predictions for today +c.execute('DELETE FROM external_predictions WHERE source = ? AND date = ?', ('grok', today)) + +# Add new Grok predictions from email +grok_picks = [ + (7, "I'M A BELIEVER", 1, 85), + (3, 'GRAND BALCON', 2, 70), + (1, 'PRINCE DE MONTFORT', 3, 65), +] + +for num, name, rank, conf in grok_picks: + c.execute('''INSERT INTO external_predictions (date, source, race_name, horse_name, horse_number, odds, rank, confidence) + VALUES (?, ?, ?, ?, ?, ?, ?, ?)''', + (today, 'grok', 'Quinte Cagnes-sur-Mer', name, num, 0, rank, conf)) + +conn.commit() +print('Updated Grok!') + +c.execute('SELECT horse_number, horse_name, rank FROM external_predictions WHERE source = ? AND date = ?', ('grok', today)) +for r in c.fetchall(): + print(f" {r[0]} - {r[1]} (rank {r[2]})") + +conn.close() diff --git a/add_jockeys.py b/add_jockeys.py new file mode 100755 index 0000000..a62a471 --- /dev/null +++ b/add_jockeys.py @@ -0,0 +1,30 @@ +import sqlite3 +conn = sqlite3.connect('/home/h3r7/turf_scraper/turf.db') +c = conn.cursor() + +# Update predictions with jockey names +# From Equidia screenshot: +jockeys = { + 7: "T. PICCONE", + 1: "C. DEMURO", + 3: "A. CRASTUS", + 8: "S. PASQUIER", + 11: "C. LECOUVRE", + 15: "E. HARDOUIN", + 16: "M. GRANDIN", + 14: "A. LEMAITRE" +} + +today = '2026-02-24' +for num, jockey in jockeys.items(): + c.execute('UPDATE predictions SET jockey = ? WHERE date = ? AND horse_number = ?', + (jockey, today, num)) + +conn.commit() +print("Updated!") + +c.execute('SELECT horse_number, horse_name, jockey, prediction_rank FROM predictions WHERE date = ? ORDER BY prediction_rank', (today,)) +for r in c.fetchall(): + print(f" {r[0]} - {r[1]} ({r[2]})") + +conn.close() diff --git a/add_odds.py b/add_odds.py new file mode 100755 index 0000000..c612938 --- /dev/null +++ b/add_odds.py @@ -0,0 +1,32 @@ +import sqlite3 +from datetime import datetime +conn = sqlite3.connect('/home/h3r7/turf_scraper/turf.db') +c = conn.cursor() + +# Add odds from scraper data +today = '2026-02-24' +now = datetime.now().strftime('%H:%M') + +odds_data = { + 7: 4.0, # I'M A BELIEVER + 1: 7.3, # PRINCE DE MONTFORT + 3: 6.7, # GRAND BALCON + 8: 18.0, # PAOLINO + 11: 19.0, # INCREMENTAL + 15: 13.0, # PRINCESSE SAPHIR + 16: 30.0, # GOLD PLAYER + 14: 18.0 # WEEMAGATEE +} + +for num, odds in odds_data.items(): + c.execute('UPDATE predictions SET odds = ?, odds_time = ? WHERE date = ? AND horse_number = ?', + (odds, now, today, num)) + +conn.commit() +print("Odds updated!") + +c.execute('SELECT horse_number, horse_name, odds, odds_time FROM predictions WHERE date = ? ORDER BY prediction_rank', (today,)) +for r in c.fetchall(): + print(f" {r[0]} - {r[1]}: {r[2]}/1 à {r[3]}") + +conn.close() diff --git a/add_preds.py b/add_preds.py new file mode 100755 index 0000000..d0dc11b --- /dev/null +++ b/add_preds.py @@ -0,0 +1,30 @@ +#!/usr/bin/env python3 +import sqlite3 +conn = sqlite3.connect('/home/h3r7/turf_scraper/turf.db') +c = conn.cursor() + +today = '2026-02-24' + +# All Canalturf predictions for Quinté +preds = [ + (7, "I'M A BELIEVER", 1), + (1, 'PRINCE DE MONTFORT', 2), + (3, 'GRAND BALCON', 3), + (8, 'PAOLINO', 4), + (11, 'INCREMENTAL', 5), + (15, 'PRINCESSE SAPHIR', 6), + (16, 'GOLD PLAYER', 7), + (14, 'WEEMAGATEE', 8), +] + +for num, name, rank in preds: + c.execute('INSERT OR REPLACE INTO predictions (date, race_name, horse_number, horse_name, prediction_rank, source) VALUES (?, ?, ?, ?, ?, ?)', + (today, 'Quinte Cagnes-sur-Mer', num, name, rank, 'canalturf')) + +conn.commit() +print('Saved!') + +c.execute('SELECT horse_number, horse_name, prediction_rank FROM predictions WHERE date = ? AND source = ?', (today, 'canalturf')) +for r in c.fetchall(): + print(f' {r[0]} - {r[1]} (rank {r[2]})') +conn.close() diff --git a/add_scheduler_job.py b/add_scheduler_job.py new file mode 100755 index 0000000..079c759 --- /dev/null +++ b/add_scheduler_job.py @@ -0,0 +1,46 @@ +#!/usr/bin/env python3 +import json +import os +import shutil +import subprocess + +JOBS_PATH = "/var/lib/docker/volumes/ow404080wwkkgkgc4oswwssc_openclaw-data/_data/.openclaw/cron/jobs.json" +BACKUP = JOBS_PATH + ".bak" + +print("📦 Sauvegarde du fichier jobs.json…") +shutil.copy(JOBS_PATH, BACKUP) + +with open(JOBS_PATH, "r", encoding="utf-8") as f: + data = json.load(f) + +# Vérifier si le job existe déjà +for job in data["jobs"]: + if job.get("name") == "turf-scheduler-08h": + print("ℹ️ Le job turf-scheduler-08h existe déjà. Rien à faire.") + exit(0) + +print("🛠 Ajout du job turf-scheduler-08h…") + +new_job = { + "id": "turf-scheduler-08h", + "name": "turf-scheduler-08h", + "enabled": True, + "schedule": { + "expr": "0 8 * * *" + }, + "command": "python3 /home/h3r7/turf_scraper/turf_scheduler.py", + "timeout": 3600, + "retries": 0 +} + +data["jobs"].append(new_job) + +with open(JOBS_PATH, "w", encoding="utf-8") as f: + json.dump(data, f, indent=2, ensure_ascii=False) + +print("✅ Job ajouté avec succès.") +print("🔄 Redémarrage d’OpenClaw…") + +subprocess.run(["docker", "restart", "openclaw-ow404080wwkkgkgc4oswwssc"]) + +print("🎉 Terminé !") diff --git a/agent_chat.html b/agent_chat.html new file mode 100755 index 0000000..514ee08 --- /dev/null +++ b/agent_chat.html @@ -0,0 +1,155 @@ + + + + + Agent Chat - H3R7Tech + + + +🏠Accueil +

+ +

💬 Agent Chat - H3R7Tech

+ +

Messages

Agents

Succès

Erreurs

+ +

+ + + ← Retour Portail +

+ +

📝 Historique

+ + + + diff --git a/agent_chat_api.py b/agent_chat_api.py new file mode 100755 index 0000000..d721243 --- /dev/null +++ b/agent_chat_api.py @@ -0,0 +1,82 @@ +#!/usr/bin/env python3 +""" +Agent Chat API - Communications entre agents H3R7Tech +""" +from flask import Flask, jsonify, request, send_from_directory +from flask_cors import CORS +import json +import os +from datetime import datetime + +app = Flask(__name__) +CORS(app) + +CHAT_FILE = '/home/h3r7/turf_scraper/agent_chat.json' + +def load_chat(): + if not os.path.exists(CHAT_FILE): + data = {"messages": []} + with open(CHAT_FILE, 'w') as f: + json.dump(data, f, indent=2) + return data + with open(CHAT_FILE, 'r') as f: + return json.load(f) + +def save_chat(data): + with open(CHAT_FILE, 'w') as f: + json.dump(data, f, indent=2) + +@app.route('/') +def index(): + return send_from_directory('/home/h3r7/turf_scraper', 'agent_chat.html') + +@app.route('/api/messages', methods=['GET']) +def get_messages(): + data = load_chat() + limit = request.args.get('limit', 50) + return jsonify(data['messages'][-int(limit):]) + +@app.route('/api/messages', methods=['POST']) +def add_message(): + data = load_chat() + req = request.json + + message = { + 'id': len(data['messages']) + 1, + 'agent': req.get('agent', 'system'), + 'type': req.get('type', 'info'), # info, warning, error, success + 'content': req.get('content', ''), + 'timestamp': datetime.now().isoformat() + } + data['messages'].append(message) + save_chat(data) + return jsonify({'success': True, 'message': message}) + +@app.route('/api/messages', methods=['DELETE']) +def clear_messages(): + data = {'messages': []} + save_chat(data) + return jsonify({'success': True}) + +@app.route('/api/stats', methods=['GET']) +def get_stats(): + data = load_chat() + messages = data['messages'] + + stats = { + 'total': len(messages), + 'par_agent': {}, + 'par_type': {} + } + + for m in messages: + agent = m.get('agent', 'unknown') + stats['par_agent'][agent] = stats['par_agent'].get(agent, 0) + 1 + + type_msg = m.get('type', 'info') + stats['par_type'][type_msg] = stats['par_type'].get(type_msg, 0) + 1 + + return jsonify(stats) + +if __name__ == '__main__': + app.run(host='0.0.0.0', port=8771, debug=False) diff --git a/agent_ia.html b/agent_ia.html new file mode 100644 index 0000000..34d7b35 --- /dev/null +++ b/agent_ia.html @@ -0,0 +1,609 @@ + + + + + + Agent IA + + + + +🏠Accueil +

+ +

Agent IA

+ ⚙ Config + +

+ +

+ + +

Bienvenue sur Agent IA

Sélectionnez un onglet et commencez une conversation

+ + + +

+ + + + diff --git a/agent_ia_config.html b/agent_ia_config.html new file mode 100644 index 0000000..db4a5fd --- /dev/null +++ b/agent_ia_config.html @@ -0,0 +1,197 @@ + + + + + + Agent IA - Configuration + + + +🏠Accueil +

+ ← Retour +

Configuration - Gestion de l'historique

+ +

Statistiques

Sessions

Messages

+ +

Supprimer l'historique

+ Cette action supprimera définitivement les messages plus anciens que la période sélectionnée. Les sessions vides seront également supprimées. +

+ +

+ Workflow cible + +

+ +

+ Période de rétention + +

+ + + +

+ +

Supprimer toutes les conversations

+ Attention : cette action est irréversible. Toutes les sessions et messages du workflow sélectionné seront supprimés. +

+ +

+ Workflow cible + +

+ + +

+ + + + diff --git a/agent_turf.py b/agent_turf.py new file mode 100755 index 0000000..9fd4128 --- /dev/null +++ b/agent_turf.py @@ -0,0 +1,347 @@ +#!/usr/bin/env python3 +""" +Agent Turf Autonome - Prédictions quotidiennes +Usage: python3 agent_turf.py [--dry-run] +""" + +import sqlite3 +import subprocess +import sys +import json +import os +from datetime import datetime, timedelta +from pathlib import Path + +DB_PATH = "/home/h3r7/turf_scraper/turf.db" +LOG_FILE = "/home/h3r7/turf_scraper/logs/agent_turf.log" +VPS_HOST = "10.0.1.1" + + +def log(msg: str): + """Log avec timestamp""" + timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S") + line = f"[{timestamp}] {msg}" + print(line) + + os.makedirs(os.path.dirname(LOG_FILE), exist_ok=True) + with open(LOG_FILE, "a") as f: + f.write(line + "\n") + + +def sync_database(): + """Sync base de données depuis VPS""" + log("=== SYNC DATABASE ===") + + # Check remote size + cmd = f"sshpass -p 'Cronstadt35*' ssh -o StrictHostKeyChecking=no h3r7@{VPS_HOST} stat -c %s {DB_PATH}" + result = subprocess.run(cmd, shell=True, capture_output=True, text=True) + + if result.returncode != 0: + log(f"ERROR: Cannot connect to VPS") + return False + + remote_size = int(result.stdout.strip()) + local_size = os.path.getsize(DB_PATH) if os.path.exists(DB_PATH) else 0 + + if remote_size == local_size: + log(f"Database up to date ({remote_size} bytes)") + return True + + log(f"Syncing database (remote: {remote_size}, local: {local_size})...") + + cmd = f"sshpass -p 'Cronstadt35*' scp -o StrictHostKeyChecking=no h3r7@{VPS_HOST}:{DB_PATH} {DB_PATH}.tmp" + result = subprocess.run(cmd, shell=True, capture_output=True) + + if result.returncode == 0: + os.rename(DB_PATH + ".tmp", DB_PATH) + log(f"Database synced successfully") + return True + else: + log(f"ERROR: Sync failed") + return False + + +def generate_predictions(): + """Génère les prédictions du jour""" + log("=== GENERATE PREDICTIONS ===") + + conn = sqlite3.connect(DB_PATH) + conn.row_factory = sqlite3.Row + cursor = conn.cursor() + + today = datetime.now().strftime("%Y-%m-%d") + + # Get today's races + cursor.execute( + """ + SELECT DISTINCT c.date_programme, c.num_reunion, c.num_course, + c.libelle, c.heure_depart_str, r.hippodrome_court + FROM pmu_courses c + JOIN pmu_reunions r ON r.date_programme = c.date_programme AND r.num_reunion = c.num_reunion + WHERE c.date_programme = ? AND r.pays_code = 'FRA' + ORDER BY c.heure_depart_str + """, + (today,), + ) + + races = cursor.fetchall() + log(f"Found {len(races)} races today") + + predictions = [] + + for race in races: + date, num_reunion, num_course, libelle, heure, hippodrome = race + + # Get scoring + cursor.execute( + """ + SELECT horse_name, horse_number, score, cote, rang_scoring + FROM scoring + WHERE date = ? AND race_name LIKE ? + ORDER BY rang_scoring + LIMIT 4 + """, + (date, f"%{libelle}%"), + ) + + scores = cursor.fetchall() + + if scores: + pred = { + "race": f"{heure} - {hippodrome} - {libelle}", + "top4": [dict(s) for s in scores], + } + predictions.append(pred) + log( + f" {pred['race'][:50]} -> {', '.join([s['horse_name'] for s in scores[:3]])}" + ) + + conn.close() + return predictions + + +def save_recommendations(predictions): + """Sauvegarde les recommandations en base""" + log("=== SAVE RECOMMENDATIONS ===") + + conn = sqlite3.connect(DB_PATH) + cursor = conn.cursor() + + today = datetime.now().strftime("%Y-%m-%d") + + # Clear today's recommendations + cursor.execute("DELETE FROM recommendations WHERE date = ?", (today,)) + + for pred in predictions: + race_name = pred["race"] + + # Add top 4 + for i, horse in enumerate(pred["top4"]): + if i < 4: # TOP 4 + cursor.execute( + """ + INSERT INTO recommendations + (date, race_name, type_pari, cheval1, numero1, cote, mise, confiance, justification) + VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?) + """, + ( + today, + race_name, + f"top{i + 1}", + horse["horse_name"], + horse["horse_number"], + horse["cote"], + 2.0, + f"TOP {i + 1}", + f"Score: {horse['score']}, Rang scoring: {horse['rang_scoring']}", + ), + ) + + # Add simple winner (top 1) + top1 = pred["top4"][0] + cursor.execute( + """ + INSERT INTO recommendations + (date, race_name, type_pari, cheval1, numero1, cote, mise, confiance, justification) + VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?) + """, + ( + today, + race_name, + "simple_gagnant", + top1["horse_name"], + top1["horse_number"], + top1["cote"], + 2.0, + "🔥 FORTE" if top1["score"] > 60 else "✅ BONNE", + f"Score: {top1['score']}, Favorite prediction", + ), + ) + + conn.commit() + conn.close() + log(f"Saved {len(predictions)} race recommendations") + + +def notify_telegram(predictions): + """Envoie les prédictions du jour via Telegram""" + import urllib.request + + config_path = "/home/h3r7/turf_scraper/config_telegram.json" + if not os.path.exists(config_path): + log("No Telegram config found, skipping notification") + return + + try: + with open(config_path, "r") as f: + cfg = json.load(f) + token = cfg.get("token", "") + chat_id = cfg.get("chat_id", "") + if not token or not chat_id: + log("Telegram config missing token or chat_id") + return + except Exception as e: + log(f"Error reading Telegram config: {e}") + return + + today = datetime.now().strftime("%d/%m/%Y") + lines = [f"🏇 *Prédictions Turf — {today}*", ""] + + if not predictions: + lines.append("Aucune prédiction disponible aujourd'hui.") + else: + for pred in predictions: + race_label = pred.get("race", "Course inconnue") + top4 = pred.get("top4", []) + lines.append(f"📍 *{race_label}*") + for i, horse in enumerate(top4, 1): + name = horse.get("horse_name", "?") + score = horse.get("score", 0) + cote = horse.get("cote", "?") + icon = "🥇" if i == 1 else ("🥈" if i == 2 else ("🥉" if i == 3 else "4️⃣")) + lines.append(f" {icon} {name} — score: {score} — cote: {cote}") + lines.append("") + + text = "\n".join(lines) + + url = f"https://api.telegram.org/bot{token}/sendMessage" + payload = json.dumps({ + "chat_id": chat_id, + "text": text, + "parse_mode": "Markdown" + }).encode("utf-8") + + try: + req = urllib.request.Request( + url, + data=payload, + headers={"Content-Type": "application/json"}, + method="POST" + ) + with urllib.request.urlopen(req, timeout=10) as resp: + result = json.loads(resp.read().decode("utf-8")) + if result.get("ok"): + log(f"Telegram notification sent ({len(predictions)} races)") + else: + log(f"Telegram API error: {result}") + except Exception as e: + log(f"Error sending Telegram notification: {e}") + + +def run_backtest_yesterday(): + """Lance le backtest sur les résultats d'hier""" + log("=== RUN BACKTEST ===") + + yesterday = (datetime.now() - timedelta(days=1)).strftime("%Y-%m-%d") + + # Check if we have results for yesterday + conn = sqlite3.connect(DB_PATH) + cursor = conn.cursor() + + cursor.execute( + """ + SELECT COUNT(*) FROM pmu_partants + WHERE date_programme = ? AND ordre_arrivee > 0 + """, + (yesterday,), + ) + + count = cursor.fetchone()[0] + conn.close() + + if count > 0: + log(f"Running backtest for {yesterday}...") + # Run backtest script + cmd = f"cd /home/h3r7/turf_scraper && python3 backtest_analyzer.py --date {yesterday}" + result = subprocess.run(cmd, shell=True, capture_output=True, text=True) + if result.returncode == 0: + log("Backtest completed") + else: + log(f"Backtest failed: {result.stderr}") + else: + log(f"No results for {yesterday}, skipping backtest") + + +def update_paris_results(): + """Met à jour les paris avec les résultats PMU""" + log("=== UPDATE PARIS RESULTS ===") + + today = datetime.now().strftime("%Y-%m-%d") + + cmd = ( + f"cd /home/h3r7/turf_scraper && python3 update_paris_results.py --date {today}" + ) + result = subprocess.run(cmd, shell=True, capture_output=True, text=True) + + if result.returncode == 0: + log(f"Paris updated: {result.stdout.strip()}") + else: + log(f"Paris update failed: {result.stderr}") + + +def main(): + """Point d'entrée principal""" + dry_run = "--dry-run" in sys.argv + + log("=" * 50) + log("🤖 AGENT TURF AUTONOME - DEMARRAGE") + log("=" * 50) + + try: + # 1. Sync database + if not dry_run: + sync_database() + + # 2. Generate predictions + predictions = generate_predictions() + + # 3. Save recommendations + if not dry_run and predictions: + save_recommendations(predictions) + + # 3b. Send Telegram notification + if not dry_run: + notify_telegram(predictions) + + # 4. Create paris from recommendations + if not dry_run and predictions: + update_paris_results() + + # 5. Run backtest on yesterday + if not dry_run: + run_backtest_yesterday() + + log("=" * 50) + log("✅ AGENT TURF - TERMINÉ") + log("=" * 50) + + except Exception as e: + log(f"❌ ERREUR: {e}") + import traceback + + traceback.print_exc() + sys.exit(1) + + +if __name__ == "__main__": + main() diff --git a/analyse_rex.py b/analyse_rex.py new file mode 100755 index 0000000..6ad8464 --- /dev/null +++ b/analyse_rex.py @@ -0,0 +1,524 @@ +#!/usr/bin/env python3 +""" +Analyse REX - Corrélations et calibration des poids du scoring +Lit historical_data + performance pour améliorer le modèle de prédiction +""" + +import sqlite3 +import json +import os +import math +from datetime import datetime, timedelta + +DB_PATH = os.environ.get("DB_PATH", "/home/h3r7/turf_scraper/turf.db") + +# ============================================================ +# UTILITAIRES STATISTIQUES +# ============================================================ + +def moyenne(valeurs): + v = [x for x in valeurs if x is not None] + return sum(v) / len(v) if v else 0 + +def ecart_type(valeurs): + v = [x for x in valeurs if x is not None] + if len(v) < 2: + return 0 + m = moyenne(v) + return math.sqrt(sum((x - m)**2 for x in v) / len(v)) + +def correlation_pearson(x_list, y_list): + """Corrélation de Pearson entre deux listes""" + pairs = [(x, y) for x, y in zip(x_list, y_list) if x is not None and y is not None] + if len(pairs) < 5: + return 0 + xs = [p[0] for p in pairs] + ys = [p[1] for p in pairs] + mx, my = moyenne(xs), moyenne(ys) + num = sum((x - mx) * (y - my) for x, y in zip(xs, ys)) + den = math.sqrt(sum((x - mx)**2 for x in xs) * sum((y - my)**2 for y in ys)) + return round(num / den, 4) if den else 0 + +def taux_top5_par_segment(conn, feature, nb_segments=5): + """ + Découpe une feature en segments et calcule le taux top5 de chaque segment. + Permet de voir si la feature est discriminante. + """ + c = conn.cursor() + c.execute(f""" + SELECT {feature}, top5 + FROM historical_data + WHERE {feature} IS NOT NULL AND {feature} > 0 + ORDER BY {feature} ASC + """) + rows = c.fetchall() + if len(rows) < nb_segments: + return [] + + segment_size = len(rows) // nb_segments + segments = [] + for i in range(nb_segments): + debut = i * segment_size + fin = debut + segment_size if i < nb_segments - 1 else len(rows) + seg = rows[debut:fin] + vals = [r[0] for r in seg] + top5s = [r[1] for r in seg] + segments.append({ + 'segment': i + 1, + 'min': round(min(vals), 2), + 'max': round(max(vals), 2), + 'nb': len(seg), + 'taux_top5': round(sum(top5s) / len(top5s) * 100, 1) + }) + return segments + +# ============================================================ +# ANALYSES PRINCIPALES +# ============================================================ + +def analyse_volume(conn): + """Statistiques générales sur les données disponibles""" + c = conn.cursor() + + c.execute(""" + SELECT COUNT(DISTINCT date) as jours, + COUNT(*) as lignes, + MIN(date) as debut, + MAX(date) as fin, + AVG(top1) as taux_gagnant, + AVG(top5) as taux_top5 + FROM historical_data + """) + row = c.fetchone() + + c.execute("SELECT COUNT(DISTINCT race_name) FROM historical_data") + nb_courses = c.fetchone()[0] + + c.execute(""" + SELECT discipline, COUNT(DISTINCT date) as nb + FROM historical_data + GROUP BY discipline + ORDER BY nb DESC + """) + disciplines = c.fetchall() + + return { + 'nb_jours': row[0], + 'nb_lignes': row[1], + 'nb_courses': nb_courses, + 'debut': row[2], + 'fin': row[3], + 'taux_gagnant': round((row[4] or 0) * 100, 1), + 'taux_top5': round((row[5] or 0) * 100, 1), + 'disciplines': disciplines, + } + + +def analyse_correlations(conn): + """ + Calcule la corrélation de chaque feature avec top5 et top1. + Features négatives avec top5 = meilleur prédicteur (ex: cote basse → top5 élevé) + """ + c = conn.cursor() + c.execute(""" + SELECT cote_directe, cote_reference, reduction_km, + forme_recente, tendance_forme, tx_victoire, tx_place, + gains_carriere, gains_annee, rang_cote, ratio_cote_field, + nb_courses, nb_victoires, age, driver_change, + indicateur_tendance, nb_disq, + top1, top5 + FROM historical_data + WHERE cote_directe > 0 + """) + rows = c.fetchall() + + features = [ + 'cote_directe', 'cote_reference', 'reduction_km', + 'forme_recente', 'tendance_forme', 'tx_victoire', 'tx_place', + 'gains_carriere', 'gains_annee', 'rang_cote', 'ratio_cote_field', + 'nb_courses', 'nb_victoires', 'age', 'driver_change', + 'indicateur_tendance', 'nb_disq' + ] + + top1_vals = [r[17] for r in rows] + top5_vals = [r[18] for r in rows] + + correlations = [] + for i, feat in enumerate(features): + feat_vals = [r[i] for r in rows] + corr_top1 = correlation_pearson(feat_vals, top1_vals) + corr_top5 = correlation_pearson(feat_vals, top5_vals) + correlations.append({ + 'feature': feat, + 'corr_top1': corr_top1, + 'corr_top5': corr_top5, + 'abs_top5': abs(corr_top5), + }) + + # Trier par corrélation absolue avec top5 + correlations.sort(key=lambda x: x['abs_top5'], reverse=True) + return correlations + + +def analyse_cote(conn): + """Analyse détaillée de la cote comme prédicteur""" + c = conn.cursor() + + # Taux de réussite par tranche de cote + tranches = [ + (0, 3, "Très favori (< 3)"), + (3, 6, "Favori (3-6)"), + (6, 10, "Second favori (6-10)"), + (10, 20, "Outsider (10-20)"), + (20, 50, "Longshot (20-50)"), + (50, 999, "Outsider extrême (50+)"), + ] + + resultats = [] + for cmin, cmax, label in tranches: + c.execute(""" + SELECT COUNT(*) as nb, + AVG(top1) as taux_gagnant, + AVG(top5) as taux_top5, + AVG(ordre_arrivee) as pos_moy + FROM historical_data + WHERE cote_directe >= ? AND cote_directe < ? AND ordre_arrivee > 0 + """, (cmin, cmax)) + row = c.fetchone() + if row[0] > 0: + resultats.append({ + 'tranche': label, + 'nb': row[0], + 'taux_gagnant': round((row[1] or 0) * 100, 1), + 'taux_top5': round((row[2] or 0) * 100, 1), + 'position_moy': round(row[3] or 0, 1), + }) + return resultats + + +def analyse_forme(conn): + """Analyse de la forme récente comme prédicteur""" + c = conn.cursor() + + tranches = [ + (0, 1.5, "Excellente (< 1.5)"), + (1.5, 3, "Bonne (1.5-3)"), + (3, 5, "Moyenne (3-5)"), + (5, 8, "Mauvaise (5-8)"), + (8, 99, "Très mauvaise (8+)"), + ] + + resultats = [] + for fmin, fmax, label in tranches: + c.execute(""" + SELECT COUNT(*) as nb, + AVG(top1) as taux_gagnant, + AVG(top5) as taux_top5 + FROM historical_data + WHERE forme_recente >= ? AND forme_recente < ? AND ordre_arrivee > 0 + """, (fmin, fmax)) + row = c.fetchone() + if row[0] > 0: + resultats.append({ + 'tranche': label, + 'nb': row[0], + 'taux_gagnant': round((row[1] or 0) * 100, 1), + 'taux_top5': round((row[2] or 0) * 100, 1), + }) + return resultats + + +def analyse_avis_entraineur(conn): + """Impact de l'avis entraîneur""" + c = conn.cursor() + c.execute(""" + SELECT avis_entraineur, + COUNT(*) as nb, + AVG(top1) as taux_gagnant, + AVG(top5) as taux_top5, + AVG(cote_directe) as cote_moy + FROM historical_data + WHERE ordre_arrivee > 0 + GROUP BY avis_entraineur + ORDER BY AVG(top5) DESC + """) + rows = c.fetchall() + return [{ + 'avis': r[0], + 'nb': r[1], + 'taux_gagnant': round((r[2] or 0) * 100, 1), + 'taux_top5': round((r[3] or 0) * 100, 1), + 'cote_moy': round(r[4] or 0, 1), + } for r in rows] + + +def analyse_driver_change(conn): + """Impact du changement de driver""" + c = conn.cursor() + c.execute(""" + SELECT driver_change, + COUNT(*) as nb, + AVG(top1) as taux_gagnant, + AVG(top5) as taux_top5 + FROM historical_data + WHERE ordre_arrivee > 0 + GROUP BY driver_change + """) + rows = c.fetchall() + return [{ + 'driver_change': 'Oui' if r[0] else 'Non', + 'nb': r[1], + 'taux_gagnant': round((r[2] or 0) * 100, 1), + 'taux_top5': round((r[3] or 0) * 100, 1), + } for r in rows] + + +def analyse_top_chevaux(conn): + """Chevaux les plus performants dans l'historique""" + c = conn.cursor() + c.execute(""" + SELECT horse_name, + COUNT(*) as nb_courses, + SUM(top1) as nb_gagnant, + SUM(top5) as nb_top5, + AVG(cote_directe) as cote_moy + FROM historical_data + WHERE ordre_arrivee > 0 + GROUP BY horse_name + HAVING COUNT(*) >= 3 + ORDER BY AVG(top5) DESC, SUM(top1) DESC + LIMIT 15 + """) + rows = c.fetchall() + return [{ + 'cheval': r[0], + 'nb_courses': r[1], + 'nb_gagnant': r[2], + 'nb_top5': r[3], + 'tx_top5': round(r[3] / r[1] * 100, 1), + 'cote_moy': round(r[4] or 0, 1), + } for r in rows] + + +# ============================================================ +# CALIBRATION DES POIDS +# ============================================================ + +def calibrer_poids(correlations): + """ + Recalcule les pondérations du scoring basé sur les corrélations REX. + Les features avec plus forte corrélation absolue reçoivent plus de poids. + """ + # Mapping feature → critère scoring actuel + feature_to_critere = { + 'cote_directe': 'cote', + 'forme_recente': 'forme', + 'tx_victoire': 'tx_victoire', + 'tx_place': 'tx_place', + 'reduction_km': 'reduction_km', + 'tendance_forme': 'tendance', + 'rang_cote': 'cote', # corrélé à cote + } + + # Agréger les corrélations par critère + criteres = { + 'cote': [], + 'forme': [], + 'tx_victoire': [], + 'tx_place': [], + 'reduction_km':[], + 'tendance': [], + 'avis': [0.05], # valeur fixe (avis entraîneur difficile à corréler) + } + + for corr in correlations: + critere = feature_to_critere.get(corr['feature']) + if critere and critere in criteres: + criteres[critere].append(corr['abs_top5']) + + # Moyenne par critère + scores = {} + for critere, vals in criteres.items(): + scores[critere] = moyenne(vals) if vals else 0.01 + + # Normaliser pour que la somme = 100% + total = sum(scores.values()) + poids_calibres = {k: round(v / total * 100, 1) for k, v in scores.items()} + + # Poids actuels (référence) + poids_actuels = { + 'cote': 20.0, + 'forme': 25.0, + 'tx_victoire': 15.0, + 'tx_place': 15.0, + 'reduction_km': 10.0, + 'tendance': 10.0, + 'avis': 5.0, + } + + return { + 'poids_actuels': poids_actuels, + 'poids_calibres': poids_calibres, + 'delta': {k: round(poids_calibres.get(k, 0) - poids_actuels.get(k, 0), 1) + for k in poids_actuels} + } + + +# ============================================================ +# RAPPORT +# ============================================================ + +def print_rapport(volume, correlations, cote_analyse, forme_analyse, + avis_analyse, driver_analyse, top_chevaux, poids): + + print(f"\n{'='*65}") + print(f"📊 ANALYSE REX — {datetime.now().strftime('%d/%m/%Y %H:%M')}") + print(f"{'='*65}") + + # Volume + print(f"\n📦 DONNÉES DISPONIBLES") + print(f" Jours : {volume['nb_jours']}") + print(f" Courses : {volume['nb_courses']}") + print(f" Partants : {volume['nb_lignes']}") + print(f" Période : {volume['debut']} → {volume['fin']}") + print(f" Taux top5 moyen : {volume['taux_top5']}%") + for disc, nb in volume['disciplines']: + print(f" {disc:<20} : {nb} courses") + + # Corrélations + print(f"\n🔗 CORRÉLATIONS FEATURES → TOP5 (triées par force)") + print(f" {'FEATURE':<22} {'CORR TOP5':>10} {'CORR TOP1':>10} {'FORCE'}") + print(f" {'─'*55}") + for c in correlations[:10]: + force = '●●●' if c['abs_top5'] > 0.15 else '●●' if c['abs_top5'] > 0.08 else '●' + direction = '▼ (inverse)' if c['corr_top5'] < 0 else '▲ (direct) ' + print(f" {c['feature']:<22} {c['corr_top5']:>10.4f} {c['corr_top1']:>10.4f} {force} {direction}") + + # Analyse cote + print(f"\n🎰 TAUX DE RÉUSSITE PAR TRANCHE DE COTE") + print(f" {'TRANCHE':<28} {'NB':>5} {'GAGNANT':>8} {'TOP5':>6} {'POS MOY':>8}") + print(f" {'─'*60}") + for t in cote_analyse: + print(f" {t['tranche']:<28} {t['nb']:>5} {t['taux_gagnant']:>7.1f}% {t['taux_top5']:>5.1f}% {t['position_moy']:>8.1f}") + + # Analyse forme + print(f"\n🏃 TAUX DE RÉUSSITE PAR FORME RÉCENTE") + print(f" {'FORME':<28} {'NB':>5} {'GAGNANT':>8} {'TOP5':>6}") + print(f" {'─'*50}") + for t in forme_analyse: + print(f" {t['tranche']:<28} {t['nb']:>5} {t['taux_gagnant']:>7.1f}% {t['taux_top5']:>5.1f}%") + + # Avis entraîneur + print(f"\n👨‍🏫 IMPACT AVIS ENTRAÎNEUR") + print(f" {'AVIS':<20} {'NB':>5} {'GAGNANT':>8} {'TOP5':>6} {'COTE MOY':>9}") + print(f" {'─'*52}") + for a in avis_analyse: + print(f" {a['avis']:<20} {a['nb']:>5} {a['taux_gagnant']:>7.1f}% {a['taux_top5']:>5.1f}% {a['cote_moy']:>9.1f}") + + # Driver change + print(f"\n🔄 IMPACT CHANGEMENT DE DRIVER") + for d in driver_analyse: + print(f" Changement {d['driver_change']:<4} : {d['nb']} courses · " + f"gagnant {d['taux_gagnant']}% · top5 {d['taux_top5']}%") + + # Top chevaux + if top_chevaux: + print(f"\n🏆 TOP CHEVAUX (≥3 courses)") + print(f" {'CHEVAL':<28} {'COURSES':>8} {'GAGNANT':>8} {'TOP5':>6} {'TX TOP5':>8} {'COTE MOY':>9}") + print(f" {'─'*70}") + for h in top_chevaux[:10]: + print(f" {h['cheval']:<28} {h['nb_courses']:>8} {h['nb_gagnant']:>8} " + f"{h['nb_top5']:>6} {h['tx_top5']:>7.1f}% {h['cote_moy']:>9.1f}") + + # Calibration poids + print(f"\n⚖️ CALIBRATION DES POIDS DU SCORING") + print(f" {'CRITÈRE':<16} {'ACTUEL':>8} {'CALIBRÉ':>8} {'DELTA':>8} RECOMMANDATION") + print(f" {'─'*60}") + for critere in poids['poids_actuels']: + actuel = poids['poids_actuels'][critere] + calibre = poids['poids_calibres'].get(critere, actuel) + delta = poids['delta'].get(critere, 0) + if delta > 2: + reco = "↑ AUGMENTER" + elif delta < -2: + reco = "↓ RÉDUIRE" + else: + reco = "→ OK" + print(f" {critere:<16} {actuel:>7.1f}% {calibre:>7.1f}% {delta:>+7.1f}% {reco}") + + nb_jours = volume['nb_jours'] + if nb_jours < 30: + print(f"\n⚠️ Données insuffisantes ({nb_jours} jours) — attendez 30+ jours avant d'appliquer la calibration") + elif nb_jours < 100: + print(f"\n✅ Données suffisantes pour calibration Phase 2 ({nb_jours} jours)") + print(f" Recommandation : appliquer les nouveaux poids dans scoring.py") + else: + print(f"\n🚀 Données suffisantes pour ML Phase 3 ({nb_jours} jours)") + print(f" Recommandation : entraîner un modèle XGBoost") + + print(f"\n{'='*65}\n") + + +# ============================================================ +# MAIN +# ============================================================ + +def main(): + print(f"\n{'='*65}") + print(f"🧠 ANALYSE REX — {datetime.now().strftime('%d/%m/%Y %H:%M')}") + print(f"{'='*65}\n") + + conn = sqlite3.connect(DB_PATH) + + # Vérifier les données + c = conn.cursor() + c.execute("SELECT COUNT(*) FROM historical_data") + nb = c.fetchone()[0] + + if nb == 0: + print("❌ Aucune donnée dans historical_data.") + print(" Lancez d'abord : python3 historical_loader.py --days 365") + conn.close() + return + + print(f"✅ {nb} lignes trouvées dans historical_data\n") + + # Analyses + print("📊 Calcul des statistiques...") + volume = analyse_volume(conn) + correlations= analyse_correlations(conn) + cote_analyse= analyse_cote(conn) + forme_analyse= analyse_forme(conn) + avis_analyse= analyse_avis_entraineur(conn) + driver_analyse= analyse_driver_change(conn) + top_chevaux = analyse_top_chevaux(conn) + poids = calibrer_poids(correlations) + + conn.close() + + # Afficher le rapport + print_rapport(volume, correlations, cote_analyse, forme_analyse, + avis_analyse, driver_analyse, top_chevaux, poids) + + # Sauvegarder le rapport JSON + rapport = { + 'date': datetime.now().isoformat(), + 'volume': volume, + 'correlations': correlations, + 'cote_analyse': cote_analyse, + 'forme_analyse': forme_analyse, + 'avis_analyse': avis_analyse, + 'driver_analyse': driver_analyse, + 'top_chevaux': top_chevaux, + 'poids_calibres': poids, + } + + turf_dir = os.environ.get('TURF_DIR', '/home/h3r7/turf_scraper') + path = f"{turf_dir}/rex_analyse_{datetime.now().strftime('%Y%m%d')}.json" + with open(path, 'w', encoding='utf-8') as f: + json.dump(rapport, f, indent=2, ensure_ascii=False) + print(f"📁 Rapport sauvegardé : {path}") + + +if __name__ == "__main__": + main() diff --git a/analytics_reports.py b/analytics_reports.py new file mode 100644 index 0000000..9b041cd --- /dev/null +++ b/analytics_reports.py @@ -0,0 +1,423 @@ +#!/usr/bin/env python3 +""" +Analytics et Rapports Automatisés - Turf Scraper +Génère des rapports quotidiens, hebdomadaires et mensuels +""" +import sqlite3 +import json +from datetime import datetime, timedelta +from typing import Dict, List, Optional +from pathlib import Path + +DB_PATH = "/home/h3r7/turf_scraper/turf.db" + + +def get_db(): + """Connexion à la base""" + conn = sqlite3.connect(DB_PATH) + conn.row_factory = sqlite3.Row + return conn + + +def get_daily_report(date: str = None) -> dict: + """ + Génère un rapport quotidien + """ + if date is None: + date = datetime.now().strftime('%Y-%m-%d') + + conn = get_db() + c = conn.cursor() + + report = { + 'date': date, + 'generated_at': datetime.now().isoformat(), + 'races': {}, + 'predictions': {}, + 'results': {} + } + + try: + c.execute(""" + SELECT id, num_reunion, num_course, libelle, libelle_court, + discipline, distance, heure_depart_str + FROM pmu_courses + WHERE date_programme = ? + ORDER BY num_reunion, num_course + """, (date,)) + + races = c.fetchall() + report['races']['count'] = len(races) + report['races']['details'] = [dict(r) for r in races[:10]] + + c.execute(""" + SELECT race_name, race_hippodrome, COUNT(*) as pred_count + FROM predictions + WHERE date = ? + GROUP BY race_name + """, (date,)) + + preds = c.fetchall() + report['predictions']['count'] = sum(p['pred_count'] for p in preds) + report['predictions']['by_race'] = [dict(p) for p in preds] + + c.execute(""" + SELECT p.nom as cheval, p.num_pmu as numero, p.ordre_arrivee as position, + p.cote_direct as cote, p.driver, c.libelle as course + FROM pmu_partants p + JOIN pmu_courses c ON p.date_programme = c.date_programme + AND p.num_reunion = c.num_reunion AND p.num_course = c.num_course + WHERE p.date_programme = ? AND p.ordre_arrivee > 0 + ORDER BY c.num_reunion, c.num_course, p.ordre_arrivee + """, (date,)) + + results = c.fetchall() + report['results']['count'] = len(results) + report['results']['details'] = [dict(r) for r in results[:20]] + + c.execute(""" + SELECT COUNT(*) as total, + SUM(CASE WHEN ordre_arrivee = 1 THEN 1 ELSE 0 END) as wins, + SUM(CASE WHEN ordre_arrivee IN (1,2,3) THEN 1 ELSE 0 END) as podiums, + AVG(CASE WHEN ordre_arrivee > 0 THEN cote_direct ELSE NULL END) as avg_cote + FROM pmu_partants + WHERE date_programme = ? AND ordre_arrivee > 0 + """, (date,)) + + stats = c.fetchone() + total = stats['total'] or 0 + wins = stats['wins'] or 0 + podiums = stats['podiums'] or 0 + + report['stats'] = { + 'total_partants': total, + 'wins': wins, + 'podiums': podiums, + 'win_rate': round(wins / total * 100, 1) if total > 0 else 0, + 'podium_rate': round(podiums / total * 100, 1) if total > 0 else 0, + 'avg_cote': round(stats['avg_cote'], 2) if stats['avg_cote'] else 0 + } + + except Exception as e: + report['error'] = str(e) + finally: + conn.close() + + return report + + +def get_weekly_report(start_date: str = None, end_date: str = None) -> dict: + """ + Génère un rapport hebdomadaire + """ + if end_date is None: + end_date = datetime.now() + else: + end_date = datetime.strptime(end_date, '%Y-%m-%d') + + if start_date is None: + start_date = end_date - timedelta(days=7) + else: + start_date = datetime.strptime(start_date, '%Y-%m-%d') + + start_str = start_date.strftime('%Y-%m-%d') + end_str = end_date.strftime('%Y-%m-%d') + + conn = get_db() + c = conn.cursor() + + report = { + 'start_date': start_str, + 'end_date': end_str, + 'generated_at': datetime.now().isoformat() + } + + try: + c.execute(""" + SELECT COUNT(DISTINCT date_programme) as race_days, + COUNT(DISTINCT id) as total_races + FROM pmu_courses + WHERE date_programme BETWEEN ? AND ? + """, (start_str, end_str)) + + overview = c.fetchone() + report['overview'] = { + 'race_days': overview['race_days'] or 0, + 'total_races': overview['total_races'] or 0 + } + + c.execute(""" + SELECT COUNT(*) as total, + SUM(CASE WHEN ordre_arrivee = 1 THEN 1 ELSE 0 END) as wins, + SUM(CASE WHEN ordre_arrivee IN (1,2,3) THEN 1 ELSE 0 END) as podiums, + AVG(CASE WHEN ordre_arrivee > 0 THEN cote_direct ELSE NULL END) as avg_cote + FROM pmu_partants + WHERE date_programme BETWEEN ? AND ? AND ordre_arrivee > 0 + """, (start_str, end_str)) + + stats = c.fetchone() + total = stats['total'] or 0 + wins = stats['wins'] or 0 + podiums = stats['podiums'] or 0 + + report['stats'] = { + 'total_partants': total, + 'wins': wins, + 'podiums': podiums, + 'win_rate': round(wins / total * 100, 1) if total > 0 else 0, + 'podium_rate': round(podiums / total * 100, 1) if total > 0 else 0, + 'avg_cote': round(stats['avg_cote'], 2) if stats['avg_cote'] else 0 + } + + c.execute(""" + SELECT driver, COUNT(*) as wins, + AVG(CASE WHEN ordre_arrivee > 0 THEN ordre_arrivee ELSE NULL END) as avg_pos + FROM pmu_partants + WHERE date_programme BETWEEN ? AND ? AND ordre_arrivee = 1 + GROUP BY driver + ORDER BY wins DESC + LIMIT 10 + """, (start_str, end_str)) + + report['top_jockeys'] = [dict(r) for r in c.fetchall()] + + c.execute(""" + SELECT discipline, COUNT(DISTINCT c.id) as races, + COUNT(*) as partants, + SUM(CASE WHEN ordre_arrivee = 1 THEN 1 ELSE 0 END) as wins + FROM pmu_courses c + LEFT JOIN pmu_partants p ON c.date_programme = p.date_programme + AND c.num_reunion = p.num_reunion AND c.num_course = p.num_course + WHERE c.date_programme BETWEEN ? AND ? + GROUP BY discipline + ORDER BY races DESC + """, (start_str, end_str)) + + report['by_discipline'] = [dict(r) for r in c.fetchall()] + + c.execute(""" + SELECT date_programme, COUNT(*) as partants, + SUM(CASE WHEN ordre_arrivee = 1 THEN 1 ELSE 0 END) as wins + FROM pmu_partants + WHERE date_programme BETWEEN ? AND ? + GROUP BY date_programme + ORDER BY date_programme + """, (start_str, end_str)) + + report['by_day'] = [dict(r) for r in c.fetchall()] + + except Exception as e: + report['error'] = str(e) + finally: + conn.close() + + return report + + +def get_monthly_report(year: int = None, month: int = None) -> dict: + """ + Génère un rapport mensuel + """ + if year is None: + year = datetime.now().year + if month is None: + month = datetime.now().month + + start_date = datetime(year, month, 1) + if month == 12: + end_date = datetime(year + 1, 1, 1) - timedelta(days=1) + else: + end_date = datetime(year, month + 1, 1) - timedelta(days=1) + + start_str = start_date.strftime('%Y-%m-%d') + end_str = end_date.strftime('%Y-%m-%d') + + conn = get_db() + c = conn.cursor() + + report = { + 'year': year, + 'month': month, + 'start_date': start_str, + 'end_date': end_str, + 'generated_at': datetime.now().isoformat() + } + + try: + c.execute(""" + SELECT COUNT(DISTINCT date_programme) as race_days, + COUNT(DISTINCT id) as total_races, + COUNT(*) as total_partants + FROM pmu_courses c + LEFT JOIN pmu_partants p ON c.date_programme = p.date_programme + AND c.num_reunion = p.num_reunion AND c.num_course = p.num_course + WHERE c.date_programme BETWEEN ? AND ? + """, (start_str, end_str)) + + overview = c.fetchone() + report['overview'] = { + 'race_days': overview['race_days'] or 0, + 'total_races': overview['total_races'] or 0, + 'total_partants': overview['total_partants'] or 0 + } + + c.execute(""" + SELECT COUNT(*) as total, + SUM(CASE WHEN ordre_arrivee = 1 THEN 1 ELSE 0 END) as wins, + SUM(CASE WHEN ordre_arrivee IN (1,2,3) THEN 1 ELSE 0 END) as podiums, + AVG(CASE WHEN ordre_arrivee > 0 THEN cote_direct ELSE NULL END) as avg_cote + FROM pmu_partants + WHERE date_programme BETWEEN ? AND ? AND ordre_arrivee > 0 + """, (start_str, end_str)) + + stats = c.fetchone() + total = stats['total'] or 0 + wins = stats['wins'] or 0 + podiums = stats['podiums'] or 0 + + report['stats'] = { + 'total_partants': total, + 'wins': wins, + 'podiums': podiums, + 'win_rate': round(wins / total * 100, 1) if total > 0 else 0, + 'podium_rate': round(podiums / total * 100, 1) if total > 0 else 0, + 'avg_cote': round(stats['avg_cote'], 2) if stats['avg_cote'] else 0 + } + + c.execute(""" + SELECT discipline, COUNT(DISTINCT c.id) as races, + SUM(CASE WHEN ordre_arrivee = 1 THEN 1 ELSE 0 END) as wins, + AVG(CASE WHEN ordre_arrivee > 0 THEN ordre_arrivee ELSE NULL END) as avg_pos + FROM pmu_courses c + LEFT JOIN pmu_partants p ON c.date_programme = p.date_programme + AND c.num_reunion = p.num_reunion AND c.num_course = p.num_course + WHERE c.date_programme BETWEEN ? AND ? + GROUP BY discipline + ORDER BY races DESC + LIMIT 20 + """, (start_str, end_str)) + + report['by_discipline'] = [dict(r) for r in c.fetchall()] + + c.execute(""" + SELECT date_programme, COUNT(*) as partants, + SUM(CASE WHEN ordre_arrivee = 1 THEN 1 ELSE 0 END) as wins + FROM pmu_partants + WHERE date_programme BETWEEN ? AND ? + GROUP BY date_programme + ORDER BY date_programme + """, (start_str, end_str)) + + report['daily_breakdown'] = [dict(r) for r in c.fetchall()] + + except Exception as e: + report['error'] = str(e) + finally: + conn.close() + + return report + + +def format_report_markdown(report: dict, report_type: str = 'daily') -> str: + """ + Formate un rapport en Markdown + """ + lines = [] + + if report_type == 'daily': + lines.append(f"# 📊 Rapport Quotidien - {report.get('date', 'N/A')}") + lines.append("") + lines.append(f"*Généré le {report.get('generated_at', '')}*") + lines.append("") + + if 'error' in report: + lines.append(f"❌ Erreur: {report['error']}") + return "\n".join(lines) + + races = report.get('races', {}) + stats = report.get('stats', {}) + + lines.append("## Résumé") + lines.append(f"- 🏇 Courses: {races.get('count', 0)}") + lines.append(f"- 🎯 Partants: {stats.get('total_partants', 0)}") + lines.append(f"- 🏆 Victoires: {stats.get('wins', 0)} ({stats.get('win_rate', 0)}%)") + lines.append(f"- 🥉 Podiums: {stats.get('podiums', 0)} ({stats.get('podium_rate', 0)}%)") + lines.append("") + + elif report_type == 'weekly': + lines.append(f"# 📈 Rapport Hebdomadaire") + lines.append(f"**{report.get('start_date', '')}** au **{report.get('end_date', '')}**") + lines.append("") + + if 'error' in report: + lines.append(f"❌ Erreur: {report['error']}") + return "\n".join(lines) + + overview = report.get('overview', {}) + stats = report.get('stats', {}) + + lines.append("## Résumé") + lines.append(f"- Jours de course: {overview.get('race_days', 0)}") + lines.append(f"- Courses totales: {overview.get('total_races', 0)}") + lines.append(f"- Victoires: {stats.get('wins', 0)} ({stats.get('win_rate', 0)}%)") + lines.append(f"- Podiums: {stats.get('podiums', 0)} ({stats.get('podium_rate', 0)}%)") + lines.append(f"- Cote moyenne: {stats.get('avg_cote', 0)}") + lines.append("") + + if report.get('top_jockeys'): + lines.append("## Top Jockeys") + for j in report['top_jockeys'][:5]: + lines.append(f"- {j['driver']}: {j['wins']} victoires") + lines.append("") + + elif report_type == 'monthly': + month_name = ['Janvier', 'Février', 'Mars', 'Avril', 'Mai', 'Juin', + 'Juillet', 'Août', 'Septembre', 'Octobre', 'Novembre', 'Décembre'] + m = report.get('month', 1) + lines.append(f"# 📅 Rapport Mensuel - {month_name[m-1]} {report.get('year', '')}") + lines.append("") + + if 'error' in report: + lines.append(f"❌ Erreur: {report['error']}") + return "\n".join(lines) + + overview = report.get('overview', {}) + stats = report.get('stats', {}) + + lines.append("## Résumé du Mois") + lines.append(f"- Jours de course: {overview.get('race_days', 0)}") + lines.append(f"- Courses: {overview.get('total_races', 0)}") + lines.append(f"- Partants: {overview.get('total_partants', 0)}") + lines.append(f"- Victoires: {stats.get('wins', 0)} ({stats.get('win_rate', 0)}%)") + lines.append(f"- Podiums: {stats.get('podiums', 0)} ({stats.get('podium_rate', 0)}%)") + lines.append(f"- Cote moyenne: {stats.get('avg_cote', 0)}") + lines.append("") + + return "\n".join(lines) + + +if __name__ == '__main__': + import sys + + if len(sys.argv) < 2: + print("Usage: python analytics_reports.py [daily|weekly|monthly] [date]") + sys.exit(1) + + report_type = sys.argv[1] + date_arg = sys.argv[2] if len(sys.argv) > 2 else None + + if report_type == 'daily': + report = get_daily_report(date_arg) + print(format_report_markdown(report, 'daily')) + elif report_type == 'weekly': + report = get_weekly_report(date_arg) + print(format_report_markdown(report, 'weekly')) + elif report_type == 'monthly': + if date_arg: + year, month = map(int, date_arg.split('-')) + else: + year, month = None, None + report = get_monthly_report(year, month) + print(format_report_markdown(report, 'monthly')) + else: + print(f"Type de rapport inconnu: {report_type}") diff --git a/api_datagouv_reference.html b/api_datagouv_reference.html new file mode 100644 index 0000000..0c16f34 --- /dev/null +++ b/api_datagouv_reference.html @@ -0,0 +1,308 @@ + + + + + + API Data.gouv.fr - Référence + + + +

📡 API Data.gouv.fr

Référence complète des endpoints - v1

+ + ← Retour au portail + +

+ + + +

+ + + + diff --git a/app.py b/app.py new file mode 100644 index 0000000..f0d83a1 --- /dev/null +++ b/app.py @@ -0,0 +1,336 @@ +#!/usr/bin/env python3 +from flask import Flask, request, jsonify, redirect, send_file +import sqlite3 +import os +import requests +import csv +import io + +app = Flask(__name__) +DB_FILE = '/home/h3r7/depenses_trello/depenses.db' + +def init_db(): + conn = sqlite3.connect(DB_FILE) + c = conn.cursor() + c.execute('''CREATE TABLE IF NOT EXISTS depenses ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + prenom TEXT, + date TEXT, + libelle TEXT, + montant REAL, + status TEXT DEFAULT 'En attente', + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP + )''') + c.execute('''CREATE TABLE IF NOT EXISTS config ( + key TEXT PRIMARY KEY, + value TEXT + )''') + c.execute('''CREATE TABLE IF NOT EXISTS prenoms ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + name TEXT UNIQUE + )''') + conn.commit() + conn.close() + +def get_db(): + conn = sqlite3.connect(DB_FILE) + conn.row_factory = sqlite3.Row + return conn + +init_db() + +def parse_date(d): + if not d: return "" + if "/" in d: + parts = d.split("/") + if len(parts) == 3: + return f"{parts[2]}-{parts[1]}-{parts[0]}" + return d + +@app.route('/') +def index(): + with open('/home/h3r7/depenses_trello/templates/index.html', 'r') as f: + return f.read() + +@app.route('/dashboard') +def dashboard(): + with open('/home/h3r7/depenses_trello/templates/dashboard.html', 'r') as f: + return f.read() + +# API Config +@app.route('/api/config') +def get_config(): + conn = get_db() + c = conn.cursor() + + # Get prenoms + c.execute('SELECT name FROM prenoms') + prenoms = [row[0] for row in c.fetchall()] + + # Get format + c.execute("SELECT value FROM config WHERE key='format'") + format_row = c.fetchone() + format_val = format_row[0] if format_row else '{prenom} - {date} - {libelle} - {montant}€' + + # Get trello config + c.execute("SELECT value FROM config WHERE key='trello'") + trello_row = c.fetchone() + trello = {} + if trello_row: + import json + trello = json.loads(trello_row[0]) + + # Get categories + c.execute("SELECT value FROM config WHERE key='categories'") + cat_row = c.fetchone() + categories = cat_row[0].split(',') if cat_row else ['Courses', 'Essence', 'Loisirs', 'Maison', 'Santé', 'Transport', 'Autre'] + + conn.close() + + return jsonify({ + 'format': format_val, + 'prenoms': prenoms, + 'categories': categories, + 'trello': trello + }) + +# Add depense +@app.route('/api/add', methods=['POST']) +def add_depense(): + conn = get_db() + c = conn.cursor() + c.execute('INSERT INTO depenses (prenom, date, libelle, montant, status, category) VALUES (?, ?, ?, ?, ?, ?)', + (request.form.get('prenom'), parse_date(request.form.get('date', '')), + request.form.get('libelle'), float(request.form.get('montant', 0)), 'En attente', request.form.get('category', 'Autre'))) + conn.commit() + conn.close() + return redirect('/?page=saisie') + +# Get depenses +@app.route('/api/depenses') +def get_depenses(): + conn = get_db() + c = conn.cursor() + c.execute('SELECT id, prenom, date, libelle, montant, status, category FROM depenses ORDER BY date DESC') + rows = c.fetchall() + conn.close() + return jsonify([dict(row) for row in rows]) + +# Delete depense +@app.route('/api/del/') +def delete_depense(id): + conn = get_db() + c = conn.cursor() + c.execute('DELETE FROM depenses WHERE id=?', (id,)) + conn.commit() + conn.close() + return redirect('/?page=saisie') + +# Clear all +@app.route('/api/clear', methods=['POST']) +def clear_depenses(): + conn = get_db() + c = conn.cursor() + c.execute('DELETE FROM depenses') + conn.commit() + conn.close() + return jsonify({'success': True}) + +# Export CSV +@app.route('/api/export') +def export_csv(): + conn = get_db() + c = conn.cursor() + c.execute('SELECT prenom, date, libelle, montant, status FROM depenses ORDER BY date DESC') + rows = c.fetchall() + conn.close() + + output = io.StringIO() + writer = csv.writer(output) + writer.writerow(['prenom', 'date', 'libelle', 'montant', 'status']) + for row in rows: + writer.writerow(row) + + output.seek(0) + return send_file( + io.BytesIO(output.getvalue().encode('utf-8')), + mimetype='text/csv', + as_attachment=True, + download_name='depenses_2026.csv' + ) + +# Import CSV +@app.route('/api/import', methods=['POST']) +def import_csv(): + if 'file' not in request.files: + return jsonify({'error': 'Aucun fichier'}), 400 + + file = request.files['file'] + if file.filename == '': + return jsonify({'error': 'Fichier vide'}), 400 + + try: + content = file.read().decode('utf-8') + reader = csv.reader(content.splitlines()) + next(reader) + + conn = get_db() + c = conn.cursor() + + imported = 0 + for row in reader: + if len(row) >= 4: + c.execute('INSERT INTO depenses (prenom, date, libelle, montant, status) VALUES (?, ?, ?, ?, ?)', + (row[0].strip(), row[1].strip(), row[2].strip(), + float(row[3]) if row[3] else 0, row[4].strip() if len(row) > 4 else 'En attente')) + imported += 1 + + conn.commit() + conn.close() + return jsonify({'success': True, 'imported': imported}) + except Exception as e: + return jsonify({'error': str(e)}), 400 + +# Send ONE to Trello +@app.route('/api/trello/send_one/', methods=['POST']) +def send_one(id): + conn = get_db() + c = conn.cursor() + c.execute("SELECT value FROM config WHERE key='trello'") + trello_row = c.fetchone() + if not trello_row: + conn.close() + return jsonify({'error': 'Config Trello manquante'}), 400 + + import json + t = json.loads(trello_row[0]) + if not t.get('api_key') or not t.get('token') or not t.get('list_id'): + conn.close() + return jsonify({'error': 'Config Trello incomplète'}), 400 + + c.execute('SELECT * FROM depenses WHERE id=?', (id,)) + d = c.fetchone() + if not d: + conn.close() + return jsonify({'error': 'Dépense non trouvée'}), 404 + + if d['status'] == 'Envoyé ✅': + conn.close() + return jsonify({'error': 'Déjà envoyé'}), 400 + + # Generate text + c.execute("SELECT value FROM config WHERE key='format'") + format_row = c.fetchone() + template = format_row[0] if format_row else '{prenom} - {date} - {libelle} - {montant}€' + + ddate = d['date'] or '' + if ddate: + ddate = '/'.join(ddate.split('-')[::-1]) + + line = template + line = line.replace('{prenom}', d['prenom'] or '') + line = line.replace('{date}', ddate) + line = line.replace('{libelle}', d['libelle'] or '') + line = line.replace('{montant}', str(d['montant'])) + + # Send to Trello + url = 'https://api.trello.com/1/cards' + params = { + 'key': t.get('api_key'), + 'token': t.get('token'), + 'idList': t.get('list_id'), + 'name': line, + 'desc': line + } + + try: + r = requests.post(url, params=params) + if r.status_code == 200: + c.execute('UPDATE depenses SET status=? WHERE id=?', ('Envoyé ✅', id)) + conn.commit() + conn.close() + return jsonify({'success': True}) + conn.close() + return jsonify({'error': r.text}), r.status_code + except Exception as e: + conn.close() + return jsonify({'error': str(e)}), 500 + +# Generate text (all) +@app.route('/api/generate', methods=['POST']) +def generate_text(): + conn = get_db() + c = conn.cursor() + c.execute("SELECT value FROM config WHERE key='format'") + format_row = c.fetchone() + template = format_row[0] if format_row else '{prenom} - {date} - {libelle} - {montant}€' + conn.close() + + data = request.json + lines = [] + for d in data.get('depenses', []): + line = template + ddate = d.get('date', '') or '' + if ddate: + ddate = '/'.join(ddate.split('-')[::-1]) + line = line.replace('{prenom}', d.get('prenom', '')) + line = line.replace('{date}', ddate) + line = line.replace('{libelle}', d.get('libelle', '')) + line = line.replace('{montant}', str(d.get('montant', 0))) + lines.append(line) + return jsonify({'text': '\n'.join(lines)}) + +# Add prenom +@app.route('/api/prenom/add', methods=['POST']) +def add_prenom(): + conn = get_db() + c = conn.cursor() + prenom = request.form.get('prenom', '').strip() + if prenom: + try: + c.execute('INSERT INTO prenoms (name) VALUES (?)', (prenom,)) + conn.commit() + except: + pass + conn.close() + return redirect('/?page=config') + +# Del prenom +@app.route('/api/prenom/del/') +def del_prenom(idx): + conn = get_db() + c = conn.cursor() + c.execute('DELETE FROM prenoms WHERE id=?', (idx+1,)) + conn.commit() + conn.close() + return redirect('/?page=config') + +# Save format +@app.route('/api/format', methods=['POST']) +def save_format(): + conn = get_db() + c = conn.cursor() + format_val = request.form.get('format', '{prenom} - {date} - {libelle} - {montant}€') + c.execute('INSERT OR REPLACE INTO config (key, value) VALUES (?, ?)', ('format', format_val)) + conn.commit() + conn.close() + return redirect('/?page=config') + +# Save trello config +@app.route('/api/trello/save', methods=['POST']) +def save_trello(): + import json + conn = get_db() + c = conn.cursor() + t = json.dumps({ + 'api_key': request.form.get('api_key', ''), + 'token': request.form.get('token', ''), + 'list_id': request.form.get('list_id', '') + }) + c.execute('INSERT OR REPLACE INTO config (key, value) VALUES (?, ?)', ('trello', t)) + conn.commit() + conn.close() + return redirect('/?page=config') + +if __name__ == '__main__': + app.run(host='0.0.0.0', port=8769, debug=False) diff --git a/archive_v5_files.py b/archive_v5_files.py new file mode 100755 index 0000000..47dbc2a --- /dev/null +++ b/archive_v5_files.py @@ -0,0 +1,127 @@ +#!/usr/bin/env python3 +""" +Archive les fichiers v5_*.json antérieurs à aujourd'hui +Usage: python3 archive_v5_files.py [--dry-run] +""" + +import os +import json +import sqlite3 +import shutil +from datetime import datetime, date +from pathlib import Path + +TURF_DIR = Path("/home/h3r7/turf_scraper") +ARCHIVE_DIR = TURF_DIR / "archive" +DB_PATH = TURF_DIR / "turf.db" +LOG_FILE = Path("/home/h3r7/logs/archive_v5.log") + +def log(message: str): + timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S") + log_entry = f"[{timestamp}] {message}" + print(log_entry) + LOG_FILE.parent.mkdir(parents=True, exist_ok=True) + with open(LOG_FILE, "a") as f: + f.write(log_entry + "\n") + +def init_archive_table(): + conn = sqlite3.connect(DB_PATH) + c = conn.cursor() + c.execute(""" + CREATE TABLE IF NOT EXISTS scraping_archives ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + filename TEXT UNIQUE NOT NULL, + execution_date TEXT NOT NULL, + runtime_sec REAL, + total_pages INTEGER, + file_size_kb INTEGER, + archive_path TEXT, + pages_success INTEGER DEFAULT 0, + pages_error INTEGER DEFAULT 0, + archived_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + archived_by TEXT DEFAULT 'cron_6h' + ) + """) + conn.commit() + conn.close() + +def extract_metadata(filepath: Path) -> dict: + try: + with open(filepath) as f: + data = json.load(f) + pages_success = sum(1 for p in data.get("pages", []) if p.get("status") == "success") + pages_error = sum(1 for p in data.get("pages", []) if p.get("status") == "error") + return { + "runtime_sec": data.get("runtime_sec"), + "total_pages": data.get("total_pages"), + "pages_success": pages_success, + "pages_error": pages_error + } + except Exception as e: + log(f"Erreur lecture {filepath}: {e}") + return {} + +def archive_files(dry_run: bool = False): + today = date.today() + today_str = today.strftime("%Y%m%d") + + init_archive_table() + log("=== Début archivage v5 ===") + + conn = sqlite3.connect(DB_PATH) + c = conn.cursor() + + archived_count = 0 + + for file in sorted(TURF_DIR.glob("v5_*.json")): + filename = file.name + parts = filename.split("_") + if len(parts) < 2: + continue + file_date_str = parts[1] + + if file_date_str >= today_str: + continue + + metadata = extract_metadata(file) + file_size_kb = file.stat().st_size // 1024 + + year = file_date_str[:4] + month = file_date_str[4:6] + target_dir = ARCHIVE_DIR / year / month + target_path = target_dir / filename + + if not dry_run: + target_dir.mkdir(parents=True, exist_ok=True) + + c.execute(""" + INSERT OR IGNORE INTO scraping_archives + (filename, execution_date, runtime_sec, total_pages, + file_size_kb, archive_path, pages_success, pages_error) + VALUES (?, ?, ?, ?, ?, ?, ?, ?) + """, ( + filename, file_date_str, metadata.get("runtime_sec"), + metadata.get("total_pages"), file_size_kb, + str(target_path), metadata.get("pages_success", 0), + metadata.get("pages_error", 0) + )) + + shutil.move(str(file), str(target_path)) + + log(f"Archivé: {filename} → {target_path}") + archived_count += 1 + + if not dry_run: + conn.commit() + conn.close() + + log(f"=== Fin archivage: {archived_count} fichiers ===") + return archived_count + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser() + parser.add_argument("--dry-run", action="store_true", help="Simulation sans déplacement") + args = parser.parse_args() + + archive_files(dry_run=args.dry_run) diff --git a/backtest.py b/backtest.py new file mode 100644 index 0000000..d1a836d --- /dev/null +++ b/backtest.py @@ -0,0 +1,325 @@ +#!/usr/bin/env python3 +""" +Backtest - Analyse des performances des prédictions +Calcule ROI, précision, et métriques par type de pari +""" + +import sqlite3 +import os +from datetime import datetime, timedelta +from collections import defaultdict + +DB_PATH = os.environ.get("DB_PATH", "/home/h3r7/turf_scraper/turf.db") + + +def get_db(): + return sqlite3.connect(DB_PATH) + + +def calculate_backtest(start_date=None, end_date=None): + """Calcule les statistiques de backtest""" + conn = get_db() + + if not start_date: + start_date = (datetime.now() - timedelta(days=30)).strftime('%Y-%m-%d') + if not end_date: + end_date = datetime.now().strftime('%Y-%m-%d') + + # Récupérer toutes les recommandations avec résultats + query = """ + SELECT + r.date, + r.race_name, + r.type_pari, + r.cheval1, + r.cheval2, + r.numero1, + r.numero2, + r.cote, + r.mise, + r.resultat, + r.gain_potentiel, + h.position as position_reel + FROM recommendations r + LEFT JOIN results h ON h.date = r.date + AND h.race_name LIKE '%' || SUBSTR(r.race_name, 1, 20) || '%' + AND h.horse_name = r.cheval1 + WHERE r.resultat IS NOT NULL + AND r.resultat != '' + AND r.date BETWEEN ? AND ? + """ + + cursor = conn.execute(query, (start_date, end_date)) + rows = cursor.fetchall() + + if not rows: + conn.close() + return { + 'summary': { + 'total_bets': 0, + 'message': 'Aucune donnée disponible pour cette période' + } + } + + # Métriques globales + stats = { + 'total_bets': len(rows), + 'gagne': 0, + 'perdu': 0, + 'mise_totale': 0, + 'gain_total': 0, + 'by_type': defaultdict(lambda: { + 'count': 0, 'gagne': 0, 'mise': 0, 'gain': 0 + }) + } + + details = [] + + for row in rows: + date, race_name, type_pari, cheval1, cheval2, numero1, numero2, cote, mise, resultat, gain_potentiel, position = row + + mise = float(mise or 1) + cote = float(cote or 1) + + stats['mise_totale'] += mise + stats['by_type'][type_pari]['mise'] += mise + + if resultat == 'GAGNE': + stats['gagne'] += 1 + stats['perdu'] += 0 + stats['gain_total'] += mise * cote + stats['by_type'][type_pari]['gagne'] += 1 + stats['by_type'][type_pari]['gain'] += mise * cote + result = 'GAGNE' + else: + stats['perdu'] += 1 + stats['gain_total'] += 0 + result = 'PERDU' + + details.append({ + 'date': date, + 'race_name': race_name[:30] if race_name else '', + 'type_pari': type_pari, + 'cheval': cheval1, + 'cote': cote, + 'mise': mise, + 'resultat': result, + 'gain': mise * cote if result == 'GAGNE' else 0 + }) + + # Calculer ROI + roi = 0 + if stats['mise_totale'] > 0: + roi = ((stats['gain_total'] - stats['mise_totale']) / stats['mise_totale']) * 100 + + # Calculer précision + precision = 0 + if stats['total_bets'] > 0: + precision = (stats['gagne'] / stats['total_bets']) * 100 + + # Métriques par type de pari + by_type_results = {} + for pari_type, data in stats['by_type'].items(): + pari_roi = 0 + if data['mise'] > 0: + pari_roi = ((data['gain'] - data['mise']) / data['mise']) * 100 + + pari_precision = 0 + if data['count'] > 0: + pari_precision = (data['gagne'] / data['count']) * 100 + + by_type_results[pari_type] = { + 'count': data['count'], + 'gagne': data['gagne'], + 'mise': data['mise'], + 'gain': data['gain'], + 'roi': round(pari_roi, 1), + 'precision': round(pari_precision, 1) + } + + # Précision par rang de prédiction + precision_by_rank = get_precision_by_rank(conn, start_date, end_date) + + conn.close() + + return { + 'period': { + 'start': start_date, + 'end': end_date + }, + 'summary': { + 'total_bets': stats['total_bets'], + 'gagne': stats['gagne'], + 'perdu': stats['perdu'], + 'precision': round(precision, 1), + 'mise_totale': round(stats['mise_totale'], 2), + 'gain_total': round(stats['gain_total'], 2), + 'roi': round(roi, 1) + }, + 'by_type': by_type_results, + 'precision_by_rank': precision_by_rank, + 'details': details[-50:] # Last 50 bets + } + + +def get_precision_by_rank(conn, start_date, end_date): + """Calcule la précision par rang de prédiction""" + + query = """ + SELECT + p.prediction_rank, + COUNT(*) as total, + SUM(CASE WHEN p.prediction_rank = r.position THEN 1 ELSE 0 END) as hits, + SUM(CASE WHEN r.position <= 3 THEN 1 ELSE 0 END) as top3 + FROM predictions p + INNER JOIN results r ON r.date = p.date + AND r.horse_name = p.horse_name + WHERE p.date BETWEEN ? AND ? + AND p.prediction_rank > 0 + AND r.position > 0 + GROUP BY p.prediction_rank + ORDER BY p.prediction_rank + """ + + cursor = conn.execute(query, (start_date, end_date)) + rows = cursor.fetchall() + + results = {} + for rank, total, hits, top3 in rows: + precision = (hits / total * 100) if total > 0 else 0 + top3_rate = (top3 / total * 100) if total > 0 else 0 + results[f'rank_{rank}'] = { + 'total': total, + 'hits': hits, + 'precision': round(precision, 1), + 'top3': top3, + 'top3_rate': round(top3_rate, 1) + } + + return results + + +def get_daily_stats(days=30): + """Statistiques quotidiennes sur les N derniers jours""" + conn = get_db() + + end_date = datetime.now().strftime('%Y-%m-%d') + start_date = (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d') + + query = """ + SELECT + date, + COUNT(*) as total_bets, + SUM(CASE WHEN resultat = 'GAGNE' THEN 1 ELSE 0 END) as gagne, + SUM(CASE WHEN resultat = 'PERDU' THEN 1 ELSE 0 END) as perdu, + SUM(CASE WHEN resultat = 'GAGNE' THEN mise * cote ELSE 0 END) as gains, + SUM(mise) as mises + FROM recommendations + WHERE date BETWEEN ? AND ? + AND resultat IS NOT NULL + AND resultat != '' + GROUP BY date + ORDER BY date DESC + """ + + cursor = conn.execute(query, (start_date, end_date)) + rows = cursor.fetchall() + + daily = [] + for date, total, gagne, perdu, gains, mises in rows: + roi = ((gains - mises) / mises * 100) if mises > 0 else 0 + precision = (gagne / total * 100) if total > 0 else 0 + + daily.append({ + 'date': date, + 'total_bets': total, + 'gagne': gagne, + 'perdu': perdu, + 'precision': round(precision, 1), + 'mises': round(mises, 2), + 'gains': round(gains, 2), + 'roi': round(roi, 1) + }) + + conn.close() + return daily + + +def get_weekly_summary(): + """Résumé hebdomadaire""" + conn = get_db() + + # Semaine en cours + today = datetime.now() + week_start = today - timedelta(days=today.weekday()) + week_start_str = week_start.strftime('%Y-%m-%d') + + query = """ + SELECT + COUNT(*) as total, + SUM(CASE WHEN resultat = 'GAGNE' THEN 1 ELSE 0 END) as gagne, + SUM(mise) as mise, + SUM(CASE WHEN resultat = 'GAGNE' THEN mise * cote ELSE 0 END) as gain + FROM recommendations + WHERE date >= ? AND resultat IS NOT NULL AND resultat != '' + """ + + cursor = conn.execute(query, (week_start_str,)) + row = cursor.fetchone() + + total, gagne, mise, gain = row + + weekly = { + 'week_start': week_start_str, + 'total_bets': total or 0, + 'gagne': gagne or 0, + 'mise': round(mise or 0, 2), + 'gain': round(gain or 0, 2), + 'roi': round(((gain - mise) / mise * 100) if mise and mise > 0 else 0, 1), + 'precision': round((gagne / total * 100) if total and total > 0 else 0, 1) + } + + # Semaine dernière + last_week_start = week_start - timedelta(days=7) + last_week_end = week_start - timedelta(days=1) + + query = """ + SELECT + COUNT(*) as total, + SUM(CASE WHEN resultat = 'GAGNE' THEN 1 ELSE 0 END) as gagne, + SUM(mise) as mise, + SUM(CASE WHEN resultat = 'GAGNE' THEN mise * cote ELSE 0 END) as gain + FROM recommendations + WHERE date BETWEEN ? AND ? + AND resultat IS NOT NULL + AND resultat != '' + """ + + cursor = conn.execute(query, (last_week_start.strftime('%Y-%m-%d'), last_week_end.strftime('%Y-%m-%d'))) + row = cursor.fetchone() + + total, gagne, mise, gain = row + + weekly['last_week'] = { + 'total_bets': total or 0, + 'gagne': gagne or 0, + 'mise': round(mise or 0, 2), + 'gain': round(gain or 0, 2), + 'roi': round(((gain - mise) / mise * 100) if mise and mise > 0 else 0, 1), + 'precision': round((gagne / total * 100) if total and total > 0 else 0, 1) + } + + conn.close() + return weekly + + +if __name__ == "__main__": + import json + + print("="*60) + print("BACKTEST - Analyse des performances") + print("="*60) + + # Test + result = calculate_backtest() + print(json.dumps(result, indent=2, default=str)) diff --git a/backtest_analyzer.py b/backtest_analyzer.py new file mode 100644 index 0000000..9d7e245 --- /dev/null +++ b/backtest_analyzer.py @@ -0,0 +1,280 @@ +#!/usr/bin/env python3 +""" +Backtest Analyzer - Analyse des prédictions vs résultats +""" + +import sqlite3 +import json +from datetime import datetime + +DB_PATH = "/home/h3r7/turf_scraper/turf.db" + +def get_connection(): + return sqlite3.connect(DB_PATH) + +def get_results_for_date(date): + """Récupère les résultats d'une date (toutes courses confondues)""" + conn = get_connection() + c = conn.execute(""" + SELECT course, cheval, position_finale, num_pmu + FROM v_resultats_complets + WHERE date_programme = ? AND position_finale > 0 + ORDER BY course, position_finale + """, (date,)) + rows = c.fetchall() + conn.close() + + # Grouper par course + courses = {} + for row in rows: + course_name = row[0] + if course_name not in courses: + courses[course_name] = [] + courses[course_name].append({ + 'horse': row[1], + 'position': row[2], + 'numero': row[3] + }) + return courses + +def get_canalturf_predictions(date): + """Récupère les prédictions Canalturf par race""" + conn = get_connection() + + # Toutes les prédictions pour la date + c = conn.execute(""" + SELECT race_name, horse_name, horse_number, source + FROM predictions + WHERE date = ? + """, (date,)) + rows = c.fetchall() + conn.close() + + # Grouper par race + races = {} + for row in rows: + race_name = row[0] if row[0] else 'UNKNOWN' + if race_name not in races: + races[race_name] = {'bases': [], 'chances': [], 'outsiders': [], 'all': []} + + entry = {'horse': row[1], 'numero': row[2]} + races[race_name]['all'].append(entry) + + if row[3] == 'canalturf_prono_bases': + races[race_name]['bases'].append(entry) + elif row[3] == 'canalturf_prono_chances': + races[race_name]['chances'].append(entry) + elif row[3] == 'canalturf_prono_outsiders': + races[race_name]['outsiders'].append(entry) + + return races + +def get_scoring_predictions(date): + """Récupère les prédictions du scoring par race""" + conn = get_connection() + c = conn.execute(""" + SELECT race_name, horse_name, horse_number, score, rang_scoring + FROM scoring + WHERE date = ? + """, (date,)) + rows = c.fetchall() + conn.close() + + races = {} + for row in rows: + race_name = row[0] if row[0] else 'UNKNOWN' + if race_name not in races: + races[race_name] = [] + races[race_name].append({ + 'horse': row[1], + 'numero': row[2], + 'score': row[3], + 'rang': row[4] + }) + + return races + +def calculate_metrics(predicted, actual): + """Calcule les métriques pour une course""" + if not predicted or not actual: + return None + + metrics = {} + + # Top1 + pred_top1 = predicted[0]['horse'].upper() if predicted else None + actual_top1 = actual[0]['horse'].upper() if actual else None + metrics['top1_hit'] = pred_top1 == actual_top1 + metrics['top1_predicted'] = pred_top1 + + # Top3 + pred_top3 = set([p['horse'].upper() for p in predicted[:3]]) + actual_top3 = set([a['horse'].upper() for a in actual[:3]]) + metrics['top3_precision'] = len(pred_top3.intersection(actual_top3)) / 3 + + # Top5 + pred_top5 = set([p['horse'].upper() for p in predicted[:5]]) + actual_top5 = set([a['horse'].upper() for a in actual[:5]]) + metrics['top5_precision'] = len(pred_top5.intersection(actual_top5)) / 5 + + # ZE2: 2/4 + pred_top4 = set([p['horse'].upper() for p in predicted[:4]]) + actual_top4 = set([a['horse'].upper() for a in actual[:4]]) + metrics['ze2_hit'] = len(pred_top4.intersection(actual_top4)) >= 2 + + return metrics + +def run_backtest(): + """Lance le backtest""" + conn = get_connection() + c = conn.execute(""" + SELECT DISTINCT date_programme + FROM v_resultats_complets + WHERE position_finale > 0 + ORDER BY date_programme DESC + """) + dates = [row[0] for row in c.fetchall()] + conn.close() + + if not dates: + print("Aucune donnée trouvée") + return None + + print(f"\n{'='*60}") + print(f"📊 BACKTEST ANALYZER") + print(f"{'='*60}") + print(f"Période: {dates[-1]} au {dates[0]} ({len(dates)} jours)") + + all_results = [] + stats = {'canalturf': {'top1': 0, 'top3': 0, 'top5': 0, 'ze2': 0, 'total': 0}, + 'scoring': {'top1': 0, 'top3': 0, 'top5': 0, 'ze2': 0, 'total': 0}} + + for date in dates: + results = get_results_for_date(date) + if not results: + continue + + canalturf_preds = get_canalturf_predictions(date) + scoring_preds = get_scoring_predictions(date) + + for race_name, race_results in results.items(): + # Canalturf + if race_name in canalturf_preds: + pred = canalturf_preds[race_name]['all'] + m = calculate_metrics(pred, race_results) + if m: + stats['canalturf']['total'] += 1 + stats['canalturf']['top1'] += 1 if m['top1_hit'] else 0 + stats['canalturf']['top3'] += m['top3_precision'] + stats['canalturf']['top5'] += m['top5_precision'] + stats['canalturf']['ze2'] += 1 if m['ze2_hit'] else 0 + + all_results.append({ + 'date': date, + 'race': race_name, + 'source': 'canalturf', + 'top1_pred': m['top1_predicted'], + 'top1_hit': m['top1_hit'], + 'ze2_hit': m['ze2_hit'], + }) + + # Scoring + if race_name in scoring_preds: + pred = scoring_preds[race_name] + m = calculate_metrics(pred, race_results) + if m: + stats['scoring']['total'] += 1 + stats['scoring']['top1'] += 1 if m['top1_hit'] else 0 + stats['scoring']['top3'] += m['top3_precision'] + stats['scoring']['top5'] += m['top5_precision'] + stats['scoring']['ze2'] += 1 if m['ze2_hit'] else 0 + + all_results.append({ + 'date': date, + 'race': race_name, + 'source': 'scoring', + 'top1_pred': m['top1_predicted'], + 'top1_hit': m['top1_hit'], + 'ze2_hit': m['ze2_hit'], + }) + + # Calcul pourcentages + for source in ['canalturf', 'scoring']: + s = stats[source] + if s['total'] > 0: + s['top1_pct'] = round(s['top1'] / s['total'] * 100, 1) + s['top3_pct'] = round(s['top3'] / s['total'] * 100, 1) + s['top5_pct'] = round(s['top5'] / s['total'] * 100, 1) + s['ze2_pct'] = round(s['ze2'] / s['total'] * 100, 1) + + return { + 'dates': dates, + 'results': all_results, + 'stats': stats, + 'generated_at': datetime.now().isoformat() + } + +def print_report(data): + stats = data['stats'] + print(f"\n📈 RÉSUMÉ GLOBAL") + print(f"{'='*60}") + + for source, label in [('canalturf', 'CANALTURF'), ('scoring', 'SCORING')]: + s = stats[source] + if s['total'] > 0: + print(f"\n{label} ({s['total']} courses analysées):") + print(f" Top1: {s['top1']}/{s['total']} = {s['top1_pct']}%") + print(f" Top3: {s['top3_pct']}%") + print(f" Top5: {s['top5_pct']}%") + print(f" ZE2: {s['ze2']}/{s['total']} = {s['ze2_pct']}%") + +def main(): + data = run_backtest() + if data: + print_report(data) + + # Sauvegarde JSON + with open('/home/h3r7/turf_scraper/backtest_result.json', 'w') as f: + json.dump(data, f, indent=2, default=str) + + # Génère markdown + md = f"""--- +date: {datetime.now().strftime('%Y-%m-%d')} +tags: [turf, backtest, analyse] +type: recherche +status: active +--- + +# Backtest - {data['dates'][-1]} au {data['dates'][0]} + +> Analyse des prédictions vs résultats officiels PMU (8 jours) + +## Résumé Global + +| Source | Courses | Top1 | Top3 | Top5 | ZE2 Hit | +|--------|---------|------|------|------|---------| +| Canalturf | {data['stats']['canalturf']['total']} | {data['stats']['canalturf']['top1_pct']}% | {data['stats']['canalturf']['top3_pct']}% | {data['stats']['canalturf']['top5_pct']}% | {data['stats']['canalturf']['ze2_pct']}% | +| Scoring | {data['stats']['scoring']['total']} | {data['stats']['scoring']['top1_pct']}% | {data['stats']['scoring']['top3_pct']}% | {data['stats']['scoring']['top5_pct']}% | {data['stats']['scoring']['ze2_pct']}% | + +## Détail + +| Date | Course | Source | Top1 Prédit | Hit | ZE2 | +|------|--------|--------|-------------|-----|-----| +""" + + for r in data['results']: + md += f"| {r['date']} | {r['race'][:30]}... | {r['source']} | {r['top1_pred'][:20] if r['top1_pred'] else 'N/A'}... | {'✅' if r['top1_hit'] else '❌'} | {'✅' if r['ze2_hit'] else '❌'} |\n" + + md += f"""\n--- +*Généré le {datetime.now().strftime('%Y-%m-%d %H:%M')}* +""" + + with open('/home/h3r7/turf_scraper/backtest_result.md', 'w') as f: + f.write(md) + + print(f"\n💾 Rapports sauvegardés:") + print(f" - /home/h3r7/turf_scraper/backtest_result.json") + print(f" - /home/h3r7/turf_scraper/backtest_result.md") + +if __name__ == "__main__": + main() diff --git a/backtest_result.md b/backtest_result.md new file mode 100644 index 0000000..0362108 --- /dev/null +++ b/backtest_result.md @@ -0,0 +1,197 @@ +--- +date: 2026-04-25 +tags: [turf, backtest, analyse] +type: recherche +status: active +--- + +# Backtest - 2026-03-31 au 2026-04-24 + +> Analyse des prédictions vs résultats officiels PMU (8 jours) + +## Résumé Global + +| Source | Courses | Top1 | Top3 | Top5 | ZE2 Hit | +|--------|---------|------|------|------|---------| +| Canalturf | 153 | 23.5% | 19.2% | 16.2% | 3.9% | +| Scoring | 19 | 10.5% | 33.3% | 45.3% | 63.2% | + +## Détail + +| Date | Course | Source | Top1 Prédit | Hit | ZE2 | +|------|--------|--------|-------------|-----|-----| +| 2026-04-24 | PRIX CLAUDIA... | canalturf | LARIANO... | ❌ | ❌ | +| 2026-04-24 | PRIX CLAUDIA... | scoring | L'AMOUR SUPREME... | ✅ | ✅ | +| 2026-04-24 | PRIX DU JARDIN DE CORBIERES... | canalturf | NUMERO DE LAUMONT... | ❌ | ❌ | +| 2026-04-24 | PRIX DU PARC DE LA PORTE D'AIX... | canalturf | KATE DU RIL... | ❌ | ❌ | +| 2026-04-24 | PRIX DU PARC DU GRAND SEMINAIR... | canalturf | MACKER D'ERONVILLE... | ❌ | ❌ | +| 2026-04-24 | PRIX MAGINUS... | canalturf | MACHIAVEL BOURBON... | ❌ | ❌ | +| 2026-04-24 | PRIX RENE PALYART - ETRIER 4 A... | canalturf | MHUM FLYNG... | ❌ | ❌ | +| 2026-04-23 | PRIX DE FERRIERES... | canalturf | SENEQUE... | ❌ | ❌ | +| 2026-04-23 | PRIX DE LA COMEDIE FRANCAISE... | canalturf | HURRICANE... | ❌ | ❌ | +| 2026-04-23 | PRIX DES BOUFFES PARISIENS... | canalturf | PAMELA BOUM... | ❌ | ❌ | +| 2026-04-23 | PRIX DU PANTHEON... | canalturf | EPHESUS... | ❌ | ❌ | +| 2026-04-23 | PRIX DU PANTHEON... | scoring | EPHESUS... | ❌ | ✅ | +| 2026-04-23 | PRIX MAURICE ZILBER... | canalturf | MANDANABA... | ✅ | ❌ | +| 2026-04-23 | PRIX NEOVA - THERMOR... | canalturf | LANCELOT DU NORD... | ❌ | ❌ | +| 2026-04-23 | PRIX SAMSUNG - GROHE... | canalturf | JOKER DARLING... | ✅ | ❌ | +| 2026-04-22 | GRAND NATIONAL DU TROT... | scoring | HORCHESTRO... | ❌ | ✅ | +| 2026-04-22 | PRIX COKTAIL JET... | canalturf | LEZZO FIGHTER... | ✅ | ❌ | +| 2026-04-22 | PRIX DE LA ROUTE DE LA DUCHESS... | canalturf | MEFIE TOI... | ✅ | ❌ | +| 2026-04-22 | PRIX DES FRAYOIRS... | canalturf | VICIOUS HARRY... | ❌ | ❌ | +| 2026-04-22 | PRIX DU CHAMP D'ALOUETTE... | canalturf | ZACAPO... | ❌ | ❌ | +| 2026-04-22 | PRIX L'ECLAIREUR-ACTU.FR... | canalturf | JASMIN GEMA... | ❌ | ❌ | +| 2026-04-22 | PRIX OTTO... | canalturf | MINO GALESTE... | ❌ | ❌ | +| 2026-04-22 | PRIX XAVIER HUNAULT... | canalturf | LEADER... | ❌ | ❌ | +| 2026-04-21 | PRIX DE JONZAC... | canalturf | L'AS DE COEUR... | ❌ | ❌ | +| 2026-04-21 | PRIX HAVNIA... | canalturf | NELSON THORIS... | ❌ | ❌ | +| 2026-04-21 | PRIX HOPPER... | canalturf | METRONOMIQUE... | ✅ | ❌ | +| 2026-04-21 | PRIX JUMENT RADIO PARIS... | canalturf | LOUBA... | ❌ | ❌ | +| 2026-04-21 | PRIX LA BATE... | canalturf | TATOO... | ❌ | ❌ | +| 2026-04-21 | PRIX LA BATE... | scoring | PRIAM DU MESNIL... | ❌ | ✅ | +| 2026-04-21 | PRIX MIRIAM... | canalturf | MAHIZ DE GUEZ... | ❌ | ❌ | +| 2026-04-21 | PRIX MODESTIA... | canalturf | MISSING... | ✅ | ❌ | +| 2026-04-21 | PRIX VANILLE... | canalturf | STECASTER... | ❌ | ❌ | +| 2026-04-20 | PRIX BOULANGERIE PATISSERIE FE... | canalturf | KING TRACK... | ✅ | ❌ | +| 2026-04-20 | PRIX DE L'EMPEREUR NAPOLEON II... | canalturf | DUBAIEARTH... | ✅ | ❌ | +| 2026-04-20 | PRIX DE LA CHAPELLE-LA-REINE... | canalturf | SORANO... | ❌ | ❌ | +| 2026-04-20 | PRIX DE LA FORET DE FONTAINEBL... | canalturf | PENTAOUR... | ❌ | ❌ | +| 2026-04-20 | PRIX DE LA FORET DE FONTAINEBL... | scoring | STAR OF ARTABAN... | ❌ | ✅ | +| 2026-04-20 | PRIX DES PINS... | canalturf | CHEVALDOR... | ❌ | ❌ | +| 2026-04-20 | PRIX DES SENTIERS DENECOURT... | canalturf | ETERNEL... | ✅ | ❌ | +| 2026-04-20 | PRIX LYONNET TRAITEUR... | canalturf | KALLISTEE... | ❌ | ❌ | +| 2026-04-20 | PRIX PASCAL LAURENT... | canalturf | MAHARAJA ATOUT... | ✅ | ❌ | +| 2026-04-20 | PRIX REGION AUVERGNE RHONE-ALP... | canalturf | NEW GRASS... | ✅ | ❌ | +| 2026-04-19 | PRIX AMADOU... | canalturf | LEOPARD DU BERLAIS... | ❌ | ❌ | +| 2026-04-19 | PRIX DE CARTHAGE... | canalturf | HAJMAH... | ❌ | ❌ | +| 2026-04-19 | PRIX DE MONFORT... | canalturf | POCA GEN... | ❌ | ✅ | +| 2026-04-19 | PRIX DE MONFORT... | scoring | XILOFONO... | ❌ | ❌ | +| 2026-04-19 | PRIX DU SOUVENIR... | canalturf | SIR TOMMY CEN... | ✅ | ❌ | +| 2026-04-19 | PRIX JEAN STERN... | canalturf | SHANNON MAESTRO... | ❌ | ❌ | +| 2026-04-19 | PRIX KATKO... | canalturf | MORVAN... | ❌ | ❌ | +| 2026-04-19 | PRIX LEON RAMBAUD... | canalturf | THELEME... | ❌ | ❌ | +| 2026-04-18 | PRIX DE L'ATLANTIQUE... | canalturf | JOSH POWER... | ❌ | ✅ | +| 2026-04-18 | PRIX DE L'ATLANTIQUE... | scoring | HARLEY GEMA... | ❌ | ✅ | +| 2026-04-18 | PRIX DE MONPLAISIR... | canalturf | STUNNING ANGEL... | ❌ | ❌ | +| 2026-04-18 | PRIX DU BOIS NOIR... | canalturf | NIAGARA DES MOTTES... | ❌ | ❌ | +| 2026-04-18 | PRIX DU MONT PILAT... | canalturf | RISK OF LOVE... | ❌ | ❌ | +| 2026-04-18 | PRIX DU PRINTEMPS... | canalturf | ENEA... | ❌ | ❌ | +| 2026-04-17 | PRIX ALETHEIA... | canalturf | KARAMBAR... | ❌ | ❌ | +| 2026-04-17 | PRIX ASCHERA... | canalturf | NONEKA... | ❌ | ❌ | +| 2026-04-17 | PRIX ATLAS... | canalturf | NICE SPEED... | ❌ | ❌ | +| 2026-04-17 | PRIX DU PHARE DE CASSIDAIGNE... | canalturf | JOY DU CARNOIS... | ❌ | ❌ | +| 2026-04-17 | PRIX DU PHARE DE SAINTE-MARIE... | canalturf | MAMZELLE D'HERIPRE... | ❌ | ❌ | +| 2026-04-17 | PRIX DU SEMAPHORE DE LA COURON... | canalturf | LUTECIA ELLIS... | ❌ | ❌ | +| 2026-04-17 | PRIX NEPTUNA... | canalturf | KENTUCKY IDEAL... | ❌ | ✅ | +| 2026-04-17 | PRIX NEPTUNA... | scoring | KATCHI QUICK... | ❌ | ✅ | +| 2026-04-17 | PRIX THEOPHILE LALLOUET... | canalturf | IDEALE DU CHENE... | ❌ | ❌ | +| 2026-04-16 | PRIX DE FUMEL... | canalturf | L'ARCHANGE... | ✅ | ❌ | +| 2026-04-16 | PRIX DE LA FONTAINE CARPEAUX... | canalturf | CREW DRAGON... | ❌ | ❌ | +| 2026-04-16 | PRIX DE LA FONTAINE CARPEAUX... | scoring | WIT... | ❌ | ✅ | +| 2026-04-16 | PRIX DU LOUVRE... | canalturf | THE LAST DANCE... | ✅ | ❌ | +| 2026-04-16 | PRIX DU PONT DE FLANDRE... | canalturf | MAZAL DES GLENAN... | ❌ | ❌ | +| 2026-04-16 | PRIX MACHADO... | canalturf | MR LOPE CEN... | ❌ | ❌ | +| 2026-04-15 | PRIX D'ENGHIEN... | canalturf | HEART AND SOUL... | ❌ | ❌ | +| 2026-04-15 | PRIX DE LA COMMUNE DE CORDEMAI... | scoring | JIM D'ALOUETTE... | ❌ | ❌ | +| 2026-04-15 | PRIX DE LA MARE A PIAT... | canalturf | ILTONE... | ✅ | ❌ | +| 2026-04-15 | PRIX DE LA SOCIETE DES COURSES... | canalturf | SPES MILICLO... | ❌ | ❌ | +| 2026-04-15 | PRIX PMU +... | canalturf | JALNA GIRL... | ✅ | ❌ | +| 2026-04-14 | PRIX CHLOE... | canalturf | NOTHINGELSEMATTERS... | ✅ | ❌ | +| 2026-04-14 | PRIX DE ROISSY... | canalturf | PROFUMO DI IENA... | ❌ | ❌ | +| 2026-04-14 | PRIX DE ROISSY... | scoring | REGALIEN... | ❌ | ❌ | +| 2026-04-14 | PRIX EPINARD... | canalturf | SAY SQUIRREL... | ❌ | ❌ | +| 2026-04-14 | PRIX STEPHANIA... | canalturf | LORD FROMENTRO... | ❌ | ❌ | +| 2026-04-13 | PRIX DE LA TAPISSERIE DE BAYEU... | canalturf | LOUIS GAILLARD... | ❌ | ❌ | +| 2026-04-13 | PRIX DES GENETS... | canalturf | STORM DES FLOS... | ❌ | ❌ | +| 2026-04-13 | PRIX DES PLAGES DU DEBARQUEMEN... | canalturf | KENZO D'EVA... | ❌ | ❌ | +| 2026-04-13 | PRIX DU BARRY... | canalturf | FIDELE AU POSTE... | ❌ | ❌ | +| 2026-04-13 | PRIX DU HARAS NATIONAL DU PIN... | canalturf | KETBERNY... | ❌ | ❌ | +| 2026-04-13 | PRIX DU VIVARAIS... | canalturf | MAGIC MARVEL... | ❌ | ❌ | +| 2026-04-13 | PRIX GASTON BRANERE... | canalturf | NO LIMITS STEVE... | ❌ | ✅ | +| 2026-04-13 | PRIX GASTON BRANERE... | scoring | TROUBLEINPARADISE... | ❌ | ❌ | +| 2026-04-13 | PRIX RENE COUETIL... | canalturf | MARBRE ROSE... | ❌ | ❌ | +| 2026-04-12 | PRIX DE FONTAINEBLEAU... | canalturf | NIGHTTIME... | ❌ | ❌ | +| 2026-04-12 | PRIX DE LA GROTTE... | canalturf | GREEN SPIRIT... | ❌ | ❌ | +| 2026-04-12 | PRIX DU COMITE REGIONAL DE L'O... | canalturf | KADOR DES TITHAIS... | ❌ | ❌ | +| 2026-04-12 | PRIX DU PAVILLON ROYAL... | canalturf | ZULU WARRIOR... | ❌ | ❌ | +| 2026-04-12 | PRIX DU PAVILLON ROYAL... | scoring | MOST GLAMOROUS... | ❌ | ❌ | +| 2026-04-12 | PRIX JACQUES LAFFITTE... | canalturf | CASAPUEBLO... | ✅ | ❌ | +| 2026-04-12 | PRIX LECLERC DISTRIBUTION... | canalturf | JOB DE CHOISEL... | ❌ | ❌ | +| 2026-04-12 | PRIX LORD SEYMOUR... | canalturf | MARQUISAT... | ❌ | ❌ | +| 2026-04-12 | PRIX YVES ET JEAN MOYON... | canalturf | MISTRAL D'HERMES... | ✅ | ❌ | +| 2026-04-11 | PRIX DE FAULQUEMONT... | canalturf | MILAN DU BOSQUET... | ❌ | ❌ | +| 2026-04-11 | PRIX DE LA LORRAINE... | canalturf | CHITCHAT... | ❌ | ❌ | +| 2026-04-11 | PRIX DU CHATEAU LA POINTE... | canalturf | ARBUS... | ✅ | ❌ | +| 2026-04-11 | PRIX DU TREMBLAY... | canalturf | PITCH PERFECT... | ✅ | ❌ | +| 2026-04-11 | PRIX EPINARD... | canalturf | MADIGAN... | ❌ | ❌ | +| 2026-04-11 | PRIX HEMINE - ETRIER 3 ANS Q4... | canalturf | NIZZA ROCCA... | ❌ | ❌ | +| 2026-04-11 | PRIX HENRI LEVESQUE... | canalturf | LA FORMIDABLE... | ❌ | ❌ | +| 2026-04-11 | PRIX HENRI LEVESQUE... | scoring | LA FORMIDABLE... | ❌ | ✅ | +| 2026-04-11 | PRIX KRISS II... | canalturf | HM MAJDALLAH... | ✅ | ❌ | +| 2026-04-11 | PRIX ROBERT AUVRAY... | canalturf | L'AS DESBOIS... | ❌ | ❌ | +| 2026-04-10 | PRIX CLARISSE... | canalturf | MARTIN DOL... | ❌ | ❌ | +| 2026-04-10 | PRIX DE LA BARRIERE DE PARIS... | canalturf | NELLY DES CHARMES... | ❌ | ❌ | +| 2026-04-10 | PRIX DE NEGRENEYS... | canalturf | LA SPEZIA DE LOU... | ✅ | ❌ | +| 2026-04-10 | PRIX DES AMIDONNIERS... | canalturf | KROONER D'ORION... | ❌ | ❌ | +| 2026-04-10 | PRIX DU FER A CHEVAL... | canalturf | LOUSTIC... | ❌ | ❌ | +| 2026-04-10 | PRIX DU PONT DES DEMOISELLES... | canalturf | LEWIS D'EVRON... | ✅ | ❌ | +| 2026-04-10 | PRIX ICLEA... | canalturf | IKURO JIEL... | ✅ | ❌ | +| 2026-04-09 | PRIX ALADDIN... | canalturf | RAFFLES CHOICE... | ❌ | ❌ | +| 2026-04-09 | PRIX JASMIN II... | canalturf | SAIN D'ESPRIT... | ✅ | ❌ | +| 2026-04-09 | PRIX LE TOUQUET... | canalturf | MELISSE DU MATHAN... | ✅ | ❌ | +| 2026-04-09 | PRIX OISELEUR... | canalturf | LASCAR D'AIRY... | ❌ | ✅ | +| 2026-04-09 | PRIX OISELEUR... | scoring | CARGO DE NUIT... | ❌ | ❌ | +| 2026-04-08 | GRAND NATIONAL DU TROT... | scoring | ISTER MAN... | ❌ | ✅ | +| 2026-04-08 | PRIX DE CABOURG... | canalturf | MYSTIQUE LOULOU... | ❌ | ❌ | +| 2026-04-08 | PRIX DE MARSEILLE... | canalturf | KOALA... | ❌ | ❌ | +| 2026-04-08 | PRIX DU CAP DE LA HEVE... | canalturf | WAKAI GO... | ❌ | ❌ | +| 2026-04-08 | PRIX DU CAP LEVY... | canalturf | EL PROFESSOR CHOP... | ❌ | ❌ | +| 2026-04-08 | PRIX DU VIEUX MARONNIER... | canalturf | FREJA... | ❌ | ❌ | +| 2026-04-08 | PRIX HENRI CALLIER... | canalturf | LYS GEMA... | ❌ | ❌ | +| 2026-04-07 | PRIX ALBIREO... | canalturf | LOUKY DE BAULON... | ❌ | ❌ | +| 2026-04-07 | PRIX BAVARIA... | canalturf | KINOU DAB... | ❌ | ❌ | +| 2026-04-07 | PRIX DE L'OPERATION OVERLORD... | canalturf | DIVINE CHRISNAT... | ❌ | ❌ | +| 2026-04-07 | PRIX DJEBEL... | canalturf | SAMANGAN... | ❌ | ❌ | +| 2026-04-07 | PRIX IMPRUDENCE... | canalturf | MY HIGHNESS... | ❌ | ❌ | +| 2026-04-07 | PRIX PROSERPINA... | canalturf | NEVERS... | ❌ | ❌ | +| 2026-04-06 | PRIX D'AUMALE... | canalturf | ZARAKHAN... | ✅ | ❌ | +| 2026-04-06 | PRIX DE LA ROCHELLE... | canalturf | CI PPO RA... | ❌ | ✅ | +| 2026-04-06 | PRIX DE LA ROCHELLE... | scoring | PRIAM DU MESNIL... | ❌ | ✅ | +| 2026-04-06 | PRIX DES CADETTES... | canalturf | GAZELLE DU BRIZAIS... | ❌ | ❌ | +| 2026-04-06 | PRIX DURTAIN... | canalturf | NOIRE WULF... | ✅ | ❌ | +| 2026-04-06 | PRIX HYUNDAI GCH CHERBOURG... | canalturf | LETTY DE BEYLEV... | ❌ | ❌ | +| 2026-04-06 | PRIX INFINY HOME... | canalturf | LISA MI FOR CLARA... | ❌ | ❌ | +| 2026-04-06 | PRIX LA NOUBA... | canalturf | SUNDAYS CHILD... | ❌ | ❌ | +| 2026-04-06 | PRIX SUZUKY GCS CHERBOURG... | canalturf | KOREA... | ❌ | ❌ | +| 2026-04-05 | PRIX CINEMA LES ENFANTS DU PAR... | canalturf | LASCAUX... | ✅ | ❌ | +| 2026-04-05 | PRIX D'HARCOURT... | canalturf | FIRST LOOK... | ❌ | ❌ | +| 2026-04-05 | PRIX DES AMIS DE L'HIPPODROME... | canalturf | MISS FRANCE PIYA... | ✅ | ❌ | +| 2026-04-05 | PRIX DU GRAND CHATELET... | canalturf | IPSO FACTO... | ❌ | ❌ | +| 2026-04-05 | PRIX L'ILLIADE... | canalturf | KLASSIC FROMENTRO... | ❌ | ❌ | +| 2026-04-05 | PRIX LA FORCE... | canalturf | ZARAKI... | ❌ | ❌ | +| 2026-04-05 | PRIX ZARKAVA... | canalturf | INDALIMOS... | ❌ | ❌ | +| 2026-04-04 | PRIX CORNELIA... | canalturf | HEURISTIQUE... | ❌ | ❌ | +| 2026-04-04 | PRIX DE BAPAUME... | canalturf | FAHARAJAH ONE... | ❌ | ❌ | +| 2026-04-04 | PRIX DE LA DORDOGNE... | canalturf | GUARDAMI... | ❌ | ❌ | +| 2026-04-04 | PRIX DES CONSEILLERS DE LA FIL... | canalturf | CANDYMAN... | ❌ | ❌ | +| 2026-04-04 | PRIX DU GERS... | canalturf | GHOSTRIDER FONT... | ❌ | ❌ | +| 2026-04-04 | PRIX JEAN PROUVE... | canalturf | KARTHAGE... | ❌ | ❌ | +| 2026-04-04 | PRIX KERJACQUES... | canalturf | KSAR... | ❌ | ❌ | +| 2026-04-04 | PRIX KERJACQUES... | scoring | INEXESS BLEU... | ✅ | ✅ | +| 2026-04-03 | PRIX CEPHENS... | canalturf | KING LOOK... | ❌ | ❌ | +| 2026-04-03 | PRIX LIBRA... | canalturf | LOUSTIC... | ❌ | ❌ | +| 2026-04-03 | PRIX NOBODY DE RETZ... | canalturf | JASPER DU BELLAY... | ❌ | ❌ | +| 2026-04-02 | PRIX ALAIN GRIMAUX... | canalturf | LASCA DE 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+ +

Chargement des idées...

+ + + + diff --git a/calculate_metrics.py b/calculate_metrics.py new file mode 100755 index 0000000..7b115b2 --- /dev/null +++ b/calculate_metrics.py @@ -0,0 +1,420 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +calculate_metrics.py - Calcul des métriques de performance prédictions vs résultats + +Usage: + python3 calculate_metrics.py # Aujourd'hui + python3 calculate_metrics.py --date 2026-04-15 # Date spécifique + python3 calculate_metrics.py --yesterday # Hier + python3 calculate_metrics.py --backfill 30 # Remplir 30 derniers jours + +Calculé après 21h (résultats PMU disponibles) +""" + +import sqlite3 +import json +import argparse +from datetime import datetime, timedelta +from collections import defaultdict + +DB_PATH = "/home/h3r7/turf_scraper/turf.db" + +# ============================================================================= +# SCHÉMA BASE DE DONNÉES +# ============================================================================= + +METRICS_SCHEMA = """ +-- Table principale des métriques par course/source +CREATE TABLE IF NOT EXISTS prediction_metrics ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + date TEXT NOT NULL, + race_time TEXT, + race_name TEXT, + race_hippodrome TEXT, + source TEXT NOT NULL, + discipline TEXT, + + -- Comptages + nb_predictions INTEGER DEFAULT 0, + nb_gagnants INTEGER DEFAULT 0, + nb_places INTEGER DEFAULT 0, + nb_top5 INTEGER DEFAULT 0, + nb_hors_top5 INTEGER DEFAULT 0, + + -- Taux + taux_gagnant REAL, + taux_place REAL, + taux_top5 REAL, + + -- Rangs + rang_moyen REAL, + ecart_rang_moyen REAL, + + -- ROI avec dividendes réels PMU + roi_sg_brut REAL, + roi_sg_net REAL, + roi_sp_brut REAL, + roi_sp_net REAL, + + -- Quinté + quinte_5sur5 INTEGER DEFAULT 0, + quinte_4sur5 INTEGER DEFAULT 0, + quinte_3sur5 INTEGER DEFAULT 0, + quinte_2sur5 INTEGER DEFAULT 0, + + -- Value + value_bet_score REAL, + top_cote_gagnante REAL, + + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + UNIQUE(date, race_time, source) +); + +-- Vue détaillée des performances +CREATE VIEW IF NOT EXISTS v_predictions_performance AS +SELECT + pr.date, + pr.race_time, + pr.race_name, + pr.race_hippodrome, + pr.horse_number, + pr.horse_name, + pr.prediction_rank, + pr.odds AS cote_prediction, + pr.source, + pa.ordre_arrivee, + pa.cote_direct AS cote_finale, + pa.driver, + + -- Indicateurs binaires + CASE WHEN pa.ordre_arrivee = 1 THEN 1 ELSE 0 END AS is_gagnant, + CASE WHEN pa.ordre_arrivee <= 3 THEN 1 ELSE 0 END AS is_place, + CASE WHEN pa.ordre_arrivee <= 5 THEN 1 ELSE 0 END AS is_top5, + + -- Écarts + ABS(COALESCE(pr.prediction_rank, 99) - COALESCE(pa.ordre_arrivee, 99)) AS ecart_rang, + + -- Value réalisée + CASE + WHEN pa.ordre_arrivee = 1 AND pa.cote_direct > pr.odds + THEN ROUND((pa.cote_direct - pr.odds) / pr.odds * 100, 1) + ELSE 0 + END AS value_realized + +FROM predictions pr +LEFT JOIN pmu_partants pa + ON pa.date_programme = pr.date + AND pa.nom = pr.horse_name; + +-- Vue résumé par source (30 jours glissants) +CREATE VIEW IF NOT EXISTS v_metrics_summary_30d AS +SELECT + source, + COUNT(*) as nb_courses, + SUM(nb_predictions) as total_predictions, + SUM(nb_gagnants) as total_gagnants, + SUM(nb_places) as total_places, + SUM(nb_top5) as total_top5, + ROUND(AVG(taux_gagnant), 2) as moy_taux_gagnant, + ROUND(AVG(taux_place), 2) as moy_taux_place, + ROUND(AVG(taux_top5), 2) as moy_taux_top5, + ROUND(AVG(roi_sg_net), 3) as moy_roi_sg, + ROUND(AVG(roi_sp_net), 3) as moy_roi_sp, + ROUND(AVG(ecart_rang_moyen), 2) as moy_ecart_rang, + SUM(quinte_5sur5) as nb_5sur5, + SUM(quinte_4sur5) as nb_4sur5, + SUM(quinte_3sur5) as nb_3sur5, + ROUND(SUM(quinte_5sur5) * 100.0 / NULLIF(COUNT(*), 0), 1) as pct_5sur5, + ROUND(SUM(quinte_4sur5) * 100.0 / NULLIF(COUNT(*), 0), 1) as pct_4sur5 +FROM prediction_metrics +WHERE date >= date('now', '-30 days') +GROUP BY source +ORDER BY moy_taux_place DESC; + +-- Vue évolution quotidienne +CREATE VIEW IF NOT EXISTS v_metrics_daily AS +SELECT + date, + source, + SUM(nb_predictions) as predictions, + SUM(nb_gagnants) as gagnants, + SUM(nb_places) as places, + SUM(nb_top5) as top5, + ROUND(AVG(taux_gagnant), 2) as taux_gagnant, + ROUND(AVG(taux_place), 2) as taux_place, + ROUND(AVG(roi_sg_net), 3) as roi_sg, + ROUND(AVG(roi_sp_net), 3) as roi_sp, + SUM(quinte_5sur5) as quinte_5sur5, + SUM(quinte_4sur5) as quinte_4sur5 +FROM prediction_metrics +GROUP BY date, source +ORDER BY date DESC; +""" + +# ============================================================================= +# FONCTIONS UTILITAIRES +# ============================================================================= + +def get_db(): + """Connexion à la base de données""" + conn = sqlite3.connect(DB_PATH) + conn.row_factory = sqlite3.Row + return conn + +def init_db(): + """Initialise les tables et vues""" + conn = get_db() + conn.executescript(METRICS_SCHEMA) + conn.commit() + conn.close() + print("✅ Tables et vues initialisées") + +def get_dividende_sg(conn, date, num_reunion, num_course, horse_number): + """Récupère le dividende Simple Gagnant pour un cheval""" + try: + row = conn.execute(""" + SELECT dividende_euro + FROM pmu_rapports + WHERE date_programme = ? + AND num_reunion = ? + AND num_course = ? + AND type_pari = 'SIMPLE_GAGNANT' + AND combinaison = ? + """, (date, num_reunion, num_course, str(horse_number))).fetchone() + return row['dividende_euro'] if row else None + except: + return None + +def get_dividende_sp(conn, date, num_reunion, num_course, horse_number): + """Récupère le dividende Simple Placé pour un cheval""" + try: + row = conn.execute(""" + SELECT dividende_euro + FROM pmu_rapports + WHERE date_programme = ? + AND num_reunion = ? + AND num_course = ? + AND type_pari = 'SIMPLE_PLACE' + AND combinaison = ? + """, (date, num_reunion, num_course, str(horse_number))).fetchone() + return row['dividende_euro'] if row else None + except: + return None + +# ============================================================================= +# CALCUL DES MÉTRIQUES +# ============================================================================= + +def calculate_course_metrics(conn, date, race_time, race_name, source): + """Calcule les métriques pour une course/source donnée""" + + # Récupérer les prédictions pour cette course/source + preds = conn.execute(""" + SELECT + pr.horse_number, + pr.horse_name, + pr.prediction_rank, + pr.odds, + pa.ordre_arrivee, + pa.cote_direct, + pa.num_reunion, + pa.num_course + FROM predictions pr + LEFT JOIN pmu_partants pa + ON pa.date_programme = pr.date + AND pa.nom = pr.horse_name + WHERE pr.date = ? + AND pr.race_time = ? + AND pr.source = ? + """, (date, race_time, source)).fetchall() + + if not preds: + return + + # Métadonnées + first_pred = preds[0] + hippodrome = conn.execute(""" + SELECT race_hippodrome FROM predictions + WHERE date = ? AND race_time = ? + LIMIT 1 + """, (date, race_time)).fetchone() + + race_hippodrome = hippodrome['race_hippodrome'] if hippodrome else None + num_reunion = first_pred['num_reunion'] if first_pred['num_reunion'] else None + num_course = first_pred['num_course'] if first_pred['num_course'] else None + + # Récupérer discipline depuis pmu_courses + discipline = None + if num_reunion and num_course: + disc_row = conn.execute(""" + SELECT discipline FROM pmu_courses + WHERE date_programme = ? AND num_reunion = ? AND num_course = ? + """, (date, num_reunion, num_course)).fetchone() + discipline = disc_row['discipline'] if disc_row else None + + # Comptages + nb_predictions = len(preds) + nb_gagnants = sum(1 for p in preds if p['ordre_arrivee'] == 1) + nb_places = sum(1 for p in preds if p['ordre_arrivee'] and p['ordre_arrivee'] <= 3) + nb_top5 = sum(1 for p in preds if p['ordre_arrivee'] and p['ordre_arrivee'] <= 5) + nb_hors_top5 = nb_predictions - nb_top5 + + # Taux + taux_gagnant = round(nb_gagnants / nb_predictions * 100, 2) if nb_predictions > 0 else 0 + taux_place = round(nb_places / nb_predictions * 100, 2) if nb_predictions > 0 else 0 + taux_top5 = round(nb_top5 / nb_predictions * 100, 2) if nb_predictions > 0 else 0 + + # Rang moyen + rangs = [p['ordre_arrivee'] for p in preds if p['ordre_arrivee']] + rang_moyen = round(sum(rangs) / len(rangs), 2) if rangs else None + + # Écart rang moyen + ecarts = [abs((p['prediction_rank'] or 99) - (p['ordre_arrivee'] or 99)) for p in preds] + ecart_rang_moyen = round(sum(ecarts) / len(ecarts), 2) if ecarts else None + + # ROI avec dividendes réels + roi_sg_values = [] + roi_sp_values = [] + + for p in preds: + if p['ordre_arrivee'] == 1 and num_reunion and num_course: + div_sg = get_dividende_sg(conn, date, num_reunion, num_course, p['horse_number']) + if div_sg and div_sg > 0: + roi_sg_values.append(div_sg - 1) + else: + roi_sg_values.append(-1) + elif p['ordre_arrivee'] and p['ordre_arrivee'] > 1: + roi_sg_values.append(-1) + + if p['ordre_arrivee'] and p['ordre_arrivee'] <= 3 and num_reunion and num_course: + div_sp = get_dividende_sp(conn, date, num_reunion, num_course, p['horse_number']) + if div_sp and div_sp > 0: + roi_sp_values.append(div_sp - 1) + else: + roi_sp_values.append(-1) + elif p['ordre_arrivee'] and p['ordre_arrivee'] > 3: + roi_sp_values.append(-1) + + roi_sg_brut = sum(roi_sg_values) if roi_sg_values else 0 + roi_sg_net = round(roi_sg_brut / len(roi_sg_values), 3) if roi_sg_values else 0 + roi_sp_brut = sum(roi_sp_values) if roi_sp_values else 0 + roi_sp_net = round(roi_sp_brut / len(roi_sp_values), 3) if roi_sp_values else 0 + + # Quinté (5 chevaux dans le top 5) + quinte_5sur5 = 1 if nb_top5 >= 5 else 0 + quinte_4sur5 = 1 if nb_top5 >= 4 else 0 + quinte_3sur5 = 1 if nb_top5 >= 3 else 0 + quinte_2sur5 = 1 if nb_top5 >= 2 else 0 + + # Value bet score + value_scores = [] + for p in preds: + if p['ordre_arrivee'] == 1 and p['cote_direct'] and p['odds']: + value = (p['cote_direct'] - p['odds']) / p['odds'] * 100 + value_scores.append(value) + value_bet_score = round(sum(value_scores) / len(value_scores), 2) if value_scores else 0 + + # Top cote gagnante + top_cotes = [p['cote_direct'] for p in preds if p['ordre_arrivee'] == 1 and p['cote_direct']] + top_cote_gagnante = max(top_cotes) if top_cotes else None + + # Insérer ou mettre à jour + conn.execute(""" + INSERT OR REPLACE INTO prediction_metrics ( + date, race_time, race_name, race_hippodrome, source, discipline, + nb_predictions, nb_gagnants, nb_places, nb_top5, nb_hors_top5, + taux_gagnant, taux_place, taux_top5, + rang_moyen, ecart_rang_moyen, + roi_sg_brut, roi_sg_net, roi_sp_brut, roi_sp_net, + quinte_5sur5, quinte_4sur5, quinte_3sur5, quinte_2sur5, + value_bet_score, top_cote_gagnante + ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) + """, ( + date, race_time, race_name, race_hippodrome, source, discipline, + nb_predictions, nb_gagnants, nb_places, nb_top5, nb_hors_top5, + taux_gagnant, taux_place, taux_top5, + rang_moyen, ecart_rang_moyen, + roi_sg_brut, roi_sg_net, roi_sp_brut, roi_sp_net, + quinte_5sur5, quinte_4sur5, quinte_3sur5, quinte_2sur5, + value_bet_score, top_cote_gagnante + )) + +def calculate_metrics(date_str): + """Calcule les métriques pour une date donnée""" + init_db() + conn = get_db() + + # Récupérer les courses avec prédictions ET résultats + courses = conn.execute(""" + SELECT DISTINCT + pr.date, + pr.race_time, + pr.race_name + FROM predictions pr + JOIN pmu_partants pa + ON pa.date_programme = pr.date + AND pa.nom = pr.horse_name + WHERE pr.date = ? + AND pa.ordre_arrivee IS NOT NULL + """, (date_str,)).fetchall() + + if not courses: + print(f"⚠️ Aucune course avec résultats pour {date_str}") + conn.close() + return + + sources = [ + 'canalturf_selections', + 'canalturf_prono_bases', + 'canalturf_prono_chances', + 'canalturf_prono_outsiders', + 'canalturf_partants' + ] + + total_calculated = 0 + for course in courses: + for source in sources: + try: + calculate_course_metrics(conn, course['date'], course['race_time'], course['race_name'], source) + total_calculated += 1 + except Exception as e: + print(f"⚠️ Erreur {course['race_time']} {source}: {e}") + + conn.commit() + conn.close() + print(f"✅ Métriques calculées pour {date_str}: {total_calculated} combinaisons course/source") + +def backfill_metrics(days=30): + """Remplit les métriques sur plusieurs jours""" + print(f"📊 Backfill sur {days} jours...") + for i in range(days): + date = (datetime.now() - timedelta(days=i)).strftime('%Y-%m-%d') + print(f" → {date}") + try: + calculate_metrics(date) + except Exception as e: + print(f" ⚠️ Erreur: {e}") + print("✅ Backfill terminé") + +# ============================================================================= +# POINT D'ENTRÉE +# ============================================================================= + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Calcul des métriques de performance") + parser.add_argument("--date", "-d", help="Date YYYY-MM-DD") + parser.add_argument("--yesterday", "-y", action="store_true", help="Calculer hier") + parser.add_argument("--backfill", "-b", type=int, help="Remplir N derniers jours") + + args = parser.parse_args() + + if args.backfill: + backfill_metrics(args.backfill) + elif args.yesterday: + date_str = (datetime.now() - timedelta(days=1)).strftime('%Y-%m-%d') + calculate_metrics(date_str) + elif args.date: + calculate_metrics(args.date) + else: + calculate_metrics(datetime.now().strftime('%Y-%m-%d')) diff --git a/clean_preds.py b/clean_preds.py new file mode 100755 index 0000000..6d6b2e7 --- /dev/null +++ b/clean_preds.py @@ -0,0 +1,31 @@ +import sqlite3 +conn = sqlite3.connect('/home/h3r7/turf_scraper/turf.db') +c = conn.cursor() + +# Delete duplicate predictions for today (keep only latest) +c.execute('DELETE FROM predictions WHERE date = "2026-02-24"') + +# Add clean predictions +preds = [ + (7, "I'M A BELIEVER", 1), + (1, 'PRINCE DE MONTFORT', 2), + (3, 'GRAND BALCON', 3), + (8, 'PAOLINO', 4), + (11, 'INCREMENTAL', 5), + (15, 'PRINCESSE SAPHIR', 6), + (16, 'GOLD PLAYER', 7), + (14, 'WEEMAGATEE', 8), +] + +today = '2026-02-24' +for num, name, rank in preds: + c.execute('INSERT INTO predictions (date, race_name, horse_number, horse_name, prediction_rank, source) VALUES (?, ?, ?, ?, ?, ?)', + (today, 'Quinte Cagnes-sur-Mer', num, name, rank, 'canalturf')) + +conn.commit() +print('Cleaned!') + +c.execute('SELECT horse_number, horse_name, prediction_rank FROM predictions WHERE date = ?', (today,)) +for r in c.fetchall(): + print(f" {r[0]} - {r[1]} (rank {r[2]})") +conn.close() diff --git a/combined_api.py b/combined_api.py new file mode 100755 index 0000000..66a4eff --- /dev/null +++ b/combined_api.py @@ -0,0 +1,3589 @@ +#!/usr/bin/env python3 +"""Combined API - Turf + Ideas""" + +from flask import Flask, jsonify, request, send_file, send_from_directory +from flask_cors import CORS +import sqlite3 +import json +import os +import requests +from datetime import datetime, timedelta + +app = Flask(__name__) +CORS(app, origins=["*"], methods=["GET", "POST", "OPTIONS"]) +DB_PATH = "/home/h3r7/turf_saas/turf_saas.db" +IDEAS_FILE = "/home/h3r7/boite_a_idees/idees.json" + + +# Basic Auth decorator +def require_auth(f): + from functools import wraps + from flask import request + + @wraps(f) + def decorated(*args, **kwargs): + from flask import make_response + import base64 + + auth = request.headers.get("Authorization", "") + expected = "Basic " + base64.b64encode("h3r7:h3r7".encode("utf-8")).decode( + "utf-8" + ) + if not auth or auth != expected: + return make_response("Unauthorized", 401) + return f(*args, **kwargs) + + return decorated + + +LLM_API_KEY = os.environ.get( + "OPENROUTER_API_KEY", + os.environ.get("OPENAI_API_KEY", os.environ.get("1MINAI_API_KEY", "")), +) +LLM_BASE_URL = os.environ.get("LLM_BASE_URL", "https://openrouter.ai/v1") +LLM_MODEL = os.environ.get("LLM_MODEL", "liquid/lfm-2.5-1.2b-instruct:free") + +# === API KEYS === +RESEND_API_KEY = os.environ.get("RESEND_API", "") +BRAVE_SEARCH_API_KEY = os.environ.get("BRAVE_SEARCH_API", "") + +SQL_SCHEMA = """ +Tables: +- pmu_partants: date_programme, num_reunion, num_course, num_pmu, id_cheval, nom, age, sexe, race, robe, pays, place_corde, nom_pere, nom_mere, nom_pere_mere, driver, driver_change, entraineur, proprietaire, eleveur, oeilleres, supplement, handicap_valeur, handicap_poids, musique, nombre_courses, nombre_victoires, nombre_places, cote_direct, cote_reference, tendance_cote, favoris, ordre_arrivee, tx_victoire, tx_place, forme_recente +- pmu_courses: date_programme, num_reunion, num_course, libelle, discipline, distance, hippodrome,px_type + +Tables relations: +- pmu_partants.date_programme = pmu_courses.date_programme +- pmu_partants.num_reunion = pmu_courses.num_reunion +- pmu_partants.num_course = pmu_courses.num_course + +Key fields: +- ordre_arrivee: 1=winner, 2=2nd, 3=3rd, 0=not finished +- favoris: 1 if favorite (cote < 5), 0 otherwise +- date_programme: YYYY-MM-DD format +- discipline: 'Plat', 'Trot', 'Attele', 'Monte' +""" + + +def generate_sql_with_llm(question: str) -> str: + """Generate SQL query from natural language using LLM""" + if not LLM_API_KEY: + return None + + try: + import litellm + + litellm.drop_params = True + + system_prompt = f"""Tu es un expert SQL. Génère SEULEMENT la requête SQL (pas d'explication). + +Schéma STRICT: +- Table pmu_partants: date_programme, num_reunion, num_course, nom, driver, entraineur, cote_direct, favoris, ordre_arrivee, tx_victoire, tx_place, forme_recente +- Table pmu_courses: date_programme, num_reunion, num_course, libelle, discipline, distance, hippodrome + +Règles OBLIGATOIRES: +- Utilise les noms de colonnes EXACTS ci-dessus +- Utilise date('now', '-X days') pour les dates relatives +- Pour "aujourd'hui", utilise date('now') +- Pour "hier", utilise date('now', '-1 day') +- Pour "cette semaine", utilise date('now', '-7 days') +- Pour "ce mois", utilise date('now', '-30 days') +- JOIN requis entre pmu_partants et pmu_courses +- Limite à 15 résultats +- Only SELECT queries allowed + +Question: {question} + +SQL:""" + + response = litellm.completion( + model=f"openrouter/{LLM_MODEL}", + messages=[ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": question}, + ], + api_key=LLM_API_KEY, + max_tokens=300, + ) + + sql = response.choices[0].message.content.strip() + + if sql.upper().startswith("SQL:"): + sql = sql[4:].strip() + if sql.upper().startswith("```SQL"): + sql = sql[6:].strip() + if sql.upper().startswith("```"): + sql = sql[3:].strip() + if sql.endswith("```"): + sql = sql[:-3].strip() + + return sql if sql.upper().startswith("SELECT") else None + + except Exception as e: + print(f"LLM SQL generation error: {e}") + return None + + +# === TURF API === +def get_db(): + conn = sqlite3.connect(DB_PATH) + conn.row_factory = sqlite3.Row + return conn + + +@app.route("/") +def index(): + return send_file("/home/h3r7/turf_saas/dashboard.html") + + +@app.route("/dashboard") +def dashboard(): + return send_file("/home/h3r7/turf_saas/dashboard.html") + + +@app.route("/n8n-chat") +@app.route("/turf/n8n-chat") +def n8n_chat(): + return send_file("/home/h3r7/turf_saas/n8n-chat.html") + + +@app.route("/turf/api/n8n-proxy", methods=["GET", "POST", "OPTIONS"]) +@app.route("/api/n8n-proxy", methods=["GET", "POST", "OPTIONS"]) +def n8n_proxy(): + """Proxy pour éviter CORS vers n8n interne""" + if request.method == "OPTIONS": + response = make_response("", 200) + response.headers["Access-Control-Allow-Origin"] = "*" + response.headers["Access-Control-Allow-Methods"] = "GET, POST, OPTIONS" + response.headers["Access-Control-Allow-Headers"] = "Content-Type" + return response + + data = request.json or {} + try: + r = requests.post( + "http://10.0.1.7:5678/webhook/openclaw", + json=data, + timeout=30, + headers={"Content-Type": "application/json"}, + ) + try: + return jsonify(r.json()), 200 + except: + return jsonify({"message": r.text}), 200 + except Exception as e: + return jsonify({"error": str(e)}), 500 + + +@app.route("/api") +@app.route("/turf/api") +@app.route("/api/today") +def api_today(): + conn = get_db() + today = datetime.now().strftime("%Y-%m-%d") + yesterday = (datetime.now() - timedelta(days=1)).strftime("%Y-%m-%d") + + # Parametre de selection des courses + race_filter = request.args.get("race", "") # Nom de la course ou vide pour toutes + + data = { + "date": today, + "races": [], + "predictions": {}, + "results": [], + "scores": {}, + "weather": {}, + } + + # Construire la condition de filtre - si un nom de course est fourni, l'utiliser directement + if race_filter: + race_condition = "AND race_name = ?" # Utilise le nom exact de la course + race_params = (race_filter,) + else: + race_condition = "" + race_params = () + + # Filtre France uniquement - matcher avec LIKE sur nom hippodrome (désactivé car pas de données aujourd'hui) + # france_condition = "AND EXISTS (SELECT 1 FROM pmu_reunions r WHERE r.pays_code='FRA' AND r.date_programme=date AND (r.hippodrome_long LIKE '%' || race_hippodrome || '%' OR r.hippodrome_court LIKE '%' || race_hippodrome || '%' OR r.hippodrome_code LIKE '%' || race_hippodrome || '%'))" + france_condition = "" + + # Filtre données suffisantes (min 10 partants) + min_partants_condition = "AND (SELECT COUNT(*) FROM predictions p2 WHERE p2.race_name=predictions.race_name AND p2.date=predictions.date AND p2.source='canalturf_partants') >= 10" + + # Recuperer toutes les courses du jour + try: + # Get full race name format: R1 ANGERS - C1 Grand prix ... (13:55) + full_race_name = get_full_race_name(conn, today) + + c = conn.execute( + f""" + SELECT DISTINCT + c.num_reunion, + c.num_course, + c.libelle, + c.libelle_court, + c.heure_depart_str, + r.hippodrome_court, + r.hippodrome_long, + r.nature + FROM pmu_courses c + JOIN pmu_reunions r + ON r.date_programme = c.date_programme AND r.num_reunion = c.num_reunion + WHERE c.date_programme=? {france_condition} + ORDER BY c.num_reunion ASC, c.num_course ASC + """, + (today,), + ) + races = c.fetchall() + + if not races: + c = conn.execute( + f""" + SELECT DISTINCT race_name, race_hippodrome, race_time + FROM predictions + WHERE date=? AND source='canalturf_partants' {france_condition} + ORDER BY race_time ASC + """, + (today,), + ) + races = c.fetchall() + + def infer_race_type(libelle="", libelle_court="", nature=""): + text = f"{libelle or ''} {libelle_court or ''} {nature or ''}".upper() + if "QUINTE" in text: + return "Quinté+" + if "TROT" in text: + return "Trot" + if "PLAT" in text: + return "Plat" + return (nature or "").strip().title() + + def fmt_race(row): + if "num_reunion" not in row.keys(): + race_name = row["race_name"] or "" + hippo = row["race_hippodrome"] or "" + time = row["race_time"] or "" + race_type = infer_race_type(race_name) + display = f"{hippo} - {race_name} - {time}".strip() + if race_type: + display = f"{display} : {race_type}" + return { + "name": race_name, + "filter": race_name, + "display_label": display, + "hippodrome": hippo, + "time": time, + "num_reunion": None, + "num_course": None, + "race_type": "", + } + num_reunion = row["num_reunion"] + num_course = row["num_course"] + hippo = row["hippodrome_court"] or row["hippodrome_long"] or "" + course = row["libelle"] or row["libelle_court"] or "" + time = row["heure_depart_str"] or "" + race_type = infer_race_type( + row["libelle"], row["libelle_court"], row["nature"] + ) + display = f"R{num_reunion} {hippo} - {course} - {time}".strip(" -") + if race_type: + display = f"{display} : {race_type}" + return { + "name": row["libelle"] or course, + "filter": row["libelle"] or course, + "display_label": display, + "hippodrome": hippo, + "time": time, + "num_reunion": num_reunion, + "num_course": num_course, + "race_type": row["nature"] or "", + } + + data["races"] = [fmt_race(r) for r in races] + + # Première course comme course principale, ou course correspondant au filtre + if races: + selected_race = None + if race_filter: + import re + + race_filter_name = race_filter + match = re.search(r"-\s+C\d+\s+(.+?)(?:\s*\(|$)", race_filter) + if match: + race_filter_name = match.group(1).strip() + selected_race = next( + ( + fmt_race(r) + for r in races + if (r["libelle"] or "") == race_filter_name + or (r["libelle_court"] or "") == race_filter_name + ), + None, + ) + first_race = selected_race or fmt_race(races[0]) + data["race"] = { + "name": first_race["display_label"] or full_race_name, + "display_label": first_race["display_label"] or full_race_name, + "filter_name": first_race["filter"], + "hippodrome": first_race["hippodrome"], + "time": first_race["time"], + "race_type": first_race["race_type"], + } + except Exception as e: + print(f"Erreur races: {e}") + data["race"] = {} + + # Partants avec cotes — 1 ligne par cheval (dédoublonnage) + try: + if race_filter: + # Extract just the race name part (after time) for better matching + race_name_part = race_filter + # Try to extract the actual race name (after " - HH:MM ") + import re + + match = re.search(r"-\s+\d{1,2}:\d{2}\s+(.+)$", race_filter) + if match: + race_name_part = match.group(1).strip() + + c = conn.execute( + """ + SELECT horse_name, horse_number, AVG(odds) as odds, prediction_rank, jockey + FROM predictions + WHERE date=? AND source='canalturf_partants' AND odds > 0 AND (race_name LIKE ? OR ? LIKE '%' || race_name || '%') + GROUP BY horse_name + ORDER BY odds ASC + """, + (today, "%" + race_name_part + "%", race_filter), + ) + else: + c = conn.execute( + """ + SELECT horse_name, horse_number, AVG(odds) as odds, prediction_rank, jockey + FROM predictions + WHERE date=? AND source='canalturf_partants' AND odds > 0 + GROUP BY horse_name + ORDER BY odds ASC + """, + (today,), + ) + data["predictions"]["partants"] = [dict(r) for r in c.fetchall()] + except: + data["predictions"]["partants"] = [] + + # Pronostic bases/chances/outsiders - aussi filtré France + for cat, src in [ + ("bases", "canalturf_prono_bases"), + ("chances", "canalturf_prono_chances"), + ("outsiders", "canalturf_prono_outsiders"), + ]: + try: + if race_filter: + # Extract just the race name part (after time) for better matching + import re + + match = re.search(r"-\s+\d{1,2}:\d{2}\s+(.+)$", race_filter) + race_name_part = race_filter + if match: + race_name_part = match.group(1).strip() + + c = conn.execute( + """ + SELECT horse_name, horse_number, MIN(prediction_rank) as prediction_rank + FROM predictions WHERE date=? AND source=? AND (race_name LIKE ? OR ? LIKE '%' || race_name || '%') + GROUP BY horse_name + ORDER BY prediction_rank + """, + (today, src, "%" + race_name_part + "%", race_filter), + ) + else: + c = conn.execute( + """ + SELECT horse_name, horse_number, MIN(prediction_rank) as prediction_rank + FROM predictions WHERE date=? AND source=? + GROUP BY horse_name + ORDER BY prediction_rank + """, + (today, src), + ) + data["predictions"][cat] = [dict(r) for r in c.fetchall()] + except: + data["predictions"][cat] = [] + + # Resultats du jour - depuis pmu_partants (toutes les courses France terminées) + try: + # Add race filter if provided + race_condition = "" + race_params = [today] + if race_filter: + import re + + match = re.search(r"-\s+\d{1,2}:\d{2}\s+(.+)$", race_filter) + race_name_part = race_filter + if match: + race_name_part = match.group(1).strip() + race_condition = "AND (c.libelle LIKE ? OR ? LIKE '%' || c.libelle || '%')" + race_params.append("%" + race_name_part + "%") + race_params.append(race_filter) + + c = conn.execute( + f""" + SELECT + pp.nom as horse_name, + pp.ordre_arrivee as position, + pp.cote_direct as odds, + pr.num_reunion, + pr.hippodrome_court, + c.heure_depart_str, + c.libelle as race_name, + c.num_course + FROM pmu_partants pp + JOIN pmu_reunions pr ON pr.date_programme=pp.date_programme AND pr.num_reunion=pp.num_reunion + JOIN pmu_courses c ON c.date_programme=pp.date_programme AND c.num_reunion=pp.num_reunion AND c.num_course=pp.num_course + WHERE pp.date_programme=? AND pp.ordre_arrivee > 0 AND pp.ordre_arrivee <= 5 + AND pr.pays_code='FRA' {race_condition} + ORDER BY c.heure_depart_str, pp.ordre_arrivee + """, + race_params, + ) + results = c.fetchall() + if results: + # Grouper par reunion + current_reunion = None + grouped = [] + for r in results: + rd = dict(r) + reunion_key = f"R{rd['num_reunion']} {rd['hippodrome_court']}" + rd["race_label"] = reunion_key + rd["race_type"] = rd["race_name"][:30] if rd["race_name"] else "" + grouped.append(rd) + data["results"] = grouped + else: + c = conn.execute( + "SELECT horse_name, position, odds FROM results WHERE date=? ORDER BY position LIMIT 5", + (today,), + ) + data["results"] = [dict(r) for r in c.fetchall()] + except Exception as e: + print(f"Erreur résultats: {e}") + data["results"] = [] + + # Fallback: utiliser v_resultats_complets si results table est vide + if not data["results"]: + try: + c = conn.execute( + """ + SELECT date_programme as date, course as race_name, cheval as horse_name, + position_finale as position, cote_direct as odds, + num_reunion, num_course + FROM v_resultats_complets + WHERE date_programme=? AND position_finale IS NOT NULL AND position_finale > 0 + ORDER BY course, position_finale + """, + (today,), + ) + rows = c.fetchall() + if rows: + data["results"] = [dict(r) for r in rows] + except Exception as e: + print(f"Erreur fallback résultats: {e}") + + # Fallback 2: afficher les résultats d'hier si pas de résultats aujourd'hui + if not data["results"]: + try: + c = conn.execute( + """ + SELECT date_programme as date, course as race_name, cheval as horse_name, + position_finale as position, cote_direct as odds, + num_reunion, num_course + FROM v_resultats_complets + WHERE date_programme=? AND position_finale IS NOT NULL AND position_finale > 0 + ORDER BY course, position_finale + """, + (yesterday,), + ) + rows = c.fetchall() + if rows: + data["results"] = [dict(r) for r in rows] + except: + pass + + # Score hier + try: + c = conn.execute( + "SELECT horse_name FROM results WHERE date=? AND position<=3", (yesterday,) + ) + result_names = [r[0] for r in c.fetchall()] + c = conn.execute( + "SELECT DISTINCT horse_name FROM predictions WHERE date=? AND source='canalturf_prono_bases'", + (yesterday,), + ) + bases_yest = [r[0] for r in c.fetchall()] + score = sum( + 1 + for p in bases_yest + if any( + p.upper() in r.upper() or r.upper() in p.upper() for r in result_names + ) + ) + data["scores"] = { + "bases": f"{score}/{len(bases_yest)}" if bases_yest else "-", + "date": yesterday, + } + except: + data["scores"] = {} + + # Weather + try: + c = conn.execute("SELECT * FROM weather ORDER BY id DESC LIMIT 1") + w = c.fetchone() + if w: + data["weather"] = dict(w) + except: + pass + + conn.close() + return jsonify(data) + + +@app.route("/api/races") +@app.route("/turf/api/races") +def api_races(): + """Liste des courses du jour avec selection""" + conn = get_db() + today = datetime.now().strftime("%Y-%m-%d") + race_filter = request.args.get( + "filter", "quinte" + ) # 'quinte', 'all', 'trot', 'plat' + + if race_filter == "quinte": + condition = "AND (race_name LIKE '%Quinte%' OR race_name LIKE '%Quinté%')" + elif race_filter == "trot": + condition = "AND race_name LIKE '%TROT%'" + elif race_filter == "plat": + condition = "AND race_name LIKE '%PLAT%'" + else: + condition = "" + + try: + c = conn.execute( + f""" + SELECT DISTINCT race_name, race_hippodrome, race_time + FROM predictions + WHERE date=? AND source='canalturf_partants' {condition} + ORDER BY race_time ASC + """, + (today,), + ) + + races = [] + for r in c.fetchall(): + # Compter les partants + c2 = conn.execute( + """ + SELECT COUNT(DISTINCT horse_name) + FROM predictions + WHERE date=? AND race_name=? AND source='canalturf_partants' + """, + (today, r[0]), + ) + partants = c2.fetchone()[0] + + races.append( + { + "name": r[0], + "hippodrome": r[1], + "time": r[2], + "partants": partants, + "is_quinte": "Quinte" in (r[0] or ""), + } + ) + + return jsonify({"date": today, "races": races, "filter": race_filter}) + except Exception as e: + return jsonify({"error": str(e)}), 500 + finally: + conn.close() + + +# === PREDICTIONS PAR COURSE === +@app.route("/api/race/") +@app.route("/turf/api/race/") +def api_race_predictions(race_name): + """Predictions pour une course specifique""" + conn = get_db() + today = datetime.now().strftime("%Y-%m-%d") + yesterday = (datetime.now() - timedelta(days=1)).strftime("%Y-%m-%d") + + # Decoder le nom de la course + import urllib.parse + + race_name = urllib.parse.unquote(race_name) + + data = { + "date": today, + "race": {"name": race_name}, + "predictions": {}, + "results": [], + "scores": {}, + } + + # Infos de la course + try: + c = conn.execute( + """ + SELECT DISTINCT race_hippodrome, race_time + FROM predictions + WHERE date=? AND race_name=? AND source='canalturf_partants' + """, + (today, race_name), + ) + row = c.fetchone() + if row: + data["race"]["hippodrome"] = row[0] + data["race"]["time"] = row[1] + except: + pass + + # Partants + try: + c = conn.execute( + """ + SELECT horse_name, horse_number, AVG(odds) as odds, prediction_rank, jockey + FROM predictions + WHERE date=? AND race_name=? AND source='canalturf_partants' AND odds > 0 + GROUP BY horse_name + ORDER BY odds ASC + """, + (today, race_name), + ) + data["predictions"]["partants"] = [dict(r) for r in c.fetchall()] + except: + data["predictions"]["partants"] = [] + + # Pronostics + for cat, src in [ + ("bases", "canalturf_prono_bases"), + ("chances", "canalturf_prono_chances"), + ("outsiders", "canalturf_prono_outsiders"), + ]: + try: + c = conn.execute( + """ + SELECT horse_name, horse_number, MIN(prediction_rank) as prediction_rank + FROM predictions + WHERE date=? AND race_name=? AND source=? + GROUP BY horse_name + ORDER BY prediction_rank + """, + (today, race_name, src), + ) + data["predictions"][cat] = [dict(r) for r in c.fetchall()] + except: + data["predictions"][cat] = [] + + # Resultats + try: + c = conn.execute( + """ + SELECT horse_name, position, odds + FROM results + WHERE date=? AND race_name LIKE ? + ORDER BY position + """, + (today, f"%{race_name[:20]}%"), + ) + data["results"] = [dict(r) for r in c.fetchall()] + except: + data["results"] = [] + + conn.close() + return jsonify(data) + + # Partants avec cotes — 1 ligne par cheval (dédoublonnage) + try: + c = conn.execute( + """ + SELECT horse_name, horse_number, AVG(odds) as odds, prediction_rank, jockey + FROM predictions + WHERE date=? AND source='canalturf_partants' AND odds > 0 + GROUP BY horse_name + ORDER BY odds ASC + """, + (today,), + ) + data["predictions"]["partants"] = [dict(r) for r in c.fetchall()] + except: + data["predictions"]["partants"] = [] + + # Pronostic bases/chances/outsiders — 1 ligne par cheval + for cat, src in [ + ("bases", "canalturf_prono_bases"), + ("chances", "canalturf_prono_chances"), + ("outsiders", "canalturf_prono_outsiders"), + ]: + try: + c = conn.execute( + """ + SELECT horse_name, horse_number, MIN(prediction_rank) as prediction_rank + FROM predictions WHERE date=? AND source=? + GROUP BY horse_name + ORDER BY prediction_rank + """, + (today, src), + ) + data["predictions"][cat] = [dict(r) for r in c.fetchall()] + except: + data["predictions"][cat] = [] + + # Legacy ours (compatibilité) + try: + c = conn.execute( + "SELECT horse_name, odds, prediction_rank FROM predictions WHERE date=?", + (today,), + ) + data["predictions"]["ours"] = [ + {"horse_name": r[0], "odds": r[1], "prediction_rank": r[2]} + for r in c.fetchall() + ] + except: + data["predictions"]["ours"] = [] + + # Résultats du jour + try: + c = conn.execute( + "SELECT horse_name, position, odds FROM results WHERE date=? ORDER BY position LIMIT 5", + (today,), + ) + data["results"] = [dict(r) for r in c.fetchall()] + except: + data["results"] = [] + + # Score hier + try: + c = conn.execute( + "SELECT horse_name FROM results WHERE date=? AND position<=3", (yesterday,) + ) + result_names = [r[0] for r in c.fetchall()] + c = conn.execute( + "SELECT DISTINCT horse_name FROM predictions WHERE date=? AND source='canalturf_prono_bases'", + (yesterday,), + ) + bases_yest = [r[0] for r in c.fetchall()] + score = sum( + 1 + for p in bases_yest + if any( + p.upper() in r.upper() or r.upper() in p.upper() for r in result_names + ) + ) + data["scores"] = { + "bases": f"{score}/{len(bases_yest)}" if bases_yest else "-", + "date": yesterday, + } + except: + data["scores"] = {} + + # Weather + try: + c = conn.execute("SELECT * FROM weather ORDER BY id DESC LIMIT 1") + w = c.fetchone() + if w: + data["weather"] = dict(w) + except: + pass + + conn.close() + return jsonify(data) + + +@app.route("/api/odds_history") +@app.route("/turf/api/odds_history") +def api_odds_history(): + conn = get_db() + today = datetime.now().strftime("%Y-%m-%d") + + try: + c = conn.execute( + """ + SELECT horse_name, horse_number, odds, MIN(scraped_at) as scraped_at + FROM odds_history WHERE date=? + GROUP BY horse_name, DATE(scraped_at), SUBSTR(scraped_at, 12, 5) + ORDER BY horse_name, scraped_at ASC + """, + (today,), + ) + rows = c.fetchall() + except: + conn.close() + return jsonify({"date": today, "horses": []}) + + horses = {} + for row in rows: + h = row["horse_name"] + if h not in horses: + horses[h] = { + "horse_name": h, + "horse_number": row["horse_number"], + "snapshots": [], + } + horses[h]["snapshots"].append( + {"odds": row["odds"], "time": row["scraped_at"][11:16]} + ) + + result = [] + for h, data in horses.items(): + snaps = data["snapshots"] + debut = snaps[0]["odds"] if snaps else 0 + actuel = snaps[-1]["odds"] if snaps else 0 + evol = ( + round(((actuel - debut) / debut) * 100, 1) + if debut > 0 and len(snaps) > 1 + else 0 + ) + result.append( + { + "horse_name": h, + "horse_number": data["horse_number"], + "odds_debut": debut, + "odds_actuel": actuel, + "evol_pct": evol, + "nb_snapshots": len(snaps), + "snapshots": snaps, + "tendance": "baisse" + if evol < -5 + else "hausse" + if evol > 5 + else "stable", + } + ) + + result.sort(key=lambda x: x["odds_actuel"]) + conn.close() + return jsonify({"date": today, "horses": result}) + + +@app.route("/api/weather") +def api_weather(): + conn = get_db() + c = conn.execute("SELECT * FROM weather ORDER BY id DESC LIMIT 4") + weather = [dict(row) for row in c.fetchall()] + conn.close() + return jsonify(weather) + + +# === IDEAS API === +def load_ideas(): + if not os.path.exists(IDEAS_FILE): + os.makedirs(os.path.dirname(IDEAS_FILE), exist_ok=True) + default = { + "categories": [ + {"id": "tech", "name": "Tech & IA", "color": "#7b2cbf"}, + {"id": "saas", "name": "SaaS", "color": "#2ec4b6"}, + {"id": "service", "name": "Service", "color": "#e94560"}, + {"id": "produit", "name": "Produit", "color": "#ff9f1c"}, + {"id": "invest", "name": "Investissement", "color": "#00d9ff"}, + ], + "ideas": [], + "next_id": 1, + } + with open(IDEAS_FILE, "w") as f: + json.dump(default, f, indent=2) + return default + with open(IDEAS_FILE, "r") as f: + return json.load(f) + + +def save_ideas(data): + os.makedirs(os.path.dirname(IDEAS_FILE), exist_ok=True) + with open(IDEAS_FILE, "w") as f: + json.dump(data, f, indent=2) + + +@app.route("/api/ideas") +def get_ideas(): + return jsonify(load_ideas()) + + +@app.route("/api/ideas", methods=["POST"]) +def add_idea(): + data = load_ideas() + new_idea = request.json + new_idea["id"] = data["next_id"] + new_idea["created"] = datetime.now().strftime("%Y-%m-%d") + data["ideas"].append(new_idea) + data["next_id"] += 1 + save_ideas(data) + return jsonify({"success": True, "id": new_idea["id"]}) + + +@app.route("/api/ideas/", methods=["GET"]) +def get_idea(idea_id): + data = load_ideas() + for i in data["ideas"]: + if i["id"] == idea_id: + return jsonify(i) + return jsonify({"error": "Not found"}), 404 + + +@app.route("/api/ideas/", methods=["PUT"]) +def update_idea(idea_id): + data = load_ideas() + for i, idea in enumerate(data["ideas"]): + if idea["id"] == idea_id: + data["ideas"][i].update(request.json) + save_ideas(data) + return jsonify({"success": True}) + return jsonify({"error": "Not found"}), 404 + + +@app.route("/api/ideas/", methods=["DELETE"]) +def delete_idea(idea_id): + data = load_ideas() + data["ideas"] = [i for i in data["ideas"] if i["id"] != idea_id] + save_ideas(data) + return jsonify({"success": True}) + + +@app.route("/idees") +@app.route("/idees/") +def idees_page(): + return send_from_directory("/home/h3r7/turf_saas", "idees_final.html") + + +@app.route("/H3R7Tech_logo.png") +@app.route("/turf/H3R7Tech_logo.png") +def serve_logo(): + return send_from_directory("/home/h3r7/turf_saas", "H3R7Tech_logo.png") + + +@app.route("/turf/") +def serve_static_turf(filename): + if filename.endswith((".html", ".md", ".png", ".svg", ".jpg", ".css", ".js")): + return send_from_directory("/home/h3r7/turf_saas", filename) + return "Not found", 404 + + +@app.route("/") +def serve_static(filename): + if filename.endswith((".html", ".md", ".png", ".svg", ".jpg")): + return send_from_directory("/home/h3r7/turf_saas", filename) + return "Not found", 404 + + +def ensure_scoring_tables(conn): + conn.execute(""" + CREATE TABLE IF NOT EXISTS scoring ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + date TEXT NOT NULL, + race_name TEXT, + horse_name TEXT, + horse_number INTEGER, + score REAL, + score_cote REAL, + score_forme REAL, + score_victoire REAL, + score_place REAL, + score_rk REAL, + score_tendance REAL, + score_avis REAL, + cote REAL, + forme_recente REAL, + tx_victoire REAL, + tx_place REAL, + avis_entraineur TEXT, + musique TEXT, + rang_scoring INTEGER, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP + ) + """) + conn.execute(""" + CREATE TABLE IF NOT EXISTS recommendations ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + date TEXT NOT NULL, + race_name TEXT, + type_pari TEXT, + cheval1 TEXT, + numero1 INTEGER, + cheval2 TEXT, + numero2 INTEGER, + cote REAL, + mise REAL, + gain_potentiel REAL, + confiance TEXT, + justification TEXT, + resultat TEXT, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP + ) + """) + + +def build_scoring_fallback(conn, date_str): + try: + import sys + + if "/home/h3r7/turf_saas" not in sys.path: + sys.path.insert(0, "/home/h3r7/turf_saas") + from scoring import build_recommendations, score_cheval + except Exception: + return [], {}, "R1C1" + + def find_race(for_date): + return conn.execute( + """ + SELECT c.num_reunion, c.num_course, c.libelle, c.libelle_court, c.heure_depart_str + FROM pmu_courses c + LEFT JOIN pmu_reunions r + ON r.date_programme = c.date_programme AND r.num_reunion = c.num_reunion + WHERE c.date_programme = ? AND COALESCE(r.pays_code, 'FRA') = 'FRA' + ORDER BY + CASE WHEN UPPER(COALESCE(c.libelle, '')) LIKE '%QUINTE%' THEN 0 ELSE 1 END, + c.num_reunion, + c.num_course + LIMIT 1 + """, + (for_date,), + ).fetchone() + + race = find_race(date_str) + if not race: + fallback_date = conn.execute( + "SELECT MAX(date_programme) FROM pmu_courses" + ).fetchone()[0] + if not fallback_date: + fallback_date = conn.execute( + "SELECT MAX(date_programme) FROM pmu_partants" + ).fetchone()[0] + if fallback_date: + date_str = fallback_date + race = find_race(date_str) + + if not race: + return [], {}, "R1C1" + + race_label = f"R{race['num_reunion']}C{race['num_course']}" + race_name = race["libelle"] or race["libelle_court"] or race_label + + rows = conn.execute( + """ + SELECT p.num_pmu, p.nom, p.musique, p.nombre_courses, p.nombre_victoires, + p.nombre_places, p.cote_direct, p.cote_reference, p.driver_change, + p.entraineur, p.tx_victoire, p.tx_place, p.forme_recente, + p.oeilleres, c.distance, c.discipline, c.nb_declares_partants + FROM pmu_partants p + LEFT JOIN pmu_courses c + ON c.date_programme = p.date_programme + AND c.num_reunion = p.num_reunion + AND c.num_course = p.num_course + WHERE p.date_programme = ? AND p.num_reunion = ? AND p.num_course = ? + ORDER BY p.num_pmu + """, + (date_str, race["num_reunion"], race["num_course"]), + ).fetchall() + + participants = [] + for row in rows: + r = dict(row) + participants.append( + { + "nom": r["nom"], + "numero": r["num_pmu"], + "musique": r.get("musique", ""), + "nombreCourses": r.get("nombre_courses", 0) or 0, + "nombreVictoires": r.get("nombre_victoires", 0) or 0, + "nombrePlaces": r.get("nombre_places", 0) or 0, + "reductionKilometrique": 0, + "avisEntraineur": "NEUTRE", + "driverChange": bool(r.get("driver_change", 0)), + "dernierRapportDirect": {"rapport": r.get("cote_direct", 0) or 0}, + "dernierRapportReference": { + "rapport": r.get("cote_reference", 0) + or r.get("cote_direct", 0) + or 0 + }, + "partants": r.get("nb_declares_partants", 0) or 0, + "distance": r.get("distance", 0) or 0, + "discipline": r.get("discipline", "PLAT") or "PLAT", + } + ) + + if not participants: + return [], {}, race_label + + scored_horses = [] + for p in participants: + score, details = score_cheval(p, participants) + scored_horses.append( + { + "nom": p["nom"], + "numero": p["numero"], + "score": score, + "details": details, + } + ) + + scored_horses = sorted(scored_horses, key=lambda x: x["score"], reverse=True) + recos = build_recommendations(scored_horses) + scores = [] + for rank, h in enumerate(scored_horses, start=1): + d = h["details"] + scores.append( + { + "race_label": race_label, + "horse_name": h["nom"], + "horse_number": h["numero"], + "score": h["score"], + "cote": d.get("cote", 0), + "forme_recente": d.get("forme_recente", 0), + "tx_victoire": d.get("tx_victoire", 0), + "avis_entraineur": d.get("avis_entraineur", "NEUTRE"), + "musique": d.get("musique", ""), + "rang_scoring": rank, + } + ) + + return scores, recos, race_name + + +@app.route("/api/scoring") +@app.route("/turf/api/scoring") +def api_scoring(): + conn = get_db() + today = datetime.now().strftime("%Y-%m-%d") + race_filter = request.args.get("race", "").strip() + ensure_scoring_tables(conn) + + def infer_race_type(libelle="", libelle_court="", nature=""): + text = f"{libelle or ''} {libelle_court or ''} {nature or ''}".upper() + if "QUINTE" in text: + return "Quinté+" + if "TROT" in text: + return "Trot" + if "PLAT" in text: + return "Plat" + return (nature or "").strip().title() + + def format_race_label(row): + reunion = row["num_reunion"] + course = row["num_course"] + hippodrome = row["hippodrome_court"] or row["hippodrome_long"] or "" + name = row["libelle"] or row["libelle_court"] or "" + time = row["heure_depart_str"] or "" + race_type = infer_race_type(row["libelle"], row["libelle_court"], row["nature"]) + label = f"R{reunion} {hippodrome} - {name} - {time}".strip(" -") + if race_type: + label = f"{label} : {race_type}" + return { + "race_label": f"R{reunion}C{course}", + "full_race_name": label, + "race_type": race_type, + "race_time": time, + "hippodrome": hippodrome, + "name": name, + } + + race_info = None + try: + if race_filter: + race_info = conn.execute( + """ + SELECT c.num_reunion, c.num_course, c.libelle, c.libelle_court, c.heure_depart_str, + r.hippodrome_court, r.hippodrome_long, r.nature + FROM pmu_courses c + JOIN pmu_reunions r ON r.date_programme=c.date_programme AND r.num_reunion=c.num_reunion + WHERE c.date_programme=? AND ( + c.libelle = ? OR c.libelle_court = ? OR c.libelle LIKE ? OR c.libelle_court LIKE ? + ) + ORDER BY c.heure_depart_str ASC, c.num_reunion ASC, c.num_course ASC + LIMIT 1 + """, + ( + today, + race_filter, + race_filter, + f"%{race_filter}%", + f"%{race_filter}%", + ), + ).fetchone() + if not race_info: + # Essayer de trouver la course qui correspond au scoring du jour + scored_race = conn.execute( + "SELECT race_name FROM scoring WHERE date=? LIMIT 1", (today,) + ).fetchone() + if scored_race and scored_race["race_name"]: + race_name_db = scored_race["race_name"] + race_info = conn.execute( + """ + SELECT c.num_reunion, c.num_course, c.libelle, c.libelle_court, c.heure_depart_str, + r.hippodrome_court, r.hippodrome_long, r.nature + FROM pmu_courses c + JOIN pmu_reunions r ON r.date_programme=c.date_programme AND r.num_reunion=c.num_reunion + WHERE c.date_programme=? AND ( + c.libelle = ? OR c.libelle LIKE ? OR c.libelle_court LIKE ? + ) + LIMIT 1 + """, + (today, race_name_db, f"%{race_name_db}%", f"%{race_name_db}%"), + ).fetchone() + if not race_info: + race_info = conn.execute( + """ + SELECT c.num_reunion, c.num_course, c.libelle, c.libelle_court, c.heure_depart_str, + r.hippodrome_court, r.hippodrome_long, r.nature + FROM pmu_courses c + JOIN pmu_reunions r ON r.date_programme=c.date_programme AND r.num_reunion=c.num_reunion + WHERE c.date_programme=? AND COALESCE(r.pays_code, 'FRA') = 'FRA' + ORDER BY c.heure_depart_str ASC, c.num_reunion ASC, c.num_course ASC + LIMIT 1 + """, + (today,), + ).fetchone() + except Exception: + race_info = None + + if race_info: + race_meta = format_race_label(race_info) + race_label = race_meta["race_label"] + full_race_name = race_meta["full_race_name"] + else: + race_label = "R1C1" + full_race_name = "R1C1" + + # Scores du jour + try: + c = conn.execute( + """ + SELECT horse_name, horse_number, score, COALESCE(cote,0) as cote, forme_recente, + tx_victoire, avis_entraineur, musique, rang_scoring + FROM scoring WHERE date=? + ORDER BY rang_scoring ASC + """, + (today,), + ) + scores = [dict(r) for r in c.fetchall()] + # Add race label and full race name to each score + for s in scores: + s["race_label"] = race_label + s["full_race_name"] = full_race_name + except: + scores = [] + + if not scores: + scores, recos, fallback_race_name = build_scoring_fallback(conn, today) + if scores and fallback_race_name: + for s in scores: + s["race_label"] = fallback_race_name + s["full_race_name"] = ( + full_race_name if full_race_name != "R1C1" else fallback_race_name + ) + else: + # Recommandations du jour - Focus B4/B3 (ZE5) + recos = generate_ze5_recommendations(conn, today) + + # Sauvegarder les recommandations ZE5 dans la DB + save_ze5_recommendations(conn, today, recos) + + # Ajouter les recommandations depuis la table recommendations + try: + c = conn.execute( + """ + SELECT type_pari, cheval1, numero1, cheval2, numero2, + cote, mise, gain_potentiel, confiance, justification + FROM recommendations WHERE date=? + ORDER BY type_pari + """, + (today,), + ) + for r in c.fetchall(): + recos[r["type_pari"]] = dict(r) + except: + pass + + conn.close() + return jsonify( + { + "date": today, + "scores": scores, + "recommendations": recos, + "full_race_name": full_race_name, + "race_label": race_label, + } + ) + + +def generate_ze5_recommendations(conn, today): + """ + Génère des recommandations ZE5 avec focus B4/B3 + Stratégie: Bases + Chances + 1 Outsider = bonne couverture pour B4/B3 + """ + recos = {} + + try: + # Récupérer nos prédictions + bases = [ + r["horse_name"] + for r in conn.execute( + """ + SELECT DISTINCT horse_name FROM predictions + WHERE date=? AND source='canalturf_prono_bases' + ORDER BY prediction_rank LIMIT 2 + """, + (today,), + ).fetchall() + ] + + chances = [ + r["horse_name"] + for r in conn.execute( + """ + SELECT DISTINCT horse_name FROM predictions + WHERE date=? AND source='canalturf_prono_chances' + ORDER BY prediction_rank LIMIT 3 + """, + (today,), + ).fetchall() + ] + + outsiders = [ + r["horse_name"] + for r in conn.execute( + """ + SELECT DISTINCT horse_name FROM predictions + WHERE date=? AND source='canalturf_prono_outsiders' + ORDER BY prediction_rank LIMIT 2 + """, + (today,), + ).fetchall() + ] + + if not bases or not chances: + return recos + + # Get horse numbers too + def get_horse_num(name): + row = conn.execute( + "SELECT horse_number FROM predictions WHERE date=? AND horse_name=? LIMIT 1", + (today, name), + ).fetchone() + return str(row[0]) if row else "" + + # Format: "NUMERO NOM" + def format_horse(name): + num = get_horse_num(name) + return f"{num} {name}" if num else name + + # Stratégie B4/B3: 4-5 chevaux couvrant bases + chances + all_horses = bases[:2] + chances[:2] + if outsiders: + all_horses.append(outsiders[0]) + + if len(all_horses) >= 4: + combo = "-".join([format_horse(h) for h in all_horses[:5]]) + recos["ze5_b4b3"] = { + "type_pari": "ZE5 B4/B3", + "cheval1": combo, + "cote": 15.0, + "mise": 3, + "gain_potentiel": 45, + "confiance": "haute", + "justification": "Bases + Chances + Outsider. Optimise pour B4/B3", + "strategy": "b4b3", + } + + # ZE5 Conservateur: Top 2 bases + Top 2 chances + if len(bases) >= 1 and len(chances) >= 2: + combo_list = [ + format_horse(bases[0]), + format_horse(chances[0]), + format_horse(chances[1]), + ] + if len(bases) > 1: + combo_list.append(format_horse(bases[1])) + else: + combo_list.append(format_horse(chances[0])) + combo_conservative = "-".join(combo_list) + recos["ze5_conservateur"] = { + "type_pari": "ZE5 Conservateur", + "cheval1": combo_conservative, + "cote": 8.0, + "mise": 3, + "gain_potentiel": 24, + "confiance": "moyenne-haute", + "justification": "Favori + 2 Chances - Plus securise", + "strategy": "conservateur", + } + + # ZE5 Audacieux: outsiders + 1 base + if len(outsiders) >= 2 and bases: + combo_audacieux_list = [ + format_horse(bases[0]), + format_horse(outsiders[0]), + format_horse(outsiders[1]), + ] + if chances: + combo_audacieux_list.append(format_horse(chances[0])) + combo_audacieux = "-".join(combo_audacieux_list) + recos["ze5_audacieux"] = { + "type_pari": "ZE5 Audacieux", + "cheval1": combo_audacieux, + "cote": 50.0, + "mise": 1, + "gain_potentiel": 50, + "confiance": "basse", + "justification": "Base + 2 Outsiders + Chance. Rapport élevé mais moins probable", + "strategy": "audacieux", + } + + # ZE4 (équivalent Multi): Top 4 bases + chances + # Trouver les 4 premiers + if len(bases) >= 2 and len(chances) >= 2: + ze4_horses = [ + format_horse(bases[0]), + format_horse(bases[1]), + format_horse(chances[0]), + format_horse(chances[1]), + ] + combo_ze4 = "-".join(ze4_horses) + recos["ze4"] = { + "type_pari": "ZE4 (Multi 4)", + "cheval1": combo_ze4, + "cote": 20.0, + "mise": 3, + "gain_potentiel": 60, + "confiance": "haute", + "justification": "Top 2 Bases + Top 2 Chances. Trouver 4 premiers", + "strategy": "ze4", + } + + except Exception as e: + print(f"Erreur génération recommandations: {e}") + + return recos + + +def get_full_race_name(conn, date): + """Construit le nom complet de la course: R1 MARSEILLE BORELY - 13h55 Grand National du Trot - Prix ...""" + # Try to get from pmu_reunions first + r = conn.execute( + """ + SELECT r.num_reunion, r.hippodrome_court, c.heure_depart_str, c.libelle + FROM pmu_courses c + JOIN pmu_reunions r ON r.date_programme=c.date_programme AND r.num_reunion=c.num_reunion + WHERE c.date_programme=? AND r.pays_code='FRA' AND c.num_course=1 + ORDER BY r.num_reunion + LIMIT 1 + """, + (date,), + ).fetchone() + + if r: + return f"R{r[0]} {r[1]} - {r[2]} {r[3]}" + + # Fallback: get from predictions table + r2 = conn.execute( + """ + SELECT DISTINCT race_name, race_hippodrome, race_time + FROM predictions + WHERE date=? AND source='canalturf_partants' + ORDER BY race_time ASC + LIMIT 1 + """, + (date,), + ).fetchone() + + if r2: + return f"{r2[1]} - {r2[2]} {r2[0]}" + + return "Course du jour" + + +def save_ze5_recommendations(conn, today, recos): + """Sauvegarde les recommandations ZE5 dans la base de données""" + try: + race_name = get_full_race_name(conn, today) + + saved = 0 + for key, r in recos.items(): + if not (key.startswith("ze5") or key == "ze4"): + continue + + conn.execute( + """ + INSERT OR REPLACE INTO recommendations + (date, race_name, type_pari, cheval1, numero1, cote, mise, gain_potentiel, confiance, justification, scoring_version) + VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) + """, + ( + today, + race_name, + r["type_pari"], + r["cheval1"], + 0, + r.get("cote", 0), + r.get("mise", 0), + r.get("gain_potentiel", 0), + r.get("confiance", ""), + r.get("justification", ""), + key, + ), + ) + saved += 1 + conn.commit() + print( + f"✅ {len([k for k in recos if k.startswith('ze5')])} recommandations ZE5 sauvegardées" + ) + except Exception as e: + print(f"Erreur sauvegarde ZE5: {e}") + + +# === ML PREDICTIONS === +import pickle +import pandas as pd +import numpy as np +from sklearn.preprocessing import LabelEncoder + +ml_models = None +MODEL_PATH = "/home/h3r7/turf_saas/xgboost_models.pkl" + + +def load_models(): + global ml_models + if ml_models is None and os.path.exists(MODEL_PATH): + try: + with open(MODEL_PATH, "rb") as f: + ml_models = pickle.load(f) + except: + ml_models = False + return ml_models + + +def table_exists(conn, table_name): + c = conn.execute( + "SELECT 1 FROM sqlite_master WHERE type='table' AND name=?", (table_name,) + ) + return c.fetchone() is not None + + +def ensure_analytics_tables(): + conn = sqlite3.connect(DB_PATH) + tables = [ + ( + "bet_results", + """ + CREATE TABLE IF NOT EXISTS bet_results ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + date TEXT, + race_name TEXT, + type_pari TEXT, + horse_name TEXT, + horse_number INTEGER, + cote REAL, + mise REAL, + resultat TEXT, + gain REAL, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP + ) + """, + ), + ( + "daily_stats", + """ + CREATE TABLE IF NOT EXISTS daily_stats ( + date TEXT PRIMARY KEY, + total_bets INTEGER, + bets_gagne INTEGER, + bets_perdu INTEGER, + mise_totale REAL, + gain_total REAL, + precision_pct REAL, + roi_pct REAL + ) + """, + ), + ( + "stats_by_type", + """ + CREATE TABLE IF NOT EXISTS stats_by_type ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + date TEXT, + type_pari TEXT, + total_bets INTEGER, + gagne INTEGER, + perdu INTEGER, + mise_totale REAL, + gain_total REAL, + precision_pct REAL, + roi_pct REAL + ) + """, + ), + ( + "race_scores", + """ + CREATE TABLE IF NOT EXISTS race_scores ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + date TEXT, + race_name TEXT, + race_time TEXT, + hippodrome TEXT, + top5_cotes TEXT, + top5_cotes_hits INTEGER, + top5_bc TEXT, + top5_bc_hits INTEGER, + top5_bo TEXT, + top5_bo_hits INTEGER, + results_top5 TEXT, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP + ) + """, + ), + ( + "paris", + """ + CREATE TABLE IF NOT EXISTS paris ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + date_pari TEXT NOT NULL, + date_course TEXT NOT NULL, + race_name TEXT, + race_label TEXT, + hippodrome TEXT, + type_pari TEXT, + chevaux TEXT, + cheval1 TEXT, + numero1 INTEGER, + cheval2 TEXT, + numero2 INTEGER, + cheval3 TEXT, + numero3 INTEGER, + cote REAL, + mise REAL DEFAULT 1.0, + statut TEXT DEFAULT 'EN_ATTENTE', + gain REAL DEFAULT 0, + commentaire TEXT, + source_reco TEXT, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP + ) + """, + ), + ] + for name, ddl in tables: + conn.execute(ddl) + conn.close() + + +ensure_analytics_tables() + + +def load_ml_horses(conn, today): + """Load horses for ML predictions, with a fallback when historical_data is absent.""" + course_info = {} + + c = conn.execute( + """ + SELECT num_reunion, num_course, libelle, libelle_court, discipline, distance, heure_depart_str + FROM pmu_courses + WHERE date_programme = ? + ORDER BY num_reunion, num_course + """, + (today,), + ) + for row in c.fetchall(): + course_info[f"{row['num_reunion']}_{row['num_course']}"] = dict(row) + + if table_exists(conn, "historical_data"): + c = conn.execute( + """ + SELECT DISTINCT p.horse_name, p.horse_number, p.odds, + h.age, h.sexe, h.nb_courses, h.nb_victoires, h.nb_places, + h.tx_victoire, h.tx_place, h.forme_recente, h.reduction_km, + h.gains_annee, h.cote_directe, h.distance, h.discipline, + h.avis_entraineur, h.oeilleres, h.deferre, h.nb_partants, + h.rang_cote, h.ratio_cote_field, h.musique + FROM predictions p + LEFT JOIN historical_data h ON h.horse_name = p.horse_name + WHERE p.date = ? AND p.source = 'canalturf_partants' AND p.odds > 0 + """, + (today,), + ) + horses = [dict(row) for row in c.fetchall()] + return today, horses, course_info + + def fetch_fallback(date_str): + c = conn.execute( + """ + SELECT + p.date_programme AS date, + p.num_reunion, + p.num_course, + p.num_pmu AS horse_number, + p.nom AS horse_name, + p.age, + p.sexe, + p.musique, + p.nombre_courses AS nb_courses, + p.nombre_victoires AS nb_victoires, + p.nombre_places AS nb_places, + p.gains_annee_en_cours AS gains_annee, + COALESCE(p.cote_direct, 0) AS cote_directe, + COALESCE(c.distance, 0) AS distance, + COALESCE(c.discipline, 'PLAT') AS discipline, + COALESCE(c.nb_declares_partants, 0) AS nb_partants, + COALESCE(p.oeilleres, 'SANS_OEILLERES') AS oeilleres, + COALESCE(p.tx_victoire, 0) AS tx_victoire, + COALESCE(p.tx_place, 0) AS tx_place, + COALESCE(p.forme_recente, 0) AS forme_recente, + 0 AS reduction_km, + 'NEUTRE' AS avis_entraineur, + 'NON' AS deferre, + 0 AS rang_cote, + 0 AS ratio_cote_field + FROM pmu_partants p + LEFT JOIN pmu_courses c + ON c.date_programme = p.date_programme + AND c.num_reunion = p.num_reunion + AND c.num_course = p.num_course + WHERE p.date_programme = ? + ORDER BY p.num_reunion, p.num_course, p.num_pmu + """, + (date_str,), + ) + horses = [dict(row) for row in c.fetchall()] + return horses + + horses = fetch_fallback(today) + if horses: + return today, horses, course_info + + c = conn.execute("SELECT MAX(date_programme) FROM pmu_partants") + fallback_date = c.fetchone()[0] + if fallback_date: + return fallback_date, fetch_fallback(fallback_date), {} + + return today, [], {} + + +def enrich_ml_horses(horses): + """Fill missing ML fields and derive odds-based features.""" + races = {} + for horse in horses: + race_key = ( + horse.get("date") or horse.get("date_programme"), + horse.get("num_reunion"), + horse.get("num_course"), + ) + races.setdefault(race_key, []).append(horse) + + for group in races.values(): + odds_values = [] + for horse in group: + raw_odds = horse.get("odds", horse.get("cote_directe", 0)) + try: + odds = float(raw_odds or 0) + except (TypeError, ValueError): + odds = 0.0 + horse["odds"] = odds + horse["cote_directe"] = float(horse.get("cote_directe", odds) or odds or 0) + if odds > 0: + odds_values.append(odds) + + avg_odds = sum(odds_values) / len(odds_values) if odds_values else 0 + ranked = sorted( + group, key=lambda h: h.get("odds", h.get("cote_directe", 0)) or 999999 + ) + + for idx, horse in enumerate(ranked, start=1): + horse.setdefault("horse_number", horse.get("num_pmu")) + horse.setdefault("horse_name", horse.get("nom")) + horse.setdefault("age", 0) + horse.setdefault("sexe", "U") + horse.setdefault("nb_courses", 0) + horse.setdefault("nb_victoires", 0) + horse.setdefault("nb_places", 0) + horse.setdefault("tx_victoire", 0) + horse.setdefault("tx_place", 0) + horse.setdefault("forme_recente", 0) + horse.setdefault("reduction_km", 0) + horse.setdefault("gains_annee", 0) + horse.setdefault("distance", 0) + horse.setdefault("discipline", "PLAT") + horse.setdefault("avis_entraineur", "NEUTRE") + horse.setdefault("oeilleres", "SANS") + horse.setdefault("deferre", "NON") + horse.setdefault("nb_partants", len(group)) + horse.setdefault("musique", "") + horse.setdefault("rang_cote", idx) + if not horse.get("ratio_cote_field"): + horse["ratio_cote_field"] = ( + round(horse.get("odds", 0) / avg_odds, 3) if avg_odds > 0 else 0 + ) + + return horses + + +@app.route("/api/ml_predictions") +@app.route("/turf/api/ml_predictions") +def api_ml_predictions(): + models = load_models() + if not models: + return jsonify({"error": "Models not loaded"}) + + conn = get_db() + today = datetime.now().strftime("%Y-%m-%d") + date_used, horses, course_info = load_ml_horses(conn, today) + horses = enrich_ml_horses(horses) + + if not horses: + conn.close() + return jsonify( + { + "date": date_used, + "predictions": [], + "message": "No predictions available", + } + ) + + feature_cols = [ + "age", + "sexe_enc", + "nb_courses", + "nb_victoires", + "nb_places", + "tx_victoire", + "tx_place", + "forme_recente", + "reduction_km", + "gains_annee", + "cote_directe", + "distance", + "nb_partants", + "discipline_enc", + "avis_enc", + "oeilleres_enc", + "deferre_enc", + "form_1", + "form_2", + "form_3", + "form_4", + "form_5", + "form_avg", + "win_rate_adj", + "place_rate_adj", + "implied_prob", + "victories_per_race", + "places_per_race", + "earnings_per_race", + "age_win_interact", + "distance_cat", + "is_favorite", + "rang_cote", + "ratio_cote_field", + ] + + all_sexes = set(h.get("sexe", "U") or "U" for h in horses) + all_avis = set(h.get("avis_entraineur", "NEUTRE") or "NEUTRE" for h in horses) + all_oeilleres = set(h.get("oeilleres", "SANS") or "SANS" for h in horses) + all_deferre = set(h.get("deferre", "NON") or "NON" for h in horses) + all_discipline = set(h.get("discipline", "PLAT") or "PLAT" for h in horses) + + le_sexe = LabelEncoder() + le_sexe.fit(list(all_sexes) + ["U"]) + le_avis = LabelEncoder() + le_avis.fit(list(all_avis) + ["NEUTRE"]) + le_oeilleres = LabelEncoder() + le_oeilleres.fit(list(all_oeilleres) + ["SANS"]) + le_deferre = LabelEncoder() + le_deferre.fit(list(all_deferre) + ["NON"]) + le_discipline = LabelEncoder() + le_discipline.fit(list(all_discipline) + ["PLAT"]) + + predictions = [] + import re + + for horse in horses: + features = {} + for col in [ + "age", + "nb_courses", + "nb_victoires", + "nb_places", + "tx_victoire", + "tx_place", + "forme_recente", + "reduction_km", + "gains_annee", + "cote_directe", + "distance", + "nb_partants", + "rang_cote", + "ratio_cote_field", + ]: + features[col] = float(horse.get(col, 0) or 0) + + features["sexe_enc"] = le_sexe.transform([horse.get("sexe", "U") or "U"])[0] + features["avis_enc"] = le_avis.transform( + [horse.get("avis_entraineur", "NEUTRE") or "NEUTRE"] + )[0] + features["oeilleres_enc"] = le_oeilleres.transform( + [horse.get("oeilleres", "SANS") or "SANS"] + )[0] + features["deferre_enc"] = le_deferre.transform( + [horse.get("deferre", "NON") or "NON"] + )[0] + features["discipline_enc"] = le_discipline.transform( + [horse.get("discipline", "PLAT") or "PLAT"] + )[0] + + musique = horse.get("musique", "") + form_nums = re.findall(r"\d+", str(musique))[:5] + for i, fn in enumerate(form_nums): + features[f"form_{i + 1}"] = float(fn) + for i in range(len(form_nums) + 1, 6): + features[f"form_{i}"] = 0.0 + features["form_avg"] = sum(features[f"form_{i}"] for i in range(1, 6)) / 5 + + features["implied_prob"] = ( + 1 / features["cote_directe"] if features["cote_directe"] > 0 else 0 + ) + features["win_rate_adj"] = features["tx_victoire"] * np.log1p( + features["nb_courses"] + ) + features["place_rate_adj"] = features["tx_place"] * np.log1p( + features["nb_courses"] + ) + features["victories_per_race"] = features["nb_victoires"] / max( + features["nb_courses"], 1 + ) + features["places_per_race"] = features["nb_places"] / max( + features["nb_courses"], 1 + ) + features["earnings_per_race"] = features["gains_annee"] / max( + features["nb_courses"], 1 + ) + features["age_win_interact"] = features["age"] * features["tx_victoire"] + features["distance_cat"] = ( + 2.0 + if 1500 < features["distance"] <= 2000 + else (3.0 if 2000 < features["distance"] <= 2500 else 1.0) + ) + features["is_favorite"] = 1 if features["cote_directe"] < 5 else 0 + + try: + X = pd.DataFrame([features])[feature_cols] + X = X.fillna(0) + prob_top1 = float(models["model_top1"].predict_proba(X)[0][1]) + prob_top3 = float(models["model_top3"].predict_proba(X)[0][1]) + + predictions.append( + { + "horse_name": horse["horse_name"], + "horse_number": horse["horse_number"], + "odds": float(horse["odds"]), + "prob_top1": round(prob_top1 * 100, 1), + "prob_top3": round(prob_top3 * 100, 1), + "ml_score": round((prob_top1 * 0.6 + prob_top3 * 0.4) * 100, 1), + "recommendation": "top1" + if prob_top1 > 0.15 + else ("top3" if prob_top3 > 0.35 else "pass"), + "is_value_bet": 1 + if (prob_top3 > 0.35 and float(horse.get("odds", 0)) > 10) + else 0, + "is_outlier": 1 + if ( + float(horse.get("odds", 0)) <= 5 + and (prob_top1 < 0.1 and prob_top3 < 0.25) + ) + else 0, + "num_reunion": horse.get("num_reunion"), + "num_course": horse.get("num_course"), + } + ) + except Exception as e: + predictions.append( + { + "horse_name": horse["horse_name"], + "horse_number": horse["horse_number"], + "odds": float(horse["odds"]), + "error": str(e), + } + ) + + predictions.sort(key=lambda x: x.get("ml_score", 0), reverse=True) + + for pred in predictions: + course_key = f"{pred.get('num_reunion', 1)}_{pred.get('num_course', 1)}" + if course_key in course_info: + cinfo = course_info[course_key] + pred["race_label"] = ( + f"R{pred.get('num_reunion', 1)}C{pred.get('num_course', 1)}" + ) + pred["race_name"] = cinfo.get("libelle", "") + pred["hippodrome"] = cinfo.get("libelle_court", "") + pred["discipline"] = cinfo.get("discipline", "") + pred["distance"] = cinfo.get("distance", 0) + pred["heure"] = cinfo.get("heure_depart_str", "") + + conn.close() + + return jsonify( + { + "date": date_used, + "model_version": "xgboost_v1", + "predictions": predictions, + "courses": course_info, + } + ) + + +# === VITESSE === +@app.route("/api/vitesse") +@app.route("/turf/api/vitesse") +def api_vitesse(): + try: + conn = get_db() + today = datetime.now().strftime("%Y-%m-%d") + categories = ["bases", "chances", "outsiders"] + result = {"date": today, "predictions": {}} + + for category in categories: + c = conn.execute( + f""" + SELECT horse_name, horse_number + FROM predictions + WHERE date=? AND source=? AND horse_name IS NOT NULL + GROUP BY horse_name + ORDER BY MIN(prediction_rank) + """, + (today, f"canalturf_prono_{category}"), + ) + + horses = [] + for row in c.fetchall(): + horse_name = row[0] + horse_number = row[1] + + latest = conn.execute( + """ + SELECT odds, odds_prev, jockey, created_at + FROM predictions + WHERE date = ? AND horse_name = ? AND source = 'canalturf_partants' + ORDER BY created_at DESC LIMIT 1 + """, + (today, horse_name), + ).fetchone() + + pmu = conn.execute( + """ + SELECT forme_recente, tx_victoire, tx_place, nombre_courses + FROM pmu_partants + WHERE nom LIKE ? || '%' + ORDER BY date_programme DESC + LIMIT 1 + """, + (horse_name.split()[0],), + ).fetchone() + + if latest: + odds_change = "" + if latest[1] and latest[0]: + diff = latest[0] - latest[1] + if diff < 0: + odds_change = " 🔻" + elif diff > 0: + odds_change = " 🔺" + + if pmu and pmu[0] is not None: + forme_str = f"{pmu[0]:.1f}" if pmu[0] else "N/A" + speed_info = { + "avg_time_ms": None, + "races": pmu[3] or 0, + "avg_time_formatted": f"F:{forme_str} | {pmu[1]:.0f}%V/{pmu[2]:.0f}%P", + "odds": latest[0], + "odds_prev": latest[1], + "jockey": latest[2] or "", + "forme": pmu[0], + "tx_victoire": pmu[1], + "tx_place": pmu[2], + "source": "predictions+pmu", + } + else: + trend = ( + f" ({latest[1]:.1f}→{latest[0]:.1f})" if latest[1] else "" + ) + speed_info = { + "avg_time_ms": None, + "races": 0, + "avg_time_formatted": f"🚴 {latest[2][:12] if latest[2] else ''} | {latest[0]:.1f}{odds_change}", + "odds": latest[0], + "odds_prev": latest[1], + "jockey": latest[2] or "", + "source": "predictions", + } + else: + speed_info = { + "avg_time_ms": None, + "races": 0, + "avg_time_formatted": "—", + "source": "none", + } + + horses.append( + { + "horse_name": horse_name, + "horse_number": horse_number, + "speed_info": speed_info, + } + ) + + result["predictions"][category] = horses + + conn.close() + return jsonify(result) + + except Exception as e: + return jsonify({"error": str(e), "status": "error"}), 500 + + +# === BACKTEST === +from datetime import timedelta + + +def calculate_backtest_data(start_date=None, end_date=None): + if not start_date: + start_date = (datetime.now() - timedelta(days=30)).strftime("%Y-%m-%d") + if not end_date: + end_date = datetime.now().strftime("%Y-%m-%d") + + conn = get_db() + + query = """ + SELECT + r.date, + r.race_name, + r.type_pari, + r.cheval1, + r.cote, + r.mise, + r.resultat + FROM recommendations r + WHERE r.resultat IS NOT NULL + AND r.resultat != '' + AND r.date BETWEEN ? AND ? + """ + + try: + cursor = conn.execute(query, (start_date, end_date)) + rows = cursor.fetchall() + except sqlite3.OperationalError as e: + conn.close() + return { + "summary": { + "total_bets": 0, + "message": "Données backtest indisponibles", + "error": str(e), + } + } + + if not rows: + conn.close() + return {"summary": {"total_bets": 0, "message": "Aucune donnée"}} + + stats = { + "total_bets": len(rows), + "gagne": 0, + "perdu": 0, + "mise_totale": 0, + "gain_total": 0, + } + details = [] + by_type = {} + + for date, race_name, type_pari, cheval1, cote, mise, resultat in rows: + mise = float(mise or 1) + cote = float(cote or 1) + + stats["mise_totale"] += mise + + if type_pari not in by_type: + by_type[type_pari] = {"count": 0, "gagne": 0, "mise": 0, "gain": 0} + by_type[type_pari]["count"] += 1 + by_type[type_pari]["mise"] += mise + + if resultat == "GAGNE": + stats["gagne"] += 1 + stats["gain_total"] += mise * cote + by_type[type_pari]["gagne"] += 1 + by_type[type_pari]["gain"] += mise * cote + else: + stats["perdu"] += 1 + + details.append( + { + "date": date, + "race_name": (race_name or "")[:30], + "type_pari": type_pari, + "cheval": cheval1, + "cote": cote, + "mise": mise, + "resultat": resultat, + "gain": mise * cote if resultat == "GAGNE" else 0, + } + ) + + roi = ( + ((stats["gain_total"] - stats["mise_totale"]) / stats["mise_totale"] * 100) + if stats["mise_totale"] > 0 + else 0 + ) + precision = ( + (stats["gagne"] / stats["total_bets"] * 100) if stats["total_bets"] > 0 else 0 + ) + + # Par type + by_type_results = {} + for pari_type, data in by_type.items(): + pari_roi = ( + ((data["gain"] - data["mise"]) / data["mise"] * 100) + if data["mise"] > 0 + else 0 + ) + pari_precision = ( + (data["gagne"] / data["count"] * 100) if data["count"] > 0 else 0 + ) + by_type_results[pari_type] = { + "count": data["count"], + "gagne": data["gagne"], + "mise": round(data["mise"], 2), + "gain": round(data["gain"], 2), + "roi": round(pari_roi, 1), + "precision": round(pari_precision, 1), + } + + conn.close() + + return { + "period": {"start": start_date, "end": end_date}, + "summary": { + "total_bets": stats["total_bets"], + "gagne": stats["gagne"], + "perdu": stats["perdu"], + "precision": round(precision, 1), + "mise_totale": round(stats["mise_totale"], 2), + "gain_total": round(stats["gain_total"], 2), + "roi": round(roi, 1), + }, + "by_type": by_type_results, + "details": details[-50:], + } + + +@app.route("/api/backtest") +@app.route("/turf/api/backtest") +def api_backtest(): + """Backtest - lit depuis la base de données""" + start = request.args.get("start") + end = request.args.get("end") + + if not start: + start = (datetime.now() - timedelta(days=30)).strftime("%Y-%m-%d") + if not end: + end = datetime.now().strftime("%Y-%m-%d") + + conn = get_db() + + # Récupérer les détails depuis bet_results + cursor = conn.execute( + """ + SELECT b.date, b.race_name, b.type_pari, + b.horse_name, b.horse_number, + COALESCE(b.cote, 0) as cote, + b.mise, b.resultat, b.gain + FROM bet_results b + WHERE b.date BETWEEN ? AND ? + ORDER BY b.date DESC, b.id DESC + LIMIT 50 + """, + (start, end), + ) + + rows = cursor.fetchall() + + # Add race_label to each row + details = [] + import re + + for r in rows: + d = dict(r) + race_label = "R1C1" + if d.get("race_name") and "R" in str(d["race_name"]): + match = re.search(r"R(\d+)C(\d+)", str(d["race_name"])) + if match: + race_label = f"R{match.group(1)}C{match.group(2)}" + d["race_label"] = race_label + details.append(d) + + if not details: + conn.close() + return jsonify({"summary": {"total_bets": 0, "message": "Aucune donnée"}}) + + # Calculer résumé depuis la DB + cursor = conn.execute( + """ + SELECT + COUNT(*) as total, + SUM(CASE WHEN resultat = 'GAGNE' THEN 1 ELSE 0 END) as gagne, + SUM(mise) as mise, + SUM(gain) as gain + FROM bet_results + WHERE date BETWEEN ? AND ? + """, + (start, end), + ) + + row = cursor.fetchone() + total, gagne, mise, gain = row + + roi = ((gain - mise) / mise * 100) if mise > 0 else 0 + precision = (gagne / total * 100) if total > 0 else 0 + + # Par type + cursor = conn.execute( + """ + SELECT + type_pari, + COUNT(*) as total, + SUM(CASE WHEN resultat = 'GAGNE' THEN 1 ELSE 0 END) as gagne, + SUM(mise) as mise, + SUM(gain) as gain + FROM bet_results + WHERE date BETWEEN ? AND ? + GROUP BY type_pari + """, + (start, end), + ) + + by_type = {} + for row in cursor.fetchall(): + type_pari, total, gagne, mise, gain = row + pari_roi = ((gain - mise) / mise * 100) if mise > 0 else 0 + pari_precision = (gagne / total * 100) if total > 0 else 0 + by_type[type_pari] = { + "count": total, + "gagne": gagne, + "mise": round(mise or 0, 2), + "gain": round(gain or 0, 2), + "roi": round(pari_roi, 1), + "precision": round(pari_precision, 1), + } + + # Details - already prepared above with race_label + + conn.close() + + return jsonify( + { + "period": {"start": start, "end": end}, + "summary": { + "total_bets": total, + "gagne": gagne, + "perdu": total - gagne, + "precision": round(precision, 1), + "mise_totale": round(mise or 0, 2), + "gain_total": round(gain or 0, 2), + "roi": round(roi, 1), + }, + "by_type": by_type, + "details": details, + } + ) + + +@app.route("/api/stats") +@app.route("/turf/api/stats") +def api_stats(): + """Statistiques quotidiennes - lit depuis la base""" + days = int(request.args.get("days", 30)) + end_date = datetime.now().strftime("%Y-%m-%d") + start_date = (datetime.now() - timedelta(days=days)).strftime("%Y-%m-%d") + + conn = get_db() + + # Lire depuis daily_stats + if not table_exists(conn, "daily_stats"): + conn.close() + return jsonify({"daily": [], "period": {"start": start_date, "end": end_date}}) + cursor = conn.execute( + """ + SELECT date, total_bets, bets_gagne, bets_perdu, mise_totale, gain_total, precision_pct, roi_pct + FROM daily_stats + WHERE date BETWEEN ? AND ? + ORDER BY date DESC + """, + (start_date, end_date), + ) + + rows = cursor.fetchall() + + daily = [] + for row in rows: + daily.append( + { + "date": row[0], + "total_bets": row[1], + "gagne": row[2], + "perdu": row[3], + "precision": row[6], + "mises": row[4], + "gains": row[5], + "roi": row[7], + } + ) + + conn.close() + return jsonify({"daily": daily, "period": {"start": start_date, "end": end_date}}) + + +def generate_sql_from_question(question): + """Generate SQL query from natural language question - keyword fallback first, then LLM""" + + q = question.lower() + + # Keyword-based queries - prioritize common questions + keyword_patterns = [ + ("taux", "favori"), + ("taux", "victoire"), + ("taux", "reussite"), + ("jockey",), + ("driver",), + ("entraineur",), + ("aujourd",), + ("hier",), + ("programme",), + ("resultat",), + ("resultats",), + ("arrivee",), + ("gagn", "victoire", "vainqueur"), + ("cote", "evolu"), + ("cote", "chang"), + ("cote", "vari"), + ("roi",), + ("profit",), + ("gain",), + ("distance",), + ("vincennes",), + ("performances",), + ("statistiques",), + ("historique",), + ] + + # Check if question matches keyword patterns + is_keyword_query = any( + all(kw in q for kw in pattern) for pattern in keyword_patterns + ) + + # Try keyword first for common questions + if is_keyword_query: + sql = generate_sql_from_keywords(question, q) + if sql: + return sql + + # Try LLM only for complex/niche questions + sql = generate_sql_with_llm(question) + if sql: + return sql + + # Final fallback to keyword + return generate_sql_from_keywords(question, q) + + +def generate_sql_from_keywords(question, q): + """Generate SQL from keywords - extracted from generate_sql_from_question""" + + # Today's races + + # Today's races (or most recent if no today) + if "aujourd" in q or "aujourd'hui" in q or "ce jour" in q or "programme" in q: + return """SELECT c.libelle as course, p.nom as cheval, p.cote_direct as cote, p.favoris, c.discipline, p.date_programme + FROM pmu_partants p + JOIN pmu_courses c ON p.date_programme = c.date_programme AND p.num_reunion = c.num_reunion AND p.num_course = c.num_course + WHERE p.date_programme >= date('now', '-1 day') + ORDER BY p.date_programme DESC, c.num_course, p.cote_direct + LIMIT 15""" + + # Today's results (arrived horses) + if "resultat" in q or "résultat" in q or "arrivee" in q: + return """SELECT c.libelle as course, c.discipline, c.distance, + p.nom as cheval, p.ordre_arrivee as position, p.cote_direct as cote, p.driver + FROM pmu_partants p + JOIN pmu_courses c ON p.date_programme = c.date_programme AND p.num_reunion = c.num_reunion AND p.num_course = c.num_course + WHERE p.ordre_arrivee > 0 AND p.date_programme >= date('now', '-2 days') + ORDER BY p.date_programme DESC, c.num_course, p.ordre_arrivee + LIMIT 20""" + + # Yesterday's results + if "hier" in q and ("resultat" in q or "résultat" in q): + return """SELECT c.libelle as course, p.nom as cheval, p.ordre_arrivee as position, p.cote_direct as cote, c.discipline + FROM pmu_partants p + JOIN pmu_courses c ON p.date_programme = c.date_programme AND p.num_reunion = c.num_reunion AND p.num_course = c.num_course + WHERE p.date_programme = date('now', '-1 day') AND p.ordre_arrivee > 0 AND p.ordre_arrivee <= 3 + ORDER BY c.num_course, p.ordre_arrivee + LIMIT 15""" + + # Winners today + if "gagn" in q or "victoire" in q or "vainqueur" in q: + return """SELECT c.libelle as course, p.nom as cheval, p.cote_direct as cote + FROM pmu_partants p + JOIN pmu_courses c ON p.date_programme = c.date_programme AND p.num_reunion = c.num_reunion AND p.num_course = c.num_course + WHERE p.date_programme >= date('now', '-3 days') AND p.ordre_arrivee = 1 + ORDER BY p.date_programme DESC, c.num_course + LIMIT 10""" + + # Favorites win rate + if "taux" in q and ("favori" in q or "favoris" in q): + return """SELECT + COUNT(*) as total_races, + SUM(CASE WHEN ordre_arrivee = 1 THEN 1 ELSE 0 END) as wins, + ROUND(CAST(SUM(CASE WHEN ordre_arrivee <= 3 THEN 1 ELSE 0 END) AS FLOAT) / NULLIF(SUM(CASE WHEN favoris = 1 THEN 1 ELSE 0 END), 0) * 100, 1) as podium_rate, + ROUND(CAST(SUM(CASE WHEN ordre_arrivee = 1 THEN 1 ELSE 0 END) AS FLOAT) / NULLIF(SUM(CASE WHEN favoris = 1 THEN 1 ELSE 0 END), 0) * 100, 1) as win_rate + FROM pmu_partants + WHERE favoris = 1 AND ordre_arrivee > 0 AND date_programme >= date('now', '-7 days')""" + + # Odds changes + if "cote" in q and ("evolu" in q or "chang" in q or "vari" in q): + return """SELECT date_programme, nom as cheval, cote_direct, cote_reference, tendance_cote + FROM pmu_partants + WHERE date_programme >= date('now', '-2 days') + ORDER BY date_programme DESC, ABS(cote_direct - cote_reference) DESC + LIMIT 10""" + + if "taux" in q and "favori" in q: + return """SELECT + COUNT(*) as total_races, + SUM(CASE WHEN ordre_arrivee = 1 THEN 1 ELSE 0 END) as wins, + ROUND(CAST(SUM(CASE WHEN ordre_arrivee <= 3 THEN 1 ELSE 0 END) AS FLOAT) / NULLIF(SUM(CASE WHEN favoris = 1 THEN 1 ELSE 0 END), 0) * 100, 1) as podium_rate, + ROUND(CAST(SUM(CASE WHEN ordre_arrivee = 1 THEN 1 ELSE 0 END) AS FLOAT) / NULLIF(SUM(CASE WHEN favoris = 1 THEN 1 ELSE 0 END), 0) * 100, 1) as win_rate + FROM pmu_partants + WHERE favoris = 1 AND ordre_arrivee > 0 AND date_programme >= date('now', '-7 days')""" + + # ROI calculation + if "roi" in q or "profit" in q or "gain" in q: + return """SELECT + date_programme, + COUNT(*) as nb_partants, + SUM(CASE WHEN ordre_arrivee = 1 THEN (cote_direct - 1) ELSE -1 END) as profit_total, + ROUND(AVG(CASE WHEN ordre_arrivee = 1 THEN (cote_direct - 1) ELSE -1 END), 2) as profit_moyen, + SUM(CASE WHEN ordre_arrivee = 1 THEN 1 ELSE 0 END) as nb_victoires, + ROUND(CAST(SUM(CASE WHEN ordre_arrivee = 1 THEN 1 ELSE 0 END) AS FLOAT) / COUNT(*) * 100, 1) as tx_victoire + FROM pmu_partants + WHERE ordre_arrivee > 0 AND date_programme >= date('now', '-30 days') + GROUP BY date_programme + ORDER BY date_programme DESC + LIMIT 15""" + + if "jockey" in q or "driver" in q or "driver" in q: + return """SELECT + driver as jockey, + COUNT(*) as total_races, + SUM(CASE WHEN ordre_arrivee = 1 THEN 1 ELSE 0 END) as victories, + ROUND(CAST(SUM(CASE WHEN ordre_arrivee <= 3 THEN 1 ELSE 0 END) AS FLOAT) / COUNT(*) * 100, 1) as podium_rate + FROM pmu_partants + WHERE driver IS NOT NULL AND driver != '' AND ordre_arrivee > 0 + GROUP BY driver + ORDER BY victories DESC + LIMIT 10""" + + if "entraineur" in q: + return """SELECT + entraineur, + COUNT(*) as total_races, + SUM(CASE WHEN ordre_arrivee = 1 THEN 1 ELSE 0 END) as victories, + ROUND(CAST(SUM(CASE WHEN ordre_arrivee <= 3 THEN 1 ELSE 0 END) AS FLOAT) / COUNT(*) * 100, 1) as podium_rate + FROM pmu_partants + WHERE entraineur IS NOT NULL AND entraineur != '' AND ordre_arrivee > 0 + GROUP BY entraineur + ORDER BY victories DESC + LIMIT 10""" + + if "distance" in q and "vincennes" in q: + return """SELECT + c.discipline, + COUNT(*) as races, + ROUND(AVG(c.distance), 0) as avg_distance + FROM pmu_courses c + WHERE c.libelle LIKE '%Vincennes%' + GROUP BY c.discipline + ORDER BY races DESC""" + + # Horse name extraction - try various patterns + if any( + kw in q + for kw in ["performances", "statistiques", "historique", "cheval", "stats"] + ): + import re + + # Match sequences of uppercase words (horse names are all caps in DB) + # Looking for patterns like "TORTISAMBERT" or "TORTISAMBERT Z" + match = re.search(r"\b([A-Z]{4,}(?:\s+[A-Z]{4,})?)\b", question) + if match: + horse = match.group(1).strip() + return ( + """SELECT + nom as cheval, + COUNT(*) as total_races, + SUM(CASE WHEN ordre_arrivee = 1 THEN 1 ELSE 0 END) as victoires, + ROUND(AVG(cote_direct), 1) as cote_moyenne, + ROUND(CAST(SUM(CASE WHEN ordre_arrivee <= 3 THEN 1 ELSE 0 END) AS FLOAT) / COUNT(*) * 100, 1) as taux_place + FROM pmu_partants + WHERE nom LIKE ? AND ordre_arrivee > 0 + GROUP BY nom""", + (f"%{horse}%",), + ) + + # Default: recent races with results + return """SELECT c.libelle as course, c.discipline, p.nom as cheval, + p.cote_direct as cote, p.favoris, p.ordre_arrivee as position + FROM pmu_partants p + JOIN pmu_courses c ON p.date_programme = c.date_programme + AND p.num_reunion = c.num_reunion AND p.num_course = c.num_course + WHERE p.date_programme >= date('now', '-3 days') + ORDER BY p.date_programme DESC, c.num_course, p.ordre_arrivee + LIMIT 15""" + + +@app.route("/turf/api/execute-sql", methods=["POST", "OPTIONS"]) +@app.route("/api/execute-sql", methods=["POST", "OPTIONS"]) +@app.route("/turf/api/ask", methods=["GET"]) +@app.route("/api/ask", methods=["GET"]) +def execute_sql(): + """Endpoint pour exécuter des requêtes SQL ou recevoir des questions via GET""" + if request.method == "OPTIONS": + return "", 200 + + # Pour GET, générer la requête SQL depuis la question + if request.method == "GET": + question = request.args.get("question", "") + result = generate_sql_from_question(question) + if not result: + return jsonify({"error": "No question provided"}), 400 + if isinstance(result, tuple): + query, sql_params = result + else: + query, sql_params = result, () + else: + data = request.get_json() + query = data.get("query", "") if data else "" + sql_params = () + + if not query: + return jsonify({"error": "No query provided"}), 400 + + # Limiter les requêtes SELECT uniquement pour la sécurité + query_upper = query.strip().upper() + if not query_upper.startswith("SELECT"): + return jsonify({"error": "Only SELECT queries allowed"}), 403 + + try: + conn = sqlite3.connect(DB_PATH) + conn.row_factory = sqlite3.Row + cursor = conn.cursor() + cursor.execute(query, sql_params) + rows = cursor.fetchall() + results = [dict(row) for row in rows] + conn.close() + return jsonify({"results": results, "count": len(results)}) + except Exception as e: + return jsonify({"error": str(e)}), 500 + + +@app.route("/call-workflow", methods=["POST"]) +def call_workflow(): + """Endpoint pour exécuter un workflow n8n via MCP""" + import requests + + data = request.get_json() + workflow_id = data.get("workflow_id", "sHVEK4hwyUmAww3F") + question = data.get("question", "") + + if not question: + return jsonify({"error": "No question provided"}), 400 + + try: + # Call n8n MCP server + mcp_url = "https://kolifee.duckdns.org/mcp-server/http" + mcp_token = os.environ.get("MCP_TOKEN", "n8n_mcp_token_2025") + + response = requests.post( + mcp_url, + json={ + "json": { + "tool": "execute_workflow", + "arguments": { + "workflowId": workflow_id, + "data": {"question": question}, + }, + } + }, + headers={ + "Content-Type": "application/json", + "Authorization": f"Bearer {mcp_token}", + }, + timeout=60, + ) + + if response.status_code == 200: + result = response.json() + return jsonify( + {"response": result.get("text", result.get("output", str(result)))} + ) + else: + return jsonify( + {"error": f"MCP error: {response.status_code}"} + ), response.status_code + + except Exception as e: + return jsonify({"error": str(e)}), 500 + + +# === NOUVEAUX ENDPOINTS LLM & ANALYTICS === + +try: + from prompts_llm import ( + get_system_prompt, + get_sql_prompt, + get_keyword_sql, + get_suggestions_prompt, + ) + from llm_cache import get_llm_cache, get_sql_cache + + HAS_LLM_MODULES = True +except ImportError: + HAS_LLM_MODULES = False + +try: + from analytics_reports import ( + get_daily_report, + get_weekly_report, + get_monthly_report, + format_report_markdown, + ) + + HAS_ANALYTICS = True +except ImportError: + HAS_ANALYTICS = False + + +@app.route("/turf/api/report/daily", methods=["GET"]) +@app.route("/api/report/daily", methods=["GET"]) +def daily_report(): + """Rapport quotidien de performance""" + if not HAS_ANALYTICS: + return jsonify({"error": "analytics_reports module not available"}), 500 + + date = request.args.get("date") + report = get_daily_report(date) + return jsonify(report) + + +@app.route("/turf/api/report/weekly", methods=["GET"]) +@app.route("/api/report/weekly", methods=["GET"]) +def weekly_report(): + """Rapport hebdomadaire de performance""" + if not HAS_ANALYTICS: + return jsonify({"error": "analytics_reports module not available"}), 500 + + start_date = request.args.get("start") + end_date = request.args.get("end") + report = get_weekly_report(start_date, end_date) + return jsonify(report) + + +@app.route("/turf/api/report/monthly", methods=["GET"]) +@app.route("/api/report/monthly", methods=["GET"]) +def monthly_report(): + """Rapport mensuel de performance""" + if not HAS_ANALYTICS: + return jsonify({"error": "analytics_reports module not available"}), 500 + + year = request.args.get("year", type=int) + month = request.args.get("month", type=int) + report = get_monthly_report(year, month) + return jsonify(report) + + +@app.route("/turf/api/report/markdown/", methods=["GET"]) +def report_markdown(report_type): + """Retourne le rapport en format Markdown""" + if not HAS_ANALYTICS: + return jsonify({"error": "analytics_reports module not available"}), 500 + + if report_type == "daily": + date = request.args.get("date") + report = get_daily_report(date) + markdown = format_report_markdown(report, "daily") + elif report_type == "weekly": + start = request.args.get("start") + end = request.args.get("end") + report = get_weekly_report(start, end) + markdown = format_report_markdown(report, "weekly") + elif report_type == "monthly": + year = request.args.get("year", type=int) + month = request.args.get("month", type=int) + report = get_monthly_report(year, month) + markdown = format_report_markdown(report, "monthly") + else: + return jsonify({"error": "Invalid report type"}), 400 + + return jsonify({"markdown": markdown, "type": report_type}) + + +@app.route("/turf/api/suggestions", methods=["GET"]) +@app.route("/api/suggestions", methods=["GET"]) +def suggestions(): + """Retourne des suggestions de questions basées sur les données""" + if not HAS_LLM_MODULES: + return jsonify( + { + "suggestions": [ + "Quel est mon taux de réussite cette semaine?", + "Liste les 5 meilleurs jockeys", + "Performances du cheval TORTISAMBERT", + "Évolution des cotes", + "Résultats d'hier", + ] + } + ) + + conn = get_db() + c = conn.cursor() + + suggestions = [] + + try: + c.execute( + "SELECT COUNT(*) as cnt FROM pmu_partants WHERE date_programme >= date('now', '-7 days')" + ) + recent = c.fetchone()["cnt"] + + if recent > 0: + suggestions = [ + "Quel est mon taux de réussite cette semaine?", + "Liste les 5 meilleurs jockeys", + "Quel est le ROI du mois?", + "Résultats d'hier", + "Programme du jour", + ] + else: + suggestions = [ + "Derniers gagnants", + "Meilleurs entraîneurs", + "Performances à Vincennes", + "Évolution des cotes", + ] + + except: + suggestions = [ + "Quel est le taux de réussite des favoris?", + "Liste les meilleurs jockeys", + "Résultats d'hier", + ] + finally: + conn.close() + + return jsonify({"suggestions": suggestions}) + + +@app.route("/turf/api/ask-enhanced", methods=["GET", "POST"]) +def ask_enhanced(): + """Version améliorée du endpoint /ask avec cache et fallback keywords""" + if not HAS_LLM_MODULES: + return jsonify({"error": "prompts_llm module not available"}), 500 + + if request.method == "GET": + question = request.args.get("question", "") + else: + data = request.get_json() + question = data.get("question", "") if data else "" + + if not question: + return jsonify({"error": "No question provided"}), 400 + + sql_cache = get_sql_cache() + + cached_sql = sql_cache.get_sql(question) + if cached_sql: + query = cached_sql + else: + query = get_keyword_sql(question) + + if not query and LLM_API_KEY: + try: + import litellm + + litellm.drop_params = True + + prompt = get_sql_prompt(question) + response = litellm.completion( + model=f"openrouter/{LLM_MODEL}", + messages=[ + {"role": "system", "content": get_system_prompt()}, + {"role": "user", "content": prompt}, + ], + api_key=LLM_API_KEY, + max_tokens=300, + ) + + sql = response.choices[0].message.content.strip() + if sql.upper().startswith("SELECT"): + query = sql + sql_cache.set_sql(question, sql, success=True) + + except Exception as e: + print(f"LLM error: {e}") + + if not query: + return jsonify({"error": "Could not generate SQL for this question"}), 400 + + if not query.upper().startswith("SELECT"): + return jsonify({"error": "Invalid query generated"}), 400 + + try: + conn = get_db() + c = conn.cursor() + c.execute(query) + rows = c.fetchall() + results = [dict(row) for row in rows] + conn.close() + + return jsonify( + { + "question": question, + "sql": query, + "results": results, + "count": len(results), + } + ) + + except Exception as e: + return jsonify({"error": str(e), "sql": query}), 500 + + +@app.route("/turf/api/scores", methods=["POST"]) +@require_auth +def save_race_scores(): + data = request.json + if not data: + return jsonify({"error": "No data provided"}), 400 + + conn = get_db() + try: + conn.execute( + """ + INSERT OR REPLACE INTO race_scores + (date, race_name, race_time, hippodrome, top5_cotes, top5_cotes_hits, + top5_bc, top5_bc_hits, top5_bo, top5_bo_hits, results_top5) + VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) + """, + ( + data.get("date"), + data.get("race_name"), + data.get("race_time"), + data.get("hippodrome"), + json.dumps(data.get("top5_cotes", [])), + data.get("top5_cotes_hits", 0), + json.dumps(data.get("top5_bc", [])), + data.get("top5_bc_hits", 0), + json.dumps(data.get("top5_bo", [])), + data.get("top5_bo_hits", 0), + json.dumps(data.get("results_top5", [])), + ), + ) + conn.commit() + return jsonify({"status": "ok", "message": "Scores saved"}) + except Exception as e: + return jsonify({"error": str(e)}), 500 + finally: + conn.close() + + +@app.route("/turf/api/scores/history", methods=["GET"]) +@require_auth +def get_scores_history(): + limit = request.args.get("limit", 30) + conn = get_db() + try: + if not table_exists(conn, "race_scores"): + return jsonify({"results": [], "count": 0}) + c = conn.execute( + """ + SELECT date, race_name, race_time, hippodrome, + top5_cotes, top5_cotes_hits, + top5_bc, top5_bc_hits, + top5_bo, top5_bo_hits, + results_top5 + FROM race_scores + ORDER BY date DESC, race_time DESC + LIMIT ? + """, + (limit,), + ) + rows = c.fetchall() + results = [] + for r in rows: + d = dict(r) + d["top5_cotes"] = json.loads(d["top5_cotes"]) if d.get("top5_cotes") else [] + d["top5_bc"] = json.loads(d["top5_bc"]) if d.get("top5_bc") else [] + d["top5_bo"] = json.loads(d["top5_bo"]) if d.get("top5_bo") else [] + d["results_top5"] = ( + json.loads(d["results_top5"]) if d.get("results_top5") else [] + ) + results.append(d) + return jsonify({"results": results, "count": len(results)}) + except Exception as e: + return jsonify({"error": str(e)}), 500 + finally: + conn.close() + + +# ============================================================ +# PARIS / ROI - Gestion des paris manuels +# ============================================================ + + +@app.route("/api/parisroi", methods=["GET"]) +@app.route("/turf/api/parisroi", methods=["GET"]) +@require_auth +def api_parisroi(): + """Dashboard complet Paris/ROI""" + conn = get_db() + today = datetime.now().strftime("%Y-%m-%d") + try: + # Resume global depuis table paris + stats = conn.execute(""" + SELECT COUNT(*) as total_bets, + SUM(CASE WHEN statut='GAGNE' THEN 1 ELSE 0 END) as gagne, + SUM(CASE WHEN statut='PERDU' THEN 1 ELSE 0 END) as perdu, + COALESCE(SUM(mise),0) as mise_totale, + COALESCE(SUM(gain),0) as gain_total + FROM paris + WHERE statut != 'EN_ATTENTE' + """).fetchone() + stats = ( + dict(stats) + if stats + else { + "total_bets": 0, + "gagne": 0, + "perdu": 0, + "mise_totale": 0, + "gain_total": 0, + } + ) + mise = stats["mise_totale"] or 0 + gain = stats["gain_total"] or 0 + roi = ((gain - mise) / mise * 100) if mise > 0 else 0 + precision = ( + (stats["gagne"] / stats["total_bets"] * 100) + if stats["total_bets"] > 0 + else 0 + ) + stats["roi"] = round(roi, 1) + stats["precision"] = round(precision, 1) + + # Paris du jour (EN_ATTENTE) + paris_jour = conn.execute(""" + SELECT * FROM paris + WHERE statut = 'EN_ATTENTE' + ORDER BY created_at DESC + """).fetchall() + paris_jour = [dict(r) for r in paris_jour] + + # Historique des 30 derniers paris + historique = conn.execute(""" + SELECT * FROM paris + WHERE statut != 'EN_ATTENTE' + ORDER BY created_at DESC + LIMIT 30 + """).fetchall() + historique = [dict(r) for r in historique] + + # Stats par type + by_type = conn.execute(""" + SELECT type_pari, + COUNT(*) as total, + SUM(CASE WHEN statut='GAGNE' THEN 1 ELSE 0 END) as gagne, + SUM(mise) as mise, + SUM(gain) as gain + FROM paris + WHERE statut != 'EN_ATTENTE' + GROUP BY type_pari + """).fetchall() + by_type = { + r["type_pari"]: { + "total": r["total"], + "gagne": r["gagne"], + "mise": round(r["mise"] or 0, 2), + "gain": round(r["gain"] or 0, 2), + "roi": round( + ((r["gain"] or 0) - (r["mise"] or 0)) / (r["mise"] or 1) * 100, 1 + ), + } + for r in by_type + } + + # ROI curve (30 derniers jours) + roi_curve = conn.execute(""" + SELECT date, roi_pct, gain_total, mise_totale + FROM daily_stats + ORDER BY date DESC + LIMIT 30 + """).fetchall() + roi_curve = [dict(r) for r in roi_curve] + + return jsonify( + { + "summary": stats, + "paris_jour": paris_jour, + "historique": historique, + "by_type": by_type, + "roi_curve": roi_curve, + } + ) + except Exception as e: + return jsonify({"error": str(e)}), 500 + finally: + conn.close() + + +@app.route("/api/paris", methods=["GET"]) +@app.route("/turf/api/paris", methods=["GET"]) +@require_auth +def api_get_paris(): + """Liste des paris avec filtres""" + conn = get_db() + date = request.args.get("date") + statut = request.args.get("statut") + try: + query = "SELECT * FROM paris WHERE 1=1" + params = [] + if date: + query += " AND date_pari = ?" + params.append(date) + if statut: + query += " AND statut = ?" + params.append(statut) + query += " ORDER BY created_at DESC" + rows = conn.execute(query, params).fetchall() + return jsonify({"paris": [dict(r) for r in rows], "count": len(rows)}) + except Exception as e: + return jsonify({"error": str(e)}), 500 + finally: + conn.close() + + +@app.route("/api/paris", methods=["POST"]) +@app.route("/turf/api/paris", methods=["POST"]) +@require_auth +def api_add_pari(): + """Ajouter un nouveau pari""" + data = request.json + if not data: + return jsonify({"error": "Donnees requises"}), 400 + conn = get_db() + try: + today = datetime.now().strftime("%Y-%m-%d") + chevaux = data.get("chevaux", "") + c = conn.execute( + """ + INSERT INTO paris (date_pari, date_course, race_name, race_label, hippodrome, + type_pari, chevaux, cheval1, numero1, cheval2, numero2, + cheval3, numero3, cote, mise, statut, commentaire, source_reco) + VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, 'EN_ATTENTE', ?, ?) + """, + ( + today, + data.get("date_course", today), + data.get("race_name", ""), + data.get("race_label", ""), + data.get("hippodrome", ""), + data.get("type_pari", "tierce"), + chevaux, + data.get("cheval1", ""), + data.get("numero1"), + data.get("cheval2", ""), + data.get("numero2"), + data.get("cheval3", ""), + data.get("numero3"), + data.get("cote", 0), + data.get("mise", 1.0), + data.get("commentaire", ""), + data.get("source_reco", "manuel"), + ), + ) + conn.commit() + return jsonify({"status": "ok", "id": c.lastrowid}) + except Exception as e: + return jsonify({"error": str(e)}), 500 + finally: + conn.close() + + +@app.route("/api/paris/", methods=["PUT"]) +@app.route("/turf/api/paris/", methods=["PUT"]) +@require_auth +def api_update_pari(pari_id): + """Mettre a jour un pari (resultat/gain)""" + data = request.json + if not data: + return jsonify({"error": "Donnees requises"}), 400 + conn = get_db() + try: + statut = data.get("statut", "EN_ATTENTE") + gain = data.get("gain", 0) + commentaire = data.get("commentaire", "") + conn.execute( + """ + UPDATE paris SET statut = ?, gain = ?, commentaire = ? + WHERE id = ? + """, + (statut, gain, commentaire, pari_id), + ) + conn.commit() + # Mettre a jour bet_results en cascade + pari = conn.execute("SELECT * FROM paris WHERE id = ?", (pari_id,)).fetchone() + if pari and statut != "EN_ATTENTE": + conn.execute( + """ + INSERT INTO bet_results (date, race_name, type_pari, horse_name, horse_number, + cote, mise, resultat, gain) + VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?) + """, + ( + pari["date_pari"], + pari["race_name"], + pari["type_pari"], + pari["cheval1"], + pari["numero1"], + pari["cote"], + pari["mise"], + statut, + gain, + ), + ) + conn.commit() + return jsonify({"status": "ok"}) + except Exception as e: + return jsonify({"error": str(e)}), 500 + finally: + conn.close() + + +@app.route("/api/paris/", methods=["DELETE"]) +@app.route("/turf/api/paris/", methods=["DELETE"]) +@require_auth +def api_delete_pari(pari_id): + """Supprimer un pari""" + conn = get_db() + try: + conn.execute( + "DELETE FROM paris WHERE id = ? AND statut = 'EN_ATTENTE'", (pari_id,) + ) + conn.commit() + return jsonify({"status": "ok"}) + except Exception as e: + return jsonify({"error": str(e)}), 500 + finally: + conn.close() + + +@app.route("/api/paris/auto-settle", methods=["POST"]) +@app.route("/turf/api/paris/auto-settle", methods=["POST"]) +@require_auth +def api_auto_settle(): + """Regler automatiquement les paris du jour via resultats PMU""" + conn = get_db() + today = datetime.now().strftime("%Y-%m-%d") + try: + paris = conn.execute( + """ + SELECT * FROM paris + WHERE date_course = ? AND statut = 'EN_ATTENTE' + """, + (today,), + ).fetchall() + settled = 0 + for pari in paris: + p = dict(pari) + chevaux_paris = [p.get("numero1"), p.get("numero2"), p.get("numero3")] + chevaux_paris = [c for c in chevaux_paris if c] + if not chevaux_paris: + continue + # Recuperer resultats + results = conn.execute( + """ + SELECT nom, ordre_arrivee FROM pmu_partants + WHERE date_programme = ? AND ordre_arrivee > 0 + ORDER BY ordre_arrivee ASC + """, + (today,), + ).fetchall() + top3 = [ + r["ordre_arrivee"] for r in results if r["ordre_arrivee"] in [1, 2, 3] + ] + if not top3: + continue + # Verifier si les numeros paris correspondent + # Simplifie: on verifie si tous les numeros sont dans le top3 + is_gagne = all(n in top3 for n in chevaux_paris) + statut = "GAGNE" if is_gagne else "PERDU" + gain = 0 + if is_gagne and p.get("cote"): + gain = p["mise"] * p["cote"] + conn.execute( + """ + UPDATE paris SET statut = ?, gain = ? WHERE id = ? + """, + (statut, gain, p["id"]), + ) + settled += 1 + conn.commit() + return jsonify({"status": "ok", "settled": settled}) + except Exception as e: + return jsonify({"error": str(e)}), 500 + finally: + conn.close() + + +# === EMAIL SENDING (Resend) === + + +@app.route("/api/send-email", methods=["POST", "OPTIONS"]) +@app.route("/turf/api/send-email", methods=["POST", "OPTIONS"]) +def send_email(): + """Envoyer un email via Resend API""" + if request.method == "OPTIONS": + return "", 200 + + if not RESEND_API_KEY: + return jsonify({"error": "RESEND_API key not configured in environment"}), 500 + + data = request.get_json() + if not data: + return jsonify({"error": "JSON body required"}), 400 + + to = data.get("to") + subject = data.get("subject") + html = data.get("html") + text = data.get("text") + from_addr = data.get("from", "H3R7Tech ") + + if not to or not subject: + return jsonify({"error": "Missing required fields: to, subject"}), 400 + if not html and not text: + return jsonify({"error": "Missing email body: provide html or text"}), 400 + + try: + payload = { + "from": from_addr, + "to": [to] if isinstance(to, str) else to, + "subject": subject, + } + if html: + payload["html"] = html + if text: + payload["text"] = text + + resp = requests.post( + "https://api.resend.com/emails", + headers={ + "Authorization": f"Bearer {RESEND_API_KEY}", + "Content-Type": "application/json", + }, + json=payload, + timeout=15, + ) + + if resp.status_code in (200, 201): + result = resp.json() + return jsonify( + { + "success": True, + "id": result.get("id"), + "message": "Email sent successfully", + } + ) + else: + return jsonify( + {"error": f"Resend API error: {resp.status_code}", "details": resp.text} + ), resp.status_code + + except Exception as e: + return jsonify({"error": str(e)}), 500 + + +# === BRAVE SEARCH === + + +@app.route("/api/brave-search", methods=["GET", "POST", "OPTIONS"]) +@app.route("/turf/api/brave-search", methods=["GET", "POST", "OPTIONS"]) +def brave_search(): + """Recherche web via Brave Search API""" + if request.method == "OPTIONS": + return "", 200 + + if not BRAVE_SEARCH_API_KEY: + return jsonify( + {"error": "BRAVE_SEARCH_API key not configured in environment"} + ), 500 + + if request.method == "GET": + q = request.args.get("q", "") + count = int(request.args.get("count", 10)) + offset = int(request.args.get("offset", 0)) + search_type = request.args.get("type", "web") + else: + data = request.get_json() or {} + q = data.get("q", data.get("query", "")) + count = int(data.get("count", 10)) + offset = int(data.get("offset", 0)) + search_type = data.get("type", "web") + + if not q: + return jsonify({"error": "Missing query parameter: q"}), 400 + + count = min(count, 20) # Limit to 20 results max + + try: + # Choose endpoint based on type + if search_type == "news": + url = "https://api.search.brave.com/res/v1/news/search" + else: + url = "https://api.search.brave.com/res/v1/web/search" + + resp = requests.get( + url, + headers={ + "X-Subscription-Token": BRAVE_SEARCH_API_KEY, + "Accept": "application/json", + }, + params={ + "q": q, + "count": count, + "offset": offset, + "search_lang": "fr", + }, + timeout=10, + ) + + if resp.status_code == 200: + result = resp.json() + # Normalize response + if search_type == "news": + items = result.get("results", []) + normalized = [ + { + "title": r.get("title", ""), + "url": r.get("url", ""), + "description": r.get("description", ""), + "age": r.get("age", ""), + "source": r.get("source", {}).get("title", ""), + } + for r in items + ] + else: + items = result.get("web", {}).get("results", []) + normalized = [ + { + "title": r.get("title", ""), + "url": r.get("url", ""), + "description": r.get("description", ""), + "age": r.get("age", ""), + "language": r.get("language", ""), + } + for r in items + ] + + return jsonify( + { + "query": q, + "type": search_type, + "count": len(normalized), + "results": normalized, + } + ) + else: + return jsonify( + { + "error": f"Brave Search API error: {resp.status_code}", + "details": resp.text, + } + ), resp.status_code + + except Exception as e: + return jsonify({"error": str(e)}), 500 + + + +@app.route("/turf/api/predictions_analysis", methods=["GET"]) +def api_predictions_analysis(): + """Analyse des predictions vs resultats reels""" + from datetime import datetime, timedelta + + days = int(request.args.get("days", 30)) + end_date = datetime.now().strftime("%Y-%m-%d") + start_date = (datetime.now() - timedelta(days=days)).strftime("%Y-%m-%d") + + conn = get_db() + cursor = conn.cursor() + + stats = { + "canalturf": {"total": 0, "top1_pct": 0, "top3_pct": 0, "top5_pct": 0, "ze2_pct": 0}, + "scoring": {"total": 0, "top1_pct": 0, "top3_pct": 0, "top5_pct": 0, "ze2_pct": 0}, + } + + for source in ["canalturf", "scoring"]: + pred_table = "predictions" if source == "canalturf" else "scoring" + pred_col = "predicted_1" if source == "canalturf" else "horse_number" + try: + cursor.execute( + f""" + SELECT c.libelle, c.ordre_arrivee, c.numero, p.{pred_col} + FROM pmu_courses c + LEFT JOIN {pred_table} p ON c.date_programme = p.date_programme + AND c.num_course = p.num_course + WHERE c.date_course BETWEEN ? AND ? + AND c.ordre_arrivee IS NOT NULL + ORDER BY c.date_course DESC + """, + (start_date, end_date), + ) + races = {} + for row in cursor.fetchall(): + race, ordre, numero, pred = row + if race not in races: + races[race] = {"actual": [], "predicted": []} + if ordre and ordre > 0: + races[race]["actual"].append(str(numero)) + if pred: + races[race]["predicted"].append(str(pred)) + + top1_hit = top3_hit = 0 + total = len(races) + for race, data in races.items(): + actual = set(data["actual"][:3]) + pred_top1 = data["predicted"][0] if data["predicted"] else None + actual_top1 = data["actual"][0] if data["actual"] else None + if pred_top1 and actual_top1 and pred_top1 == actual_top1: + top1_hit += 1 + if len(set(data["predicted"][:3]) & actual) >= 1: + top3_hit += 1 + + if total > 0: + stats[source]["total"] = total + stats[source]["top1_pct"] = round(top1_hit / total * 100, 1) + stats[source]["top3_pct"] = round(top3_hit / total * 100, 1) + except Exception as e: + print(f"Erreur {source}: {e}") + + conn.close() + return jsonify({"stats": stats, "period": {"start": start_date, "end": end_date}}) + + +if __name__ == "__main__": + app.run(host="0.0.0.0", port=8790, debug=False) diff --git a/compare_all.py b/compare_all.py new file mode 100755 index 0000000..d879ec4 --- /dev/null +++ b/compare_all.py @@ -0,0 +1,79 @@ +#!/usr/bin/env python3 +""" +Compare All Predictions (Our + External Sources) +""" +import sqlite3 +from datetime import datetime +from collections import defaultdict + +DB_PATH = "/home/h3r7/turf_scraper/turf.db" + +def compare_all(date): + conn = sqlite3.connect(DB_PATH) + c = conn.cursor() + + print(f"\n{'='*60}") + print(f"COMPARAISON COMPLÈTE - {date}") + print(f"{'='*60}") + + # Our predictions + print("\n[NOTRE SYSTÈME]") + c.execute("SELECT horse_name, odds, prediction_rank FROM predictions WHERE date = ? ORDER BY prediction_rank", (date,)) + our_preds = c.fetchall() + for name, odds, rank in our_preds: + print(f" {rank}. {name} ({odds})") + + # External predictions grouped by source + c.execute("SELECT DISTINCT source FROM external_predictions WHERE date = ?", (date,)) + sources = [r[0] for r in c.fetchall()] + + for source in sources: + print(f"\n[{source.upper()}]") + c.execute("SELECT horse_name, odds, rank, confidence FROM external_predictions WHERE date = ? AND source = ? ORDER BY rank", (date, source)) + preds = c.fetchall() + for name, odds, rank, conf in preds: + conf_str = f" ({conf}%)" if conf else "" + print(f" {rank}. {name} ({odds}){conf_str}") + + # Results + print("\n[RÉSULTATS]") + c.execute("SELECT horse_name, position FROM results WHERE date = ? AND position <= 5", (date,)) + results = c.fetchall() + for name, pos in results: + print(f" {pos}. {name}") + + # Score + if results: + result_names = [r[0] for r in results] + + print(f"\n[SCORE - 3 premiers]") + + # Our system + our_hits = sum(1 for p in our_preds if p[0] in result_names[:3]) + print(f" Nous: {our_hits}/3") + + # Each source + for source in sources: + c.execute("SELECT horse_name FROM external_predictions WHERE date = ? AND source = ? ORDER BY rank", (date, source)) + preds = [r[0] for r in c.fetchall()] + hits = sum(1 for p in preds if p in result_names[:3]) + print(f" {source}: {hits}/3") + + conn.close() + +def add_external(date, source, race_name, horse_name, odds, rank, confidence=None): + conn = sqlite3.connect(DB_PATH) + c = conn.cursor() + c.execute(''' + INSERT INTO external_predictions (date, source, race_name, horse_name, odds, rank, confidence) + VALUES (?, ?, ?, ?, ?, ?, ?) + ''', (date, source, race_name, horse_name, odds, rank, confidence)) + conn.commit() + conn.close() + +if __name__ == "__main__": + import sys + if len(sys.argv) > 1: + compare_all(sys.argv[1]) + else: + compare_all(datetime.now().strftime('%Y-%m-%d')) diff --git a/compare_models.py b/compare_models.py new file mode 100644 index 0000000..6f8c4c9 --- /dev/null +++ b/compare_models.py @@ -0,0 +1,207 @@ +#!/usr/bin/env python3 +""" +Compare scoring models performance vs actual results +""" +import sqlite3 +import sys + +DB_PATH = "/home/h3r7/turf_scraper/turf.db" + +def get_results(date): + """Get actual race results""" + conn = sqlite3.connect(DB_PATH) + conn.row_factory = sqlite3.Row + c = conn.cursor() + + c.execute(""" + SELECT race_name, horse_name, position + FROM results + WHERE date = ? + ORDER BY race_name, position + """, (date,)) + + results = {} + for row in c.fetchall(): + race = row['race_name'] + if race not in results: + results[race] = [] + results[race].append({ + 'horse': row['horse_name'], + 'position': row['position'] + }) + + conn.close() + return results + +def get_predictions_v1(date): + """Get scoring v1 predictions""" + conn = sqlite3.connect(DB_PATH) + conn.row_factory = sqlite3.Row + c = conn.cursor() + + c.execute(""" + SELECT race_name, horse_name, score, rang_scoring + FROM scoring + WHERE date = ? AND scoring_version = 'v1' + ORDER BY race_name, rang_scoring + """, (date,)) + + results = {} + for row in c.fetchall(): + race = row['race_name'] + if race not in results: + results[race] = [] + results[race].append({ + 'horse': row['horse_name'], + 'score': row['score'], + 'rank': row['rang_scoring'] + }) + + conn.close() + return results + +def get_predictions_v2(date): + """Get scoring v2 predictions""" + conn = sqlite3.connect(DB_PATH) + conn.row_factory = sqlite3.Row + c = conn.cursor() + + c.execute(""" + SELECT race_name, horse_name, score, rang_scoring + FROM scoring + WHERE date = ? AND scoring_version = 'v2' + ORDER BY race_name, rang_scoring + """, (date,)) + + results = {} + for row in c.fetchall(): + race = row['race_name'] + if race not in results: + results[race] = [] + results[race].append({ + 'horse': row['horse_name'], + 'score': row['score'], + 'rank': row['rang_scoring'] + }) + + conn.close() + return results + +def get_canalturf(date): + """Get CanalTurf predictions""" + conn = sqlite3.connect(DB_PATH) + conn.row_factory = sqlite3.Row + c = conn.cursor() + + c.execute(""" + SELECT race_name, horse_name, prediction_rank + FROM predictions + WHERE date = ? AND source = 'canalturf_partants' + ORDER BY race_name, prediction_rank + """, (date,)) + + results = {} + for row in c.fetchall(): + race = row['race_name'] + if race not in results: + results[race] = [] + results[race].append({ + 'horse': row['horse_name'], + 'rank': row['prediction_rank'] + }) + + conn.close() + return results + +def calculate_hits(predictions, actual, top_n=3): + """Calculate hits for top N predictions""" + hits = 0 + for race, actual_horses in actual.items(): + if race not in predictions: + continue + + pred_horses = [h['horse'] for h in predictions[race][:top_n]] + actual_top = [h['horse'] for h in actual_horses[:top_n]] + + for p in pred_horses: + if p in actual_top: + hits += 1 + + return hits + +def compare_models(date): + print(f"\n{'='*70}") + print(f"COMPARAISON DES MODÈLES - {date}") + print(f"{'='*70}\n") + + # Get data + actual = get_results(date) + v1 = get_predictions_v1(date) + v2 = get_predictions_v2(date) + canal = get_canalturf(date) + + if not actual: + print("Aucun résultat trouvé pour cette date") + return + + print(f"Courses avec résultats: {len(actual)}") + + # Calculate hits for each model + print(f"\n{'MODÈLE':<20} | {'TOP 1':<8} | {'TOP 3':<8} | {'TOP 5':<8}") + print("-" * 50) + + # Top 1 + v1_hits = calculate_hits(v1, actual, 1) + v2_hits = calculate_hits(v2, actual, 1) + canal_hits = calculate_hits(canal, actual, 1) + total_races = len(actual) + + print(f"Scoring V1 | {v1_hits}/{total_races} ({v1_hits*100/total_races:.0f}%) | - | - ") + print(f"Scoring V2 | {v2_hits}/{total_races} ({v2_hits*100/total_races:.0f}%) | - | - ") + print(f"CanalTurf | {canal_hits}/{total_races} ({canal_hits*100/total_races:.0f}%) | - | - ") + + # Top 3 + v1_hits = calculate_hits(v1, actual, 3) + v2_hits = calculate_hits(v2, actual, 3) + canal_hits = calculate_hits(canal, actual, 3) + + print(f"Scoring V1 | - | {v1_hits}/{total_races*3} ({v1_hits*100/(total_races*3):.0f}%) | - ") + print(f"Scoring V2 | - | {v2_hits}/{total_races*3} ({v2_hits*100/(total_races*3):.0f}%) | - ") + print(f"CanalTurf | - | {canal_hits}/{total_races*3} ({canal_hits*100/(total_races*3):.0f}%) | - ") + + # Top 5 + v1_hits = calculate_hits(v1, actual, 5) + v2_hits = calculate_hits(v2, actual, 5) + canal_hits = calculate_hits(canal, actual, 5) + + print(f"Scoring V1 | - | - | {v1_hits}/{total_races*5} ({v1_hits*100/(total_races*5):.0f}%)") + print(f"Scoring V2 | - | - | {v2_hits}/{total_races*5} ({v2_hits*100/(total_races*5):.0f}%)") + print(f"CanalTurf | - | - | {canal_hits}/{total_races*5} ({canal_hits*100/(total_races*5):.0f}%)") + + # Detailed per race + print(f"\n{'='*70}") + print("DÉTAIL PAR COURSE") + print(f"{'='*70}") + + for race, actual_horses in actual.items(): + print(f"\n🏇 {race}") + print(f" Résultat: {' / '.join([h['horse'] for h in actual_horses[:5]])}") + + if race in v1: + print(f" V1: {' / '.join([h['horse'] for h in v1[race][:3]])}") + else: + print(f" V1: Pas de prédictions") + + if race in v2: + print(f" V2: {' / '.join([h['horse'] for h in v2[race][:3]])}") + else: + print(f" V2: Pas de prédictions") + + if race in canal: + print(f" CT: {' / '.join([h['horse'] for h in canal[race][:3]])}") + else: + print(f" CT: Pas de prédictions") + +if __name__ == "__main__": + date = sys.argv[1] if len(sys.argv) > 1 else "2026-04-06" + compare_models(date) \ No newline at end of file diff --git a/compare_predictions.py b/compare_predictions.py new file mode 100755 index 0000000..a41cb82 --- /dev/null +++ b/compare_predictions.py @@ -0,0 +1,68 @@ +#!/usr/bin/env python3 +""" +Compare Grok vs Our predictions +""" +import sqlite3 +from datetime import datetime + +DB_PATH = "/home/h3r7/turf_scraper/turf.db" + +def add_grok_prediction(date, race_name, horse_name, odds, rank): + conn = sqlite3.connect(DB_PATH) + c = conn.cursor() + c.execute(''' + INSERT INTO grok_predictions (date, race_name, horse_name, odds, rank) + VALUES (?, ?, ?, ?, ?) + ''', (date, race_name, horse_name, odds, rank)) + conn.commit() + conn.close() + print(f"Added: {horse_name}") + +def compare_predictions(date): + conn = sqlite3.connect(DB_PATH) + c = conn.cursor() + + print(f"\n{'='*50}") + print(f"COMPARAISON - {date}") + print(f"{'='*50}") + + # Our predictions + print("\n[NOS PREDICTIONS]") + c.execute("SELECT horse_name, odds, prediction_rank FROM predictions WHERE date = ? ORDER BY prediction_rank", (date,)) + our_preds = c.fetchall() + for name, odds, rank in our_preds: + print(f" {rank}. {name} (cote: {odds})") + + # Grok predictions + print("\n[PREDICTIONS GROK]") + c.execute("SELECT horse_name, odds, rank FROM grok_predictions WHERE date = ? ORDER BY rank", (date,)) + grok_preds = c.fetchall() + for name, odds, rank in grok_preds: + print(f" {rank}. {name} (cote: {odds})") + + # Results (if available) + print("\n[RÉSULTATS]") + c.execute("SELECT horse_name, position FROM results WHERE date = ? AND position <= 5", (date,)) + results = c.fetchall() + for name, pos in results: + print(f" {pos}. {name}") + + # Comparison + if our_preds and grok_preds and results: + result_names = [r[0] for r in results] + + our_hits = sum(1 for p in our_preds if p[0] in result_names[:3]) + grok_hits = sum(1 for p in grok_preds if p[0] in result_names[:3]) + + print(f"\n[SCORE]") + print(f" Nos picks dans les 3 premiers: {our_hits}/3") + print(f" Grok picks dans les 3 premiers: {grok_hits}/3") + + conn.close() + +if __name__ == "__main__": + import sys + if len(sys.argv) > 1: + compare_predictions(sys.argv[1]) + else: + compare_predictions(datetime.now().strftime('%Y-%m-%d')) diff --git a/crm_api.py b/crm_api.py new file mode 100755 index 0000000..aa32bfb --- /dev/null +++ b/crm_api.py @@ -0,0 +1,89 @@ +#!/usr/bin/env python3 +from flask import Flask, jsonify, request, send_from_directory +from flask_cors import CORS +import json +import os +from datetime import datetime + +app = Flask(__name__) +CORS(app) + +CRM_FILE = '/home/h3r7/turf_scraper/crm_prospects.json' + +def load_crm(): + if not os.path.exists(CRM_FILE): + data = {'prospects': [], 'last_id': 0} + with open(CRM_FILE, 'w') as f: + json.dump(data, f, indent=2) + return data + with open(CRM_FILE, 'r') as f: + return json.load(f) + +def save_crm(data): + with open(CRM_FILE, 'w') as f: + json.dump(data, f, indent=2) + +@app.route('/') +def index(): + return send_from_directory('/home/h3r7/turf_scraper', 'crm_dashboard.html') + +@app.route('/api/prospects', methods=['GET']) +def get_prospects(): + data = load_crm() + return jsonify(data) + +@app.route('/api/prospects', methods=['POST']) +def add_prospect(): + data = load_crm() + req = request.json + + # Generate string ID if not provided + prospect_id = req.get('id') or f"prospect_{data['last_id'] + 1}" + data['last_id'] += 1 + + prospect = { + 'id': prospect_id, + 'nom': req.get('nom', ''), + 'entreprise': req.get('entreprise', ''), + 'tel': req.get('tel', ''), + 'email': req.get('email', ''), + 'secteur': req.get('secteur', ''), + 'statut': req.get('statut', 'nouveau'), + 'score': req.get('score', 0), + 'notes': req.get('notes', ''), + 'adresse': req.get('adresse', ''), + 'ville': req.get('ville', ''), + 'categorie': req.get('categorie', ''), + 'created': datetime.now().strftime('%Y-%m-%d') + } + data['prospects'].append(prospect) + save_crm(data) + return jsonify({'success': True, 'prospect': prospect}) + +@app.route('/api/prospects/', methods=['PUT']) +def update_prospect(prospect_id): + data = load_crm() + req = request.json + + for p in data['prospects']: + if str(p['id']) == str(prospect_id): + p.update(req) + save_crm(data) + return jsonify({'success': True, 'prospect': p}) + + return jsonify({'error': 'Prospect non trouvé'}), 404 + +@app.route('/api/prospects/', methods=['DELETE']) +def delete_prospect(prospect_id): + data = load_crm() + initial_len = len(data['prospects']) + data['prospects'] = [p for p in data['prospects'] if str(p['id']) != str(prospect_id)] + + if len(data['prospects']) < initial_len: + save_crm(data) + return jsonify({'success': True}) + + return jsonify({'error': 'Prospect non trouvé'}), 404 + +if __name__ == '__main__': + app.run(host='0.0.0.0', port=8770, debug=False) diff --git a/crm_candidatures.html b/crm_candidatures.html new file mode 100755 index 0000000..6dea526 --- /dev/null +++ b/crm_candidatures.html @@ -0,0 +1,395 @@ + + + + + CRM Candidatures + + + +🏠Accueil +

🎯 CRM Candidatures

Suivi de recherche d'emploi - Pipeline

+ +

Total

À Postuler

En Attente

Entretiens

Offres

Refus

+ +

➕ Nouvelle Candidature

+ + + +

+ + + Date publication +

+ +

📝 À Postuler

⏳ En Attente

🎤 Entretien 1

🎓 Entretien 2

✅ Offre

❌ Refus

+ + + + + + diff --git a/crm_dashboard.html b/crm_dashboard.html new file mode 100755 index 0000000..333432d --- /dev/null +++ b/crm_dashboard.html @@ -0,0 +1,324 @@ + + + + + CRM H3R7 - Gestion Prospects + + + +🏠Accueil +

📊 CRM H3R7 - Gestion des Prospects

+ +

Total Prospects

Nouveaux

Qualifiés

Gagnés

+ +

➕ Nouveau Prospect

+ + + +

+ +

+ + + + + + + +

+ +

+ Chargement... +

+ + ← Retour au Portail + + + + + + + diff --git a/crm_dashboard_new.html b/crm_dashboard_new.html new file mode 100755 index 0000000..2a87f8b --- /dev/null +++ b/crm_dashboard_new.html @@ -0,0 +1,316 @@ + + + + + CRM H3R7 - Gestion Prospects + + + +🏠Accueil +

+ +

📊 CRM H3R7 - Gestion des Prospects

+ +

Total Prospects

Nouveaux

Qualifiés

Gagnés

+ +

➕ Nouveau Prospect

+ + + +

+ +

+ + + + + + +

+ +

+ Chargement... +

+ + ← Retour au Portail + + + + + + + diff --git a/crm_simple.html b/crm_simple.html new file mode 100755 index 0000000..0157b77 --- /dev/null +++ b/crm_simple.html @@ -0,0 +1,205 @@ + + + + +CRM H3R7 + + + +🏠Accueil +

CRM H3R7

Total

Nouveau

Qualifies

Gagnes

+ + + + + + + + +

+ + + + diff --git a/dashboard.html b/dashboard.html new file mode 100644 index 0000000..fc27b28 --- /dev/null +++ b/dashboard.html @@ -0,0 +1,2464 @@ + + + + + + + 🏇 Turf Dashboard — H3R7Tech + + + + + + +🏠Accueil + + + + + + + + + +

+ + + + + + + + + + + +

+ +

+ + +

+ +
+

+ + +

+ +
+

+ + +

+ +
+

+ + +

+ +
+

+ + +

+Période: + + +

+ + +

+ +
+

+ + +

+ +

+ + +

+ + +

+

+ +

+ + + + + diff --git a/dashboard_api.py b/dashboard_api.py new file mode 100755 index 0000000..791d9d6 --- /dev/null +++ b/dashboard_api.py @@ -0,0 +1,1159 @@ +#!/usr/bin/env python3 +print("STARTING API...") +""" +API serve - Turf Dashboard Data +Includes ML predictions using XGBoost models +""" + +from flask import Flask, jsonify, send_file, send_from_directory, request +import sqlite3 +from datetime import datetime, timedelta +import pickle +import os + +try: + import pandas as pd + import numpy as np + from sklearn.preprocessing import LabelEncoder + + ML_AVAILABLE = True +except ImportError: + ML_AVAILABLE = False + pd = None + np = None + LabelEncoder = None + +app = Flask(__name__) +DB_PATH = "/home/h3r7/turf_saas/turf_saas.db" +MODEL_PATH = "/home/h3r7/turf_saas/xgboost_models.pkl" + +ml_models = None + + +def load_models(): + """Load XGBoost models""" + global ml_models + if ml_models is None and os.path.exists(MODEL_PATH): + try: + with open(MODEL_PATH, "rb") as f: + ml_models = pickle.load(f) + print("✅ XGBoost models loaded") + except Exception as e: + print(f"⚠️ Failed to load models: {e}") + ml_models = False + return ml_models + + +def get_db(): + conn = sqlite3.connect(DB_PATH) + conn.row_factory = sqlite3.Row + return conn + + +def ensure_ml_cache_table(conn): + """Crée la table ml_predictions_cache si elle n'existe pas, et ajoute les colonnes manquantes""" + conn.execute(""" + CREATE TABLE IF NOT EXISTS ml_predictions_cache ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + date TEXT NOT NULL, + num_reunion INTEGER, + num_course INTEGER, + horse_name TEXT, + horse_number INTEGER, + odds REAL, + prob_top1 REAL, + prob_top3 REAL, + ml_score REAL, + recommendation TEXT, + is_value_bet INTEGER DEFAULT 0, + is_outlier INTEGER DEFAULT 0, + race_label TEXT, + race_name TEXT, + hippodrome TEXT, + discipline TEXT, + distance REAL, + heure TEXT, + risque_label TEXT DEFAULT 'neutral', + risque_score INTEGER DEFAULT 50, + model_version TEXT DEFAULT 'xgboost_v1', + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + UNIQUE(date, num_reunion, num_course, horse_name) + ) + """) + conn.execute(""" + CREATE INDEX IF NOT EXISTS idx_ml_cache_date + ON ml_predictions_cache(date) + """) + # Migration : ajouter colonnes risque si table existante sans elles + try: + conn.execute("ALTER TABLE ml_predictions_cache ADD COLUMN risque_label TEXT DEFAULT 'neutral'") + except Exception: + pass + try: + conn.execute("ALTER TABLE ml_predictions_cache ADD COLUMN risque_score INTEGER DEFAULT 50") + except Exception: + pass + conn.commit() + + +def get_ml_from_cache(conn, date): + """Lit les prédictions ML depuis le cache BDD. Retourne (predictions, course_info) ou (None, None)""" + ensure_ml_cache_table(conn) + cursor = conn.execute( + """SELECT * FROM ml_predictions_cache WHERE date = ? ORDER BY ml_score DESC""", + (date,) + ) + rows = cursor.fetchall() + if not rows: + return None, None + + predictions = [] + course_info = {} + for row in rows: + r = dict(row) + pred = { + "horse_name": r["horse_name"], + "horse_number": r["horse_number"], + "odds": r["odds"], + "prob_top1": r["prob_top1"], + "prob_top3": r["prob_top3"], + "ml_score": r["ml_score"], + "recommendation": r["recommendation"], + "is_value_bet": r["is_value_bet"], + "is_outlier": r["is_outlier"], + "num_reunion": r["num_reunion"], + "num_course": r["num_course"], + "race_label": r["race_label"], + "race_name": r["race_name"], + "hippodrome": r["hippodrome"], + "discipline": r["discipline"], + "distance": r["distance"], + "heure": r["heure"], + "risque_label": r["risque_label"] if "risque_label" in r.keys() else "neutral", + "risque_score": r["risque_score"] if "risque_score" in r.keys() else 50, + } + predictions.append(pred) + key = f"{r['num_reunion']}_{r['num_course']}" + if key not in course_info: + course_info[key] = { + "libelle": r["race_name"], + "libelle_court": r["hippodrome"], + "discipline": r["discipline"], + "distance": r["distance"], + "heure_depart_str": r["heure"], + } + return predictions, course_info + + +def save_ml_to_cache(conn, date, predictions, model_version="xgboost_v1"): + """Sauvegarde les prédictions ML dans le cache BDD (INSERT OR REPLACE)""" + ensure_ml_cache_table(conn) + # Supprimer les anciennes entrées du jour pour permettre le refresh + conn.execute("DELETE FROM ml_predictions_cache WHERE date = ?", (date,)) + # Calculer le risque par course (grouper les chevaux avec tous leurs scores ML) + from collections import defaultdict + race_horses = defaultdict(list) + for p in predictions: + key = (p.get("num_reunion"), p.get("num_course")) + race_horses[key].append({ + "odds": p.get("odds", 999), + "ml_score": p.get("ml_score", 0), + "prob_top1":p.get("prob_top1", 0), + "prob_top3":p.get("prob_top3", 0), + }) + + race_risque = {} + for key, partants in race_horses.items(): + label, score = calculate_risque(partants) + race_risque[key] = (label or "neutral", score or 50) + + for p in predictions: + rkey = (p.get("num_reunion"), p.get("num_course")) + rl, rs = race_risque.get(rkey, ("neutral", 50)) + conn.execute(""" + INSERT INTO ml_predictions_cache + (date, num_reunion, num_course, horse_name, horse_number, odds, + prob_top1, prob_top3, ml_score, recommendation, is_value_bet, is_outlier, + race_label, race_name, hippodrome, discipline, distance, heure, + risque_label, risque_score, model_version) + VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?) + """, ( + date, + p.get("num_reunion"), + p.get("num_course"), + p.get("horse_name"), + p.get("horse_number"), + p.get("odds"), + p.get("prob_top1"), + p.get("prob_top3"), + p.get("ml_score"), + p.get("recommendation"), + p.get("is_value_bet", 0), + p.get("is_outlier", 0), + p.get("race_label"), + p.get("race_name"), + p.get("hippodrome"), + p.get("discipline"), + p.get("distance"), + p.get("heure"), + rl, + rs, + model_version, + )) + conn.commit() + + +def calculate_risque(partants): + """ + Calcule le niveau de risque d'une course à partir des scores ML et des cotes. + + Logique : + - SAFE (vert) : un favori ML domine clairement, écart > 25pts avec le 2e + - TRAP (rouge) : 3+ chevaux avec ml_score > 40 ET aucun ne dépasse 65 + OU favori de cote < 5 avec prob_top1 < 20% (outsider ML) + - NEUTRAL (orange) : cas intermédiaires + + Retourne (label, score) où score est une valeur 0-100 (100 = très sûr) + """ + if not partants: + return None, None + + # Trier par ml_score desc (ou prob_top1 si ml_score absent) + sorted_p = sorted(partants, key=lambda x: x.get("ml_score") or x.get("prob_top1") or 0, reverse=True) + + top1_score = sorted_p[0].get("ml_score") or sorted_p[0].get("prob_top1") or 0 + top2_score = sorted_p[1].get("ml_score") or sorted_p[1].get("prob_top1") or 0 if len(sorted_p) > 1 else 0 + top3_score = sorted_p[2].get("ml_score") or sorted_p[2].get("prob_top1") or 0 if len(sorted_p) > 2 else 0 + + gap_1_2 = top1_score - top2_score # écart entre 1er et 2e ML + gap_1_3 = top1_score - top3_score # écart entre 1er et 3e ML + + # Nombre de concurrents avec ml_score > 40 (dangereux) + nb_dangerous = sum(1 for p in sorted_p if (p.get("ml_score") or 0) > 40) + + # Détection favori de cote surpris par le ML + odds_fav = sorted(partants, key=lambda x: x.get("odds") or 999) + fav_odds = odds_fav[0].get("odds") or 999 if odds_fav else 999 + fav_ml = odds_fav[0].get("ml_score") or odds_fav[0].get("prob_top1") or 0 if odds_fav else 0 + fav_surprise = fav_odds < 5 and fav_ml < 25 # favori de cote ignoré par le ML + + # --- SAFE : domination claire --- + if top1_score >= 65 and gap_1_2 >= 20: + score = min(100, int(50 + gap_1_2 * 1.5)) + return "safe", score + + # --- TRAP : course très ouverte ou favori piégé --- + if fav_surprise: + return "trap", max(10, int(35 - (25 - fav_ml))) + if nb_dangerous >= 4 and top1_score < 70: + return "trap", max(10, int(40 - nb_dangerous * 2)) + if gap_1_2 < 8 and top2_score > 45: + return "trap", max(15, int(30 + gap_1_2)) + + # --- NEUTRAL : cas intermédiaires --- + # score 35-64 selon l'avantage du leader + score = min(64, max(35, int(35 + gap_1_2 * 1.2))) + return "neutral", score + + + +def table_exists(conn, table_name): + c = conn.execute( + "SELECT 1 FROM sqlite_master WHERE type='table' AND name=?", (table_name,) + ) + return c.fetchone() is not None + + +def load_ml_horses(conn, today): + course_info = {} + + # Get course info + c = conn.execute( + """ + SELECT num_reunion, num_course, libelle, libelle_court, discipline, distance, heure_depart_str + FROM pmu_courses + WHERE date_programme = ? + ORDER BY num_reunion, num_course + """, + (today,), + ) + for row in c.fetchall(): + course_info[f"{row['num_reunion']}_{row['num_course']}"] = dict(row) + + if table_exists(conn, "historical_data"): + c = conn.execute( + """ + SELECT DISTINCT p.horse_name, p.horse_number, p.odds, + h.age, h.sexe, h.nb_courses, h.nb_victoires, h.nb_places, + h.tx_victoire, h.tx_place, h.forme_recente, h.reduction_km, + h.gains_annee, h.cote_directe, h.distance, h.discipline, + h.avis_entraineur, h.oeilleres, h.deferre, h.nb_partants, + h.rang_cote, h.ratio_cote_field, h.musique + FROM predictions p + LEFT JOIN historical_data h ON h.horse_name = p.horse_name + WHERE p.date = ? AND p.source = 'canalturf_partants' AND p.odds > 0 + """, + (today,), + ) + horses = [dict(row) for row in c.fetchall()] + return today, horses, course_info + + c = conn.execute( + """ + SELECT + p.date_programme AS date, + p.num_reunion, + p.num_course, + p.num_pmu AS horse_number, + p.nom AS horse_name, + p.age, + p.sexe, + p.musique, + p.nombre_courses AS nb_courses, + p.nombre_victoires AS nb_victoires, + p.nombre_places AS nb_places, + p.gains_annee_en_cours AS gains_annee, + COALESCE(p.cote_direct, 0) AS cote_directe, + COALESCE(c.distance, 0) AS distance, + COALESCE(c.discipline, 'PLAT') AS discipline, + COALESCE(c.nb_declares_partants, 0) AS nb_partants, + COALESCE(p.oeilleres, 'SANS_OEILLERES') AS oeilleres, + COALESCE(p.tx_victoire, 0) AS tx_victoire, + COALESCE(p.tx_place, 0) AS tx_place, + COALESCE(p.forme_recente, 0) AS forme_recente, + 0 AS reduction_km, + 'NEUTRE' AS avis_entraineur, + 'NON' AS deferre, + 0 AS rang_cote, + 0 AS ratio_cote_field + FROM pmu_partants p + LEFT JOIN pmu_courses c + ON c.date_programme = p.date_programme + AND c.num_reunion = p.num_reunion + AND c.num_course = p.num_course + INNER JOIN pmu_reunions r + ON r.date_programme = p.date_programme + AND r.num_reunion = p.num_reunion + WHERE p.date_programme = ? AND r.pays_code = 'FRA' + ORDER BY p.num_reunion, p.num_course, p.num_pmu + """, + (today,), + ) + horses = [dict(row) for row in c.fetchall()] + course_info = {} + if horses: + c = conn.execute( + """ + SELECT num_reunion, num_course, libelle, libelle_court, discipline, distance, heure_depart_str + FROM pmu_courses + WHERE date_programme = ? + ORDER BY num_reunion, num_course + """, + (today,), + ) + for row in c.fetchall(): + course_info[f"{row['num_reunion']}_{row['num_course']}"] = dict(row) + return today, horses, course_info + + c = conn.execute("SELECT MAX(date_programme) FROM pmu_partants") + fallback_date = c.fetchone()[0] + if fallback_date: + c = conn.execute( + """ + SELECT + p.date_programme AS date, + p.num_reunion, + p.num_course, + p.num_pmu AS horse_number, + p.nom AS horse_name, + p.age, + p.sexe, + p.musique, + p.nombre_courses AS nb_courses, + p.nombre_victoires AS nb_victoires, + p.nombre_places AS nb_places, + p.gains_annee_en_cours AS gains_annee, + COALESCE(p.cote_direct, 0) AS cote_directe, + COALESCE(c.distance, 0) AS distance, + COALESCE(c.discipline, 'PLAT') AS discipline, + COALESCE(c.nb_declares_partants, 0) AS nb_partants, + COALESCE(p.oeilleres, 'SANS_OEILLERES') AS oeilleres, + COALESCE(p.tx_victoire, 0) AS tx_victoire, + COALESCE(p.tx_place, 0) AS tx_place, + COALESCE(p.forme_recente, 0) AS forme_recente, + 0 AS reduction_km, + 'NEUTRE' AS avis_entraineur, + 'NON' AS deferre, + 0 AS rang_cote, + 0 AS ratio_cote_field + FROM pmu_partants p + LEFT JOIN pmu_courses c + ON c.date_programme = p.date_programme + AND c.num_reunion = p.num_reunion + AND c.num_course = p.num_course + WHERE p.date_programme = ? + ORDER BY p.num_reunion, p.num_course, p.num_pmu + """, + (fallback_date,), + ) + return fallback_date, [dict(row) for row in c.fetchall()], {} + + return today, [], {} + + +def enrich_ml_horses(horses): + races = {} + for horse in horses: + race_key = ( + horse.get("date") or horse.get("date_programme"), + horse.get("num_reunion"), + horse.get("num_course"), + ) + races.setdefault(race_key, []).append(horse) + + for group in races.values(): + odds_values = [] + for horse in group: + raw_odds = horse.get("odds", horse.get("cote_directe", 0)) + try: + odds = float(raw_odds or 0) + except (TypeError, ValueError): + odds = 0.0 + horse["odds"] = odds + horse["cote_directe"] = float(horse.get("cote_directe", odds) or odds or 0) + if odds > 0: + odds_values.append(odds) + + avg_odds = sum(odds_values) / len(odds_values) if odds_values else 0 + ranked = sorted( + group, key=lambda h: h.get("odds", h.get("cote_directe", 0)) or 999999 + ) + + for idx, horse in enumerate(ranked, start=1): + horse.setdefault("horse_number", horse.get("num_pmu")) + horse.setdefault("horse_name", horse.get("nom")) + horse.setdefault("age", 0) + horse.setdefault("sexe", "U") + horse.setdefault("nb_courses", 0) + horse.setdefault("nb_victoires", 0) + horse.setdefault("nb_places", 0) + horse.setdefault("tx_victoire", 0) + horse.setdefault("tx_place", 0) + horse.setdefault("forme_recente", 0) + horse.setdefault("reduction_km", 0) + horse.setdefault("gains_annee", 0) + horse.setdefault("distance", 0) + horse.setdefault("discipline", "PLAT") + horse.setdefault("avis_entraineur", "NEUTRE") + horse.setdefault("oeilleres", "SANS") + horse.setdefault("deferre", "NON") + horse.setdefault("nb_partants", len(group)) + horse.setdefault("musique", "") + horse.setdefault("rang_cote", idx) + if not horse.get("ratio_cote_field"): + horse["ratio_cote_field"] = ( + round(horse.get("odds", 0) / avg_odds, 3) if avg_odds > 0 else 0 + ) + + return horses + + +def prepare_features_from_db(horse_data): + """Convert database rows to ML features""" + df = pd.DataFrame([horse_data]) + + # Encode categorical + for col in ["discipline", "sexe", "avis_entraineur", "oeilleres", "deferre"]: + if col in df.columns: + df[col] = df[col].fillna("UNKNOWN") + + return df + + +@app.route("/") +def index(): + return send_file("/home/h3r7/turf_saas/dashboard.html") + + +@app.route("/turf/") +@app.route("/turf") +def turf_index(): + return send_file("/home/h3r7/turf_saas/dashboard.html") + + +@app.route("/turf/") +def turf_static(filename): + return send_from_directory("/home/h3r7/turf_saas", filename) + + +@app.route("/api/today") +@app.route("/turf/api") +def api_today(): + conn = get_db() + today = datetime.now().strftime("%Y-%m-%d") + + race_filter = request.args.get("race", "") + + data = { + "date": today, + "races": [], + "race": {}, + "predictions": {}, + "results": [], + "weather": {}, + "scores": {}, + } + + # Construire la condition de filtre + if race_filter: + race_condition = "AND race_name = ?" + race_params = (race_filter,) + else: + race_condition = "" + race_params = () + + # Récupérer toutes les courses du jour + try: + query_params = (today,) + race_params if race_condition else (today,) + c = conn.execute( + f""" + SELECT DISTINCT race_name, race_hippodrome, race_time + FROM predictions + WHERE date=? AND source='canalturf_partants' {race_condition} + ORDER BY race_time ASC + """, + query_params, + ) + races = c.fetchall() + + data["races"] = [ + {"name": r[0], "hippodrome": r[1], "time": r[2]} for r in races + ] + + if races: + data["race"] = { + "name": f"{races[0][1]} - {races[0][2]} {races[0][0]}", + "hippodrome": races[0][1] if len(races[0]) > 1 else "", + "time": races[0][2] if len(races[0]) > 2 else "", + } + except Exception as e: + print(f"Erreur races: {e}") + + # Prédictions du jour — partants avec cotes uniquement + try: + if race_filter: + c = conn.execute( + """ + SELECT horse_name, horse_number, AVG(odds) as odds, prediction_rank, source, jockey + FROM predictions + WHERE date = ? AND source = 'canalturf_partants' AND odds > 0 AND race_name = ? + GROUP BY horse_name + ORDER BY odds ASC + """, + (today, race_filter), + ) + else: + c = conn.execute( + """ + SELECT horse_name, horse_number, AVG(odds) as odds, prediction_rank, source, jockey + FROM predictions + WHERE date = ? AND source = 'canalturf_partants' AND odds > 0 + GROUP BY horse_name + ORDER BY odds ASC + """, + (today,), + ) + data["predictions"]["partants"] = [dict(row) for row in c.fetchall()] + except Exception as e: + print(f"Erreur partants: {e}") + data["predictions"]["partants"] = [] + + # Pronostic (bases, chances, outsiders) + for cat, src in [ + ("bases", "canalturf_prono_bases"), + ("chances", "canalturf_prono_chances"), + ("outsiders", "canalturf_prono_outsiders"), + ]: + try: + if race_filter: + c = conn.execute( + """ + SELECT DISTINCT horse_name, horse_number, prediction_rank + FROM predictions WHERE date = ? AND source = ? AND race_name = ? + ORDER BY prediction_rank + """, + (today, src, race_filter), + ) + else: + c = conn.execute( + """ + SELECT DISTINCT horse_name, horse_number, prediction_rank + FROM predictions WHERE date = ? AND source = ? + ORDER BY prediction_rank + """, + (today, src), + ) + data["predictions"][cat] = [dict(row) for row in c.fetchall()] + except: + data["predictions"][cat] = [] + + # Résultats du jour + c = conn.execute( + "SELECT horse_name, position, odds FROM results WHERE date = ? ORDER BY position LIMIT 5", + (today,), + ) + data["results"] = [dict(row) for row in c.fetchall()] + + # Weather + c = conn.execute("SELECT * FROM weather ORDER BY id DESC LIMIT 1") + row = c.fetchone() + if row: + data["weather"] = dict(row) + partants_list = data.get("predictions", {}).get("partants", []) + if partants_list: + print("DEBUG: partants found") + risque_label, risque_course = calculate_risque(partants_list) + data["risque_label"] = risque_label + data["risque_course"] = risque_course + + # Score hier + yesterday = (datetime.now() - timedelta(days=1)).strftime("%Y-%m-%d") + data["scores"]["date"] = yesterday + c = conn.execute( + "SELECT horse_name FROM results WHERE date = ? AND position <= 3", (yesterday,) + ) + result_names = [r[0] for r in c.fetchall()] + c = conn.execute( + "SELECT DISTINCT horse_name FROM predictions WHERE date = ? AND source='canalturf_prono_bases'", + (yesterday,), + ) + our_preds = [r[0] for r in c.fetchall()] + our_score = sum(1 for p in our_preds if p in result_names) + data["scores"]["bases"] = f"{our_score}/{len(our_preds)}" if our_preds else "-" + + conn.close() + return jsonify(data) + + +@app.route("/api/odds_history") +@app.route("/turf/api/odds_history") +def api_odds_history(): + conn = get_db() + today = datetime.now().strftime("%Y-%m-%d") + + c = conn.execute( + """ + SELECT horse_name, horse_number, odds, scraped_at + FROM odds_history + WHERE date = ? + ORDER BY horse_name, scraped_at ASC + """, + (today,), + ) + rows = c.fetchall() + conn.close() + + horses = {} + for row in rows: + h = row["horse_name"] + if h not in horses: + horses[h] = { + "horse_name": h, + "horse_number": row["horse_number"], + "snapshots": [], + } + horses[h]["snapshots"].append( + {"odds": row["odds"], "time": row["scraped_at"][11:16]} + ) + + result = [] + for h, data in horses.items(): + snaps = data["snapshots"] + debut = snaps[0]["odds"] if snaps else 0 + actuel = snaps[-1]["odds"] if snaps else 0 + evol_pct = ( + round(((actuel - debut) / debut) * 100, 1) + if debut > 0 and len(snaps) > 1 + else 0 + ) + result.append( + { + "horse_name": h, + "horse_number": data["horse_number"], + "odds_debut": debut, + "odds_actuel": actuel, + "evol_pct": evol_pct, + "nb_snapshots": len(snaps), + "snapshots": snaps, + "tendance": "baisse" + if evol_pct < -5 + else "hausse" + if evol_pct > 5 + else "stable", + } + ) + + result.sort(key=lambda x: x["odds_actuel"]) + return jsonify({"date": today, "horses": result}) + + +@app.route("/api/weather") +def api_weather(): + conn = get_db() + c = conn.execute("SELECT * FROM weather ORDER BY id DESC LIMIT 4") + weather = [dict(row) for row in c.fetchall()] + conn.close() + return jsonify(weather) + + +@app.route("/api/ml_predictions") +@app.route("/turf/api/ml_predictions") +def api_ml_predictions(): + """ML-powered predictions using XGBoost — cache BDD activé""" + if not ML_AVAILABLE: + return jsonify({"error": "ML libraries not available"}) + + conn = get_db() + today = datetime.now().strftime("%Y-%m-%d") + force_refresh = request.args.get("refresh", "0") == "1" + + # --- LECTURE CACHE --- + if not force_refresh: + cached_preds, cached_courses = get_ml_from_cache(conn, today) + if cached_preds: + conn.close() + return jsonify({ + "date": today, + "model_version": "xgboost_v1", + "predictions": cached_preds, + "courses": cached_courses, + "from_cache": True, + }) + + # --- CALCUL ML --- + models = load_models() + + if not models or models is True: + conn.close() + return jsonify( + { + "error": "Models not loaded", + "message": "Run train_xgboost.py first to train the models", + } + ) + date_used, horses, course_info = load_ml_horses(conn, today) + horses = enrich_ml_horses(horses) + + if not horses: + conn.close() + return jsonify( + { + "date": date_used, + "predictions": [], + "message": "No predictions available", + } + ) + + # Use exact feature columns from training + feature_cols = [ + "age", + "sexe_enc", + "nb_courses", + "nb_victoires", + "nb_places", + "tx_victoire", + "tx_place", + "forme_recente", + "reduction_km", + "gains_annee", + "cote_directe", + "distance", + "nb_partants", + "discipline_enc", + "avis_enc", + "oeilleres_enc", + "deferre_enc", + "form_1", + "form_2", + "form_3", + "form_4", + "form_5", + "form_avg", + "win_rate_adj", + "place_rate_adj", + "implied_prob", + "victories_per_race", + "places_per_race", + "earnings_per_race", + "age_win_interact", + "distance_cat", + "is_favorite", + "rang_cote", + "ratio_cote_field", + ] + + # Get all unique values for encoding + all_sexes = set(h.get("sexe", "U") or "U" for h in horses) + all_avis = set(h.get("avis_entraineur", "NEUTRE") or "NEUTRE" for h in horses) + all_oeilleres = set(h.get("oeilleres", "SANS") or "SANS" for h in horses) + all_deferre = set(h.get("deferre", "NON") or "NON" for h in horses) + all_discipline = set(h.get("discipline", "PLAT") or "PLAT" for h in horses) + + le_sexe = LabelEncoder() + le_sexe.fit(list(all_sexes) + ["U"]) + le_avis = LabelEncoder() + le_avis.fit(list(all_avis) + ["NEUTRE"]) + le_oeilleres = LabelEncoder() + le_oeilleres.fit(list(all_oeilleres) + ["SANS"]) + le_deferre = LabelEncoder() + le_deferre.fit(list(all_deferre) + ["NON"]) + le_discipline = LabelEncoder() + le_discipline.fit(list(all_discipline) + ["PLAT"]) + + predictions = [] + + for horse in horses: + features = {} + + # Numeric features + for col in [ + "age", + "nb_courses", + "nb_victoires", + "nb_places", + "tx_victoire", + "tx_place", + "forme_recente", + "reduction_km", + "gains_annee", + "cote_directe", + "distance", + "nb_partants", + "rang_cote", + "ratio_cote_field", + ]: + features[col] = float(horse.get(col, 0) or 0) + + # Encoded categorical + features["sexe_enc"] = le_sexe.transform([horse.get("sexe", "U") or "U"])[0] + features["avis_enc"] = le_avis.transform( + [horse.get("avis_entraineur", "NEUTRE") or "NEUTRE"] + )[0] + features["oeilleres_enc"] = le_oeilleres.transform( + [horse.get("oeilleres", "SANS") or "SANS"] + )[0] + features["deferre_enc"] = le_deferre.transform( + [horse.get("deferre", "NON") or "NON"] + )[0] + features["discipline_enc"] = le_discipline.transform( + [horse.get("discipline", "PLAT") or "PLAT"] + )[0] + + # Form features (parse from musique) + musique = horse.get("musique", "") + import re + + form_nums = re.findall(r"\d+", str(musique))[:5] + for i, fn in enumerate(form_nums): + features[f"form_{i + 1}"] = float(fn) + for i in range(len(form_nums) + 1, 6): + features[f"form_{i}"] = 0.0 + features["form_avg"] = sum(features[f"form_{i}"] for i in range(1, 6)) / 5 + + # Derived features + features["implied_prob"] = ( + 1 / features["cote_directe"] if features["cote_directe"] > 0 else 0 + ) + features["win_rate_adj"] = features["tx_victoire"] * np.log1p( + features["nb_courses"] + ) + features["place_rate_adj"] = features["tx_place"] * np.log1p( + features["nb_courses"] + ) + features["victories_per_race"] = features["nb_victoires"] / max( + features["nb_courses"], 1 + ) + features["places_per_race"] = features["nb_places"] / max( + features["nb_courses"], 1 + ) + features["earnings_per_race"] = features["gains_annee"] / max( + features["nb_courses"], 1 + ) + features["age_win_interact"] = features["age"] * features["tx_victoire"] + features["distance_cat"] = ( + 2.0 + if 1500 < features["distance"] <= 2000 + else (3.0 if 2000 < features["distance"] <= 2500 else 1.0) + ) + features["is_favorite"] = 1 if features["cote_directe"] < 5 else 0 + + # Make prediction + try: + X = pd.DataFrame([features])[feature_cols] + X = X.fillna(0) + + prob_top1 = float(models["model_top1"].predict_proba(X)[0][1]) + prob_top3 = float(models["model_top3"].predict_proba(X)[0][1]) + + predictions.append( + { + "horse_name": horse["horse_name"], + "horse_number": horse["horse_number"], + "odds": float(horse["odds"]), + "prob_top1": round(prob_top1 * 100, 1), + "prob_top3": round(prob_top3 * 100, 1), + "ml_score": round((prob_top1 * 0.6 + prob_top3 * 0.4) * 100, 1), + "recommendation": "top1" + if prob_top1 > 0.15 + else ("top3" if prob_top3 > 0.35 else "pass"), + "is_value_bet": 1 + if (prob_top3 > 0.35 and float(horse.get("odds", 0)) > 10) + else 0, + "is_outlier": 1 + if ( + float(horse.get("odds", 0)) <= 5 + and (prob_top1 < 0.1 and prob_top3 < 0.25) + ) + else 0, + "num_reunion": horse.get("num_reunion"), + "num_course": horse.get("num_course"), + } + ) + except Exception as e: + predictions.append( + { + "horse_name": horse["horse_name"], + "horse_number": horse["horse_number"], + "odds": horse["odds"], + "error": str(e), + } + ) + + # Sort by ML score + predictions.sort(key=lambda x: x.get("ml_score", 0), reverse=True) + + # Add course info to predictions + for pred in predictions: + course_key = f"{pred.get('num_reunion')}_{pred.get('num_course')}" + if course_key in course_info: + cinfo = course_info[course_key] + pred["race_label"] = f"R{pred.get('num_reunion')}C{pred.get('num_course')}" + pred["race_name"] = cinfo.get("libelle", "") + pred["hippodrome"] = cinfo.get("libelle_court", "") + pred["discipline"] = cinfo.get("discipline", "") + pred["distance"] = cinfo.get("distance", 0) + pred["heure"] = cinfo.get("heure_depart_str", "") + + # --- CALCUL RISQUE PAR COURSE + INJECTION DANS PREDICTIONS --- + from collections import defaultdict as _dd + _race_horses_ml = _dd(list) + for p in predictions: + key = (p.get("num_reunion"), p.get("num_course")) + _race_horses_ml[key].append({ + "odds": p.get("odds", 999), + "ml_score": p.get("ml_score", 0), + "prob_top1": p.get("prob_top1", 0), + "prob_top3": p.get("prob_top3", 0), + }) + _race_risque_map = {} + for key, partants in _race_horses_ml.items(): + label, score = calculate_risque(partants) + _race_risque_map[key] = (label or "neutral", score or 50) + for p in predictions: + rkey = (p.get("num_reunion"), p.get("num_course")) + rl, rs = _race_risque_map.get(rkey, ("neutral", 50)) + p["risque_label"] = rl + p["risque_score"] = rs + + # --- SAUVEGARDE CACHE --- + try: + save_ml_to_cache(conn, today, predictions) + except Exception as e_cache: + pass # cache non bloquant + + conn.close() + + return jsonify( + { + "date": date_used, + "model_version": "xgboost_v1", + "predictions": predictions, + "courses": course_info, + "from_cache": False, + } + ) + + +@app.route("/api/ml_predictions/refresh") +@app.route("/turf/api/ml_predictions/refresh") +def api_ml_predictions_refresh(): + """Force le recalcul des prédictions ML et met à jour le cache""" + conn = get_db() + today = datetime.now().strftime("%Y-%m-%d") + ensure_ml_cache_table(conn) + conn.execute("DELETE FROM ml_predictions_cache WHERE date = ?", (today,)) + conn.commit() + conn.close() + # Déléguer au endpoint principal avec force_refresh + from flask import redirect, url_for + return redirect(url_for("api_ml_predictions") + "?refresh=1") + + +@app.route("/api/scoring") +@app.route("/turf/api/scoring") +def api_scoring(): + """Get scoring data for dashboard - today only, filtered by race if provided""" + race = request.args.get("race", "") + today = datetime.now().strftime("%Y-%m-%d") + conn = get_db() + query = """ + SELECT date, race_name, horse_name, horse_number, score, + score_cote, score_forme, score_victoire, score_place, + cote, forme_recente, tx_victoire, tx_place, + rang_scoring, avis_entraineur, musique + FROM scoring + WHERE date = ? + """ + params = [today] + if race: + query += " AND race_name LIKE ?" + params.append(f"%{race}%") + query += " ORDER BY rang_scoring ASC" + c = conn.execute(query, params) + scores = [dict(row) for row in c.fetchall()] + conn.close() + return jsonify({"scores": scores, "recommendations": {}}) + + +# === RAPPORTS AUTOMATISÉS === +try: + from analytics_reports import ( + get_daily_report, + get_weekly_report, + get_monthly_report, + ) + + HAS_ANALYTICS = True +except ImportError: + HAS_ANALYTICS = False + + +@app.route("/turf/api/report/daily") +def api_report_daily(): + """Rapport quotidien""" + if not HAS_ANALYTICS: + return jsonify({"error": "analytics module not available"}), 500 + date = request.args.get("date") + return jsonify(get_daily_report(date)) + + +@app.route("/turf/api/report/weekly") +def api_report_weekly(): + """Rapport hebdomadaire""" + if not HAS_ANALYTICS: + return jsonify({"error": "analytics module not available"}), 500 + start_date = request.args.get("start") + end_date = request.args.get("end") + return jsonify(get_weekly_report(start_date, end_date)) + + +@app.route("/turf/api/report/monthly") +def api_report_monthly(): + """Rapport mensuel""" + if not HAS_ANALYTICS: + return jsonify({"error": "analytics module not available"}), 500 + year = request.args.get("year", type=int) + month = request.args.get("month", type=int) + return jsonify(get_monthly_report(year, month)) + + +@app.route("/turf/api/suggestions") +def api_suggestions(): + """Suggestions de questions""" + conn = get_db() + c = conn.cursor() + + suggestions = [] + try: + c.execute( + "SELECT COUNT(*) as cnt FROM pmu_partants WHERE date_programme >= date('now', '-7 days')" + ) + recent = c.fetchone()[0] + + if recent > 0: + suggestions = [ + "Quel est mon taux de réussite cette semaine?", + "Liste les 5 meilleurs jockeys", + "Quel est le ROI du mois?", + "Résultats d'hier", + "Programme du jour", + ] + else: + suggestions = [ + "Derniers gagnants", + "Meilleurs entraîneurs", + "Performances à Vincennes", + "Évolution des cotes", + ] + except: + suggestions = [ + "Quel est le taux de réussite des favoris?", + "Liste les meilleurs jockeys", + "Résultats d'hier", + ] + finally: + conn.close() + + return jsonify({"suggestions": suggestions}) + + + + +@app.route("/turf/api/metrics/summary") +@app.route("/turf/api/metrics/summary/") +def metrics_summary(): + days = min(max(request.args.get("days", 30, type=int), 1), 365) + try: + conn = get_db() + date_filter = f"-{int(days)} days" + cur = conn.execute( + "SELECT source, COUNT(*) as nb_courses, SUM(nb_predictions) as total_predictions, " + "SUM(nb_gagnants) as total_gagnants, SUM(nb_places) as total_places, SUM(nb_top5) as total_top5, " + "ROUND(AVG(taux_gagnant), 2) as moy_taux_gagnant, ROUND(AVG(taux_place), 2) as moy_taux_place, " + "ROUND(AVG(taux_top5), 2) as moy_taux_top5, ROUND(SUM(roi_sg_net), 3) as roi_sg_cumul, " + "ROUND(SUM(roi_sp_net), 3) as roi_sp_cumul, ROUND(AVG(ecart_rang_moyen), 2) as moy_ecart_rang, " + "SUM(quinte_5sur5) as nb_5sur5, SUM(quinte_4sur5) as nb_4sur5, SUM(quinte_3sur5) as nb_3sur5 " + "FROM prediction_metrics WHERE date >= date('now', ?) GROUP BY source ORDER BY moy_taux_place DESC", + (date_filter,)) + cols = [d[0] for d in cur.description] + rows = [dict(zip(cols, row)) for row in cur.fetchall()] + conn.close() + return jsonify({"summary": rows}) + except Exception as e: + return jsonify({"error": True, "message": str(e)}) + +@app.route("/turf/api/metrics/daily") +@app.route("/turf/api/metrics/daily/") +def metrics_daily(): + days = min(max(request.args.get("days", 30, type=int), 1), 365) + try: + conn = get_db() + date_filter = f"-{int(days)} days" + cur = conn.execute( + "SELECT date, source, SUM(nb_predictions) as predictions, SUM(nb_gagnants) as gagnants, " + "SUM(nb_places) as places, SUM(nb_top5) as top5, ROUND(AVG(taux_gagnant), 2) as taux_gagnant, " + "ROUND(AVG(taux_place), 2) as taux_place, ROUND(AVG(roi_sg_net), 3) as roi_sg, " + "ROUND(AVG(roi_sp_net), 3) as roi_sp, SUM(quinte_5sur5) as quinte_5sur5, " + "SUM(quinte_4sur5) as quinte_4sur5 " + "FROM prediction_metrics WHERE date >= date('now', ?) GROUP BY date, source ORDER BY date DESC", + (date_filter,)) + cols = [d[0] for d in cur.description] + rows = [dict(zip(cols, row)) for row in cur.fetchall()] + conn.close() + return jsonify({"daily": rows}) + except Exception as e: + return jsonify({"error": True, "message": str(e)}) + +if __name__ == "__main__": + load_models() + app.run(host="0.0.0.0", port=8791, debug=False) diff --git a/datagouv_explorer.html b/datagouv_explorer.html new file mode 100644 index 0000000..f467cf5 --- /dev/null +++ b/datagouv_explorer.html @@ -0,0 +1,915 @@ + + + + + + Data.gouv.fr Explorer Pro + + + + +🏠Accueil +

📊 Data.gouv.fr Explorer Pro

Explorez, analysez et exploitez les données ouvertes françaises

+ + + +

+ +

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...

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+ + +

+ +

+ + + + +

+ +

+ 📂 Résultats (0) +

+ +

+ + +

+ +

+ + +

+ 🔄 Réutilisations +

+ + +

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+ + +

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📍 Recherche géographique

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+ + +

📈 Datasets par mois

📊 Top 10 Organisations

📁 Formats des ressources

+ + +

📜 Recherches récentes

+ +

⭐ Favoris

+ +

+ + +

🛠️ Console API

+ +

+ URL: +

Prêt...

+ + +

+ ← Retour aux résultats +

+ + + + diff --git a/db.py b/db.py new file mode 100644 index 0000000..4f4719b --- /dev/null +++ b/db.py @@ -0,0 +1,139 @@ +#!/usr/bin/env python3 +"""Module PostgreSQL pour la persistance des conversations Agent IA.""" +import psycopg2 +import psycopg2.extras +from contextlib import contextmanager + +PG_HOST = "10.0.3.3" +PG_PORT = 5432 +PG_DB = "n8n" +PG_USER = "fpNKWWEZnfaVjvWS" +PG_PASS = "MiGvyqnsKWUD7SzKgOoSnUAedUqX3US6" + + +def _connect(): + return psycopg2.connect(host=PG_HOST, port=PG_PORT, dbname=PG_DB, user=PG_USER, password=PG_PASS) + + +@contextmanager +def get_db(): + conn = _connect() + try: + yield conn + conn.commit() + except Exception: + conn.rollback() + raise + finally: + conn.close() + + +def get_workflows(): + with get_db() as conn: + with conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor) as cur: + cur.execute("SELECT id, slug, name, webhook_url, description, is_active, mode FROM agent_chat_workflows WHERE is_active = true ORDER BY id;") + return cur.fetchall() + + +def get_sessions(workflow_slug): + with get_db() as conn: + with conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor) as cur: + cur.execute(""" + SELECT s.id, s.session_id, s.title, s.created_at, s.updated_at + FROM agent_chat_sessions s + JOIN agent_chat_workflows w ON s.workflow_id = w.id + WHERE w.slug = %s + ORDER BY s.updated_at DESC; + """, (workflow_slug,)) + return cur.fetchall() + + +def get_messages(session_id, workflow_slug): + with get_db() as conn: + with conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor) as cur: + cur.execute(""" + SELECT m.id, m.role, m.content, m.created_at + FROM agent_chat_messages m + JOIN agent_chat_workflows w ON m.workflow_id = w.id + WHERE m.session_id = %s AND w.slug = %s + ORDER BY m.created_at ASC; + """, (session_id, workflow_slug)) + return cur.fetchall() + + +def create_session(session_id, workflow_id, title=None): + with get_db() as conn: + with conn.cursor() as cur: + cur.execute(""" + INSERT INTO agent_chat_sessions (session_id, workflow_id, title) + VALUES (%s, %s, %s) + ON CONFLICT (session_id, workflow_id) DO NOTHING; + """, (session_id, workflow_id, title)) + + +def save_message(session_id, workflow_id, role, content): + with get_db() as conn: + with conn.cursor() as cur: + cur.execute(""" + INSERT INTO agent_chat_messages (session_id, workflow_id, role, content) + VALUES (%s, %s, %s, %s) + RETURNING id; + """, (session_id, workflow_id, role, content)) + msg_id = cur.fetchone()[0] + cur.execute(""" + UPDATE agent_chat_sessions SET updated_at = NOW() + WHERE session_id = %s AND workflow_id = %s; + """, (session_id, workflow_id)) + return msg_id + + +def delete_session(session_id, workflow_slug): + with get_db() as conn: + with conn.cursor() as cur: + cur.execute(""" + DELETE FROM agent_chat_sessions + WHERE session_id = %s AND workflow_id = (SELECT id FROM agent_chat_workflows WHERE slug = %s); + """, (session_id, workflow_slug)) + return cur.rowcount + + +def rename_session(session_id, workflow_slug, new_title): + with get_db() as conn: + with conn.cursor() as cur: + cur.execute(""" + UPDATE agent_chat_sessions + SET title = %s, updated_at = NOW() + WHERE session_id = %s AND workflow_id = (SELECT id FROM agent_chat_workflows WHERE slug = %s); + """, (new_title, session_id, workflow_slug)) + return cur.rowcount + + +def delete_messages_before(days, workflow_slug): + with get_db() as conn: + with conn.cursor() as cur: + cur.execute(""" + DELETE FROM agent_chat_messages + WHERE workflow_id = (SELECT id FROM agent_chat_workflows WHERE slug = %s) + AND created_at < NOW() - INTERVAL '%s days'; + """, (workflow_slug, days)) + deleted = cur.rowcount + cur.execute(""" + DELETE FROM agent_chat_sessions s + WHERE s.workflow_id = (SELECT id FROM agent_chat_workflows WHERE slug = %s) + AND NOT EXISTS (SELECT 1 FROM agent_chat_messages m WHERE m.session_id = s.session_id AND m.workflow_id = s.workflow_id); + """, (workflow_slug,)) + return deleted + + +def search_messages(query, workflow_slug, limit=20): + with get_db() as conn: + with conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor) as cur: + cur.execute(""" + SELECT DISTINCT m.session_id, m.content, m.role, m.created_at + FROM agent_chat_messages m + WHERE m.workflow_id = (SELECT id FROM agent_chat_workflows WHERE slug = %s) + AND m.content ILIKE %s + ORDER BY m.created_at DESC + LIMIT %s; + """, (workflow_slug, f'%{query}%', limit)) + return cur.fetchall() diff --git a/favicon.ico b/favicon.ico new file mode 100644 index 0000000..8811151 --- /dev/null +++ b/favicon.ico @@ -0,0 +1,6 @@ + +301 Moved +

301 Moved

+The document has moved +here. + diff --git a/fetch_results.py b/fetch_results.py new file mode 100644 index 0000000..fb03b22 --- /dev/null +++ b/fetch_results.py @@ -0,0 +1,99 @@ +#!/usr/bin/env python3 +""" +Récupération des résultats des courses 1h après la course +""" +import sqlite3 +import subprocess +import schedule +import time +import logging +from datetime import datetime, timedelta + +DB_PATH = "/home/h3r7/turf_scraper/turf.db" +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + +def get_db(): + conn = sqlite3.connect(DB_PATH) + conn.row_factory = sqlite3.Row + return conn + +def should_fetch_results(): + """Vérifie s'il est temps de récupérer les résultats (1h après la dernière course)""" + conn = get_db() + today = datetime.now().strftime('%Y-%m-%d') + + c = conn.execute(""" + SELECT DISTINCT race_time + FROM predictions + WHERE date=? AND source='canalturf_partants' + ORDER BY race_time DESC + LIMIT 1 + """, (today,)) + last_race = c.fetchone() + conn.close() + + if not last_race or not last_race['race_time']: + return True + + try: + race_hour = int(last_race['race_time'].split(':')[0]) + race_min = int(last_race['race_time'].split(':')[1]) + now = datetime.now() + + race_datetime = now.replace(hour=race_hour, minute=race_min, second=0) + + if (now - race_datetime).total_seconds() < 3600: + return False + return True + except: + return True + +def fetch_and_save_results(): + """Récupère les résultats via pmu_results.py""" + logger.info("=== Récupération des résultats PMU ===") + + if not should_fetch_results(): + logger.info("Pas encore 1h après les courses") + return + + try: + result = subprocess.run( + ['python3', '/home/h3r7/turf_scraper/pmu_results.py'], + capture_output=True, + text=True, + timeout=60, + cwd='/home/h3r7/turf_scraper' + ) + + if result.returncode == 0: + logger.info("✅ Résultats récupérés avec succès") + else: + logger.error(f"❌ Erreur: {result.stderr}") + + except Exception as e: + logger.error(f"❌ Exception: {e}") + + logger.info("=== Fin récupération résultats ===") + +def run_result_fetch(): + """Point d'entrée pour le scheduler""" + fetch_and_save_results() + +def start_scheduler(): + """Démarre le scheduler pour récupération automatique""" + logger.info("Démarrage scheduler résultats...") + + schedule.every().hour.do(run_result_fetch) + + while True: + schedule.run_pending() + time.sleep(60) + +if __name__ == '__main__': + import sys + + if len(sys.argv) > 1 and sys.argv[1] == '--once': + fetch_and_save_results() + else: + start_scheduler() diff --git a/gemini_agent.html b/gemini_agent.html new file mode 100644 index 0000000..b2b79db --- /dev/null +++ b/gemini_agent.html @@ -0,0 +1,134 @@ + + + + + + Agent IA - Gemini + + + +🏠Accueil +

Agent IA - Google Gemini

Propulsé par n8n

+ +

+ + +

+ + + + diff --git a/guide_gitea.html b/guide_gitea.html new file mode 100755 index 0000000..9fd1f2e --- /dev/null +++ b/guide_gitea.html @@ -0,0 +1,291 @@ + + + + + + Guide Git & Gitea - H3R7Tech + + + +🏠Accueil + +

+ Init + Workflow + Commandes + Règles + Résolution +

+ +

🐙 Guide Git & Gitea - H3R7Tech

+ +

Ce guide détaille les bonnes pratiques pour utiliser Git avec Gitea sur le VPS H3R7Tech.

+ + +

⚙️ 1. Configuration Initiale

+ +

Configurer Git

git config --global user.name "H3R7"
+git config --global user.email "h3r7@tech.local"
+git config --global init.defaultBranch master
+git config --global pull.rebase false

+ +

Cloner un Repo

# Via HTTPS (recommandé pour automate)
+git clone /gitea/admin/h3r7tech.git
+
+# Via SSH (pour developement)
+git clone git@178.18.250.53:admin/h3r7tech.git

+ + +

🔄 2. Workflow Standard

+ +

+ 💡 Règle d'or: Toujours travailler sur la branche dev pour les développements, puis fusionner vers master pour la production. +

+ +

Schéma du Workflow

master (production)
+   ↑
+   └── dev (développement)
+         ↑
+         └── feature/xyz (nouvelle功能)
+

+ +

Créer une Feature

# 1. Se placer sur dev
+git checkout dev
+
+# 2. Mettre à jour dev
+git pull origin dev
+
+# 3. Créer une branche feature
+git checkout -b feature/nom-feature
+
+# 4. Travailler sur la功能...
+git add .
+git commit -m "Description claire du changement"
+
+# 5. Pousser sur Gitea
+git push origin feature/nom-feature

+ + +

⌨️ 3. Commandes Essentielles

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Action	Commande
Statut actuel	`git status`
Voir les changements	`git diff`
Ajouter fichiers	`git add .`
Commit avec message	`git commit -m "message"`
Voir historique	`git log --oneline -10`
Basculer branche	`git checkout nom-branche`
Fusionner dev → master	`git checkout master && git merge dev`
Annuler dernier commit	`git reset --soft HEAD~1`

+ + +

📋 4. Règles de Bon Usage

+ +

Messages de Commit

✓ git commit -m "Ajout filtre catégorie CRM"
✓ git commit -m "Fix bug render() surCRM"
✗ git commit -m "modif"
✗ git commit -m "fix"

+ +

Fréquence de Push

+ Push fréquent: Au moins 1x/jour ou à chaque功能 complète. +

+ +

Protection Branches

+ + + + + + + + + + + + + + + + + +

Branche	Règle
`master`	lecture seule, push via merge depuis dev uniquement
`dev`	push autorisé après tests locaux
`feature/*`	push fréquent, supprimée après merge

+ + +

🔧 5. Résolution des Conflits

+ +

+ ⚠️ Avant un merge: Toujours pull la dernière version! +

+ +

En cas de conflit

# 1. Mettre à jour dev
+git checkout dev
+git pull origin dev
+
+# 2. Fusionner dans votre branche
+git checkout feature/ma-branche
+git merge dev
+
+# 3. Résoudre les conflits manuellement
+# (éditer les fichiers en conflit)
+
+# 4. Ajouter les fichiers résolus
+git add .
+git commit -m "Résolution conflits merge dev"
+git push origin feature/ma-branche

+ +

Méthode Alternative: Rebase

#Instead de merge, utiliser rebase pour historique propre
+git checkout feature/ma-branche
+git rebase dev

+ + +

📝 6. Exemple Pratique

+ +

Modifier le CRM sur le VPS

# Connexion SSH
+ssh -i /path/to/key h3r7@178.18.250.53
+
+# Aller dans le dossier projet
+cd /home/h3r7
+
+# Bas/turf_scraperculer sur dev
+git checkout dev
+
+# Mettre à jour
+git pull origin dev
+
+# Créer branche pour la功能
+git checkout -b fix/crm-filtre
+
+# ... Faire les modifications ...
+
+# Commit
+git add crm_dashboard.html
+git commit -m "Corrige filtre catégorie CRM"
+
+# Pousser
+git push origin fix/crm-filtre

+ +

Créer une Pull Request

Aller sur Gitea
Cliquer sur "Pull Requests" → "New Pull Request"
Sélectionner: fix/crm-filtre → dev
Décrire les changements
Valider le merge après review

+ + +

🛠️ 7. Accès Rapides

+ + + + + + + + + + + + + + + + + + + + + + + + + + +

Service	URL
Gitea	/gitea/
Repo H3R7Tech	/gitea/admin/h3r7tech
CRM Prod	/crm/
CRM Dev	/crm/
Dashboard Turf	/turf/

+ +

🐾 Version 1.0

+ + + diff --git a/h3r7tech_business.html b/h3r7tech_business.html new file mode 100755 index 0000000..b295571 --- /dev/null +++ b/h3r7tech_business.html @@ -0,0 +1,269 @@ + + + + + + 💼 H3R7Tech - Services Digitaux + + + + +🏠Accueil + + +

+ +

💼 Nos Services

🖨️ Print On Demand

Création de produits personnalisés (t-shirts, mugs, stickers)

15-40% marge

🏪 Site Vitrine Artisan

Site web 1 page pour artisans et petits commerces

199€ one-shot

📊 CRM sur Mesure

Gestion prospects, suivi clients, pipeline

49-99€/mois

🏇 Turf Predictions

Prédictions IA pour les courses hippiques

9.90-99€/mois

+ + +

🎯 Niche #1: Print On Demand

🛍️ Comment ça marche

Tu crées un design
Client commande sur ta boutique
Printful imprime et expédie
Tu touches la marge

+ T-shirts + Mugs + Stickers + Posters +

+ + +

🎯 Niche #2: Services aux Artisans

🔧 Le problème

80% des artisans n'ont pas de site web et perdent des clients.

La solution

Site web vitrine (199€)
Gestion Google Business (49€/mois)
Photos produits (99€)
Formation réseaux sociaux (149€)

+ + +

🎯 Niche #3: Turf Betting Service

🏇 Monetisation

+ Gratuit +

1 prédiction/jour (email)

+ Essentiel 9.90€/mois +

3 prédictions + analyse

+ Premium 29.90€/mois +

5 prédictions + money management

+ VIP 99€/mois +

Picks exclusifs + suivi

+ + +

📈 Objectifs 2026

100€

Mars

500€

Mai

1000€

Juillet

2500€

Novembre

+ + +

🚀 Lançons-nous!

Tu veux commander un service ou discuter d'un projet?

+ Me contacter +

+ + diff --git a/historical_loader.py b/historical_loader.py new file mode 100755 index 0000000..3be100e --- /dev/null +++ b/historical_loader.py @@ -0,0 +1,501 @@ +#!/usr/bin/env python3 +""" +Historical Loader - Récupère 1 an d'historique Quinté+ via API PMU +Sauvegarde toutes les features + résultats en BDD pour entraîner le modèle ML +""" + +import requests +import sqlite3 +import json +import time +import os +import re +from datetime import datetime, timedelta + +DB_PATH = os.environ.get("DB_PATH", "/home/h3r7/turf_scraper/turf.db") +HEADERS = {'User-Agent': 'Mozilla/5.0', 'Accept': 'application/json'} +BASE_URL = "https://turfinfo.api.pmu.fr/rest/client/1/programme" + +# ============================================================ +# BASE DE DONNÉES +# ============================================================ + +def init_historical_table(): + conn = sqlite3.connect(DB_PATH) + c = conn.cursor() + c.execute(''' + CREATE TABLE IF NOT EXISTS historical_data ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + + -- Identifiants course + date TEXT NOT NULL, + race_name TEXT, + hippodrome TEXT, + distance INTEGER, + discipline TEXT, + allocation REAL, + nb_partants INTEGER, + heure TEXT, + + -- Identifiants cheval + horse_name TEXT NOT NULL, + horse_number INTEGER, + driver TEXT, + driver_change INTEGER DEFAULT 0, + + -- Features cheval + age INTEGER, + sexe TEXT, + musique TEXT, + nb_courses INTEGER, + nb_victoires INTEGER, + nb_places INTEGER, + nb_places_2 INTEGER, + nb_places_3 INTEGER, + gains_carriere REAL, + gains_annee REAL, + gains_victoires REAL, + reduction_km INTEGER, + avis_entraineur TEXT, + oeilleres TEXT, + deferre TEXT, + + -- Features marché + cote_directe REAL, + cote_reference REAL, + indicateur_tendance REAL, + est_favori INTEGER DEFAULT 0, + + -- Features dérivées (calculées) + tx_victoire REAL, + tx_place REAL, + forme_recente REAL, + tendance_forme REAL, + nb_disq INTEGER, + rang_cote INTEGER, + ratio_cote_field REAL, + + -- Résultat officiel (variable cible) + ordre_arrivee INTEGER, + temps_obtenu INTEGER, + top1 INTEGER DEFAULT 0, + top3 INTEGER DEFAULT 0, + top5 INTEGER DEFAULT 0, + + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + + UNIQUE(date, horse_name, horse_number) + ) + ''') + + # Index pour accélérer les requêtes ML + c.execute('CREATE INDEX IF NOT EXISTS idx_hist_date ON historical_data(date)') + c.execute('CREATE INDEX IF NOT EXISTS idx_hist_top5 ON historical_data(top5)') + c.execute('CREATE INDEX IF NOT EXISTS idx_hist_horse ON historical_data(horse_name)') + + conn.commit() + conn.close() + print(f"✅ Table historical_data initialisée : {DB_PATH}") + + +# ============================================================ +# DÉCODEUR MUSIQUE +# ============================================================ + +def parse_musique(musique): + if not musique: + return {'forme_recente': 99, 'tendance': 0, 'nb_disq': 0} + + clean = re.sub(r'$\d+$', '', musique) + resultats = re.findall(r'(\d+|D|0)([amphsc]?)', clean) + + positions = [] + for pos, disc in resultats[:10]: + if pos == 'D': + positions.append(99) + else: + positions.append(int(pos)) + + if not positions: + return {'forme_recente': 99, 'tendance': 0, 'nb_disq': 0} + + nb_disq = positions.count(99) + positions_clean = [p for p in positions if p != 99] + + recentes = positions_clean[:3] + forme_recente = sum(recentes) / len(recentes) if recentes else 99 + + if len(positions_clean) >= 4: + debut = sum(positions_clean[-4:]) / 4 + fin = sum(positions_clean[:4]) / 4 + tendance = round(debut - fin, 2) + else: + tendance = 0 + + return { + 'forme_recente': round(forme_recente, 2), + 'tendance': tendance, + 'nb_disq': nb_disq + } + + +# ============================================================ +# API PMU +# ============================================================ + +def get_programme(date_pmu): + url = f"{BASE_URL}/{date_pmu}/reunions" + r = requests.get(url, headers=HEADERS, timeout=15) + if r.status_code != 200: + return None + return r.json().get('programme', {}).get('reunions', []) + + +def find_quinte(reunions): + for reunion in reunions: + for course in reunion.get('courses', []): + paris = [p['typePari'] for p in course.get('paris', [])] + libelle = course.get('libelle', '') + if any('QUINTE' in p for p in paris) or 'PARIS-TURF' in libelle: + heure_ts = course.get('heureDepart', 0) + heure = datetime.fromtimestamp(heure_ts/1000).strftime('%H:%M') if heure_ts else '13:55' + return { + 'num_reunion': reunion['numOfficiel'], + 'num_course': course['numOrdre'], + 'libelle': libelle, + 'hippodrome': reunion['hippodrome']['libelleCourt'], + 'distance': course.get('distance', 0), + 'discipline': course.get('discipline', ''), + 'allocation': course.get('montantPrix', 0), + 'nb_partants': course.get('nombreDeclaresPartants', 0), + 'heure': heure, + 'arrivee_def': course.get('arriveeDefinitive', False), + } + return None + + +def get_participants(date_pmu, num_r, num_c): + url = f"{BASE_URL}/{date_pmu}/R{num_r}/C{num_c}/participants" + r = requests.get(url, headers=HEADERS, timeout=15) + if r.status_code != 200: + return [] + return r.json().get('participants', []) + + +# ============================================================ +# EXTRACTION FEATURES +# ============================================================ + +def extract_features(p, course_info, all_participants): + """Extrait toutes les features d'un partant + résultat""" + + musique_stats = parse_musique(p.get('musique', '')) + + # Cote directe + rapport_direct = p.get('dernierRapportDirect', {}) or {} + cote_directe = rapport_direct.get('rapport', 0) or 0 + est_favori = 1 if rapport_direct.get('favoris', False) else 0 + indicateur = rapport_direct.get('nombreIndicateurTendance', 0) or 0 + + # Cote référence + rapport_ref = p.get('dernierRapportReference', {}) or {} + cote_reference = rapport_ref.get('rapport', 0) or 0 + + # Stats carrière + nb_courses = p.get('nombreCourses', 0) or 0 + nb_victoires = p.get('nombreVictoires', 0) or 0 + nb_places = p.get('nombrePlaces', 0) or 0 + nb_p2 = p.get('nombrePlacesSecond', 0) or 0 + nb_p3 = p.get('nombrePlacesTroisieme', 0) or 0 + + tx_vic = round(nb_victoires / nb_courses * 100, 2) if nb_courses else 0 + tx_place = round(nb_places / nb_courses * 100, 2) if nb_courses else 0 + + # Gains + gains = p.get('gainsParticipant', {}) or {} + gains_carriere = gains.get('gainsCarriere', 0) or 0 + gains_annee = gains.get('gainsAnneeEnCours', 0) or 0 + gains_victoires= gains.get('gainsVictoires', 0) or 0 + + # Rang cote dans le field + all_cotes = sorted([ + (x.get('dernierRapportDirect', {}) or {}).get('rapport', 999) or 999 + for x in all_participants + ]) + rang_cote = all_cotes.index(cote_directe) + 1 if cote_directe in all_cotes else 99 + + # Ratio cote / moyenne field + cotes_valides = [c for c in all_cotes if c < 900] + moy_cote = sum(cotes_valides) / len(cotes_valides) if cotes_valides else 1 + ratio_cote = round(cote_directe / moy_cote, 3) if moy_cote else 0 + + # Résultat + ordre = p.get('ordreArrivee', 0) or 0 + top1 = 1 if ordre == 1 else 0 + top3 = 1 if 1 <= ordre <= 3 else 0 + top5 = 1 if 1 <= ordre <= 5 else 0 + + return { + # Course + 'date': None, # rempli par l'appelant + 'race_name': course_info['libelle'], + 'hippodrome': course_info['hippodrome'], + 'distance': course_info['distance'], + 'discipline': course_info['discipline'], + 'allocation': course_info['allocation'], + 'nb_partants': course_info['nb_partants'], + 'heure': course_info['heure'], + + # Cheval + 'horse_name': p.get('nom', ''), + 'horse_number': p.get('numPmu', 0), + 'driver': p.get('driver', ''), + 'driver_change': 1 if p.get('driverChange', False) else 0, + + # Features + 'age': p.get('age', 0), + 'sexe': p.get('sexe', ''), + 'musique': p.get('musique', ''), + 'nb_courses': nb_courses, + 'nb_victoires': nb_victoires, + 'nb_places': nb_places, + 'nb_places_2': nb_p2, + 'nb_places_3': nb_p3, + 'gains_carriere': gains_carriere, + 'gains_annee': gains_annee, + 'gains_victoires': gains_victoires, + 'reduction_km': p.get('reductionKilometrique', 0), + 'avis_entraineur': p.get('avisEntraineur', 'NEUTRE'), + 'oeilleres': p.get('oeilleres', ''), + 'deferre': p.get('deferre', ''), + + # Marché + 'cote_directe': cote_directe, + 'cote_reference': cote_reference, + 'indicateur_tendance': indicateur, + 'est_favori': est_favori, + + # Dérivées + 'tx_victoire': tx_vic, + 'tx_place': tx_place, + 'forme_recente': musique_stats['forme_recente'], + 'tendance_forme': musique_stats['tendance'], + 'nb_disq': musique_stats['nb_disq'], + 'rang_cote': rang_cote, + 'ratio_cote_field': ratio_cote, + + # Résultat + 'ordre_arrivee': ordre, + 'temps_obtenu': p.get('tempsObtenu', 0), + 'top1': top1, + 'top3': top3, + 'top5': top5, + } + + +# ============================================================ +# SAUVEGARDE +# ============================================================ + +def save_batch(rows): + if not rows: + return 0 + conn = sqlite3.connect(DB_PATH) + c = conn.cursor() + saved = 0 + for row in rows: + try: + c.execute(''' + INSERT OR IGNORE INTO historical_data + (date, race_name, hippodrome, distance, discipline, allocation, nb_partants, heure, + horse_name, horse_number, driver, driver_change, + age, sexe, musique, nb_courses, nb_victoires, nb_places, nb_places_2, nb_places_3, + gains_carriere, gains_annee, gains_victoires, reduction_km, avis_entraineur, + oeilleres, deferre, + cote_directe, cote_reference, indicateur_tendance, est_favori, + tx_victoire, tx_place, forme_recente, tendance_forme, nb_disq, + rang_cote, ratio_cote_field, + ordre_arrivee, temps_obtenu, top1, top3, top5) + VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?) + ''', ( + row['date'], row['race_name'], row['hippodrome'], row['distance'], + row['discipline'], row['allocation'], row['nb_partants'], row['heure'], + row['horse_name'], row['horse_number'], row['driver'], row['driver_change'], + row['age'], row['sexe'], row['musique'], row['nb_courses'], + row['nb_victoires'], row['nb_places'], row['nb_places_2'], row['nb_places_3'], + row['gains_carriere'], row['gains_annee'], row['gains_victoires'], + row['reduction_km'], row['avis_entraineur'], row['oeilleres'], row['deferre'], + row['cote_directe'], row['cote_reference'], row['indicateur_tendance'], row['est_favori'], + row['tx_victoire'], row['tx_place'], row['forme_recente'], + row['tendance_forme'], row['nb_disq'], row['rang_cote'], row['ratio_cote_field'], + row['ordre_arrivee'], row['temps_obtenu'], row['top1'], row['top3'], row['top5'] + )) + saved += c.rowcount + except Exception as e: + print(f" ⚠️ {row.get('horse_name','?')}: {e}") + conn.commit() + conn.close() + return saved + + +# ============================================================ +# MAIN +# ============================================================ + +def main(): + import argparse + parser = argparse.ArgumentParser() + parser.add_argument('--days', type=int, default=365, help='Nombre de jours à récupérer') + parser.add_argument('--start', type=str, default=None, help='Date de début YYYY-MM-DD') + parser.add_argument('--delay', type=float, default=0.5, help='Délai entre requêtes (s)') + args = parser.parse_args() + + print(f"\n{'='*60}") + print(f"📚 HISTORICAL LOADER — {datetime.now().strftime('%d/%m/%Y %H:%M')}") + print(f"{'='*60}") + + init_historical_table() + + # Vérifier ce qui est déjà en BDD + conn = sqlite3.connect(DB_PATH) + c = conn.cursor() + c.execute("SELECT COUNT(DISTINCT date) as jours, COUNT(*) as lignes FROM historical_data") + existing = c.fetchone() + c.execute("SELECT MIN(date), MAX(date) FROM historical_data") + date_range = c.fetchone() + conn.close() + + print(f"\n📊 Données existantes : {existing[0]} jours · {existing[1]} lignes") + if date_range[0]: + print(f" Période : {date_range[0]} → {date_range[1]}") + + # Calculer la plage de dates + if args.start: + start_date = datetime.strptime(args.start, '%Y-%m-%d') + else: + start_date = datetime.now() - timedelta(days=args.days) + + end_date = datetime.now() - timedelta(days=1) # Hier (résultats definitifs) + + total_days = (end_date - start_date).days + 1 + print(f"\n📅 Période à charger : {start_date.strftime('%d/%m/%Y')} → {end_date.strftime('%d/%m/%Y')} ({total_days} jours)") + print(f"⏱️ Délai entre requêtes : {args.delay}s") + print(f"⏳ Durée estimée : ~{round(total_days * args.delay * 2 / 60, 1)} minutes\n") + + # Stats + total_courses = 0 + total_partants = 0 + total_saved = 0 + errors = 0 + skipped = 0 + + current = start_date + day_num = 0 + + while current <= end_date: + day_num += 1 + date_str = current.strftime('%Y-%m-%d') + date_pmu = current.strftime('%d%m%Y') + + # Vérifier si déjà chargé + conn = sqlite3.connect(DB_PATH) + c = conn.cursor() + c.execute("SELECT COUNT(*) FROM historical_data WHERE date=?", (date_str,)) + already = c.fetchone()[0] + conn.close() + + if already > 0: + print(f" [{day_num:3}/{total_days}] {date_str} — déjà chargé ({already} partants) ⏭️") + skipped += 1 + current += timedelta(days=1) + continue + + try: + # Récupérer le programme + reunions = get_programme(date_pmu) + if not reunions: + print(f" [{day_num:3}/{total_days}] {date_str} — pas de programme") + current += timedelta(days=1) + time.sleep(args.delay) + continue + + # Trouver le Quinté+ + quinte = find_quinte(reunions) + if not quinte: + print(f" [{day_num:3}/{total_days}] {date_str} — pas de Quinté+") + current += timedelta(days=1) + time.sleep(args.delay) + continue + + if not quinte['arrivee_def']: + print(f" [{day_num:3}/{total_days}] {date_str} — arrivée non définitive") + current += timedelta(days=1) + time.sleep(args.delay) + continue + + time.sleep(args.delay) + + # Récupérer les participants + participants = get_participants(date_pmu, quinte['num_reunion'], quinte['num_course']) + if not participants: + print(f" [{day_num:3}/{total_days}] {date_str} — pas de participants") + current += timedelta(days=1) + time.sleep(args.delay) + continue + + # Extraire les features + rows = [] + for p in participants: + if p.get('statut') not in ('PARTANT', None): + continue + features = extract_features(p, quinte, participants) + features['date'] = date_str + rows.append(features) + + # Sauvegarder + saved = save_batch(rows) + total_courses += 1 + total_partants += len(rows) + total_saved += saved + + winner = next((r['horse_name'] for r in rows if r['top1']), '?') + print(f" [{day_num:3}/{total_days}] {date_str} — {quinte['libelle'][:30]:<30} {len(rows)} partants · gagnant: {winner} ✅") + + except requests.exceptions.Timeout: + print(f" [{day_num:3}/{total_days}] {date_str} — timeout ⏱️") + errors += 1 + time.sleep(2) + except Exception as e: + print(f" [{day_num:3}/{total_days}] {date_str} — erreur: {e} ❌") + errors += 1 + time.sleep(1) + + current += timedelta(days=1) + time.sleep(args.delay) + + # Résumé final + print(f"\n{'='*60}") + print(f"✅ CHARGEMENT TERMINÉ") + print(f"{'='*60}") + print(f" Courses chargées : {total_courses}") + print(f" Partants total : {total_partants}") + print(f" Lignes sauvegardées : {total_saved}") + print(f" Déjà présents : {skipped} jours") + print(f" Erreurs : {errors}") + + # Statistiques BDD finale + conn = sqlite3.connect(DB_PATH) + c = conn.cursor() + c.execute("SELECT COUNT(DISTINCT date), COUNT(*), AVG(top5) FROM historical_data") + stats = c.fetchone() + conn.close() + + print(f"\n📊 BDD HISTORIQUE FINALE :") + print(f" Jours total : {stats[0]}") + print(f" Lignes total : {stats[1]}") + print(f" Taux top5 moy : {round((stats[2] or 0)*100, 1)}%") + print(f"{'='*60}\n") + + +if __name__ == "__main__": + main() diff --git a/horse_detail_enhanced.py b/horse_detail_enhanced.py new file mode 100755 index 0000000..eda1d90 --- /dev/null +++ b/horse_detail_enhanced.py @@ -0,0 +1,222 @@ +#!/usr/bin/env python3 +""" +Enhanced Horse Detail Scraper - With ALL factors +Ferrure, Oeillères, Jockey stats, Distance aptitude +""" +import requests +from bs4 import BeautifulSoup +import json +from datetime import datetime +import re +import sqlite3 + +HEADERS = { + 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36', + 'Accept-Language': 'fr-FR,fr;q=0.9,en;q=0.8', +} + +DB_PATH = "/home/h3r7/turf_scraper/turf.db" + +def init_horse_db(): + """Initialize horse detail table""" + conn = sqlite3.connect(DB_PATH) + c = conn.cursor() + + c.execute(''' + CREATE TABLE IF NOT EXISTS horses_details ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + date TEXT, + horse_name TEXT, + horse_id TEXT, + age INTEGER, + sex TEXT, + father TEXT, + mother TEXT, + trainer TEXT, + jockey TEXT, + last_odds REAL, + wins INTEGER, + placed INTEGER, + total_races INTEGER, + earnings REAL, + form_music TEXT, + ferrure TEXT, + oeilleres TEXT, + recent_form TEXT, + best_distance TEXT, + best_terrain TEXT, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP + ) + ''') + + conn.commit() + conn.close() + +def save_horse(horse_data): + """Save horse to database""" + conn = sqlite3.connect(DB_PATH) + c = conn.cursor() + + c.execute(''' + INSERT INTO horses_details ( + date, horse_name, horse_id, age, sex, father, mother, + trainer, jockey, last_odds, wins, placed, total_races, + earnings, form_music, ferrure, oeilleres, recent_form, + best_distance, best_terrain + ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) + ''', ( + datetime.now().strftime('%Y-%m-%d'), + horse_data.get('name'), + horse_data.get('id'), + horse_data.get('age'), + horse_data.get('sex'), + horse_data.get('father'), + horse_data.get('mother'), + horse_data.get('trainer'), + horse_data.get('jockey'), + horse_data.get('odds'), + horse_data.get('wins'), + horse_data.get('placed'), + horse_data.get('total_races'), + horse_data.get('earnings'), + horse_data.get('form_music'), + horse_data.get('ferrure'), + horse_data.get('oeilleres'), + horse_data.get('recent_form'), + horse_data.get('best_distance'), + horse_data.get('best_terrain') + )) + + conn.commit() + conn.close() + +def scrape_horse_detail(url): + """Scrape full horse details from Canalturf""" + try: + r = requests.get(url, headers=HEADERS, timeout=15) + soup = BeautifulSoup(r.text, 'html.parser') + + text = soup.get_text(separator=' | ', strip=True) + + data = { + 'url': url, + 'id': re.search(r'idcheval=(\d+)', url).group(1) if 'idcheval' in url else None, + 'source': 'canalturf' + } + + # Name + title = soup.find('title') + if title: + data['name'] = title.text.split('-')[0].strip() + + # Age/Sex + if 'Sexe/Age' in text: + match = re.search(r'Sexe/Age : ([MF]\d)', text) + if match: + sex_age = match.group(1) + data['sex'] = sex_age[0] + data['age'] = int(sex_age[1:]) + + # Father/Mother + if 'Père :' in text: + match = re.search(r'Père : ([^|]+)', text) + if match: data['father'] = match.group(1).strip() + + if 'Mère :' in text: + match = re.search(r'Mère : ([^|]+)', text) + if match: data['mother'] = match.group(1).strip() + + # Trainer + if 'Entraineur' in text: + match = re.search(r'Entraineur : ([^|]+)', text) + if match: data['trainer'] = match.group(1).strip() + + # Odds + if 'Cote' in text: + match = re.search(r'(\d+[\.,]\d+)', text) + if match: data['odds'] = float(match.group(1).replace(',', '.')) + + # Stats + if 'Victoire' in text: + match = re.search(r'Victoire$s$ : (\d+)', text) + if match: data['wins'] = int(match.group(1)) + + if 'Placé' in text: + match = re.search(r'Placé$s$ : (\d+)', text) + if match: data['placed'] = int(match.group(1)) + + if 'Course' in text: + match = re.search(r'Course$s$ : (\d+)', text) + if match: data['total_races'] = int(match.group(1)) + + # Earnings + if 'Gains' in text: + match = re.search(r'(\d+[\d\s]*)', text) + if match: data['earnings'] = match.group(1).replace(' ', '') + + # Form music + if 'Perf.' in text: + match = re.search(r'Perf\. : ([^|]+)', text) + if match: data['form_music'] = match.group(1).strip() + + # FERROUR - From zone-turf style data + # Look for indicators in text + ferrure_indicators = { + 'Da': 'Déferré Antérieur', + 'Dm': 'Déferré Membres', + 'Dp': 'Déferré Postérieur', + 'DD': 'Déferré des 4', + '': 'Ferré' + } + data['ferrure'] = 'Non détecté' + + # OEILLERES + oeillere_indicators = { + 'O': 'Oeillères', + 'Oa': 'Oeillères australiennes', + 'E': 'Élastiques' + } + data['oeilleres'] = 'Non détecté' + + # Recent form (last 5 races) + recent = [] + for link in soup.select('a[href*="/resultats-PMU/"]')[:5]: + txt = link.get_text(strip=True) + if txt: + recent.append(txt) + data['recent_form'] = ' | '.join(recent) if recent else '' + + # Best distance (inferred from performances) + # This would need historical analysis + data['best_distance'] = 'À analyser' + data['best_terrain'] = 'À analyser' + + return data + + except Exception as e: + return {'url': url, 'error': str(e)} + +# Test +if __name__ == "__main__": + init_horse_db() + + print("="*50) + print("HORSE DETAIL SCRAPER - ENHANCED") + print("="*50) + + # Test with PASSIONATA + url = "https://www.canalturf.com/courses_fiche_cheval.php?idcheval=516052" + horse = scrape_horse_detail(url) + + print(f"\nHorse: {horse.get('name')}") + print(f" Age/Sex: {horse.get('sex')}{horse.get('age')}") + print(f" Trainer: {horse.get('trainer')}") + print(f" Odds: {horse.get('odds')}") + print(f" Wins: {horse.get('wins')}, Placed: {horse.get('placed')}, Races: {horse.get('total_races')}") + print(f" Form: {horse.get('form_music')}") + print(f" Ferrure: {horse.get('ferrure')}") + print(f" Oeillères: {horse.get('oeilleres')}") + + # Save to DB + save_horse(horse) + print(f"\n✅ Saved to database!") diff --git a/horse_detail_scraper.py b/horse_detail_scraper.py new file mode 100755 index 0000000..8401b3a --- /dev/null +++ b/horse_detail_scraper.py @@ -0,0 +1,172 @@ +#!/usr/bin/env python3 +""" +Horse Detail Scraper - Get individual horse data for RUNTIME V4 +""" +import requests +from bs4 import BeautifulSoup +import json +from datetime import datetime +import re + +HEADERS = { + 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36', + 'Accept-Language': 'fr-FR,fr;q=0.9,en;q=0.8', +} + +def get_horse_id_from_url(url): + """Extract horse ID from Canalturf URL""" + match = re.search(r'idcheval=(\d+)', url) + if match: + return match.group(1) + return None + +def scrape_canalturf_horse(horse_url): + """Scrape horse details from Canalturf""" + try: + r = requests.get(horse_url, headers=HEADERS, timeout=15) + soup = BeautifulSoup(r.text, 'html.parser') + + data = { + 'url': horse_url, + 'source': 'canalturf', + 'scraped_at': datetime.now().isoformat(), + 'status': 'success' + } + + # Get title + title = soup.find('title') + if title: + data['horse_name'] = title.text.split('-')[0].strip() + + # Extract all text for parsing + text = soup.get_text(separator=' | ', strip=True) + + # PEDIGREE + if 'Père :' in text: + match = re.search(r'Père : ([^|]+)', text) + if match: data['father'] = match.group(1).strip() + + if 'Mère :' in text: + match = re.search(r'Mère : ([^|]+)', text) + if match: data['mother'] = match.group(1).strip() + + # AGE & SEX + if 'Sexe/Age' in text: + match = re.search(r'Sexe/Age : ([^|]+)', text) + if match: data['sex_age'] = match.group(1).strip() + + # ENTRAINEUR + if 'Entraineur' in text: + match = re.search(r'Entraineur : ([^|]+)', text) + if match: data['trainer'] = match.group(1).strip() + + # COTE + if 'Cote' in text: + match = re.search(r'Cote.*?(\d+[\.,]\d+)', text) + if match: data['cote'] = match.group(1).replace(',', '.') + + # DERNIERES PERFORMANCES + performances = [] + for link in soup.select('a[href*="/resultats-PMU/"]'): + perf_text = link.get_text(strip=True) + if perf_text: + performances.append(perf_text) + + if performances: + data['recent_performances'] = performances[:10] # Last 10 + + # STATS + if 'Victoire' in text: + match = re.search(r'Victoire$s$ : (\d+)', text) + if match: data['wins'] = int(match.group(1)) + + if 'Placé' in text: + match = re.search(r'Placé$s$ : (\d+)', text) + if match: data['placed'] = int(match.group(1)) + + if 'Course' in text: + match = re.search(r'Course$s$ : (\d+)', text) + if match: data['total_races'] = int(match.group(1)) + + # GAINS + if 'Gains :' in text: + match = re.search(r'Gains : (\d+[\d\s]*)', text) + if match: data['earnings'] = match.group(1).replace(' ', '').strip() + + # MUSIC (Form) + if 'Perf.' in text: + match = re.search(r'Perf\. : ([^|]+)', text) + if match: data['form_music'] = match.group(1).strip() + + return data + + except Exception as e: + return { + 'url': horse_url, + 'source': 'canalturf', + 'status': 'error', + 'error': str(e) + } + +def get_race_horses_urls(race_url): + """Get all horse URLs from a race page""" + try: + r = requests.get(race_url, headers=HEADERS, timeout=15) + soup = BeautifulSoup(r.text, 'html.parser') + + horse_urls = [] + for link in soup.select('a[href*="fiche_cheval"]'): + href = link.get('href', '') + if 'idcheval' in href: + # Fix double domain issue + href = href.replace('https://www.canalturf.comhttps://www.canalturf.com', 'https://www.canalturf.com') + if not href.startswith('http'): + href = 'https://www.canalturf.com' + href + horse_urls.append(href) + + return list(set(horse_urls)) + except Exception as e: + print(f"Error getting horse URLs: {e}") + return [] + +def main(): + print("="*50) + print("🐴 HORSE DETAIL SCRAPER - RUNTIME V4") + print("="*50) + + # Example: Get horses from Quinté page + quinte_url = "https://www.canalturf.com/courses_quinte.php" + + print(f"\n📋 Getting horses from: {quinte_url}") + horse_urls = get_race_horses_urls(quinte_url) + print(f" Found {len(horse_urls)} horses") + + # Scrape first 5 as demo + results = [] + for i, url in enumerate(horse_urls[:5]): + print(f"\n[{i+1}/{min(5, len(horse_urls))}] Scraping: {url[:60]}...") + data = scrape_canalturf_horse(url) + results.append(data) + if data['status'] == 'success': + print(f" ✅ {data.get('horse_name', 'Unknown')}") + else: + print(f" ❌ {data.get('error', 'Error')}") + + # Save + output = f"/home/h3r7/turf_scraper/horses_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" + with open(output, 'w', encoding='utf-8') as f: + json.dump({ + 'timestamp': datetime.now().isoformat(), + 'race_url': quinte_url, + 'total_horses': len(horse_urls), + 'horses': results + }, f, indent=2, ensure_ascii=False) + + print(f"\n{'='*50}") + print(f"✅ Saved to {output}") + print(f"{'='*50}\n") + + return results + +if __name__ == "__main__": + main() diff --git a/horse_detail_v2.py b/horse_detail_v2.py new file mode 100755 index 0000000..c23ad5a --- /dev/null +++ b/horse_detail_v2.py @@ -0,0 +1,195 @@ +#!/usr/bin/env python3 +""" +Enhanced Horse Detail Scraper - v2 +Ferrure, Oeillères, Jockey stats +""" +import requests +from bs4 import BeautifulSoup +import json +from datetime import datetime +import re +import sqlite3 + +HEADERS = { + 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36', +} + +DB_PATH = "/home/h3r7/turf_scraper/turf.db" + +def init_horse_db(): + """Initialize horse detail table""" + conn = sqlite3.connect(DB_PATH) + c = conn.cursor() + + c.execute(''' + CREATE TABLE IF NOT EXISTS horses_details ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + date TEXT, + horse_name TEXT, + horse_id TEXT, + age INTEGER, + sex TEXT, + trainer TEXT, + jockey TEXT, + last_odds REAL, + wins INTEGER, + placed INTEGER, + total_races INTEGER, + earnings REAL, + form_music TEXT, + ferrure TEXT, + oeilleres TEXT, + recent_form TEXT, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP + ) + ''') + + conn.commit() + conn.close() + +def save_horse(horse_data): + """Save horse to database""" + conn = sqlite3.connect(DB_PATH) + c = conn.cursor() + + c.execute(''' + INSERT INTO horses_details ( + date, horse_name, horse_id, age, sex, trainer, jockey, + last_odds, wins, placed, total_races, earnings, + form_music, ferrure, oeilleres, recent_form + ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) + ''', ( + datetime.now().strftime('%Y-%m-%d'), + horse_data.get('name'), + horse_data.get('id'), + horse_data.get('age'), + horse_data.get('sex'), + horse_data.get('trainer'), + horse_data.get('jockey'), + horse_data.get('odds'), + horse_data.get('wins'), + horse_data.get('placed'), + horse_data.get('total_races'), + horse_data.get('earnings'), + horse_data.get('form_music'), + horse_data.get('ferrure'), + horse_data.get('oeilleres'), + horse_data.get('recent_form') + )) + + conn.commit() + conn.close() + print(f"✅ Saved: {horse_data.get('name')}") + +def scrape_horse(horse_id): + """Scrape horse from Canalturf""" + url = f"https://www.canalturf.com/courses_fiche_cheval.php?idcheval={horse_id}" + + try: + r = requests.get(url, headers=HEADERS, timeout=15) + soup = BeautifulSoup(r.text, 'html.parser') + + text = soup.get_text(separator=' | ', strip=True) + + data = { + 'url': url, + 'id': horse_id, + 'name': '', + 'age': None, + 'sex': '', + 'trainer': '', + 'jockey': '', + 'odds': None, + 'wins': 0, + 'placed': 0, + 'total_races': 0, + 'earnings': 0, + 'form_music': '', + 'ferrure': '', + 'oeilleres': '', + 'recent_form': '' + } + + # Name + title = soup.find('title') + if title: + data['name'] = title.text.split('-')[0].strip() + + # Sex/Age - format "F4" or "M5" + if 'Sexe/Age' in text: + match = re.search(r'Sexe/Age\s*:\s*([MF])(\d)', text) + if match: + data['sex'] = match.group(1) + data['age'] = int(match.group(2)) + + # Trainer + if 'Entraineur' in text: + match = re.search(r'Entraineur\s*:\s*([^|]+)', text) + if match: + data['trainer'] = match.group(1).strip() + + # Odds + if 'Cote' in text: + match = re.search(r'(\d+[.,]\d+)', text) + if match: + data['odds'] = float(match.group(1).replace(',', '.')) + + # Form/Music + if 'Perf.' in text: + match = re.search(r'Perf\.\s*:\s*([A-Za-z0-9()hsm]+)', text) + if match: + data['form_music'] = match.group(1).strip() + + # Stats + # Victoires + if 'Victoire' in text: + match = re.search(r'(\d+)\s*$', text.split('Victoire')[1].split('|')[0] if 'Victoire' in text else '') + # Simplified - look for numbers + for num in re.findall(r'Victoire$s$\s*:\s*(\d+)', text): + data['wins'] = int(num) + break + + # Recent performances + recent = [] + for link in soup.select('a[href*="/resultats-PMU/"]')[:5]: + txt = link.get_text(strip=True) + if txt and len(txt) > 5: + recent.append(txt[:50]) + + data['recent_form'] = ' | '.join(recent) + + return data + + except Exception as e: + return {'id': horse_id, 'error': str(e)} + +# Test +if __name__ == "__main__": + init_horse_db() + + print("="*50) + print("HORSE DETAIL SCRAPER v2 - ENHANCED") + print("="*50) + + # Test with PASSIONATA + horse_id = "516052" + horse = scrape_horse(horse_id) + + print(f"\n{horse.get('name')}:") + print(f" Age/Sex: {horse.get('sex')}{horse.get('age')}") + print(f" Trainer: {horse.get('trainer')}") + print(f" Odds: {horse.get('odds')}") + print(f" Form: {horse.get('form_music')}") + + save_horse(horse) + + # Test with EMSILORD + print("\n" + "-"*30) + horse2 = scrape_horse("518372") + print(f"\n{horse2.get('name')}:") + print(f" Age/Sex: {horse2.get('sex')}{horse2.get('age')}") + print(f" Trainer: {horse2.get('trainer')}") + print(f" Odds: {horse2.get('odds')}") + print(f" Form: {horse2.get('form_music')}") + + save_horse(horse2) diff --git a/ideas_api.py b/ideas_api.py new file mode 100755 index 0000000..9b06521 --- /dev/null +++ b/ideas_api.py @@ -0,0 +1,92 @@ +#!/usr/bin/env python3 +""" +Ideas Management API +""" +from flask import Flask, jsonify, request, send_file, send_from_directory +import json +import os +from datetime import datetime + +app = Flask(__name__) +IDEAS_FILE = '/home/h3r7/boite_a_idees/idees.json' + +# Default structure +default_data = { + "categories": [ + {"id": "tech", "name": "Tech & IA", "color": "#7b2cbf"}, + {"id": "saas", "name": "SaaS", "color": "#2ec4b6"}, + {"id": "service", "name": "Service", "color": "#e94560"}, + {"id": "produit", "name": "Produit", "color": "#ff9f1c"}, + {"id": "invest", "name": "Investissement", "color": "#00d9ff"} + ], + "ideas": [ + { + "id": 1, + "title": "🤖 Turf Predictor AI", + "category": "saas", + "subcategory": "IA", + "description": "Système automatisé de prédictions hippiques avec IA. 4 sources de données.", + "status": "teste", + "potential": "eleve", + "revenue": 0, + "created": "2026-02-21", + "notes": "Test 24/02: 3/3 placé" + } + ], + "next_id": 2 +} + +def load_data(): + if not os.path.exists(IDEAS_FILE): + with open(IDEAS_FILE, 'w') as f: + json.dump(default_data, f, indent=2) + with open(IDEAS_FILE, 'r') as f: + return json.load(f) + +def save_data(data): + with open(IDEAS_FILE, 'w') as f: + json.dump(data, f, indent=2) + +@app.route('/') +def index(): + return send_file('/home/h3r7/turf_scraper/dashboard.html') + +@app.route('/api/ideas') +def get_ideas(): + return jsonify(load_data()) + +@app.route('/api/ideas', methods=['POST']) +def add_idea(): + data = load_data() + new_idea = request.json + new_idea['id'] = data['next_id'] + new_idea['created'] = datetime.now().strftime('%Y-%m-%d') + data['ideas'].append(new_idea) + data['next_id'] += 1 + save_data(data) + return jsonify({"success": True, "id": new_idea['id']}) + +@app.route('/api/ideas/', methods=['PUT']) +def update_idea(idea_id): + data = load_data() + for i, idea in enumerate(data['ideas']): + if idea['id'] == idea_id: + data['ideas'][i].update(request.json) + save_data(data) + return jsonify({"success": True}) + return jsonify({"error": "Not found"}), 404 + +@app.route('/api/ideas/', methods=['DELETE']) +def delete_idea(idea_id): + data = load_data() + data['ideas'] = [i for i in data['ideas'] if i['id'] != idea_id] + save_data(data) + return jsonify({"success": True}) + + +@app.route('/idees') +def idees(): + return send_from_directory('/home/h3r7/turf_scraper', 'idees_final.html') + +if __name__ == '__main__': + app.run(host='0.0.0.0', port=8765, debug=False) diff --git a/idees_final.html b/idees_final.html new file mode 100755 index 0000000..81ac541 --- /dev/null +++ b/idees_final.html @@ -0,0 +1,182 @@ + + + + + + 💡 Boîte à Idées + + + +🏠Accueil +

💡 Boîte à Idées

+ + + +

+ + + + + + + +

+ + +

+ +

Chargement...

+ ← Retour au Portail + + + + diff --git a/idees_local.html b/idees_local.html new file mode 100755 index 0000000..594f55a --- /dev/null +++ b/idees_local.html @@ -0,0 +1,123 @@ + + + + + + 💡 Boîte à Idées + + + +🏠Accueil +

💡 Boîte à Idées

+ +

➕ Nouvelle Idée

+ + + + + + + +

+ +

+ + ← Retour au Portail + + + + diff --git a/idees_simple.html b/idees_simple.html new file mode 100755 index 0000000..70fbb21 --- /dev/null +++ b/idees_simple.html @@ -0,0 +1,49 @@ + + + + + + 💡 Boîte à Idées + + + +🏠Accueil +

💡 Boîte à Idées

Chargement...

+ + + + diff --git a/idees_statiques.html b/idees_statiques.html new file mode 100755 index 0000000..0ed8629 --- /dev/null +++ b/idees_statiques.html @@ -0,0 +1,110 @@ + + + + + + 💡 Boîte à Idées + + + +🏠Accueil +

💡 Boîte à Idées

+ ← Retour au Portail + +

🤖 Turf Predictor AI

+ SaaS + teste + 💰 0€ | 📅 2026-02-21 +

Système automatisé de prédictions hippiques avec IA. 4 sources de données.

+ +

🌐 Template site web pour les professionnels

+ Tech & IA + idee + 💰 0€ | 📅 2026-02-24 +

Créer un template versionné sur github permettant de déployer un site web pour : +- Restaurant (menu, réservations, galerie photos) +- Cordonnier (services, horaires, contact) +- Boulangerie (produits, commander en ligne) +- Artisans (portfolio, devis, contact) + +✅ FONCTIONNALITÉS INCLUSES: +🏠 Site Restaurant: Design chic, menu configurable, calendrier réservation, Google Maps, Avis Clients +⚙️ Admin: Modification temps réel du menu, ajouter/supprimer plats +🛠️ TECHNOLOGIE: HTML + CSS + JavaScript, JSON, Python Flask

+ +

🤖 Agent IA Support Client

+ Tech & IA + idee + 💰 0€ | 📅 2026-02-24 +

Assistant IA pour gérer les demandes clients 24/7 sur site e-commerce. Utilise les APIs pour répondre aux questions produits.

+ +

📱 App Fitness Collectif

+ Produit + idee + 💰 0€ | 📅 2026-02-24 +

Application mobile pour défis fitness entre amis/collègues. Défis quotidiens, classements, rewards.

+ +

📊 Dashboard Trading Crypto

+ SaaS + idee + 💰 0€ | 📅 2026-02-24 +

Dashboard de suivi des cryptos avec alerts, analyses techniques automatisées et signaux d'achat/vente.

+ +

🔍 Scrapper annuaires pour trouver prospects

+ Tech & IA + idee + 💰 0€ | 📅 2026-02-24 +

Scraper les annuaires en ligne pour trouver des artisans/commerchants sans site web et les prospecter. +CIBLE: Artisans, Commerces, Services +ANNUAIRES: PagesJaunes, Mappy, Chrono-Facile +TECHNOLOGIE: Python + BeautifulSoup +REVENUS: Fichier prospects 29-49€, Prospection 99-149€

+ + diff --git a/improve_historical_data.py b/improve_historical_data.py new file mode 100644 index 0000000..9658966 --- /dev/null +++ b/improve_historical_data.py @@ -0,0 +1,361 @@ +#!/usr/bin/env python3 +""" +Improved Historical Data Loader +- Fills gaps in historical data +- Adds more features for better ML +- Supports bulk loading for more training data +""" + +import requests +import sqlite3 +import json +import time +import os +import re +from datetime import datetime, timedelta +from concurrent.futures import ThreadPoolExecutor, as_completed +import sys + +DB_PATH = os.environ.get("DB_PATH", "/home/h3r7/turf_scraper/turf.db") +HEADERS = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64)', 'Accept': 'application/json'} +BASE_URL = "https://turfinfo.api.pmu.fr/rest/client/1/programme" + + +def get_db_connection(): + return sqlite3.connect(DB_PATH) + + +def get_missing_dates(): + """Find dates missing from historical_data.""" + conn = get_db_connection() + c = conn.cursor() + + c.execute("SELECT DISTINCT date FROM historical_data") + existing = set(row[0] for row in c.fetchall()) + conn.close() + + start_date = datetime(2025, 1, 1) + end_date = datetime(2026, 12, 31) + + all_dates = [] + current = start_date + while current <= end_date: + date_str = current.strftime('%Y-%m-%d') + if date_str not in existing: + all_dates.append(date_str) + current += timedelta(days=1) + + return all_dates + + +def get_programme(date_pmu): + """Get program for a given date.""" + try: + url = f"{BASE_URL}/{date_pmu}/reunions" + r = requests.get(url, headers=HEADERS, timeout=15) + if r.status_code != 200: + return [] + return r.json().get('programme', {}).get('reunions', []) + except Exception as e: + print(f"Error fetching {date_pmu}: {e}") + return [] + + +def find_all_quintes(reunions): + """Find all courses (not just Quinte+) - more training data.""" + courses = [] + for reunion in reunions: + for course in reunion.get('courses', []): + paris = [p['typePari'] for p in course.get('paris', [])] + libelle = course.get('libelle', '') + + # Skip if no participants + nb_partants = course.get('nombreDeclaresPartants', 0) + if nb_partants < 10: + continue + + heure_ts = course.get('heureDepart', 0) + heure = datetime.fromtimestamp(heure_ts/1000).strftime('%H:%M') if heure_ts else '13:55' + + courses.append({ + 'num_reunion': reunion['numOfficiel'], + 'num_course': course['numOrdre'], + 'libelle': libelle, + 'hippodrome': reunion['hippodrome']['libelleCourt'], + 'distance': course.get('distance', 0), + 'discipline': course.get('discipline', ''), + 'allocation': course.get('montantPrix', 0), + 'nb_partants': nb_partants, + 'heure': heure, + 'arrivee_def': course.get('arriveeDefinitive', False), + }) + return courses + + +def get_participants(date_pmu, num_r, num_c): + """Get participants for a course.""" + try: + url = f"{BASE_URL}/{date_pmu}/R{num_r}/C{num_c}/participants" + r = requests.get(url, headers=HEADERS, timeout=15) + if r.status_code != 200: + return [] + return r.json().get('participants', []) + except Exception as e: + print(f"Error fetching participants: {e}") + return [] + + +def parse_musique(musique): + """Parse the musique (form) string.""" + if not musique: + return {'forme_recente': 99, 'tendance': 0, 'nb_disq': 0, 'best_pos': 99} + + clean = re.sub(r'$\d+$', '', musique) + resultats = re.findall(r'(\d+|D|0)([amphsc]?)', clean) + + positions = [] + for pos, disc in resultats[:10]: + if pos == 'D': + positions.append(99) + else: + positions.append(int(pos)) + + if not positions: + return {'forme_recente': 99, 'tendance': 0, 'nb_disq': 0, 'best_pos': 99} + + nb_disq = positions.count(99) + positions_clean = [p for p in positions if p != 99] + + recentes = positions_clean[:3] + forme_recente = sum(recentes) / len(recentes) if recentes else 99 + + best_pos = min(positions_clean) if positions_clean else 99 + + if len(positions_clean) >= 4: + debut = sum(positions_clean[-4:]) / 4 + fin = sum(positions_clean[:4]) / 4 + tendance = round(debut - fin, 2) + else: + tendance = 0 + + return { + 'forme_recente': round(forme_recente, 2), + 'tendance': tendance, + 'nb_disq': nb_disq, + 'best_pos': best_pos + } + + +def extract_features(p, course_info, all_participants): + """Extract all features from a participant.""" + musique_stats = parse_musique(p.get('musique', '')) + + # Odds + rapport_direct = p.get('dernierRapportDirect', {}) or {} + cote_directe = rapport_direct.get('rapport', 0) or 0 + est_favori = 1 if rapport_direct.get('favoris', False) else 0 + + rapport_ref = p.get('dernierRapportReference', {}) or {} + cote_reference = rapport_ref.get('rapport', 0) or 0 + + # Career stats + nb_courses = p.get('nombreCourses', 0) or 0 + nb_victoires = p.get('nombreVictoires', 0) or 0 + nb_places = p.get('nombrePlaces', 0) or 0 + nb_p2 = p.get('nombrePlacesSecond', 0) or 0 + nb_p3 = p.get('nombrePlacesTroisieme', 0) or 0 + + tx_vic = round(nb_victoires / nb_courses * 100, 2) if nb_courses else 0 + tx_place = round(nb_places / nb_courses * 100, 2) if nb_courses else 0 + + # Earnings + gains = p.get('gainsParticipant', {}) or {} + gains_carriere = gains.get('gainsCarriere', 0) or 0 + gains_annee = gains.get('gainsAnneeEnCours', 0) or 0 + gains_victoires = gains.get('gainsVictoires', 0) or 0 + + # Odds rank + all_cotes = sorted([ + (x.get('dernierRapportDirect', {}) or {}).get('rapport', 999) or 999 + for x in all_participants + ]) + rang_cote = all_cotes.index(cote_directe) + 1 if cote_directe in all_cotes else 99 + + cotes_valides = [c for c in all_cotes if c < 900] + moy_cote = sum(cotes_valides) / len(cotes_valides) if cotes_valides else 1 + ratio_cote = round(cote_directe / moy_cote, 3) if moy_cote else 0 + + # Result + ordre = p.get('ordreArrivee', 0) or 0 + top1 = 1 if ordre == 1 else 0 + top3 = 1 if 1 <= ordre <= 3 else 0 + top5 = 1 if 1 <= ordre <= 5 else 0 + + # Driver/jockey + driver = p.get('driver', {}) or {} + jockey_name = driver.get('nom', '') if driver else '' + if not jockey_name: + jockey_name = p.get('jockey', {}).get('nom', '') if p.get('jockey') else '' + + # Equipment + oeilleres = p.get('oeilleres', '') + deferre = p.get('deferre', '') + + return { + 'date': None, + 'race_name': course_info['libelle'], + 'hippodrome': course_info['hippodrome'], + 'distance': course_info['distance'], + 'discipline': course_info['discipline'], + 'allocation': course_info['allocation'], + 'nb_partants': course_info['nb_partants'], + 'heure': course_info['heure'], + 'horse_name': p.get('nom', ''), + 'horse_number': p.get('numero', 0), + 'driver': jockey_name, + 'age': p.get('age', 0) or 0, + 'sexe': p.get('sexe', 'U'), + 'musique': p.get('musique', ''), + 'nb_courses': nb_courses, + 'nb_victoires': nb_victoires, + 'nb_places': nb_places, + 'nb_places_2': nb_p2, + 'nb_places_3': nb_p3, + 'gains_carriere': gains_carriere, + 'gains_annee': gains_annee, + 'gains_victoires': gains_victoires, + 'reduction_km': p.get('reductionKm', 0) or 0, + 'avis_entraineur': p.get('avisEntraineur', 'NEUTRE') or 'NEUTRE', + 'oeilleres': oeilleres or 'SANS', + 'deferre': deferre or 'NON', + 'cote_directe': cote_directe, + 'cote_reference': cote_reference, + 'indicateur_tendance': rapport_direct.get('nombreIndicateurTendance', 0) or 0, + 'est_favori': est_favori, + 'tx_victoire': tx_vic, + 'tx_place': tx_place, + 'forme_recente': musique_stats['forme_recente'], + 'tendance_forme': musique_stats['tendance'], + 'nb_disq': musique_stats['nb_disq'], + 'rang_cote': rang_cote, + 'ratio_cote_field': ratio_cote, + 'ordre_arrivee': ordre, + 'temps_obtenu': p.get('tempsObtenu', 0) or 0, + 'top1': top1, + 'top3': top3, + 'top5': top5, + } + + +def load_date(date_str): + """Load all course data for a specific date.""" + date_pmu = datetime.strptime(date_str, '%Y-%m-%d').strftime('%d%m%Y') + + reunions = get_programme(date_pmu) + if not reunions: + return 0 + + courses = find_all_quintes(reunions) + if not courses: + return 0 + + total_loaded = 0 + conn = get_db_connection() + c = conn.cursor() + + for course in courses: + participants = get_participants(date_pmu, course['num_reunion'], course['num_course']) + if not participants: + continue + + for p in participants: + try: + features = extract_features(p, course, participants) + features['date'] = date_str + + c.execute(''' + INSERT OR IGNORE INTO historical_data + (date, race_name, hippodrome, distance, discipline, allocation, nb_partants, heure, + horse_name, horse_number, driver, age, sexe, musique, nb_courses, nb_victoires, + nb_places, nb_places_2, nb_places_3, gains_carriere, gains_annee, gains_victoires, + reduction_km, avis_entraineur, oeilleres, deferre, cote_directe, cote_reference, + indicateur_tendance, est_favori, tx_victoire, tx_place, forme_recente, tendance_forme, + nb_disq, rang_cote, ratio_cote_field, ordre_arrivee, temps_obtenu, top1, top3, top5) + VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, + ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) + ''', ( + features['date'], features['race_name'], features['hippodrome'], features['distance'], + features['discipline'], features['allocation'], features['nb_partants'], features['heure'], + features['horse_name'], features['horse_number'], features['driver'], features['age'], + features['sexe'], features['musique'], features['nb_courses'], features['nb_victoires'], + features['nb_places'], features['nb_places_2'], features['nb_places_3'], + features['gains_carriere'], features['gains_annee'], features['gains_victoires'], + features['reduction_km'], features['avis_entraineur'], features['oeilleres'], + features['deferre'], features['cote_directe'], features['cote_reference'], + features['indicateur_tendance'], features['est_favori'], features['tx_victoire'], + features['tx_place'], features['forme_recente'], features['tendance_forme'], + features['nb_disq'], features['rang_cote'], features['ratio_cote_field'], + features['ordre_arrivee'], features['temps_obtenu'], features['top1'], + features['top3'], features['top5'] + )) + + if c.rowcount > 0: + total_loaded += 1 + + except Exception as e: + print(f" Error loading participant: {e}") + + conn.commit() + conn.close() + + return total_loaded + + +def main(): + print(f"\n{'='*60}") + print("Improved Historical Data Loader") + print(f"{'='*60}\n") + + # Get missing dates + missing = get_missing_dates() + print(f"Found {len(missing)} missing dates") + + if not missing: + print("No missing dates to load!") + return + + # Load missing dates (limit to avoid timeout) + dates_to_load = missing[:30] # Load max 30 dates at once + + print(f"Loading {len(dates_to_load)} dates...\n") + + total = 0 + for i, date in enumerate(dates_to_load): + print(f"[{i+1}/{len(dates_to_load)}] Loading {date}...", end=" ") + loaded = load_date(date) + print(f"✓ {loaded} rows") + total += loaded + + # Rate limiting + if i < len(dates_to_load) - 1: + time.sleep(0.5) + + print(f"\n{'='*60}") + print(f"Total loaded: {total} rows") + + # Show updated stats + conn = get_db_connection() + c = conn.cursor() + c.execute("SELECT COUNT(*), COUNT(DISTINCT date) FROM historical_data") + count, days = c.fetchone() + c.execute("SELECT MIN(date), MAX(date) FROM historical_data") + min_date, max_date = c.fetchone() + conn.close() + + print(f"Total in DB: {count} rows, {days} days") + print(f"Date range: {min_date} to {max_date}") + print(f"{'='*60}\n") + + +if __name__ == "__main__": + main() diff --git a/llm_cache.py b/llm_cache.py new file mode 100644 index 0000000..4acc476 --- /dev/null +++ b/llm_cache.py @@ -0,0 +1,260 @@ +#!/usr/bin/env python3 +""" +Cache LLM - Turf Scraper +Réduction des appels API par mise en cache des réponses +""" +import json +import hashlib +import os +import time +from datetime import datetime, timedelta +from pathlib import Path +from typing import Optional, Any + + +class LLMCache: + """Cache pour réponses LLM avec expiration""" + + def __init__(self, cache_dir: str = None, ttl_hours: int = 24): + """ + Args: + cache_dir: Répertoire pour le cache (défaut: ~/.cache/turf_llm/) + ttl_hours: Time-to-live en heures (défaut: 24h) + """ + if cache_dir is None: + cache_dir = os.path.expanduser("~/.cache/turf_llm") + + self.cache_dir = Path(cache_dir) + self.cache_dir.mkdir(parents=True, exist_ok=True) + self.ttl = timedelta(hours=ttl_hours) + + def _hash_key(self, key: str) -> str: + """Génère un hash pour la clé""" + return hashlib.sha256(key.encode()).hexdigest() + + def _get_cache_path(self, key: str) -> Path: + """Retourne le chemin du fichier cache""" + hash_key = self._hash_key(key) + return self.cache_dir / f"{hash_key}.json" + + def get(self, key: str) -> Optional[dict]: + """ + Récupère une valeur du cache + + Args: + key: Clé de recherche + + Returns: + dict avec 'response' et 'timestamp' ou None si expiré/absent + """ + cache_path = self._get_cache_path(key) + + if not cache_path.exists(): + return None + + try: + with open(cache_path, 'r', encoding='utf-8') as f: + data = json.load(f) + + cached_time = datetime.fromisoformat(data.get('timestamp', '')) + + if datetime.now() - cached_time > self.ttl: + cache_path.unlink() + return None + + return data + + except (json.JSONDecodeError, ValueError, OSError): + return None + + def set(self, key: str, response: Any, metadata: dict = None) -> bool: + """ + Sauvegarde une réponse dans le cache + + Args: + key: Clé de recherche + response: Réponse à sauvegarder + metadata: Métadonnées additionnelles + + Returns: + True si succès + """ + cache_path = self._get_cache_path(key) + + data = { + 'key': key, + 'response': response, + 'timestamp': datetime.now().isoformat(), + 'metadata': metadata or {} + } + + try: + with open(cache_path, 'w', encoding='utf-8') as f: + json.dump(data, f, indent=2, ensure_ascii=False) + return True + except OSError: + return False + + def delete(self, key: str) -> bool: + """Supprime une entrée du cache""" + cache_path = self._get_cache_path(key) + try: + if cache_path.exists(): + cache_path.unlink() + return True + except OSError: + pass + return False + + def clear(self) -> int: + """Supprime tout le cache""" + count = 0 + for f in self.cache_dir.glob("*.json"): + try: + f.unlink() + count += 1 + except OSError: + pass + return count + + def clear_expired(self) -> int: + """Supprime les entrées expirées""" + count = 0 + now = datetime.now() + + for f in self.cache_dir.glob("*.json"): + try: + with open(f, 'r', encoding='utf-8') as fp: + data = json.load(fp) + + cached_time = datetime.fromisoformat(data.get('timestamp', '')) + + if now - cached_time > self.ttl: + f.unlink() + count += 1 + except (json.JSONDecodeError, ValueError, OSError): + pass + + return count + + def get_stats(self) -> dict: + """Retourne des statistiques sur le cache""" + files = list(self.cache_dir.glob("*.json")) + total_size = sum(f.stat().st_size for f in files) + + now = datetime.now() + expired = 0 + + for f in files: + try: + with open(f, 'r', encoding='utf-8') as fp: + data = json.load(fp) + cached_time = datetime.fromisoformat(data.get('timestamp', '')) + if now - cached_time > self.ttl: + expired += 1 + except: + pass + + return { + 'total_entries': len(files), + 'total_size_bytes': total_size, + 'expired_entries': expired, + 'active_entries': len(files) - expired + } + + +class QuestionCache: + """Cache spécifique pour les questions SQL""" + + def __init__(self, db_path: str = None): + if db_path is None: + db_path = os.path.expanduser("~/.cache/turf_llm/sql_cache.json") + + self.cache_file = Path(db_path) + self.cache = self._load() + + def _load(self) -> dict: + """Charge le cache depuis le fichier""" + if self.cache_file.exists(): + try: + with open(self.cache_file, 'r', encoding='utf-8') as f: + return json.load(f) + except: + pass + return {} + + def _save(self): + """Sauvegarde le cache""" + try: + self.cache_file.parent.mkdir(parents=True, exist_ok=True) + with open(self.cache_file, 'w', encoding='utf-8') as f: + json.dump(self.cache, f, indent=2) + except: + pass + + def get_sql(self, question: str) -> Optional[str]: + """Récupère SQL pour une question similaire""" + normalized = question.lower().strip() + + if normalized in self.cache: + return self.cache[normalized].get('sql') + + for key, value in self.cache.items(): + if self._similarity(normalized, key) > 0.7: + return value.get('sql') + + return None + + def set_sql(self, question: str, sql: str, success: bool = True): + """Sauvegarde SQL pour une question""" + normalized = question.lower().strip() + + self.cache[normalized] = { + 'sql': sql, + 'success': success, + 'timestamp': datetime.now().isoformat(), + 'count': self.cache.get(normalized, {}).get('count', 0) + 1 + } + self._save() + + def _similarity(self, s1: str, s2: str) -> float: + """Calcule similarité simple entre deux strings""" + words1 = set(s1.split()) + words2 = set(s2.split()) + + if not words1 or not words2: + return 0.0 + + intersection = len(words1 & words2) + union = len(words1 | words2) + + return intersection / union if union > 0 else 0.0 + + def get_frequent_questions(self, limit: int = 10) -> list: + """Retourne les questions les plus fréquentes""" + sorted_questions = sorted( + self.cache.items(), + key=lambda x: x[1].get('count', 0), + reverse=True + ) + return [q[0] for q in sorted_questions[:limit]] + + +_global_cache = None +_sql_cache = None + + +def get_llm_cache() -> LLMCache: + """Singleton pour le cache global""" + global _global_cache + if _global_cache is None: + _global_cache = LLMCache() + return _global_cache + + +def get_sql_cache() -> QuestionCache: + """Singleton pour le cache SQL""" + global _sql_cache + if _sql_cache is None: + _sql_cache = QuestionCache() + return _sql_cache diff --git a/map_visual.html b/map_visual.html new file mode 100644 index 0000000..25831a7 --- /dev/null +++ b/map_visual.html @@ -0,0 +1,514 @@ + + + + + + H3R7Tech - Architecture Visuelle + + + + + +🏠Accueil + + + +

+ +

+ + + +

Fichiers:0

Liens:0

API:0

+ +

GROUPES

API

Database

Scraper

Core

+ + + + + + diff --git a/metrics_alerts.py b/metrics_alerts.py new file mode 100755 index 0000000..cbdb9ca --- /dev/null +++ b/metrics_alerts.py @@ -0,0 +1,345 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +metrics_alerts.py - Alertes Telegram + Email pour les métriques de performance + +Usage: + python3 metrics_alerts.py --daily # Alerte quotidienne (si notable) + python3 metrics_alerts.py --weekly # Rapport hebdomadaire + python3 metrics_alerts.py --test # Test formatage + python3 metrics_alerts.py --daily --email # Alerte + email si ROI > 1.0€ + python3 metrics_alerts.py --weekly --email # Rapport hebdo par email +""" + +import sqlite3 +import argparse +import requests +from datetime import datetime, timedelta +from pathlib import Path + +DB_PATH = "/home/h3r7/turf_scraper/turf.db" +TELEGRAM_DIR = "/home/h3r7/turf_scraper" + +# Email alert configuration (via combined_api Resend endpoint on port 8765) +COMBINED_API_URL = "http://localhost:8765" +ALERT_EMAIL_TO = "ronanyves26@gmail.com" # destinataire des alertes email + +# Seuil ROI pour déclencher une alerte email automatique (euros par mise) +ROI_ALERT_THRESHOLD = 1.0 + +# ============================================================================= +# FONCTIONS UTILITAIRES +# ============================================================================= + +def get_db(): + """Connexion a la base de donnees""" + conn = sqlite3.connect(DB_PATH) + conn.row_factory = sqlite3.Row + return conn + +def save_telegram_message(message, filename): + """Sauvegarde le message Telegram dans un fichier""" + filepath = Path(TELEGRAM_DIR) / filename + with open(filepath, 'w', encoding='utf-8') as f: + f.write(message) + print("Message Telegram sauvegarde: {}".format(filepath)) + return filepath + + +def send_email_alert(subject, message_text): + """ + Envoie une alerte email via le endpoint /api/send-email de combined_api (port 8765). + Convertit le message texte en HTML style. + Retourne True si envoi reussi, False sinon. + """ + # Convertir le texte brut en HTML lisible + html_lines = [] + for line in message_text.splitlines(): + stripped = line.strip() + if not stripped: + html_lines.append("
") + else: + # Echapper les caracteres HTML basiques + escaped = stripped.replace("&", "&").replace("<", "<").replace(">", ">") + html_lines.append( + "

{}

".format(escaped) + ) + + html_body = """ +

+ H3R7Tech — Alerte Performance Turf +

+ {} +

+ Alerte automatique generee par metrics_alerts.py — H3R7Tech
+ Dashboard: Turf Dashboard +

+ """.format("".join(html_lines)) + + try: + resp = requests.post( + "{}/api/send-email".format(COMBINED_API_URL), + json={ + "to": ALERT_EMAIL_TO, + "subject": subject, + "html": html_body, + }, + timeout=15, + ) + if resp.status_code in (200, 201): + result = resp.json() + print("Email envoye a {} — id: {}".format(ALERT_EMAIL_TO, result.get("id", "?"))) + return True + else: + try: + data = resp.json() + error_msg = data.get("error", data.get("details", resp.text[:300])) + except Exception: + error_msg = resp.text[:300] + print("Erreur Resend ({}): {}".format(resp.status_code, error_msg)) + return False + except Exception as e: + print("Impossible d'envoyer l'email: {}".format(e)) + return False + + +# ============================================================================= +# ALERTE QUOTIDIENNE +# ============================================================================= + +def check_daily_alerts(date_str): + """Verifie les evenements notables du jour. Retourne (message, has_roi_alert) ou None.""" + conn = get_db() + + # Recuperer les metriques du jour + metrics = conn.execute(""" + SELECT + source, + SUM(nb_predictions) as total_pred, + SUM(nb_gagnants) as total_gagn, + SUM(nb_places) as total_place, + SUM(nb_top5) as total_top5, + AVG(taux_gagnant) as taux_g, + AVG(taux_place) as taux_p, + AVG(roi_sg_net) as roi_sg, + SUM(quinte_5sur5) as q5 + FROM prediction_metrics + WHERE date = ? + GROUP BY source + """, (date_str,)).fetchall() + + if not metrics: + print("Aucune metrique pour {}".format(date_str)) + return None + + # Verifier les criteres d'alerte + alerts = [] + has_roi_alert = False + + for m in metrics: + # Quinte 5/5 + if m['q5'] and m['q5'] > 0: + alerts.append("QUINTE 5/5 {}: Quinte 5/5 trouve!".format(m['source'])) + + # ROI exceptionnel (> seuil ROI_ALERT_THRESHOLD) + if m['roi_sg'] and m['roi_sg'] > ROI_ALERT_THRESHOLD: + alerts.append("ROI ELEVE {}: ROI SG +{:.2f}€/mise".format(m['source'], m['roi_sg'])) + has_roi_alert = True + + # Taux place excellent + if m['taux_p'] and m['taux_p'] > 70: + alerts.append("TAUX PLACE {}: {:.1f}% place (excellent)".format(m['source'], m['taux_p'])) + + if not alerts: + print("Aucun evenement notable aujourd'hui") + return None + + # Formater le message + date_fmt = datetime.strptime(date_str, '%Y-%m-%d').strftime('%d/%m/%Y') + message = "ALERTE PERFORMANCE\nDate: {}\n{}\n\n{}\n\nDetails: Dashboard Turf".format( + date_fmt, + "=" * 30, + "\n".join(alerts) + ) + + conn.close() + return message, has_roi_alert + + +# ============================================================================= +# RAPPORT HEBDOMADAIRE +# ============================================================================= + +def generate_weekly_report(): + """Genere le rapport hebdomadaire de performance. Retourne (message, total_roi_sg).""" + conn = get_db() + + # Dates de la semaine + today = datetime.now() + start_date = (today - timedelta(days=7)).strftime('%Y-%m-%d') + end_date = today.strftime('%Y-%m-%d') + + # Periode precedente pour comparaison + prev_start = (today - timedelta(days=14)).strftime('%Y-%m-%d') + prev_end = start_date + + # Metriques de la semaine + current_metrics = conn.execute(""" + SELECT + source, + COUNT(*) as nb_courses, + SUM(nb_predictions) as total_pred, + SUM(nb_gagnants) as total_gagn, + SUM(nb_places) as total_place, + ROUND(AVG(taux_gagnant), 1) as taux_g, + ROUND(AVG(taux_place), 1) as taux_p, + ROUND(SUM(roi_sg_net), 3) as roi_sg_cumul, + ROUND(SUM(roi_sp_net), 3) as roi_sp_cumul, + SUM(quinte_5sur5) as q5, + SUM(quinte_4sur5) as q4 + FROM prediction_metrics + WHERE date BETWEEN ? AND ? + GROUP BY source + ORDER BY taux_p DESC + """, (start_date, end_date)).fetchall() + + # Metriques semaine precedente + prev_metrics = conn.execute(""" + SELECT + source, + ROUND(AVG(taux_gagnant), 1) as taux_g, + ROUND(AVG(taux_place), 1) as taux_p + FROM prediction_metrics + WHERE date BETWEEN ? AND ? + GROUP BY source + """, (prev_start, prev_end)).fetchall() + + prev_dict = {m['source']: m for m in prev_metrics} + + # Formater le message + start_fmt = datetime.strptime(start_date, '%Y-%m-%d').strftime('%d/%m') + end_fmt = datetime.strptime(end_date, '%Y-%m-%d').strftime('%d/%m') + + message = "RAPPORT PERFORMANCE\nSemaine {} - {}\n{}\n\n".format( + start_fmt, end_fmt, "=" * 30 + ) + + # Bilan global + total_roi_sg = sum(m['roi_sg_cumul'] or 0 for m in current_metrics) + total_roi_sp = sum(m['roi_sp_cumul'] or 0 for m in current_metrics) + total_q5 = sum(m['q5'] or 0 for m in current_metrics) + total_q4 = sum(m['q4'] or 0 for m in current_metrics) + + # Detail par source + for m in current_metrics: + source_name = m['source'].replace('canalturf_', '').replace('_', ' ').title() + + # Comparaison avec semaine precedente + prev = prev_dict.get(m['source']) + diff_g = 0 + diff_p = 0 + if prev: + diff_g = m['taux_g'] - prev['taux_g'] + diff_p = m['taux_p'] - prev['taux_p'] + + arrow_g = 'hausse' if diff_g > 0 else 'baisse' if diff_g < 0 else 'stable' + arrow_p = 'hausse' if diff_p > 0 else 'baisse' if diff_p < 0 else 'stable' + + message += "{}\n Courses: {}\n Taux gagnant: {}% ({})\n Taux place: {}% ({})\n ROI SG: {:+.2f}€\n ROI SP: {:+.2f}€\n\n".format( + source_name, + m['nb_courses'], + m['taux_g'], arrow_g, + m['taux_p'], arrow_p, + m['roi_sg_cumul'], + m['roi_sp_cumul'] + ) + + # Bilan global + bilan = "Semaine positive" if total_roi_sg > 0 else "Semaine negative" + + message += "{}\nQuinte: 5/5 = {} courses | 4/5 = {} courses\n\nBilan global:\n ROI SG cumule: {:+.2f}€\n ROI SP cumule: {:+.2f}€\n\n{}\n{}".format( + "=" * 30, + total_q5, total_q4, + total_roi_sg, total_roi_sp, + bilan, "=" * 30 + ) + + conn.close() + return message, total_roi_sg + +# ============================================================================= +# POINT D'ENTREE +# ============================================================================= + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Alertes metriques de performance") + parser.add_argument("--daily", "-d", action="store_true", help="Alerte quotidienne") + parser.add_argument("--weekly", "-w", action="store_true", help="Rapport hebdomadaire") + parser.add_argument("--test", "-t", action="store_true", help="Test formatage") + parser.add_argument( + "--email", "-e", action="store_true", + help="Envoyer par email (auto si ROI > {:.1f}€)".format(ROI_ALERT_THRESHOLD) + ) + parser.add_argument("--date", help="Date YYYY-MM-DD (defaut: hier)") + + args = parser.parse_args() + + if args.test: + date_str = args.date or (datetime.now() - timedelta(days=1)).strftime('%Y-%m-%d') + result = check_daily_alerts(date_str) + if result: + msg, has_roi = result + print(msg) + if args.email: + print("\n--- Test envoi email ---") + send_email_alert("[TEST] Alerte Performance Turf — {}".format(date_str), msg) + result_w = generate_weekly_report() + if result_w: + msg_w, roi_w = result_w + print(msg_w) + + elif args.daily: + date_str = args.date or (datetime.now() - timedelta(days=1)).strftime('%Y-%m-%d') + result = check_daily_alerts(date_str) + if result: + msg, has_roi = result + filename = "telegram_metrics_daily_{}.txt".format(date_str.replace('-', '')) + save_telegram_message(msg, filename) + + # Envoi email si ROI > seuil (automatique) OU si flag --email passe + if args.email or has_roi: + date_fmt = datetime.strptime(date_str, '%Y-%m-%d').strftime('%d/%m/%Y') + subject = "Alerte Turf — ROI exceptionnel {}".format(date_fmt) + sent = send_email_alert(subject, msg) + if not sent: + print("Email non envoye (voir logs ci-dessus)") + + elif args.weekly: + result = generate_weekly_report() + if result: + msg, total_roi = result + filename = "telegram_metrics_weekly_{}.txt".format(datetime.now().strftime('%Y%m%d')) + save_telegram_message(msg, filename) + + # Envoi email du rapport hebdo si --email active + if args.email: + start_fmt = (datetime.now() - timedelta(days=7)).strftime('%d/%m') + end_fmt = datetime.now().strftime('%d/%m') + roi_status = "positif" if total_roi > 0 else "negatif" + subject = "Rapport Hebdo Turf {}-{} — ROI {} ({:+.2f}€)".format( + start_fmt, end_fmt, roi_status, total_roi + ) + sent = send_email_alert(subject, msg) + if not sent: + print("Email non envoye (voir logs ci-dessus)") + else: + parser.print_help() diff --git a/mobile_ideas.html b/mobile_ideas.html new file mode 100755 index 0000000..19538fb --- /dev/null +++ b/mobile_ideas.html @@ -0,0 +1,44 @@ + + + + + + 💡 Idées + + + +🏠Accueil +

💡 Mes Idées

Chargement...

+ + + + diff --git a/model_optimizer.py b/model_optimizer.py new file mode 100644 index 0000000..33f2e03 --- /dev/null +++ b/model_optimizer.py @@ -0,0 +1,221 @@ +#!/usr/bin/env python3 +""" +Optimisation du modèle de scoring turf + Méthodes pour affiner le modèle avec précision +""" +import sqlite3 +import numpy as np +from datetime import datetime, timedelta +from collections import defaultdict + +DB_PATH = "/home/h3r7/turf_scraper/turf.db" + +def analyze_scoring_accuracy(): + """Analyse la précision actuelle du scoring""" + conn = sqlite3.connect(DB_PATH) + conn.row_factory = sqlite3.Row + c = conn.cursor() + + # Get all results with scoring + c.execute(""" + SELECT + r.date, + r.race_name, + r.horse_name as actual_horse, + r.position as actual_position, + s.horse_name as scored_horse, + s.score as scoring_score, + s.rang_scoring as scoring_rank + FROM results r + JOIN scoring s ON r.date = s.date AND r.race_name = s.race_name AND r.horse_name = s.horse_name + WHERE s.scoring_version = 'v2' + ORDER BY r.date, r.race_name + """) + + # Calculate hit rates + races = defaultdict(list) + for row in c.fetchall(): + races[(row['date'], row['race_name'])].append({ + 'horse': row['actual_horse'], + 'position': row['actual_position'], + 'scored_horse': row['scored_horse'], + 'score': row['scoring_score'], + 'rank': row['scoring_rank'] + }) + + # Calculate metrics + total_races = len(races) + top1_hits = 0 + top3_hits = 0 + top5_hits = 0 + + for race_key, horses in races.items(): + actual_top1 = [h['horse'] for h in horses if h['position'] == 1] + actual_top3 = [h['horse'] for h in horses if h['position'] <= 3] + actual_top5 = [h['horse'] for h in horses if h['position'] <= 5] + + pred_top1 = [h['scored_horse'] for h in horses if h['rank'] == 1] + pred_top3 = [h['scored_horse'] for h in horses if h['rank'] <= 3] + pred_top5 = [h['scored_horse'] for h in horses if h['rank'] <= 5] + + if any(p == actual_top1[0] for p in pred_top1 if actual_top1): + top1_hits += 1 + if any(p in actual_top3 for p in pred_top3): + top3_hits += 1 + if any(p in actual_top5 for p in pred_top5): + top5_hits += 1 + + conn.close() + + print("="*60) + print("ANALYSE PRÉCISION SCORING V2") + print("="*60) + print(f"Total courses analysées: {total_races}") + print(f"Top 1 hit rate: {top1_hits}/{total_races} = {top1_hits*100/total_races:.1f}%") + print(f"Top 3 hit rate: {top3_hits}/{total_races} = {top3_hits*100/total_races:.1f}%") + print(f"Top 5 hit rate: {top5_hits}/{total_races} = {top5_hits*100/total_races:.1f}%") + + return races + +def analyze_feature_importance(): + """Analyse l'importance des features dans le scoring""" + conn = sqlite3.connect(DB_PATH) + c = conn.cursor() + + # Get scoring components + c.execute(""" + SELECT + AVG(score_cote) as avg_cote, + AVG(score_forme) as avg_forme, + AVG(score_victoire) as avg_victoire, + AVG(score_place) as avg_place, + AVG(score_avis) as avg_avis + FROM scoring + WHERE scoring_version = 'v2' + """) + + row = c.fetchone() + print("\n" + "="*60) + print("IMPORTANCE DES COMPOSANTES DU SCORE") + print("="*60) + print(f"Score Côte (odds): {row[0]:.2f}") + print(f"Score Forme: {row[1]:.2f}") + print(f"Score Victoire: {row[2]:.2f}") + print(f"Score Place: {row[3]:.2f}") + print(f"Score Avis: {row[4]:.2f}") + + conn.close() + +def identify_patterns(): + """Identifie les patterns qui améliorent les prédictions""" + conn = sqlite3.connect(DB_PATH) + conn.row_factory = sqlite3.Row + c = conn.cursor() + + print("\n" + "="*60) + print("PATTERNS IDENTIFIÉS") + print("="*60) + + # Position vs Score - correlation + c.execute(""" + SELECT + AVG(s.score) as avg_score, + r.position + FROM scoring s + JOIN results r ON s.date = r.date AND s.race_name = r.race_name AND s.horse_name = r.horse_name + WHERE s.scoring_version = 'v2' + GROUP BY r.position + ORDER BY r.position + """) + + print("\n1. Score moyen par position:") + for row in c.fetchall(): + print(f" Position {row[1]}: Score avg = {row[0]:.1f}") + + # Côte vs Position - les faibles cotes gagnent plus souvent? + c.execute(""" + SELECT + CASE + WHEN p.odds < 5 THEN 'Favori (<5)' + WHEN p.odds < 10 THEN 'Coef 5-10' + WHEN p.odds < 20 THEN 'Coef 10-20' + ELSE 'Outsider (>20)' + END as category, + COUNT(*) as total, + SUM(CASE WHEN r.position <= 3 THEN 1 ELSE 0 END) as top3, + ROUND(CAST(SUM(CASE WHEN r.position <= 3 THEN 1 ELSE 0 END) AS FLOAT) / COUNT(*) * 100, 1) as pct + FROM predictions p + JOIN results r ON p.date = r.date AND p.race_name = r.race_name AND p.horse_name = r.horse_name + WHERE p.source = 'canalturf_partants' + GROUP BY category + ORDER BY pct DESC + """) + + print("\n2. Taux de Top3 par catégorie de cote:") + for row in c.fetchall(): + print(f" {row[0]}: {row[3]}% ({row[2]}/{row[1]})") + + # Forme du cheval (musique) - les récents gains comptent? + c.execute(""" + SELECT + s.forme_recente, + COUNT(*) as total, + SUM(CASE WHEN r.position <= 3 THEN 1 ELSE 0 END) as top3, + ROUND(CAST(SUM(CASE WHEN r.position <= 3 THEN 1 ELSE 0 END) AS FLOAT) / COUNT(*) * 100, 1) as pct + FROM scoring s + JOIN results r ON s.date = r.date AND s.race_name = r.race_name AND s.horse_name = r.horse_name + WHERE s.scoring_version = 'v2' AND s.forme_recente IS NOT NULL + GROUP BY s.forme_recente + HAVING total > 5 + ORDER BY pct DESC + LIMIT 10 + """) + + print("\n3. Forme récente (musique) vs réussite:") + for row in c.fetchall(): + print(f" {row[0]}: {row[3]}% ({row[2]}/{row[1]})") + + conn.close() + +def suggest_improvements(): + """Suggère des améliorations basées sur l'analyse""" + print("\n" + "="*60) + print("AMÉLORATIONS SUGGÉRÉES") + print("="*60) + + suggestions = """ + 1. FEATURE ENGINEERING + - Ajouter: forme sur 5 dernières courses (poids + élevé) + - Ajouter: performance sur même distance/hippodrome + - Ajouter: historique jockey/cheval ensemble + + 2. POIDS DES COMPOSANTES + - Réduire le poids du score Côte (peu prédictif) + - Augmenter le poids de la forme récente + - Ajouter un component "tendance" (évolution cotes) + + 3. MACHINE LEARNING + - Passer de scoring linéaire à XGBoost/LogisticRegression + - Utiliser cross-validation pour éviter overfitting + - Ajouter regularization + + 4. ENSEMBLE + - Combiner scoring V2 + CanalTurf + un modèle ML + - Pondérer selon historique de précision + + 5. BACKTESTING + - Tester sur 30+ jours de données + - Calculer ROI théorique + - Ajuster seuils de sélection + """ + print(suggestions) + +def run_full_analysis(): + """Lance l'analyse complète""" + analyze_scoring_accuracy() + analyze_feature_importance() + identify_patterns() + suggest_improvements() + +if __name__ == "__main__": + run_full_analysis() \ No newline at end of file diff --git a/multi_scraper.py b/multi_scraper.py new file mode 100755 index 0000000..9d2f5f3 --- /dev/null +++ b/multi_scraper.py @@ -0,0 +1,113 @@ +#!/usr/bin/env python3 +""" +Multi-site turf scraper +Sources: Equidia, ZETurf, Canalturf, Boturfers +""" +import requests +from bs4 import BeautifulSoup +import json +from datetime import datetime +import re + +HEADERS = { + 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36' +} + +def scrape_equidia(): + """Scrape Equidia - résultats détaillés""" + url = "https://www.equidia.fr/courses" + try: + r = requests.get(url, headers=HEADERS, timeout=10) + soup = BeautifulSoup(r.text, 'html.parser') + courses = [] + for link in soup.select('a[href*="/courses/2026-"]'): + href = link.get('href', '') + if 'R1C' in href: + courses.append(f"https://www.equidia.fr{href}") + return list(set(courses))[:5] + except Exception as e: + print(f"Equidia error: {e}") + return [] + +def scrape_zeturf(): + """Scrape ZETurf - cotes""" + url = "https://www.zeturf.fr/fr/courses-du-jour" + try: + r = requests.get(url, headers=HEADERS, timeout=10) + soup = BeautifulSoup(r.text, 'html.parser') + courses = [] + for link in soup.select('a[href*="/course-du-jour/"]'): + href = link.get('href', '') + courses.append(f"https://www.zeturf.fr{href}") + return list(set(courses))[:5] + except Exception as e: + print(f"ZETurf error: {e}") + return [] + +def scrape_canalturf(): + """Scrape Canalturf - pronostics""" + url = "https://www.canalturf.com/courses_chevaux_jour.php" + try: + r = requests.get(url, headers=HEADERS, timeout=10) + soup = BeautifulSoup(r.text, 'html.parser') + links = [] + for link in soup.select('a'): + href = link.get('href', '') + if 'quinte' in href.lower(): + links.append(f"https://www.canalturf.com{href}") + return list(set(links))[:5] + except Exception as e: + print(f"Canalturf error: {e}") + return [] + +def scrape_boturfers(): + """Scrape Boturfers - pronostics + stats""" + url = "https://www.boturfers.fr" + try: + r = requests.get(url, headers=HEADERS, timeout=10) + soup = BeautifulSoup(r.text, 'html.parser') + courses = [] + for link in soup.select('a[href*="/quinte"]'): + href = link.get('href', '') + courses.append(f"https://www.boturfers.fr{href}") + return list(set(courses))[:5] + except Exception as e: + print(f"Boturfers error: {e}") + return [] + +def main(): + print(f"=== Turf Multi-Scraper {datetime.now().strftime('%Y-%m-%d %H:%M')} ===") + + print("\n[1/4] Scraping Equidia...") + equidia_courses = scrape_equidia() + print(f" Found {len(equidia_courses)} courses") + + print("\n[2/4] Scraping ZETurf...") + zeturf_courses = scrape_zeturf() + print(f" Found {len(zeturf_courses)} courses") + + print("\n[3/4] Scraping Canalturf...") + canalturf_courses = scrape_canalturf() + print(f" Found {len(canalturf_courses)} courses") + + print("\n[4/4] Scraping Boturfers...") + boturfers_courses = scrape_boturfers() + print(f" Found {len(boturfers_courses)} courses") + + data = { + 'timestamp': datetime.now().isoformat(), + 'equidia': equidia_courses, + 'zeturf': zeturf_courses, + 'canalturf': canalturf_courses, + 'boturfers': boturfers_courses + } + + output_file = f"/home/h3r7/turf_scraper/courses_{datetime.now().strftime('%Y%m%d_%H%M')}.json" + with open(output_file, 'w') as f: + json.dump(data, f, indent=2) + + print(f"\n✅ Saved to {output_file}") + return data + +if __name__ == "__main__": + main() diff --git a/multi_scraper_v5.py b/multi_scraper_v5.py new file mode 100755 index 0000000..3f2c515 --- /dev/null +++ b/multi_scraper_v5.py @@ -0,0 +1,699 @@ +#!/usr/bin/env python3 +""" +Turf Scraper v5 - REALTIME DATABASE SAVING +Saves predictions immediately as they're scraped +Parser robuste intégré : canalturf (partants + pronostic + sélections), boturfers (infos course) +""" +import requests +from bs4 import BeautifulSoup +import json +from datetime import datetime +import time +from concurrent.futures import ThreadPoolExecutor, as_completed +import threading +import sqlite3 +import re +import os + +DB_PATH = "/home/h3r7/turf_scraper/turf.db" +HEADERS = { + 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36', + 'Accept-Language': 'fr-FR,fr;q=0.9,en;q=0.8', +} + +lock = threading.Lock() +counter = {"total": 0, "done": 0} + +# ============== DATABASE FUNCTIONS ============== + +def init_db(): + """Initialize database""" + conn = sqlite3.connect(DB_PATH) + conn = sqlite3.connect(DB_PATH) + conn = sqlite3.connect(DB_PATH) + c = conn.cursor() + + c.execute(''' + CREATE TABLE IF NOT EXISTS predictions ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + date TEXT NOT NULL, + race_name TEXT, + race_hippodrome TEXT, + race_time TEXT, + horse_number INTEGER, + horse_name TEXT, + odds REAL, + prediction_rank INTEGER, + source TEXT, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + jockey TEXT, + odds_time TEXT, + odds_prev REAL + ) + ''') + + # Ajouter les colonnes jockey/odds_time si elles n'existent pas (migration) + for col, coltype in [("jockey", "TEXT"), ("odds_time", "TEXT"), ("odds_prev", "REAL")]: + try: + c.execute(f"ALTER TABLE predictions ADD COLUMN {col} {coltype}") + except sqlite3.OperationalError: + pass # Colonne déjà présente + + c.execute(''' + CREATE TABLE IF NOT EXISTS results ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + date TEXT NOT NULL, + race_name TEXT, + race_hippodrome TEXT, + position INTEGER, + horse_name TEXT, + odds REAL, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP + ) + ''') + + c.execute(''' + CREATE TABLE IF NOT EXISTS performance ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + prediction_date TEXT, + race_date TEXT, + horse_name TEXT, + predicted_rank INTEGER, + actual_position INTEGER, + hit BOOLEAN, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP + ) + ''') + + # Table odds_history : historique des cotes intraday + c.execute(''' + CREATE TABLE IF NOT EXISTS odds_history ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + date TEXT NOT NULL, + race_name TEXT, + race_hippodrome TEXT, + horse_number INTEGER, + horse_name TEXT, + odds REAL NOT NULL, + scraped_at TEXT NOT NULL, + source TEXT DEFAULT 'canalturf' + ) + ''') + + + c.execute(''' + CREATE TABLE IF NOT EXISTS race_meta ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + date TEXT NOT NULL, + race_name TEXT, + race_hippodrome TEXT, + race_time TEXT, + race_timestamp INTEGER, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP + ) + ''') + + conn.commit() + conn.close() + print(f"✅ DB initialized: {DB_PATH}") + +def add_prediction(date, race_name, race_hippodrome, race_time, horse_number, horse_name, + odds, prediction_rank, source, jockey="", odds_time=None): + """Add a prediction with OR IGNORE to avoid duplicates""" + c = conn.cursor() + c.execute(''' + INSERT OR IGNORE INTO predictions + (date, race_name, race_hippodrome, race_time, horse_number, horse_name, odds, prediction_rank, source, jockey, odds_time) + VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) + ''', (date, race_name, race_hippodrome, race_time, horse_number, horse_name, + odds, prediction_rank, source, jockey, odds_time or datetime.now().isoformat())) + + c.execute(''' + CREATE TABLE IF NOT EXISTS race_meta ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + date TEXT NOT NULL, + race_name TEXT, + race_hippodrome TEXT, + race_time TEXT, + race_timestamp INTEGER, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP + ) + ''') + + conn.commit() + conn.close() + +def add_result(date, race_name, race_hippodrome, position, horse_name, odds): + """Add a race result""" + conn = sqlite3.connect(DB_PATH) + c = conn.cursor() + c.execute(''' + INSERT INTO results (date, race_name, race_hippodrome, position, horse_name, odds) + VALUES (?, ?, ?, ?, ?, ?) + ''', (date, race_name, race_hippodrome, position, horse_name, odds)) + + c.execute(''' + CREATE TABLE IF NOT EXISTS race_meta ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + date TEXT NOT NULL, + race_name TEXT, + race_hippodrome TEXT, + race_time TEXT, + race_timestamp INTEGER, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP + ) + ''') + + conn.commit() + conn.close() + +# ============== SCRAPER FUNCTIONS ============== + +def fetch_url(args): + url, site = args + try: + r = requests.get(url, headers=HEADERS, timeout=12) + soup = BeautifulSoup(r.text, 'html.parser') + for s in soup(["script", "style"]): + s.decompose() + text = soup.get_text(separator='\n', strip=True)[:8000] + + with lock: + counter["done"] += 1 + pct = (counter["done"] / counter["total"]) * 100 + print(f" [{pct:.0f}%] {site}: OK") + + return {'url': url, 'site': site, 'content': text, 'status': 'success'} + except Exception as e: + with lock: + counter["done"] += 1 + return {'url': url, 'site': site, 'error': str(e), 'status': 'error'} + +# ============== PARSERS ROBUSTES ============== + +def parse_canalturf_quinte(content): + """ + Extrait depuis courses_quinte.php : + - Infos course (nom, hippodrome, heure, distance, allocation) + - Liste des partants (numéro, cheval, jockey, cote) + - Pronostic structuré (bases, chances régulières, outsiders) + """ + result = { + "course": {}, + "partants": [], + "pronostic": {"bases": [], "chances": [], "outsiders": []} + } + lines = [l.strip() for l in content.split('\n') if l.strip()] + + # Nom de la course + for line in lines: + if re.search(r'^PRIX\s+[A-Z]', line): + result["course"]["nom"] = line.strip() + break + + # Hippodrome + m = re.search(r'hippodrome de\s+([A-Z\-]+)', content, re.IGNORECASE) + if m: + result["course"]["hippodrome"] = m.group(1).strip() + + # Heure + m = re.search(r'(\d{1,2}:\d{2})', content) + if m: + result["course"]["heure"] = m.group(1) + + # Distance + m = re.search(r'(\d{3,4})m', content) + if m: + result["course"]["distance"] = int(m.group(1)) + + # Type de course + for t in ['TROT ATTELE', 'TROT MONTE', 'PLAT', 'OBSTACLE', 'HAIES', 'STEEPLE']: + if t in content.upper(): + result["course"]["type"] = t + break + + # Partants : on cherche des blocs numéro / NOM / Jockey / cote + # On s'arrête dès qu'on a trouvé la section "Liste des partants" pour éviter + # de parser aussi le bloc pronostic qui contient les mêmes noms sans cote + liste_idx = content.find("Liste des partants") + prono_idx = content.find("Le pronostic du Quinté+") + partants_zone = content[liste_idx:prono_idx] if liste_idx != -1 and prono_idx != -1 else content + lines_partants = [l.strip() for l in partants_zone.split('\n') if l.strip()] + + seen_nums = set() + i = 0 + while i < len(lines_partants): + if re.match(r'^\d{1,2}$', lines_partants[i]): + num = int(lines_partants[i]) + if 1 <= num <= 20 and num not in seen_nums and i + 2 < len(lines_partants): + nom_cheval = lines_partants[i + 1] + jockey = lines_partants[i + 2] + cote = None + if i + 3 < len(lines_partants) and re.match(r'[\d\.]+/\d', lines_partants[i + 3]): + try: + cote = float(lines_partants[i + 3].split('/')[0]) + except: + pass + i += 4 + else: + i += 3 + # Valider que le nom est bien en majuscules + if re.search(r'[A-Z]{3,}', nom_cheval) and re.search(r'[A-Z]', jockey): + seen_nums.add(num) + result["partants"].append({ + "numero": num, + "cheval": nom_cheval.strip(), + "jockey": jockey.strip(), + "cote": cote + }) + continue + i += 1 + + # Pronostic : extraire uniquement les chevaux dans la section dédiée + # On délimite chaque section entre son mot-clé et le suivant + section_keywords = ["Base(s)", "Chance(s) régulière(s)", "Outsider(s)", "Le cheval du Quinté+"] + + def extract_horses_between(start_kw, end_kws): + horses = [] + idx_start = content.find(start_kw) + if idx_start == -1: + return horses + idx_end = len(content) + for kw in end_kws: + idx = content.find(kw, idx_start + len(start_kw)) + if idx != -1 and idx < idx_end: + idx_end = idx + snippet = content[idx_start:idx_end] + for m in re.finditer(r'(\d{1,2})\s+([A-Z][A-Z\s\-\']+?)\s*$', snippet): + try: + horses.append({"numero": int(m.group(1)), "cheval": m.group(2).strip()}) + except: + pass + return horses + + result["pronostic"]["bases"] = extract_horses_between("Base(s)", ["Chance(s) régulière(s)", "Outsider(s)", "Le cheval"]) + result["pronostic"]["chances"] = extract_horses_between("Chance(s) régulière(s)", ["Outsider(s)", "Le cheval"]) + result["pronostic"]["outsiders"] = extract_horses_between("Outsider(s)", ["Le cheval", "Partants détaillés"]) + + return result + + +def parse_canalturf_selections(content): + """ + Extrait depuis courses_chevaux_jour.php : + Sélections gagnantes/placées par course (hippodrome, heure, cheval, jockey, cote PMU) + """ + selections = [] + today = datetime.now().strftime('%Y-%m-%d') + + for m in re.finditer( + r'C(\d+)\s*[-–]\s*(PRIX[^(]+)\((\d{1,2}:\d{2})$\s*' + r'(\d{1,2})\s*[-–]\s*([A-Z][A-Z\s\'\-]+?)\s*$([^)]+)$', + content + ): + race_name = m.group(2).strip() + race_time = m.group(3) + horse_num = int(m.group(4)) + horse_name = m.group(5).strip() + jockey = m.group(6).strip() + + after = content[m.end():m.end() + 100] + cote_m = re.search(r'(\d+\.?\d*)\s*PMU', after) + cote = float(cote_m.group(1)) if cote_m else 0.0 + + selections.append({ + "date": today, + "race_name": race_name, + "race_time": race_time, + "horse_number": horse_num, + "horse_name": horse_name, + "jockey": jockey, + "cote_pmu": cote, + }) + + return selections + + +def parse_boturfers_quinte(content): + """ + Extrait depuis boturfers.fr/quinte-du-jour : + Infos course (nb partants, distance, météo, probabilités) + """ + info = {} + + m = re.search(r'(\d+)\s*partants', content) + if m: + info["nb_partants"] = int(m.group(1)) + + m = re.search(r'(\d+)°C', content) + if m: + info["temperature"] = int(m.group(1)) + + probs = re.findall(r'(\d+)%\s*\nen (\d+) cheval', content) + if probs: + info["probabilites"] = {f"top{p[1]}": int(p[0]) for p in probs} + + return info + + +def save_parsed_data(quinte_data, selections, today): + """Sauvegarde en BDD toutes les données parsées""" + conn = sqlite3.connect(DB_PATH) + c = conn.cursor() + now = datetime.now().isoformat() + saved = 0 + + course = quinte_data.get("course", {}) + race_name = course.get("nom", "Quinté+") + hippodrome = course.get("hippodrome", "") + race_time = course.get("heure", "13:55") + + # 1. Partants avec cotes + for p in quinte_data.get("partants", []): + try: + c.execute(''' + INSERT OR IGNORE INTO predictions + (date, race_name, race_hippodrome, race_time, horse_number, horse_name, + odds, prediction_rank, source, jockey, odds_time) + VALUES (?, ?, ?, ?, ?, ?, ?, 0, ?, ?, ?) + ''', (today, race_name, hippodrome, race_time, + p["numero"], p["cheval"], p.get("cote") or 0, + "canalturf_partants", p.get("jockey", ""), now)) + saved += c.rowcount + except Exception as e: + print(f" ⚠️ Partant {p['cheval']}: {e}") + + # 2. Pronostic (bases=1, chances=2, outsiders=3) + for category, rank in [("bases", 1), ("chances", 2), ("outsiders", 3)]: + for horse in quinte_data.get("pronostic", {}).get(category, []): + try: + c.execute(''' + INSERT OR IGNORE INTO predictions + (date, race_name, race_hippodrome, race_time, horse_number, horse_name, + odds, prediction_rank, source, odds_time) + VALUES (?, ?, ?, ?, ?, ?, 0, ?, ?, ?) + ''', (today, race_name, hippodrome, race_time, + horse["numero"], horse["cheval"], rank, + f"canalturf_prono_{category}", now)) + saved += c.rowcount + except Exception as e: + print(f" ⚠️ Prono {horse['cheval']}: {e}") + + # 3. Sélections autres courses + for sel in selections: + try: + c.execute(''' + INSERT OR IGNORE INTO predictions + (date, race_name, race_hippodrome, race_time, horse_number, horse_name, + odds, prediction_rank, source, jockey, odds_time) + VALUES (?, ?, ?, ?, ?, ?, ?, 0, ?, ?, ?) + ''', (sel["date"], sel["race_name"], hippodrome, sel["race_time"], + sel["horse_number"], sel["horse_name"], sel.get("cote_pmu") or 0, + "canalturf_selections", sel.get("jockey", ""), now)) + saved += c.rowcount + except Exception as e: + print(f" ⚠️ Sélection {sel['horse_name']}: {e}") + + + c.execute(''' + CREATE TABLE IF NOT EXISTS race_meta ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + date TEXT NOT NULL, + race_name TEXT, + race_hippodrome TEXT, + race_time TEXT, + race_timestamp INTEGER, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP + ) + ''') + + conn.commit() + c.execute('SELECT COUNT(*) FROM predictions WHERE date = ?', (today,)) + total_today = c.fetchone()[0] + conn.close() + return saved, total_today + + +def save_race_meta(quinte_data, today): + """Sauvegarde l'heure de la course (HH:MM + timestamp) dans race_meta.""" + course = quinte_data.get("course", {}) + race_name = course.get("nom", "Quinté+") + hippodrome = course.get("hippodrome", "") + race_time = course.get("heure", "13:55") + + # Convertir HH:MM en timestamp du jour + try: + dt = datetime.strptime(f"{today} {race_time}", "%Y-%m-%d %H:%M") + ts = int(dt.timestamp()) + except: + ts = None + + conn = sqlite3.connect(DB_PATH) + c = conn.cursor() + c.execute(''' + INSERT INTO race_meta (date, race_name, race_hippodrome, race_time, race_timestamp) + VALUES (?, ?, ?, ?, ?) + ''', (today, race_name, hippodrome, race_time, ts)) + conn.commit() + conn.close() + + print(f"🕒 Heure course sauvegardée : {race_time} (ts={ts})") + +def save_odds_history(quinte_data, today): + """ + Sauvegarde un snapshot des cotes dans odds_history à chaque run. + Permet de suivre l'évolution des cotes tout au long de la journée. + """ + conn = sqlite3.connect(DB_PATH) + c = conn.cursor() + now = datetime.now().isoformat() + saved = 0 + + course = quinte_data.get("course", {}) + race_name = course.get("nom", "Quinté+") + hippodrome = course.get("hippodrome", "") + + for p in quinte_data.get("partants", []): + cote = p.get("cote") + if not cote or cote <= 0: + continue + c.execute(''' + INSERT INTO odds_history + (date, race_name, race_hippodrome, horse_number, horse_name, odds, scraped_at, source) + VALUES (?, ?, ?, ?, ?, ?, ?, ?) + ''', (today, race_name, hippodrome, + p["numero"], p["cheval"], cote, now, "canalturf")) + saved += c.rowcount + + + c.execute(''' + CREATE TABLE IF NOT EXISTS race_meta ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + date TEXT NOT NULL, + race_name TEXT, + race_hippodrome TEXT, + race_time TEXT, + race_timestamp INTEGER, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP + ) + ''') + + conn.commit() + conn.close() + return saved + + +def print_odds_evolution(today): + """ + Affiche l'évolution des cotes depuis le début de la journée. + Compare le premier snapshot du matin avec le snapshot actuel. + """ + conn = sqlite3.connect(DB_PATH) + c = conn.cursor() + + # Récupérer tous les snapshots du jour + c.execute(''' + SELECT horse_name, odds, scraped_at + FROM odds_history + WHERE date = ? + ORDER BY horse_name, scraped_at ASC + ''', (today,)) + rows = c.fetchall() + conn.close() + + if not rows: + return + + # Grouper par cheval + horses = {} + for horse, odds, ts in rows: + if horse not in horses: + horses[horse] = [] + horses[horse].append((odds, ts)) + + # Afficher l'évolution + print(f"\n📈 ÉVOLUTION DES COTES — {today}") + print(f"{'-'*60}") + print(f" {'CHEVAL':<25} {'MATIN':<8} {'ACTUEL':<8} {'ÉVOL':<8} TENDANCE") + print(f"{'-'*60}") + + evolutions = [] + for horse, snapshots in horses.items(): + if len(snapshots) < 1: + continue + cote_debut = snapshots[0][0] + cote_actuel = snapshots[-1][0] + nb_snapshots = len(snapshots) + if cote_debut > 0: + evol_pct = ((cote_actuel - cote_debut) / cote_debut) * 100 + else: + evol_pct = 0 + evolutions.append((horse, cote_debut, cote_actuel, evol_pct, nb_snapshots)) + + # Trier par cote actuelle + for horse, debut, actuel, evol, nb in sorted(evolutions, key=lambda x: x[2]): + if evol < -5: + tendance = "📉 BAISSE" + elif evol > 5: + tendance = "📈 HAUSSE" + else: + tendance = "➡️ STABLE" + evol_str = f"{evol:+.0f}%" if nb > 1 else "1er snap" + print(f" {horse:<25} {debut:<8} {actuel:<8} {evol_str:<8} {tendance}") + + print(f"{'-'*60}") + print(f" ({len(evolutions)} chevaux, {rows[0][2][:16] if rows else '?'} → maintenant)") + + +# ============== URL LIST ============== + +def get_urls(): + """ALL 7 WORKING SITES""" + sites = { + 'equidia': ['https://www.equidia.fr/courses', 'https://www.equidia.fr/courses/2026-02-24'], + 'zeturf': ['https://www.zeturf.fr/fr/courses-du-jour', 'https://www.zeturf.fr/en'], + 'canalturf': ['https://www.canalturf.com/courses_chevaux_jour.php', 'https://www.canalturf.com/courses_quinte.php'], + 'boturfers': ['https://www.boturfers.fr', 'https://www.boturfers.fr/quinte-du-jour', 'https://www.boturfers.fr/quinte-de-demain'], + 'zone-turf': ['https://www.zone-turf.fr', 'https://www.zone-turf.fr/programmes/'], + 'genybet': ['https://www.genybet.fr', 'https://www.genybet.fr/courses/'], + 'ruedesjoueurs': ['https://www.ruedesjoueurs.com/turf.html', 'https://www.ruedesjoueurs.com/turf/pronostics.html'] + } + urls = [] + for site, pages in sites.items(): + for url in pages: + urls.append((url, site)) + return urls + +# ============== MAIN ============== + +def main(): + start = time.time() + print(f"\n{'='*50}") + print(f"🐾 TURF SCRAPER v5 - REALTIME SAVING") + print(f"{'='*50}\n") + + init_db() + + urls = get_urls() + counter["total"] = len(urls) + + print(f"📡 Fetching {len(urls)} pages...\n") + + results = [] + with ThreadPoolExecutor(max_workers=10) as executor: + futures = {executor.submit(fetch_url, u): u for u in urls} + for future in as_completed(futures): + results.append(future.result()) + + elapsed = time.time() - start + today = datetime.now().strftime('%Y-%m-%d') + + print(f"\n📊 Parsing predictions...") + + quinte_data = {"course": {}, "partants": [], "pronostic": {}} + selections = [] + boturfers_info = {} + + for r in results: + if r['status'] != 'success': + continue + site = r['site'] + url = r['url'] + content = r['content'] + + if site == 'canalturf': + if 'quinte' in url: + quinte_data = parse_canalturf_quinte(content) + nb_p = len(quinte_data['partants']) + nb_b = len(quinte_data['pronostic'].get('bases', [])) + print(f" canalturf quinté : {nb_p} partants, {nb_b} base(s) trouvé(s)") + else: + selections = parse_canalturf_selections(content) + print(f" canalturf sélections : {len(selections)} course(s)") + + elif site == 'boturfers' and 'quinte-du-jour' in url: + boturfers_info = parse_boturfers_quinte(content) + temp = boturfers_info.get('temperature', '?') + print(f" boturfers : {boturfers_info.get('nb_partants', '?')} partants, {temp}°C") + + # Sauvegarde BDD + saved, total_today = save_parsed_data(quinte_data, selections, today) + print(f"\n💾 {saved} nouvelles entrées insérées en BDD") + + # Snapshot cotes dans odds_history + odds_saved = save_odds_history(quinte_data, today) + print(f"📊 {odds_saved} cotes sauvegardées dans odds_history") + + # Afficher l'évolution des cotes + print_odds_evolution(today) + + # Affichage résumé Quinté+ + if quinte_data["partants"]: + course = quinte_data["course"] + print(f"\n{'='*55}") + print(f"🏇 {course.get('nom', 'Quinté+')} — {course.get('hippodrome', '')} {course.get('heure', '')} ({course.get('distance', '')}m)") + print(f"{'─'*55}") + print(f" {'N°':<4} {'CHEVAL':<25} {'JOCKEY':<20} COTE") + print(f"{'─'*55}") + for p in sorted(quinte_data["partants"], key=lambda x: x.get("cote") or 999): + cote_str = str(p['cote']) if p['cote'] else "?" + print(f" {p['numero']:<4} {p['cheval']:<25} {p['jockey']:<20} {cote_str}") + bases = [h['cheval'] for h in quinte_data['pronostic'].get('bases', [])] + if bases: + print(f"\n ⭐ Bases : {', '.join(bases)}") + chances = [h['cheval'] for h in quinte_data['pronostic'].get('chances', [])] + if chances: + print(f" 🎯 Chances : {', '.join(chances)}") + outsiders = [h['cheval'] for h in quinte_data['pronostic'].get('outsiders', [])] + if outsiders: + print(f" 🔍 Outsiders : {', '.join(outsiders)}") + print(f"{'='*55}") + + # Stats par site + by_site = {} + for r in results: + s = r['site'] + by_site[s] = by_site.get(s, 0) + (1 if r['status'] == 'success' else 0) + + print(f"\n📊 STATS:") + for site, count in by_site.items(): + print(f" {site}: {count} pages") + + # Sauvegarde JSON + output = f"{os.environ.get('TURF_DIR', '/home/h3r7/turf_scraper')}/v5_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" + with open(output, 'w', encoding='utf-8') as f: + json.dump({ + 'timestamp': datetime.now().isoformat(), + 'runtime_sec': round(elapsed, 2), + 'total_pages': len(urls), + 'pages': results + }, f, indent=2, ensure_ascii=False) + + print(f"\n{'='*50}") + print(f"✅ DONE! {len(results)} pages in {elapsed:.1f}s") + print(f"💾 {total_today} prédictions en BDD pour aujourd'hui") + print(f"📁 {output}") + print(f"{'='*50}\n") + +if __name__ == "__main__": + main() diff --git a/n8n-chat.html b/n8n-chat.html new file mode 100644 index 0000000..9569633 --- /dev/null +++ b/n8n-chat.html @@ -0,0 +1,281 @@ + + + + + + N8N Workflow Chat - 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+ +

+ + +

+ + + + \ No newline at end of file diff --git a/n8n-workflow-import-instructions.md b/n8n-workflow-import-instructions.md new file mode 100644 index 0000000..c0262a9 --- /dev/null +++ b/n8n-workflow-import-instructions.md @@ -0,0 +1,114 @@ +# Import du workflow n8n OpenClaw Chat + +## Fichier à importer +`/home/h3r7/turf_scraper/n8n-openclaw-workflow.json` + +## Instructions d'import + +### Méthode 1 : Via l'interface n8n + +1. **Accède à n8n** + - URL : `https://kolifee.duckdns.org` + - Login avec tes identifiants + +2. **Importe le workflow** + - Clique sur le bouton **"+"** (Nouveau workflow) en haut à droite + - Ou va dans **Workflows** → **Import from File** + - Sélectionne le fichier `n8n-openclaw-workflow.json` + +3. **Active le workflow** + - Clique sur le toggle **"Active"** en haut à droite + - Le webhook `/webhook/openclaw` sera automatiquement disponible + +### Méthode 2 : Copier-coller le JSON + +1. Ouvre n8n : `https://kolifee.duckdns.org` +2. Crée un nouveau workflow +3. Clique sur les **3 points** (⋮) en haut à droite +4. Sélectionne **"Import from URL or File"** +5. Choisis **"Paste JSON"** +6. Copie tout le contenu de `n8n-openclaw-workflow.json` +7. Colle-le et clique **"Import"** + +## Structure du workflow + +``` +┌─────────────────┐ +│ Webhook │ ← Reçoit les messages POST /webhook/openclaw +│ (openclaw) │ +└────────┬────────┘ + │ + ▼ +┌─────────────────┐ +│ Process Query │ ← Traite la question (date, stats, etc.) +│ (Code Node) │ +└────────┬────────┘ + │ + ▼ +┌─────────────────┐ +│ Respond to │ ← Renvoie la réponse JSON +│ Webhook │ +└─────────────────┘ +``` + +## Fonctionnalités incluses + +Le workflow répond automatiquement à : +- 📅 **"date"** → Affiche la date et l'heure +- 📋 **"workflow"** → Liste les workflows actifs +- 📊 **"stats"** / **"système"** → Statistiques système +- ✅ **"test"** / **"ping"** → Test de connexion +- 💬 **Autre message** → Confirmation de réception + +## Test après import + +1. Active le workflow +2. Va sur `https://portal-kolifee.duckdns.org/turf/n8n-chat` +3. Envoie un message de test +4. Tu devrais recevoir une réponse formatée + +## Alternative : Créer manuellement + +Si l'import ne fonctionne pas : + +### 1. Webhook +- Type : Webhook +- Method : POST +- Path : `openclaw` +- Response Mode : **"Using 'Respond to Webhook' Node"** + +### 2. Code Node +```javascript +const query = $input.item.json.query || "Aucune question"; +let response = ""; + +if (query.toLowerCase().includes('date')) { + response = `📅 ${new Date().toLocaleDateString('fr-FR')}`; +} else if (query.toLowerCase().includes('test')) { + response = "✅ Connexion OK!"; +} else { + response = `💬 Message reçu: "${query}"`; +} + +return { + json: { + response: response, + timestamp: new Date().toISOString() + } +}; +``` + +### 3. Respond to Webhook +- Respond With : **JSON** +- Response Body : `{{ JSON.stringify({ message: $json.response }) }}` + +## Troubleshooting + +**Erreur "Unused Respond to Webhook"** +→ Vérifie que le nœud "Respond to Webhook" est bien connecté après le Code node + +**Webhook non trouvé** +→ Vérifie que le workflow est **activé** (toggle en haut à droite) + +**Pas de réponse** +→ Regarde les logs d'exécution dans n8n (onglet "Executions") diff --git a/organigramme_h3r7tech.html b/organigramme_h3r7tech.html new file mode 100755 index 0000000..6243636 --- /dev/null +++ b/organigramme_h3r7tech.html @@ -0,0 +1,318 @@ + + + + + Organigramme H3R7Tech + + + +🏠Accueil +

🏢 Organigramme H3R7Tech

Organisation et missions des agents

+ + +

👑

H3R7

CEO / Fondateur

+ + +

Agents

Tâches/jour

24/7

Disponibilité

100%

Automatisé

+ + +

Niveau 1 - Direction

🤖

AgentChief

Superviseur Global

🎯 Missions

Orchestration des agents
Décisions stratégiques
Rapports quotidiens
Gestion des priorities

+ OpenClawDashboard +

+ + +

Niveau 2 - Agents Opérationnels

🔍

AgentScraper

Collecte de Données

🎯 Missions

Scraping annuaires pro
Extraction données prospects
Veille concurrentielle
Nettoyage/dédoublonnage
Mise à jour CRM

+ PythonScrapyCRM +

+ +

📞

AgentSales

Prospection Commerciale

🎯 Missions

Appels sortants
Découverte besoins
Présentation offres
Prise de rendez-vous
Relances prospects

+ CRMScriptsPhone +

+ +

📧

AgentMailing

Email Marketing

🎯 Missions

Campagnes email
Séquences prospection
Templates professionnels
Tracking ouvertures/clics
A/B testing

+ EmailSendGridAnalytics +

+ +

💬

AgentSupport

Relation Client

🎯 Missions

Réponses aux demandes
SAV et suivi
Foire aux questions
Gestion tickets
Satisfaction client

+ TelegramEmailChat +

+ +

📊

AgentAnalyst

Analyste Données

🎯 Missions

KPI tracking
Rapports quotidiens
Analyse conversions
Prévisions business
Recommendations

+ AnalyticsExcelIA +

+ +

🎨

AgentDesign

Créateur Contenu

🎯 Missions

Logos et branding
Sites web
Visuels marketing
Présentations
Templates

+ CanvaFigmaIA +

+ + +

🔄 Flux de Travail Quotidien

🔍 Scraper

09:00

📧 Mailing

10:00

📞 Appels

14:00

📊 Bilan

18:00

+ + +

🛠️ Services H3R7Tech

🌐

Sites Web Pro

Vitrine
E-commerce
Responsive design

📈

Marketing Digital

SEO/SEA
Réseaux sociaux
Content marketing

🤖

Automatisation

CRM
Scraping
Workflows

🏇

Turf Analytics

Prédictions
Statistiques
Gestion paris

+ +

+ ← Retour au Portail +

+ + diff --git a/parse_predictions.py b/parse_predictions.py new file mode 100755 index 0000000..215a014 --- /dev/null +++ b/parse_predictions.py @@ -0,0 +1,68 @@ +#!/usr/bin/env python3 +"""Parse Canalturf predictions and save to DB""" +import json +import sqlite3 +from datetime import datetime + +DB_PATH = "/home/h3r7/turf_scraper/turf.db" + +# Load today's scraper output +with open('/home/h3r7/turf_scraper/v5_20260224_100313.json') as f: + data = json.load(f) + +# Find Canalturf quinté content +quinte_data = None +for page in data['pages']: + if page['site'] == 'canalturf' and 'PRIX RAUBA CAPEU' in page['content']: + quinte_data = page['content'] + break + +if not quinte_data: + print("No Quinté data found!") + exit() + +# Parse predictions from Canalturf +import re + +# Extract horses from the content +horses = [] + +# Base (7 - I'M A BELIEVER) +base_match = re.search(r'Base$s$\s+(\d+)\s+([^$]+)\s+\(([^)]+)$', quinte_data) +if base_match: + num, name, jockey = base_match.groups() + horses.append((name.strip(), int(num), 90)) # 90% confidence for base + +# Chance régulière +chance_matches = re.findall(r'Chance$s$ régulière$s$\s+(\d+)\s+([^$]+)\s+\(([^)]+)$', quinte_data) +for num, name, jockey in chance_matches: + horses.append((name.strip(), int(num), 70)) + +# Outsider +outsider_matches = re.findall(r'Outsider$s$\s+(\d+)\s+([^$]+)\s+\(([^)]+)$', quinte_data) +for num, name, jockey in outsider_matches: + horses.append((name.strip(), int(num), 50)) + +print(f"Parsed {len(horses)} horses from Canalturf") + +# Save to DB +conn = sqlite3.connect(DB_PATH) +c = conn.cursor() + +today = "2026-02-24" + +for name, num, conf in horses: + c.execute(""" + INSERT INTO predictions (date, race_name, horse_number, horse_name, odds, prediction_rank, confidence, source) + VALUES (?, ?, ?, ?, ?, ?, ?, ?) + """, (today, "Quinté Cagnes-sur-Mer", num, name, 0, len(horses)+1, conf, "canalturf")) + +conn.commit() +print(f"Saved {len(horses)} predictions!") + +# Show what we saved +c.execute("SELECT horse_name, horse_number, confidence, source FROM predictions WHERE date = ?", (today,)) +for row in c.fetchall(): + print(f" {row[1]} - {row[0]} ({row[2]}%) - {row[3]}") + +conn.close() diff --git a/performance_tracker.py b/performance_tracker.py new file mode 100755 index 0000000..4de87f9 --- /dev/null +++ b/performance_tracker.py @@ -0,0 +1,135 @@ +#!/usr/bin/env python3 +""" +Turf Performance Tracker +Calculates REX after results are in +""" +import sqlite3 +from datetime import datetime, timedelta +import requests +from bs4 import BeautifulSoup +import re + +DB_PATH = "/home/h3r7/turf_scraper/turf.db" + +HEADERS = { + 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36', +} + +def get_yesterday_results(): + """Scrape yesterday's results from Zone-Turf""" + url = "https://www.zone-turf.fr/rapports/" + try: + r = requests.get(url, headers=HEADERS, timeout=15) + soup = BeautifulSoup(r.text, 'html.parser') + # Extract results - simplified + return [] + except: + return [] + +def calculate_performance(): + """Calculate performance from predictions vs results""" + conn = sqlite3.connect(DB_PATH) + c = conn.cursor() + + # Get predictions without performance + c.execute(''' + SELECT p.id, p.date, p.race_name, p.horse_name, p.prediction_rank, r.position + FROM predictions p + LEFT JOIN results r ON p.date = r.date AND p.horse_name = r.horse_name + WHERE p.date >= date('now', '-7 days') + AND p.id NOT IN (SELECT id FROM performance) + ''') + + pending = c.fetchall() + + added = 0 + for row in pending: + pred_id, date, race, horse, pred_rank, actual_pos = row + + if actual_pos: + hit = (pred_rank == actual_pos) + c.execute(''' + INSERT INTO performance (prediction_date, race_date, horse_name, predicted_rank, actual_position, hit) + VALUES (?, ?, ?, ?, ?, ?) + ''', (date, date, horse, pred_rank, actual_pos, hit)) + added += 1 + + conn.commit() + + # Get overall stats + c.execute(''' + SELECT + COUNT(*) as total, + SUM(CASE WHEN hit = 1 THEN 1 ELSE 0 END) as hits, + ROUND(CAST(SUM(CASE WHEN hit = 1 THEN 1 ELSE 0 END) AS FLOAT) / COUNT(*) * 100, 1) as hit_rate + FROM performance + ''') + stats = c.fetchone() + + conn.close() + + return added, stats + +def main(): + print("="*50) + print("TURF PERFORMANCE TRACKER") + print("="*50) + + # First, try to get results from Zone-Turf + print("\n1. Scraping results...") + results = get_yesterday_results() + print(f" Found {len(results)} results") + + # Calculate performance + print("\n2. Calculating performance...") + added, stats = calculate_performance() + + print(f"\n📊 REX:") + print(f" Total predictions: {stats[0]}") + print(f" Hits: {stats[1]}") + print(f" Hit rate: {stats[2]}%") + + if stats[0] > 0: + # Calculate ROI (simplified) + print(f"\n💰 ROI Analysis:") + + conn = sqlite3.connect(DB_PATH) + c = conn.cursor() + + # Get predictions with results + c.execute(''' + SELECT p.horse_name, p.odds, p.prediction_rank, r.position, r.odds + FROM predictions p + JOIN results r ON p.date = r.date AND p.horse_name = r.horse_name + WHERE p.prediction_rank <= 3 + ''') + + total_stake = 0 + total_return = 0 + + for row in c.fetchall(): + horse, pred_odds, pred_rank, actual_pos, result_odds = row + stake = 2 # 2 euros per bet + total_stake += stake + + # If placed (top 5), win money + if actual_pos and actual_pos <= 5: + # Simple placed bet calculation + if actual_pos == 1: + total_return += stake * (result_odds - 1) + else: + total_return += stake * 0.5 # Placed usually pays ~1.5x + else: + total_return -= stake + + roi = ((total_return / total_stake) * 100) if total_stake > 0 else 0 + print(f" Stake: {total_stake}") + print(f" Return: {total_return:.2f}") + print(f" ROI: {roi:.1f}%") + + conn.close() + + print("\n" + "="*50) + +if __name__ == "__main__": + main() diff --git a/pmu_results.py b/pmu_results.py new file mode 100755 index 0000000..2f9713d --- /dev/null +++ b/pmu_results.py @@ -0,0 +1,968 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +pmu_results.py — Récupération complète des données PMU via API client/7 +========================================================================= +Sources : https://online.turfinfo.api.pmu.fr/rest/client/7 + +Endpoints utilisés : + /programme/DDMMYYYY?meteo=true → programme + météo + ordreArrivee + /programme/.../R{n}/C{n}/participants → partants + cotes + stats + résultats + /programme/.../R{n}/C{n}/rapports-definitifs → gains + +Schéma DB (nouvelles tables, compatibles avec turf.db existant) : + pmu_reunions — une ligne par réunion du jour + pmu_courses — une ligne par course + pmu_partants — un cheval par course (stats + cotes + résultat) + pmu_rapports — gains par type de pari + pmu_meteo — météo par réunion + +Usage : + python3 pmu_results.py # aujourd'hui + python3 pmu_results.py --date 23032026 # date en DDMMYYYY + python3 pmu_results.py --show # afficher résultats BDD sans scraper + python3 pmu_results.py --bilan # bilan prédictions vs arrivées + python3 pmu_results.py --yesterday # raccourci hier +""" + +import sqlite3 +import requests +import json +import base64 +import time +import argparse +import re +from datetime import datetime, timedelta +from pathlib import Path + +# ───────────────────────────────────────────────────────── +# CONFIG +# ───────────────────────────────────────────────────────── +DB_PATH = "/home/h3r7/turf_scraper/turf.db" +OUTPUT_DIR = Path("/home/h3r7/turf_scraper") +API_BASE = "https://online.turfinfo.api.pmu.fr/rest/client/7" + +HEADERS = { + "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36", + "Accept": "application/json", + "Accept-Language": "fr-FR,fr;q=0.9", + "Referer": "https://www.pmu.fr/", +} + +SESSION = requests.Session() +SESSION.headers.update(HEADERS) + + +# ───────────────────────────────────────────────────────── +# SCHÉMA BASE DE DONNÉES +# ───────────────────────────────────────────────────────── +SCHEMA = """ +CREATE TABLE IF NOT EXISTS pmu_reunions ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + date_programme TEXT NOT NULL, + num_reunion INTEGER NOT NULL, + num_externe INTEGER, + nature TEXT, + statut TEXT, + audience TEXT, + hippodrome_code TEXT, + hippodrome_court TEXT, + hippodrome_long TEXT, + pays_code TEXT, + pays_libelle TEXT, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + UNIQUE(date_programme, num_reunion) +); + +CREATE TABLE IF NOT EXISTS pmu_meteo ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + date_programme TEXT NOT NULL, + num_reunion INTEGER NOT NULL, + nebulositecode TEXT, + nebulosite_court TEXT, + nebulosite_long TEXT, + temperature INTEGER, + force_vent INTEGER, + direction_vent TEXT, + date_prevision INTEGER, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + UNIQUE(date_programme, num_reunion) +); + +CREATE TABLE IF NOT EXISTS pmu_courses ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + date_programme TEXT NOT NULL, + num_reunion INTEGER NOT NULL, + num_course INTEGER NOT NULL, + num_externe INTEGER, + libelle TEXT, + libelle_court TEXT, + heure_depart INTEGER, + heure_depart_str TEXT, + distance INTEGER, + distance_unit TEXT, + parcours TEXT, + discipline TEXT, + specialite TEXT, + type_piste TEXT, + corde TEXT, + condition_age TEXT, + condition_sexe TEXT, + categorie_particularite TEXT, + nb_declares_partants INTEGER, + montant_prix INTEGER, + montant_1er INTEGER, + montant_2eme INTEGER, + montant_3eme INTEGER, + montant_4eme INTEGER, + montant_5eme INTEGER, + penetrometre_intitule TEXT, + penetrometre_valeur TEXT, + statut TEXT, + categorie_statut TEXT, + arrivee_definitive INTEGER DEFAULT 0, + rapports_disponibles INTEGER DEFAULT 0, + duree_course_ms INTEGER, + conditions TEXT, + ordre_arrivee_json TEXT, + incidents_json TEXT, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + UNIQUE(date_programme, num_reunion, num_course) +); + +CREATE TABLE IF NOT EXISTS pmu_partants ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + date_programme TEXT NOT NULL, + num_reunion INTEGER NOT NULL, + num_course INTEGER NOT NULL, + num_pmu INTEGER NOT NULL, + id_cheval TEXT, + nom TEXT, + age INTEGER, + sexe TEXT, + race TEXT, + robe TEXT, + pays TEXT, + place_corde INTEGER, + nom_pere TEXT, + nom_mere TEXT, + nom_pere_mere TEXT, + driver TEXT, + driver_change INTEGER DEFAULT 0, + entraineur TEXT, + proprietaire TEXT, + eleveur TEXT, + pays_entrainement TEXT, + oeilleres TEXT, + supplement INTEGER DEFAULT 0, + handicap_valeur REAL, + handicap_poids INTEGER, + musique TEXT, + nombre_courses INTEGER DEFAULT 0, + nombre_victoires INTEGER DEFAULT 0, + nombre_places INTEGER DEFAULT 0, + nombre_places_2eme INTEGER DEFAULT 0, + nombre_places_3eme INTEGER DEFAULT 0, + gains_carriere INTEGER DEFAULT 0, + gains_victoires INTEGER DEFAULT 0, + gains_place INTEGER DEFAULT 0, + gains_annee_en_cours INTEGER DEFAULT 0, + gains_annee_precedente INTEGER DEFAULT 0, + cote_direct REAL, + cote_reference REAL, + tendance_cote TEXT, + favoris INTEGER DEFAULT 0, + ordre_arrivee INTEGER DEFAULT 0, + statut_partant TEXT, + distance_cheval_prec TEXT, + distance_cheval_prec_code INTEGER, + commentaire_apres_course TEXT, + indicateur_inedit INTEGER DEFAULT 0, + tx_victoire REAL, + tx_place REAL, + forme_recente REAL, + tendance_forme REAL, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + UNIQUE(date_programme, num_reunion, num_course, num_pmu) +); + +CREATE TABLE IF NOT EXISTS pmu_rapports ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + date_programme TEXT NOT NULL, + num_reunion INTEGER NOT NULL, + num_course INTEGER NOT NULL, + type_pari TEXT NOT NULL, + famille_pari TEXT, + mise_base INTEGER, + combinaison TEXT NOT NULL, + dividende INTEGER, + dividende_euro REAL, + nb_gagnants REAL, + libelle TEXT, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + UNIQUE(date_programme, num_reunion, num_course, type_pari, combinaison) +); +""" + +# Vues recréées séparément (DROP + CREATE) +VUES = [ + ("v_resultats_complets", """ +CREATE VIEW v_resultats_complets AS +SELECT + p.date_programme, + p.num_reunion, + p.num_course, + c.libelle AS course, + c.heure_depart_str AS heure, + r.hippodrome_long AS hippodrome, + c.discipline, + c.distance, + c.type_piste, + c.penetrometre_intitule AS penetrometre, + m.temperature, + m.nebulosite_court AS meteo, + m.force_vent, + m.direction_vent, + p.num_pmu, + p.nom AS cheval, + p.age, + p.sexe, + p.race, + p.driver, + p.entraineur, + p.oeilleres, + p.handicap_valeur, + p.musique, + p.nombre_courses, + p.nombre_victoires, + p.nombre_places, + p.gains_carriere, + p.gains_annee_en_cours, + p.cote_direct, + p.cote_reference, + p.tendance_cote, + p.tx_victoire, + p.tx_place, + p.forme_recente, + p.tendance_forme, + p.ordre_arrivee AS position_finale, + p.distance_cheval_prec, + p.commentaire_apres_course, + p.nom_pere, + p.nom_mere, + p.pays_entrainement +FROM pmu_partants p +JOIN pmu_courses c ON c.date_programme=p.date_programme + AND c.num_reunion=p.num_reunion + AND c.num_course=p.num_course +JOIN pmu_reunions r ON r.date_programme=p.date_programme + AND r.num_reunion=p.num_reunion +LEFT JOIN pmu_meteo m ON m.date_programme=p.date_programme + AND m.num_reunion=p.num_reunion +"""), + ("v_bilan_predictions", """ +CREATE VIEW v_bilan_predictions AS +SELECT + pr.date, + pr.race_name, + pr.horse_name, + pr.horse_number, + pr.prediction_rank, + pr.odds AS cote_pred, + pr.source, + pa.ordre_arrivee AS position_reelle, + pa.cote_direct AS cote_finale, + pa.commentaire_apres_course, + CASE + WHEN pa.ordre_arrivee = 1 THEN 'GAGNANT' + WHEN pa.ordre_arrivee <= 3 THEN 'PLACE' + WHEN pa.ordre_arrivee <= 5 THEN 'TOP5' + WHEN pa.ordre_arrivee > 5 THEN 'HORS' + ELSE 'INCONNU' + END AS resultat +FROM predictions pr +LEFT JOIN pmu_partants pa + ON pa.date_programme = pr.date + AND pa.nom = pr.horse_name + AND pa.num_pmu = pr.horse_number +"""), +] + + +# ───────────────────────────────────────────────────────── +# INITIALISATION DB +# ───────────────────────────────────────────────────────── +def init_db(): + conn = sqlite3.connect(DB_PATH) + conn.executescript(SCHEMA) + for vue_name, vue_sql in VUES: + conn.execute(f"DROP VIEW IF EXISTS {vue_name}") + conn.execute(vue_sql) + conn.commit() + conn.close() + print("✅ Schéma DB initialisé (5 tables + 2 vues)") + + +# ───────────────────────────────────────────────────────── +# APPELS API +# ───────────────────────────────────────────────────────── +def api_get(path, params=None, retries=3): + url = API_BASE + path + for attempt in range(retries): + try: + r = SESSION.get(url, params=params, timeout=15) + if r.status_code == 200: + return r.json() + elif r.status_code == 404: + return None + else: + print(f" ⚠️ HTTP {r.status_code}") + time.sleep(1) + except Exception as e: + if attempt == retries - 1: + print(f" ❌ Erreur réseau : {e}") + return None + + +def ts_to_hhmm(ts_ms): + if not ts_ms: + return "" + try: + return datetime.fromtimestamp(ts_ms / 1000).strftime("%H:%M") + except Exception: + return "" + + +# ───────────────────────────────────────────────────────── +# CALCUL STATS MUSIQUE +# ───────────────────────────────────────────────────────── +def parse_musique(musique): + if not musique: + return None, None, None, None + clean = re.sub(r'$\d+$', '', musique) + resultats = re.findall(r'(\d+|D|0)([amphsc]?)', clean) + positions = [] + for pos, _ in resultats[:10]: + positions.append(99 if pos == 'D' else int(pos)) + if not positions: + return None, None, None, None + nb = len(positions) + vict = positions.count(1) + plac = sum(1 for p in positions if 1 <= p <= 3) + rec = [p for p in positions[:3] if p != 99] + forme_recente = round(sum(rec) / len(rec), 1) if rec else None + if len(positions) >= 4: + tendance = round(sum(positions[-4:]) / 4 - sum(positions[:4]) / 4, 1) + else: + tendance = 0.0 + return ( + round(vict / nb * 100, 1) if nb else None, + round(plac / nb * 100, 1) if nb else None, + forme_recente, + tendance, + ) + + +# ───────────────────────────────────────────────────────── +# SAUVEGARDE RÉUNION +# ───────────────────────────────────────────────────────── +def save_reunion(conn, date_str, reunion): + hipp = reunion.get("hippodrome", {}) + pays = reunion.get("pays", {}) + conn.execute(""" + INSERT OR REPLACE INTO pmu_reunions + (date_programme, num_reunion, num_externe, nature, statut, audience, + hippodrome_code, hippodrome_court, hippodrome_long, pays_code, pays_libelle) + VALUES (?,?,?,?,?,?,?,?,?,?,?) + """, ( + date_str, + reunion.get("numOfficiel"), + reunion.get("numExterne"), + reunion.get("nature"), + reunion.get("statut"), + reunion.get("audience"), + hipp.get("code"), + hipp.get("libelleCourt"), + hipp.get("libelleLong"), + pays.get("code"), + pays.get("libelle"), + )) + + +# ───────────────────────────────────────────────────────── +# SAUVEGARDE MÉTÉO +# ───────────────────────────────────────────────────────── +def save_meteo(conn, date_str, num_reunion, meteo): + if not meteo: + return + conn.execute(""" + INSERT OR REPLACE INTO pmu_meteo + (date_programme, num_reunion, nebulositecode, nebulosite_court, + nebulosite_long, temperature, force_vent, direction_vent, date_prevision) + VALUES (?,?,?,?,?,?,?,?,?) + """, ( + date_str, num_reunion, + meteo.get("nebulositeCode"), + meteo.get("nebulositeLibelleCourt"), + meteo.get("nebulositeLibelleLong"), + meteo.get("temperature"), + meteo.get("forceVent"), + meteo.get("directionVent"), + meteo.get("datePrevision"), + )) + + +# ───────────────────────────────────────────────────────── +# SAUVEGARDE COURSE +# ───────────────────────────────────────────────────────── +def save_course(conn, date_str, num_reunion, course): + heure_ts = course.get("heureDepart") + penet = course.get("penetrometre") or {} + incidents = course.get("incidents", []) + ordre_arr = course.get("ordreArrivee", []) + + conn.execute(""" + INSERT OR REPLACE INTO pmu_courses + (date_programme, num_reunion, num_course, num_externe, + libelle, libelle_court, heure_depart, heure_depart_str, + distance, distance_unit, parcours, discipline, specialite, + type_piste, corde, condition_age, condition_sexe, + categorie_particularite, nb_declares_partants, + montant_prix, montant_1er, montant_2eme, montant_3eme, montant_4eme, montant_5eme, + penetrometre_intitule, penetrometre_valeur, + statut, categorie_statut, arrivee_definitive, rapports_disponibles, + duree_course_ms, conditions, ordre_arrivee_json, incidents_json) + VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?) + """, ( + date_str, num_reunion, + course.get("numOrdre"), + course.get("numExterne"), + course.get("libelle"), + course.get("libelleCourt"), + heure_ts, + ts_to_hhmm(heure_ts), + course.get("distance"), + course.get("distanceUnit"), + course.get("parcours"), + course.get("discipline"), + course.get("specialite"), + course.get("typePiste"), + course.get("corde"), + course.get("conditionAge"), + course.get("conditionSexe"), + course.get("categorieParticularite"), + course.get("nombreDeclaresPartants"), + course.get("montantPrix"), + course.get("montantOffert1er"), + course.get("montantOffert2eme"), + course.get("montantOffert3eme"), + course.get("montantOffert4eme"), + course.get("montantOffert5eme"), + penet.get("intitule"), + penet.get("commentaire"), + course.get("statut"), + course.get("categorieStatut"), + int(bool(course.get("arriveeDefinitive"))), + int(bool(course.get("rapportsDefinitifsDisponibles"))), + course.get("dureeCourse"), + course.get("conditions"), + json.dumps(ordre_arr) if ordre_arr else None, + json.dumps(incidents) if incidents else None, + )) + + +# ───────────────────────────────────────────────────────── +# SAUVEGARDE PARTANTS +# ───────────────────────────────────────────────────────── +def save_partants(conn, date_str, num_reunion, num_course, participants): + saved = 0 + for p in participants: + robe = p.get("robe") or {} + gains = p.get("gainsParticipant") or {} + rdr = p.get("dernierRapportDirect") or {} + rref = p.get("dernierRapportReference") or {} + dist_prec = p.get("distanceChevalPrecedent") or {} + comm = p.get("commentaireApresCourse") or {} + musique = p.get("musique", "") + tx_vic, tx_plac, forme_rec, tendance_f = parse_musique(musique) + + conn.execute(""" + INSERT OR REPLACE INTO pmu_partants + (date_programme, num_reunion, num_course, + num_pmu, id_cheval, nom, age, sexe, race, robe, pays, place_corde, + nom_pere, nom_mere, nom_pere_mere, + driver, driver_change, entraineur, proprietaire, eleveur, pays_entrainement, + oeilleres, supplement, handicap_valeur, handicap_poids, + musique, nombre_courses, nombre_victoires, nombre_places, + nombre_places_2eme, nombre_places_3eme, + gains_carriere, gains_victoires, gains_place, + gains_annee_en_cours, gains_annee_precedente, + cote_direct, cote_reference, tendance_cote, favoris, + ordre_arrivee, statut_partant, + distance_cheval_prec, distance_cheval_prec_code, + commentaire_apres_course, indicateur_inedit, + tx_victoire, tx_place, forme_recente, tendance_forme) + VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?, + ?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?) + """, ( + date_str, num_reunion, num_course, + p.get("numPmu"), + p.get("idCheval"), + p.get("nom"), + p.get("age"), + p.get("sexe"), + p.get("race"), + robe.get("libelleLong"), + p.get("pays"), + p.get("placeCorde"), + p.get("nomPere"), + p.get("nomMere"), + p.get("nomPereMere"), + p.get("driver"), + int(bool(p.get("driverChange"))), + p.get("entraineur"), + p.get("proprietaire"), + p.get("eleveur"), + p.get("paysEntrainement"), + p.get("oeilleres"), + p.get("supplement", 0), + p.get("handicapValeur"), + p.get("handicapPoids"), + musique, + p.get("nombreCourses", 0), + p.get("nombreVictoires", 0), + p.get("nombrePlaces", 0), + p.get("nombrePlacesSecond", 0), + p.get("nombrePlacesTroisieme", 0), + gains.get("gainsCarriere", 0), + gains.get("gainsVictoires", 0), + gains.get("gainsPlace", 0), + gains.get("gainsAnneeEnCours", 0), + gains.get("gainsAnneePrecedente", 0), + rdr.get("rapport"), + rref.get("rapport"), + rref.get("indicateurTendance"), + int(bool(rdr.get("favoris"))), + p.get("ordreArrivee", 0), + p.get("statut"), + dist_prec.get("libelleLong"), + dist_prec.get("code"), + comm.get("texte"), + int(bool(p.get("indicateurInedit"))), + tx_vic, tx_plac, forme_rec, tendance_f, + )) + saved += 1 + return saved + + +# ───────────────────────────────────────────────────────── +# SAUVEGARDE RAPPORTS +# ───────────────────────────────────────────────────────── +def save_rapports(conn, date_str, num_reunion, num_course, data): + saved = 0 + if not data or not isinstance(data, list): + return 0 + for bloc in data: + type_pari = bloc.get("typePari", "") + famille = bloc.get("famillePari", "") + mise_base = bloc.get("miseBase", 0) + for rap in bloc.get("rapports", []): + combinaison = str(rap.get("combinaison", "")) + dividende = rap.get("dividende", 0) or 0 + try: + conn.execute(""" + INSERT OR IGNORE INTO pmu_rapports + (date_programme, num_reunion, num_course, + type_pari, famille_pari, mise_base, + combinaison, dividende, dividende_euro, nb_gagnants, libelle) + VALUES (?,?,?,?,?,?,?,?,?,?,?) + """, ( + date_str, num_reunion, num_course, + type_pari, famille, mise_base, + combinaison, + dividende, + round(dividende / 100, 2), + rap.get("nombreGagnants"), + rap.get("libelle"), + )) + saved += 1 + except Exception: + pass + return saved + + +# ───────────────────────────────────────────────────────── +# BILAN PRÉDICTIONS VS ARRIVÉES +# ───────────────────────────────────────────────────────── +def afficher_bilan(date_str): + conn = sqlite3.connect(DB_PATH) + conn.row_factory = sqlite3.Row + rows = conn.execute( + "SELECT * FROM v_bilan_predictions WHERE date=? ORDER BY race_name, prediction_rank", + (date_str,) + ).fetchall() + conn.close() + + if not rows: + print(f"Aucune donnée de bilan pour {date_str} (prédictions ou partants manquants)") + return + + from collections import defaultdict + par_course = defaultdict(list) + for r in rows: + par_course[r["race_name"]].append(dict(r)) + + total_g = total_p = total_t = total = 0 + print(f"\n{'='*65}") + print(f" 📊 BILAN PRÉDICTIONS vs ARRIVÉES — {date_str}") + print(f"{'='*65}") + + for course, chevaux in sorted(par_course.items()): + print(f"\n 🏇 {course}") + print(f" {'N°':<4} {'CHEVAL':<22} {'RANG PRÉDIT':<12} {'POSITION':<9} RÉSULTAT") + print(f" {'-'*60}") + for ch in chevaux: + pos = ch["position_reelle"] or "?" + res = ch["resultat"] or "-" + em = {"GAGNANT": "🥇", "PLACE": "🥈", "TOP5": "✅", "HORS": "❌"}.get(res, "·") + print(f" {ch['horse_number']:<4} {ch['horse_name']:<22} #{ch['prediction_rank']:<11} {str(pos):<9} {em} {res}") + total += 1 + if res == "GAGNANT": total_g += 1 + if res in ("GAGNANT", "PLACE"): total_p += 1 + if res in ("GAGNANT", "PLACE", "TOP5"): total_t += 1 + + print(f"\n TOTAL : {total} prédictions analysées") + print(f" 🥇 Gagnants : {total_g} | 🥈 Placés top3 : {total_p} | ✅ Top5 : {total_t}") + print(f"{'='*65}\n") + + +# ───────────────────────────────────────────────────────── +# AFFICHAGE RÉSULTATS BDD +# ───────────────────────────────────────────────────────── +def afficher_resultats(date_str): + conn = sqlite3.connect(DB_PATH) + conn.row_factory = sqlite3.Row + + courses = conn.execute(""" + SELECT c.num_reunion, c.num_course, c.libelle, c.heure_depart_str, + r.hippodrome_court, c.discipline, c.distance, + c.penetrometre_intitule, c.nb_declares_partants, + m.temperature, m.nebulosite_court, m.direction_vent, m.force_vent + FROM pmu_courses c + JOIN pmu_reunions r ON r.date_programme=c.date_programme AND r.num_reunion=c.num_reunion + LEFT JOIN pmu_meteo m ON m.date_programme=c.date_programme AND m.num_reunion=c.num_reunion + WHERE c.date_programme=? AND c.arrivee_definitive=1 + ORDER BY c.num_reunion, c.num_course + """, (date_str,)).fetchall() + + if not courses: + print(f"\nAucune course terminée en BDD pour {date_str}") + conn.close() + return + + print(f"\n{'='*65}") + print(f" RÉSULTATS PMU — {date_str} ({len(courses)} course(s))") + print(f"{'='*65}") + + for co in courses: + print(f"\n R{co['num_reunion']}C{co['num_course']} | {co['libelle']}") + meteo_str = "" + if co["temperature"]: + meteo_str = f" | {co['temperature']}°C {co['nebulosite_court']} vent {co['direction_vent']} {co['force_vent']}km/h" + print(f" {co['hippodrome_court']} — {co['heure_depart_str']} — {co['discipline']} {co['distance']}m — {co['nb_declares_partants']} partants{meteo_str}") + if co["penetrometre_intitule"]: + print(f" Terrain : {co['penetrometre_intitule']}") + + top5 = conn.execute(""" + SELECT num_pmu, nom, driver, ordre_arrivee, + cote_direct, distance_cheval_prec, commentaire_apres_course + FROM pmu_partants + WHERE date_programme=? AND num_reunion=? AND num_course=? + AND ordre_arrivee BETWEEN 1 AND 5 + ORDER BY ordre_arrivee + """, (date_str, co["num_reunion"], co["num_course"])).fetchall() + + if top5: + print(f" {'Pos':<4} {'N°':<4} {'CHEVAL':<22} {'Driver':<16} {'Cote':>6} Ecart") + print(f" {'-'*64}") + for ch in top5: + cote = f"{ch['cote_direct']:.1f}" if ch["cote_direct"] else "-" + ecart = ch["distance_cheval_prec"] or "" + print(f" {ch['ordre_arrivee']:<4} {ch['num_pmu']:<4} {ch['nom']:<22} {ch['driver'] or '':<16} {cote:>6} {ecart}") + if ch["commentaire_apres_course"]: + print(f" → {ch['commentaire_apres_course'][:80]}") + + # Rapports principaux + raps = conn.execute(""" + SELECT type_pari, combinaison, dividende_euro, nb_gagnants + FROM pmu_rapports + WHERE date_programme=? AND num_reunion=? AND num_course=? + AND type_pari IN ('SIMPLE_GAGNANT','SIMPLE_PLACE','COUPLE_GAGNANT', + 'TIERCE','QUARTE_PLUS','QUINTE_PLUS') + ORDER BY CASE type_pari + WHEN 'SIMPLE_GAGNANT' THEN 1 + WHEN 'SIMPLE_PLACE' THEN 2 + WHEN 'COUPLE_GAGNANT' THEN 3 + WHEN 'TIERCE' THEN 4 + WHEN 'QUARTE_PLUS' THEN 5 + WHEN 'QUINTE_PLUS' THEN 6 END, combinaison + """, (date_str, co["num_reunion"], co["num_course"])).fetchall() + + if raps: + print(f" Rapports :") + for rap in raps: + nb = f"({int(rap['nb_gagnants'])} gagnants)" if rap["nb_gagnants"] else "" + print(f" {rap['type_pari']:<22} {rap['combinaison']:<12} {rap['dividende_euro']:>8.2f}€ {nb}") + + conn.close() + print() + + +# ───────────────────────────────────────────────────────── +# EXPORT JSON +# ───────────────────────────────────────────────────────── +def export_json(date_str, date_ddmmyyyy): + conn = sqlite3.connect(DB_PATH) + conn.row_factory = sqlite3.Row + + def rows(sql): + return [dict(r) for r in conn.execute(sql, (date_str,)).fetchall()] + + out = { + "date": date_str, + "generated_at": datetime.now().isoformat(), + "reunions": rows("SELECT * FROM pmu_reunions WHERE date_programme=?"), + "courses": rows("SELECT * FROM pmu_courses WHERE date_programme=?"), + "partants": rows("SELECT * FROM pmu_partants WHERE date_programme=?"), + "rapports": rows("SELECT * FROM pmu_rapports WHERE date_programme=?"), + } + conn.close() + + fname = OUTPUT_DIR / f"pmu_{date_ddmmyyyy}.json" + with open(fname, "w", encoding="utf-8") as f: + json.dump(out, f, indent=2, ensure_ascii=False, default=str) + print(f" 📁 Export JSON : {fname}\n") + + +# ───────────────────────────────────────────────────────── +# ORCHESTRATEUR PRINCIPAL +# ───────────────────────────────────────────────────────── +def run(date_ddmmyyyy: str): + d = datetime.strptime(date_ddmmyyyy, "%d%m%Y") + date_str = d.strftime("%Y-%m-%d") + + print(f"\n{'='*65}") + print(f" 🏇 PMU SCRAPER — {date_str} (API client/7)") + print(f"{'='*65}\n") + + init_db() + conn = sqlite3.connect(DB_PATH) + + # ── 1. Programme ───────────────────────────────────── + print("📡 Récupération du programme...") + prog = api_get(f"/programme/{date_ddmmyyyy}", params={"meteo": "true"}) + if not prog: + print("❌ Programme indisponible.") + conn.close() + return + + reunions = prog.get("programme", {}).get("reunions", []) + print(f" ✅ {len(reunions)} réunion(s)\n") + + total_courses = total_partants = total_rapports = 0 + + for reunion in reunions: + r_num = reunion.get("numOfficiel") + hipp = reunion.get("hippodrome", {}).get("libelleCourt", "?") + statut_r = reunion.get("statut", "") + courses = reunion.get("courses", []) + + print(f" ── R{r_num} {hipp} ({statut_r}) — {len(courses)} course(s)") + + save_reunion(conn, date_str, reunion) + save_meteo(conn, date_str, r_num, reunion.get("meteo")) + conn.commit() + + for course in courses: + c_num = course.get("numOrdre") + libelle = course.get("libelleCourt", f"C{c_num}") + heure = ts_to_hhmm(course.get("heureDepart")) + terminee = bool(course.get("arriveeDefinitive")) + + print(f" C{c_num} {libelle:<20} {heure} ", end="", flush=True) + + save_course(conn, date_str, r_num, course) + total_courses += 1 + + # ── 2. Participants ─────────────────────────── + parts = api_get( + f"/programme/{date_ddmmyyyy}/R{r_num}/C{c_num}/participants", + params={"withCotes": "true"} + ) + time.sleep(0.2) + + if parts and "participants" in parts: + n = save_partants(conn, date_str, r_num, c_num, parts["participants"]) + total_partants += n + print(f"{n} partants ", end="", flush=True) + else: + print("(pas de partants) ", end="", flush=True) + + # ── 3. Rapports (si terminée) ───────────────── + if terminee: + raps = api_get( + f"/programme/{date_ddmmyyyy}/R{r_num}/C{c_num}/rapports-definitifs" + ) + time.sleep(0.2) + if raps and isinstance(raps, list): + n = save_rapports(conn, date_str, r_num, c_num, raps) + total_rapports += n + print(f"✅ {n} rapports", flush=True) + else: + print("(pas de rapports)", flush=True) + else: + print("(en attente)", flush=True) + + conn.commit() + + calculate_and_save_scores(conn, date_str) + + conn.close() + + print(f"\n{'='*65}") + print(f" ✅ TERMINÉ — Courses: {total_courses} | Partants: {total_partants} | Rapports: {total_rapports}") + print(f"{'='*65}\n") + + export_json(date_str, date_ddmmyyyy) + afficher_resultats(date_str) + afficher_bilan(date_str) + + +def calculate_and_save_scores(conn, date_str): + """Calcule et sauvegarde les scores Top 5 pour chaque course terminée""" + conn.row_factory = sqlite3.Row + courses = conn.execute(""" + SELECT date_programme, num_reunion, num_course, libelle, heure_depart_str, + ordre_arrivee_json + FROM pmu_courses + WHERE date_programme = ? AND arrivee_definitive = 1 + """, (date_str,)).fetchall() + + if not courses: + print(" Aucune course terminée pour calcul des scores") + return + + for course in courses: + if not course['ordre_arrivee_json']: + continue + + arrivee = json.loads(course['ordre_arrivee_json']) + if not arrivee: + continue + + race_name = f"R{course['num_reunion']}C{course['num_course']} - {course['libelle']}" + + partants = conn.execute(""" + SELECT nom, num_pmu, ordre_arrivee, cote_direct + FROM pmu_partants + WHERE date_programme = ? AND num_reunion = ? AND num_course = ? + ORDER BY cote_direct ASC + """, (date_str, course['num_reunion'], course['num_course'])).fetchall() + + if not partants: + continue + + arrived_names = [p['nom'] for p in partants if p['ordre_arrivee'] and 1 <= p['ordre_arrivee'] <= 5] + + top5_cotes = [p['nom'] for p in partants[:5] if p['nom']] + + bases = [p['nom'] for p in conn.execute(""" + SELECT DISTINCT pr.horse_name + FROM predictions pr + WHERE pr.date = ? AND pr.source = 'Bases' + """, (date_str,)).fetchall()] + + chances = [p['nom'] for p in conn.execute(""" + SELECT DISTINCT pr.horse_name + FROM predictions pr + WHERE pr.date = ? AND pr.source = 'Chances' + """, (date_str,)).fetchall()] + + outsiders = [p['nom'] for p in conn.execute(""" + SELECT DISTINCT pr.horse_name + FROM predictions pr + WHERE pr.date = ? AND pr.source = 'Outsiders' + """, (date_str,)).fetchall()] + + top5_bc = (bases + chances)[:5] + top5_bo = (bases + outsiders)[:5] + + hits_cotes = sum(1 for h in top5_cotes if h in arrived_names) + hits_bc = sum(1 for h in top5_bc if h in arrived_names) + hits_bo = sum(1 for h in top5_bo if h in arrived_names) + + hippodrome = conn.execute(""" + SELECT r.hippodrome_long + FROM pmu_reunions r + WHERE r.date_programme = ? AND r.num_reunion = ? + """, (date_str, course['num_reunion'])).fetchone() + + hippodrome_name = hippodrome['hippodrome_long'] if hippodrome else '' + + data = { + 'date': date_str, + 'race_name': race_name, + 'race_time': course['heure_depart_str'], + 'hippodrome': hippodrome_name, + 'top5_cotes': top5_cotes, + 'top5_cotes_hits': hits_cotes, + 'top5_bc': top5_bc, + 'top5_bc_hits': hits_bc, + 'top5_bo': top5_bo, + 'top5_bo_hits': hits_bo, + 'results_top5': arrived_names + } + + try: + auth = base64.b64encode(b'h3r7:h3r7').decode('ascii') + resp = requests.post( + 'http://localhost:8765/turf/api/scores', + json=data, + headers={'Authorization': f'Basic {auth}'}, + timeout=10 + ) + print(f" 💾 Scores sauvegardés: {race_name} - Hits: Cotes:{hits_cotes} BC:{hits_bc} BO:{hits_bo}") + except Exception as e: + print(f" ⚠️ Erreur sauvegarde scores: {e}") + + +# ───────────────────────────────────────────────────────── +# POINT D'ENTRÉE +# ───────────────────────────────────────────────────────── +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="PMU Results — API client/7") + parser.add_argument("--date", "-d", + default=datetime.now().strftime("%d%m%Y"), + help="Date DDMMYYYY (défaut: aujourd'hui)") + parser.add_argument("--yesterday", "-y", action="store_true") + parser.add_argument("--show", "-s", action="store_true", + help="Afficher résultats BDD sans scraper") + parser.add_argument("--bilan", "-b", action="store_true", + help="Afficher bilan prédictions vs arrivées") + args = parser.parse_args() + + if args.yesterday: + date_fmt = (datetime.now() - timedelta(days=1)).strftime("%d%m%Y") + else: + date_fmt = args.date + + date_iso = datetime.strptime(date_fmt, "%d%m%Y").strftime("%Y-%m-%d") + + if args.show: + init_db() + afficher_resultats(date_iso) + elif args.bilan: + init_db() + afficher_bilan(date_iso) + else: + run(date_fmt) diff --git a/populate_analytics.py b/populate_analytics.py new file mode 100644 index 0000000..abebc97 --- /dev/null +++ b/populate_analytics.py @@ -0,0 +1,294 @@ +#!/usr/bin/env python3 +""" +Populate Analytics Tables +Remplit les tables de statistiques depuis les données existantes +""" + +import sqlite3 +import os +from datetime import datetime, timedelta + +DB_PATH = "/home/h3r7/turf_scraper/turf.db" + + +def get_db(): + return sqlite3.connect(DB_PATH) + + +def populate_bet_results(): + """Remplit bet_results depuis recommendations""" + conn = get_db() + c = conn.cursor() + + # Vérifier si déjà rempli + c.execute("SELECT COUNT(*) FROM bet_results") + if c.fetchone()[0] > 0: + print("✅ bet_results déjà rempli") + conn.close() + return + + # Récupérer depuis recommendations + c.execute(""" + SELECT date, race_name, type_pari, cheval1, numero1, cote, mise, resultat + FROM recommendations + WHERE resultat IS NOT NULL AND resultat != '' + ORDER BY date + """) + + rows = c.fetchall() + + for row in rows: + date, race_name, type_pari, cheval1, numero1, cote, mise, resultat = row + + gain = 0 + if resultat == 'GAGNE': + gain = float(cote or 1) * float(mise or 1) + + c2 = conn.cursor() + c2.execute(""" + INSERT INTO bet_results (date, race_name, type_pari, horse_name, horse_number, cote, mise, resultat, gain, model_source) + VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?) + """, (date, race_name, type_pari, cheval1, numero1, cote, mise, resultat, gain, source_reco or 'manual')) + + conn.commit() + print(f"✅ {len(rows)} paris ajoutés dans bet_results") + conn.close() + + +def populate_daily_stats(): + """Remplit daily_stats depuis bet_results""" + conn = get_db() + c = conn.cursor() + + # Vérifier si déjà rempli + c.execute("SELECT COUNT(*) FROM daily_stats") + if c.fetchone()[0] > 0: + print("✅ daily_stats déjà rempli") + conn.close() + return + + # Grouper par date + c.execute(""" + SELECT + date, + COUNT(*) as total, + SUM(CASE WHEN resultat = 'GAGNE' THEN 1 ELSE 0 END) as gagne, + SUM(mise) as mise, + SUM(gain) as gain + FROM bet_results + GROUP BY date + ORDER BY date + """) + + rows = c.fetchall() + + for row in rows: + date, total, gagne, mise, gain = row + + precision = (gagne / total * 100) if total > 0 else 0 + roi = ((gain - mise) / mise * 100) if mise > 0 else 0 + + c2 = conn.cursor() + c2.execute(""" + INSERT INTO daily_stats (date, total_bets, bets_gagne, bets_perdu, mise_totale, gain_total, precision_pct, roi_pct) + VALUES (?, ?, ?, ?, ?, ?, ?, ?) + """, (date, total, gagne, total - gagne, mise, gain, precision, roi)) + + conn.commit() + print(f"✅ {len(rows)} jours ajoutés dans daily_stats") + conn.close() + + +def populate_stats_by_type(): + """Remplit stats_by_type depuis bet_results""" + conn = get_db() + c = conn.cursor() + + # Vérifier si déjà rempli + c.execute("SELECT COUNT(*) FROM stats_by_type") + if c.fetchone()[0] > 0: + print("✅ stats_by_type déjà rempli") + conn.close() + return + + # Grouper par date et type + c.execute(""" + SELECT + date, + type_pari, + COUNT(*) as total, + SUM(CASE WHEN resultat = 'GAGNE' THEN 1 ELSE 0 END) as gagne, + SUM(mise) as mise, + SUM(gain) as gain + FROM bet_results + GROUP BY date, type_pari + ORDER BY date, type_pari + """) + + rows = c.fetchall() + + for row in rows: + date, type_pari, total, gagne, mise, gain = row + + precision = (gagne / total * 100) if total > 0 else 0 + roi = ((gain - mise) / mise * 100) if mise > 0 else 0 + + c2 = conn.cursor() + c2.execute(""" + INSERT INTO stats_by_type (date, type_pari, total_bets, gagne, perdu, mise_totale, gain_total, precision_pct, roi_pct) + VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?) + """, (date, type_pari, total, gagne, total - gagne, mise, gain, precision, roi)) + + conn.commit() + print(f"✅ {len(rows)} lignes ajoutées dans stats_by_type") + conn.close() + + +def update_daily_stats(): + """Met à jour les statistiques quotidiennes (à appeler après chaque nouveau résultat)""" + conn = get_db() + c = conn.cursor() + + # Récupérer les dates sans stats ou avec nouveaux paris + c.execute(""" + SELECT DISTINCT date FROM bet_results + WHERE date NOT IN (SELECT date FROM daily_stats) + ORDER BY date + """) + + dates = [r[0] for r in c.fetchall()] + + for date in dates: + c.execute(""" + SELECT + COUNT(*) as total, + SUM(CASE WHEN resultat = 'GAGNE' THEN 1 ELSE 0 END) as gagne, + SUM(mise) as mise, + SUM(gain) as gain + FROM bet_results + WHERE date = ? + """, (date,)) + + row = c.fetchone() + if row: + total, gagne, mise, gain = row + precision = (gagne / total * 100) if total > 0 else 0 + roi = ((gain - mise) / mise * 100) if mise > 0 else 0 + + c.execute(""" + INSERT OR REPLACE INTO daily_stats (date, total_bets, bets_gagne, bets_perdu, mise_totale, gain_total, precision_pct, roi_pct) + VALUES (?, ?, ?, ?, ?, ?, ?, ?) + """, (date, total, gagne, total - gagne, mise, gain, precision, roi)) + + conn.commit() + print(f"✅ {len(dates)} dates mises à jour dans daily_stats") + conn.close() + + +def get_stats_from_db(start_date=None, end_date=None): + """Récupère les stats depuis la base""" + conn = get_db() + c = conn.cursor() + + if not start_date: + start_date = (datetime.now() - timedelta(days=30)).strftime('%Y-%m-%d') + if not end_date: + end_date = datetime.now().strftime('%Y-%m-%d') + + # Daily stats + c.execute(""" + SELECT date, total_bets, bets_gagne, bets_perdu, mise_totale, gain_total, precision_pct, roi_pct + FROM daily_stats + WHERE date BETWEEN ? AND ? + ORDER BY date DESC + """, (start_date, end_date)) + + daily = [] + for row in c.fetchall(): + daily.append({ + 'date': row[0], + 'total_bets': row[1], + 'gagne': row[2], + 'perdu': row[3], + 'precision': row[6], + 'mises': row[4], + 'gains': row[5], + 'roi': row[7] + }) + + # Summary + c.execute(""" + SELECT + SUM(total_bets) as total, + SUM(bets_gagne) as gagne, + SUM(mise_totale) as mise, + SUM(gain_total) as gain + FROM daily_stats + WHERE date BETWEEN ? AND ? + """, (start_date, end_date)) + + row = c.fetchone() + total, gagne, mise, gain = row + + summary = { + 'total_bets': total or 0, + 'gagne': gagne or 0, + 'perdu': (total or 0) - (gagne or 0), + 'precision': (gagne / total * 100) if total and total > 0 else 0, + 'mise_totale': mise or 0, + 'gain_total': gain or 0, + 'roi': ((gain - mise) / mise * 100) if mise and mise > 0 else 0 + } + + # By type + c.execute(""" + SELECT + type_pari, + SUM(total_bets) as total, + SUM(gagne) as gagne, + SUM(mise_totale) as mise, + SUM(gain_total) as gain + FROM stats_by_type + WHERE date BETWEEN ? AND ? + GROUP BY type_pari + """, (start_date, end_date)) + + by_type = {} + for row in c.fetchall(): + type_pari, total, gagne, mise, gain = row + roi = ((gain - mise) / mise * 100) if mise > 0 else 0 + precision = (gagne / total * 100) if total > 0 else 0 + + by_type[type_pari] = { + 'count': total, + 'gagne': gagne, + 'mise': mise, + 'gain': gain, + 'roi': roi, + 'precision': precision + } + + conn.close() + + return { + 'period': {'start': start_date, 'end': end_date}, + 'summary': summary, + 'daily': daily, + 'by_type': by_type + } + + +if __name__ == "__main__": + print("="*50) + print("Population des tables analytics") + print("="*50) + + populate_bet_results() + populate_daily_stats() + populate_stats_by_type() + + print("\n✅ Tables analytics populées!") + + # Test + stats = get_stats_from_db() + print(f"\nRésumé: {stats['summary']}") diff --git a/portail.html b/portail.html new file mode 100755 index 0000000..3bb2c9b --- /dev/null +++ b/portail.html @@ -0,0 +1,305 @@ + + + + + + 🐾 H3R7Tech - Portail + + + +🏠Accueil +

🐾 H3R7Tech

Portail des services & applications

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🔍 Brave Search — Recherche web intégrée

+ + + +

+ +

+ +

+ + +

📊 CRM Prospects

Gestion des prospects artisans, boulangeries, garages

+ ✅ Opérationnel + + + +

💰 Dépenses Trello

Suivi des dépenses avec export Trello

+ ✅ Opérationnel + + + +

💼 Discord Leads

Dashboard leads Discord - Analyse automatique

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📦 POD Manager

Gestion Print on Demand - Designs, produits, ventes

+ ✅ Opérationnel + + + +

🎨 Guide Niches POD

Analyse des 6 niches POD rentables

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📝 Prompts IA

Catalogue de prompts par profession - Juriste, Graphiste, Commercial...

+ ✅ 110+ + + + + +

🤖 Agent AI Skills

Base de connaissances de skills pour agents IA

+ ✅ Nouveau + + + +

🧠 Agent IA

Chat multi-workflows avec historique persistant

+ ✅ Nouveau + + + + +

🏇 Turf Dashboard

Analyses et prédictions courses hippiques

+ ✅ Opérationnel + + + + +

🗺️ Cartographie Projets

Visualisation des 12 projets H3R7Tech - Architecture et status

+✅ Nouveau + + + + +

💡 Boîte à Idées

Suivi de tous les Projets H3R7Tech

+ ✅ Opérationnel + + + +

🔧 Gitea (Git)

Dépôt de code (accès privé requis)

+ ✅ Opérationnel + + + +

📂 Data.gouv.fr Explorer

Explorer les datasets ouverts de l'administration française

+ ✅ Nouveau + + + +

📡 API Data.gouv.fr

Référence complète des endpoints de l'API

+ ✅ Nouveau + + + + +

🍽️ Template Restaurant

Site web pour restaurant

+ ✅ + + + +

🥖 Template Boulangerie

Site web pour boulangerie

+ ✅ + + + +

🔨 Template Artisan

Site web pour artisan

+ ✅ + + + + +

📋 CRM Candidatures

Kanban des candidatures

+ ✅ Opérationnel + +

+ + + + + + diff --git a/portal_server.py b/portal_server.py new file mode 100755 index 0000000..906c62c --- /dev/null +++ b/portal_server.py @@ -0,0 +1,831 @@ +#!/usr/bin/env python3 +from flask import Flask, send_from_directory, jsonify, request, make_response +import os +import json +import requests +import subprocess +import db + +app = Flask(__name__) + +DASHBOARD_API_URL = "http://localhost:8791" +COMBINED_API_URL = "http://localhost:8790" +COMBINED_API_URL = "http://localhost:8790" + + +@app.route("/") +def portal(): + return send_from_directory("/home/h3r7/turf_saas", "portail.html") + + +@app.route("/favicon.ico") +def favicon(): + return send_from_directory("/home/h3r7/turf_saas", "favicon.ico") +@app.route("/prompts", methods=["GET", "POST", "PUT", "DELETE", "PATCH"]) +@app.route("/prompts/", methods=["GET", "POST", "PUT", "DELETE", "PATCH"]) +@app.route("/prompts/", methods=["GET", "POST", "PUT", "DELETE", "PATCH"]) +def proxy_prompts(subpath=""): + PROMPTS_API_URL = "http://localhost:8781" + full_url = PROMPTS_API_URL + ("/" + subpath if subpath else "/") + if request.query_string: + full_url += "?" + request.query_string.decode() + try: + headers = { + k: v + for k, v in request.headers + if k.lower() + not in ("host", "content-length", "transfer-encoding", "connection") + } + raw_body = request.get_data() + resp = requests.request( + request.method, + full_url, + headers=headers, + data=raw_body, + cookies=request.cookies, + allow_redirects=False, + timeout=10, + ) + if resp.status_code in (301, 302, 303, 307, 308): + location = resp.headers.get("Location", "") + if location.startswith("/") and not location.startswith("/prompts"): + location = "/prompts" + location + r = make_response(b"", resp.status_code) + r.headers["Location"] = location + return r + response = make_response(resp.content, resp.status_code) + for k, v in resp.headers.items(): + if k.lower() not in ("content-encoding", "transfer-encoding", "connection"): + response.headers[k] = v + return response + except Exception as e: + return f"Erreur proxy prompts: {e}", 502 + + +@app.route("/business") +def business(): + return send_from_directory("/home/h3r7/depenses_trello/templates", "business.html") + + +@app.route( + "/depenses", methods=["GET", "POST", "PUT", "DELETE", "PATCH"], strict_slashes=False +) +@app.route( + "/depenses/", + methods=["GET", "POST", "PUT", "DELETE", "PATCH"], + strict_slashes=False, +) +@app.route( + "/depenses/", methods=["GET", "POST", "PUT", "DELETE", "PATCH"] +) +def proxy_depenses(subpath=""): + backend_url = "http://localhost:8769" + if subpath: + full_url = f"{backend_url}/{subpath}" + else: + full_url = backend_url + + if request.query_string: + full_url += "?" + request.query_string.decode() + + try: + print(f"[DEPENSES PROXY] {request.method} {full_url}") + + # Transmettre les headers bruts sans reparser + headers = { + k: v + for k, v in request.headers + if k.lower() + not in ("host", "content-length", "transfer-encoding", "connection") + } + + # Body brut transmis tel quel — Content-Type préservé + raw_body = request.get_data() + + resp = requests.request( + request.method, + full_url, + headers=headers, + data=raw_body, + cookies=request.cookies, + allow_redirects=False, + timeout=10, + ) + print(f"[DEPENSES PROXY] resp={resp.status_code}") + + if resp.status_code in (301, 302, 303, 307, 308): + location = resp.headers.get("Location", "") + if location.startswith("/") and not location.startswith("/depenses"): + location = "/depenses" + location + response = make_response(b"", resp.status_code) + response.headers["Location"] = location + response.headers["Content-Length"] = "0" + return response + + if resp.status_code >= 400: + return resp.content, resp.status_code + + response = make_response(resp.content, resp.status_code) + for k, v in resp.headers.items(): + if k.lower() not in ("content-encoding", "transfer-encoding", "connection"): + response.headers[k] = v + return response + except Exception as e: + return f"Erreur proxy depenses: {e}", 502 + + +@app.route("/skills", methods=["GET", "POST", "PUT", "DELETE", "PATCH"]) +@app.route("/skills/", methods=["GET", "POST", "PUT", "DELETE", "PATCH"]) +@app.route("/skills/", methods=["GET", "POST", "PUT", "DELETE", "PATCH"]) +def proxy_skills(subpath=""): + SKILLS_API_URL = "http://localhost:8772" + full_url = SKILLS_API_URL + ("/" + subpath if subpath else "/") + if request.query_string: + full_url += "?" + request.query_string.decode() + try: + headers = { + k: v + for k, v in request.headers + if k.lower() + not in ("host", "content-length", "transfer-encoding", "connection") + } + raw_body = request.get_data() + resp = requests.request( + request.method, + full_url, + headers=headers, + data=raw_body, + cookies=request.cookies, + allow_redirects=False, + timeout=10, + ) + if resp.status_code in (301, 302, 303, 307, 308): + location = resp.headers.get("Location", "") + if location.startswith("/") and not location.startswith("/skills"): + location = "/skills" + location + r = make_response(b"", resp.status_code) + r.headers["Location"] = location + return r + response = make_response(resp.content, resp.status_code) + for k, v in resp.headers.items(): + if k.lower() not in ("content-encoding", "transfer-encoding", "connection"): + response.headers[k] = v + return response + except Exception as e: + return f"Erreur proxy skills: {e}", 502 + + +@app.route("/crm", methods=["GET", "POST", "PUT", "DELETE", "PATCH"]) +@app.route("/crm/", methods=["GET", "POST", "PUT", "DELETE", "PATCH"]) +@app.route("/crm/", methods=["GET", "POST", "PUT", "DELETE", "PATCH"]) +def proxy_crm(subpath=""): + CRM_API_URL = "http://localhost:8770" + full_url = CRM_API_URL + ("/" + subpath if subpath else "/") + if request.query_string: + full_url += "?" + request.query_string.decode() + try: + headers = { + k: v + for k, v in request.headers + if k.lower() + not in ("host", "content-length", "transfer-encoding", "connection") + } + raw_body = request.get_data() + resp = requests.request( + request.method, + full_url, + headers=headers, + data=raw_body, + cookies=request.cookies, + allow_redirects=False, + timeout=10, + ) + if resp.status_code in (301, 302, 303, 307, 308): + location = resp.headers.get("Location", "") + if location.startswith("/") and not location.startswith("/crm"): + location = "/crm" + location + r = make_response(b"", resp.status_code) + r.headers["Location"] = location + return r + response = make_response(resp.content, resp.status_code) + for k, v in resp.headers.items(): + if k.lower() not in ("content-encoding", "transfer-encoding", "connection"): + response.headers[k] = v + return response + except Exception as e: + return f"Erreur proxy crm: {e}", 502 + + +@app.route("/gitea", methods=["GET", "POST", "PUT", "DELETE", "PATCH"]) +@app.route("/gitea/", methods=["GET", "POST", "PUT", "DELETE", "PATCH"]) +@app.route("/gitea/", methods=["GET", "POST", "PUT", "DELETE", "PATCH"]) +def proxy_gitea(subpath=""): + GITEA_API_URL = "http://localhost:3000" + full_url = GITEA_API_URL + ("/" + subpath if subpath else "/") + if request.query_string: + full_url += "?" + request.query_string.decode() + try: + headers = { + k: v + for k, v in request.headers + if k.lower() + not in ("host", "content-length", "transfer-encoding", "connection") + } + raw_body = request.get_data() + resp = requests.request( + request.method, + full_url, + headers=headers, + data=raw_body, + cookies=request.cookies, + allow_redirects=False, + timeout=10, + ) + if resp.status_code in (301, 302, 303, 307, 308): + location = resp.headers.get("Location", "") + if location.startswith("/") and not location.startswith("/gitea"): + location = "/gitea" + location + r = make_response(b"", resp.status_code) + r.headers["Location"] = location + return r + response = make_response(resp.content, resp.status_code) + for k, v in resp.headers.items(): + if k.lower() not in ("content-encoding", "transfer-encoding", "connection"): + response.headers[k] = v + return response + except Exception as e: + return f"Erreur proxy gitea: {e}", 502 + + +@app.route("/boite_a_idees.html") +def boite_a_idees(): + return send_from_directory("/home/h3r7/depenses_trello", "boite_a_idees.html") + + +@app.route("/niches_business.html") +def niches_business(): + return send_from_directory("/home/h3r7/depenses_trello/templates", "business.html") + + +@app.route("/template_restaurant_json.html") +def template_restaurant(): + return send_from_directory( + "/home/h3r7/turf_saas", "template_restaurant_json.html" + ) + + +@app.route("/template_boulangerie_final.html") +def template_boulangerie(): + return send_from_directory( + "/home/h3r7/turf_saas", "template_boulangerie_final.html" + ) + + +@app.route("/template_artisan_final.html") +def template_artisan(): + return send_from_directory("/home/h3r7/turf_saas", "template_artisan_final.html") + + +@app.route("/template_restaurant_final.html") +def template_restaurant_final(): + return send_from_directory( + "/home/h3r7/turf_saas", "template_restaurant_final.html" + ) + + +@app.route("/template_complet.html") +def template_complet(): + return send_from_directory("/home/h3r7/turf_saas", "template_complet.html") + + +@app.route("/boite_a_idees_dashboard") +def boite_a_idees_dashboard(): + return send_from_directory( + "/home/h3r7/turf_saas", "boite_a_idees_dashboard.html" + ) + + +@app.route("/datagouv_explorer.html") +def datagouv_explorer(): + return send_from_directory("/home/h3r7/turf_saas", "datagouv_explorer.html") + + +@app.route("/api_datagouv_reference.html") +def api_datagouv_reference(): + return send_from_directory("/home/h3r7/turf_saas", "api_datagouv_reference.html") + + +# Agent IA - Page principale +@app.route("/agent-ia") +@app.route("/agent-ia/") +def agent_ia(): + return send_from_directory("/home/h3r7/turf_saas", "agent_ia.html") + + +# Agent IA - Page de config +@app.route("/agent-ia/config") +@app.route("/agent-ia/config/") +def agent_ia_config(): + return send_from_directory("/home/h3r7/turf_saas", "agent_ia_config.html") + + +# Ancienne page (compatibilité) +@app.route("/agent-ia/legacy") +def agent_ia_legacy(): + return send_from_directory("/home/h3r7/turf_saas", "gemini_agent.html") + + +# --- API Chat --- + + +@app.route("/api/chat/workflows", methods=["GET"]) +def api_chat_workflows(): + try: + workflows = db.get_workflows() + return jsonify([dict(w) for w in workflows]) + except Exception as e: + return jsonify({"error": str(e)}), 500 +@app.route("/api/chat/nvidia-models", methods=["GET"]) +def api_nvidia_models(): + return jsonify([ + {"id": k, "name": v.split("/")[-1].replace("-instruct", "").replace("-", " ").title(), "full_id": v} + for k, v in NVIDIA_MODELS.items() + ]) + + + +@app.route("/api/chat/sessions", methods=["GET"]) +def api_chat_sessions(): + workflow_slug = request.args.get("workflow") + if not workflow_slug: + return jsonify({"error": "Paramètre workflow requis"}), 400 + try: + sessions = db.get_sessions(workflow_slug) + return jsonify([dict(s) for s in sessions]) + except Exception as e: + return jsonify({"error": str(e)}), 500 + + +@app.route("/api/chat/history", methods=["GET"]) +def api_chat_history(): + session_id = request.args.get("session_id") + workflow_slug = request.args.get("workflow") + if not session_id or not workflow_slug: + return jsonify({"error": "session_id et workflow requis"}), 400 + try: + messages = db.get_messages(session_id, workflow_slug) + return jsonify([dict(m) for m in messages]) + except Exception as e: + return jsonify({"error": str(e)}), 500 + + +@app.route("/api/chat/search", methods=["GET"]) +def api_chat_search(): + query = request.args.get("q") + workflow_slug = request.args.get("workflow") + if not query or not workflow_slug: + return jsonify({"error": "q et workflow requis"}), 400 + try: + results = db.search_messages(query, workflow_slug) + return jsonify([dict(r) for r in results]) + except Exception as e: + return jsonify({"error": str(e)}), 500 + + +@app.route("/api/chat/session", methods=["POST"]) +def api_chat_session_create(): + data = request.json or {} + session_id = data.get("session_id") + workflow_slug = data.get("workflow") + title = data.get("title") + if not session_id or not workflow_slug: + return jsonify({"error": "session_id et workflow requis"}), 400 + try: + workflows = db.get_workflows() + wf = next((w for w in workflows if w["slug"] == workflow_slug), None) + if not wf: + return jsonify({"error": "Workflow introuvable"}), 404 + db.create_session(session_id, wf["id"], title) + return jsonify({"status": "ok", "session_id": session_id}) + except Exception as e: + return jsonify({"error": str(e)}), 500 + + +@app.route("/api/chat/session", methods=["PUT"]) +def api_chat_session_rename(): + data = request.json or {} + session_id = request.args.get("session_id") + workflow_slug = request.args.get("workflow") + new_title = data.get("title") + if not session_id or not workflow_slug: + return jsonify({"error": "session_id et workflow requis"}), 400 + try: + db.rename_session(session_id, workflow_slug, new_title) + return jsonify({"status": "ok"}) + except Exception as e: + return jsonify({"error": str(e)}), 500 + + +@app.route("/api/chat/session", methods=["DELETE"]) +def api_chat_session_delete(): + session_id = request.args.get("session_id") + workflow_slug = request.args.get("workflow") + if not session_id or not workflow_slug: + return jsonify({"error": "session_id et workflow requis"}), 400 + try: + deleted = db.delete_session(session_id, workflow_slug) + return jsonify({"status": "ok", "deleted": deleted}) + except Exception as e: + return jsonify({"error": str(e)}), 500 + + +@app.route("/api/chat/cleanup", methods=["POST"]) +def api_chat_cleanup(): + data = request.json or {} + days = data.get("days") + workflow_slug = data.get("workflow") + if not days or not workflow_slug: + return jsonify({"error": "days et workflow requis"}), 400 + try: + deleted = db.delete_messages_before(days, workflow_slug) + return jsonify({"status": "ok", "deleted": deleted}) + except Exception as e: + return jsonify({"error": str(e)}), 500 + + +# --- Webhook proxy avec persistance --- + +OPENCLAW_TOKEN = "szDd75yG15ZRADWEhGus3dfNoQve7ZhdxpGq9gudeEzoVhUqB0TomVJd90XcwkUz" +OPENCLAW_CONTAINER = "openclaw-ow404080wwkkgkgc4oswwssc" +NVIDIA_API_KEY = "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" +NVIDIA_API_URL = "https://integrate.api.nvidia.com/v1/chat/completions" +NVIDIA_MODEL = "meta/llama-3.1-8b-instruct" # Default model +NVIDIA_MODELS = { + "llama-3.1-8b": "meta/llama-3.1-8b-instruct", + "llama-3.1-70b": "meta/llama-3.1-70b-instruct", + "llama-3.3-70b": "meta/llama-3.3-70b-instruct", + "llama-4-scout": "meta/llama-4-scout-17b-16e-instruct", + "nemotron-mini": "nvidia/nemotron-mini-4b-instruct", + "nemotron-super": "nvidia/llama-3.3-nemotron-super-49b-v1", + "mistral-small": "mistralai/mistral-small-3.1-24b-instruct-2503", + "mistral-medium": "mistralai/mistral-medium-3-instruct", + "mistral-large": "mistralai/mistral-large-3-675b-instruct-2512", + "qwen-coder": "qwen/qwen3-coder-480b-a35b-instruct", + "gemma-3": "google/gemma-3-27b-it", + "deepseek": "deepseek-ai/deepseek-v3.2", +} + + + +@app.route("/webhook/telegram", methods=["POST"]) +def telegram_webhook(): + try: + data = request.get_data() + resp = requests.post( + "http://localhost:5003/webhook", + data=data, + headers={"Content-Type": "application/json"}, + timeout=30, + ) + return resp.content, resp.status_code + except Exception as e: + return jsonify({"error": str(e)}), 500 + + +@app.route("/webhook/", methods=["POST"]) +def webhook_proxy(workflow_slug): + try: + workflows = db.get_workflows() + wf = next((w for w in workflows if w["slug"] == workflow_slug), None) + if not wf: + return jsonify({"error": f'Workflow "{workflow_slug}" introuvable'}), 404 + + session_id = request.headers.get("X-Session-ID", "default") + user_message = request.json.get("message", "") + + db.create_session(session_id, wf["id"]) + db.save_message(session_id, wf["id"], "user", user_message) + + mode = wf.get("mode", "n8n") + + if mode == "direct": + # OpenClaw gateway bind sur 127.0.0.1 uniquement → docker exec + import subprocess + + escaped_msg = user_message.replace('"', '\\"').replace("\n", "\\n") + cmd = [ + "docker", + "exec", + OPENCLAW_CONTAINER, + "curl", + "-s", + "http://127.0.0.1:18789/v1/chat/completions", + "-H", + "Content-Type: application/json", + "-H", + f"Authorization: Bearer {OPENCLAW_TOKEN}", + "-d", + f'{{"model":"openclaw","messages":[{{"role":"user","content":"{escaped_msg}"}}],"max_tokens":4096,"temperature":0.7}}', + ] + result = subprocess.run(cmd, capture_output=True, text=True, timeout=60) + if result.returncode != 0: + raise Exception(f"docker exec failed: {result.stderr}") + data = json.loads(result.stdout) + ai_response = ( + data.get("choices", [{}])[0] + .get("message", {}) + .get("content", str(data)) + ) + elif mode == "nvidia": + # Appel direct a l API Nvidia NIM + # Recuperer le modele choisi (par defaut: llama-3.1-8b) + model_key = request.json.get("model", "llama-3.1-8b") + model_id = NVIDIA_MODELS.get(model_key, NVIDIA_MODEL) + resp = requests.post( + NVIDIA_API_URL, + headers={ + "Authorization": f"Bearer {NVIDIA_API_KEY}", + "Content-Type": "application/json", + }, + json={ + "model": model_id, + "messages": [{"role": "user", "content": user_message}], + "max_tokens": 1024, + "temperature": 0.7, + }, + timeout=60, + ) + data = resp.json() + ai_response = ( + data.get("choices", [{}])[0] + .get("message", {}) + .get("content", str(data)) + ) + else: + # Proxy vers webhook n8n + resp = requests.post( + wf["webhook_url"], + headers={ + "Content-Type": "application/json", + "X-Session-ID": session_id, + }, + json=request.json, + timeout=60, + ) + ai_response = resp.text + + db.save_message(session_id, wf["id"], "ai", ai_response) + return ai_response, 200, {"Content-Type": "text/plain; charset=utf-8"} + except Exception as e: + return str(e), 500 + + +# --- Anciens webhooks (compatibilité) --- + + +@app.route("/webhook/gemini-agent", methods=["POST"]) +def webhook_proxy_legacy_gemini(): + return webhook_proxy("llm-models") + + +@app.route("/webhook/openclaw-web", methods=["POST"]) +def webhook_proxy_legacy_openclaw(): + return webhook_proxy("openclaw") + + +TELEGRAM_TOKEN = "8649773134:AAFqzZVtSHfPPFDadcte1B-1h23nZ8DmdYE" + + +@app.route("/webhook/telegram-opencode", methods=["POST"]) +def webhook_proxy_telegram(): + """Handle Telegram messages directly""" + try: + data = request.get_json(force=True, silent=True) + + print(f"[TELEGRAM] Raw: {request.data}") + print(f"[TELEGRAM] Remote: {request.remote_addr}") + print(f"[TELEGRAM] Headers: {request.headers.get('X-Forwarded-For', 'none')}") + + if not data: + return jsonify({"error": "No data", "raw": request.data.decode()}), 400 + + # Accept both direct message and Telegram update format + message = data.get("message") or data.get("update", {}).get("message") + + if not message: + return jsonify({"data": data, "keys": list(data.keys())}), 400 + + chat_id = message["chat"]["id"] + text = message.get("text", "") + user_id = message["from"]["id"] + + if text.startswith("/start"): + reply = "🤖 *OpencdPilot Bot*\n\nPilotez OpenCode depuis Telegram!" + elif text.startswith("/help"): + reply = "*Commandes:*\n/start - Démarrer\n/help - Aide\n/status - Statut" + elif text.startswith("/status"): + reply = "✅ *Système actif*\n- OpenCode: OK\n- Bot: OK" + else: + # Call OpenCode API + opencode_resp = requests.post( + "http://localhost:8792/api/opencode", json={"prompt": text}, timeout=180 + ) + opencode_data = opencode_resp.json() + reply = opencode_data.get("output", opencode_data.get("error", "Erreur"))[ + :4000 + ] + + # Send reply + requests.post( + f"https://api.telegram.org/bot{TELEGRAM_TOKEN}/sendMessage", + json={"chat_id": chat_id, "text": reply, "parse_mode": "Markdown"}, + ) + + return jsonify({"ok": True}) + except Exception as e: + print(f"[TELEGRAM] Error: {e}") + return jsonify({"error": str(e)}), 500 + + +# --- Turf Dashboard --- + + +@app.route("/dashboard") +@app.route("/dashboard.html") +def dashboard(): + return send_from_directory("/home/h3r7/turf_saas", "dashboard_system.html") + + +@app.route("/turf/") +@app.route("/turf") +def turf_index(): + return send_from_directory("/home/h3r7/turf_saas", "dashboard.html") + + +@app.route("/turf/") +def turf_static(filename): + return send_from_directory("/home/h3r7/turf_saas", filename) + + +# --- POD Routes --- +@app.route("/pod/") +@app.route("/pod/") +def pod_static(filename=""): + return send_from_directory( + "/home/h3r7/turf_saas/POD", filename if filename else "pod_manager.html" + ) + + +@app.route("/turf/api") +@app.route("/turf/api/") +@app.route("/turf/api/") +def api_proxy(api_path=""): + if api_path.startswith("vitesse"): + url = f"{COMBINED_API_URL}/turf/api/{api_path}" + elif api_path.startswith("n8n-proxy"): + url = f"{COMBINED_API_URL}/turf/api/{api_path}" + elif api_path.startswith("backtest"): + url = f"{COMBINED_API_URL}/turf/api/{api_path}" + elif api_path.startswith("stats"): + url = f"{COMBINED_API_URL}/turf/api/{api_path}" + elif api_path.startswith("predictions_analysis"): + url = f"{COMBINED_API_URL}/turf/api/{api_path}" + elif api_path.startswith("parisroi"): + url = f"{COMBINED_API_URL}/turf/api/{api_path}" + elif api_path.startswith("paris"): + url = f"{COMBINED_API_URL}/turf/api/{api_path}" + elif api_path.startswith("scoring"): + url = f"{DASHBOARD_API_URL}/turf/api/{api_path}" + elif api_path: + url = f"{DASHBOARD_API_URL}/turf/api/{api_path}" + else: + url = f"{DASHBOARD_API_URL}/turf/api" + try: + fwd_method = request.method + fwd_json = request.get_json(silent=True) if fwd_method in ("POST", "PUT", "PATCH") else None + fwd_headers = {"Content-Type": "application/json"} + if request.headers.get("Authorization"): + fwd_headers["Authorization"] = request.headers.get("Authorization") + resp = requests.request(method=fwd_method, url=url, json=fwd_json, timeout=30, + headers=fwd_headers) + return resp.content, resp.status_code, {"Content-Type": "application/json"} + except Exception as e: + return jsonify({"error": str(e), "url": url}), 500 + + +@app.route("/api/opencode", methods=["POST"]) +def opencode_api(): + """Execute OpenCode commands via API""" + try: + data = request.get_json() + prompt = data.get("prompt", "") + if not prompt: + return jsonify({"error": "No prompt provided"}), 400 + + # Execute opencode with the wrapper script + result = subprocess.run( + ["/home/h3r7/opencode_wrapper.sh", prompt], + cwd="/home/h3r7", + capture_output=True, + text=True, + timeout=180, + ) + + return jsonify( + { + "output": result.stdout, + "error": result.stderr, + "returncode": result.returncode, + } + ) + except subprocess.TimeoutExpired: + return jsonify({"error": "Timeout"}), 504 + except Exception as e: + return jsonify({"error": str(e)}), 500 + + + +@app.route("/candidatures/") +def candidatures_index(): + return send_from_directory("/home/h3r7/turf_saas", "crm_candidatures.html") + +@app.route("/candidatures/") +def candidatures_static(filename): + return send_from_directory("/home/h3r7/turf_saas", filename) + +@app.route("/map") +def map_visual(): + return send_from_directory("/home/h3r7/turf_saas", "map_visual.html") + +@app.route("/architecture.json") +def architecture_json(): + return send_from_directory("/home/h3r7/turf_saas", "architecture.json") + +if __name__ == "__main__": + app.run(host="0.0.0.0", port=8792, debug=False) + + +# Telegram Bot Webhook Proxy +def telegram_webhook(): + """Proxy Telegram webhook to bot service""" + try: + data = request.get_data() + resp = requests.post( + "http://localhost:5003/webhook", + data=data, + headers={"Content-Type": "application/json"}, + timeout=30, + ) + return resp.content, resp.status_code + except Exception as e: + return jsonify({"error": str(e)}), 500 + + +PROMPTS_API_URL = "http://localhost:8781" + + +@app.route("/testprompts", methods=["GET", "POST", "PUT", "DELETE", "PATCH"]) +def proxy_prompts_test(): + return "TEST_PROMPTS_OK" + full_url = PROMPTS_API_URL + ("/" + subpath if subpath else "/") + if request.query_string: + full_url += "?" + request.query_string.decode() + try: + headers = { + k: v + for k, v in request.headers + if k.lower() + not in ( + "host", + "content-length", + "transfer-encoding", + "connection", + "authorization", + ) + } + raw_body = request.get_data() + resp = requests.request( + request.method, + full_url, + headers=headers, + data=raw_body, + cookies=request.cookies, + allow_redirects=False, + timeout=10, + ) + if resp.status_code in (301, 302, 303, 307, 308): + location = resp.headers.get("Location", "") + if location.startswith("/") and not location.startswith("/prompts"): + location = "/prompts" + location + r = make_response(b"", resp.status_code) + r.headers["Location"] = location + return r + response = make_response(resp.content, resp.status_code) + for k, v in resp.headers.items(): + if k.lower() not in ("content-encoding", "transfer-encoding", "connection"): + response.headers[k] = v + return response + except Exception as e: + return f"Erreur proxy prompts: {e}", 502 + + diff --git a/prompts_llm.py b/prompts_llm.py new file mode 100644 index 0000000..97fcaf2 --- /dev/null +++ b/prompts_llm.py @@ -0,0 +1,290 @@ +#!/usr/bin/env python3 +""" +Prompts LLM Centralisés - Turf Scraper +""" +import os + +TURF_DB_SCHEMA = """ +Tables disponibles dans turf.db: + +1. predictions + - id (INTEGER PRIMARY KEY) + - date (TEXT) - format YYYY-MM-DD + - race_name (TEXT) - ex: "Quinté+" + - race_hippodrome (TEXT) - ex: "Vincennes" + - race_time (TEXT) - ex: "13:55" + - horse_number (INTEGER) + - horse_name (TEXT) + - odds (REAL) - cote + - prediction_rank (INTEGER) - position prédite (1-5) + - source (TEXT) - origine du prono + +2. pmu_courses (table des courses) + - id (INTEGER PRIMARY KEY) + - date_programme (TEXT) - YYYY-MM-DD + - num_reunion (INTEGER) + - num_course (INTEGER) + - libelle (TEXT) - nom de la course + - libelle_court (TEXT) + - discipline (TEXT) - PLAT, TROT, ATTELE, MONTE + - distance (INTEGER) + - heure_depart_str (TEXT) - ex: "13:55" + +3. pmu_partants (table des partants/chevaux) + - id (INTEGER PRIMARY KEY) + - date_programme (TEXT) - YYYY-MM-DD + - num_reunion (INTEGER) + - num_course (INTEGER) + - num_pmu (INTEGER) - numéro du cheval + - nom (TEXT) - nom du cheval + - driver (TEXT) - jockey + - entraineur (TEXT) + - cote_direct (REAL) - cote + - ordre_arrivee (INTEGER) - position finale (0 = pas couru) + - favoris (INTEGER) - 1 si favori + - tx_victoire (REAL) - taux de victoire % + - tx_place (REAL) - taux podium % + - forme_recente (REAL) + +4. odds_history + - id (INTEGER PRIMARY KEY) + - date (TEXT) + - id_race (TEXT) + - scraped_at (TEXT) + - odds_json (TEXT) - cotes au moment X +""" + +SAMPLE_DATA = """ +Exemples de données réelles (2026-03-26): +- Hippodrome: CHANTILLY +- Course: PRIX DE LA JOURNEE DES PLANTES +- Type: PLAT +- Partants: TORTISAMBERT (3) @ 4.5, WIT (8) @ 7.1, BIN ZARAK (7) @ 12.7 +- Résultat: TORTISAMBERT 1er, WIT 2ème, BIN ZARAK 3ème +""" + +SYSTEM_PROMPT = """Tu es un expert en paris hippiques et turf français. +Tu as accès à une base de données SQLite (turf.db) contenant: +- Prédictions de pronostics (table: predictions) +- Résultats de courses PMU (table: pmu_partants) +- Historique des cotes (table: odds_history) +- Statistiques de performance (table: performance_stats) + +{schema} + +Règles importantes: +1. Tu génères ONLY des requêtes SQL SELECT (jamais INSERT/UPDATE/DELETE) +2. Les dates sont au format YYYY-MM-DD (ex: 2026-03-26) +3. Le taux de réussite se calcule: (victoires / total) * 100 +4. Le ROI se calcule: (gains misés - mises) / mises * 100 +5. Un cheval "placé" est sur le podium (positions 1, 2 ou 3) +6. Les positions 0 = pas encore couru + +Réponds en français. Sois précis et concis.""" + +SQL_GENERATION_PROMPT = """ +Génère une requête SQL SQLite pour répondre à cette question. + +Question: {question} + +Contraintes: +- ONLY SELECT queries (pas de INSERT/UPDATE/DELETE) +- Utilise les noms de colonnes exactes du schéma fourni +- Retourne uniquement le SQL, pas d'explication +- Si impossible, retourne "IMPOSSIBLE: [raison]" + +Exemples de requêtes valides: + +Q: "taux de réussite des favoris" +SQL: SELECT + AVG(CASE WHEN position = 1 THEN 100.0 ELSE 0.0 END) as win_rate, + AVG(CASE WHEN position IN (1,2,3) THEN 100.0 ELSE 0.0 END) as podium_rate, + COUNT(*) as total_races +FROM pmu_partants +WHERE numero IN (1,2,3) +AND date_armee >= date('now', '-30 days') +AND position > 0 + +Q: "meilleurs jockeys par victoires" +SQL: SELECT driver, COUNT(*) as wins, + AVG(CASE WHEN position IN (1,2,3) THEN 100.0 ELSE 0.0 END) as podium_rate, + AVG(position) as avg_position +FROM pmu_partants +WHERE position = 1 +AND date_armee >= date('now', '-30 days') +GROUP BY driver +ORDER BY wins DESC +LIMIT 10 + +Q: "performances du cheval TORTISAMBERT" +SQL: SELECT * FROM performance_stats WHERE horse_name LIKE '%TORTISAMBERT%' + +Q: "{question}" +SQL:""" + +CONVERSATION_PROMPT = """ +Historique de la conversation: +{history} + +Dernière question: {question} + +Analyse cette question en tenant compte du contexte précédent. +Si la question fait référence à un élément de l'historique, utilise-le. +Si la question est vague, fournis une réponse utile basée sur les données disponibles. + +Contexte DB: +- predictions: tes prédictions +- pmu_partants: résultats réels +- odds_history: évolution des cotes + +Réponds de façon conversationnelle en français.""" + +REPORT_PROMPTS = { + "daily": """ +Génère un rapport quotidien de performance pour la date {date}. + +Inclure: +1. Nombre de courses aujourd'hui +2. Résultats (positions des partants) +3. Taux de réussite des prédictions +4. ROI du jour +5. Meilleure cote réalisée + +Format: Markdown français concis. +""", + "weekly": """ +Génère un rapport hebdomadaire pour la semaine du {start_date} au {end_date}. + +Inclure: +1. Total courses, paris, gains +2. Taux de réussite global +3. Meilleurs jockeys/chiens cette semaine +4. Évolution vs semaine précédente +5. Recommandations + +Format: Markdown français concis. +""", + "monthly": """ +Génère un rapport mensuel pour {month} {year}. + +Inclure: +1. Bilan全局 (courses, paris, gains, ROI) +2. Meilleures performances par hippodrome +3. Trends (amélioration/détérioration) +4. Statistiques détaillées + +Format: Markdown français concis. +""" +} + +SUGGESTIONS_PROMPT = """ +Basé sur les données disponibles dans turf.db, génère 5 suggestions de questions +que l'utilisateur pourrait poser. + +Les suggestions doivent couvrir différents aspects: +- Statistiques de performance +- Analyse de courses/chevaux spécifiques +- Historique et tendances +- ROI et gains + +Retourne SEULEMENT une liste de questions, une par ligne, sans numérotation.""" + +KEYWORD_PATTERNS = { + "favoris": """ +SELECT + AVG(CASE WHEN ordre_arrivee = 1 THEN 100.0 ELSE 0.0 END) as win_rate, + AVG(CASE WHEN ordre_arrivee IN (1,2,3) THEN 100.0 ELSE 0.0 END) as podium_rate, + COUNT(*) as total_races +FROM pmu_partants +WHERE favoris = 1 AND ordre_arrivee > 0 +AND date_programme >= date('now', '-7 days')""", + + "jockeys": """ +SELECT driver, COUNT(*) as wins, + AVG(CASE WHEN ordre_arrivee IN (1,2,3) THEN 100.0 ELSE 0.0 END) as podium_rate, + AVG(ordre_arrivee) as avg_position +FROM pmu_partants +WHERE ordre_arrivee = 1 AND date_programme >= date('now', '-30 days') +GROUP BY driver ORDER BY wins DESC LIMIT 10""", + + "entraineurs": """ +SELECT entraineur, COUNT(*) as wins, + AVG(CASE WHEN ordre_arrivee IN (1,2,3) THEN 100.0 ELSE 0.0 END) as podium_rate +FROM pmu_partants +WHERE ordre_arrivee = 1 AND date_programme >= date('now', '-30 days') +GROUP BY entraineur ORDER BY wins DESC LIMIT 10""", + + "programme": """ +SELECT c.id, c.num_reunion, c.num_course, c.libelle, c.discipline, c.distance, c.heure_depart_str, + COUNT(p.id) as partants +FROM pmu_courses c +LEFT JOIN pmu_partants p ON c.date_programme = p.date_programme + AND c.num_reunion = p.num_reunion AND c.num_course = p.num_course +WHERE c.date_programme = date('now') +GROUP BY c.id +ORDER BY c.num_reunion, c.num_course""", + + "resultats": """ +SELECT p.nom as cheval, p.num_pmu as numero, p.ordre_arrivee as position, + p.cote_direct as cote, p.driver, c.libelle as course +FROM pmu_partants p +JOIN pmu_courses c ON p.date_programme = c.date_programme + AND p.num_reunion = c.num_reunion AND p.num_course = c.num_course +WHERE p.date_programme = date('now', '-1 day') +AND p.ordre_arrivee > 0 +ORDER BY c.num_reunion, c.num_course, p.ordre_arrivee""", + + "gagnants": """ +SELECT p.nom as cheval, p.cote_direct as cote, c.libelle as course, p.date_programme +FROM pmu_partants p +JOIN pmu_courses c ON p.date_programme = c.date_programme + AND p.num_reunion = c.num_reunion AND p.num_course = c.num_course +WHERE p.ordre_arrivee = 1 AND p.cote_direct > 0 +ORDER BY p.date_programme DESC +LIMIT 10""", + + "cote": """ +SELECT p.nom as cheval, p.cote_direct as cote_initiale, + p.ordre_arrivee as position, p.date_programme +FROM pmu_partants p +WHERE p.date_programme >= date('now', '-7 days') +AND p.ordre_arrivee > 0 +ORDER BY p.date_programme DESC""", +} + + +def get_system_prompt() -> str: + """Retourne le prompt système complet""" + return SYSTEM_PROMPT.format(schema=TURF_DB_SCHEMA) + + +def get_sql_prompt(question: str) -> str: + """Retourne le prompt pour génération SQL""" + return SQL_GENERATION_PROMPT.format(question=question) + + +def get_conversation_prompt(question: str, history: str = "") -> str: + """Retourne le prompt pour conversation""" + if history: + return CONVERSATION_PROMPT.format(question=question, history=history) + return f"Question: {question}\n\n{get_sql_prompt(question)}" + + +def get_report_prompt(report_type: str, **kwargs) -> str: + """Retourne le prompt pour un rapport""" + template = REPORT_PROMPTS.get(report_type, "") + return template.format(**kwargs) + + +def get_suggestions_prompt() -> str: + """Retourne le prompt pour suggestions""" + return SUGGESTIONS_PROMPT + + +def get_keyword_sql(question: str) -> str | None: + """Retourne SQL basé sur mots-clés (fallback)""" + q = question.lower() + for keyword, sql in KEYWORD_PATTERNS.items(): + if keyword in q: + return sql + return None diff --git a/results_scraper.py b/results_scraper.py new file mode 100755 index 0000000..8e775dd --- /dev/null +++ b/results_scraper.py @@ -0,0 +1,383 @@ +#!/usr/bin/env python3 +""" +Results Scraper - API PMU officielle +Scrape les résultats réels du Quinté+, les sauvegarde en BDD +et calcule le taux de réussite des prédictions. +À lancer à 21h via cron ou OpenClaw. +""" + +import requests +import sqlite3 +import json +from datetime import datetime + +import os; DB_PATH = os.environ.get("DB_PATH", "/home/h3r7/turf_scraper/turf.db") +HEADERS = {'User-Agent': 'Mozilla/5.0', 'Accept': 'application/json'} + +# ============================================================ +# API PMU +# ============================================================ + +def get_programme(date_str): + """ + Récupère le programme complet du jour via l'API PMU. + date_str : format DDMMYYYY + Retourne la liste des réunions avec leurs courses. + """ + url = f"https://turfinfo.api.pmu.fr/rest/client/1/programme/{date_str}/reunions" + r = requests.get(url, headers=HEADERS, timeout=15) + r.raise_for_status() + data = r.json() + return data.get("programme", {}).get("reunions", []) + + +def get_participants(date_str, num_reunion, num_course): + """ + Récupère les participants + ordreArrivee pour une course donnée. + ordreArrivee = position finale officielle (0 = non classé/disqualifié) + """ + url = f"https://turfinfo.api.pmu.fr/rest/client/1/programme/{date_str}/R{num_reunion}/C{num_course}/participants" + r = requests.get(url, headers=HEADERS, timeout=15) + r.raise_for_status() + return r.json().get("participants", []) + + +def find_quinte(reunions): + """ + Identifie la course Quinté+ du jour (pariMultiCourses=True ou libelle contient 'PARIS-TURF'). + Retourne (num_reunion, num_course, libelle, hippodrome) ou None. + """ + for reunion in reunions: + for course in reunion.get("courses", []): + libelle = course.get("libelle", "") + paris_types = [p["typePari"] for p in course.get("paris", [])] + if any("QUINTE" in p for p in paris_types) or "PARIS-TURF" in libelle: + return ( + reunion["numOfficiel"], + course["numOrdre"], + libelle, + reunion["hippodrome"]["libelleCourt"], + course.get("arriveeDefinitive", False) + ) + return None + + +# ============================================================ +# BASE DE DONNÉES +# ============================================================ + +def init_db_results(): + """Crée les tables si elles n'existent pas encore.""" + conn = sqlite3.connect(DB_PATH) + c = conn.cursor() + + # Table results : arrivée officielle + c.execute(''' + CREATE TABLE IF NOT EXISTS results ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + date TEXT NOT NULL, + race_name TEXT, + race_hippodrome TEXT, + position INTEGER, + horse_name TEXT, + odds REAL, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP + ) + ''') + + # Table performance : comparaison prédictions vs résultats + c.execute(''' + CREATE TABLE IF NOT EXISTS performance ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + date TEXT NOT NULL, + race_name TEXT, + horse_name TEXT, + predicted_rank INTEGER, + actual_position INTEGER, + hit_top5 BOOLEAN, + hit_winner BOOLEAN, + source TEXT, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP + ) + ''') + + conn.commit() + conn.close() + + +def save_results(date, race_name, hippodrome, participants): + """Sauvegarde les positions officielles en BDD (évite les doublons).""" + conn = sqlite3.connect(DB_PATH) + c = conn.cursor() + saved = 0 + + for p in participants: + position = p.get("ordreArrivee", 0) + if position == 0: + continue # Non classé / disqualifié + horse = p.get("nom", "") + # Cote finale (rapport direct simple gagnant) + rapport = p.get("dernierRapportDirect", {}) + odds = rapport.get("rapport", 0.0) if rapport else 0.0 + + # Vérifier si déjà inséré + c.execute( + "SELECT id FROM results WHERE date=? AND race_name=? AND horse_name=? AND position=?", + (date, race_name, horse, position) + ) + if c.fetchone(): + continue + + c.execute(''' + INSERT INTO results (date, race_name, race_hippodrome, position, horse_name, odds) + VALUES (?, ?, ?, ?, ?, ?) + ''', (date, race_name, hippodrome, position, horse, odds)) + saved += c.rowcount + + conn.commit() + conn.close() + return saved + + +def compare_predictions(date, race_name): + """ + Compare les prédictions du jour avec les résultats réels. + Retourne un dict avec les stats de performance. + """ + conn = sqlite3.connect(DB_PATH) + c = conn.cursor() + + # Récupérer toutes les prédictions du jour, puis dédoublonner en Python + # Priorité source : bases > chances > outsiders > partants + c.execute(''' + SELECT horse_name, prediction_rank, source + FROM predictions + WHERE date=? AND source LIKE 'canalturf%' + ORDER BY prediction_rank ASC, odds ASC + ''', (date,)) + rows = c.fetchall() + + # Dédoublonner : pour chaque cheval, garder la source la plus précise + SOURCE_PRIORITY = { + 'canalturf_prono_bases': 1, + 'canalturf_prono_chances': 2, + 'canalturf_prono_outsiders': 3, + 'canalturf_partants': 4, + 'canalturf_selections': 5, + } + seen = {} + for horse, rank, source in rows: + prio = SOURCE_PRIORITY.get(source, 9) + if horse not in seen or prio < SOURCE_PRIORITY.get(seen[horse][2], 9): + seen[horse] = (horse, rank, source) + predictions = list(seen.values()) + + # Récupérer les résultats réels + c.execute(''' + SELECT horse_name, position + FROM results + WHERE date=? AND race_name LIKE ? + ORDER BY position ASC + ''', (date, f"%{race_name[:15]}%")) + results = {row[0]: row[1] for row in c.fetchall()} + + if not results: + conn.close() + return None + + # Top 5 réel + top5_real = {h for h, pos in results.items() if pos <= 5} + winner_real = next((h for h, pos in results.items() if pos == 1), None) + + # Calcul des hits + hits_top5 = [] + hits_winner = [] + performance_rows = [] + + for horse, pred_rank, source in predictions: + actual_pos = results.get(horse, 99) + hit_top5 = horse in top5_real + hit_winner = horse == winner_real + + if hit_top5: + hits_top5.append(horse) + if hit_winner: + hits_winner.append(horse) + + # Sauvegarder en table performance (structure existante) + c.execute("SELECT id FROM performance WHERE prediction_date=? AND horse_name=?", + (date, horse)) + if not c.fetchone(): + c.execute(''' + INSERT INTO performance + (prediction_date, race_date, horse_name, predicted_rank, actual_position, hit) + VALUES (?, ?, ?, ?, ?, ?) + ''', (date, date, horse, pred_rank, actual_pos, hit_top5)) + + performance_rows.append({ + "cheval": horse, + "pred_rank": pred_rank, + "actual_pos": actual_pos, + "hit_top5": hit_top5, + "hit_winner": hit_winner, + "source": source + }) + + conn.commit() + + # Stats globales + bases = [p for p in performance_rows if p["source"] == "canalturf_prono_bases"] + chances = [p for p in performance_rows if p["source"] == "canalturf_prono_chances"] + outsiders = [p for p in performance_rows if p["source"] == "canalturf_prono_outsiders"] + partants = [p for p in performance_rows if p["source"] == "canalturf_partants"] + + nb_pred = len(performance_rows) + nb_top5 = len(hits_top5) + + stats = { + "date": date, + "race_name": race_name, + "total_predictions": nb_pred, + "hits_top5": nb_top5, + "hit_rate_top5": round(nb_top5 / nb_pred * 100, 1) if nb_pred else 0, + "winner": winner_real, + "winner_predicted": winner_real in [p["cheval"] for p in performance_rows], + "bases_hit": [p["cheval"] for p in bases if p["hit_top5"]], + "bases_miss": [p["cheval"] for p in bases if not p["hit_top5"]], + "top5_real": sorted([(h, pos) for h, pos in results.items() if pos <= 5], key=lambda x: x[1]), + "details": performance_rows + } + + conn.close() + return stats + + +# ============================================================ +# RAPPORT +# ============================================================ + +def print_report(stats): + """Affiche un rapport détaillé en console.""" + if not stats: + print("❌ Aucune donnée à comparer.") + return + + print(f"\n{'='*60}") + print(f"📊 BILAN QUINTÉ+ — {stats['date']}") + print(f"🏇 {stats['race_name']}") + print(f"{'='*60}") + + # Arrivée réelle + print(f"\n🏆 ARRIVÉE OFFICIELLE (Top 5) :") + for horse, pos in stats["top5_real"]: + print(f" {pos}. {horse}") + + # Gagnant prédit ? + winner = stats["winner"] + if stats["winner_predicted"]: + print(f"\n✅ GAGNANT PRÉDIT : {winner}") + else: + print(f"\n❌ Gagnant non prédit : {winner}") + + # Bases + print(f"\n⭐ BASES :") + for h in stats["bases_hit"]: + print(f" ✅ {h} (dans le top 5)") + for h in stats["bases_miss"]: + print(f" ❌ {h} (hors top 5)") + + # Taux de réussite global + print(f"\n📈 TAUX DE RÉUSSITE : {stats['hit_rate_top5']}% ({stats['hits_top5']}/{stats['total_predictions']} chevaux dans le top 5)") + + # Top 5 favori (cotes les plus basses) + partants_hits = [p for p in stats["details"] if p["source"] == "canalturf_partants" and p["hit_top5"]] + print(f"\n💰 FAVORIS PLACÉS : {', '.join([p['cheval'] for p in partants_hits]) or 'aucun'}") + + print(f"{'='*60}\n") + + +def save_report_json(stats, date): + """Sauvegarde le rapport en JSON pour archivage.""" + path = f"{os.environ.get('TURF_DIR', '/home/h3r7/turf_scraper')}/perf_{date.replace('-','')}.json" + with open(path, 'w', encoding='utf-8') as f: + json.dump(stats, f, indent=2, ensure_ascii=False) + return path + + +# ============================================================ +# MAIN +# ============================================================ + +def main(): + today = datetime.now().strftime('%Y-%m-%d') + date_pmu = datetime.now().strftime('%d%m%Y') + + print(f"\n{'='*60}") + print(f"🏇 RESULTS SCRAPER — {datetime.now().strftime('%d/%m/%Y %H:%M')}") + print(f"{'='*60}\n") + + # Init BDD + init_db_results() + + # Récupérer le programme + print("📡 Récupération du programme PMU...") + try: + reunions = get_programme(date_pmu) + print(f" ✅ {len(reunions)} réunion(s) trouvée(s)") + except Exception as e: + print(f" ❌ Erreur API PMU : {e}") + return + + # Trouver le Quinté+ + quinte = find_quinte(reunions) + if not quinte: + print(" ❌ Quinté+ non trouvé dans le programme") + return + + num_r, num_c, libelle, hippodrome, arrivee_def = quinte + print(f" 🏇 Quinté+ : R{num_r}C{num_c} — {libelle} ({hippodrome})") + print(f" Arrivée définitive : {'✅ OUI' if arrivee_def else '⏳ PAS ENCORE'}") + + if not arrivee_def: + print("\n⚠️ La course n'est pas encore terminée. Relancez après la course.") + return + + # Récupérer les participants avec résultats + print(f"\n📡 Récupération des résultats R{num_r}C{num_c}...") + try: + participants = get_participants(date_pmu, num_r, num_c) + print(f" ✅ {len(participants)} participants récupérés") + except Exception as e: + print(f" ❌ Erreur : {e}") + return + + # Trier par position + classes = sorted( + [p for p in participants if p.get("ordreArrivee", 0) > 0], + key=lambda x: x["ordreArrivee"] + ) + + print(f"\n🏆 TOP 5 OFFICIEL :") + for p in classes[:5]: + cote = p.get("dernierRapportDirect", {}).get("rapport", "?") if p.get("dernierRapportDirect") else "?" + print(f" {p['ordreArrivee']}. {p['nom']:<25} cote={cote}") + + # Sauvegarder les résultats + saved = save_results(today, libelle, hippodrome, participants) + print(f"\n💾 {saved} résultats sauvegardés en BDD") + + # Comparer avec les prédictions + print(f"\n🔍 Comparaison avec les prédictions...") + stats = compare_predictions(today, libelle) + + if stats: + print_report(stats) + path = save_report_json(stats, today) + print(f"📁 Rapport sauvegardé : {path}") + else: + print("⚠️ Aucune prédiction trouvée pour aujourd'hui en BDD.") + print(" Vérifiez que multi_scraper_v5.py a bien tourné ce matin.") + + +if __name__ == "__main__": + main() diff --git a/run_api.sh b/run_api.sh new file mode 100755 index 0000000..9539484 --- /dev/null +++ b/run_api.sh @@ -0,0 +1,5 @@ +#!/bin/bash +# Toujours passer par systemd pour éviter les doublons +systemctl restart combined_api +sleep 2 +systemctl is-active combined_api && echo "✅ API active" || echo "❌ API down" diff --git a/run_api.sh.disabled b/run_api.sh.disabled new file mode 100755 index 0000000..b2d5b1e --- /dev/null +++ b/run_api.sh.disabled @@ -0,0 +1,4 @@ +#!/bin/bash +cd /home/h3r7/turf_scraper +source /home/h3r7/turf_scraper/venv/bin/activate +exec python3 combined_api.py diff --git a/run_pmu_range.sh b/run_pmu_range.sh new file mode 100755 index 0000000..8c97bf7 --- /dev/null +++ b/run_pmu_range.sh @@ -0,0 +1,33 @@ +#!/bin/bash + +# Début et fin +START="2026-01-01" +END="2026-03-21" + +# Conversion en timestamps +start_ts=$(date -d "$START" +%s) +end_ts=$(date -d "$END" +%s) + +echo "=== Lancement PMU du $START au $END ===" + +current_ts=$start_ts + +while [ $current_ts -le $end_ts ]; do + # Format JJMMAAAA + DATE=$(date -d "@$current_ts" +%d%m%Y) + + echo "----------------------------------------" + echo "📅 Traitement date : $DATE" + echo "----------------------------------------" + + # Exécution de la commande + python3 /home/h3r7/turf_scraper/pmu_results.py --date "$DATE" 2>&1 | head -60 + + # Pause légère pour éviter de spammer l’API + sleep 1 + + # Date suivante + current_ts=$(( current_ts + 86400 )) +done + +echo "=== Terminé ===" diff --git a/run_results.sh b/run_results.sh new file mode 100755 index 0000000..eec0989 --- /dev/null +++ b/run_results.sh @@ -0,0 +1,4 @@ +#!/bin/bash +export DB_PATH=/home/h3r7/turf_scraper/turf.db +export TURF_DIR=/home/h3r7/turf_scraper +python3 /home/h3r7/turf_scraper/pmu_results.py diff --git a/run_scoring.sh b/run_scoring.sh new file mode 100755 index 0000000..c47867b --- /dev/null +++ b/run_scoring.sh @@ -0,0 +1,4 @@ +#!/bin/bash +export DB_PATH=/home/h3r7/turf_scraper/turf.db +export TURF_DIR=/home/h3r7/turf_scraper +python3 /home/h3r7/turf_scraper/scoring_v2.py diff --git a/run_scraper.sh b/run_scraper.sh new file mode 100755 index 0000000..9d88d73 --- /dev/null +++ b/run_scraper.sh @@ -0,0 +1,4 @@ +#!/bin/bash +export DB_PATH=/home/h3r7/turf_scraper/turf.db +export TURF_DIR=/home/h3r7/turf_scraper +python3 /home/h3r7/turf_scraper/multi_scraper_v5.py diff --git a/scoring.py b/scoring.py new file mode 100755 index 0000000..ae87509 --- /dev/null +++ b/scoring.py @@ -0,0 +1,603 @@ +#!/usr/bin/env python3 +""" +Scoring Engine - Analyse complète des partants via API PMU +Calcule un score composite et recommande Simple Gagnant, Simple Placé, Couplé +Sauvegarde en BDD + JSON + rapport Telegram +""" + +import requests +import sqlite3 +import json +import re +from datetime import datetime + +import os; DB_PATH = os.environ.get("DB_PATH", "/home/h3r7/turf_scraper/turf.db") +HEADERS = {'User-Agent': 'Mozilla/5.0', 'Accept': 'application/json'} + +# ============================================================ +# DÉCODEUR MUSIQUE +# ============================================================ + +def parse_musique(musique): + """ + Décode la musique PMU : ex "1a0a(25)0a4a2a" + Retourne des stats sur les 10 dernières courses (hors recul) + Position 1 = course la plus récente + """ + if not musique: + return {} + + # Supprimer les indices de recul (25), (24) etc. + clean = re.sub(r'$\d+$', '', musique) + + # Extraire les résultats : chiffre + lettre discipline + # a=attelé, m=monté, p=plat, h=haies, s=steeple, c=cross + resultats = re.findall(r'(\d+|D|0)([amphsc]?)', clean) + + positions = [] + for pos, disc in resultats[:10]: # 10 dernières + if pos == 'D': + positions.append(99) # Disqualifié + else: + positions.append(int(pos)) + + if not positions: + return {} + + # Stats + nb_courses = len(positions) + nb_victoires = positions.count(1) + nb_places = sum(1 for p in positions if 1 <= p <= 3) + nb_top5 = sum(1 for p in positions if 1 <= p <= 5) + nb_disq = positions.count(99) + + # Forme récente (3 dernières) + recentes = [p for p in positions[:3] if p != 99] + forme_recente = sum(recentes) / len(recentes) if recentes else 99 + + # Tendance : amélioration ou dégradation + if len(positions) >= 4: + debut = sum(positions[-4:]) / 4 # anciennes + fin = sum(positions[:4]) / 4 # récentes + tendance = debut - fin # positif = amélioration + else: + tendance = 0 + + return { + 'positions': positions, + 'nb_courses': nb_courses, + 'nb_victoires': nb_victoires, + 'nb_places': nb_places, + 'nb_top5': nb_top5, + 'nb_disq': nb_disq, + 'forme_recente': round(forme_recente, 1), + 'tendance': round(tendance, 1), + 'tx_victoire': round(nb_victoires / nb_courses * 100, 1) if nb_courses else 0, + 'tx_place': round(nb_places / nb_courses * 100, 1) if nb_courses else 0, + } + + +# ============================================================ +# SCORING COMPOSITE +# ============================================================ + +def score_cheval(p, all_participants): + """ + Calcule un score composite 0-100 pour un cheval. + Critères pondérés : + - Cote (20%) : inverse de la cote = plus c'est bas, mieux c'est + - Forme récente (25%) : positions des 3 dernières courses + - Taux victoire carrière (15%) + - Taux placé carrière (15%) + - Réduction kilométrique (10%) : vitesse de référence + - Tendance (10%) : amélioration récente + - Avis entraîneur (5%) + """ + score = 0 + details = {} + + # 1. COTE (20 pts) — plus la cote est basse, plus le cheval est favori + cote = 0 + rapport = p.get('dernierRapportDirect', {}) + if rapport: + cote = rapport.get('rapport', 0) + if not cote: + rapport_ref = p.get('dernierRapportReference', {}) + cote = rapport_ref.get('rapport', 99) if rapport_ref else 99 + + # Normaliser : cote 1 = 20pts, cote 10 = 10pts, cote 50+ = 2pts + if cote > 0: + score_cote = max(2, min(20, 20 / (1 + cote * 0.15))) + else: + score_cote = 2 + score += score_cote + details['cote'] = round(cote, 1) + details['score_cote'] = round(score_cote, 1) + + # 2. FORME RÉCENTE (25 pts) + musique_stats = parse_musique(p.get('musique', '')) + forme = musique_stats.get('forme_recente', 99) + if forme <= 1: + score_forme = 25 + elif forme <= 2: + score_forme = 20 + elif forme <= 3: + score_forme = 15 + elif forme <= 5: + score_forme = 10 + elif forme <= 8: + score_forme = 5 + else: + score_forme = 0 + score += score_forme + details['forme_recente'] = forme + details['score_forme'] = score_forme + + # 3. TAUX VICTOIRE CARRIÈRE (15 pts) + nb_courses_total = p.get('nombreCourses', 0) + nb_victoires_total = p.get('nombreVictoires', 0) + tx_vic = (nb_victoires_total / nb_courses_total * 100) if nb_courses_total else 0 + score_vic = min(15, tx_vic * 0.5) + score += score_vic + details['tx_victoire'] = round(tx_vic, 1) + details['score_victoire'] = round(score_vic, 1) + + # 4. TAUX PLACÉ CARRIÈRE (15 pts) + nb_places_total = p.get('nombrePlaces', 0) + tx_place = (nb_places_total / nb_courses_total * 100) if nb_courses_total else 0 + score_place = min(15, tx_place * 0.2) + score += score_place + details['tx_place'] = round(tx_place, 1) + details['score_place'] = round(score_place, 1) + + # 5. RÉDUCTION KILOMÉTRIQUE (10 pts) — plus c'est bas, plus c'est rapide + rk = p.get('reductionKilometrique', 0) + if rk > 0: + # Normaliser : RK 72000 = excellent, RK 78000 = moyen + all_rk = [x.get('reductionKilometrique', 0) for x in all_participants if x.get('reductionKilometrique', 0) > 0] + if all_rk: + min_rk = min(all_rk) + max_rk = max(all_rk) + if max_rk > min_rk: + score_rk = 10 * (1 - (rk - min_rk) / (max_rk - min_rk)) + else: + score_rk = 5 + else: + score_rk = 5 + else: + score_rk = 0 + score += score_rk + details['rk'] = rk + details['score_rk'] = round(score_rk, 1) + + # 6. TENDANCE (10 pts) — amélioration récente + tendance = musique_stats.get('tendance', 0) + score_tendance = min(10, max(0, 5 + tendance)) + score += score_tendance + details['tendance'] = tendance + details['score_tendance'] = round(score_tendance, 1) + + # 7. AVIS ENTRAÎNEUR (5 pts) + avis = p.get('avisEntraineur', 'NEUTRE') + score_avis = {'POSITIF': 5, 'TRES_POSITIF': 5, 'NEUTRE': 2, 'NEGATIF': 0, 'TRES_NEGATIF': 0}.get(avis, 2) + score += score_avis + details['avis_entraineur'] = avis + details['score_avis'] = score_avis + + # Bonus driver change négatif + if p.get('driverChange', False): + score -= 3 + details['driver_change'] = True + + details['score_total'] = round(score, 1) + details['musique'] = p.get('musique', '') + details['nb_victoires'] = nb_victoires_total + details['nb_places'] = nb_places_total + details['nb_courses'] = nb_courses_total + + return round(score, 1), details + + +# ============================================================ +# RECOMMANDATIONS PARIS +# ============================================================ + +def build_recommendations(scored_horses): + """ + Construit les recommandations de paris basées sur le scoring. + """ + # Trier par score décroissant + ranked = sorted(scored_horses, key=lambda x: x['score'], reverse=True) + + top1 = ranked[0] + top2 = ranked[1] + top3 = ranked[2] + + # Calcul de la confiance + def confiance(score): + if score >= 55: return "🔥 FORTE" + elif score >= 45: return "✅ BONNE" + elif score >= 35: return "⚠️ MOYENNE" + else: return "❓ FAIBLE" + + reco = { + 'simple_gagnant': { + 'cheval': top1['nom'], + 'numero': top1['numero'], + 'cote': top1['details']['cote'], + 'score': top1['score'], + 'confiance': confiance(top1['score']), + 'mise_suggeree': 2.0, + 'gain_potentiel': round(2.0 * top1['details']['cote'], 2), + 'justification': _justif_gagnant(top1), + }, + 'simple_place': { + 'cheval': top1['nom'], + 'numero': top1['numero'], + 'cote': round(top1['details']['cote'] / 4, 2), # Cote placé ≈ cote/4 + 'score': top1['score'], + 'confiance': confiance(top1['score'] + 10), # Placé plus facile + 'mise_suggeree': 3.0, + 'gain_potentiel': round(3.0 * (top1['details']['cote'] / 4), 2), + 'justification': 'Même cheval qu\'en Simple Gagnant, pari sécurisé', + }, + 'couple_gagnant': { + 'cheval1': top1['nom'], + 'numero1': top1['numero'], + 'cheval2': top2['nom'], + 'numero2': top2['numero'], + 'score_combo': round((top1['score'] + top2['score']) / 2, 1), + 'confiance': confiance((top1['score'] + top2['score']) / 2 - 5), + 'mise_suggeree': 2.0, + 'justification': _justif_couple(top1, top2), + }, + 'couple_place': { + 'cheval1': top1['nom'], + 'numero1': top1['numero'], + 'cheval2': top3['nom'], + 'numero2': top3['numero'], + 'score_combo': round((top1['score'] + top3['score']) / 2, 1), + 'confiance': confiance((top1['score'] + top3['score']) / 2), + 'mise_suggeree': 2.0, + 'justification': 'Combinaison favorite + outsider solide', + }, + 'top3_scores': [ + {'rang': i+1, 'nom': h['nom'], 'numero': h['numero'], + 'score': h['score'], 'cote': h['details']['cote'], + 'forme': h['details']['forme_recente'], + 'tx_vic': h['details']['tx_victoire'], + 'avis': h['details']['avis_entraineur'], + 'musique': h['details']['musique']} + for i, h in enumerate(ranked[:5]) + ], + 'budget_total': 9.0, + 'generated_at': datetime.now().isoformat(), + } + + return reco + + +def _justif_gagnant(horse): + parts = [] + d = horse['details'] + if d['forme_recente'] <= 2: + parts.append(f"forme excellente ({d['forme_recente']} de moy.)") + if d['tx_victoire'] >= 20: + parts.append(f"bon taux de victoire ({d['tx_victoire']}%)") + if d['avis_entraineur'] == 'POSITIF': + parts.append("avis entraîneur positif") + if d['tendance'] > 2: + parts.append("en progression") + if d['cote'] <= 5: + parts.append(f"grand favori ({d['cote']}/1)") + return " • ".join(parts) if parts else "Meilleur score composite" + + +def _justif_couple(h1, h2): + return f"{h1['nom']} (score {h1['score']}) + {h2['nom']} (score {h2['score']})" + + +# ============================================================ +# BASE DE DONNÉES +# ============================================================ + +def init_scoring_table(): + conn = sqlite3.connect(DB_PATH) + c = conn.cursor() + c.execute(''' + CREATE TABLE IF NOT EXISTS scoring ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + date TEXT NOT NULL, + race_name TEXT, + horse_name TEXT, + horse_number INTEGER, + score REAL, + score_cote REAL, + score_forme REAL, + score_victoire REAL, + score_place REAL, + score_rk REAL, + score_tendance REAL, + score_avis REAL, + cote REAL, + forme_recente REAL, + tx_victoire REAL, + tx_place REAL, + avis_entraineur TEXT, + musique TEXT, + rang_scoring INTEGER, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP + ) + ''') + c.execute(''' + CREATE TABLE IF NOT EXISTS recommendations ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + date TEXT NOT NULL, + race_name TEXT, + type_pari TEXT, + cheval1 TEXT, + numero1 INTEGER, + cheval2 TEXT, + numero2 INTEGER, + cote REAL, + mise REAL, + gain_potentiel REAL, + confiance TEXT, + justification TEXT, + resultat TEXT, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP + ) + ''') + conn.commit() + conn.close() + + +def save_scoring(date, race_name, scored_horses, recommendations): + conn = sqlite3.connect(DB_PATH) + c = conn.cursor() + + # Supprimer les anciens scores du jour + c.execute("DELETE FROM scoring WHERE date=? AND race_name=?", (date, race_name)) + c.execute("DELETE FROM recommendations WHERE date=? AND race_name=?", (date, race_name)) + + # Sauvegarder les scores + ranked = sorted(scored_horses, key=lambda x: x['score'], reverse=True) + for rang, h in enumerate(ranked, 1): + d = h['details'] + c.execute(''' + INSERT INTO scoring + (date, race_name, horse_name, horse_number, score, score_cote, score_forme, + score_victoire, score_place, score_rk, score_tendance, score_avis, + cote, forme_recente, tx_victoire, tx_place, avis_entraineur, musique, rang_scoring) + VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?) + ''', (date, race_name, h['nom'], h['numero'], h['score'], + d['score_cote'], d['score_forme'], d['score_victoire'], + d['score_place'], d['score_rk'], d['score_tendance'], d['score_avis'], + d['cote'], d['forme_recente'], d['tx_victoire'], d['tx_place'], + d['avis_entraineur'], d['musique'], rang)) + + # Sauvegarder les recommandations + paris = [ + ('simple_gagnant', recommendations['simple_gagnant']), + ('simple_place', recommendations['simple_place']), + ('couple_gagnant', recommendations['couple_gagnant']), + ('couple_place', recommendations['couple_place']), + ] + for type_pari, reco in paris: + c1 = reco.get('cheval', reco.get('cheval1', '')) + n1 = reco.get('numero', reco.get('numero1', 0)) + c2 = reco.get('cheval2', '') + n2 = reco.get('numero2', 0) + c.execute(''' + INSERT INTO recommendations + (date, race_name, type_pari, cheval1, numero1, cheval2, numero2, + cote, mise, gain_potentiel, confiance, justification) + VALUES (?,?,?,?,?,?,?,?,?,?,?,?) + ''', (date, race_name, type_pari, c1, n1, c2, n2, + reco.get('cote', 0), reco.get('mise_suggeree', 2), + reco.get('gain_potentiel', 0), + reco.get('confiance', ''), reco.get('justification', ''))) + + conn.commit() + conn.close() + + +# ============================================================ +# RAPPORT CONSOLE + JSON +# ============================================================ + +def print_report(scored_horses, recommendations, race_info): + ranked = sorted(scored_horses, key=lambda x: x['score'], reverse=True) + race_name = race_info.get('nom', 'Quinté+') + hippodrome = race_info.get('hippodrome', '') + heure = race_info.get('heure', '') + + print(f"\n{'='*65}") + print(f"🏇 SCORING — {race_name}") + print(f" {hippodrome} — {heure}") + print(f"{'='*65}") + print(f" {'RANG':<5} {'N°':<4} {'CHEVAL':<25} {'SCORE':<7} {'COTE':<7} {'FORME':<7} {'TX-V':<6} {'AVIS'}") + print(f"{'─'*65}") + + for rang, h in enumerate(ranked, 1): + d = h['details'] + star = '⭐' if rang <= 3 else ' ' + print(f" {star}{rang:<4} {h['numero']:<4} {h['nom']:<25} {h['score']:<7} {d['cote']:<7} {d['forme_recente']:<7} {d['tx_victoire']:<6}% {d['avis_entraineur']}") + + print(f"\n{'='*65}") + print(f"💰 RECOMMANDATIONS PARIS") + print(f"{'='*65}") + + sg = recommendations['simple_gagnant'] + print(f"\n 🎯 SIMPLE GAGNANT") + print(f" ➤ N°{sg['numero']} {sg['cheval']} @ {sg['cote']}/1") + print(f" Mise : {sg['mise_suggeree']}€ | Gain potentiel : {sg['gain_potentiel']}€") + print(f" Confiance : {sg['confiance']}") + print(f" {sg['justification']}") + + sp = recommendations['simple_place'] + print(f"\n 🛡️ SIMPLE PLACÉ") + print(f" ➤ N°{sp['numero']} {sp['cheval']} @ {sp['cote']}/1") + print(f" Mise : {sp['mise_suggeree']}€ | Gain potentiel : {sp['gain_potentiel']}€") + print(f" Confiance : {sp['confiance']}") + + cg = recommendations['couple_gagnant'] + print(f"\n 🔗 COUPLÉ GAGNANT") + print(f" ➤ N°{cg['numero1']} {cg['cheval1']} + N°{cg['numero2']} {cg['cheval2']}") + print(f" Mise : {cg['mise_suggeree']}€ | Confiance : {cg['confiance']}") + print(f" {cg['justification']}") + + cp = recommendations['couple_place'] + print(f"\n 🔗 COUPLÉ PLACÉ") + print(f" ➤ N°{cp['numero1']} {cp['cheval1']} + N°{cp['numero2']} {cp['cheval2']}") + print(f" Mise : {cp['mise_suggeree']}€ | Confiance : {cp['confiance']}") + + print(f"\n 💼 Budget total suggéré : {recommendations['budget_total']}€") + print(f"{'='*65}\n") + + +def save_json(data, date): + path = f"{os.environ.get('TURF_DIR', '/home/h3r7/turf_scraper')}/scoring_{date.replace('-','')}.json" + with open(path, 'w', encoding='utf-8') as f: + json.dump(data, f, indent=2, ensure_ascii=False) + return path + + +def format_telegram(recommendations, race_info): + sg = recommendations['simple_gagnant'] + sp = recommendations['simple_place'] + cg = recommendations['couple_gagnant'] + cp = recommendations['couple_place'] + top3 = recommendations['top3_scores'] + + msg = f"""🏇 *QUINTÉ+ — {race_info.get('hippodrome','?')} {race_info.get('heure','?')}* +_{race_info.get('nom','')}_ + +📊 *TOP 3 SCORING* +""" + for h in top3[:3]: + msg += f" {h['rang']}. N°{h['numero']} *{h['nom']}* — score {h['score']} | cote {h['cote']} | forme {h['forme']}\n" + + msg += f""" +💰 *RECOMMANDATIONS* + +🎯 *Simple Gagnant* : N°{sg['numero']} {sg['cheval']} @ {sg['cote']}/1 + Mise {sg['mise_suggeree']}€ → gain potentiel {sg['gain_potentiel']}€ + {sg['confiance']} | {sg['justification']} + +🛡️ *Simple Placé* : N°{sp['numero']} {sp['cheval']} + Mise {sp['mise_suggeree']}€ | {sp['confiance']} + +🔗 *Couplé Gagnant* : {cg['numero1']}-{cg['numero2']} ({cg['cheval1']} / {cg['cheval2']}) + Mise {cg['mise_suggeree']}€ | {cg['confiance']} + +🔗 *Couplé Placé* : {cp['numero1']}-{cp['numero2']} ({cp['cheval1']} / {cp['cheval2']}) + Mise {cp['mise_suggeree']}€ | {cp['confiance']} + +💼 Budget total : {recommendations['budget_total']}€ +""" + return msg + + +# ============================================================ +# MAIN +# ============================================================ + +def main(): + today = datetime.now().strftime('%Y-%m-%d') + date_pmu = datetime.now().strftime('%d%m%Y') + + print(f"\n{'='*65}") + print(f"🧠 SCORING ENGINE — {datetime.now().strftime('%d/%m/%Y %H:%M')}") + print(f"{'='*65}\n") + + init_scoring_table() + + # Récupérer le programme + print("📡 Récupération du programme PMU...") + try: + url = f"https://turfinfo.api.pmu.fr/rest/client/1/programme/{date_pmu}/reunions" + r = requests.get(url, headers=HEADERS, timeout=15) + reunions = r.json().get('programme', {}).get('reunions', []) + print(f" ✅ {len(reunions)} réunion(s)") + except Exception as e: + print(f" ❌ {e}") + return + + # Trouver le Quinté+ + quinte = None + for reunion in reunions: + for course in reunion.get('courses', []): + libelle = course.get('libelle', '') + paris_types = [p["typePari"] for p in course.get("paris", [])] + if any("QUINTE" in p for p in paris_types) or "PARIS-TURF" in libelle: + quinte = (reunion['numOfficiel'], course['numOrdre'], + libelle, reunion['hippodrome']['libelleCourt'], + course.get('heureDepart', 0)) + break + if quinte: + break + + if not quinte: + print("❌ Quinté+ non trouvé") + return + + num_r, num_c, libelle, hippodrome, heure_ts = quinte + heure = datetime.fromtimestamp(heure_ts/1000).strftime('%H:%M') if heure_ts else '13:55' + race_info = {'nom': libelle, 'hippodrome': hippodrome, 'heure': heure} + print(f" 🏇 {libelle} — {hippodrome} {heure}") + + # Récupérer les participants + print(f"\n📡 Récupération des participants R{num_r}C{num_c}...") + try: + url = f"https://turfinfo.api.pmu.fr/rest/client/1/programme/{date_pmu}/R{num_r}/C{num_c}/participants" + r = requests.get(url, headers=HEADERS, timeout=15) + participants = [p for p in r.json().get('participants', []) if p.get('statut') == 'PARTANT'] + print(f" ✅ {len(participants)} partants") + except Exception as e: + print(f" ❌ {e}") + return + + # Calculer le scoring + print(f"\n🧠 Calcul du scoring composite...") + scored_horses = [] + for p in participants: + score, details = score_cheval(p, participants) + scored_horses.append({ + 'nom': p['nom'], + 'numero': p['numPmu'], + 'score': score, + 'details': details, + }) + + # Construire les recommandations + recommendations = build_recommendations(scored_horses) + + # Afficher le rapport + print_report(scored_horses, recommendations, race_info) + + # Sauvegarder en BDD + save_scoring(today, libelle, scored_horses, recommendations) + print(f"💾 Scoring sauvegardé en BDD") + + # Sauvegarder en JSON + output = { + 'date': today, + 'race': race_info, + 'scored_horses': sorted(scored_horses, key=lambda x: x['score'], reverse=True), + 'recommendations': recommendations, + } + json_path = save_json(output, today) + print(f"📁 JSON : {json_path}") + + # Message Telegram + telegram_msg = format_telegram(recommendations, race_info) + telegram_path = f"{os.environ.get('TURF_DIR', '/home/h3r7/turf_scraper')}/telegram_scoring_{today.replace('-','')}.txt" + with open(telegram_path, 'w', encoding='utf-8') as f: + f.write(telegram_msg) + print(f"📱 Message Telegram : {telegram_path}") + print(f"\n{'─'*65}") + print(telegram_msg) + +if __name__ == "__main__": + main() diff --git a/scoring_v2.py b/scoring_v2.py new file mode 100755 index 0000000..48dec94 --- /dev/null +++ b/scoring_v2.py @@ -0,0 +1,320 @@ +#!/usr/bin/env python3 +""" +Scoring Engine V2 - ZE 2 sur 4 Optimise +Cote: 10%, Forme: 30%, Bonus outsider +""" + +import requests +import sqlite3 +import json +import re +from datetime import datetime + +DB_PATH = "/home/h3r7/turf_scraper/turf.db" +HEADERS = {'User-Agent': 'Mozilla/5.0', 'Accept': 'application/json'} + +def get_cote_from_db(horse_name, date_course): + """Recupere la cote depuis la table predictions (plus recente et non nulle)""" + conn = sqlite3.connect(DB_PATH) + conn.row_factory = sqlite3.Row + c = conn.execute(""" + SELECT odds FROM predictions + WHERE date=? AND horse_name LIKE ? AND odds > 0 + ORDER BY created_at DESC LIMIT 1 + """, (date_course, f"%{horse_name}%")) + r = c.fetchone() + conn.close() + return r['odds'] if r else 0 + +def parse_musique(musique): + if not musique: + return {} + clean = re.sub(r'$\d+$', '', musique) + resultats = re.findall(r'(\d+|D|0)([amphsc]?)', clean) + positions = [] + for pos, disc in resultats[:10]: + positions.append(99 if pos == 'D' else int(pos)) + if not positions: + return {} + nb_courses = len(positions) + nb_victoires = positions.count(1) + nb_places = sum(1 for p in positions if 1 <= p <= 3) + recentes = [p for p in positions[:3] if p != 99] + forme_recente = sum(recentes) / len(recentes) if recentes else 99 + tendance = (sum(positions[-4:]) / 4 - sum(positions[:4]) / 4) if len(positions) >= 4 else 0 + return { + 'forme_recente': round(forme_recente, 1), + 'tendance': round(tendance, 1), + 'tx_victoire': round(nb_victoires / nb_courses * 100, 1) if nb_courses else 0, + 'tx_place': round(nb_places / nb_courses * 100, 1) if nb_courses else 0, + } + +def score_cheval_v2(p, all_participants, today): + score = 0 + details = {} + + # 1. COTE - Essaye PMU API, sinon DB + horse_name = p.get('nom', '') + cote = 0 + + # Essayer d'abord depuis l'API PMU + rapport = p.get('dernierRapportDirect', {}) + if rapport: + cote = rapport.get('rapport', 0) + if not cote: + rapport_ref = p.get('dernierRapportReference', {}) + cote = rapport_ref.get('rapport', 0) if rapport_ref else 0 + + # Fallback: aller chercher dans la DB + if not cote or cote == 0: + cote = get_cote_from_db(horse_name, today) + + # Si toujours pas de cote, utiliser 99 comme valeur par defaut + if not cote or cote == 0: + cote = 99.0 + + score_cote = max(2, min(10, 20 / (1 + cote * 0.15))) if cote > 0 else 2 + score += score_cote + details['cote'] = round(cote, 1) + details['score_cote'] = round(score_cote, 1) + + # 2. FORME - AUGMENTE a 30 pts + musique_stats = parse_musique(p.get('musique', '')) + forme = musique_stats.get('forme_recente', 99) + score_forme = 30 if forme <= 1 else 25 if forme <= 2 else 20 if forme <= 3 else 15 if forme <= 5 else 8 if forme <= 8 else 0 + score += score_forme + details['forme_recente'] = forme + details['score_forme'] = score_forme + + # 3. TAUX VICTOIRE (15 pts) + nb_courses_total = p.get('nombreCourses', 0) + nb_victoires_total = p.get('nombreVictoires', 0) + tx_vic = (nb_victoires_total / nb_courses_total * 100) if nb_courses_total else 0 + score_vic = min(15, tx_vic * 0.5) + score += score_vic + details['tx_victoire'] = round(tx_vic, 1) + details['score_victoire'] = round(score_vic, 1) + + # 4. TAUX PLACE (15 pts) + nb_places_total = p.get('nombrePlaces', 0) + tx_place = (nb_places_total / nb_courses_total * 100) if nb_courses_total else 0 + score_place = min(15, tx_place * 0.2) + score += score_place + details['tx_place'] = round(tx_place, 1) + details['score_place'] = round(score_place, 1) + + # 5. REDUCTION KM (10 pts) + rk = p.get('reductionKilometrique', 0) + all_rk = [x.get('reductionKilometrique', 0) for x in all_participants if x.get('reductionKilometrique', 0) > 0] + if rk > 0 and all_rk: + score_rk = 10 * (1 - (rk - min(all_rk)) / (max(all_rk) - min(all_rk))) if max(all_rk) > min(all_rk) else 5 + else: + score_rk = 0 + score += score_rk + details['rk'] = rk + details['score_rk'] = round(score_rk, 1) + + # 6. TENDANCE (10 pts) + tendance = musique_stats.get('tendance', 0) + score_tendance = min(10, max(0, 5 + tendance)) + score += score_tendance + details['tendance'] = tendance + details['score_tendance'] = round(score_tendance, 1) + + # 7. AVIS ENTRAINEUR (5 pts) + avis = p.get('avisEntraineur', 'NEUTRE') + score_avis = {'POSITIF': 5, 'TRES_POSITIF': 5, 'NEUTRE': 2, 'NEGATIF': 0, 'TRES_NEGATIF': 0}.get(avis, 2) + score += score_avis + details['avis_entraineur'] = avis + details['score_avis'] = score_avis + + # 8. BONUS OUTSIDER (5 pts) + bonus_outsider = 5 if forme <= 3 and cote >= 10 else 0 + score += bonus_outsider + details['bonus_outsider'] = bonus_outsider + + # Driver change penalty + if p.get('driverChange', False): + score -= 3 + details['driver_change'] = True + + details['score_total'] = round(score, 1) + details['musique'] = p.get('musique', '') + details['nb_victoires'] = nb_victoires_total + details['nb_places'] = nb_places_total + details['nb_courses'] = nb_courses_total + + return round(score, 1), details + +def get_ze2sur4_combinaisons(top4): + combinaisons = [] + for i in range(4): + for j in range(i+1, 4): + c1 = top4[i] + c2 = top4[j] + combinaisons.append({ + 'cheval1': c1['nom'], + 'numero1': c1['numero'], + 'cheval2': c2['nom'], + 'numero2': c2['numero'], + 'mise': 1.0, + }) + return combinaisons + +def build_recommendations_v2(scored_horses): + ranked = sorted(scored_horses, key=lambda x: x['score'], reverse=True) + if len(ranked) < 4: + return None + + top1, top2, top3, top4 = ranked[0], ranked[1], ranked[2], ranked[3] + top4_list = ranked[:4] + + def confiance(s): + return "FORTE" if s >= 55 else "BONNE" if s >= 45 else "MOYENNE" if s >= 35 else "FAIBLE" + + ze2_combinaisons = get_ze2sur4_combinaisons(top4_list) + mise_ze2 = len(ze2_combinaisons) * 1.0 + + return { + 'simple_gagnant': { + 'cheval': top1['nom'], 'numero': top1['numero'], 'cote': top1['details']['cote'], + 'score': top1['score'], 'confiance': confiance(top1['score']), + 'mise_suggeree': 2.0, 'gain_potentiel': round(2.0 * top1['details']['cote'], 2) + }, + 'ze2_sur_4': { + 'top4': [{'nom': h['nom'], 'numero': h['numero']} for h in top4_list], + 'combinaisons': ze2_combinaisons, + 'mise_totale': mise_ze2, + 'nb_combinaisons': len(ze2_combinaisons), + 'confiance': confiance((top1['score'] + top2['score'] + top3['score'] + top4['score']) / 4), + 'explication': 'Jouer les 6 combinaisons de 2 chevaux parmi les 4 premiers' + }, + 'outsider': _find_outsider(ranked), + 'budget_total': 2.0 + mise_ze2, + } + +def _find_outsider(ranked): + for h in ranked[3:7]: + d = h['details'] + if d['cote'] >= 12 and d['forme_recente'] <= 4 and d['bonus_outsider'] == 5: + return { + 'cheval': h['nom'], 'numero': h['numero'], 'cote': d['cote'], + 'mise_suggeree': 1.0, 'gain_potentiel': round(1.0 * d['cote'], 2) + } + return None + +def save_to_db(scored_horses, date_course, hippodrome, libelle): + conn = sqlite3.connect(DB_PATH) + cursor = conn.cursor() + + cursor.execute("DELETE FROM scoring WHERE date = ?", (date_course,)) + + for i, h in enumerate(scored_horses, 1): + d = h['details'] + cursor.execute(""" + INSERT INTO scoring (date, race_name, horse_number, horse_name, score, + score_cote, score_forme, score_victoire, score_place, score_rk, + score_tendance, score_avis, cote, forme_recente, tx_victoire, tx_place, + avis_entraineur, musique, rang_scoring, scoring_version) + VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, 'v2') + """, (date_course, libelle, h['numero'], h['nom'], h['score'], + d.get('score_cote', 0), d.get('score_forme', 0), d.get('score_victoire', 0), + d.get('score_place', 0), d.get('score_rk', 0), d.get('score_tendance', 0), + d.get('score_avis', 0), d.get('cote', 0), d.get('forme_recente', 0), + d.get('tx_victoire', 0), d.get('tx_place', 0), d.get('avis_entraineur', ''), + d.get('musique', ''), i)) + + conn.commit() + conn.close() + print(f"💾 {len(scored_horses)} scores enregistres en BDD pour {date_course}") + +def main(): + today = datetime.now().strftime('%Y-%m-%d') + date_pmu = datetime.now().strftime('%d%m%Y') + print(f"=== SCORING V2 - ZE2 SUR4 OPTIMISE === {datetime.now().strftime('%d/%m/%Y %H:%M')} ===") + + try: + url = f"https://turfinfo.api.pmu.fr/rest/client/1/programme/{date_pmu}/reunions" + r = requests.get(url, headers=HEADERS, timeout=15) + reunions = r.json().get('programme', {}).get('reunions', []) + except Exception as e: + print(f"Erreur: {e}") + return + + quinte = None + for reunion in reunions: + for course in reunion.get('courses', []): + paris_types = [p["typePari"] for p in course.get("paris", [])] + if any("QUINTE" in p for p in paris_types) or "PARIS-TURF" in course.get('libelle', ''): + quinte = (reunion['numOfficiel'], course['numOrdre'], course.get('libelle', ''), + reunion['hippodrome']['libelleCourt'], course.get('heureDepart', 0)) + break + if quinte: + break + + if not quinte: + # Fallback: utiliser la premiere reunion francaise avec predictions + conn = sqlite3.connect(DB_PATH) + conn.row_factory = sqlite3.Row + r = conn.execute(""" + SELECT r.num_reunion, r.hippodrome_court, c.num_course, c.libelle + FROM pmu_courses c + JOIN pmu_reunions r ON r.date_programme=c.date_programme AND r.num_reunion=c.num_reunion + WHERE c.date_programme=? AND r.pays_code='FRA' AND c.num_course=1 + AND EXISTS (SELECT 1 FROM predictions p WHERE p.date=? AND p.source='canalturf_partants' + AND p.race_name LIKE '%' || c.libelle || '%') + ORDER BY c.heure_depart_str ASC LIMIT 1 + """, (today, today)).fetchone() + conn.close() + if r: + quinte = (r['num_reunion'], r['num_course'], r['libelle'], r['hippodrome_court'], 0) + else: + print("Aucune course trouvee") + return + + num_r, num_c, libelle, hippodrome, heure_ts = quinte + heure = datetime.fromtimestamp(heure_ts/1000).strftime('%H:%M') if heure_ts else '13:55' + print(f"Course: {libelle} - {hippodrome} {heure}") + + try: + url = f"https://turfinfo.api.pmu.fr/rest/client/1/programme/{date_pmu}/R{num_r}/C{num_c}/participants" + r = requests.get(url, headers=HEADERS, timeout=15) + participants = [p for p in r.json().get('participants', []) if p.get('statut') == 'PARTANT'] + except Exception as e: + print(f"Erreur: {e}") + return + + scored_horses = [] + for p in participants: + score, details = score_cheval_v2(p, participants, today) + scored_horses.append({'nom': p['nom'], 'numero': p['numPmu'], 'score': score, 'details': details}) + + ranked = sorted(scored_horses, key=lambda x: x['score'], reverse=True) + print(f"\n=== TOP 4 ===") + for i, h in enumerate(ranked[:4], 1): + d = h['details'] + print(f"{i}. #{h['numero']:>2} {h['nom']:<20} Score:{h['score']:.1f} Cote:{d['cote']:.1f}") + + save_to_db(ranked, today, hippodrome, libelle) + + reco = build_recommendations_v2(scored_horses) + if reco: + print(f"\n=== RECOMMANDATIONS ===") + sg = reco['simple_gagnant'] + print(f"\n🎯 SIMPLE GAGNANT:") + print(f" #{sg['numero']} {sg['cheval']} @ {sg['cote']}/1 (mise {sg['mise_suggeree']}EUR)") + + ze2 = reco['ze2_sur_4'] + print(f"\n🎰 ZE 2 SUR 4 (TOP 4: {', '.join([h['nom'] for h in ze2['top4']])}") + print(f" Mise totale: {ze2['mise_totale']}EUR ({ze2['nb_combinaisons']} combis x 1EUR)") + print(f" Confiance: {ze2['confiance']}") + print(f" Combinaisons:") + for c in ze2['combinaisons']: + print(f" {c['numero1']}-{c['cheval1']} + {c['numero2']}-{c['cheval2']}") + + print(f"\n💰 BUDGET TOTAL: {reco['budget_total']}EUR") + print(f" - Simple Gagnant: 2EUR") + print(f" - ZE 2 sur 4: {ze2['mise_totale']}EUR") + +if __name__ == "__main__": + main() diff --git a/scoring_v2_json.py b/scoring_v2_json.py new file mode 100755 index 0000000..48dec94 --- /dev/null +++ b/scoring_v2_json.py @@ -0,0 +1,320 @@ +#!/usr/bin/env python3 +""" +Scoring Engine V2 - ZE 2 sur 4 Optimise +Cote: 10%, Forme: 30%, Bonus outsider +""" + +import requests +import sqlite3 +import json +import re +from datetime import datetime + +DB_PATH = "/home/h3r7/turf_scraper/turf.db" +HEADERS = {'User-Agent': 'Mozilla/5.0', 'Accept': 'application/json'} + +def get_cote_from_db(horse_name, date_course): + """Recupere la cote depuis la table predictions (plus recente et non nulle)""" + conn = sqlite3.connect(DB_PATH) + conn.row_factory = sqlite3.Row + c = conn.execute(""" + SELECT odds FROM predictions + WHERE date=? AND horse_name LIKE ? AND odds > 0 + ORDER BY created_at DESC LIMIT 1 + """, (date_course, f"%{horse_name}%")) + r = c.fetchone() + conn.close() + return r['odds'] if r else 0 + +def parse_musique(musique): + if not musique: + return {} + clean = re.sub(r'$\d+$', '', musique) + resultats = re.findall(r'(\d+|D|0)([amphsc]?)', clean) + positions = [] + for pos, disc in resultats[:10]: + positions.append(99 if pos == 'D' else int(pos)) + if not positions: + return {} + nb_courses = len(positions) + nb_victoires = positions.count(1) + nb_places = sum(1 for p in positions if 1 <= p <= 3) + recentes = [p for p in positions[:3] if p != 99] + forme_recente = sum(recentes) / len(recentes) if recentes else 99 + tendance = (sum(positions[-4:]) / 4 - sum(positions[:4]) / 4) if len(positions) >= 4 else 0 + return { + 'forme_recente': round(forme_recente, 1), + 'tendance': round(tendance, 1), + 'tx_victoire': round(nb_victoires / nb_courses * 100, 1) if nb_courses else 0, + 'tx_place': round(nb_places / nb_courses * 100, 1) if nb_courses else 0, + } + +def score_cheval_v2(p, all_participants, today): + score = 0 + details = {} + + # 1. COTE - Essaye PMU API, sinon DB + horse_name = p.get('nom', '') + cote = 0 + + # Essayer d'abord depuis l'API PMU + rapport = p.get('dernierRapportDirect', {}) + if rapport: + cote = rapport.get('rapport', 0) + if not cote: + rapport_ref = p.get('dernierRapportReference', {}) + cote = rapport_ref.get('rapport', 0) if rapport_ref else 0 + + # Fallback: aller chercher dans la DB + if not cote or cote == 0: + cote = get_cote_from_db(horse_name, today) + + # Si toujours pas de cote, utiliser 99 comme valeur par defaut + if not cote or cote == 0: + cote = 99.0 + + score_cote = max(2, min(10, 20 / (1 + cote * 0.15))) if cote > 0 else 2 + score += score_cote + details['cote'] = round(cote, 1) + details['score_cote'] = round(score_cote, 1) + + # 2. FORME - AUGMENTE a 30 pts + musique_stats = parse_musique(p.get('musique', '')) + forme = musique_stats.get('forme_recente', 99) + score_forme = 30 if forme <= 1 else 25 if forme <= 2 else 20 if forme <= 3 else 15 if forme <= 5 else 8 if forme <= 8 else 0 + score += score_forme + details['forme_recente'] = forme + details['score_forme'] = score_forme + + # 3. TAUX VICTOIRE (15 pts) + nb_courses_total = p.get('nombreCourses', 0) + nb_victoires_total = p.get('nombreVictoires', 0) + tx_vic = (nb_victoires_total / nb_courses_total * 100) if nb_courses_total else 0 + score_vic = min(15, tx_vic * 0.5) + score += score_vic + details['tx_victoire'] = round(tx_vic, 1) + details['score_victoire'] = round(score_vic, 1) + + # 4. TAUX PLACE (15 pts) + nb_places_total = p.get('nombrePlaces', 0) + tx_place = (nb_places_total / nb_courses_total * 100) if nb_courses_total else 0 + score_place = min(15, tx_place * 0.2) + score += score_place + details['tx_place'] = round(tx_place, 1) + details['score_place'] = round(score_place, 1) + + # 5. REDUCTION KM (10 pts) + rk = p.get('reductionKilometrique', 0) + all_rk = [x.get('reductionKilometrique', 0) for x in all_participants if x.get('reductionKilometrique', 0) > 0] + if rk > 0 and all_rk: + score_rk = 10 * (1 - (rk - min(all_rk)) / (max(all_rk) - min(all_rk))) if max(all_rk) > min(all_rk) else 5 + else: + score_rk = 0 + score += score_rk + details['rk'] = rk + details['score_rk'] = round(score_rk, 1) + + # 6. TENDANCE (10 pts) + tendance = musique_stats.get('tendance', 0) + score_tendance = min(10, max(0, 5 + tendance)) + score += score_tendance + details['tendance'] = tendance + details['score_tendance'] = round(score_tendance, 1) + + # 7. AVIS ENTRAINEUR (5 pts) + avis = p.get('avisEntraineur', 'NEUTRE') + score_avis = {'POSITIF': 5, 'TRES_POSITIF': 5, 'NEUTRE': 2, 'NEGATIF': 0, 'TRES_NEGATIF': 0}.get(avis, 2) + score += score_avis + details['avis_entraineur'] = avis + details['score_avis'] = score_avis + + # 8. BONUS OUTSIDER (5 pts) + bonus_outsider = 5 if forme <= 3 and cote >= 10 else 0 + score += bonus_outsider + details['bonus_outsider'] = bonus_outsider + + # Driver change penalty + if p.get('driverChange', False): + score -= 3 + details['driver_change'] = True + + details['score_total'] = round(score, 1) + details['musique'] = p.get('musique', '') + details['nb_victoires'] = nb_victoires_total + details['nb_places'] = nb_places_total + details['nb_courses'] = nb_courses_total + + return round(score, 1), details + +def get_ze2sur4_combinaisons(top4): + combinaisons = [] + for i in range(4): + for j in range(i+1, 4): + c1 = top4[i] + c2 = top4[j] + combinaisons.append({ + 'cheval1': c1['nom'], + 'numero1': c1['numero'], + 'cheval2': c2['nom'], + 'numero2': c2['numero'], + 'mise': 1.0, + }) + return combinaisons + +def build_recommendations_v2(scored_horses): + ranked = sorted(scored_horses, key=lambda x: x['score'], reverse=True) + if len(ranked) < 4: + return None + + top1, top2, top3, top4 = ranked[0], ranked[1], ranked[2], ranked[3] + top4_list = ranked[:4] + + def confiance(s): + return "FORTE" if s >= 55 else "BONNE" if s >= 45 else "MOYENNE" if s >= 35 else "FAIBLE" + + ze2_combinaisons = get_ze2sur4_combinaisons(top4_list) + mise_ze2 = len(ze2_combinaisons) * 1.0 + + return { + 'simple_gagnant': { + 'cheval': top1['nom'], 'numero': top1['numero'], 'cote': top1['details']['cote'], + 'score': top1['score'], 'confiance': confiance(top1['score']), + 'mise_suggeree': 2.0, 'gain_potentiel': round(2.0 * top1['details']['cote'], 2) + }, + 'ze2_sur_4': { + 'top4': [{'nom': h['nom'], 'numero': h['numero']} for h in top4_list], + 'combinaisons': ze2_combinaisons, + 'mise_totale': mise_ze2, + 'nb_combinaisons': len(ze2_combinaisons), + 'confiance': confiance((top1['score'] + top2['score'] + top3['score'] + top4['score']) / 4), + 'explication': 'Jouer les 6 combinaisons de 2 chevaux parmi les 4 premiers' + }, + 'outsider': _find_outsider(ranked), + 'budget_total': 2.0 + mise_ze2, + } + +def _find_outsider(ranked): + for h in ranked[3:7]: + d = h['details'] + if d['cote'] >= 12 and d['forme_recente'] <= 4 and d['bonus_outsider'] == 5: + return { + 'cheval': h['nom'], 'numero': h['numero'], 'cote': d['cote'], + 'mise_suggeree': 1.0, 'gain_potentiel': round(1.0 * d['cote'], 2) + } + return None + +def save_to_db(scored_horses, date_course, hippodrome, libelle): + conn = sqlite3.connect(DB_PATH) + cursor = conn.cursor() + + cursor.execute("DELETE FROM scoring WHERE date = ?", (date_course,)) + + for i, h in enumerate(scored_horses, 1): + d = h['details'] + cursor.execute(""" + INSERT INTO scoring (date, race_name, horse_number, horse_name, score, + score_cote, score_forme, score_victoire, score_place, score_rk, + score_tendance, score_avis, cote, forme_recente, tx_victoire, tx_place, + avis_entraineur, musique, rang_scoring, scoring_version) + VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, 'v2') + """, (date_course, libelle, h['numero'], h['nom'], h['score'], + d.get('score_cote', 0), d.get('score_forme', 0), d.get('score_victoire', 0), + d.get('score_place', 0), d.get('score_rk', 0), d.get('score_tendance', 0), + d.get('score_avis', 0), d.get('cote', 0), d.get('forme_recente', 0), + d.get('tx_victoire', 0), d.get('tx_place', 0), d.get('avis_entraineur', ''), + d.get('musique', ''), i)) + + conn.commit() + conn.close() + print(f"💾 {len(scored_horses)} scores enregistres en BDD pour {date_course}") + +def main(): + today = datetime.now().strftime('%Y-%m-%d') + date_pmu = datetime.now().strftime('%d%m%Y') + print(f"=== SCORING V2 - ZE2 SUR4 OPTIMISE === {datetime.now().strftime('%d/%m/%Y %H:%M')} ===") + + try: + url = f"https://turfinfo.api.pmu.fr/rest/client/1/programme/{date_pmu}/reunions" + r = requests.get(url, headers=HEADERS, timeout=15) + reunions = r.json().get('programme', {}).get('reunions', []) + except Exception as e: + print(f"Erreur: {e}") + return + + quinte = None + for reunion in reunions: + for course in reunion.get('courses', []): + paris_types = [p["typePari"] for p in course.get("paris", [])] + if any("QUINTE" in p for p in paris_types) or "PARIS-TURF" in course.get('libelle', ''): + quinte = (reunion['numOfficiel'], course['numOrdre'], course.get('libelle', ''), + reunion['hippodrome']['libelleCourt'], course.get('heureDepart', 0)) + break + if quinte: + break + + if not quinte: + # Fallback: utiliser la premiere reunion francaise avec predictions + conn = sqlite3.connect(DB_PATH) + conn.row_factory = sqlite3.Row + r = conn.execute(""" + SELECT r.num_reunion, r.hippodrome_court, c.num_course, c.libelle + FROM pmu_courses c + JOIN pmu_reunions r ON r.date_programme=c.date_programme AND r.num_reunion=c.num_reunion + WHERE c.date_programme=? AND r.pays_code='FRA' AND c.num_course=1 + AND EXISTS (SELECT 1 FROM predictions p WHERE p.date=? AND p.source='canalturf_partants' + AND p.race_name LIKE '%' || c.libelle || '%') + ORDER BY c.heure_depart_str ASC LIMIT 1 + """, (today, today)).fetchone() + conn.close() + if r: + quinte = (r['num_reunion'], r['num_course'], r['libelle'], r['hippodrome_court'], 0) + else: + print("Aucune course trouvee") + return + + num_r, num_c, libelle, hippodrome, heure_ts = quinte + heure = datetime.fromtimestamp(heure_ts/1000).strftime('%H:%M') if heure_ts else '13:55' + print(f"Course: {libelle} - {hippodrome} {heure}") + + try: + url = f"https://turfinfo.api.pmu.fr/rest/client/1/programme/{date_pmu}/R{num_r}/C{num_c}/participants" + r = requests.get(url, headers=HEADERS, timeout=15) + participants = [p for p in r.json().get('participants', []) if p.get('statut') == 'PARTANT'] + except Exception as e: + print(f"Erreur: {e}") + return + + scored_horses = [] + for p in participants: + score, details = score_cheval_v2(p, participants, today) + scored_horses.append({'nom': p['nom'], 'numero': p['numPmu'], 'score': score, 'details': details}) + + ranked = sorted(scored_horses, key=lambda x: x['score'], reverse=True) + print(f"\n=== TOP 4 ===") + for i, h in enumerate(ranked[:4], 1): + d = h['details'] + print(f"{i}. #{h['numero']:>2} {h['nom']:<20} Score:{h['score']:.1f} Cote:{d['cote']:.1f}") + + save_to_db(ranked, today, hippodrome, libelle) + + reco = build_recommendations_v2(scored_horses) + if reco: + print(f"\n=== RECOMMANDATIONS ===") + sg = reco['simple_gagnant'] + print(f"\n🎯 SIMPLE GAGNANT:") + print(f" #{sg['numero']} {sg['cheval']} @ {sg['cote']}/1 (mise {sg['mise_suggeree']}EUR)") + + ze2 = reco['ze2_sur_4'] + print(f"\n🎰 ZE 2 SUR 4 (TOP 4: {', '.join([h['nom'] for h in ze2['top4']])}") + print(f" Mise totale: {ze2['mise_totale']}EUR ({ze2['nb_combinaisons']} combis x 1EUR)") + print(f" Confiance: {ze2['confiance']}") + print(f" Combinaisons:") + for c in ze2['combinaisons']: + print(f" {c['numero1']}-{c['cheval1']} + {c['numero2']}-{c['cheval2']}") + + print(f"\n💰 BUDGET TOTAL: {reco['budget_total']}EUR") + print(f" - Simple Gagnant: 2EUR") + print(f" - ZE 2 sur 4: {ze2['mise_totale']}EUR") + +if __name__ == "__main__": + main() diff --git a/scraper_artisans.py b/scraper_artisans.py new file mode 100755 index 0000000..76f2772 --- /dev/null +++ b/scraper_artisans.py @@ -0,0 +1,288 @@ +#!/usr/bin/env python3 +""" +H3R7Tech - Web Scraper pour Artisans +===================================== +Extraction de données depuis Pages Jaunes / Google Maps +Stockage vers CRM + Export Google Sheets + +Auteur: H3R7Tech +Date: 25/02/2026 +""" + +import requests +from bs4 import BeautifulSoup +import json +import csv +import os +from datetime import datetime +import urllib.parse + +# Configuration +CRM_FILE = '/home/h3r7/turf_scraper/crm_prospects.json' +EXPORT_DIR = '/home/h3r7/turf_scraper/exports/' + +# Créer le dossier exports +os.makedirs(EXPORT_DIR, exist_ok=True) + +class ScraperArtisans: + """Classe principale pour le scraping des artisans""" + + def __init__(self): + self.headers = { + 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36', + 'Accept-Language': 'fr-FR,fr;q=0.9,en;q=0.8', + } + + def search_pagesjaunes(self, profession, ville, cp): + """ + Recherche sur Pages Jaunes + Note: Souvent bloqué par Cloudflare + """ + url = f"https://www.pagesjaunes.fr/annuaire/{ville.lower()}-{cp}/{profession.lower()}" + + try: + r = requests.get(url, headers=self.headers, timeout=10) + if r.status_code == 200: + return self._parse_pagesjaunes(r.text) + except Exception as e: + print(f"❌ PagesJaunes bloqué: {e}") + + return [] + + def _parse_pagesjaunes(self, html): + """Parse le HTML de Pages Jaunes""" + soup = BeautifulSoup(html, 'html.parser') + results = [] + + for item in soup.select('.bi-thu__ItemSearchResult')[:20]: + try: + name = item.select_one('.bi-thu__Title') + addr = item.select_one('.bi-thu__Address') + phone = item.select_one('.bi-thu__PhoneNumber') + + if name: + results.append({ + 'nom': name.get_text(strip=True), + 'adresse': addr.get_text(strip=True) if addr else '', + 'telephone': phone.get_text(strip=True) if phone else '', + 'website': '', + 'note': '', + 'avis': '' + }) + except: + continue + + return results + + def search_google_maps(self, profession, ville, cp): + """ + Recherche via Google Maps (méthode alternative) + Utilise la recherche Google classique + """ + query = f"{profession} {ville} {cp}" + url = f"https://www.google.com/search?q={urllib.parse.quote(query)}+annuaire" + + try: + r = requests.get(url, headers=self.headers, timeout=15) + if r.status_code == 200: + return self._parse_google_results(r.text) + except Exception as e: + print(f"❌ Google bloqué: {e}") + + return [] + + def _parse_google_results(self, html): + """Parse les résultats Google""" + soup = BeautifulSoup(html, 'html.parser') + results = [] + + # Chercher les éléments de résultats + for item in soup.select('.g')[:15]: + try: + title = item.select_one('h3') + if title: + text = title.get_text(strip=True) + # Chercher téléphone dans le texte + phone = '' + if '06' in text or '07' in text: + for word in text.split(): + if word.startswith('0') and len(word) == 10: + phone = word + + results.append({ + 'nom': text[:100], + 'adresse': '', + 'telephone': phone, + 'website': '', + 'note': '', + 'avis': '' + }) + except: + continue + + return results + + +class CRMManager: + """Gestion du CRM local""" + + def __init__(self): + self.file = CRM_FILE + + def load(self): + if os.path.exists(self.file): + with open(self.file, 'r') as f: + return json.load(f) + return {"prospects": [], "last_id": 0} + + def save(self, data): + with open(self.file, 'w') as f: + json.dump(data, f, indent=2) + + def add_prospect(self, data): + """Ajouter un prospect au CRM""" + crm = self.load() + + crm['last_id'] += 1 + prospect = { + 'id': crm['last_id'], + 'nom': data.get('nom', ''), + 'entreprise': data.get('entreprise', data.get('nom', '')), + 'tel': data.get('telephone', '').replace(' ', '').replace('.', ''), + 'email': '', + 'secteur': data.get('profession', 'Artisan'), + 'statut': 'nouveau', + 'score': self._calculate_score(data), + 'notes': f"Adresse: {data.get('adresse', '')} | Note: {data.get('note', '')}", + 'source': 'Scraping', + 'created': datetime.now().isoformat(), + 'updated': datetime.now().isoformat() + } + + crm['prospects'].append(prospect) + self.save(crm) + + return prospect['id'] + + def _calculate_score(self, data): + """Calculer le score de qualification""" + score = 1 + + if data.get('telephone'): + score += 1 + if data.get('note'): + score += 1 + if data.get('avis'): + score += 1 + + return min(score, 5) + + def export_csv(self, filename=None): + """Exporter vers CSV""" + if not filename: + filename = f"prospects_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv" + + crm = self.load() + filepath = os.path.join(EXPORT_DIR, filename) + + with open(filepath, 'w', newline='', encoding='utf-8') as f: + writer = csv.writer(f) + writer.writerow(['ID', 'Nom', 'Entreprise', 'Téléphone', 'Email', 'Secteur', 'Statut', 'Score', 'Adresse', 'Source', 'Date création']) + + for p in crm['prospects']: + writer.writerow([ + p.get('id', ''), + p.get('nom', ''), + p.get('entreprise', ''), + p.get('tel', ''), + p.get('email', ''), + p.get('secteur', ''), + p.get('statut', ''), + p.get('score', ''), + p.get('notes', ''), + p.get('source', ''), + p.get('created', '')[:10] + ]) + + return filepath + + def get_stats(self): + """Obtenir les statistiques""" + crm = self.load() + prospects = crm['prospects'] + + stats = { + 'total': len(prospects), + 'par_statut': {}, + 'par_secteur': {}, + 'score_moyen': 0 + } + + total_score = 0 + for p in prospects: + statut = p.get('statut', 'nouveau') + stats['par_statut'][statut] = stats['par_statut'].get(statut, 0) + 1 + + secteur = p.get('secteur', 'Autre') + stats['par_secteur'][secteur] = stats['par_secteur'].get(secteur, 0) + 1 + + total_score += p.get('score', 0) + + if prospects: + stats['score_moyen'] = round(total_score / len(prospects), 1) + + return stats + + +def main(): + """Fonction principale - Demo""" + print("=" * 60) + print("🏢 H3R7Tech - Web Scraper pour Artisans") + print("=" * 60) + + # Demo: Ajouter des prospects simulate + demo_data = [ + { + 'nom': 'Dupont Philippe', + 'entreprise': 'Dupont Cordonnerie', + 'telephone': '0320123456', + 'adresse': '45 Rue Nationale, Lille 59000', + 'profession': 'Cordonnier', + 'note': '4.5', + 'avis': '120' + }, + { + 'nom': 'Martin Jean', + 'entreprise': 'Martin Réparation', + 'telephone': '0320987654', + 'adresse': '12 Avenue de la République, Lille 59000', + 'profession': 'Cordonnier', + 'note': '4.2', + 'avis': '85' + } + ] + + # Ajouter au CRM + crm = CRMManager() + for data in demo_data: + cid = crm.add_prospect(data) + print(f"✅ Ajouté: {data['nom']} (ID: {cid})") + + # Exporter vers CSV + csv_file = crm.export_csv() + print(f"\n💾 Export CSV: {csv_file}") + + # Afficher les stats + stats = crm.get_stats() + print(f"\n📊 Statistiques CRM:") + print(f" Total prospects: {stats['total']}") + print(f" Score moyen: {stats['score_moyen']}") + + print("\n" + "=" * 60) + print("🚀 Pour lancer une recherche réelle:") + print(" python3 scraper_artisans.py --profession 'cordonnier' --ville 'lille' --cp '59000'") + print("=" * 60) + + +if __name__ == '__main__': + main() diff --git a/scraper_manual.html b/scraper_manual.html new file mode 100755 index 0000000..97016b5 --- /dev/null +++ b/scraper_manual.html @@ -0,0 +1,71 @@ + + + + + Scraper Pros - H3R7Tech + + + +🏠Accueil +

🏢 H3R7Tech - Scraper Pros

+ +

Rechercher des professionnels

+ + + + +

+ +

+ + + + diff --git a/scraper_pro.html b/scraper_pro.html new file mode 100755 index 0000000..c96886d --- /dev/null +++ b/scraper_pro.html @@ -0,0 +1,163 @@ + + + + + Scraper Pro - H3R7Tech + + + +🏠Accueil +

+ +

🔍 Scraper Pro - H3R7Tech

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💾 Derniers Ajouts

Chargement...

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+ 📊 Ouvrir CRM | + ← Retour Portail +

+ + + + diff --git a/simple_ideas.html b/simple_ideas.html new file mode 100755 index 0000000..befdaf6 --- /dev/null +++ b/simple_ideas.html @@ -0,0 +1,41 @@ + + + + + + 💡 Boîte à Idées + + + +🏠Accueil +

💡 Boîte à Idées

+ + + + diff --git a/simple_idees.html b/simple_idees.html new file mode 100755 index 0000000..8a96d21 --- /dev/null +++ b/simple_idees.html @@ -0,0 +1,105 @@ + + + + + + 💡 Idées + + + +🏠Accueil +

💡 Boîte à Idées

+ +

➕ Nouvelle Idée

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+ +

Chargement...

+ + + + diff --git a/start_vitesse.sh b/start_vitesse.sh new file mode 100755 index 0000000..c90c8b3 --- /dev/null +++ b/start_vitesse.sh @@ -0,0 +1,3 @@ +#!/bin/bash +cd /home/h3r7/turf_scraper +python3 vitesse_api.py diff --git a/template_artisan_final.html b/template_artisan_final.html new file mode 100755 index 0000000..cf2ca6e --- /dev/null +++ b/template_artisan_final.html @@ -0,0 +1,253 @@ + + + + + + Artisan Multi-services - Rénovation & Bricolage + + + +🏠Accueil +

🔧 Artisan Multi-services

+ + +

+ +

Artisan Multi-services

Rénovation • Bricolage • Petits travaux

📍 Paris & Île-de-France

+ +

Expérience

15 ans

Certification

RGE

Zone

Paris & IDF

Réponse

24h

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Mes Services

🏠

Rénovation

Rénovation complète, second œuvre, aménagement intérieur, mise aux normes.

🔨

Bricolage

Montage meubles, peinture, pose de parquet, jointoiement, petites réparations.

⚡

Électricité

Prises, interrupteurs, éclairage, mise aux normes électriques.

🚰

Plomberie

Réparation fuites, remplacement robinets, installation sanitaires.

🚪

Menuiserie

Porte, fenêtre, placard, escalier, terrasse bois.

🧹

Nettoyage

Nettoyage post-travaux, entretient espaces verts.

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Mes Réalisations

Rénovation salon - Paris 15

Aménagement cuisine - Lyon

Rénovation salle de bain

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Mes Tarifs

Heure

45€ /h

Petits bricolages
Réparations diverses
Déplacement inclus

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Demi-journée

180€ /demi-jour

3h de travail
Petits travaux
Matériel inclus
Déplacement offert

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Journée

320€ /jour

7h de travail
Gros chantier
Matériel inclus
Déplacement offert

+ + +

+ +

Me Contacter

📍 Zone d'intervention

Paris et toute l'Île-de-France

+
+

📞 Contact

📞 06 12 34 56 78

✉️ contact@artisan-multi.fr

+
+

🕐 Disponibilité

Lundi - Samedi: 8h - 19h

Intervention rapide sous 48h

+ +

+ + + + diff --git a/template_boulangerie_final.html b/template_boulangerie_final.html new file mode 100755 index 0000000..fcefb60 --- /dev/null +++ b/template_boulangerie_final.html @@ -0,0 +1,183 @@ + + + + + + La Petite Boulangerie - Pain artisanal & Gateaux + + + +🏠Accueil +

🍞 La Petite Boulangerie

+ +

La Petite Boulangerie

Pain artisanal & gateaux faits maison

📍 15 Rue du Commerce, 75001 Paris

+ +

Ouvert

Lun-Sam

Service

6h-19h

Téléphone

01 42 00 00 00

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Nos Produits

Baguette tradition

Farine française, levée naturelle

1.90€

Croissant

Beurre AOP, thérapeut

1.50€

Pain au chocolat

2 carrés de chocolat noir

2.00€

Flan pâtissier

Crème vanillée, caramel

3.50€

Brioche tressée

Beurre frais, sucre perlé

4.00€

Éclair au chocolat

Crème pâtissière, fondant

3.50€

Macarons (x6)

Assortiment fruité

12.00€

Cake aux fruits

Fruits confits, rhum

5.00€

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Notre Galerie

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Nous Contacter

📍 Adresse

15 Rue du Commerce, 75001 Paris

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+

📞 Commande

📞 01 42 00 00 00

✉️ contact@lapetiteboulangerie.fr

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+

🕐 Horaires

Lundi - Samedi: 6h00 - 19h00

Dimanche: 7h00 - 13h00

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+ + + + diff --git a/template_complet.html b/template_complet.html new file mode 100755 index 0000000..a934ce4 --- /dev/null +++ b/template_complet.html @@ -0,0 +1,276 @@ + + + + + + Template Site Pro - Demo + + + +🏠Accueil +

Mon Entreprise

+ +

Votre entreprise mérite un site pro

Une présence web de qualité à petit prix pour attirer plus de clients

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Mes Services

🎨

Création de site web

Site web sur-mesure, responsive et optimisé pour tous les écrans. Design moderne qui reflète votre expertise.

🔍

Référencement SEO

Optimisation pour Google.Soyez visible quand vos clients cherchent vos services.

📱

Site mobile

Version smartphone optimisée pour une expérience utilisateur parfaite.

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Maintenance

Je m'occupe de tout : mises à jour, hébergement, sauvegardes.

📧

Email pro

Adresse email personnalisée @votreentreprise.fr.

📊

Analytics

Suivez les visites et optimisez votre stratégie.

+ +

À propos

Fort de mon expérience dans le numérique, j'aide les artisans et petits commerçants à se doter d'une présence web professionnelle sans se ruiner.

Mon objectif : rendre le web accessible à tous les professionnels, quel que soit leur budget.

50+

Sites créés

100%

Clients satisfaits

24h

Délai moyen

🏢

+ +

Me contacter

Parlons de votre projet !

+ +

+ + + + + + diff --git a/template_restaurant_final.html b/template_restaurant_final.html new file mode 100755 index 0000000..1cf292e --- /dev/null +++ b/template_restaurant_final.html @@ -0,0 +1,444 @@ + + + + + + Le Jardin Secret - Restaurant Gastronomique Paris + + + + +🏠Accueil +

🍽️ Le Jardin Secret

+ + +

+ +

Le Jardin Secret

Cuisine française raffinée au cœur de Paris

📍 42 Rue de la Paix, 75002 Paris

+ + +

+ +

📅

Ouvert

Mardi - Dimanche

🕐

Service

12h-14h / 19h-22h

📞

Réservation

01 42 86 00 00

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Notre Menu

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🥗 Entrées

+ + + + +

🍖 Plats

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+ + +

🍰 Desserts

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Notre Galerie

Salle principale

Terrasse

Cuisine ouverte

Vue jardín

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Nos Avis

⭐⭐⭐⭐⭐

"Une expérience culinaire exceptionnelle. Le foie gras est divin et le service impeccable. Je recommande!"

- Marie D.

⭐⭐⭐⭐⭐

"Le meilleur restaurant gastronomique du quartier. Rapport qualité-prix excellent pour cette gamme."

- Jean-Pierre M.

⭐⭐⭐⭐⭐

"Décor magnifique, cuisine raffinée. Réservation obligatoire le week-end!"

- Sophie L.

+ +

Nous Contacter

📍 Adresse

42 Rue de la Paix

75002 Paris, France

+ + 📍 Itinéraire + +

+ +

+
+

📞 Réservation

📞 01 42 86 00 00

✉️ contact@lejardinsecret.fr

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+

🕐 Horaires

Mardi - Samedi: 12h-14h / 19h-22h

Dimanche: 12h-15h

Lundi: Fermé

+ + + + +

+ 📅 Date + +

+ 🕐 Horaire + +

+ 12:00 + 12:30 + 13:00 + 13:30 + 19:00 + 19:30 + 20:00 + 20:30 + 21:00 + 21:30 +

+ + +

+ + + + + diff --git a/template_restaurant_json.html b/template_restaurant_json.html new file mode 100755 index 0000000..00eed51 --- /dev/null +++ b/template_restaurant_json.html @@ -0,0 +1,297 @@ + + + + + + Le Jardin Secret - Restaurant Gastronomique Paris + + + + +🏠Accueil +

🍽️ Le Jardin Secret

+ +

Le Jardin Secret

Cuisine française raffinée au cœur de Paris

📍 42 Rue de la Paix, 75002 Paris

+ + +

+ +

+ + + +

Notre Galerie

Salle principale

Terrasse

Cuisine ouverte

Vue exceptionnelle

+ +

Nos Avis

+ +

Nous Contacter

+ +

+ + + + + + diff --git a/templates/dashboard.html b/templates/dashboard.html new file mode 100644 index 0000000..5e63b06 --- /dev/null +++ b/templates/dashboard.html @@ -0,0 +1,157 @@ + + + + + + 📊 Dépenses Dashboard + + + + + +🏠Accueil +

📊 Dépenses Dashboard

+ ✏️ Saisie + 📊 Dashboard + ⚙️ Config +

+ +

💰 Total: 0.00€

+ +

📊 Filtres

Mois

Personne

Catégorie

+ +

📈 Graphiques

Bar chart

Camembert

+ + + + diff --git a/templates/index.html b/templates/index.html new file mode 100644 index 0000000..7ffff31 --- /dev/null +++ b/templates/index.html @@ -0,0 +1,510 @@ + + + + + + 💸 Dépenses Trello + + + + + + +🏠Accueil +

💸 Dépenses Trello

+ ✏️ Saisie + 📊 Dashboard + ⚙️ Config +

+ + + + + + + + + + + diff --git a/test_ideas.html b/test_ideas.html new file mode 100755 index 0000000..5dd5ac1 --- /dev/null +++ b/test_ideas.html @@ -0,0 +1,29 @@ + + + + + Test Ideas + + + +🏠Accueil +

Test API

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+ + + diff --git a/test_recs.py b/test_recs.py new file mode 100644 index 0000000..43fda23 --- /dev/null +++ b/test_recs.py @@ -0,0 +1,11 @@ +from db import get_connection +from datetime import datetime + +today = datetime.now().strftime("%Y-%m-%d") +conn = get_connection() +c = conn.execute( + "SELECT type_pari, cheval1 FROM recommendations WHERE date=?", (today,) +) +for r in c.fetchall(): + print(r) +conn.close() diff --git a/tests/e2e/test_scenarios.py b/tests/e2e/test_scenarios.py new file mode 100644 index 0000000..129378f --- /dev/null +++ b/tests/e2e/test_scenarios.py @@ -0,0 +1,463 @@ +""" +Tests E2E Playwright — SaaS Turf Prédictions IA +Sprint 8 — QA, Beta Fermee, Go/No-Go +Ticket: HRT-34 + +Scénarios couverts : +1. Inscription → choix plan free → voir top 3 +2. Upgrade premium → Stripe checkout → accès toutes courses +3. Abonnement pro → export CSV → accès API +4. Annulation abonnement → downgrade free + +Navigateurs : Chrome, Firefox, Safari mobile (via Playwright) +Screenshots automatiques sur échec +""" + +import asyncio +import os +from pathlib import Path +from datetime import datetime + +import pytest +from playwright.async_api import async_playwright, Page, Browser, BrowserContext + +# === Configuration === +BASE_URL = os.environ.get("APP_URL", "http://localhost:8792") +SCREENSHOT_DIR = Path("tests/screenshots") +SCREENSHOT_DIR.mkdir(parents=True, exist_ok=True) + +# Stripe test card (mode test uniquement) +STRIPE_TEST_CARD = "4242 4242 4242 4242" +STRIPE_EXPIRY = "12/30" +STRIPE_CVC = "123" +STRIPE_ZIP = "75001" + +# Credentials beta +BETA_PROMO_CODE = "BETA2026" +TEST_USER_EMAIL_BASE = "testqa+{}@h3r7.tech" +TEST_USER_PASSWORD = "TestQA_2026!" + + +def screenshot_on_fail(test_name: str, page: Page): + """Decorator/helper to take screenshot on test failure.""" + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + path = SCREENSHOT_DIR / f"FAIL_{test_name}_{timestamp}.png" + return str(path) + + +# === Fixtures === + + +@pytest.fixture(scope="session") +def event_loop(): + loop = asyncio.get_event_loop_policy().new_event_loop() + yield loop + loop.close() + + +@pytest.fixture(scope="module") +async def playwright_instance(): + async with async_playwright() as p: + yield p + + +@pytest.fixture(params=["chromium", "firefox", "webkit"]) +async def browser(playwright_instance, request): + browser_type = getattr(playwright_instance, request.param) + # Webkit simule Safari mobile + if request.param == "webkit": + b = await browser_type.launch(headless=True) + yield b, "safari_mobile" + else: + b = await browser_type.launch(headless=True) + yield b, request.param + await b.close() + + +@pytest.fixture +async def context_page(browser): + b, browser_name = browser + if browser_name == "safari_mobile": + # Simule iPhone 13 + ctx = await b.new_context( + viewport={"width": 390, "height": 844}, + user_agent=( + "Mozilla/5.0 (iPhone; CPU iPhone OS 15_0 like Mac OS X) " + "AppleWebKit/605.1.15 (KHTML, like Gecko) Version/15.0 Mobile/15E148 Safari/604.1" + ), + ) + else: + ctx = await b.new_context(viewport={"width": 1280, "height": 800}) + page = await ctx.new_page() + yield page, browser_name + await ctx.close() + + +# === Helper functions === + + +async def register_user(page: Page, email: str, password: str, promo: str = None): + """Inscrit un nouvel utilisateur.""" + await page.goto(f"{BASE_URL}/register") + await page.fill('[name="email"]', email) + await page.fill('[name="password"]', password) + await page.fill('[name="confirm_password"]', password) + if promo: + promo_field = page.locator('[name="promo_code"]') + if await promo_field.count() > 0: + await promo_field.fill(promo) + await page.click('[type="submit"]') + await page.wait_for_url(f"{BASE_URL}/**", timeout=10_000) + + +async def login_user(page: Page, email: str, password: str): + """Connecte un utilisateur existant.""" + await page.goto(f"{BASE_URL}/login") + await page.fill('[name="email"]', email) + await page.fill('[name="password"]', password) + await page.click('[type="submit"]') + await page.wait_for_url(f"{BASE_URL}/**", timeout=10_000) + + +async def fill_stripe_form(page: Page): + """Remplit le formulaire Stripe Checkout (mode test).""" + # Stripe embeds iframe — on attend le frame + stripe_frame = page.frame_locator('iframe[name^="__privateStripeFrame"]').first + await stripe_frame.locator('[placeholder="Card number"]').fill(STRIPE_TEST_CARD) + await stripe_frame.locator('[placeholder="MM / YY"]').fill(STRIPE_EXPIRY) + await stripe_frame.locator('[placeholder="CVC"]').fill(STRIPE_CVC) + await stripe_frame.locator('[placeholder="ZIP"]').fill(STRIPE_ZIP) + + +# === Test Scénario 1 : Inscription → plan free → voir top 3 === + + +@pytest.mark.asyncio +async def test_inscription_plan_free_top3(context_page): + """ + Scénario 1 : Inscription → choix plan free → voir top 3 prédictions + """ + page, browser_name = context_page + test_name = f"test_inscription_plan_free_top3_{browser_name}" + unique_email = TEST_USER_EMAIL_BASE.format( + f"free_{datetime.now().strftime('%H%M%S')}" + ) + + try: + # 1. Inscription + await register_user(page, unique_email, TEST_USER_PASSWORD) + + # 2. Choix du plan free (si une page de choix de plan existe) + if "plan" in page.url or "pricing" in page.url: + free_plan_btn = page.locator( + '[data-plan="free"], [data-testid="plan-free"], text=Free, text=Gratuit' + ) + await free_plan_btn.first.click() + await page.wait_for_timeout(1000) + + # 3. Accès au dashboard + await page.goto(f"{BASE_URL}/dashboard") + await page.wait_for_load_state("networkidle", timeout=15_000) + + # 4. Vérification : le top 3 est visible + top3_section = page.locator( + '[data-testid="top3"], .top3, #top3, .predictions-top3' + ) + if await top3_section.count() > 0: + await top3_section.first.wait_for(state="visible", timeout=5_000) + assert await top3_section.first.is_visible(), "Top 3 section non visible" + else: + # Fallback : chercher des cards de chevaux + prediction_cards = page.locator( + ".prediction-card, .horse-card, [data-horse]" + ) + count = await prediction_cards.count() + assert count >= 1, ( + f"Aucune prédiction visible pour plan free (browser: {browser_name})" + ) + + # 5. Vérification : pas d'accès aux routes premium + await page.goto(f"{BASE_URL}/api/races?all=true") + content = await page.content() + # Un plan free ne devrait pas voir toutes les courses sans restriction + # Le check exact dépend de l'implémentation — ici on vérifie juste la réponse 200 ou 403 + assert page.url is not None + + print(f"✅ [{browser_name}] Scénario 1 PASS — {unique_email}") + + except Exception as e: + await page.screenshot(path=screenshot_on_fail(test_name, page)) + raise AssertionError(f"[{browser_name}] Scénario 1 FAIL: {e}") from e + + +# === Test Scénario 2 : Upgrade premium → Stripe → accès toutes courses === + + +@pytest.mark.asyncio +async def test_upgrade_premium_stripe(context_page): + """ + Scénario 2 : Upgrade premium → Stripe checkout → accès toutes courses + """ + page, browser_name = context_page + test_name = f"test_upgrade_premium_{browser_name}" + unique_email = TEST_USER_EMAIL_BASE.format( + f"premium_{datetime.now().strftime('%H%M%S')}" + ) + + try: + # 1. Inscription free + await register_user(page, unique_email, TEST_USER_PASSWORD) + await page.goto(f"{BASE_URL}/dashboard") + await page.wait_for_load_state("networkidle", timeout=15_000) + + # 2. Aller à la page upgrade/pricing + upgrade_btn = page.locator( + '[data-testid="upgrade"], [href*="pricing"], [href*="upgrade"], text=Upgrade, text=Premium' + ) + await upgrade_btn.first.click() + await page.wait_for_timeout(1000) + + # 3. Sélectionner plan premium + premium_btn = page.locator( + '[data-plan="premium"], [data-testid="plan-premium"], text=Premium, text=Pro' + ) + await premium_btn.first.click() + await page.wait_for_timeout(1000) + + # 4. Stripe Checkout + # Attendre la redirection vers Stripe ou l'ouverture du formulaire + try: + await page.wait_for_url("**/stripe.com/**", timeout=8_000) + # Mode redirection Stripe + await page.fill( + '[placeholder="Card number"]', STRIPE_TEST_CARD.replace(" ", "") + ) + await page.fill('[placeholder="MM / YY"]', STRIPE_EXPIRY) + await page.fill('[placeholder="CVC"]', STRIPE_CVC) + except Exception: + # Mode embedded Stripe + await fill_stripe_form(page) + + await page.click( + '[type="submit"], [data-testid="submit"], text=Pay, text=Payer' + ) + # Attendre le retour sur l'app + await page.wait_for_url(f"{BASE_URL}/**", timeout=30_000) + + # 5. Vérification : accès à toutes les courses + await page.goto(f"{BASE_URL}/dashboard") + await page.wait_for_load_state("networkidle", timeout=15_000) + + # Le badge premium doit être visible + premium_badge = page.locator( + '[data-testid="premium-badge"], .premium-badge, .badge-premium, text=Premium' + ) + assert ( + await premium_badge.count() > 0 + or "premium" in (await page.content()).lower() + ), f"Badge premium non détecté après upgrade (browser: {browser_name})" + + print(f"✅ [{browser_name}] Scénario 2 PASS — {unique_email}") + + except Exception as e: + await page.screenshot(path=screenshot_on_fail(test_name, page)) + raise AssertionError(f"[{browser_name}] Scénario 2 FAIL: {e}") from e + + +# === Test Scénario 3 : Abonnement pro → export CSV → accès API === + + +@pytest.mark.asyncio +async def test_pro_export_csv_api_access(context_page): + """ + Scénario 3 : Abonnement pro → export CSV → accès API + """ + page, browser_name = context_page + test_name = f"test_pro_export_csv_{browser_name}" + unique_email = TEST_USER_EMAIL_BASE.format( + f"pro_{datetime.now().strftime('%H%M%S')}" + ) + + try: + # 1. Inscription + upgrade pro (via API admin pour les tests) + await register_user(page, unique_email, TEST_USER_PASSWORD) + + # Promotion directe via API de test (si disponible) + api_resp = await page.request.post( + f"{BASE_URL}/api/test/set-plan", + data={"email": unique_email, "plan": "pro"}, + ) + if api_resp.status != 200: + pytest.skip("API de promotion de plan non disponible — nécessite HRT-31") + + # 2. Se connecter + await login_user(page, unique_email, TEST_USER_PASSWORD) + await page.goto(f"{BASE_URL}/dashboard") + await page.wait_for_load_state("networkidle", timeout=15_000) + + # 3. Export CSV + async with page.expect_download() as download_info: + export_btn = page.locator( + '[data-testid="export-csv"], [href*="csv"], text=Export CSV, text=Exporter CSV' + ) + await export_btn.first.click() + download = await download_info.value + assert download.suggested_filename.endswith(".csv"), ( + f"Fichier téléchargé n'est pas un CSV: {download.suggested_filename}" + ) + # Sauvegarder pour vérification + await download.save_as(f"/tmp/qa_export_{browser_name}.csv") + + # 4. Accès API avec clé + api_key_section = page.locator('[data-testid="api-key"], .api-key, #api-key') + if await api_key_section.count() > 0: + api_key = await api_key_section.first.inner_text() + api_key = api_key.strip() + # Tester l'API directement + api_resp = await page.request.get( + f"{BASE_URL}/api/races", + headers={"X-API-Key": api_key}, + ) + assert api_resp.status == 200, ( + f"API access refusé avec clé valide (status: {api_resp.status})" + ) + + print(f"✅ [{browser_name}] Scénario 3 PASS — {unique_email}") + + except Exception as e: + await page.screenshot(path=screenshot_on_fail(test_name, page)) + raise AssertionError(f"[{browser_name}] Scénario 3 FAIL: {e}") from e + + +# === Test Scénario 4 : Annulation abonnement → downgrade free === + + +@pytest.mark.asyncio +async def test_annulation_downgrade_free(context_page): + """ + Scénario 4 : Annulation abonnement → downgrade free + """ + page, browser_name = context_page + test_name = f"test_annulation_downgrade_{browser_name}" + unique_email = TEST_USER_EMAIL_BASE.format( + f"cancel_{datetime.now().strftime('%H%M%S')}" + ) + + try: + # 1. Inscription + set plan premium via API test + await register_user(page, unique_email, TEST_USER_PASSWORD) + api_resp = await page.request.post( + f"{BASE_URL}/api/test/set-plan", + data={"email": unique_email, "plan": "premium"}, + ) + if api_resp.status != 200: + pytest.skip("API de promotion de plan non disponible — nécessite HRT-31") + + await login_user(page, unique_email, TEST_USER_PASSWORD) + + # 2. Aller à la page de gestion d'abonnement + await page.goto(f"{BASE_URL}/account/subscription") + await page.wait_for_load_state("networkidle", timeout=10_000) + + # 3. Annuler l'abonnement + cancel_btn = page.locator( + '[data-testid="cancel-subscription"], text=Annuler, text=Cancel subscription, ' + "text=Résilier" + ) + await cancel_btn.first.click() + + # Confirmation modal + confirm_btn = page.locator( + '[data-testid="confirm-cancel"], text=Confirmer, text=Confirm, text=Oui' + ) + if await confirm_btn.count() > 0: + await confirm_btn.first.click() + + await page.wait_for_timeout(2000) + + # 4. Vérification : retour au plan free + await page.goto(f"{BASE_URL}/dashboard") + await page.wait_for_load_state("networkidle", timeout=15_000) + content = await page.content() + + # Le badge premium ne doit plus être actif + premium_active = page.locator( + '[data-testid="premium-badge"].active, .premium-active' + ) + assert await premium_active.count() == 0, ( + f"Badge premium encore actif après annulation (browser: {browser_name})" + ) + + # Le plan free doit être indiqué + free_indicator = page.locator( + '[data-plan="free"], .badge-free, text=Plan Free, text=Gratuit' + ) + # Ou simplement vérifier l'absence de mention "premium actif" + assert ( + "annulé" in content.lower() + or "cancelled" in content.lower() + or "free" in content.lower() + or await free_indicator.count() > 0 + ), f"Downgrade vers free non confirmé (browser: {browser_name})" + + print(f"✅ [{browser_name}] Scénario 4 PASS — {unique_email}") + + except Exception as e: + await page.screenshot(path=screenshot_on_fail(test_name, page)) + raise AssertionError(f"[{browser_name}] Scénario 4 FAIL: {e}") from e + + +# === Test Sécurité : Plan free ne peut pas accéder routes premium === + + +@pytest.mark.asyncio +async def test_autorisation_plan_free_acces_premium_refuse(context_page): + """ + Test d'autorisation : un utilisateur free ne peut pas accéder aux routes premium. + """ + page, browser_name = context_page + test_name = f"test_auth_plan_free_acces_bloque_{browser_name}" + unique_email = TEST_USER_EMAIL_BASE.format( + f"authtest_{datetime.now().strftime('%H%M%S')}" + ) + + try: + await register_user(page, unique_email, TEST_USER_PASSWORD) + await login_user(page, unique_email, TEST_USER_PASSWORD) + + # Tenter d'accéder à une route premium + premium_routes = [ + "/api/races?all=true", + "/api/export/csv", + "/api/predictions/all", + ] + + for route in premium_routes: + resp = await page.request.get(f"{BASE_URL}{route}") + assert resp.status in (403, 401, 402), ( + f"Route premium accessible par plan free: {route} → HTTP {resp.status} (browser: {browser_name})" + ) + + print(f"✅ [{browser_name}] Test autorisation PASS — {unique_email}") + + except Exception as e: + await page.screenshot(path=screenshot_on_fail(test_name, page)) + raise AssertionError(f"[{browser_name}] Test autorisation FAIL: {e}") from e + + +if __name__ == "__main__": + # Exécution directe pour debug + import subprocess + + subprocess.run( + [ + "python", + "-m", + "pytest", + __file__, + "-v", + "--tb=short", + "--html=tests/reports/e2e_report.html", + "--self-contained-html", + ] + ) diff --git a/tests/load/locustfile.py b/tests/load/locustfile.py new file mode 100644 index 0000000..71d445d --- /dev/null +++ b/tests/load/locustfile.py @@ -0,0 +1,305 @@ +""" +Tests de charge Locust — SaaS Turf Prédictions IA +Sprint 8 — QA, Beta Fermee, Go/No-Go +Ticket: HRT-34 + +Scénarios : +- Test normal : 100 users simultanés, 10 min +- Test spike : 500 users en 2 min + +Cibles : +- p95 latence < 500ms +- error rate < 0.1% + +Usage : + # Test normal (100 users, 10 min) + locust -f tests/load/locustfile.py --host http://localhost:8792 \ + --users 100 --spawn-rate 10 --run-time 10m \ + --headless --csv tests/reports/load_normal + + # Test spike (500 users, 2 min) + locust -f tests/load/locustfile.py --host http://localhost:8792 \ + --users 500 --spawn-rate 250 --run-time 2m \ + --headless --csv tests/reports/load_spike + + # Interface web + locust -f tests/load/locustfile.py --host http://localhost:8792 +""" + +import random +import json +from locust import HttpUser, TaskSet, task, between, events +from locust.env import Environment +import logging + +logger = logging.getLogger(__name__) + +# === Credentials de test === +TEST_USERS = [ + {"email": f"loadtest+{i}@h3r7.tech", "password": "TestLoad_2026!"} + for i in range(50) +] + + +# === Helper de login === +class AuthMixin: + """Mixin pour authentification JWT.""" + + token: str = None + + def login(self): + """Login et stocke le token JWT.""" + user = random.choice(TEST_USERS) + with self.client.post( + "/api/auth/login", + json={"email": user["email"], "password": user["password"]}, + catch_response=True, + name="/api/auth/login", + ) as resp: + if resp.status_code == 200: + data = resp.json() + self.token = data.get("access_token") or data.get("token") + resp.success() + else: + self.token = None + resp.failure(f"Login failed: {resp.status_code}") + + def auth_headers(self): + if self.token: + return {"Authorization": f"Bearer {self.token}"} + return {} + + +# === Comportement utilisateur Free === +class FreePlanTaskSet(TaskSet): + """Simule un utilisateur Free qui consulte les prédictions top 3.""" + + def on_start(self): + self.user.login() + + @task(5) + def voir_dashboard(self): + with self.client.get( + "/dashboard", + catch_response=True, + name="/dashboard", + ) as resp: + if resp.status_code in (200, 304): + resp.success() + else: + resp.failure(f"Dashboard: {resp.status_code}") + + @task(8) + def voir_predictions_aujourd_hui(self): + with self.client.get( + "/api", + headers=self.user.auth_headers(), + catch_response=True, + name="/api (today predictions)", + ) as resp: + if resp.status_code == 200: + data = resp.json() + if isinstance(data, (list, dict)): + resp.success() + else: + resp.failure("Réponse inattendue") + elif resp.status_code in (401, 403): + resp.success() # Normal si token expiré + else: + resp.failure(f"Predictions: {resp.status_code}") + + @task(3) + def voir_courses(self): + with self.client.get( + "/api/races", + headers=self.user.auth_headers(), + catch_response=True, + name="/api/races", + ) as resp: + if resp.status_code in (200, 401, 403): + resp.success() + else: + resp.failure(f"Races: {resp.status_code}") + + @task(2) + def voir_scoring(self): + with self.client.get( + "/api/scoring", + headers=self.user.auth_headers(), + catch_response=True, + name="/api/scoring", + ) as resp: + if resp.status_code in (200, 401, 403): + resp.success() + else: + resp.failure(f"Scoring: {resp.status_code}") + + @task(1) + def voir_portail(self): + with self.client.get( + "/", + catch_response=True, + name="/ (portail)", + ) as resp: + if resp.status_code in (200, 304): + resp.success() + else: + resp.failure(f"Portail: {resp.status_code}") + + +# === Comportement utilisateur Premium === +class PremiumPlanTaskSet(TaskSet): + """Simule un utilisateur Premium avec accès complet.""" + + def on_start(self): + self.user.login() + + @task(4) + def voir_dashboard_complet(self): + with self.client.get( + "/dashboard", + headers=self.user.auth_headers(), + catch_response=True, + name="/dashboard (premium)", + ) as resp: + if resp.status_code in (200, 304): + resp.success() + else: + resp.failure(f"Dashboard premium: {resp.status_code}") + + @task(6) + def voir_toutes_courses(self): + with self.client.get( + "/api/races?all=true", + headers=self.user.auth_headers(), + catch_response=True, + name="/api/races?all=true", + ) as resp: + if resp.status_code in (200, 403): + resp.success() + else: + resp.failure(f"All races: {resp.status_code}") + + @task(3) + def export_csv(self): + with self.client.get( + "/api/export/csv", + headers=self.user.auth_headers(), + catch_response=True, + name="/api/export/csv", + ) as resp: + if resp.status_code in (200, 403): + resp.success() + else: + resp.failure(f"CSV export: {resp.status_code}") + + @task(2) + def voir_historique(self): + with self.client.get( + "/api/odds_history", + headers=self.user.auth_headers(), + catch_response=True, + name="/api/odds_history", + ) as resp: + if resp.status_code in (200, 403): + resp.success() + else: + resp.failure(f"Odds history: {resp.status_code}") + + @task(1) + def appel_api_externe(self): + """Simule un accès API via clé (plan pro).""" + with self.client.get( + "/api/races", + headers={"X-API-Key": "test-api-key-qa"}, + catch_response=True, + name="/api/races (api-key)", + ) as resp: + if resp.status_code in (200, 401, 403): + resp.success() + else: + resp.failure(f"API key access: {resp.status_code}") + + +# === Utilisateurs Locust === + + +class FreeUser(AuthMixin, HttpUser): + """Utilisateur plan free (70% du trafic).""" + + tasks = [FreePlanTaskSet] + wait_time = between(1, 4) + weight = 70 + + +class PremiumUser(AuthMixin, HttpUser): + """Utilisateur plan premium (30% du trafic).""" + + tasks = [PremiumPlanTaskSet] + wait_time = between(0.5, 2) + weight = 30 + + +# === Events et rapports === + + +@events.test_stop.add_listener +def on_test_stop(environment: Environment, **kwargs): + """Analyse les résultats et écrit un rapport.""" + stats = environment.stats + total = stats.total + + report_lines = [ + "=" * 60, + "RAPPORT TEST DE CHARGE — SaaS Turf IA", + "=" * 60, + f"Total requests : {total.num_requests}", + f"Failures : {total.num_failures}", + f"Error rate : {total.fail_ratio * 100:.2f}%", + f"Avg response time : {total.avg_response_time:.1f}ms", + f"95th percentile : {total.get_response_time_percentile(0.95):.1f}ms", + f"99th percentile : {total.get_response_time_percentile(0.99):.1f}ms", + f"Max response time : {total.max_response_time:.1f}ms", + f"RPS (avg) : {total.total_rps:.2f}", + "", + "CRITERES DE SUCCES :", + ] + + p95 = total.get_response_time_percentile(0.95) + error_rate = total.fail_ratio * 100 + + p95_ok = p95 < 500 + error_ok = error_rate < 0.1 + + report_lines.append( + f" p95 < 500ms : {'✅ PASS' if p95_ok else '❌ FAIL'} ({p95:.1f}ms)" + ) + report_lines.append( + f" error rate < 0.1% : {'✅ PASS' if error_ok else '❌ FAIL'} ({error_rate:.2f}%)" + ) + report_lines.append("") + report_lines.append( + f"VERDICT GLOBAL : {'✅ GO' if (p95_ok and error_ok) else '❌ NO-GO'}" + ) + report_lines.append("=" * 60) + + report = "\n".join(report_lines) + print(report) + + # Écrire dans un fichier + import os + from pathlib import Path + from datetime import datetime + + Path("tests/reports").mkdir(parents=True, exist_ok=True) + report_file = ( + f"tests/reports/load_report_{datetime.now().strftime('%Y%m%d_%H%M%S')}.txt" + ) + with open(report_file, "w") as f: + f.write(report) + print(f"\nRapport sauvegardé : {report_file}") + + # Exit code non-0 si les critères ne sont pas respectés + if not (p95_ok and error_ok): + logger.error("CRITÈRES DE PERFORMANCE NON RESPECTÉS — NO-GO") + environment.process_exit_code = 1 diff --git a/tests/run_qa.sh b/tests/run_qa.sh new file mode 100644 index 0000000..2feb37c --- /dev/null +++ b/tests/run_qa.sh @@ -0,0 +1,188 @@ +#!/bin/bash +# ============================================================ +# Script principal QA — SaaS Turf Prédictions IA +# Sprint 8 — QA, Beta Fermee, Go/No-Go +# Ticket: HRT-34 +# +# Usage : bash tests/run_qa.sh [APP_URL] +# ============================================================ + +set -e + +APP_URL="${1:-http://localhost:8792}" +REPORT_DIR="tests/reports" +VENV="venv" +TIMESTAMP=$(date +%Y%m%d_%H%M%S) + +mkdir -p "$REPORT_DIR" + +echo "============================================================" +echo "QA SPRINT 8 — SaaS Turf Prédictions IA" +echo "Target : $APP_URL" +echo "Date : $(date)" +echo "============================================================" + +# Vérifier l'environnement +if [ ! -d "$VENV" ]; then + echo "❌ Venv non trouvé. Créer l'environnement virtuel d'abord." + exit 1 +fi + +source "$VENV/bin/activate" + +# Installer les dépendances de test si nécessaire +pip install pytest pytest-asyncio pytest-html playwright locust bandit safety 2>/dev/null | tail -5 + +# Installer les navigateurs Playwright +python -m playwright install chromium firefox webkit 2>/dev/null || true + +PASS=0 +FAIL=0 +RESULTS=() + +# ============================================================ +# 1. Tests E2E Playwright +# ============================================================ +echo "" +echo "--- Tests E2E Playwright ---" +if APP_URL="$APP_URL" python -m pytest tests/e2e/ \ + -v --tb=short \ + --html="$REPORT_DIR/e2e_report_${TIMESTAMP}.html" \ + --self-contained-html \ + -q 2>&1 | tee "$REPORT_DIR/e2e_output_${TIMESTAMP}.log"; then + echo "✅ E2E : PASS" + RESULTS+=("✅ Tests E2E Playwright : PASS") + ((PASS++)) +else + echo "❌ E2E : FAIL" + RESULTS+=("❌ Tests E2E Playwright : FAIL — voir $REPORT_DIR/e2e_report_${TIMESTAMP}.html") + ((FAIL++)) +fi + +# ============================================================ +# 2. Tests de sécurité (injection SQL, JWT, autorisation) +# ============================================================ +echo "" +echo "--- Tests Sécurité ---" +if APP_URL="$APP_URL" python -m pytest tests/security/test_security.py \ + -v --tb=short \ + --html="$REPORT_DIR/security_report_${TIMESTAMP}.html" \ + --self-contained-html \ + -q 2>&1 | tee "$REPORT_DIR/security_output_${TIMESTAMP}.log"; then + echo "✅ Sécurité : PASS" + RESULTS+=("✅ Tests Sécurité (JWT, SQL injection, autorisation) : PASS") + ((PASS++)) +else + echo "❌ Sécurité : FAIL" + RESULTS+=("❌ Tests Sécurité : FAIL — voir $REPORT_DIR/security_report_${TIMESTAMP}.html") + ((FAIL++)) +fi + +# ============================================================ +# 3. Scan Bandit (vulnérabilités code Python) +# ============================================================ +echo "" +echo "--- Scan Bandit ---" +if bandit -r . \ + --exclude ./venv,./tests \ + -ll \ + -f html \ + -o "$REPORT_DIR/bandit_report_${TIMESTAMP}.html" \ + 2>&1 | tee "$REPORT_DIR/bandit_output_${TIMESTAMP}.log"; then + echo "✅ Bandit : PASS" + RESULTS+=("✅ Bandit (sécurité code) : PASS") + ((PASS++)) +else + echo "⚠️ Bandit : vulnérabilités détectées" + RESULTS+=("⚠️ Bandit : voir $REPORT_DIR/bandit_report_${TIMESTAMP}.html") +fi + +# ============================================================ +# 4. Tests de charge Locust (100 users, 3 min) +# ============================================================ +echo "" +echo "--- Tests de charge Locust (100 users, 3 min) ---" +if locust \ + -f tests/load/locustfile.py \ + --host "$APP_URL" \ + --users 100 \ + --spawn-rate 10 \ + --run-time 3m \ + --headless \ + --csv "$REPORT_DIR/load_normal_${TIMESTAMP}" \ + 2>&1 | tee "$REPORT_DIR/load_output_${TIMESTAMP}.log"; then + echo "✅ Locust 100 users : PASS" + RESULTS+=("✅ Tests de charge 100 users : PASS") + ((PASS++)) +else + echo "❌ Locust 100 users : FAIL" + RESULTS+=("❌ Tests de charge 100 users : FAIL — voir $REPORT_DIR/load_output_${TIMESTAMP}.log") + ((FAIL++)) +fi + +# ============================================================ +# 5. Test spike (500 users, 2 min) — optionnel +# ============================================================ +echo "" +echo "--- Test spike Locust (500 users, 2 min) ---" +if locust \ + -f tests/load/locustfile.py \ + --host "$APP_URL" \ + --users 500 \ + --spawn-rate 250 \ + --run-time 2m \ + --headless \ + --csv "$REPORT_DIR/load_spike_${TIMESTAMP}" \ + 2>&1 | tee "$REPORT_DIR/spike_output_${TIMESTAMP}.log"; then + echo "✅ Spike 500 users : PASS" + RESULTS+=("✅ Test spike 500 users : PASS") + ((PASS++)) +else + echo "❌ Spike 500 users : FAIL" + RESULTS+=("❌ Test spike 500 users : FAIL") + ((FAIL++)) +fi + +# ============================================================ +# 6. OWASP ZAP (si Docker disponible) +# ============================================================ +if command -v docker &>/dev/null; then + echo "" + echo "--- OWASP ZAP ---" + if bash tests/security/run_owasp_zap.sh "$APP_URL" 2>&1 | tee "$REPORT_DIR/zap_main_${TIMESTAMP}.log"; then + echo "✅ OWASP ZAP : PASS" + RESULTS+=("✅ OWASP ZAP (zero vulnérabilité critique) : PASS") + ((PASS++)) + else + echo "❌ OWASP ZAP : FAIL" + RESULTS+=("❌ OWASP ZAP : vulnérabilités critiques détectées") + ((FAIL++)) + fi +else + RESULTS+=("⚠️ OWASP ZAP : skippé (Docker non disponible)") +fi + +# ============================================================ +# Rapport final +# ============================================================ +echo "" +echo "============================================================" +echo "RAPPORT QA FINAL — Sprint 8" +echo "============================================================" +for r in "${RESULTS[@]}"; do + echo " $r" +done +echo "" +echo "Passed : $PASS" +echo "Failed : $FAIL" +echo "" + +if [ "$FAIL" -eq 0 ]; then + echo "✅ VERDICT : GO — Tous les tests passent" + echo " Lancement public autorisé" + exit 0 +else + echo "❌ VERDICT : NO-GO — $FAIL test(s) en échec" + echo " Corriger avant lancement public" + exit 1 +fi diff --git a/tests/security/run_owasp_zap.sh b/tests/security/run_owasp_zap.sh new file mode 100644 index 0000000..0dd9edc --- /dev/null +++ b/tests/security/run_owasp_zap.sh @@ -0,0 +1,100 @@ +#!/bin/bash +# ============================================================ +# Script OWASP ZAP — SaaS Turf Prédictions IA +# Sprint 8 — QA, Beta Fermee, Go/No-Go +# Ticket: HRT-34 +# +# Prérequis : Docker, APP en cours d'exécution +# Usage : bash tests/security/run_owasp_zap.sh [APP_URL] +# ============================================================ + +set -e + +APP_URL="${1:-http://localhost:8792}" +REPORT_DIR="tests/reports" +ZAP_IMAGE="ghcr.io/zaproxy/zaproxy:stable" +TIMESTAMP=$(date +%Y%m%d_%H%M%S) + +mkdir -p "$REPORT_DIR" + +echo "============================================================" +echo "OWASP ZAP — Scan SaaS Turf IA" +echo "Target : $APP_URL" +echo "============================================================" + +# Vérifier que Docker est disponible +if ! command -v docker &>/dev/null; then + echo "❌ Docker non disponible. Installer Docker pour exécuter OWASP ZAP." + exit 1 +fi + +# Vérifier que l'app est en cours d'exécution +if ! curl -sf "$APP_URL" >/dev/null 2>&1; then + echo "❌ App non accessible sur $APP_URL" + echo " Démarrer l'application avant de lancer le scan." + exit 1 +fi + +echo "✅ App accessible sur $APP_URL" +echo "" +echo "Démarrage du scan OWASP ZAP (mode baseline)..." + +# Scan ZAP en mode baseline (rapide, non-destructif) +docker run --rm \ + --network host \ + -v "$(pwd)/tests/reports:/zap/wrk:rw" \ + "$ZAP_IMAGE" \ + zap-baseline.py \ + -t "$APP_URL" \ + -r "zap_report_${TIMESTAMP}.html" \ + -J "zap_report_${TIMESTAMP}.json" \ + -l WARN \ + 2>&1 | tee "$REPORT_DIR/zap_output_${TIMESTAMP}.log" + +ZAP_EXIT=$? + +# Analyser le rapport JSON +REPORT_JSON="$REPORT_DIR/zap_report_${TIMESTAMP}.json" +if [ -f "$REPORT_JSON" ]; then + CRITICAL=$(python3 -c " +import json, sys +try: + data = json.load(open('$REPORT_JSON')) + alerts = data.get('site', [{}])[0].get('alerts', []) + critical = [a for a in alerts if a.get('riskcode', '0') == '3'] + high = [a for a in alerts if a.get('riskcode', '0') == '2'] + print(f'CRITICAL:{len(critical)},HIGH:{len(high)}') + for a in critical: + print(f' [CRITICAL] {a.get(\"alert\", \"?\")}') + for a in high: + print(f' [HIGH] {a.get(\"alert\", \"?\")}') +except Exception as e: + print(f'CRITICAL:?,HIGH:? (parse error: {e})') +" 2>/dev/null || echo "CRITICAL:?,HIGH:?") + + echo "" + echo "============================================================" + echo "RÉSULTATS OWASP ZAP" + echo "============================================================" + echo "$CRITICAL" + + CRIT_COUNT=$(echo "$CRITICAL" | head -1 | cut -d: -f2 | cut -d, -f1) + if [ "$CRIT_COUNT" = "0" ]; then + echo "" + echo "✅ PASS — Zéro vulnérabilité critique" + else + echo "" + echo "❌ FAIL — $CRIT_COUNT vulnérabilité(s) critique(s) détectée(s)" + echo " Action requise avant Go/No-Go" + fi +else + echo "⚠️ Rapport JSON non généré" +fi + +echo "" +echo "Rapport HTML : $REPORT_DIR/zap_report_${TIMESTAMP}.html" +echo "Rapport JSON : $REPORT_DIR/zap_report_${TIMESTAMP}.json" +echo "Log complet : $REPORT_DIR/zap_output_${TIMESTAMP}.log" +echo "============================================================" + +exit $ZAP_EXIT diff --git a/tests/security/test_security.py b/tests/security/test_security.py new file mode 100644 index 0000000..b251225 --- /dev/null +++ b/tests/security/test_security.py @@ -0,0 +1,320 @@ +""" +Tests de sécurité — SaaS Turf Prédictions IA +Sprint 8 — QA, Beta Fermee, Go/No-Go +Ticket: HRT-34 + +Couverture : +- Test injection SQL sur tous les inputs +- Test authentification : JWT expiration, refresh, logout +- Test autorisation : plan free ne peut pas accéder routes premium +- (OWASP ZAP est exécuté séparément via script shell) +""" + +import pytest +import requests +import time +import base64 +import json +import os + +BASE_URL = os.environ.get("APP_URL", "http://localhost:8792") + +# === Payloads injection SQL === +SQL_INJECTION_PAYLOADS = [ + "' OR '1'='1", + "' OR 1=1--", + "'; DROP TABLE users;--", + "' UNION SELECT null,null,null--", + "1'; SELECT * FROM users--", + "admin'--", + "' OR 'x'='x", + "1 OR 1=1", + "%27 OR %271%27=%271", +] + +# === Payloads XSS === +XSS_PAYLOADS = [ + "", + "

", + "javascript:alert(1)", + "⏱️ ${speedText}`; + } + }); + } + }); + + // Ajouter la colonne vitesse dans la table des partants + const oddsTable = document.querySelector('.odds-table'); + if (oddsTable) { + // Ajouter l'en-tête + const headerRow = oddsTable.querySelector('thead tr'); + if (headerRow && headerRow.children.length === 3) { + const th = document.createElement('th'); + th.textContent = 'Vitesse'; + headerRow.appendChild(th); + } + + // Ajouter les données + const rows = oddsTable.querySelectorAll('tbody tr'); + rows.forEach(row => { + const horseName = row.cells[1]?.textContent?.trim().split('⭐')[0]?.trim(); + if (horseName) { + let speedInfo = null; + for (let section of ['bases', 'chances', 'outsiders']) { + speedInfo = speedData.predictions?.[section]?.find(h => h.horse_name === horseName)?.speed_info; + if (speedInfo) break; + } + const speedText = speedInfo ? speedInfo.avg_time_formatted : 'N/A'; + + const td = document.createElement('td'); + td.className = 'speed-val'; + td.textContent = speedText; + row.appendChild(td); + } + }); + } + }, 2000); // Attendre 2 secondes que le contenu soit chargé + }); + } catch (error) { + console.error('Erreur:', error); + } +} + +// Lancer la fonction quand la page est chargée +document.addEventListener('DOMContentLoaded', addSpeedInfo); diff --git a/vitesse_styles.css b/vitesse_styles.css new file mode 100644 index 0000000..9f22f03 --- /dev/null +++ b/vitesse_styles.css @@ -0,0 +1,22 @@ +/* Styles pour la vitesse */ +.prono-horse-speed { + font-size: 10px; + color: var(--text2); + margin-left: auto; + white-space: nowrap; + opacity: 0.8; +} + +.speed-val { + font-family: 'Space Mono', monospace; + font-size: 11px; + color: var(--cyan); + font-weight: 600; +} + +.empty-horses { + padding: 20px; + text-align: center; + color: var(--text2); + font-style: italic; +} diff --git a/weather_module.py b/weather_module.py new file mode 100755 index 0000000..54eb3d1 --- /dev/null +++ b/weather_module.py @@ -0,0 +1,170 @@ +#!/usr/bin/env python3 +""" +Weather Module - Open-Meteo API Integration +Adds weather context for horse racing analysis +""" +import requests +from datetime import datetime + +# Hippodrome coordinates (France) +HIPPODROMES = { + 'auteuil': (48.8718, 2.2525), + ' Chantilly': (49.1939, 2.4744), + 'deauville': (49.3563, 0.0775), + 'vincennes': (48.8414, 2.4375), + 'longchamp': (48.8641, 2.2372), + 'saint-cloud': (48.8419, 2.1039), + 'pau': (43.2917, -0.3708), + 'cagnes-sur-mer': (43.6689, 7.1914), + 'lyon-parilly': (45.7378, 4.8092), + 'marseille': (43.2964, 5.3695), +} + +def get_weather(hippodrome, date=None): + """ + Get weather for hippodrome from Open-Meteo API (FREE!) + + Args: + hippodrome: Name of hippodrome + date: Optional date (YYYY-MM-DD), defaults to today + + Returns: + dict: Weather data + """ + if hippodrome.lower() not in HIPPODROMES: + return {'error': f'Hippodrome not found: {hippodrome}'} + + lat, lon = HIPPODROMES[hippodrome.lower()] + + # Open-Meteo API (free, no key needed) + url = f"https://api.open-meteo.com/v1/forecast" + params = { + 'latitude': lat, + 'longitude': lon, + 'current': 'temperature_2m,relative_humidity_2m,precipitation,rain,weather_code,wind_speed_10m,wind_direction_10m', + 'timezone': 'Europe/Paris' + } + + if date: + # Historical data + url = f"https://archive-api.open-meteo.com/v1/archive" + params['start_date'] = date + params['end_date'] = date + + try: + r = requests.get(url, params=params, timeout=10) + data = r.json() + + if 'current' in data: + current = data['current'] + return { + 'hippodrome': hippodrome, + 'temperature': current.get('temperature_2m'), + 'humidity': current.get('relative_humidity_2m'), + 'precipitation': current.get('precipitation'), + 'rain': current.get('rain'), + 'weather_code': current.get('weather_code'), + 'wind_speed': current.get('wind_speed_10m'), + 'wind_direction': current.get('wind_direction_10m'), + 'timestamp': datetime.now().isoformat() + } + elif 'daily' in data: + return { + 'hippodrome': hippodrome, + 'date': date, + 'temperature_max': data['daily'].get('temperature_2m_max', [None])[0], + 'temperature_min': data['daily'].get('temperature_2m_min', [None])[0], + 'precipitation_sum': data['daily'].get('precipitation_sum', [None])[0], + 'rain_sum': data['daily'].get('rain_sum', [None])[0], + 'wind_speed_max': data['daily'].get('wind_speed_10m_max', [None])[0], + } + + except Exception as e: + return {'error': str(e)} + +def weather_code_to_desc(code): + """Convert weather code to description""" + codes = { + 0: "Ciel dégagé", + 1: "Mainly clear", + 2: "Partly cloudy", + 3: "Overcast", + 45: "Brouillard", + 48: "Fog", + 51: "Bruine légère", + 53: "Bruine modérée", + 55: "Bruine dense", + 61: "Pluie légère", + 63: "Pluie modérée", + 65: "Pluie forte", + 71: "Neige légère", + 73: "Neige modérée", + 75: "Neige forte", + 80: "Averses légères", + 81: "Averses modérées", + 82: "Averses fortes", + 95: "Orage", + } + return codes.get(code, f"Code {code}") + +def get_ground_condition(weather_data): + """ + Determine ground condition based on weather + + Returns: BON, SOUPLE, LOURD, TERRE PERTURBÉE, etc. + """ + if 'error' in weather_data: + return 'INCONNU' + + precip = weather_data.get('precipitation', 0) + rain = weather_data.get('rain', 0) + humidity = weather_data.get('humidity', 0) + + # Simple logic for turf conditions + if precip > 10 or rain > 5: + return 'LOURD' + elif precip > 5 or rain > 2: + return 'SOUPLE' + elif precip > 1: + return 'BON' + else: + return 'BON' + +def analyze_weather_impact(weather_data, horse_history): + """ + Analyze how horse performs in current weather + + Args: + weather_data: Current weather conditions + horse_history: List of past performances with weather + + Returns: + dict: Weather impact analysis + """ + analysis = { + 'ground_condition': get_ground_condition(weather_data), + 'recommendation': 'NEUTRAL' + } + + # Check ground preference based on history + # This would need historical data to be implemented + + return analysis + +# Example usage +if __name__ == "__main__": + # Test + print("="*50) + print("Weather Module - Test") + print("="*50) + + for hippo in ['Auteuil', 'Vincennes', 'Pau']: + print(f"\n{hippo}:") + w = get_weather(hippo) + if 'error' not in w: + print(f" Temp: {w.get('temperature')}°C") + print(f" Humidity: {w.get('humidity')}%") + print(f" Wind: {w.get('wind_speed')} km/h") + print(f" Ground: {get_ground_condition(w)}") + else: + print(f" Error: {w.get('error')}") diff --git a/weather_module_v2.py b/weather_module_v2.py new file mode 100755 index 0000000..a7a82ad --- /dev/null +++ b/weather_module_v2.py @@ -0,0 +1,262 @@ +#!/usr/bin/env python3 +""" +Weather Module v2 - With Database Storage +Tracks weather history for performance analysis +""" +import requests +from datetime import datetime +import sqlite3 + +DB_PATH = "/home/h3r7/turf_scraper/turf.db" + +# Hippodrome coordinates +HIPPODROMES = { + 'auteuil': (48.8718, 2.2525), + 'chantilly': (49.1939, 2.4744), + 'deauville': (49.3563, 0.0775), + 'vincennes': (48.8414, 2.4375), + 'longchamp': (48.8641, 2.2372), + 'saint-cloud': (48.8419, 2.1039), + 'pau': (43.2917, -0.3708), + 'cagnes-sur-mer': (43.6689, 7.1914), + 'lyon-parilly': (45.7378, 4.8092), + 'lyon': (45.7378, 4.8092), + 'marseille': (43.2964, 5.3695), + 'cagnes': (43.6689, 7.1914), + 'le croise larroche': (50.6692, 3.0775), + 'mons': (50.4547, 3.9522), +} + +def init_weather_db(): + """Initialize weather table""" + conn = sqlite3.connect(DB_PATH) + c = conn.cursor() + + c.execute(''' + CREATE TABLE IF NOT EXISTS weather ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + date TEXT NOT NULL, + hippodrome TEXT, + temperature REAL, + humidity INTEGER, + wind_speed REAL, + precipitation REAL, + weather_code INTEGER, + ground_condition TEXT, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP + ) + ''') + + c.execute(''' + CREATE TABLE IF NOT EXISTS horse_weather_performance ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + date TEXT, + race_date TEXT, + horse_name TEXT, + position INTEGER, + hippodrome TEXT, + temperature REAL, + ground_condition TEXT, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP + ) + ''') + + conn.commit() + conn.close() + print("Weather tables initialized!") + +def get_weather(hippodrome, date=None): + """Get weather from Open-Meteo API""" + hippo_key = hippodrome.lower().strip() + + # Try to find matching hippodrome + coords = None + for name, coord in HIPPODROMES.items(): + if name in hippo_key or hippo_key in name: + coords = coord + break + + if not coords: + return {'error': f'Hippodrome non trouvé: {hippodrome}'} + + lat, lon = coords + + url = "https://api.open-meteo.com/v1/forecast" + params = { + 'latitude': lat, + 'longitude': lon, + 'current': 'temperature_2m,relative_humidity_2m,precipitation,rain,weather_code,wind_speed_10m', + 'timezone': 'Europe/Paris' + } + + try: + r = requests.get(url, params=params, timeout=10) + data = r.json() + + if 'current' in data: + current = data['current'] + weather = { + 'hippodrome': hippodrome, + 'temperature': current.get('temperature_2m'), + 'humidity': current.get('relative_humidity_2m'), + 'precipitation': current.get('precipitation'), + 'rain': current.get('rain'), + 'weather_code': current.get('weather_code'), + 'wind_speed': current.get('wind_speed_10m'), + 'ground_condition': get_ground_condition(current), + 'timestamp': datetime.now().isoformat() + } + return weather + + except Exception as e: + return {'error': str(e)} + + return {'error': 'Unknown error'} + +def get_ground_condition(weather_data): + """Determine ground condition""" + if isinstance(weather_data, dict): + precip = weather_data.get('precipitation', 0) + else: + precip = 0 + + if precip > 10: + return 'TRES_LOURD' + elif precip > 5: + return 'LOURD' + elif precip > 1: + return 'SOUPLE' + else: + return 'BON' + +def weather_code_to_desc(code): + """Weather code to description""" + codes = { + 0: "Ciel dégagé", 1: "Mainly clear", 2: "Partly cloudy", 3: "Overcast", + 45: "Brouillard", 48: "Brouillard", + 51: "Bruine légère", 53: "Bruine", 55: "Bruine dense", + 61: "Pluie légère", 63: "Pluie", 65: "Pluie forte", + 71: "Neige légère", 73: "Neige", 75: "Neige forte", + 80: "Averses", 81: "Averses", 82: "Averses fortes", + 95: "Orage", 96: "Orage grêle", + } + return codes.get(code, f"Code {code}") + +def save_weather(date, hippodrome, weather): + """Save weather to database""" + conn = sqlite3.connect(DB_PATH) + c = conn.cursor() + + c.execute(''' + INSERT INTO weather (date, hippodrome, temperature, humidity, wind_speed, precipitation, weather_code, ground_condition) + VALUES (?, ?, ?, ?, ?, ?, ?, ?) + ''', ( + date, + hippodrome, + weather.get('temperature'), + weather.get('humidity'), + weather.get('wind_speed'), + weather.get('precipitation'), + weather.get('weather_code'), + weather.get('ground_condition') + )) + + conn.commit() + conn.close() + print(f"Weather saved: {hippodrome} - {weather.get('ground_condition')}") + +def save_horse_weather_performance(date, race_date, horse_name, position, hippodrome, temperature, ground_condition): + """Save horse performance with weather""" + conn = sqlite3.connect(DB_PATH) + c = conn.cursor() + + c.execute(''' + INSERT INTO horse_weather_performance (date, race_date, horse_name, position, hippodrome, temperature, ground_condition) + VALUES (?, ?, ?, ?, ?, ?, ?) + ''', (date, race_date, horse_name, position, hippodrome, temperature, ground_condition)) + + conn.commit() + conn.close() + +def get_horse_weather_stats(horse_name): + """Get horse performance by weather conditions""" + conn = sqlite3.connect(DB_PATH) + c = conn.cursor() + + c.execute(''' + SELECT + ground_condition, + COUNT(*) as races, + SUM(CASE WHEN position <= 3 THEN 1 ELSE 0 END) as podiums, + ROUND(CAST(SUM(CASE WHEN position <= 3 THEN 1 ELSE 0 END) AS FLOAT) / COUNT(*) * 100, 1) as podium_rate + FROM horse_weather_performance + WHERE horse_name = ? + GROUP BY ground_condition + ''', (horse_name,)) + + results = c.fetchall() + conn.close() + + stats = {} + for row in results: + stats[row[0]] = { + 'races': row[1], + 'podiums': row[2], + 'podium_rate': row[3] + } + + return stats + +def analyze_weather_advantage(horse_name): + """Analyze horse performance by weather""" + stats = get_horse_weather_stats(horse_name) + + if not stats: + return {'message': 'Pas de données météo pour ce cheval'} + + # Find best ground condition + best = None + best_rate = 0 + for condition, data in stats.items(): + if data['podium_rate'] > best_rate: + best_rate = data['podium_rate'] + best = condition + + return { + 'horse': horse_name, + 'best_ground': best, + 'best_rate': best_rate, + 'all_conditions': stats + } + +# Test +if __name__ == "__main__": + init_weather_db() + + print("\n" + "="*50) + print("WEATHER MODULE v2 - TEST") + print("="*50) + + # Get weather for today's races + hippodromes = ['Auteuil', 'Vincennes', 'Pau', 'Cagnes-sur-Mer'] + today = datetime.now().strftime('%Y-%m-%d') + + for hippo in hippodromes: + w = get_weather(hippo) + if 'error' not in w: + print(f"\n{hippo}:") + print(f" {w.get('temperature')}°C, {w.get('humidity')}%, {w.get('wind_speed')} km/h") + print(f" Terrain: {w.get('ground_condition')}") + save_weather(today, hippo, w) + + # Test horse weather analysis + print("\n" + "="*50) + print("HORSE WEATHER ANALYSIS TEST") + print("="*50) + + # Save some test data + save_horse_weather_performance(today, today, 'PASSIONATA', 1, 'Auteuil', 13, 'BON') + save_horse_weather_performance(today, today, 'PASSIONATA', 2, 'Vincennes', 10, 'SOUPLE') + + result = analyze_weather_advantage('PASSIONATA') + print(f"\nPASSIONATA: {result}")

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🐙 Guide Git & Gitea - H3R7Tech

⚙️ 1. Configuration Initiale