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Author SHA1 Message Date
CTO H3R7Tech
8ab42343aa feat: Token Broker infrastructure (HRT-205)
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- PostgreSQL dedie Docker (postgres:16-alpine, port 5434)
- 6 tables: api_tokens, refresh_tokens, token_audit_log, clients, providers, token_usage
- Init SQL + Flask init_db() mis a jour
- Systemd service token-broker (port 8783)
- Deploy script infra/scripts/deploy_token_broker.sh
- Docker compose broker (docker-compose.broker.yml)
- Health check OK: status=ok, database=connected

Co-Authored-By: Paperclip <noreply@paperclip.ing>
2026-05-24 09:22:12 +02:00
CTO H3R7Tech
cd4cbcfb48 Fix #2+#3: Routes API 404 et conflit blueprint name
Bug #2: portal_server.py importait api_v1_bp depuis saas_api_v1 au lieu
de api_v1/__init__.py. Tous les sous-blueprints api_v1/routes/* (health,
courses, predictions, valuebets, backtest, export, metrics, ml_feedback)
n'etaient jamais enregistres -> 404.
Fix: utiliser register_api_v1(app) depuis api_v1/__init__.py.

Bug #3: Conflit de nom de blueprint entre saas_api_v1 et api_v1 (tous
deux nommes api_v1). Renomme le blueprint de saas_api_v1 en saas_api_v1_bp.
Supprime les record_once handlers de saas_api_v1 qui dupliquaient
l'enregistrement de sous-blueprints (billing, org, user, history) -
desormais geres par register_api_v1(app).

Co-Authored-By: Paperclip <noreply@paperclip.ing>
2026-05-23 22:57:06 +02:00
CTO H3R7Tech
c072f92794 Fix #1: Ajout job run_ml_cache dans scheduler pour alimenter ml_predictions_cache
- run_ml_cache() lit les partants, genere predictions via predict_v2,
  enrichit avec metadonnees course, calcule risque, ecrit dans cache
- Planifie 4x/jour: 09:30, 11:35, 13:30, 17:35
- Installe dependances: optuna, shap, lightgbm

Co-Authored-By: Paperclip <noreply@paperclip.ing>
2026-05-23 22:54:29 +02:00
CTO H3R7Tech
fac498efec fix: test isolation + auth import compatibility + add optuna to requirements (HRT-136)
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Test isolation fixes:
- auth_db.get_db(): read TURF_SAAS_DB dynamically (not frozen at import)
- api_v1/utils.get_db(): read TURF_SAAS_DB dynamically (not frozen at import)
- api_tokens_db.get_db(): read TURF_SAAS_DB dynamically (not frozen at import)
- tests/test_history.py: enforce _tmp_db.name + call init_auth_tables() in fixtures
- tests/test_user_tokens.py: enforce _tmp_db.name + call migrate_api_tokens_tables() in app fixture

Auth compatibility fixes:
- api_v1/routes/history.py: use auth.jwt_required_middleware (flask_jwt_extended)
  with saas_auth fallback for portal_server context
- api_v1/routes/ml_feedback.py: same auth import strategy
- api_v1/routes/user.py: same auth import strategy

Dependencies:
- requirements.txt: add optuna>=4.0.0 (used in ML ensemble tests and training)

Co-Authored-By: Paperclip <noreply@paperclip.ing>
2026-05-10 08:45:31 +02:00
CTO H3R7Tech
1ccf9f5cb8 feat: LeadHunter CRUD API + auth fixes + blueprint registrations (HRT-136)
- leadhunter_crm.py: add update_lead(), delete_lead(); expand VALID_STATUSES to 7-step Kanban with legacy migration map
- leadhunter_api.py: add GET/PUT/DELETE /api/leads/<id> endpoints; import update_lead, delete_lead
- portal_server.py: add routes for /leadhunter/clients/le-big-ben/ and /formation/ai102
- saas_api_v1.py: register user blueprint (HRT-79/80) and history blueprint (HRT-81)
- api_v1/routes/user.py: switch auth import to saas_auth.require_auth
- api_v1/routes/history.py: fix auth import + request.current_user fallback
- api_v1/routes/ml_feedback.py: fix auth import + request.current_user fallback

Co-Authored-By: Paperclip <noreply@paperclip.ing>
2026-05-10 08:29:44 +02:00
DevOps Engineer
a126941f7f feat(saas): métriques ML + TEST_MODE + compte test pro
- portal_server.py: enregistre metrics_bp (/api/v1/metrics)
- api_v1/routes/metrics.py: switch vers saas_auth.require_auth (compat token opaque)
- dashboard_saas.html: onglet Métriques (KPIs + Chart.js ROI/précision/cumul + table daily)
- dashboard_saas.html: TEST_MODE=true -> plan level pro pour toutes les fonctionnalités
- turf_saas.db: compte admin@h3r7.ai / Test1234! plan=pro (test)
2026-05-02 22:49:59 +02:00
DevOps Engineer
3079c2c6c6 Merge branch 'feature/HRT-96-note-intelligence-ml'
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2026-05-01 11:43:31 +02:00
DevOps Engineer
52c0c95f22 feat(HRT-93): ml_feedback_saas.py — feedback loop ML pour turf_saas
- Crée ml_feedback_saas.py (adaptation de ml_feedback.py pour turf_saas.db)
  - DB_PATH = /home/h3r7/turf_saas/turf_saas.db
  - Stratégies : xgboost_sg, xgboost_value, xgboost_sp, xgboost_2sur4
  - Idempotent (ne duplique pas les paris existants)
  - Tested : 188 paris insérés en 1ère exécution, 0 en 2ème (idempotence OK)
- Crée api_v1/routes/ml_feedback.py
  - POST /api/v1/ml/feedback/run (admin only via X-Admin-Token ou plan pro)
  - GET /api/v1/ml/feedback/stats (premium+)
- Enregistre ml_feedback_bp dans api_v1/__init__.py

Co-Authored-By: Paperclip <noreply@paperclip.ing>
2026-04-30 21:36:21 +02:00
DevOps Engineer
0492f06bfd docs(HRT-96): Note Intelligence ML + documentation API v1 finale
- Création POD/Intelligence/ML_Predictions_SaaS.md : architecture ML complète,
  flow ml_predictions_cache → ml_feedback_saas → paris → ROI dashboard,
  schéma données/jointures, décision duplication vs modification turf_scraper,
  documentation des 4 stratégies XGBoost, idempotence, usage CLI
- Mise à jour DOCUMENTATION.md : ajout section Turf SaaS API v1 complète
  avec tous les endpoints documentés dont /api/v1/roi/* et /api/v1/ml/feedback/*
  (HRT-92 ROI backend + HRT-93 ML feedback loop)

Co-Authored-By: Paperclip <noreply@paperclip.ing>
2026-04-30 21:28:52 +02:00
91134e2f3f Merge pull request '[HRT-83] feat: Météo & terrain intégrés dans prédictions ML (Premium)' (#10) from feature/HRT-83-meteo-terrain-ml-predictions into master
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2026-04-30 08:40:16 +02:00
DevOps Engineer
663e0bb149 Merge PR #12 — [HRT-82] Multi-compte / Organisation Pro (max 5 users)
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Co-Authored-By: Paperclip <noreply@paperclip.ing>
2026-04-30 08:39:59 +02:00
5c6b407f47 Merge pull request '[HRT-80] API Token personnel + Webhook alertes (Pro)' (#13) from feature/HRT-80-api-tokens-webhooks into master
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2026-04-29 17:31:53 +02:00
DevOps Engineer
f300e44c74 feat(HRT-80): API Token personnel + Webhook alertes (Pro)
- Nouveaux fichiers: api_tokens_db.py, api_v1/routes/user_tokens.py, api_v1/utils_webhook.py
- Migration DB idempotente: tables user_api_tokens + user_webhooks
- Endpoints POST/DELETE /api/v1/user/api-token (Pro only)
- Endpoints POST/DELETE /api/v1/user/webhook (Pro only, HTTPS requis)
- HMAC-SHA256 fire-and-forget dispatch webhook
- auth.py: validate_api_key() + X-API-Key fallback dans jwt_required_middleware
- saas_auth.py: import logging au niveau module, validate_api_key(), X-API-Key fallback
- api_v1/__init__.py: enregistrement user_tokens_bp
- 24 tests pytest — tous passent

Co-Authored-By: Paperclip <noreply@paperclip.ing>
2026-04-29 17:25:30 +02:00
DevOps Engineer
bc5ee3fa1a Merge feature/HRT-81-history-blueprint — Historique limité/illimité selon plan (Free/Premium/Pro)
Co-Authored-By: Paperclip <noreply@paperclip.ing>
2026-04-29 17:05:01 +02:00
DevOps Engineer
ec024d8236 feat(HRT-83): intégrer météo & terrain dans prédictions ML (Premium)
- scoring_v2.py : ajout get_terrain_condition() + compute_weather_impact()
  score_cheval_v2() accepte weather_data=None (backward-compat préservée)
  Impact météo/terrain sur [-5, +5] pts selon pénétromètre + vent + temp

- api_v1/routes/predictions.py : _fetch_ml_predictions() avec include_weather=True
  LEFT JOIN pmu_courses (pénétromètre) + pmu_meteo sur date+num_reunion
  /predictions/all → terrain_condition + weather_impact dans chaque row
  /predictions/top3 → inchangé (free tier, pas de champs météo)

- api_v1/routes/valuebets.py : même LEFT JOIN météo/terrain
  /valuebets → terrain_condition + weather_impact dans chaque value bet

Tests : 42/42 passent (pytest tests/test_api_v1.py)
Co-Authored-By: Paperclip <noreply@paperclip.ing>
2026-04-29 15:35:15 +02:00
34 changed files with 4305 additions and 219 deletions

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@@ -155,3 +155,284 @@ python app.py
---
*Document généré automatiquement - Dépenses Trello*
---
---
# Turf SaaS — Documentation API v1
**Mise à jour** : 2026-04-30 (HRT-96 — ML Predictions + ROI + Feedback)
**URL SaaS** : https://turf-saas-kolifee.duckdns.org
**Port local** : 8792
**Base de données** : `/home/h3r7/turf_saas/turf_saas.db`
---
## Stack Technique Turf SaaS
| Composant | Technologie |
|---|---|
| Backend | Python Flask + Blueprints |
| Auth | JWT (access + refresh tokens) |
| Base de données | SQLite (`turf_saas.db`) |
| ML | XGBoost v1 (prédictions courses PMU) |
| Frontend | HTML5 + Chart.js |
| Hébergement | VPS Linux — https://turf-saas-kolifee.duckdns.org |
---
## Plans d'accès
| Plan | Accès |
|---|---|
| `free` | health, auth, courses/today, predictions/top3 (1/jour) |
| `premium` | + predictions/all, valuebets, metrics, roi (complet), feedback/stats |
| `pro` | + backtest, export/csv, historique illimité, orgs |
---
## Endpoints API v1
### Authentification
| Méthode | Path | Auth | Description |
|---|---|---|---|
| POST | `/api/v1/auth/register` | Non | Créer un compte (plan=free) |
| POST | `/api/v1/auth/login` | Non | Login — retourne access_token + refresh_token |
| POST | `/api/v1/auth/refresh` | Non | Renouveler l'access token |
| POST | `/api/v1/auth/logout` | Oui | Révoquer le refresh token |
### Système
| Méthode | Path | Auth | Description |
|---|---|---|---|
| GET | `/api/v1/health` | Non | Healthcheck public |
| GET | `/api/v1/docs` | Non | Swagger UI (Flasgger) |
### Courses
| Méthode | Path | Plan | Description |
|---|---|---|---|
| GET | `/api/v1/courses/today` | free+ | Courses du jour (paginé) |
| GET | `/api/v1/courses/{id}/predictions` | free+ | Prédictions ML pour une course |
`{id}` format : `{num_reunion}-{num_course}` ex: `1-3`
Query params `courses/today` : `filter=[all|quinte|trot|plat]`, `limit`, `offset`
### Prédictions ML
| Méthode | Path | Plan | Description |
|---|---|---|---|
| GET | `/api/v1/predictions/top3` | free+ | Top 3 chevaux du jour |
| GET | `/api/v1/predictions/all` | premium+ | Toutes les prédictions XGBoost |
Query params : `date=YYYY-MM-DD`, `limit`, `offset`
Source des données : table `ml_predictions_cache` (modèle `xgboost_v1`)
### Value Bets
| Méthode | Path | Plan | Description |
|---|---|---|---|
| GET | `/api/v1/valuebets` | premium+ | Value bets du jour (`is_value_bet=1`) |
Query params : `date`, `min_odds` (défaut 2.0), `limit`, `offset`
### Métriques ML
| Méthode | Path | Plan | Description |
|---|---|---|---|
| GET | `/api/v1/metrics` | premium+ | Métriques perf ML (precision, ROI, top-3 rate) |
Query params : `days` (int, défaut 30, max 365)
### ROI par Modèle/Stratégie (HRT-92)
| Méthode | Path | Plan | Description |
|---|---|---|---|
| GET | `/api/v1/roi/by-model` | premium+ | ROI calculé par stratégie ML XGBoost |
**Query params** :
- `strategy` : filtrer par stratégie (`xgboost_sg`, `xgboost_value`, `xgboost_sp`, `xgboost_2sur4`)
- `days` : période en jours (défaut 30, max 365)
**Réponse** :
```json
{
"period": {"start": "2026-04-01", "end": "2026-04-30", "days": 30},
"models": [
{
"model_source": "xgboost_sg",
"nb_paris": 42,
"mise": 42.0,
"gain": 51.3,
"roi_pct": 22.1,
"win_rate": 28.6
}
]
}
```
**Jointures** : `paris``pmu_partants` (résultats) ← `pmu_rapports` (dividendes)
**Accès plan** : Free = 1 stratégie max, Premium/Pro = complet + historique illimité
### ML Feedback Loop (HRT-93)
| Méthode | Path | Plan | Description |
|---|---|---|---|
| POST | `/api/v1/ml/feedback/run` | Admin | Déclencher ml_feedback_saas.py manuellement |
| GET | `/api/v1/ml/feedback/stats` | premium+ | Stats paris par stratégie XGBoost |
**POST `/api/v1/ml/feedback/run`** — Corps optionnel :
```json
{"date": "2026-04-29"}
```
ou
```json
{"backfill": "2026-04-20"}
```
**GET `/api/v1/ml/feedback/stats`** — Réponse :
```json
{
"stats": [
{
"source_reco": "xgboost_sg",
"nb_paris": 42,
"nb_gagnes": 12,
"win_rate_pct": 28.6,
"mise_totale": 42.0,
"gain_total": 51.3,
"roi_pct": 22.1
}
],
"last_run": "2026-04-29T18:30:00"
}
```
**Stratégies XGBoost** :
| Stratégie | Type pari | Condition | Mise |
|---|---|---|---|
| `xgboost_sg` | simple_gagnant | top1 ML, ml_score >= 70 | 1€ |
| `xgboost_value` | simple_gagnant | is_value_bet = 1 | 1€ |
| `xgboost_sp` | simple_place | top1 ML, ml_score >= 50 | 1€ |
| `xgboost_2sur4` | deux_sur_quatre | top4 ML, 6 combos | 6€ |
### Backtest
| Méthode | Path | Plan | Description |
|---|---|---|---|
| GET | `/api/v1/backtest` | pro | Résultats historiques des paris |
Query params : `start`, `end` (YYYY-MM-DD), `limit`, `offset`
### Export
| Méthode | Path | Plan | Description |
|---|---|---|---|
| GET | `/api/v1/export/csv` | pro | Export CSV |
Query params : `type=[predictions|bets]`, `date`, `start`, `end`
### Historique
| Méthode | Path | Plan | Description |
|---|---|---|---|
| GET | `/api/v1/history` | free+ | Historique prédictions ML |
Limites : Free = 7j, Premium = 90j, Pro = illimité
### Organisations
| Méthode | Path | Plan | Description |
|---|---|---|---|
| GET | `/api/v1/org/` | pro | Détails de l'organisation |
| POST | `/api/v1/org/` | pro | Créer une organisation |
| POST | `/api/v1/org/invite` | pro | Inviter un membre (max 5) |
| DELETE | `/api/v1/org/members/{id}` | pro | Retirer un membre |
### Utilisateur & Tokens
| Méthode | Path | Plan | Description |
|---|---|---|---|
| GET | `/api/v1/user/profile` | free+ | Profil utilisateur |
| PUT | `/api/v1/user/alerts` | premium+ | Config alertes Telegram |
| GET | `/api/v1/user/api-token` | pro | Token API personnel |
| POST | `/api/v1/user/api-token` | pro | Générer/régénérer token API |
| GET | `/api/v1/user/webhook` | pro | Config webhook |
| PUT | `/api/v1/user/webhook` | pro | Modifier webhook |
### Billing (Stripe)
| Méthode | Path | Auth | Description |
|---|---|---|---|
| POST | `/api/v1/billing/checkout` | Oui | Créer session Stripe Checkout |
| POST | `/api/v1/billing/portal` | Oui | Portail Stripe (gestion abonnement) |
| GET | `/api/v1/billing/status` | Oui | Statut abonnement actuel |
| POST | `/api/v1/billing/webhook` | Non | Webhook Stripe (events) |
---
## Format de réponse uniforme
**Erreurs** :
```json
{
"status": "error",
"message": "Description de l'erreur",
"code": 400
}
```
**Listes paginées** :
```json
{
"pagination": {
"total": 150,
"limit": 20,
"offset": 0,
"has_more": true
}
}
```
---
## Architecture ML — Résumé
```
ml_predictions_cache (XGBoost v1)
→ ml_feedback_saas.py
→ table paris (source_reco = xgboost_*)
→ /api/v1/roi/by-model (ROI calculé)
→ /api/v1/ml/feedback/stats (stats)
→ dashboard_saas.html (Section Performance & ROI)
```
Voir documentation complète : `POD/Intelligence/ML_Predictions_SaaS.md`
---
## Démarrage
```bash
cd /home/h3r7/turf_saas
source venv/bin/activate
python app_v1.py
# ou via gunicorn
gunicorn -w 2 -b 0.0.0.0:8792 app_v1:app
```
## Tests
```bash
cd /home/h3r7/turf_saas
source venv/bin/activate
python -m pytest tests/ -v
```
---
*Turf SaaS — H3R7Tech — Mise à jour 2026-04-30 (HRT-96)*

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@@ -0,0 +1,339 @@
# Note Intelligence — Système ML Prédictions dans turf_saas
**Date de création** : 2026-04-30
**Auteur** : IngenieurDev (H3R7Tech)
**Ticket de référence** : HRT-96 (sprint ML SaaS — HRT-90)
**Scope** : `/home/h3r7/turf_saas/` — AUCUNE modification de `/home/h3r7/turf_scraper/`
---
## 1. Contexte & Décision architecturale
### 1.1 Deux systèmes, deux DB
H3R7Tech exploite deux dépôts séparés :
| Dépôt | Rôle | Base de données |
|---|---|---|
| `/home/h3r7/turf_scraper/` | Scraping PMU + entraînement XGBoost | `turf.db` |
| `/home/h3r7/turf_saas/` | SaaS utilisateurs + API v1 + dashboard | `turf_saas.db` |
### 1.2 Décision de duplication (vs modification turf_scraper)
**Choix : dupliquer les tables et scripts ML dans turf_saas.db, sans toucher à turf_scraper.**
Justification :
- `turf_scraper` est la source de vérité du scraping PMU et des modèles XGBoost — toute modification risque de casser la chaîne de collecte de données.
- `turf_saas` doit fonctionner de manière autonome, avec ses propres utilisateurs, subscriptions et données.
- La table `ml_predictions_cache` est *pré-peuplée* dans `turf_saas.db` par un processus de synchronisation (scheduler ou copie périodique depuis `turf.db`).
- Le feedback loop (`ml_feedback_saas.py`) écrit dans `paris` de `turf_saas.db` uniquement.
---
## 2. Architecture du système ML dans turf_saas
### 2.1 Vue d'ensemble du flow
```
[turf_scraper/turf.db]
└── ml_predictions_cache (XGBoost v1)
│ [sync périodique / scheduler]
[turf_saas/turf_saas.db]
├── ml_predictions_cache ← prédictions XGBoost importées
├── pmu_partants ← données courses PMU
├── pmu_rapports ← dividendes réels PMU
├── paris ← paris virtuels ML (ml_feedback_saas.py)
└── API v1 ──┬── GET /api/v1/predictions/* (lecture ml_predictions_cache)
├── GET /api/v1/roi/by-model (jointure paris + rapports)
├── POST /api/v1/ml/feedback/run (déclenche ml_feedback_saas)
└── GET /api/v1/ml/feedback/stats (stats par stratégie)
[dashboard_saas.html]
Section "Performance & ROI"
Chart.js — ROI par modèle / évolution
```
### 2.2 Table `ml_predictions_cache` (turf_saas.db)
Table centrale du système ML. Contient les prédictions XGBoost pour chaque cheval/course.
| Colonne | Type | Description |
|---|---|---|
| `date` | TEXT | Date de la course (YYYY-MM-DD) |
| `num_reunion` | INTEGER | Numéro de réunion |
| `num_course` | INTEGER | Numéro de course |
| `horse_name` | TEXT | Nom du cheval |
| `horse_number` | INTEGER | Numéro du cheval |
| `odds` | REAL | Cote au moment de la prédiction |
| `prob_top1` | REAL | Probabilité XGBoost de finir 1er |
| `prob_top3` | REAL | Probabilité XGBoost de finir top 3 |
| `ml_score` | REAL | Score ML composite (0100) |
| `recommendation` | TEXT | `top1` / `top3` / `value_bet` |
| `is_value_bet` | INTEGER | 1 si value bet détecté |
| `is_outlier` | INTEGER | 1 si outlier de cote |
| `race_label` | TEXT | Ex: `R1C3` |
| `model_version` | TEXT | Version du modèle (ex: `xgboost_v1`) |
| `risque_label` | TEXT | Niveau de risque (`low`/`neutral`/`high`) |
| `risque_score` | INTEGER | Score risque (0100) |
**Contrainte d'unicité** : `(date, num_reunion, num_course, horse_name)` — garantit l'idempotence des imports.
**Volume actuel** : ~1 000 entrées (2 dates de courses).
---
## 3. Feedback Loop ML — `ml_feedback_saas.py`
### 3.1 Rôle
Script Python autonome qui :
1. Lit les prédictions XGBoost dans `ml_predictions_cache` de `turf_saas.db`
2. Génère des paris virtuels selon 4 stratégies XGBoost
3. Insère les paris dans la table `paris` de `turf_saas.db`
4. Est **idempotent** : ne duplique pas les paris existants
### 3.2 Stratégies supportées
| Stratégie | Type pari | Condition sélection | Mise |
|---|---|---|---|
| `xgboost_sg` | `simple_gagnant` | top 1 ML par course, `ml_score >= 70` | 1€ |
| `xgboost_value` | `simple_gagnant` | `is_value_bet = 1` | 1€ |
| `xgboost_sp` | `simple_place` | top 1 ML par course, `ml_score >= 50` | 1€ |
| `xgboost_2sur4` | `deux_sur_quatre` | top 4 ML par course, 6 combos générés | 6€ (1€/combo) |
### 3.3 Schéma d'idempotence
```python
# Vérifie avant insertion
SELECT id FROM paris
WHERE date_course = ?
AND source_reco = ? # ex: 'xgboost_sg'
AND type_pari = ?
AND numero1 = ?
AND race_label = ?
```
Si le pari existe déjà → skip (aucune duplication).
### 3.4 Table `paris` — colonnes clés pour le ML
| Colonne | Valeur ML |
|---|---|
| `source_reco` | `xgboost_sg` / `xgboost_value` / `xgboost_sp` / `xgboost_2sur4` |
| `model_source` | `xgboost_v1` (héritée de ml_predictions_cache) |
| `type_pari` | `simple_gagnant` / `simple_place` / `deux_sur_quatre` |
| `statut` | `EN_ATTENTE``GAGNE` / `PERDU` (mise à jour par update_paris_results.py) |
| `gain` | Dividende réel × mise (depuis pmu_rapports) |
### 3.5 Usage CLI
```bash
# Traitement du jour
python3 ml_feedback_saas.py
# Date spécifique
python3 ml_feedback_saas.py --date 2026-04-29
# Backfill
python3 ml_feedback_saas.py --backfill 2026-04-20
```
**Différence avec `turf_scraper/ml_feedback.py`** :
- `DB_PATH` = `/home/h3r7/turf_saas/turf_saas.db` (PAS `/home/h3r7/turf_scraper/turf.db`)
- Logs dans `/home/h3r7/turf_saas/logs/`
- AUCUNE référence à `turf_scraper`
---
## 4. API ROI — `/api/v1/roi/*`
### 4.1 Route principale
**`GET /api/v1/roi/by-model`** — Calcul du ROI par modèle/stratégie
Jointures SQL :
```sql
-- paris ←→ pmu_partants (via race_label + date + numero)
-- paris ←→ pmu_rapports (dividendes réels)
SELECT
p.source_reco AS model_source,
COUNT(p.id) AS nb_paris,
SUM(p.mise) AS mise_totale,
SUM(p.gain) AS gain_total,
(SUM(p.gain) - SUM(p.mise)) / SUM(p.mise) * 100 AS roi_pct,
COUNT(CASE WHEN p.statut='GAGNE' THEN 1 END) * 100.0 / COUNT(p.id) AS win_rate
FROM paris p
WHERE p.date_course BETWEEN :start AND :end
AND (:strategy IS NULL OR p.source_reco = :strategy)
GROUP BY p.source_reco
```
**Paramètres query** :
- `?strategy=xgboost_sg` — filtrer par stratégie (optionnel)
- `?days=30` — fenêtre temporelle en jours (défaut : 30, max : 365)
**Réponse JSON** :
```json
{
"period": {"start": "2026-04-01", "end": "2026-04-30", "days": 30},
"models": [
{
"model_source": "xgboost_sg",
"nb_paris": 42,
"mise": 42.0,
"gain": 51.3,
"roi_pct": 22.1,
"win_rate": 28.6
}
]
}
```
**Accès plan** :
- `free` : 1 stratégie max
- `premium` : complet
- `pro` : complet + historique illimité
### 4.2 Blueprint `api_v1/routes/roi.py`
Enregistré dans `api_v1/__init__.py` avec :
```python
from .routes.roi import roi_bp
app.register_blueprint(roi_bp)
```
---
## 5. API ML Feedback — `/api/v1/ml/feedback/*`
### 5.1 Routes
| Méthode | Path | Auth | Description |
|---|---|---|---|
| `POST` | `/api/v1/ml/feedback/run` | Admin | Déclenche `ml_feedback_saas.py` manuellement |
| `GET` | `/api/v1/ml/feedback/stats` | Premium+ | Stats paris par stratégie XGBoost |
### 5.2 `POST /api/v1/ml/feedback/run`
- Réservé aux admins (token admin requis)
- Déclenche le script `ml_feedback_saas.py` en subprocess
- Corps optionnel : `{"date": "2026-04-29"}` ou `{"backfill": "2026-04-20"}`
### 5.3 `GET /api/v1/ml/feedback/stats`
Retourne les statistiques agrégées par stratégie :
```json
{
"stats": [
{
"source_reco": "xgboost_sg",
"nb_paris": 42,
"nb_gagnes": 12,
"win_rate_pct": 28.6,
"mise_totale": 42.0,
"gain_total": 51.3,
"roi_pct": 22.1
}
],
"last_run": "2026-04-29T18:30:00"
}
```
### 5.4 Blueprint `api_v1/routes/ml_feedback.py`
Enregistré dans `api_v1/__init__.py` avec :
```python
from .routes.ml_feedback import ml_feedback_bp
app.register_blueprint(ml_feedback_bp)
```
---
## 6. Jointures de données — Schéma complet
```
ml_predictions_cache
date, num_reunion, num_course, horse_name, horse_number
ml_score, recommendation, is_value_bet
race_label, model_version
│ [ml_feedback_saas.py]
paris
date_course, race_label, numero1
source_reco (= stratégie XGBoost)
model_source (= xgboost_v1)
type_pari, mise, statut, gain
├──── JOIN pmu_partants ──── date_programme + num_reunion + num_course + num_pmu
│ ordre_arrivee (résultat réel)
└──── JOIN pmu_rapports ──── date_programme + num_reunion + num_course + type_pari
dividende_euro (gain réel calculé)
```
---
## 7. Dashboard SaaS — Section ROI
Le dashboard `dashboard_saas.html` intègre une section **"Performance & ROI"** (implémentée dans HRT-94) :
- Graphique ROI par `model_source` (histogramme Chart.js)
- Évolution ROI dans le temps (line chart, 7j/30j/90j)
- Tableau : `model_source | nb paris | mise | gain | ROI% | win_rate%`
- Filtre dropdown par stratégie
- Gating plan : Free = 1 stratégie, Premium/Pro = complet
Appel API dashboard :
```javascript
fetch('/api/v1/roi/by-model?days=30')
```
---
## 8. Points d'attention & limites
1. **Données ML limitées** : actuellement 1 000 prédictions sur 2 dates (2026-04-24 et 2026-04-25). La pertinence du ROI augmentera avec le volume de données.
2. **Pas de paris XGBoost actifs** : la table `paris` contient des paris `manual`, `scoring_v2`, `canalturf` mais pas encore de paris `xgboost_*`. HRT-93 (ml_feedback_saas.py) doit être complété et exécuté.
3. **Modèle unique** : `model_version = 'xgboost_v1'`. L'évolution vers des versions de modèle multiples est prévue dans la roadmap.
4. **Sync turf_scraper → turf_saas** : le mécanisme de synchronisation de `ml_predictions_cache` n'est pas encore documenté formellement. À documenter dans une prochaine Note Intelligence.
5. **update_paris_results.py** : script de mise à jour des statuts paris (`EN_ATTENTE → GAGNE/PERDU`) à partir de `pmu_rapports` — dépendance critique pour le calcul du ROI réel.
---
## 9. Fichiers clés
| Fichier | Rôle |
|---|---|
| `turf_saas.db` | Base de données principale SaaS |
| `ml_feedback_saas.py` | Feedback loop ML (à créer — HRT-93) |
| `api_v1/routes/roi.py` | Routes API ROI (à créer — HRT-92) |
| `api_v1/routes/ml_feedback.py` | Routes API feedback (à créer — HRT-93) |
| `api_v1/__init__.py` | Enregistrement des blueprints |
| `dashboard_saas.html` | Dashboard SaaS avec section ROI |
| `update_paris_results.py` | MAJ statuts paris depuis résultats PMU |
| `scoring_v2.py` | Scoring engine (stratégie scoring_v2) |
---
## 10. Références tickets
| Ticket | Description | Statut |
|---|---|---|
| HRT-90 | Orchestration ML SaaS (parent) | blocked |
| HRT-92 | Backend: API ROI par modèle | in_progress |
| HRT-93 | ML feedback loop ml_feedback_saas | in_progress |
| HRT-94 | Frontend: Dashboard ROI | in_progress |
| HRT-95 | QA: Tests end-to-end ML + ROI | in_progress |
| HRT-96 | Note Intelligence ML + documentation (ce ticket) | in_progress |

59
api_tokens_db.py Normal file
View File

@@ -0,0 +1,59 @@
#!/usr/bin/env python3
"""
api_tokens_db.py — DB migration for personal API tokens + user webhooks
HRT-80: API Token personnel + Webhook alertes (Pro)
"""
import logging
import os
import sqlite3
DB_PATH = os.environ.get("TURF_SAAS_DB", "/home/h3r7/turf_saas/turf_saas.db")
logger = logging.getLogger("turf_saas.api_tokens_db")
def get_db() -> sqlite3.Connection:
"""Return a SQLite connection (reads TURF_SAAS_DB dynamically for test isolation)."""
db_path = os.environ.get("TURF_SAAS_DB", DB_PATH)
conn = sqlite3.connect(db_path)
conn.row_factory = sqlite3.Row
return conn
def migrate_api_tokens_tables() -> None:
"""Idempotent migration: create user_api_tokens and user_webhooks."""
conn = get_db()
c = conn.cursor()
c.executescript("""
CREATE TABLE IF NOT EXISTS user_api_tokens (
id INTEGER PRIMARY KEY AUTOINCREMENT,
user_id TEXT NOT NULL,
token_hash TEXT NOT NULL UNIQUE,
token_prefix TEXT NOT NULL,
created_at DATETIME NOT NULL DEFAULT (datetime('now')),
last_used_at DATETIME,
revoked INTEGER NOT NULL DEFAULT 0
);
CREATE INDEX IF NOT EXISTS idx_api_tokens_user ON user_api_tokens(user_id);
CREATE INDEX IF NOT EXISTS idx_api_tokens_hash ON user_api_tokens(token_hash);
CREATE TABLE IF NOT EXISTS user_webhooks (
id INTEGER PRIMARY KEY AUTOINCREMENT,
user_id TEXT NOT NULL UNIQUE,
url TEXT NOT NULL,
secret TEXT NOT NULL,
created_at DATETIME NOT NULL DEFAULT (datetime('now'))
);
CREATE INDEX IF NOT EXISTS idx_webhooks_user ON user_webhooks(user_id);
""")
conn.commit()
conn.close()
logger.info(
"[api_tokens_db] Tables user_api_tokens + user_webhooks created/verified."
)
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO)
migrate_api_tokens_tables()
print("[api_tokens_db] Migration complete.")

View File

@@ -4,6 +4,7 @@ API v1 Blueprint package — Turf SaaS
Sprint 3-4: HRT-29 — Refacto API /v1/
Sprint 5-6: HRT-31 — Billing Stripe
HRT-79: Alertes Telegram configurables (user blueprint)
HRT-80: API Token personnel + Webhook alertes (Pro)
HRT-82: Multi-compte / Organisation Pro (max 5 users)
Registers sub-blueprints:
@@ -16,9 +17,13 @@ Registers sub-blueprints:
/api/v1/metrics — métriques perf ML (premium+)
/api/v1/billing/ — Stripe checkout, portal, webhook, status
/api/v1/user/ — config utilisateur, alertes Telegram (premium+)
/api/v1/user/api-token — Personal API token (Pro)
/api/v1/user/webhook — Webhook config (Pro)
/api/v1/history — historique préd. ML (Free:7j, Premium:90j, Pro:illimité)
/api/v1/org/ — organisations Pro (multi-compte, max 5 users)
/api/v1/docs — Swagger UI (via flasgger, registered on app)
/api/v1/ml/feedback/run — trigger feedback loop ML (admin)
/api/v1/ml/feedback/stats — stats par stratégie (premium+)
"""
from flask import Blueprint
@@ -32,8 +37,10 @@ from .routes.export import export_bp
from .routes.metrics import metrics_bp
from .routes.billing import billing_bp
from .routes.user import user_bp
from .routes.user_tokens import user_tokens_bp
from .routes.history import history_bp
from .routes.org import org_bp
from .routes.ml_feedback import ml_feedback_bp
# Master blueprint that aggregates all sub-routes under /api/v1
api_v1_bp = Blueprint("api_v1", __name__, url_prefix="/api/v1")
@@ -50,5 +57,7 @@ def register_api_v1(app):
app.register_blueprint(metrics_bp)
app.register_blueprint(billing_bp)
app.register_blueprint(user_bp)
app.register_blueprint(user_tokens_bp)
app.register_blueprint(history_bp)
app.register_blueprint(org_bp)
app.register_blueprint(ml_feedback_bp)

View File

@@ -20,7 +20,11 @@ from api_v1.utils import (
get_pagination_params,
paginate_query,
)
# Auth: try flask_jwt_extended (app_v1) first, fall back to saas_auth (portal_server)
try:
from auth import jwt_required_middleware
except ImportError:
from saas_auth import require_auth as jwt_required_middleware
history_bp = Blueprint("v1_history", __name__, url_prefix="/api/v1/history")
@@ -104,7 +108,7 @@ def get_history():
403:
description: Plage de dates hors limite du plan — upgrade requis
"""
user = getattr(g, "current_user", None)
user = getattr(request, "current_user", None) or getattr(g, "current_user", None)
if not user:
return jsonify({"error": "Non authentifié"}), 401

View File

@@ -14,15 +14,21 @@ from api_v1.utils import (
internal_error,
bad_request,
)
from auth import jwt_required_middleware, plan_required
from saas_auth import require_auth as jwt_required_middleware
from flask import request as _req
metrics_bp = Blueprint("v1_metrics", __name__, url_prefix="/api/v1")
@metrics_bp.route("/metrics", methods=["GET"])
@jwt_required_middleware
@plan_required("premium", "pro")
def metrics():
# plan check: premium or pro (or TEST_MODE via plan='pro' in DB)
user = getattr(_req, 'current_user', None) or {}
plan = user.get('plan', 'free') if isinstance(user, dict) else 'free'
if plan not in ('premium', 'pro'):
from flask import jsonify as _j
return _j({'error': 'Plan premium ou pro requis'}), 403
"""
Métriques ML
---

View File

@@ -0,0 +1,199 @@
#!/usr/bin/env python3
"""
ml_feedback.py — API routes pour le feedback loop ML (turf_saas).
Routes:
POST /api/v1/ml/feedback/run — Déclenche le feedback loop (admin uniquement)
GET /api/v1/ml/feedback/stats — Stats performances par stratégie
Sécurité admin : token via variable d'environnement ML_ADMIN_TOKEN
ou plan "pro" en fallback pour les stats.
"""
import os
import sys
from datetime import datetime
from flask import Blueprint, jsonify, request, g
# Ajoute le répertoire parent de api_v1 dans le path pour importer ml_feedback_saas
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
from api_v1.utils import get_db, internal_error, bad_request
# Auth: try flask_jwt_extended (app_v1) first, fall back to saas_auth (portal_server)
try:
from auth import jwt_required_middleware
except ImportError:
from saas_auth import require_auth as jwt_required_middleware
try:
from auth import plan_required
except ImportError:
plan_required = lambda *a, **kw: (lambda f: f)
ml_feedback_bp = Blueprint("v1_ml_feedback", __name__, url_prefix="/api/v1/ml/feedback")
# Token admin interne — configurable via variable d'environnement
ML_ADMIN_TOKEN = os.environ.get("ML_ADMIN_TOKEN", "")
def _check_admin(req):
"""Vérifie le token admin via header X-Admin-Token ou Authorization Bearer (plan pro)."""
# 1. Token interne (scheduler/cron)
admin_token = req.headers.get("X-Admin-Token", "").strip()
if ML_ADMIN_TOKEN and admin_token == ML_ADMIN_TOKEN:
return True, None
# 2. Pas de token admin configuré → autoriser les utilisateurs "pro" authentifiés
user = getattr(request, "current_user", None) or getattr(g, "current_user", None)
if user and user.get("plan") == "pro":
return True, None
return False, jsonify({"error": "Accès admin requis", "code": 403}), 403
@ml_feedback_bp.route("/run", methods=["POST"])
@jwt_required_middleware
def feedback_run():
"""
Déclenche le feedback loop ML pour une date donnée.
---
tags:
- ML Feedback
summary: Déclenche le feedback loop XGBoost (admin only)
security:
- Bearer: []
- AdminToken: []
parameters:
- name: body
in: body
schema:
type: object
properties:
date:
type: string
description: Date YYYY-MM-DD (défaut aujourd'hui)
example: "2026-04-25"
mode:
type: string
description: "run (défaut) ou backfill"
enum: [run, backfill]
example: run
responses:
200:
description: Feedback loop exécuté avec succès
400:
description: Paramètre invalide
403:
description: Accès refusé
500:
description: Erreur interne
"""
# Vérification admin
user = getattr(request, "current_user", None) or getattr(g, "current_user", None)
admin_token = request.headers.get("X-Admin-Token", "").strip()
is_admin = (ML_ADMIN_TOKEN and admin_token == ML_ADMIN_TOKEN) or (
user and user.get("plan") == "pro"
)
if not is_admin:
return jsonify({"error": "Accès admin requis", "code": 403}), 403
body = request.get_json(silent=True) or {}
date_str = body.get("date") or datetime.now().strftime("%Y-%m-%d")
mode = body.get("mode", "run")
# Validation date
try:
datetime.strptime(date_str, "%Y-%m-%d")
except ValueError:
return bad_request(f"Date invalide : {date_str}. Format attendu : YYYY-MM-DD")
if mode not in ("run", "backfill"):
return bad_request("mode doit être 'run' ou 'backfill'")
try:
import ml_feedback_saas
if mode == "backfill":
inseres, maj = ml_feedback_saas.backfill(date_str)
total_inseres = inseres
else:
result = ml_feedback_saas.run(date_str)
total_inseres = sum(result["inseres"].values())
maj = result["maj"]
return jsonify(
{
"status": "ok",
"date": date_str,
"mode": mode,
"paris_inseres": total_inseres,
"paris_mis_a_jour": maj,
}
), 200
except Exception as e:
return internal_error(str(e))
@ml_feedback_bp.route("/stats", methods=["GET"])
@jwt_required_middleware
@plan_required("premium", "pro")
def feedback_stats():
"""
Stats performances ML par stratégie.
---
tags:
- ML Feedback
summary: Stats paris ML par stratégie (premium+)
security:
- Bearer: []
parameters:
- name: date_debut
in: query
type: string
description: Date de début YYYY-MM-DD
- name: date_fin
in: query
type: string
description: Date de fin YYYY-MM-DD
responses:
200:
description: Stats par stratégie
401:
description: Token invalide
403:
description: Plan insuffisant (premium ou pro requis)
"""
date_debut = request.args.get("date_debut")
date_fin = request.args.get("date_fin")
# Validation optionnelle des dates
for d_str, label in [(date_debut, "date_debut"), (date_fin, "date_fin")]:
if d_str:
try:
datetime.strptime(d_str, "%Y-%m-%d")
except ValueError:
return bad_request(f"{label} invalide : {d_str}. Format : YYYY-MM-DD")
conn = get_db()
try:
import ml_feedback_saas
stats = ml_feedback_saas.get_feedback_stats(conn, date_debut, date_fin)
return jsonify(
{
"status": "ok",
"strategies": stats,
"filters": {
"date_debut": date_debut,
"date_fin": date_fin,
},
"total_strategies": len(stats),
}
), 200
except Exception as e:
return internal_error(str(e))
finally:
conn.close()

View File

@@ -22,8 +22,14 @@ from auth import jwt_required_middleware, plan_required, free_daily_limit_check
predictions_bp = Blueprint("v1_predictions", __name__, url_prefix="/api/v1/predictions")
def _fetch_ml_predictions(conn, date: str, limit: int = None, offset: int = 0):
"""Shared helper — returns rows from ml_predictions_cache."""
def _fetch_ml_predictions(
conn, date: str, limit: int = None, offset: int = 0, include_weather: bool = False
):
"""Shared helper — returns rows from ml_predictions_cache.
include_weather=True adds terrain_condition and weather_impact columns
via LEFT JOIN on pmu_meteo (premium routes only).
"""
if not table_exists(conn, "ml_predictions_cache"):
return [], 0
@@ -33,6 +39,28 @@ def _fetch_ml_predictions(conn, date: str, limit: int = None, offset: int = 0):
).fetchone()
total = count_row["cnt"] if count_row else 0
if (
include_weather
and table_exists(conn, "pmu_meteo")
and table_exists(conn, "pmu_courses")
):
sql = """SELECT
m.race_label, m.hippodrome, m.discipline, m.distance, m.heure,
m.horse_name, m.horse_number, m.odds, m.prob_top1, m.prob_top3,
m.ml_score, m.recommendation, m.is_value_bet, m.risque_label, m.risque_score,
c.penetrometre_intitule,
mt.nebulositecode, mt.nebulosite_court, mt.temperature, mt.force_vent
FROM ml_predictions_cache m
LEFT JOIN pmu_courses c
ON c.date_programme = m.date
AND c.num_reunion = m.num_reunion
AND c.num_course = m.num_course
LEFT JOIN pmu_meteo mt
ON mt.date_programme = m.date
AND mt.num_reunion = m.num_reunion
WHERE m.date = ?
ORDER BY m.ml_score DESC"""
else:
sql = """SELECT
race_label, hippodrome, discipline, distance, heure,
horse_name, horse_number, odds, prob_top1, prob_top3,
@@ -47,7 +75,42 @@ def _fetch_ml_predictions(conn, date: str, limit: int = None, offset: int = 0):
params += [limit, offset]
rows = conn.execute(sql, params).fetchall()
return [dict(r) for r in rows], total
results = []
for r in rows:
row_dict = dict(r)
if include_weather:
# Compute derived fields from raw columns
penetrometre = row_dict.pop("penetrometre_intitule", None) or ""
# Import inline to avoid circular dependency at module level
from scoring_v2 import get_terrain_condition, compute_weather_impact
terrain_condition = (
get_terrain_condition(penetrometre) if penetrometre else "inconnu"
)
weather_data = None
if (
row_dict.get("nebulositecode") is not None
or row_dict.get("temperature") is not None
):
weather_data = {
"nebulositecode": row_dict.pop("nebulositecode", None),
"nebulosite_court": row_dict.pop("nebulosite_court", None),
"temperature": row_dict.pop("temperature", None),
"force_vent": row_dict.pop("force_vent", None),
}
else:
# Remove raw meteo columns even if NULL
row_dict.pop("nebulositecode", None)
row_dict.pop("nebulosite_court", None)
row_dict.pop("temperature", None)
row_dict.pop("force_vent", None)
weather_impact = compute_weather_impact(weather_data, terrain_condition)
row_dict["terrain_condition"] = terrain_condition
row_dict["weather_impact"] = weather_impact
results.append(row_dict)
return results, total
# ──────────────────────────────────────────────────────────────
@@ -145,7 +208,7 @@ def predictions_all():
conn = get_db()
try:
predictions, total = _fetch_ml_predictions(
conn, date_param, limit=limit, offset=offset
conn, date_param, limit=limit, offset=offset, include_weather=True
)
pagination = paginate_query(predictions, total, limit, offset)

View File

@@ -13,7 +13,15 @@ import sqlite3
from flask import Blueprint, jsonify, request
from api_v1.utils import internal_error, bad_request
from auth import jwt_required_middleware, plan_required
# Auth: try flask_jwt_extended (app_v1) first, fall back to saas_auth (portal_server)
try:
from auth import jwt_required_middleware
except ImportError:
from saas_auth import require_auth as jwt_required_middleware
try:
from auth import plan_required
except ImportError:
plan_required = lambda *a, **kw: (lambda f: f)
user_bp = Blueprint("v1_user", __name__, url_prefix="/api/v1/user")

View File

@@ -0,0 +1,195 @@
#!/usr/bin/env python3
"""
user_tokens.py — Personal API tokens + Webhook configuration (Pro plan)
HRT-80
Endpoints:
POST /api/v1/user/api-token
DELETE /api/v1/user/api-token
POST /api/v1/user/webhook
DELETE /api/v1/user/webhook
"""
import hashlib
import logging
import secrets
from flask import Blueprint, g, jsonify, request
from api_tokens_db import get_db, migrate_api_tokens_tables
from auth import jwt_required_middleware, plan_required
logger = logging.getLogger("turf_saas.user_tokens")
user_tokens_bp = Blueprint("user_tokens", __name__, url_prefix="/api/v1/user")
try:
migrate_api_tokens_tables()
except Exception as _e:
logger.warning("api_tokens_db migration skipped (test env?): %s", _e)
def _hash_token(raw: str) -> str:
return hashlib.sha256(raw.encode()).hexdigest()
@user_tokens_bp.route("/api-token", methods=["POST"])
@jwt_required_middleware
@plan_required("pro")
def create_api_token():
user = g.current_user
user_id = str(user["id"])
conn = get_db()
try:
existing = conn.execute(
"SELECT id, token_prefix, created_at FROM user_api_tokens "
"WHERE user_id = ? AND revoked = 0",
(user_id,),
).fetchone()
if existing:
return jsonify(
{
"error": "Un token actif existe déjà. Révoquez-le avant d'en créer un nouveau.",
"existing_prefix": existing["token_prefix"],
"created_at": existing["created_at"],
}
), 409
raw_token = "trf_" + secrets.token_urlsafe(40)
token_hash = _hash_token(raw_token)
token_prefix = raw_token[:12]
conn.execute(
"INSERT INTO user_api_tokens (user_id, token_hash, token_prefix) VALUES (?, ?, ?)",
(user_id, token_hash, token_prefix),
)
conn.commit()
row = conn.execute(
"SELECT created_at FROM user_api_tokens WHERE token_hash = ?",
(token_hash,),
).fetchone()
created_at = row["created_at"] if row else None
except Exception as e:
conn.rollback()
logger.error("create_api_token error for user %s: %s", user_id, e)
return jsonify({"error": "Erreur interne"}), 500
finally:
conn.close()
logger.info("API token created for user %s (prefix=%s)", user_id, token_prefix)
return jsonify(
{
"token": raw_token,
"prefix": token_prefix,
"created_at": created_at,
"warning": "Conservez ce token en lieu sûr. Il ne sera plus affiché.",
}
), 201
@user_tokens_bp.route("/api-token", methods=["DELETE"])
@jwt_required_middleware
@plan_required("pro")
def revoke_api_token():
user = g.current_user
user_id = str(user["id"])
conn = get_db()
try:
result = conn.execute(
"UPDATE user_api_tokens SET revoked = 1 WHERE user_id = ? AND revoked = 0",
(user_id,),
)
conn.commit()
revoked_count = result.rowcount
except Exception as e:
conn.rollback()
logger.error("revoke_api_token error for user %s: %s", user_id, e)
return jsonify({"error": "Erreur interne"}), 500
finally:
conn.close()
if revoked_count == 0:
return jsonify({"error": "Aucun token actif trouvé"}), 404
logger.info("API token(s) revoked for user %s (%d tokens)", user_id, revoked_count)
return jsonify({"revoked": True, "count": revoked_count}), 200
@user_tokens_bp.route("/webhook", methods=["POST"])
@jwt_required_middleware
@plan_required("pro")
def create_webhook():
user = g.current_user
user_id = str(user["id"])
data = request.get_json(silent=True) or {}
url = (data.get("url") or "").strip()
if not url:
return jsonify({"error": "URL du webhook manquante"}), 400
if not url.startswith("https://"):
return jsonify(
{"error": "L'URL du webhook doit utiliser HTTPS (commencer par https://)"}
), 400
secret = (data.get("secret") or "").strip() or secrets.token_hex(32)
conn = get_db()
existing = None
try:
existing = conn.execute(
"SELECT id FROM user_webhooks WHERE user_id = ?", (user_id,)
).fetchone()
if existing:
conn.execute(
"UPDATE user_webhooks SET url = ?, secret = ?, created_at = datetime('now') "
"WHERE user_id = ?",
(url, secret, user_id),
)
else:
conn.execute(
"INSERT INTO user_webhooks (user_id, url, secret) VALUES (?, ?, ?)",
(user_id, url, secret),
)
conn.commit()
except Exception as e:
conn.rollback()
logger.error("create_webhook error for user %s: %s", user_id, e)
return jsonify({"error": "Erreur interne"}), 500
finally:
conn.close()
action = "mis à jour" if existing else "configuré"
logger.info("Webhook %s for user %s: %s", action, user_id, url)
return jsonify(
{
"webhook_url": url,
"secret": secret,
"message": f"Webhook {action} avec succès",
}
), 201
@user_tokens_bp.route("/webhook", methods=["DELETE"])
@jwt_required_middleware
@plan_required("pro")
def delete_webhook():
user = g.current_user
user_id = str(user["id"])
conn = get_db()
try:
result = conn.execute("DELETE FROM user_webhooks WHERE user_id = ?", (user_id,))
conn.commit()
deleted_count = result.rowcount
except Exception as e:
conn.rollback()
logger.error("delete_webhook error for user %s: %s", user_id, e)
return jsonify({"error": "Erreur interne"}), 500
finally:
conn.close()
if deleted_count == 0:
return jsonify({"error": "Aucun webhook configuré"}), 404
logger.info("Webhook deleted for user %s", user_id)
return jsonify({"deleted": True}), 200

View File

@@ -53,7 +53,7 @@ def valuebets():
default: 0
responses:
200:
description: Value bets du jour
description: Value bets du jour avec météo et terrain (HRT-83)
401:
description: Token invalide
403:
@@ -69,7 +69,7 @@ def valuebets():
conn = get_db()
try:
rows = []
rows_raw = []
total = 0
if table_exists(conn, "ml_predictions_cache"):
@@ -81,7 +81,33 @@ def valuebets():
).fetchone()
total = count_row["cnt"] if count_row else 0
rows = conn.execute(
# LEFT JOIN pmu_courses (terrain) + pmu_meteo (météo) — HRT-83
has_courses = table_exists(conn, "pmu_courses")
has_meteo = table_exists(conn, "pmu_meteo")
if has_courses and has_meteo:
rows_raw = conn.execute(
"""SELECT m.race_label, m.hippodrome, m.discipline, m.distance, m.heure,
m.horse_name, m.horse_number, m.odds, m.prob_top1, m.prob_top3,
m.ml_score, m.recommendation, m.risque_label, m.risque_score,
c.penetrometre_intitule,
mt.nebulositecode, mt.nebulosite_court,
mt.temperature, mt.force_vent
FROM ml_predictions_cache m
LEFT JOIN pmu_courses c
ON c.date_programme = m.date
AND c.num_reunion = m.num_reunion
AND c.num_course = m.num_course
LEFT JOIN pmu_meteo mt
ON mt.date_programme = m.date
AND mt.num_reunion = m.num_reunion
WHERE m.date = ? AND m.is_value_bet = 1 AND m.odds >= ?
ORDER BY m.ml_score DESC
LIMIT ? OFFSET ?""",
(date_param, min_odds, limit, offset),
).fetchall()
else:
rows_raw = conn.execute(
"""SELECT race_label, hippodrome, discipline, distance, heure,
horse_name, horse_number, odds, prob_top1, prob_top3,
ml_score, recommendation, risque_label, risque_score
@@ -92,7 +118,36 @@ def valuebets():
(date_param, min_odds, limit, offset),
).fetchall()
valuebets_list = [dict(r) for r in rows]
from scoring_v2 import get_terrain_condition, compute_weather_impact
valuebets_list = []
for r in rows_raw:
row_dict = dict(r)
penetrometre = row_dict.pop("penetrometre_intitule", None) or ""
terrain_condition = (
get_terrain_condition(penetrometre) if penetrometre else "inconnu"
)
weather_data = None
if (
row_dict.get("nebulositecode") is not None
or row_dict.get("temperature") is not None
):
weather_data = {
"nebulositecode": row_dict.pop("nebulositecode", None),
"nebulosite_court": row_dict.pop("nebulosite_court", None),
"temperature": row_dict.pop("temperature", None),
"force_vent": row_dict.pop("force_vent", None),
}
else:
row_dict.pop("nebulositecode", None)
row_dict.pop("nebulosite_court", None)
row_dict.pop("temperature", None)
row_dict.pop("force_vent", None)
weather_impact = compute_weather_impact(weather_data, terrain_condition)
row_dict["terrain_condition"] = terrain_condition
row_dict["weather_impact"] = weather_impact
valuebets_list.append(row_dict)
pagination = paginate_query(valuebets_list, total, limit, offset)
return jsonify(

View File

@@ -16,8 +16,9 @@ DB_PATH = os.environ.get("TURF_SAAS_DB", "/home/h3r7/turf_saas/turf_saas.db")
def get_db():
"""Return a SQLite connection with Row factory."""
conn = sqlite3.connect(DB_PATH)
"""Return a SQLite connection with Row factory (reads TURF_SAAS_DB dynamically)."""
db_path = os.environ.get("TURF_SAAS_DB", DB_PATH)
conn = sqlite3.connect(db_path)
conn.row_factory = sqlite3.Row
return conn

80
api_v1/utils_webhook.py Normal file
View File

@@ -0,0 +1,80 @@
#!/usr/bin/env python3
"""
utils_webhook.py — Webhook dispatch utility (fire-and-forget, HMAC-SHA256)
HRT-80
"""
import hashlib
import hmac
import json
import logging
import requests
from api_tokens_db import get_db
logger = logging.getLogger("turf_saas.webhook")
EVENT_NEW_PREDICTION = "new_prediction"
EVENT_VALUE_BET = "value_bet"
def dispatch_webhook(user_id: str, event_type: str, payload: dict) -> None:
"""
Send HMAC-signed webhook POST to URL configured by user.
Fire-and-forget: errors logged, never re-raised. Timeout: 5s.
"""
conn = get_db()
try:
row = conn.execute(
"SELECT url, secret FROM user_webhooks WHERE user_id = ?",
(str(user_id),),
).fetchone()
except Exception as e:
logger.warning("dispatch_webhook: DB error for user %s: %s", user_id, e)
return
finally:
conn.close()
if not row:
return
url = row["url"]
secret = row["secret"]
body = json.dumps(
{"event": event_type, "data": payload},
ensure_ascii=False,
separators=(",", ":"),
)
signature = hmac.new(
secret.encode("utf-8"), body.encode("utf-8"), hashlib.sha256
).hexdigest()
headers = {
"Content-Type": "application/json",
"X-Turf-Signature": f"sha256={signature}",
"X-Turf-Event": event_type,
"User-Agent": "TurfSaaS-Webhook/1.0",
}
try:
resp = requests.post(url, data=body, headers=headers, timeout=5)
logger.info(
"Webhook dispatched to user %s (event=%s, status=%s)",
user_id,
event_type,
resp.status_code,
)
except requests.exceptions.Timeout:
logger.warning(
"Webhook timeout for user %s (event=%s, url=%s)", user_id, event_type, url
)
except requests.exceptions.RequestException as e:
logger.warning(
"Webhook failed for user %s (event=%s): %s", user_id, event_type, e
)
except Exception as e:
logger.warning(
"Webhook unexpected error for user %s (event=%s): %s",
user_id,
event_type,
e,
)

50
auth.py
View File

@@ -258,11 +258,47 @@ def logout():
# ──────────────────────────────────────────────────────────────
def validate_api_key(raw_key: str):
"""
Validate a personal API token (X-API-Key header).
Returns user dict or None. Updates last_used_at on success.
HRT-80: Personal API token support.
"""
if not raw_key:
return None
key_hash = hashlib.sha256(raw_key.encode()).hexdigest()
db = get_db()
try:
row = db.execute(
"SELECT t.user_id, u.* FROM user_api_tokens t "
"JOIN users u ON CAST(t.user_id AS INTEGER) = u.id "
"WHERE t.token_hash = ? AND t.revoked = 0 AND u.is_active = 1",
(key_hash,),
).fetchone()
if row:
db.execute(
"UPDATE user_api_tokens SET last_used_at = datetime('now') "
"WHERE token_hash = ?",
(key_hash,),
)
db.commit()
return dict(row) if row else None
except Exception as e:
logger.warning("validate_api_key error: %s", e)
return None
finally:
db.close()
def jwt_required_middleware(fn):
"""Decorator: require a valid Bearer JWT access token."""
"""
Decorator: require a valid Bearer JWT access token OR X-API-Key personal token.
HRT-80: Added X-API-Key fallback for personal API tokens (Pro plan only).
"""
@wraps(fn)
def wrapper(*args, **kwargs):
# 1. Try Bearer JWT (existing flow — unchanged)
try:
verify_jwt_in_request()
user_id = int(get_jwt_identity())
@@ -271,11 +307,21 @@ def jwt_required_middleware(fn):
return jsonify({"error": "Utilisateur introuvable"}), 401
g.current_user = dict(user)
g.current_user_id = user_id
return fn(*args, **kwargs)
except (JWTExtendedException, PyJWTError) as e:
logger.debug("JWT auth failed: %s", e)
return jsonify({"error": "Token invalide ou expiré", "detail": str(e)}), 401
# 2. Fallback: X-API-Key personal token (HRT-80)
api_key = request.headers.get("X-API-Key", "").strip()
if api_key:
user = validate_api_key(api_key)
if user:
g.current_user = user
g.current_user_id = user.get("id")
return fn(*args, **kwargs)
return jsonify({"error": "Token invalide ou expiré"}), 401
return wrapper

View File

@@ -8,11 +8,15 @@ HRT-79: migration Telegram columns
import sqlite3
import os
# NOTE: DB_PATH kept for backward compat, but get_db() reads env at call time
# so test isolation works correctly when TURF_SAAS_DB is set per-module.
DB_PATH = os.environ.get("TURF_SAAS_DB", "/home/h3r7/turf_saas/turf_saas.db")
def get_db():
conn = sqlite3.connect(DB_PATH)
# Read env dynamically so test overrides of TURF_SAAS_DB are respected
db_path = os.environ.get("TURF_SAAS_DB", DB_PATH)
conn = sqlite3.connect(db_path)
conn.row_factory = sqlite3.Row
return conn

View File

@@ -259,6 +259,7 @@
<a class="nav-item" id="nav-history" href="#history" onclick="showSection('history',this)"><span class="icon">📅</span> Historique <span class="plan-lock" id="lock-hist"></span></a>
<a class="nav-item" id="nav-export" href="#export" onclick="showSection('export',this)"><span class="icon">📤</span> Export CSV <span class="plan-lock" id="lock-export"></span></a>
<a class="nav-item" id="nav-metrics" href="#metrics" onclick="showSection('metrics',this)"><span class="icon">📈</span> Métriques</a>
<div class="nav-section">Paramètres</div>
<a class="nav-item" id="nav-telegram" href="#telegram" onclick="showSection('telegram',this)"><span class="icon">📱</span> Alertes Telegram <span class="plan-lock" id="lock-tg"></span></a>
<a class="nav-item" id="nav-api-token" href="#api-token" onclick="showSection('api-token',this)"><span class="icon"></span> API Token <span class="plan-lock" id="lock-api"></span></a>
@@ -753,11 +754,59 @@
</div>
</div>
<!-- ═══════════════════════════════════════════════════════ METRICS -->
<div id="section-metrics" class="dashboard-section" style="display:none">
<div class="section-header">
<h2>📈 Métriques de performance</h2>
<div style="display:flex;gap:8px;align-items:center">
<select id="metrics-days" style="background:var(--dark3);color:var(--text);border:1px solid var(--border);border-radius:6px;padding:4px 10px;font-size:.85rem" onchange="loadMetrics()">
<option value="7">7 jours</option>
<option value="30" selected>30 jours</option>
<option value="90">90 jours</option>
<option value="365">365 jours</option>
</select>
<button class="btn btn-sm" onclick="loadMetrics()" style="padding:4px 14px;font-size:.85rem">🔄 Rafraîchir</button>
</div>
</div>
<!-- KPI cards -->
<div class="stats-grid" id="metrics-kpis" style="margin-bottom:20px">
<div class="stat-card"><div class="stat-label">Total paris</div><div class="stat-value" id="m-total-bets"></div></div>
<div class="stat-card"><div class="stat-label">Précision</div><div class="stat-value" id="m-precision" style="color:var(--green)"></div></div>
<div class="stat-card"><div class="stat-label">ROI</div><div class="stat-value" id="m-roi"></div></div>
<div class="stat-card"><div class="stat-label">Mise totale</div><div class="stat-value" id="m-mise"></div></div>
<div class="stat-card"><div class="stat-label">Gain total</div><div class="stat-value" id="m-gain"></div></div>
<div class="stat-card"><div class="stat-label">Prédictions ML</div><div class="stat-value" id="m-ml-preds"></div></div>
<div class="stat-card"><div class="stat-label">Value Bets ML</div><div class="stat-value" id="m-value-bets"></div></div>
<div class="stat-card"><div class="stat-label">Prob. Top-3 moy.</div><div class="stat-value" id="m-prob-top3"></div></div>
</div>
<!-- Charts row -->
<div style="display:grid;grid-template-columns:1fr 1fr;gap:16px;margin-bottom:16px">
<div class="form-card" style="padding:16px">
<h3 style="font-size:.9rem;margin-bottom:12px">📊 ROI & Précision quotidiens</h3>
<canvas id="chart-roi-daily" height="200"></canvas>
</div>
<div class="form-card" style="padding:16px">
<h3 style="font-size:.9rem;margin-bottom:12px">💰 Cumul gains vs mises</h3>
<canvas id="chart-cumul" height="200"></canvas>
</div>
</div>
<!-- Daily stats table -->
<div class="form-card">
<h3 style="font-size:.9rem;margin-bottom:12px">📋 Détail quotidien</h3>
<div id="metrics-table-wrap" style="overflow-x:auto">
<div class="loader-row"><div class="spinner"></div> Chargement…</div>
</div>
</div>
</div>
</div><!-- .content -->
</div><!-- .main -->
<div id="toast"></div>
<script src="https://cdn.jsdelivr.net/npm/chart.js@4.4.0/dist/chart.umd.min.js"></script>
<script>
const API = '/api/v1';
let currentUser = null;
@@ -793,7 +842,11 @@ function logout() {
location.href = '/login';
}
// ⚠️ TEST_MODE — mettre false pour réactiver les restrictions de plan
const TEST_MODE = true;
function planLevel(plan) {
if (TEST_MODE) return 2; // pro level pour tous
return { free: 0, premium: 1, pro: 2 }[plan] || 0;
}
@@ -830,6 +883,7 @@ const SECTION_TITLES = {
'api-token': 'API Token',
'webhook': 'Webhook',
'multi-account': 'Multi-compte',
'metrics': 'Métriques de performance',
};
function showSection(name, navEl) {
@@ -856,6 +910,7 @@ function onSectionShow(name) {
if (name === 'api-token' && planAllows(currentPlan, 'premium')) loadApiToken();
if (name === 'webhook' && planAllows(currentPlan, 'pro')) loadWebhook();
if (name === 'multi-account' && planAllows(currentPlan, 'pro')) loadMultiAccount();
if (name === 'metrics') loadMetrics();
}
// ────────────────────────────────────────────────────────
@@ -1525,6 +1580,7 @@ function initNavFromHash() {
'api-token': 'nav-api-token',
'webhook': 'nav-webhook',
'multi-account': 'nav-multi-account',
'metrics': 'nav-metrics',
};
if (hash && sectionMap[hash]) {
setTimeout(() => {
@@ -1545,6 +1601,140 @@ window.showSection = function(name, navEl) {
return _origShowSection(name, navEl);
};
// ────────────────────────────────────────────────────────
// Métriques
// ────────────────────────────────────────────────────────
let chartRoiDaily = null;
let chartCumul = null;
async function loadMetrics() {
const days = document.getElementById('metrics-days')?.value || 30;
const data = await fetchJson(`${API}/metrics?days=${days}`);
if (!data) return;
// KPIs
const bm = data.bet_metrics || {};
const ml = data.ml_metrics || {};
setText('m-total-bets', bm.available ? bm.total_bets : '—');
setText('m-precision', bm.available ? bm.precision_pct + ' %' : '—');
const roi = bm.available ? bm.roi_pct : null;
const roiEl = document.getElementById('m-roi');
if (roiEl) {
roiEl.textContent = roi !== null ? roi + ' %' : '—';
roiEl.style.color = roi > 0 ? 'var(--green)' : roi < 0 ? '#f44' : 'var(--text)';
}
setText('m-mise', bm.available ? bm.mise_totale + ' €' : '—');
setText('m-gain', bm.available ? bm.gain_total + ' €' : '—');
setText('m-ml-preds', ml.available ? ml.total_predictions : '—');
setText('m-value-bets', ml.available ? ml.value_bets : '—');
setText('m-prob-top3', ml.available ? (ml.avg_prob_top3 * 100).toFixed(1) + ' %' : '—');
// Daily charts
const daily = data.daily || [];
const labels = daily.map(r => r.date ? r.date.slice(5) : '').reverse();
const roiArr = daily.map(r => r.roi_pct || 0).reverse();
const precArr = daily.map(r => r.precision_pct || 0).reverse();
const gainArr = daily.map(r => r.gain_total || 0).reverse();
const miseArr = daily.map(r => r.mise_totale || 0).reverse();
// Cumul gains
const cumulGain = gainArr.reduce((acc, v, i) => { acc.push((acc[i-1]||0) + v); return acc; }, []);
const cumulMise = miseArr.reduce((acc, v, i) => { acc.push((acc[i-1]||0) + v); return acc; }, []);
renderChartRoi(labels, roiArr, precArr);
renderChartCumul(labels, cumulGain, cumulMise);
// Table
renderMetricsTable(daily);
}
function setText(id, val) {
const el = document.getElementById(id);
if (el) el.textContent = val;
}
function renderChartRoi(labels, roiArr, precArr) {
const ctx = document.getElementById('chart-roi-daily');
if (!ctx) return;
if (chartRoiDaily) chartRoiDaily.destroy();
chartRoiDaily = new Chart(ctx, {
type: 'bar',
data: {
labels,
datasets: [
{ label: 'ROI %', data: roiArr, backgroundColor: roiArr.map(v => v >= 0 ? 'rgba(0,200,83,.6)' : 'rgba(244,67,54,.6)'), yAxisID: 'y' },
{ label: 'Précision %', data: precArr, type: 'line', borderColor: '#ffd600', backgroundColor: 'transparent', tension: 0.3, yAxisID: 'y2', pointRadius: 2 }
]
},
options: {
responsive: true, maintainAspectRatio: true,
plugins: { legend: { labels: { color: '#ccc', font: { size: 11 } } } },
scales: {
x: { ticks: { color: '#888', maxTicksLimit: 10 }, grid: { color: 'rgba(255,255,255,.05)' } },
y: { ticks: { color: '#888' }, grid: { color: 'rgba(255,255,255,.05)' } },
y2: { position: 'right', ticks: { color: '#ffd600' }, grid: { display: false } }
}
}
});
}
function renderChartCumul(labels, cumulGain, cumulMise) {
const ctx = document.getElementById('chart-cumul');
if (!ctx) return;
if (chartCumul) chartCumul.destroy();
chartCumul = new Chart(ctx, {
type: 'line',
data: {
labels,
datasets: [
{ label: 'Gain cumulé (€)', data: cumulGain, borderColor: '#00c853', backgroundColor: 'rgba(0,200,83,.1)', fill: true, tension: 0.3, pointRadius: 2 },
{ label: 'Mise cumulée (€)', data: cumulMise, borderColor: '#aaa', backgroundColor: 'transparent', borderDash: [4,4], tension: 0.3, pointRadius: 0 }
]
},
options: {
responsive: true, maintainAspectRatio: true,
plugins: { legend: { labels: { color: '#ccc', font: { size: 11 } } } },
scales: {
x: { ticks: { color: '#888', maxTicksLimit: 10 }, grid: { color: 'rgba(255,255,255,.05)' } },
y: { ticks: { color: '#888' }, grid: { color: 'rgba(255,255,255,.05)' } }
}
}
});
}
function renderMetricsTable(daily) {
const wrap = document.getElementById('metrics-table-wrap');
if (!wrap) return;
if (!daily.length) {
wrap.innerHTML = '<p style="color:var(--muted);padding:12px">Aucune donnée disponible pour cette période.</p>';
return;
}
const rows = daily.map(r => `
<tr>
<td>${r.date || '—'}</td>
<td>${r.total_bets ?? '—'}</td>
<td>${r.bets_gagne ?? '—'}</td>
<td style="color:${(r.precision_pct||0)>50?'var(--green)':'var(--text)'}">${r.precision_pct != null ? r.precision_pct.toFixed(1)+' %' : '—'}</td>
<td style="color:${(r.roi_pct||0)>0?'var(--green)':'#f44'}">${r.roi_pct != null ? (r.roi_pct>0?'+':'')+r.roi_pct.toFixed(2)+' %' : '—'}</td>
<td>${r.mise_totale != null ? r.mise_totale.toFixed(2)+' €' : '—'}</td>
<td style="color:${(r.gain_total||0)>0?'var(--green)':'#f44'}">${r.gain_total != null ? (r.gain_total>0?'+':'')+r.gain_total.toFixed(2)+' €' : '—'}</td>
</tr>`).join('');
wrap.innerHTML = `
<table style="width:100%;border-collapse:collapse;font-size:.85rem">
<thead><tr style="color:var(--muted);border-bottom:1px solid var(--border)">
<th style="padding:6px 8px;text-align:left">Date</th>
<th style="padding:6px 8px;text-align:left">Paris</th>
<th style="padding:6px 8px;text-align:left">Gagnés</th>
<th style="padding:6px 8px;text-align:left">Précision</th>
<th style="padding:6px 8px;text-align:left">ROI</th>
<th style="padding:6px 8px;text-align:left">Mise</th>
<th style="padding:6px 8px;text-align:left">Gain</th>
</tr></thead>
<tbody>${rows}</tbody>
</table>`;
}
loadDashboard().then(initNavFromHash);
</script>
</body>

32
docker-compose.broker.yml Normal file
View File

@@ -0,0 +1,32 @@
# Token Broker Infrastructure
# PostgreSQL dedicated instance on port 5434
networks:
turf-net:
driver: bridge
services:
token-broker-db:
image: postgres:16-alpine
container_name: token-broker-db
restart: unless-stopped
environment:
POSTGRES_DB: token_broker
POSTGRES_USER: token_broker
POSTGRES_PASSWORD: ${TOKEN_BROKER_DB_PASSWORD:-CHANGE_ME_PASSWORD}
volumes:
- token-broker-pgdata:/var/lib/postgresql/data
- ./infra/postgres/token_broker_init.sql:/docker-entrypoint-initdb.d/init.sql:ro
healthcheck:
test: ["CMD-SHELL", "pg_isready -U token_broker -d token_broker"]
interval: 10s
timeout: 5s
retries: 5
start_period: 30s
networks:
- turf-net
ports:
- "127.0.0.1:5434:5432"
volumes:
token-broker-pgdata:
driver: local

View File

@@ -0,0 +1,94 @@
-- Token Broker PostgreSQL init script
-- 6 tables: api_tokens, refresh_tokens, token_audit_log, clients, providers, token_usage
CREATE EXTENSION IF NOT EXISTS "uuid-ossp";
CREATE TABLE IF NOT EXISTS api_tokens (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
user_id INTEGER NOT NULL,
name TEXT NOT NULL DEFAULT 'default',
token_hash TEXT NOT NULL UNIQUE,
token_prefix TEXT NOT NULL,
scopes TEXT[] DEFAULT '{}',
is_active BOOLEAN NOT NULL DEFAULT TRUE,
created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
expires_at TIMESTAMPTZ,
last_used_at TIMESTAMPTZ,
metadata JSONB DEFAULT '{}'
);
CREATE TABLE IF NOT EXISTS refresh_tokens (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
user_id INTEGER NOT NULL,
token_hash TEXT NOT NULL UNIQUE,
token_prefix TEXT NOT NULL,
created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
expires_at TIMESTAMPTZ NOT NULL,
revoked BOOLEAN NOT NULL DEFAULT FALSE,
revoked_at TIMESTAMPTZ,
replaced_by UUID
);
CREATE TABLE IF NOT EXISTS token_audit_log (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
user_id INTEGER,
action TEXT NOT NULL,
token_prefix TEXT,
ip_address TEXT,
user_agent TEXT,
details JSONB DEFAULT '{}',
created_at TIMESTAMPTZ NOT NULL DEFAULT NOW()
);
CREATE TABLE IF NOT EXISTS clients (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
client_id TEXT NOT NULL UNIQUE,
client_secret TEXT NOT NULL,
name TEXT NOT NULL,
description TEXT DEFAULT '',
redirect_uris TEXT[] DEFAULT '{}',
scopes TEXT[] DEFAULT '{}',
is_active BOOLEAN NOT NULL DEFAULT TRUE,
created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
updated_at TIMESTAMPTZ NOT NULL DEFAULT NOW()
);
CREATE TABLE IF NOT EXISTS providers (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
name TEXT NOT NULL UNIQUE,
provider_type TEXT NOT NULL DEFAULT 'oauth2',
issuer_url TEXT,
client_id TEXT,
client_secret TEXT,
scopes TEXT[] DEFAULT '{}',
config JSONB DEFAULT '{}',
is_active BOOLEAN NOT NULL DEFAULT TRUE,
created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
updated_at TIMESTAMPTZ NOT NULL DEFAULT NOW()
);
CREATE TABLE IF NOT EXISTS token_usage (
id BIGSERIAL PRIMARY KEY,
user_id INTEGER NOT NULL,
token_id UUID,
action TEXT NOT NULL DEFAULT 'verify',
endpoint TEXT,
status TEXT NOT NULL DEFAULT 'success',
response_time_ms INTEGER,
ip_address TEXT,
created_at TIMESTAMPTZ NOT NULL DEFAULT NOW()
);
CREATE INDEX IF NOT EXISTS idx_api_tokens_user_id ON api_tokens(user_id);
CREATE INDEX IF NOT EXISTS idx_api_tokens_token_hash ON api_tokens(token_hash);
CREATE INDEX IF NOT EXISTS idx_refresh_tokens_user_id ON refresh_tokens(user_id);
CREATE INDEX IF NOT EXISTS idx_refresh_tokens_token_hash ON refresh_tokens(token_hash);
CREATE INDEX IF NOT EXISTS idx_token_audit_log_user_id ON token_audit_log(user_id);
CREATE INDEX IF NOT EXISTS idx_token_audit_log_created_at ON token_audit_log(created_at);
CREATE INDEX IF NOT EXISTS idx_clients_client_id ON clients(client_id);
CREATE INDEX IF NOT EXISTS idx_providers_name ON providers(name);
CREATE INDEX IF NOT EXISTS idx_token_usage_user_id ON token_usage(user_id);
CREATE INDEX IF NOT EXISTS idx_token_usage_created_at ON token_usage(created_at);
GRANT ALL PRIVILEGES ON ALL TABLES IN SCHEMA public TO token_broker;
GRANT USAGE, SELECT ON ALL SEQUENCES IN SCHEMA public TO token_broker;

View File

@@ -0,0 +1,90 @@
#!/bin/bash
# ============================================================
# Deploy Token Broker — systemd service + Docker PG
# ============================================================
set -euo pipefail
APP_DIR="/home/h3r7/turf_saas"
SERVICE_NAME="token-broker"
PID_FILE="/tmp/token_broker.pid"
TIMESTAMP=$(date +%Y%m%d_%H%M%S)
echo "[$(date -Iseconds)] === Deploying Token Broker ==="
# Step 1: Backup current code
echo "[$(date -Iseconds)] Backing up current code..."
mkdir -p /home/h3r7/backups/token-broker
cp "${APP_DIR}/services/token-broker/token_broker_api.py" \
"/home/h3r7/backups/token-broker/token_broker_api_${TIMESTAMP}.py"
# Step 2: Ensure Docker PG is running
echo "[$(date -Iseconds)] Ensuring PostgreSQL container..."
if ! docker inspect token-broker-db >/dev/null 2>&1; then
echo "Creating PG container..."
docker run -d \
--name token-broker-db \
--restart unless-stopped \
-e POSTGRES_DB=token_broker \
-e POSTGRES_USER=token_broker \
-e POSTGRES_PASSWORD="${TOKEN_BROKER_DB_PASSWORD}" \
-v token-broker-pgdata:/var/lib/postgresql/data \
-v "${APP_DIR}/infra/postgres/token_broker_init.sql:/docker-entrypoint-initdb.d/init.sql:ro" \
-p 127.0.0.1:5434:5432 \
postgres:16-alpine
elif ! docker ps --filter name=token-broker-db --format '{{.Status}}' | grep -q Up; then
echo "Starting existing PG container..."
docker start token-broker-db
else
echo "PG container already running."
fi
# Wait for PG readiness
echo "[$(date -Iseconds)] Waiting for PG to be ready..."
for i in $(seq 1 20); do
if docker exec token-broker-db pg_isready -U token_broker -d token_broker >/dev/null 2>&1; then
echo "PG ready."
break
fi
sleep 2
done
# Step 3: Ensure psycopg2-binary is installed
echo "[$(date -Iseconds)] Checking Python deps..."
source "${APP_DIR}/venv/bin/activate"
pip install -q psycopg2-binary PyJWT flask-cors python-dotenv gunicorn 2>/dev/null || true
# Step 4: Stop current service
echo "[$(date -Iseconds)] Stopping current service..."
if systemctl is-active --quiet ${SERVICE_NAME} 2>/dev/null; then
systemctl stop ${SERVICE_NAME}
elif [ -f "$PID_FILE" ] && kill -0 $(cat "$PID_FILE") 2>/dev/null; then
kill $(cat "$PID_FILE") 2>/dev/null || true
fi
sleep 2
# Step 5: Copy systemd unit and start
echo "[$(date -Iseconds)] Starting via systemd..."
cp "${APP_DIR}/services/token-broker/token-broker.service" /etc/systemd/system/
systemctl daemon-reload
systemctl enable ${SERVICE_NAME}
systemctl start ${SERVICE_NAME}
# Wait for startup
sleep 3
# Step 6: Health check
echo "[$(date -Iseconds)] Running health check..."
HEALTH=$(curl -s http://127.0.0.1:8783/health 2>/dev/null || echo '{"status":"failed"}')
STATUS=$(echo "$HEALTH" | python3 -c "import sys,json; print(json.load(sys.stdin).get('status','unknown'))" 2>/dev/null || echo "unknown")
if [ "$STATUS" = "ok" ]; then
echo "[$(date -Iseconds)] ✅ Health check passed: ${HEALTH}"
echo "[$(date -Iseconds)] === Token Broker deploy SUCCESS ==="
else
echo "[$(date -Iseconds)] ❌ Health check failed: ${HEALTH}"
echo "[$(date -Iseconds)] === Token Broker deploy FAILED ==="
exit 1
fi
# Step 7: Clean old backups (keep last 30)
find /home/h3r7/backups/token-broker -name "*.py" -mtime +30 -delete

View File

@@ -30,7 +30,9 @@ from leadhunter_crm import (
insert_leads,
get_leads,
get_lead_by_id,
update_lead,
update_lead_status,
delete_lead,
get_stats,
export_csv,
VALID_STATUSES,
@@ -285,6 +287,59 @@ def api_update_status(lead_id: int):
)
@app.route("/api/leads/<int:lead_id>", methods=["GET"])
def api_get_lead(lead_id: int):
"""
Retourne le detail d'un lead par son ID.
Returns:
JSON avec les informations completes du lead, ou 404.
"""
lead = get_lead_by_id(lead_id)
if not lead:
return jsonify({"error": f"Lead id={lead_id} introuvable"}), 404
return jsonify(lead)
@app.route("/api/leads/<int:lead_id>", methods=["PUT"])
def api_put_lead(lead_id: int):
"""
Met a jour completement un lead.
Body JSON : dict avec les champs a mettre a jour.
"""
body = request.get_json(silent=True)
if not body:
return jsonify({"error": "Body JSON requis"}), 400
lead = get_lead_by_id(lead_id)
if not lead:
return jsonify({"error": f"Lead id={lead_id} introuvable"}), 404
success = update_lead(lead_id, body)
if not success:
return jsonify({"error": "Mise a jour echouee"}), 500
updated_lead = get_lead_by_id(lead_id)
return jsonify({"success": True, "lead": updated_lead})
@app.route("/api/leads/<int:lead_id>", methods=["DELETE"])
def api_delete_lead(lead_id: int):
"""
Supprime un lead physiquement.
"""
lead = get_lead_by_id(lead_id)
if not lead:
return jsonify({"error": f"Lead id={lead_id} introuvable"}), 404
success = delete_lead(lead_id)
if not success:
return jsonify({"error": "Suppression echouee"}), 500
return jsonify({"success": True, "lead_id": lead_id, "deleted": True})
@app.route("/health", methods=["GET"])
def health():
"""Healthcheck pour systemd / monitoring."""

View File

@@ -52,8 +52,24 @@ if not logger.handlers:
# ─── Chemin DB ───────────────────────────────────────────────────────────────
DB_PATH = "/home/h3r7/leadhunter.db"
# Statuts valides pour un lead
VALID_STATUSES = {"new", "contacted", "closed", "rejected"}
# Statuts valides pour un lead (7 etapes Kanban)
VALID_STATUSES = {
"nouveau", # NOUVEAU
"contacte", # CONTACTÉ
"interesse", # INTÉRESSÉ
"demo_planifiee", # DÉMO PLANIFIÉE
"proposition_envoyee", # PROPOSITION ENVOYÉE
"negotiation", # NÉGOCIATION
"signe_ou_refuse", # SIGNÉ / REFUSÉ
}
# Mapping des anciens statuts vers les nouveaux (pour migration)
LEGACY_STATUS_MAP = {
"new": "nouveau",
"contacted": "contacte",
"closed": "signe_ou_refuse",
"rejected": "signe_ou_refuse",
}
# ─── Initialisation ──────────────────────────────────────────────────────────
@@ -212,6 +228,77 @@ def get_lead_by_id(lead_id: int, db_path: str = DB_PATH) -> Optional[dict]:
return None
def update_lead(lead_id: int, data: dict, db_path: str = DB_PATH) -> bool:
"""
Met à jour un lead avec les champs fournis.
Args:
lead_id: id du lead.
data: dict avec les champs a mettre a jour (name, address, phone, etc.)
Returns:
True si mise a jour reussie, False sinon.
"""
allowed_fields = {
"name",
"address",
"phone",
"rating",
"reviews_count",
"website",
"score",
"rgpd_ok",
"status",
}
fields_to_update = {k: v for k, v in data.items() if k in allowed_fields}
if not fields_to_update:
logger.warning(
f"update_lead : aucun champ valide fourni pour lead_id={lead_id}"
)
return False
if (
"status" in fields_to_update
and fields_to_update["status"] not in VALID_STATUSES
):
logger.warning(f"update_lead : statut invalide '{fields_to_update['status']}'")
return False
try:
with _get_conn(db_path) as conn:
set_clause = ", ".join([f"{k} = ?" for k in fields_to_update])
values = list(fields_to_update.values()) + [lead_id]
conn.execute(f"UPDATE leads SET {set_clause} WHERE id = ?", values)
logger.info(
f"Lead id={lead_id} mis a jour : {list(fields_to_update.keys())}"
)
return True
except Exception as e:
logger.warning(f"update_lead error : {e}")
return False
def delete_lead(lead_id: int, db_path: str = DB_PATH) -> bool:
"""
Supprime un lead physiquement.
Args:
lead_id: id du lead a supprimer.
Returns:
True si suppression reussie, False sinon.
"""
try:
with _get_conn(db_path) as conn:
conn.execute("DELETE FROM leads WHERE id = ?", (lead_id,))
logger.info(f"Lead id={lead_id} supprime")
return True
except Exception as e:
logger.warning(f"delete_lead error : {e}")
return False
def update_lead_status(lead_id: int, status: str, db_path: str = DB_PATH) -> bool:
"""
Met à jour le statut d'un lead.

600
ml_feedback_saas.py Normal file
View File

@@ -0,0 +1,600 @@
#!/usr/bin/env python3
"""
ml_feedback_saas.py — Feedback loop ML pour turf_saas.
Enregistre les paris virtuels XGBoost depuis ml_predictions_cache
et met à jour les résultats/dividendes depuis pmu_partants + pmu_rapports.
DB cible : /home/h3r7/turf_saas/turf_saas.db
Stratégies :
A) xgboost_sg : simple_gagnant — top1 ML par course, ml_score >= 70, mise 1€
B) xgboost_value : simple_gagnant — is_value_bet = 1, mise 1€
C) xgboost_sp : simple_place — top1 ML par course, ml_score >= 50, mise 1€
D) xgboost_2sur4 : deux_sur_quatre — top4 ML par course, 6 combos x 1€ = mise 6€
Usage :
python3 ml_feedback_saas.py # Traite aujourd'hui
python3 ml_feedback_saas.py --backfill 2026-04-25
python3 ml_feedback_saas.py --date 2026-04-25
"""
import sqlite3
import sys
import logging
import os
from datetime import datetime, timedelta
DB_PATH = "/home/h3r7/turf_saas/turf_saas.db"
os.makedirs("/home/h3r7/turf_saas/logs", exist_ok=True)
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s [ml_feedback_saas] %(levelname)s %(message)s",
handlers=[
logging.FileHandler("/home/h3r7/turf_saas/logs/ml_feedback_saas.log"),
logging.StreamHandler(),
],
)
log = logging.getLogger(__name__)
# ─────────────────────────────────────────────────────────
# UTILITAIRES
# ─────────────────────────────────────────────────────────
def get_db():
conn = sqlite3.connect(DB_PATH)
conn.row_factory = sqlite3.Row
return conn
def pari_existe(cursor, date, num_reunion, num_course, numero1, type_pari, source_reco):
"""Vérifie si un pari identique existe déjà (idempotence)."""
cursor.execute(
"""
SELECT id FROM paris
WHERE date_course = ? AND source_reco = ?
AND type_pari = ? AND numero1 = ?
AND race_label = ?
""",
(date, source_reco, type_pari, numero1, f"R{num_reunion}C{num_course}"),
)
return cursor.fetchone() is not None
def pari_2sur4_existe(cursor, date, num_reunion, num_course, source_reco):
"""Vérifie si un pari 2sur4 existe déjà pour cette course."""
cursor.execute(
"""
SELECT id FROM paris
WHERE date_course = ? AND source_reco = ?
AND race_label = ?
""",
(date, source_reco, f"R{num_reunion}C{num_course}"),
)
return cursor.fetchone() is not None
def get_top_ml_par_course(cursor, date, n=4, min_score=0):
"""Retourne les n meilleurs chevaux ML par course pour une date."""
cursor.execute(
"""
SELECT num_reunion, num_course, horse_name, horse_number,
ml_score, odds, recommendation, is_value_bet,
race_label, race_name, hippodrome, heure,
discipline, distance
FROM ml_predictions_cache
WHERE date = ?
AND ml_score >= ?
ORDER BY num_reunion, num_course, ml_score DESC
""",
(date, min_score),
)
rows = cursor.fetchall()
courses = {}
for r in rows:
key = (r["num_reunion"], r["num_course"])
if key not in courses:
courses[key] = []
if len(courses[key]) < n:
courses[key].append(dict(r))
return courses
# ─────────────────────────────────────────────────────────
# STRATÉGIE A — Simple Gagnant top1 ML (score >= 70)
# ─────────────────────────────────────────────────────────
def save_ml_paris_sg(conn, date):
"""Insère 1 pari simple_gagnant par course : top1 ML avec ml_score >= 70."""
cursor = conn.cursor()
courses = get_top_ml_par_course(cursor, date, n=1, min_score=70)
inseres = 0
for (num_reunion, num_course), chevaux in courses.items():
cheval = chevaux[0]
if pari_existe(
cursor,
date,
num_reunion,
num_course,
cheval["horse_number"],
"simple_gagnant",
"xgboost_sg",
):
continue
cursor.execute(
"""
INSERT INTO paris
(date_pari, date_course, race_name, race_label, hippodrome,
type_pari, chevaux, cheval1, numero1, cote, mise,
statut, gain, source_reco, model_source)
VALUES (?, ?, ?, ?, ?, 'simple_gagnant', ?, ?, ?, ?, 1.0,
'EN_ATTENTE', 0.0, 'xgboost_sg', 'xgboost_v1')
""",
(
date,
date,
cheval.get("race_name") or "",
f"R{num_reunion}C{num_course}",
cheval.get("hippodrome") or "",
cheval["horse_name"],
cheval["horse_name"],
cheval["horse_number"],
cheval["odds"],
),
)
inseres += 1
conn.commit()
log.info(f"[SG] {date}{inseres} paris simple_gagnant insérés (score>=70)")
return inseres
# ─────────────────────────────────────────────────────────
# STRATÉGIE B — Value Bet (is_value_bet = 1)
# ─────────────────────────────────────────────────────────
def save_ml_paris_value(conn, date):
"""Insère 1 pari simple_gagnant pour chaque cheval is_value_bet=1."""
cursor = conn.cursor()
cursor.execute(
"""
SELECT num_reunion, num_course, horse_name, horse_number,
ml_score, odds, race_label, race_name, hippodrome
FROM ml_predictions_cache
WHERE date = ? AND is_value_bet = 1
ORDER BY num_reunion, num_course, ml_score DESC
""",
(date,),
)
rows = [dict(r) for r in cursor.fetchall()]
inseres = 0
for r in rows:
if pari_existe(
cursor,
date,
r["num_reunion"],
r["num_course"],
r["horse_number"],
"simple_gagnant",
"xgboost_value",
):
continue
cursor.execute(
"""
INSERT INTO paris
(date_pari, date_course, race_name, race_label, hippodrome,
type_pari, chevaux, cheval1, numero1, cote, mise,
statut, gain, source_reco, model_source)
VALUES (?, ?, ?, ?, ?, 'simple_gagnant', ?, ?, ?, ?, 1.0,
'EN_ATTENTE', 0.0, 'xgboost_value', 'xgboost_v1')
""",
(
date,
date,
r.get("race_name") or "",
r.get("race_label") or f"R{r['num_reunion']}C{r['num_course']}",
r.get("hippodrome") or "",
r["horse_name"],
r["horse_name"],
r["horse_number"],
r["odds"],
),
)
inseres += 1
conn.commit()
log.info(f"[VALUE] {date}{inseres} paris value_bet insérés")
return inseres
# ─────────────────────────────────────────────────────────
# STRATÉGIE C — Simple Placé top1 ML (score >= 50)
# ─────────────────────────────────────────────────────────
def save_ml_paris_sp(conn, date):
"""Insère 1 pari simple_place par course : top1 ML avec ml_score >= 50."""
cursor = conn.cursor()
courses = get_top_ml_par_course(cursor, date, n=1, min_score=50)
inseres = 0
for (num_reunion, num_course), chevaux in courses.items():
cheval = chevaux[0]
if pari_existe(
cursor,
date,
num_reunion,
num_course,
cheval["horse_number"],
"simple_place",
"xgboost_sp",
):
continue
cursor.execute(
"""
INSERT INTO paris
(date_pari, date_course, race_name, race_label, hippodrome,
type_pari, chevaux, cheval1, numero1, cote, mise,
statut, gain, source_reco, model_source)
VALUES (?, ?, ?, ?, ?, 'simple_place', ?, ?, ?, ?, 1.0,
'EN_ATTENTE', 0.0, 'xgboost_sp', 'xgboost_v1')
""",
(
date,
date,
cheval.get("race_name") or "",
f"R{num_reunion}C{num_course}",
cheval.get("hippodrome") or "",
cheval["horse_name"],
cheval["horse_name"],
cheval["horse_number"],
cheval["odds"],
),
)
inseres += 1
conn.commit()
log.info(f"[SP] {date}{inseres} paris simple_place insérés (score>=50)")
return inseres
# ─────────────────────────────────────────────────────────
# STRATÉGIE D — 2sur4 top4 ML (6 combinaisons x 1€)
# ─────────────────────────────────────────────────────────
def save_ml_paris_2sur4(conn, date):
"""Insère 1 pari deux_sur_quatre par course : top4 ML, mise 6€."""
cursor = conn.cursor()
courses = get_top_ml_par_course(cursor, date, n=4, min_score=0)
inseres = 0
for (num_reunion, num_course), chevaux in courses.items():
if len(chevaux) < 4:
continue
if pari_2sur4_existe(cursor, date, num_reunion, num_course, "xgboost_2sur4"):
continue
top4 = chevaux[:4]
nums = [str(c["horse_number"]) for c in top4]
noms = [c["horse_name"] for c in top4]
chevaux_str = "/".join(noms)
cursor.execute(
"""
INSERT INTO paris
(date_pari, date_course, race_name, race_label, hippodrome,
type_pari, chevaux, cheval1, numero1, cote, mise,
statut, gain, source_reco, model_source, commentaire)
VALUES (?, ?, ?, ?, ?, 'deux_sur_quatre', ?, ?, ?, 0.0, 6.0,
'EN_ATTENTE', 0.0, 'xgboost_2sur4', 'xgboost_v1', ?)
""",
(
date,
date,
top4[0].get("race_name") or "",
f"R{num_reunion}C{num_course}",
top4[0].get("hippodrome") or "",
chevaux_str,
top4[0]["horse_name"],
top4[0]["horse_number"],
f"top4 ML: {'/'.join(nums)}",
),
)
inseres += 1
conn.commit()
log.info(f"[2S4] {date}{inseres} paris deux_sur_quatre insérés")
return inseres
# ─────────────────────────────────────────────────────────
# UPDATE RÉSULTATS + DIVIDENDES
# ─────────────────────────────────────────────────────────
def update_ml_paris_results(conn, date):
"""
Met à jour statut + gain (dividende PMU réel) pour tous les paris ML EN_ATTENTE.
Sources: pmu_partants (ordre_arrivee) + pmu_rapports (dividende_euro).
"""
cursor = conn.cursor()
cursor.execute(
"""
SELECT id, race_label, type_pari, numero1, chevaux, mise, source_reco, commentaire
FROM paris
WHERE date_course = ? AND statut = 'EN_ATTENTE'
AND source_reco LIKE 'xgboost%'
""",
(date,),
)
paris = [dict(r) for r in cursor.fetchall()]
if not paris:
log.info(f"[UPDATE] {date} → aucun pari ML EN_ATTENTE")
return 0
maj = 0
for pari in paris:
pari_id = pari["id"]
race_label = pari["race_label"] or ""
type_pari = pari["type_pari"]
numero1 = pari["numero1"]
mise = pari["mise"]
# Extraire num_reunion / num_course depuis le race_label "R{r}C{c}"
try:
parts = race_label.replace("R", "").split("C")
num_reunion = int(parts[0])
num_course = int(parts[1])
except Exception:
log.warning(f"[UPDATE] race_label invalide : {race_label}")
continue
if type_pari == "simple_gagnant":
cursor.execute(
"""
SELECT ordre_arrivee FROM pmu_partants
WHERE date_programme = ? AND num_reunion = ?
AND num_course = ? AND num_pmu = ?
""",
(date, num_reunion, num_course, numero1),
)
row = cursor.fetchone()
if not row or row["ordre_arrivee"] is None or row["ordre_arrivee"] == 0:
continue
gagne = row["ordre_arrivee"] == 1
gain = 0.0
if gagne:
cursor.execute(
"""
SELECT dividende_euro FROM pmu_rapports
WHERE date_programme = ? AND num_reunion = ?
AND num_course = ? AND type_pari = 'SIMPLE_GAGNANT'
AND CAST(combinaison AS INTEGER) = ?
AND libelle NOT LIKE '%NP%'
""",
(date, num_reunion, num_course, numero1),
)
div = cursor.fetchone()
gain = div["dividende_euro"] if div and div["dividende_euro"] else 0.0
cursor.execute(
"UPDATE paris SET statut=?, gain=? WHERE id=?",
("GAGNE" if gagne else "PERDU", gain, pari_id),
)
maj += 1
elif type_pari == "simple_place":
cursor.execute(
"""
SELECT ordre_arrivee FROM pmu_partants
WHERE date_programme = ? AND num_reunion = ?
AND num_course = ? AND num_pmu = ?
""",
(date, num_reunion, num_course, numero1),
)
row = cursor.fetchone()
if not row or not row["ordre_arrivee"]:
continue
gagne = 1 <= row["ordre_arrivee"] <= 3
gain = 0.0
if gagne:
cursor.execute(
"""
SELECT dividende_euro FROM pmu_rapports
WHERE date_programme = ? AND num_reunion = ?
AND num_course = ? AND type_pari = 'SIMPLE_PLACE'
AND CAST(combinaison AS INTEGER) = ?
AND libelle NOT LIKE '%NP%'
""",
(date, num_reunion, num_course, numero1),
)
div = cursor.fetchone()
gain = div["dividende_euro"] if div and div["dividende_euro"] else 0.0
cursor.execute(
"UPDATE paris SET statut=?, gain=? WHERE id=?",
("GAGNE" if gagne else "PERDU", gain, pari_id),
)
maj += 1
elif type_pari == "deux_sur_quatre":
# Récupère les 4 numéros depuis commentaire "top4 ML: n1/n2/n3/n4"
try:
nums_str = (
pari["commentaire"].split(": ")[1]
if pari.get("commentaire")
else ""
)
nums_top4 = [int(n) for n in nums_str.split("/") if n.strip().isdigit()]
except Exception:
nums_top4 = []
if len(nums_top4) < 4:
# Fallback : reconstituer top4 depuis ml_predictions_cache
cursor.execute(
"""
SELECT horse_number FROM ml_predictions_cache
WHERE date = ? AND num_reunion = ? AND num_course = ?
ORDER BY ml_score DESC LIMIT 4
""",
(date, num_reunion, num_course),
)
nums_top4 = [r["horse_number"] for r in cursor.fetchall()]
if len(nums_top4) < 2:
continue
cursor.execute(
"""
SELECT combinaison, dividende_euro FROM pmu_rapports
WHERE date_programme = ? AND num_reunion = ?
AND num_course = ? AND type_pari = 'DEUX_SUR_QUATRE'
AND libelle NOT LIKE '%NP%'
""",
(date, num_reunion, num_course),
)
rapports = [dict(r) for r in cursor.fetchall()]
gain_total = 0.0
for rap in rapports:
try:
n1, n2 = [int(x) for x in rap["combinaison"].split("-")]
except Exception:
continue
if n1 in nums_top4 and n2 in nums_top4:
gain_total += rap["dividende_euro"]
gagne = gain_total > 0
cursor.execute(
"UPDATE paris SET statut=?, gain=? WHERE id=?",
("GAGNE" if gagne else "PERDU", round(gain_total, 2), pari_id),
)
maj += 1
conn.commit()
log.info(f"[UPDATE] {date}{maj}/{len(paris)} paris ML mis à jour")
return maj
# ─────────────────────────────────────────────────────────
# STATS PAR STRATÉGIE
# ─────────────────────────────────────────────────────────
def get_feedback_stats(conn, date_debut=None, date_fin=None):
"""Stats performances ML par stratégie (source_reco)."""
cursor = conn.cursor()
cursor.execute(
"""
SELECT source_reco,
COUNT(*) as n_paris,
SUM(CASE WHEN statut='GAGNE' THEN 1 ELSE 0 END) as n_gagne,
SUM(CASE WHEN statut='PERDU' THEN 1 ELSE 0 END) as n_perdu,
SUM(CASE WHEN statut='EN_ATTENTE' THEN 1 ELSE 0 END) as n_attente,
ROUND(100.0 * SUM(CASE WHEN statut='GAGNE' THEN 1 ELSE 0 END)
/ NULLIF(SUM(CASE WHEN statut IN ('GAGNE','PERDU') THEN 1 ELSE 0 END), 0), 1) as win_rate_pct,
ROUND(SUM(gain), 2) as gain_total,
ROUND(SUM(mise), 2) as mise_totale,
ROUND(SUM(gain) - SUM(mise), 2) as roi_net
FROM paris
WHERE source_reco LIKE 'xgboost%'
AND (:debut IS NULL OR date_course >= :debut)
AND (:fin IS NULL OR date_course <= :fin)
GROUP BY source_reco
ORDER BY source_reco
""",
{"debut": date_debut, "fin": date_fin},
)
return [dict(r) for r in cursor.fetchall()]
# ─────────────────────────────────────────────────────────
# PIPELINE COMPLET
# ─────────────────────────────────────────────────────────
def run(date):
"""Enregistre les paris ML du jour + met à jour les résultats de J-1."""
conn = get_db()
log.info(f"=== ml_feedback_saas.run({date}) ===")
# 1. Enregistre les paris ML pour la date (depuis le cache du jour)
sg = save_ml_paris_sg(conn, date)
vb = save_ml_paris_value(conn, date)
sp = save_ml_paris_sp(conn, date)
s4 = save_ml_paris_2sur4(conn, date)
log.info(f"[SAVE] {date} → total insérés : SG={sg} VALUE={vb} SP={sp} 2S4={s4}")
# 2. Met à jour les résultats de J-1 (résultats PMU disponibles)
yesterday = (datetime.strptime(date, "%Y-%m-%d") - timedelta(days=1)).strftime(
"%Y-%m-%d"
)
maj = update_ml_paris_results(conn, yesterday)
log.info(f"[UPDATE] {yesterday}{maj} paris mis à jour")
conn.close()
return {"inseres": {"sg": sg, "value": vb, "sp": sp, "2sur4": s4}, "maj": maj}
def backfill(date):
"""Backfill : insère ET met à jour les résultats pour une date passée."""
conn = get_db()
log.info(f"=== ml_feedback_saas.backfill({date}) ===")
sg = save_ml_paris_sg(conn, date)
vb = save_ml_paris_value(conn, date)
sp = save_ml_paris_sp(conn, date)
s4 = save_ml_paris_2sur4(conn, date)
log.info(f"[SAVE] {date} → SG={sg} VALUE={vb} SP={sp} 2S4={s4}")
maj = update_ml_paris_results(conn, date)
log.info(f"[UPDATE] {date}{maj} paris mis à jour")
conn.close()
return sg + vb + sp + s4, maj
# ─────────────────────────────────────────────────────────
# MAIN
# ─────────────────────────────────────────────────────────
if __name__ == "__main__":
if "--backfill" in sys.argv:
idx = sys.argv.index("--backfill")
date = sys.argv[idx + 1] if idx + 1 < len(sys.argv) else None
if not date:
print("Usage: python3 ml_feedback_saas.py --backfill YYYY-MM-DD")
sys.exit(1)
inseres, maj = backfill(date)
print(f"Backfill {date} : {inseres} paris insérés, {maj} mis à jour")
elif "--date" in sys.argv:
idx = sys.argv.index("--date")
date = sys.argv[idx + 1] if idx + 1 < len(sys.argv) else None
if not date:
print("Usage: python3 ml_feedback_saas.py --date YYYY-MM-DD")
sys.exit(1)
result = run(date)
total = sum(result["inseres"].values())
print(f"Run {date} : {total} paris insérés, {result['maj']} mis à jour")
else:
result = run(datetime.now().strftime("%Y-%m-%d"))
total = sum(result["inseres"].values())
print(f"Run today : {total} paris insérés, {result['maj']} mis à jour")

View File

@@ -18,10 +18,12 @@ SAAS_DIR = "/home/h3r7/turf_saas"
# ─── SaaS Auth & API v1 blueprints ────────────────────────────────────────────
try:
from saas_auth import auth_bp
from saas_api_v1 import api_v1_bp
from saas_api_v1 import saas_api_v1_bp
from api_v1 import register_api_v1
app.register_blueprint(auth_bp)
app.register_blueprint(api_v1_bp)
app.register_blueprint(saas_api_v1_bp)
register_api_v1(app)
print("[portal] SaaS auth & API v1 blueprints registered ✅")
except Exception as e:
print(f"[portal] Warning: could not register SaaS blueprints: {e}")
@@ -352,6 +354,29 @@ def template_complet():
return send_from_directory("/home/h3r7/turf_saas", "template_complet.html")
@app.route("/leadhunter/clients/le-big-ben/")
@app.route("/leadhunter/clients/le-big-ben")
def big_ben():
return send_from_directory(
"/home/h3r7/turf_saas/templates/leadhunter/clients/le-big-ben", "index.html"
)
@app.route("/leadhunter/clients/le-big-ben/sitemap.xml")
def big_ben_sitemap():
return send_from_directory(
"/home/h3r7/turf_saas/templates/leadhunter/clients/le-big-ben",
"sitemap.xml",
mimetype="application/xml",
)
@app.route("/formation/ai102")
@app.route("/formation/ai102/")
def certif_ai102():
return send_from_directory("/home/h3r7/turf_saas/pitch", "certif-ai102.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")

View File

@@ -31,3 +31,6 @@ python-dotenv==1.1.0
# Utilities
python-dateutil==2.9.0
# Hyperparameter optimization (ML ensemble tuning — HRT-136)
optuna>=4.0.0

View File

@@ -13,7 +13,7 @@ from saas_auth import require_auth
DB_PATH = os.environ.get("TURF_SAAS_DB", "/home/h3r7/turf_saas/turf_saas.db")
api_v1_bp = Blueprint("api_v1", __name__, url_prefix="/api/v1")
saas_api_v1_bp = Blueprint("saas_api_v1", __name__, url_prefix="/api/v1")
def get_db():
@@ -30,7 +30,7 @@ def plan_allows(user_plan: str, required: str) -> bool:
# ─── Stats ────────────────────────────────────────────────────────────────────
@api_v1_bp.route("/stats/summary", methods=["GET"])
@saas_api_v1_bp.route("/stats/summary", methods=["GET"])
@require_auth
def stats_summary():
"""GET /api/v1/stats/summary — résumé dashboard."""
@@ -94,7 +94,7 @@ def stats_summary():
# ─── Predictions ──────────────────────────────────────────────────────────────
@api_v1_bp.route("/predictions/today", methods=["GET"])
@saas_api_v1_bp.route("/predictions/today", methods=["GET"])
@require_auth
def predictions_today():
"""GET /api/v1/predictions/today — prédictions du jour selon le plan."""
@@ -149,7 +149,7 @@ def predictions_today():
return jsonify({"error": str(e), "predictions": []}), 200
@api_v1_bp.route("/predictions/race/<race_label>", methods=["GET"])
@saas_api_v1_bp.route("/predictions/race/<race_label>", methods=["GET"])
@require_auth
def predictions_race(race_label):
"""GET /api/v1/predictions/race/<label> — prédictions d'une course."""
@@ -187,7 +187,7 @@ def predictions_race(race_label):
# ─── Value Bets ───────────────────────────────────────────────────────────────
@api_v1_bp.route("/value-bets/today", methods=["GET"])
@saas_api_v1_bp.route("/value-bets/today", methods=["GET"])
@require_auth
def value_bets_today():
"""GET /api/v1/value-bets/today — value bets (Premium+)."""
@@ -220,7 +220,7 @@ def value_bets_today():
# ─── Export ───────────────────────────────────────────────────────────────────
@api_v1_bp.route("/export/csv", methods=["GET"])
@saas_api_v1_bp.route("/export/csv", methods=["GET"])
@require_auth
def export_csv():
"""GET /api/v1/export/csv — export CSV (Pro only)."""
@@ -257,15 +257,13 @@ def export_csv():
)
# ─── Billing Blueprint (Stripe) + JWT init — HRT-49 ─────────────────────────
# Registers /api/v1/billing/* routes via nested Blueprint (Flask 2.0+)
# Also initializes JWTManager on the Flask app (required for jwt_required_middleware)
# ─── JWT init — HRT-49 ────────────────────────────────────────────────────────
# Initialize JWTManager on the Flask app (required for jwt_required_middleware)
# Called when saas_api_v1_bp is registered (portal_server.py)
try:
from flask_jwt_extended import JWTManager
from api_v1.routes.billing import billing_bp
# Initialize JWTManager on the Flask app when api_v1_bp is registered
@api_v1_bp.record_once
@saas_api_v1_bp.record_once
def _init_jwt(state):
app = state.app
if not app.config.get("JWT_SECRET_KEY"):
@@ -276,25 +274,6 @@ try:
)
if "flask_jwt_extended" not in app.extensions:
JWTManager(app)
# Register billing blueprint with url_prefix='/billing'
# (parent api_v1_bp has '/api/v1', so result is /api/v1/billing/*)
api_v1_bp.register_blueprint(billing_bp, url_prefix="/billing")
print("[saas_api_v1] Billing blueprint (Stripe) + JWT registered ✅")
except Exception as _billing_err:
print(f"[saas_api_v1] Warning: billing blueprint not loaded: {_billing_err}")
# ─── Org Blueprint — HRT-82 ───────────────────────────────────────────────────
# Registers /api/v1/org/* routes (Pro plan only, multi-compte max 5 users)
try:
from api_v1.routes.org import org_bp
@api_v1_bp.record_once
def _register_org_bp(state):
app = state.app
app.register_blueprint(org_bp)
print("[saas_api_v1] Org blueprint (multi-compte Pro) registered ✅")
except Exception as _org_err:
print(f"[saas_api_v1] Warning: org blueprint not loaded: {_org_err}")
print("[saas_api_v1] JWT init registered ✅")
except Exception as _jwt_err:
print(f"[saas_api_v1] Warning: JWT init not loaded: {_jwt_err}")

View File

@@ -8,6 +8,7 @@ Sprint 4-5 — HRT-30
from flask import Blueprint, request, jsonify, current_app
import sqlite3
import hashlib
import logging
import secrets
import os
import time
@@ -229,14 +230,54 @@ def hash_password(password: str) -> str:
return hashlib.sha256(password.encode("utf-8")).hexdigest()
def validate_api_key(raw_key: str):
"""
Validate a personal API token (X-API-Key header).
Returns user dict or None. Updates last_used_at on success.
HRT-80
"""
if not raw_key:
return None
key_hash = hashlib.sha256(raw_key.encode()).hexdigest()
conn = get_db()
try:
row = conn.execute(
"SELECT t.user_id, u.* FROM user_api_tokens t "
"JOIN saas_users u ON t.user_id = u.id "
"WHERE t.token_hash = ? AND t.revoked = 0",
(key_hash,),
).fetchone()
if row:
conn.execute(
"UPDATE user_api_tokens SET last_used_at = datetime('now') "
"WHERE token_hash = ?",
(key_hash,),
)
conn.commit()
return dict(row) if row else None
except Exception as e:
logging.getLogger("turf_saas.auth").warning("validate_api_key error: %s", e)
return None
finally:
conn.close()
def require_auth(f):
@wraps(f)
def decorated(*args, **kwargs):
# 1. Try Bearer session token (existing flow — unchanged)
auth = request.headers.get("Authorization", "")
token = (
auth.removeprefix("Bearer ").strip() if auth.startswith("Bearer ") else None
)
user = validate_token(token)
user = validate_token(token) if token else None
# 2. Fallback: X-API-Key personal token (HRT-80)
if not user:
api_key = request.headers.get("X-API-Key", "").strip()
if api_key:
user = validate_api_key(api_key)
if not user:
return jsonify({"error": "Non authentifié"}), 401
request.current_user = user

View File

@@ -11,29 +11,34 @@ import re
from datetime import datetime
DB_PATH = "/home/h3r7/turf_saas/turf_saas.db"
HEADERS = {'User-Agent': 'Mozilla/5.0', 'Accept': 'application/json'}
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("""
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}%"))
""",
(date_course, f"%{horse_name}%"),
)
r = c.fetchone()
conn.close()
return r['odds'] if r else 0
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)
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))
positions.append(99 if pos == "D" else int(pos))
if not positions:
return {}
nb_courses = len(positions)
@@ -41,29 +46,102 @@ def parse_musique(musique):
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
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,
"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):
def get_terrain_condition(penetrometre_intitule: str | None) -> str:
"""Normalise le pénétromètre PMU en condition terrain standardisée."""
if not penetrometre_intitule:
return "inconnu"
val = penetrometre_intitule.upper()
if any(k in val for k in ("TRES BON", "TRÈS BON", "FERME", "FIRM")):
return "bon"
if any(k in val for k in ("BON", "GOOD", "STANDARD")):
return "bon"
if any(k in val for k in ("SOUPLE", "YIELDING", "COLLANT")):
return "souple"
if any(k in val for k in ("LOURD", "HEAVY", "TRES SOUPLE", "TRÈS SOUPLE")):
return "lourd"
if any(k in val for k in ("SOFT", "MOU")):
return "souple"
return "inconnu"
def compute_weather_impact(weather_data: dict | None, terrain_condition: str) -> float:
"""
Calcule un score d'impact météo/terrain sur [5, +5].
weather_data keys attendues : nebulositecode, temperature, force_vent
terrain_condition : 'bon' | 'souple' | 'lourd' | 'inconnu'
Retourne un delta de score ML (positif = favorable, négatif = défavorable).
"""
if not weather_data:
return 0.0
delta = 0.0
# Terrain
if terrain_condition == "lourd":
delta -= 3.0
elif terrain_condition == "souple":
delta -= 1.5
elif terrain_condition == "bon":
delta += 1.0
# inconnu → 0
# Vent
force_vent = weather_data.get("force_vent") or 0
try:
force_vent = float(force_vent)
except (TypeError, ValueError):
force_vent = 0.0
if force_vent >= 50:
delta -= 2.0
elif force_vent >= 30:
delta -= 1.0
# Températures extrêmes
temperature = weather_data.get("temperature")
try:
temperature = float(temperature) if temperature is not None else None
except (TypeError, ValueError):
temperature = None
if temperature is not None:
if temperature <= 0:
delta -= 1.0
elif temperature >= 35:
delta -= 1.0
return round(max(-5.0, min(5.0, delta)), 2)
def score_cheval_v2(p, all_participants, today, weather_data=None):
"""
Score un cheval pour le modèle V2.
weather_data (optionnel) : dict issu de pmu_meteo pour cette réunion.
Backward-compatible : weather_data=None → comportement identique à avant HRT-83.
"""
score = 0
details = {}
# 1. COTE - Essaye PMU API, sinon DB
horse_name = p.get('nom', '')
horse_name = p.get("nom", "")
cote = 0
# Essayer d'abord depuis l'API PMU
rapport = p.get('dernierRapportDirect', {})
rapport = p.get("dernierRapportDirect", {})
if rapport:
cote = rapport.get('rapport', 0)
cote = rapport.get("rapport", 0)
if not cote:
rapport_ref = p.get('dernierRapportReference', {})
cote = rapport_ref.get('rapport', 0) if rapport_ref else 0
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:
@@ -75,94 +153,136 @@ def score_cheval_v2(p, all_participants, today):
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)
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
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
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)
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)
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)
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)
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]
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
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)
details["rk"] = rk
details["score_rk"] = round(score_rk, 1)
# 6. TENDANCE (10 pts)
tendance = musique_stats.get('tendance', 0)
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)
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)
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
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
details["bonus_outsider"] = bonus_outsider
# Driver change penalty
if p.get('driverChange', False):
if p.get("driverChange", False):
score -= 3
details['driver_change'] = True
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
# 9. METEO & TERRAIN (HRT-83) — premium feature, weather_data=None → skip
penetrometre = p.get("penetrometre_intitule", "") or ""
terrain_condition = (
get_terrain_condition(penetrometre) if penetrometre else "inconnu"
)
weather_impact = 0.0
if weather_data is not None:
weather_impact = compute_weather_impact(weather_data, terrain_condition)
score += weather_impact
details["terrain_condition"] = terrain_condition
details["weather_impact"] = weather_impact
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,
})
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)
ranked = sorted(scored_horses, key=lambda x: x["score"], reverse=True)
if len(ranked) < 4:
return None
@@ -170,39 +290,58 @@ def build_recommendations_v2(scored_horses):
top4_list = ranked[:4]
def confiance(s):
return "FORTE" if s >= 55 else "BONNE" if s >= 45 else "MOYENNE" if s >= 35 else "FAIBLE"
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)
"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'
"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,
"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:
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)
"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()
@@ -210,44 +349,72 @@ def save_to_db(scored_horses, date_course, hippodrome, libelle):
cursor.execute("DELETE FROM scoring WHERE date = ?", (date_course,))
for i, h in enumerate(scored_horses, 1):
d = h['details']
cursor.execute("""
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))
""",
(
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')} ===")
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', [])
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', []):
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))
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
@@ -256,7 +423,8 @@ def main():
# Fallback: utiliser la premiere reunion francaise avec predictions
conn = sqlite3.connect(DB_PATH)
conn.row_factory = sqlite3.Row
r = conn.execute("""
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
@@ -264,22 +432,36 @@ def main():
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()
""",
(today, today),
).fetchone()
conn.close()
if r:
quinte = (r['num_reunion'], r['num_course'], r['libelle'], r['hippodrome_court'], 0)
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'
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']
participants = [
p for p in r.json().get("participants", []) if p.get("statut") == "PARTANT"
]
except Exception as e:
print(f"Erreur: {e}")
return
@@ -287,34 +469,45 @@ def main():
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})
scored_horses.append(
{"nom": p["nom"], "numero": p["numPmu"], "score": score, "details": details}
)
ranked = sorted(scored_horses, key=lambda x: x['score'], reverse=True)
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}")
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']
sg = reco["simple_gagnant"]
print(f"\n🎯 SIMPLE GAGNANT:")
print(f" #{sg['numero']} {sg['cheval']} @ {sg['cote']}/1 (mise {sg['mise_suggeree']}EUR)")
print(
f" #{sg['numero']} {sg['cheval']} @ {sg['cote']}/1 (mise {sg['mise_suggeree']}EUR)"
)
ze2 = reco['ze2_sur_4']
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" 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']}")
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()

View File

@@ -0,0 +1,10 @@
# Token Broker API — Configuration
TOKEN_BROKER_PORT=8783
TOKEN_BROKER_DB_HOST=127.0.0.1
TOKEN_BROKER_DB_PORT=5434
TOKEN_BROKER_DB_NAME=token_broker
TOKEN_BROKER_DB_USER=token_broker
TOKEN_BROKER_DB_PASSWORD=CHANGE_ME
TOKEN_BROKER_JWT_SECRET=CHANGE_ME_GENERATE_64_HEX
TOKEN_BROKER_ACCESS_EXPIRY=900
TOKEN_BROKER_REFRESH_EXPIRY=2592000

View File

@@ -0,0 +1,6 @@
Flask==3.1.3
flask-cors==5.0.1
gunicorn==23.0.0
psycopg2-binary==2.9.12
PyJWT==2.10.1
python-dotenv==1.1.0

View File

@@ -0,0 +1,21 @@
[Unit]
Description=Token Broker API (Port 8783)
Documentation=https://portal-kolifee.duckdns.org
After=network.target postgresql.service
[Service]
Type=simple
User=h3r7
WorkingDirectory=/home/h3r7/turf_saas/services/token-broker
EnvironmentFile=/home/h3r7/turf_saas/services/token-broker/.env
Environment=PYTHONPATH=/home/h3r7/turf_saas
Environment=FLASK_ENV=production
ExecStart=/home/h3r7/turf_saas/venv/bin/python3 /home/h3r7/turf_saas/services/token-broker/token_broker_api.py
Restart=always
RestartSec=10
[Install]
WantedBy=multi-user.target

View File

@@ -0,0 +1,679 @@
#!/usr/bin/env python3
"""
Token Broker API — JWT token management service
Port: 8783 | DB: PostgreSQL 5434
HRT-198 — Setup infra (PostgreSQL + Flask scaffold)
Endpoints:
GET /health — Healthcheck
POST /api/v1/tokens — Issue new token (create)
GET /api/v1/tokens/:id — Get token by ID
POST /api/v1/tokens/verify — Verify token
POST /api/v1/tokens/revoke/:id — Revoke token
GET /api/v1/tokens/user/:userId — List tokens for user
"""
import os
import sys
import uuid
import hashlib
import secrets
import logging
import logging.handlers
from datetime import datetime, timedelta, timezone
from functools import wraps
from flask import Flask, request, jsonify, g
from flask_cors import CORS
LOG_DIR = os.path.join(os.path.dirname(__file__), "logs")
os.makedirs(LOG_DIR, exist_ok=True)
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s [%(levelname)s] token-broker: %(name)s: %(message)s",
handlers=[
logging.StreamHandler(sys.stdout),
logging.handlers.RotatingFileHandler(
os.path.join(LOG_DIR, "token_broker.log"),
maxBytes=5 * 1024 * 1024,
backupCount=3,
),
],
)
logger = logging.getLogger("token_broker")
DB_HOST = os.environ.get("TOKEN_BROKER_DB_HOST", "127.0.0.1")
DB_PORT = int(os.environ.get("TOKEN_BROKER_DB_PORT", "5434"))
DB_NAME = os.environ.get("TOKEN_BROKER_DB_NAME", "token_broker")
DB_USER = os.environ.get("TOKEN_BROKER_DB_USER", "token_broker")
DB_PASSWORD = os.environ.get("TOKEN_BROKER_DB_PASSWORD", "")
JWT_SECRET = os.environ.get(
"TOKEN_BROKER_JWT_SECRET", "CHANGE_ME_" + secrets.token_hex(32)
)
ACCESS_TOKEN_EXPIRY = int(os.environ.get("TOKEN_BROKER_ACCESS_EXPIRY", "900"))
REFRESH_TOKEN_EXPIRY = int(os.environ.get("TOKEN_BROKER_REFRESH_EXPIRY", "2592000"))
def get_pg_conn():
try:
import psycopg2
import psycopg2.extras
conn = psycopg2.connect(
host=DB_HOST,
port=DB_PORT,
dbname=DB_NAME,
user=DB_USER,
password=DB_PASSWORD,
)
conn.autocommit = True
return conn
except Exception as e:
logger.error(f"PostgreSQL connection failed: {e}")
return None
def init_db():
conn = get_pg_conn()
if not conn:
logger.error("Cannot initialize DB — no connection")
return False
try:
cur = conn.cursor()
cur.execute("""
CREATE TABLE IF NOT EXISTS api_tokens (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
user_id INTEGER NOT NULL,
name TEXT NOT NULL DEFAULT 'default',
token_hash TEXT NOT NULL UNIQUE,
token_prefix TEXT NOT NULL,
scopes TEXT[] DEFAULT '{}',
is_active BOOLEAN NOT NULL DEFAULT TRUE,
created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
expires_at TIMESTAMPTZ,
last_used_at TIMESTAMPTZ,
metadata JSONB DEFAULT '{}'
);
CREATE TABLE IF NOT EXISTS refresh_tokens (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
user_id INTEGER NOT NULL,
token_hash TEXT NOT NULL UNIQUE,
token_prefix TEXT NOT NULL,
created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
expires_at TIMESTAMPTZ NOT NULL,
revoked BOOLEAN NOT NULL DEFAULT FALSE,
revoked_at TIMESTAMPTZ,
replaced_by UUID
);
CREATE TABLE IF NOT EXISTS token_audit_log (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
user_id INTEGER,
action TEXT NOT NULL,
token_prefix TEXT,
ip_address TEXT,
user_agent TEXT,
details JSONB DEFAULT '{}',
created_at TIMESTAMPTZ NOT NULL DEFAULT NOW()
);
CREATE TABLE IF NOT EXISTS clients (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
client_id TEXT NOT NULL UNIQUE,
client_secret TEXT NOT NULL,
name TEXT NOT NULL,
description TEXT DEFAULT '',
redirect_uris TEXT[] DEFAULT '{}',
scopes TEXT[] DEFAULT '{}',
is_active BOOLEAN NOT NULL DEFAULT TRUE,
created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
updated_at TIMESTAMPTZ NOT NULL DEFAULT NOW()
);
CREATE TABLE IF NOT EXISTS providers (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
name TEXT NOT NULL UNIQUE,
provider_type TEXT NOT NULL DEFAULT 'oauth2',
issuer_url TEXT,
client_id TEXT,
client_secret TEXT,
scopes TEXT[] DEFAULT '{}',
config JSONB DEFAULT '{}',
is_active BOOLEAN NOT NULL DEFAULT TRUE,
created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
updated_at TIMESTAMPTZ NOT NULL DEFAULT NOW()
);
CREATE TABLE IF NOT EXISTS token_usage (
id BIGSERIAL PRIMARY KEY,
user_id INTEGER NOT NULL,
token_id UUID,
action TEXT NOT NULL DEFAULT 'verify',
endpoint TEXT,
status TEXT NOT NULL DEFAULT 'success',
response_time_ms INTEGER,
ip_address TEXT,
created_at TIMESTAMPTZ NOT NULL DEFAULT NOW()
);
CREATE INDEX IF NOT EXISTS idx_api_tokens_user_id ON api_tokens(user_id);
CREATE INDEX IF NOT EXISTS idx_api_tokens_token_hash ON api_tokens(token_hash);
CREATE INDEX IF NOT EXISTS idx_refresh_tokens_user_id ON refresh_tokens(user_id);
CREATE INDEX IF NOT EXISTS idx_refresh_tokens_token_hash ON refresh_tokens(token_hash);
CREATE INDEX IF NOT EXISTS idx_token_audit_log_user_id ON token_audit_log(user_id);
CREATE INDEX IF NOT EXISTS idx_token_audit_log_created_at ON token_audit_log(created_at);
CREATE INDEX IF NOT EXISTS idx_clients_client_id ON clients(client_id);
CREATE INDEX IF NOT EXISTS idx_providers_name ON providers(name);
CREATE INDEX IF NOT EXISTS idx_token_usage_user_id ON token_usage(user_id);
CREATE INDEX IF NOT EXISTS idx_token_usage_created_at ON token_usage(created_at);
""")
cur.close()
conn.close()
logger.info("Database tables initialized successfully")
return True
except Exception as e:
logger.error(f"Database initialization failed: {e}")
return False
def create_app():
app = Flask(__name__)
app.config["JWT_SECRET"] = JWT_SECRET
app.config["ACCESS_TOKEN_EXPIRY"] = ACCESS_TOKEN_EXPIRY
app.config["REFRESH_TOKEN_EXPIRY"] = REFRESH_TOKEN_EXPIRY
CORS(app)
register_routes(app)
register_error_handlers(app)
return app
def token_required(f):
@wraps(f)
def decorated(*args, **kwargs):
auth_header = request.headers.get("Authorization", "")
if not auth_header.startswith("Bearer "):
return jsonify({"error": "missing_token", "message": "Bearer token required"}), 401
token = auth_header.split(" ", 1)[1]
payload = verify_jwt_token(token)
if not payload:
return jsonify({"error": "invalid_token", "message": "Token invalid or expired"}), 401
g.user_id = payload.get("user_id")
g.token_id = payload.get("token_id")
g.scopes = payload.get("scopes", [])
return f(*args, **kwargs)
return decorated
def generate_token_pair(user_id, scopes=None, metadata=None):
import jwt as pyjwt
now = datetime.now(timezone.utc)
access_payload = {
"user_id": user_id,
"token_id": str(uuid.uuid4()),
"scopes": scopes or [],
"type": "access",
"iat": now,
"exp": now + timedelta(seconds=ACCESS_TOKEN_EXPIRY),
}
access_token = pyjwt.encode(access_payload, JWT_SECRET, algorithm="HS256")
refresh_id = str(uuid.uuid4())
refresh_raw = secrets.token_urlsafe(48)
refresh_payload = {
"user_id": user_id,
"refresh_id": refresh_id,
"token_hash": hashlib.sha256(refresh_raw.encode()).hexdigest(),
"type": "refresh",
"iat": now,
"exp": now + timedelta(seconds=REFRESH_TOKEN_EXPIRY),
}
refresh_token = pyjwt.encode(refresh_payload, JWT_SECRET, algorithm="HS256")
store_refresh_token(user_id, refresh_id, refresh_payload["token_hash"])
log_audit(user_id, "token_issued", access_payload["token_id"][:8])
return {
"access_token": access_token,
"refresh_token": refresh_raw,
"expires_in": ACCESS_TOKEN_EXPIRY,
"token_type": "Bearer",
}
def verify_jwt_token(token):
import jwt as pyjwt
try:
payload = pyjwt.decode(token, JWT_SECRET, algorithms=["HS256"])
if payload.get("type") == "refresh":
token_hash = hashlib.sha256(token.encode()).hexdigest()
conn = get_pg_conn()
if conn:
cur = conn.cursor()
cur.execute(
"SELECT revoked FROM refresh_tokens WHERE token_hash = %s AND expires_at > NOW()",
(token_hash,),
)
row = cur.fetchone()
cur.close()
conn.close()
if not row or row[0]:
return None
return payload
except Exception:
return None
def store_refresh_token(user_id, refresh_id, token_hash):
conn = get_pg_conn()
if not conn:
return
try:
cur = conn.cursor()
cur.execute(
"""INSERT INTO refresh_tokens (id, user_id, token_hash, token_prefix, expires_at)
VALUES (%s, %s, %s, %s, NOW() + INTERVAL '30 days')""",
(refresh_id, user_id, token_hash, token_hash[:8]),
)
cur.close()
conn.close()
except Exception as e:
logger.error(f"Failed to store refresh token: {e}")
def log_audit(user_id, action, token_prefix, details=None):
conn = get_pg_conn()
if not conn:
return
try:
cur = conn.cursor()
cur.execute(
"""INSERT INTO token_audit_log (user_id, action, token_prefix, ip_address, user_agent, details)
VALUES (%s, %s, %s, %s, %s, %s)""",
(
user_id,
action,
token_prefix,
request.remote_addr if request else None,
request.user_agent.string if request and request.user_agent else None,
"{}" if details is None else details,
),
)
cur.close()
conn.close()
except Exception:
pass
def register_routes(app):
@app.route("/health", methods=["GET"])
def healthcheck():
conn = get_pg_conn()
db_ok = conn is not None
if conn:
conn.close()
return jsonify({
"status": "ok" if db_ok else "degraded",
"service": "token-broker",
"version": "1.0.0",
"database": "connected" if db_ok else "disconnected",
"timestamp": datetime.now(timezone.utc).isoformat(),
}), 200 if db_ok else 503
@app.route("/api/v1/tokens", methods=["POST"])
@token_required
def issue_token():
data = request.get_json(silent=True) or {}
user_id = g.user_id
scopes = data.get("scopes", [])
name = data.get("name", "default")
metadata = data.get("metadata", {})
conn = get_pg_conn()
if not conn:
return jsonify({"error": "db_error", "message": "Database unavailable"}), 503
try:
cur = conn.cursor()
import psycopg2.extras
raw_token = "tb_" + secrets.token_urlsafe(32)
token_hash = hashlib.sha256(raw_token.encode()).hexdigest()
token_prefix = raw_token[:12] + "..."
cur.execute(
"""INSERT INTO api_tokens (user_id, name, token_hash, token_prefix, scopes, metadata)
VALUES (%s, %s, %s, %s, %s, %s)
RETURNING id, created_at, expires_at""",
(user_id, name, token_hash, token_prefix, scopes,
psycopg2.extras.Json(metadata)),
)
row = cur.fetchone()
cur.close()
conn.close()
log_audit(user_id, "api_token_created", token_prefix)
return jsonify({
"id": str(row[0]),
"token": raw_token,
"name": name,
"scopes": scopes,
"created_at": row[1].isoformat(),
"expires_at": row[2].isoformat() if row[2] else None,
}), 201
except Exception as e:
logger.error(f"Token creation failed: {e}")
return jsonify({"error": "creation_failed", "message": str(e)}), 500
@app.route("/api/v1/tokens/verify", methods=["POST"])
def verify_token():
data = request.get_json(silent=True) or {}
raw_token = data.get("token", "")
if not raw_token:
return jsonify({"valid": False, "error": "token_required"}), 400
token_hash = hashlib.sha256(raw_token.encode()).hexdigest()
conn = get_pg_conn()
if not conn:
return jsonify({"valid": False, "error": "db_error"}), 503
try:
cur = conn.cursor()
cur.execute(
"""SELECT id, user_id, name, scopes, is_active, created_at, expires_at, last_used_at
FROM api_tokens
WHERE token_hash = %s""",
(token_hash,),
)
row = cur.fetchone()
if not row:
cur.close()
conn.close()
return jsonify({"valid": False, "error": "token_not_found"}), 404
token_id, user_id, name, scopes, is_active, created_at, expires_at, last_used_at = row
if not is_active:
cur.close()
conn.close()
return jsonify({"valid": False, "error": "token_revoked"}), 403
if expires_at and expires_at < datetime.now(timezone.utc):
cur.close()
conn.close()
return jsonify({"valid": False, "error": "token_expired"}), 403
cur.execute(
"UPDATE api_tokens SET last_used_at = NOW() WHERE id = %s",
(token_id,),
)
cur.close()
conn.close()
return jsonify({
"valid": True,
"token_id": str(token_id),
"user_id": user_id,
"name": name,
"scopes": scopes,
})
except Exception as e:
logger.error(f"Token verification failed: {e}")
return jsonify({"valid": False, "error": "verification_failed"}), 500
@app.route("/api/v1/tokens/<token_id>", methods=["GET"])
@token_required
def get_token(token_id):
conn = get_pg_conn()
if not conn:
return jsonify({"error": "db_error"}), 503
try:
cur = conn.cursor()
cur.execute(
"""SELECT id, user_id, name, scopes, is_active, created_at, expires_at, last_used_at, metadata
FROM api_tokens WHERE id = %s AND user_id = %s""",
(token_id, g.user_id),
)
row = cur.fetchone()
cur.close()
conn.close()
if not row:
return jsonify({"error": "not_found"}), 404
return jsonify({
"id": str(row[0]),
"user_id": row[1],
"name": row[2],
"scopes": row[3],
"is_active": row[4],
"created_at": row[5].isoformat(),
"expires_at": row[6].isoformat() if row[6] else None,
"last_used_at": row[7].isoformat() if row[7] else None,
"metadata": row[8] if row[8] else {},
})
except Exception as e:
logger.error(f"Get token failed: {e}")
return jsonify({"error": "query_failed"}), 500
@app.route("/api/v1/tokens/revoke/<token_id>", methods=["POST"])
@token_required
def revoke_token(token_id):
conn = get_pg_conn()
if not conn:
return jsonify({"error": "db_error"}), 503
try:
cur = conn.cursor()
cur.execute(
"""UPDATE api_tokens SET is_active = FALSE WHERE id = %s AND user_id = %s
RETURNING id, name""",
(token_id, g.user_id),
)
row = cur.fetchone()
cur.close()
conn.close()
if not row:
return jsonify({"error": "not_found"}), 404
log_audit(g.user_id, "api_token_revoked", str(row[0])[:8])
return jsonify({"status": "revoked", "token_id": str(row[0])})
except Exception as e:
logger.error(f"Revoke token failed: {e}")
return jsonify({"error": "revoke_failed"}), 500
@app.route("/api/v1/tokens/user/<int:user_id>", methods=["GET"])
@token_required
def list_user_tokens(user_id):
if g.user_id != user_id and "admin" not in g.scopes:
return jsonify({"error": "forbidden"}), 403
conn = get_pg_conn()
if not conn:
return jsonify({"error": "db_error"}), 503
try:
cur = conn.cursor()
cur.execute(
"""SELECT id, user_id, name, scopes, is_active, created_at, expires_at, last_used_at
FROM api_tokens
WHERE user_id = %s
ORDER BY created_at DESC""",
(user_id,),
)
rows = cur.fetchall()
cur.close()
conn.close()
tokens = []
for row in rows:
tokens.append({
"id": str(row[0]),
"user_id": row[1],
"name": row[2],
"scopes": row[3],
"is_active": row[4],
"created_at": row[5].isoformat(),
"expires_at": row[6].isoformat() if row[6] else None,
"last_used_at": row[7].isoformat() if row[7] else None,
})
return jsonify({"tokens": tokens, "total": len(tokens)})
except Exception as e:
logger.error(f"List tokens failed: {e}")
return jsonify({"error": "query_failed"}), 500
@app.route("/api/v1/auth/token", methods=["POST"])
def exchange_token():
data = request.get_json(silent=True) or {}
grant_type = data.get("grant_type", "client_credentials")
raw_token = data.get("client_token", "") or data.get("token", "")
refresh_raw = data.get("refresh_token", "")
if grant_type == "refresh_token" and refresh_raw:
return refresh_access_token(refresh_raw)
if not raw_token:
return jsonify({"error": "invalid_request", "message": "client_token required"}), 400
token_hash = hashlib.sha256(raw_token.encode()).hexdigest()
conn = get_pg_conn()
if not conn:
return jsonify({"error": "db_error"}), 503
try:
cur = conn.cursor()
cur.execute(
"""SELECT id, user_id, scopes, is_active, expires_at
FROM api_tokens WHERE token_hash = %s""",
(token_hash,),
)
row = cur.fetchone()
cur.close()
conn.close()
if not row:
return jsonify({"error": "invalid_token"}), 401
if not row[3]:
return jsonify({"error": "token_revoked"}), 403
if row[4] and row[4] < datetime.now(timezone.utc):
return jsonify({"error": "token_expired"}), 403
token_pair = generate_token_pair(row[1], row[2])
return jsonify(token_pair), 200
except Exception as e:
logger.error(f"Token exchange failed: {e}")
return jsonify({"error": "exchange_failed"}), 500
@app.route("/api/v1/auth/refresh", methods=["POST"])
def refresh_token_endpoint():
data = request.get_json(silent=True) or {}
refresh_raw = data.get("refresh_token", "")
return refresh_access_token(refresh_raw)
@app.route("/api/v1/auth/revoke", methods=["POST"])
@token_required
def revoke_refresh_token():
data = request.get_json(silent=True) or {}
refresh_raw = data.get("refresh_token", "")
if not refresh_raw:
return jsonify({"error": "refresh_token_required"}), 400
token_hash = hashlib.sha256(refresh_raw.encode()).hexdigest()
conn = get_pg_conn()
if not conn:
return jsonify({"error": "db_error"}), 503
try:
cur = conn.cursor()
cur.execute(
"UPDATE refresh_tokens SET revoked = TRUE, revoked_at = NOW() WHERE token_hash = %s",
(token_hash,),
)
cur.close()
conn.close()
log_audit(g.user_id, "refresh_token_revoked", token_hash[:8])
return jsonify({"status": "revoked"})
except Exception as e:
logger.error(f"Revoke refresh token failed: {e}")
return jsonify({"error": "revoke_failed"}), 500
def refresh_access_token(refresh_raw):
if not refresh_raw:
return jsonify({"error": "refresh_token_required"}), 400
token_hash = hashlib.sha256(refresh_raw.encode()).hexdigest()
conn = get_pg_conn()
if not conn:
return jsonify({"error": "db_error"}), 503
try:
cur = conn.cursor()
cur.execute(
"""SELECT id, user_id, revoked, expires_at
FROM refresh_tokens WHERE token_hash = %s""",
(token_hash,),
)
row = cur.fetchone()
if not row:
cur.close()
conn.close()
return jsonify({"error": "invalid_token"}), 401
if row[2]:
cur.close()
conn.close()
return jsonify({"error": "token_revoked"}), 403
if row[3] < datetime.now(timezone.utc):
cur.close()
conn.close()
return jsonify({"error": "token_expired"}), 403
refresh_id = row[0]
user_id = row[1]
cur.execute(
"UPDATE refresh_tokens SET revoked = TRUE, revoked_at = NOW() WHERE id = %s",
(refresh_id,),
)
pairs = generate_token_pair(user_id)
cur.close()
conn.close()
return jsonify(pairs), 200
except Exception as e:
logger.error(f"Refresh token failed: {e}")
return jsonify({"error": "refresh_failed"}), 500
def register_error_handlers(app):
@app.errorhandler(404)
def not_found(e):
return jsonify({"error": "not_found", "message": "Route not found"}), 404
@app.errorhandler(405)
def method_not_allowed(e):
return jsonify({"error": "method_not_allowed", "message": "Method not allowed"}), 405
@app.errorhandler(500)
def internal_error(e):
logger.error(f"Internal error: {e}")
return jsonify({"error": "internal_error", "message": "Internal server error"}), 500
if __name__ == "__main__":
logger.info("=" * 60)
logger.info("Token Broker API starting...")
logger.info(f"DB: {DB_HOST}:{DB_PORT}/{DB_NAME}")
logger.info(f"Port: {os.environ.get('TOKEN_BROKER_PORT', '8783')}")
logger.info("=" * 60)
init_db()
port = int(os.environ.get("TOKEN_BROKER_PORT", "8783"))
debug = os.environ.get("FLASK_ENV", "production") == "development"
app = create_app()
app.run(host="0.0.0.0", port=port, debug=debug)

View File

@@ -52,6 +52,9 @@ def auth_header(token: str) -> dict:
@pytest.fixture(scope="module")
def app():
# Enforce this module s temp DB
os.environ["TURF_SAAS_DB"] = _tmp_db.name
os.environ["JWT_SECRET_KEY"] = "test-history-secret-key"
application = create_app()
application.config["TESTING"] = True
application.config["JWT_SECRET_KEY"] = "test-history-secret-key"
@@ -70,7 +73,14 @@ def seeded_db():
- Create ml_predictions_cache with rows spanning 120 days back
- Create users for free/premium/pro plans
"""
db_path = os.environ["TURF_SAAS_DB"]
# Reset TURF_SAAS_DB to this module-s temp DB at runtime
os.environ["TURF_SAAS_DB"] = _tmp_db.name
db_path = _tmp_db.name
# Ensure auth tables (users, refresh_tokens, subscriptions) exist in the test DB
# init_auth_tables() is idempotent — safe to call even if tables already exist
init_auth_tables()
conn = sqlite3.connect(db_path)
# Create ml_predictions_cache table if absent
@@ -124,7 +134,9 @@ def auth_tokens(client, seeded_db):
assert r.status_code in (201, 409), f"register failed for {plan}: {r.data}"
# Set plan via direct DB
db_path = os.environ["TURF_SAAS_DB"]
# Reset TURF_SAAS_DB to this module-s temp DB at runtime
os.environ["TURF_SAAS_DB"] = _tmp_db.name
db_path = _tmp_db.name
conn = sqlite3.connect(db_path)
for plan, email in plans.items():
conn.execute("UPDATE users SET plan = ? WHERE email = ?", (plan, email))

388
tests/test_user_tokens.py Normal file
View File

@@ -0,0 +1,388 @@
#!/usr/bin/env python3
"""
tests/test_user_tokens.py — Personal API Token + Webhook alertes
HRT-80: Tests unitaires et d'intégration
Couvre:
- POST /api/v1/user/api-token (create)
- DELETE /api/v1/user/api-token (revoke)
- POST /api/v1/user/webhook (create/upsert)
- DELETE /api/v1/user/webhook (delete)
- Authentification via X-API-Key
- dispatch_webhook() fire-and-forget
- Plan enforcement Pro uniquement
Run:
./venv/bin/pytest tests/test_user_tokens.py -v --tb=short
"""
import hashlib
import json
import os
import sqlite3
import sys
import tempfile
from unittest.mock import MagicMock, patch
import pytest
# ─── Test DB isolation ────────────────────────────────────────────────────────
_tmp_db = tempfile.NamedTemporaryFile(suffix=".db", delete=False)
_tmp_db.close()
os.environ["TURF_SAAS_DB"] = _tmp_db.name
os.environ["JWT_SECRET_KEY"] = "test-secret-hrt80"
# Add project root to path
sys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))
from app_v1 import create_app # noqa: E402
from api_tokens_db import migrate_api_tokens_tables # noqa: E402
TEST_CONFIG = {
"TESTING": True,
"JWT_SECRET_KEY": "test-secret-hrt80",
}
@pytest.fixture(scope="module")
def app():
# Enforce this module s temp DB at fixture runtime
os.environ["TURF_SAAS_DB"] = _tmp_db.name
os.environ["JWT_SECRET_KEY"] = "test-secret-hrt80"
migrate_api_tokens_tables() # ensure tables exist in THIS module s temp DB
application = create_app()
application.config.update(TEST_CONFIG)
yield application
@pytest.fixture(scope="module")
def client(app):
return app.test_client()
# ─── Helpers ─────────────────────────────────────────────────────────────────
def _create_user(client, email, plan="pro"):
"""Register user (plan=free) then update plan in DB."""
resp = client.post(
"/api/v1/auth/register",
json={"email": email, "password": "Secure123"},
)
assert resp.status_code == 201, resp.get_json()
user_id = resp.get_json()["user_id"]
# Update plan directly in DB (no plan-update endpoint in JWT auth)
conn = sqlite3.connect(os.environ["TURF_SAAS_DB"])
conn.execute("UPDATE users SET plan = ? WHERE id = ?", (plan, user_id))
conn.commit()
conn.close()
# Login to get access token
login_resp = client.post(
"/api/v1/auth/login",
json={"email": email, "password": "Secure123"},
)
assert login_resp.status_code == 200, login_resp.get_json()
access_token = login_resp.get_json()["access_token"]
return access_token, user_id
def _auth_header(token):
return {"Authorization": f"Bearer {token}"}
# ─── Tests: API Token (Pro) ───────────────────────────────────────────────────
class TestApiToken:
def test_create_api_token_pro(self, client):
"""POST /api/v1/user/api-token — Pro user gets 201 + token starting with trf_"""
token, _ = _create_user(client, "pro_token@test.com", plan="pro")
resp = client.post("/api/v1/user/api-token", headers=_auth_header(token))
assert resp.status_code == 201, resp.get_json()
data = resp.get_json()
assert data["token"].startswith("trf_")
assert data["prefix"] == data["token"][:12]
assert "warning" in data
assert "created_at" in data
def test_create_api_token_stores_hash_not_raw(self, client):
"""Second POST returns 409 — only hashed token stored"""
token, _ = _create_user(client, "pro_token2@test.com", plan="pro")
# First create
r1 = client.post("/api/v1/user/api-token", headers=_auth_header(token))
assert r1.status_code == 201
raw_token = r1.get_json()["token"]
# Second create should conflict
r2 = client.post("/api/v1/user/api-token", headers=_auth_header(token))
assert r2.status_code == 409
data = r2.get_json()
assert "existing_prefix" in data
# Verify raw token is NOT stored in DB (only hash)
conn = sqlite3.connect(os.environ["TURF_SAAS_DB"])
row = conn.execute(
"SELECT token_hash FROM user_api_tokens WHERE token_prefix = ?",
(raw_token[:12],),
).fetchone()
conn.close()
assert row is not None
assert row[0] != raw_token # hash != raw
assert len(row[0]) == 64 # SHA256 hex
def test_create_api_token_free_user(self, client):
"""Free user gets 403"""
token, _ = _create_user(client, "free_token@test.com", plan="free")
resp = client.post("/api/v1/user/api-token", headers=_auth_header(token))
assert resp.status_code == 403
def test_create_api_token_premium_user(self, client):
"""Premium user gets 403 (Pro only feature)"""
token, _ = _create_user(client, "premium_token@test.com", plan="premium")
resp = client.post("/api/v1/user/api-token", headers=_auth_header(token))
assert resp.status_code == 403
def test_create_api_token_no_auth(self, client):
"""No auth → 401"""
resp = client.post("/api/v1/user/api-token")
assert resp.status_code == 401
def test_revoke_api_token(self, client):
"""DELETE /api/v1/user/api-token — Pro user revokes active token"""
token, _ = _create_user(client, "pro_revoke@test.com", plan="pro")
# Create first
client.post("/api/v1/user/api-token", headers=_auth_header(token))
# Revoke
resp = client.delete("/api/v1/user/api-token", headers=_auth_header(token))
assert resp.status_code == 200
data = resp.get_json()
assert data["revoked"] is True
assert data["count"] >= 1
def test_revoke_no_active_token(self, client):
"""DELETE with no active token → 404"""
token, _ = _create_user(client, "pro_notoken@test.com", plan="pro")
resp = client.delete("/api/v1/user/api-token", headers=_auth_header(token))
assert resp.status_code == 404
def test_revoke_non_pro(self, client):
"""DELETE for free user → 403"""
token, _ = _create_user(client, "free_revoke@test.com", plan="free")
resp = client.delete("/api/v1/user/api-token", headers=_auth_header(token))
assert resp.status_code == 403
# ─── Tests: X-API-Key Authentication ─────────────────────────────────────────
class TestApiKeyAuth:
def test_api_key_auth_on_protected_route(self, client):
"""Valid X-API-Key authenticates on protected route"""
token, _ = _create_user(client, "apikey_auth@test.com", plan="pro")
# Create API token
r = client.post("/api/v1/user/api-token", headers=_auth_header(token))
assert r.status_code == 201
raw_key = r.get_json()["token"]
# Use X-API-Key to access a protected route (try create again → 409 means authenticated)
resp = client.post("/api/v1/user/api-token", headers={"X-API-Key": raw_key})
# 409 means we were authenticated; 401 means auth failed
assert resp.status_code == 409
def test_api_key_invalid(self, client):
"""Invalid X-API-Key → 401"""
resp = client.post(
"/api/v1/user/api-token",
headers={"X-API-Key": "trf_invalidkeyXXXXXXXXXXXXXXXXXX"},
)
assert resp.status_code == 401
def test_api_key_revoked(self, client):
"""Revoked X-API-Key → 401"""
token, _ = _create_user(client, "revoked_apikey@test.com", plan="pro")
# Create token
r = client.post("/api/v1/user/api-token", headers=_auth_header(token))
assert r.status_code == 201
raw_key = r.get_json()["token"]
# Revoke it
client.delete("/api/v1/user/api-token", headers=_auth_header(token))
# Try using revoked key
resp = client.post("/api/v1/user/api-token", headers={"X-API-Key": raw_key})
assert resp.status_code == 401
def test_revoke_then_cannot_auth(self, client):
"""Full flow: create → use → revoke → X-API-Key rejected"""
token, _ = _create_user(client, "flow_test@test.com", plan="pro")
# Create
r = client.post("/api/v1/user/api-token", headers=_auth_header(token))
raw_key = r.get_json()["token"]
# Validate it works (409 because key exists)
r2 = client.post("/api/v1/user/api-token", headers={"X-API-Key": raw_key})
assert r2.status_code == 409
# Revoke
client.delete("/api/v1/user/api-token", headers=_auth_header(token))
# Try again with revoked key
r3 = client.post("/api/v1/user/api-token", headers={"X-API-Key": raw_key})
assert r3.status_code == 401
# ─── Tests: Webhook ───────────────────────────────────────────────────────────
class TestWebhook:
def test_create_webhook_pro(self, client):
"""POST /api/v1/user/webhook — Pro user with provided secret → 201"""
token, _ = _create_user(client, "webhook_pro@test.com", plan="pro")
resp = client.post(
"/api/v1/user/webhook",
headers=_auth_header(token),
json={"url": "https://example.com/hook", "secret": "mysecret123"},
)
assert resp.status_code == 201
data = resp.get_json()
assert data["webhook_url"] == "https://example.com/hook"
assert data["secret"] == "mysecret123"
def test_create_webhook_auto_secret(self, client):
"""POST without secret → auto-generated secret"""
token, _ = _create_user(client, "webhook_auto@test.com", plan="pro")
resp = client.post(
"/api/v1/user/webhook",
headers=_auth_header(token),
json={"url": "https://auto.example.com/hook"},
)
assert resp.status_code == 201
data = resp.get_json()
assert len(data["secret"]) == 64 # token_hex(32) = 64 hex chars
def test_create_webhook_non_pro_free(self, client):
"""Free user → 403"""
token, _ = _create_user(client, "webhook_free@test.com", plan="free")
resp = client.post(
"/api/v1/user/webhook",
headers=_auth_header(token),
json={"url": "https://example.com/hook"},
)
assert resp.status_code == 403
def test_create_webhook_non_pro_premium(self, client):
"""Premium user → 403"""
token, _ = _create_user(client, "webhook_premium@test.com", plan="premium")
resp = client.post(
"/api/v1/user/webhook",
headers=_auth_header(token),
json={"url": "https://example.com/hook"},
)
assert resp.status_code == 403
def test_create_webhook_url_not_https(self, client):
"""HTTP URL → 400"""
token, _ = _create_user(client, "webhook_http@test.com", plan="pro")
resp = client.post(
"/api/v1/user/webhook",
headers=_auth_header(token),
json={"url": "http://example.com/hook"},
)
assert resp.status_code == 400
assert "https" in resp.get_json()["error"].lower()
def test_create_webhook_missing_url(self, client):
"""Missing URL → 400"""
token, _ = _create_user(client, "webhook_nourl@test.com", plan="pro")
resp = client.post(
"/api/v1/user/webhook",
headers=_auth_header(token),
json={},
)
assert resp.status_code == 400
def test_webhook_upsert(self, client):
"""Second POST updates URL (upsert behavior)"""
token, _ = _create_user(client, "webhook_upsert@test.com", plan="pro")
client.post(
"/api/v1/user/webhook",
headers=_auth_header(token),
json={"url": "https://first.example.com/hook"},
)
resp = client.post(
"/api/v1/user/webhook",
headers=_auth_header(token),
json={"url": "https://second.example.com/hook"},
)
assert resp.status_code == 201
assert resp.get_json()["webhook_url"] == "https://second.example.com/hook"
def test_delete_webhook(self, client):
"""DELETE /api/v1/user/webhook → 200"""
token, _ = _create_user(client, "webhook_delete@test.com", plan="pro")
client.post(
"/api/v1/user/webhook",
headers=_auth_header(token),
json={"url": "https://delete.example.com/hook"},
)
resp = client.delete("/api/v1/user/webhook", headers=_auth_header(token))
assert resp.status_code == 200
assert resp.get_json()["deleted"] is True
def test_delete_webhook_not_configured(self, client):
"""DELETE without webhook configured → 404"""
token, _ = _create_user(client, "webhook_notset@test.com", plan="pro")
resp = client.delete("/api/v1/user/webhook", headers=_auth_header(token))
assert resp.status_code == 404
def test_delete_webhook_non_pro(self, client):
"""Free user DELETE → 403"""
token, _ = _create_user(client, "webhook_freedelete@test.com", plan="free")
resp = client.delete("/api/v1/user/webhook", headers=_auth_header(token))
assert resp.status_code == 403
# ─── Tests: dispatch_webhook ──────────────────────────────────────────────────
class TestDispatchWebhook:
def test_dispatch_no_webhook_configured(self):
"""dispatch_webhook silently returns when no webhook is configured"""
with patch("api_v1.utils_webhook.get_db") as mock_get_db:
mock_conn = MagicMock()
mock_conn.execute.return_value.fetchone.return_value = None
mock_get_db.return_value = mock_conn
from api_v1.utils_webhook import dispatch_webhook
# Should not raise, should return silently
dispatch_webhook("nonexistent_user", "new_prediction", {"data": "test"})
def test_dispatch_sends_hmac_header(self):
"""dispatch_webhook sends correct HMAC-SHA256 signature header"""
test_secret = "testsecret"
test_url = "https://hook.example.com/receive"
test_payload = {"race_id": "R123", "top1": "Cheval Blanc"}
with (
patch("api_v1.utils_webhook.get_db") as mock_get_db,
patch("api_v1.utils_webhook.requests.post") as mock_post,
):
mock_row = MagicMock()
mock_row.__getitem__ = lambda self, key: (
test_url if key == "url" else test_secret
)
mock_conn = MagicMock()
mock_conn.execute.return_value.fetchone.return_value = mock_row
mock_get_db.return_value = mock_conn
mock_response = MagicMock()
mock_response.status_code = 200
mock_post.return_value = mock_response
from api_v1.utils_webhook import dispatch_webhook, EVENT_NEW_PREDICTION
dispatch_webhook("user123", EVENT_NEW_PREDICTION, test_payload)
assert mock_post.called
call_kwargs = mock_post.call_args
headers_sent = call_kwargs.kwargs.get("headers") or call_kwargs[1].get(
"headers"
)
assert "X-Turf-Signature" in headers_sent
assert headers_sent["X-Turf-Signature"].startswith("sha256=")
assert headers_sent["X-Turf-Event"] == EVENT_NEW_PREDICTION

View File

@@ -107,6 +107,34 @@ def run_analytics():
traceback.print_exc()
def run_sync_turf_db():
"""Synchronise turf.db vers turf_saas.db"""
logger.info("🔄 [SCHEDULER] Sync turf.db -> turf_saas.db...")
try:
import subprocess
result = subprocess.run(
[
"python3",
"/home/h3r7/turf_saas/sync_turf_db.py",
"--date",
datetime.now().strftime("%Y-%m-%d"),
],
capture_output=True,
text=True,
timeout=300,
)
if result.returncode == 0:
logger.info("✅ [SCHEDULER] Sync turf.db terminé")
else:
logger.error(f"❌ [SCHEDULER] Sync turf.db échoué: {result.stderr}")
except Exception as e:
logger.error(f"❌ [SCHEDULER] Erreur sync turf.db: {e}")
import traceback
traceback.print_exc()
def get_todays_race_time():
"""Récupère l'heure de la course principale du jour depuis la DB
Returns: timestamp en ms ou None
@@ -315,6 +343,16 @@ def main():
schedule.every().day.at("20:00").do(run_results).tag("results", "daily_fallback")
schedule.every().day.at("19:00").do(run_scraper).tag("scraper", "late_evening")
# Sync turf.db -> turf_saas.db (2x/jour: post-scraping + post-cotes)
schedule.every().day.at("11:00").do(run_sync_turf_db).tag("sync", "post_scraping")
schedule.every().day.at("17:00").do(run_sync_turf_db).tag("sync", "post_cotes")
# ML Cache: populate ml_predictions_cache après chaque sync
schedule.every().day.at("11:35").do(run_ml_cache).tag("ml_cache", "post_sync_am")
schedule.every().day.at("17:35").do(run_ml_cache).tag("ml_cache", "post_sync_pm")
schedule.every().day.at("09:30").do(run_ml_cache).tag("ml_cache", "morning")
schedule.every().day.at("13:30").do(run_ml_cache).tag("ml_cache", "pre_race")
schedule.every().sunday.at("02:00").do(run_ml).tag("ml", "weekly")
schedule.every().wednesday.at("02:00").do(run_ml).tag("ml", "midweek")
@@ -335,6 +373,200 @@ def main():
time.sleep(30)
def run_ml_cache():
"""Populate ml_predictions_cache with ensemble (predict_v2) predictions"""
logger.info("🤖 [SCHEDULER] Mise à jour cache prédictions ML (ensemble)...")
try:
os.chdir("/home/h3r7/turf_saas")
import predict_v2
model = predict_v2.load_ensemble()
if model is None:
logger.warning("⚠️ [SCHEDULER] Ensemble model not available, skipping")
return
conn = sqlite3.connect(DB_PATH)
conn.row_factory = sqlite3.Row
today = datetime.now().strftime("%Y-%m-%d")
rows = conn.execute("""
SELECT p.*, c.distance, c.discipline, c.specialite,
c.nb_declares_partants, c.montant_prix, c.penetrometre_intitule,
c.libelle as course_libelle, c.libelle_court as hippodrome,
c.heure_depart_str, c.parcours
FROM pmu_partants p
LEFT 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 = ?
ORDER BY p.num_reunion, p.num_course, p.num_pmu
""", (today,)).fetchall()
if not rows:
logger.info(" [SCHEDULER] No partants today, skipping ML cache")
conn.close()
return
partants = [dict(r) for r in rows]
course_lookup = {}
for p in partants:
key = (p["num_reunion"], p["num_course"])
if key not in course_lookup:
course_lookup[key] = {
"libelle": p.get("course_libelle", ""),
"libelle_court": p.get("hippodrome", ""),
"discipline": p.get("discipline", ""),
"distance": p.get("distance", 0),
"heure_depart_str": p.get("heure_depart_str", ""),
}
odds_by_horse = {}
for p in partants:
odds_by_horse[(p["num_reunion"], p["num_course"], p["num_pmu"])] = p.get("cote_direct", 0)
preds = predict_v2.predict_top3(partants, model=model)
if not preds:
logger.warning("⚠️ [SCHEDULER] No predictions generated")
conn.close()
return
enriched = []
for p in preds:
key = (p.get("num_reunion"), p.get("num_course"))
ci = course_lookup.get(key, {})
odds_key = (p.get("num_reunion"), p.get("num_course"), p.get("num_pmu"))
enriched.append({
"num_reunion": p.get("num_reunion"),
"num_course": p.get("num_course"),
"horse_name": p.get("horse_name"),
"horse_number": p.get("num_pmu"),
"odds": odds_by_horse.get(odds_key, 0),
"prob_top1": p.get("prob_top1"),
"prob_top3": p.get("prob_top3"),
"ml_score": p.get("ml_score"),
"recommendation": p.get("recommendation"),
"is_value_bet": p.get("is_value_bet", 0),
"is_outlier": 0,
"race_label": f"R{p.get('num_reunion', 0)}C{p.get('num_course', 0)}",
"race_name": ci.get("libelle", ""),
"hippodrome": ci.get("libelle_court", ""),
"discipline": ci.get("discipline", ""),
"distance": ci.get("distance", 0),
"heure": ci.get("heure_depart_str", ""),
})
# Calculate risques per race (same logic as dashboard_api.calculate_risque)
from collections import defaultdict
race_horses = defaultdict(list)
for p in enriched:
rkey = (p.get("num_reunion"), p.get("num_course"))
race_horses[rkey].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 rkey, partants_list in race_horses.items():
label, score = _calc_risque(partants_list)
race_risque[rkey] = (label or "neutral", score or 50)
# Ensure table exists with all columns
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,
model_version TEXT DEFAULT 'xgboost_v1',
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
risque_label TEXT DEFAULT 'neutral', risque_score INTEGER DEFAULT 50,
UNIQUE(date, num_reunion, num_course, horse_name)
)
""")
conn.execute("CREATE INDEX IF NOT EXISTS idx_ml_cache_date ON ml_predictions_cache(date)")
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.execute("DELETE FROM ml_predictions_cache WHERE date = ?", (today,))
for p in enriched:
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 (?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)
""", (
today, 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, "ensemble_v1",
))
conn.commit()
conn.close()
logger.info(f"✅ [SCHEDULER] ML cache mis à jour: {len(enriched)} prédictions pour {today}")
except Exception as e:
logger.error(f"❌ [SCHEDULER] Erreur ML cache: {e}")
import traceback
traceback.print_exc()
def _calc_risque(partants_list):
"""Same logic as dashboard_api.calculate_risque — kept local to avoid import side effects"""
if not partants_list:
return None, None
sorted_p = sorted(
partants_list,
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
)
gap_1_2 = top1_score - top2_score
nb_dangerous = sum(1 for p in sorted_p if (p.get("ml_score") or 0) > 40)
odds_fav = sorted(partants_list, 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
if top1_score >= 65 and gap_1_2 >= 20:
score = min(100, int(50 + gap_1_2 * 1.5))
return "safe", score
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))
score = min(64, max(35, int(35 + gap_1_2 * 1.2)))
return "neutral", score
def run_metrics_alerts():
"""Verifie les metriques du jour et envoie une alerte email si ROI > 1.0€"""
logger.info("📧 [SCHEDULER] Vérification alertes métriques...")