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>
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DevOps Engineer
@@ -22,6 +22,8 @@ Registers sub-blueprints:
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/api/v1/history — historique préd. ML (Free:7j, Premium:90j, Pro:illimité)
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/api/v1/org/ — organisations Pro (multi-compte, max 5 users)
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/api/v1/docs — Swagger UI (via flasgger, registered on app)
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/api/v1/ml/feedback/run — trigger feedback loop ML (admin)
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/api/v1/ml/feedback/stats — stats par stratégie (premium+)
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"""
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from flask import Blueprint
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@@ -38,6 +40,7 @@ from .routes.user import user_bp
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from .routes.user_tokens import user_tokens_bp
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from .routes.history import history_bp
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from .routes.org import org_bp
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from .routes.ml_feedback import ml_feedback_bp
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# Master blueprint that aggregates all sub-routes under /api/v1
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api_v1_bp = Blueprint("api_v1", __name__, url_prefix="/api/v1")
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@@ -57,3 +60,4 @@ def register_api_v1(app):
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app.register_blueprint(user_tokens_bp)
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app.register_blueprint(history_bp)
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app.register_blueprint(org_bp)
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app.register_blueprint(ml_feedback_bp)
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191
api_v1/routes/ml_feedback.py
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191
api_v1/routes/ml_feedback.py
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@@ -0,0 +1,191 @@
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#!/usr/bin/env python3
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"""
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ml_feedback.py — API routes pour le feedback loop ML (turf_saas).
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Routes:
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POST /api/v1/ml/feedback/run — Déclenche le feedback loop (admin uniquement)
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GET /api/v1/ml/feedback/stats — Stats performances par stratégie
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Sécurité admin : token via variable d'environnement ML_ADMIN_TOKEN
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ou plan "pro" en fallback pour les stats.
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"""
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import os
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import sys
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from datetime import datetime
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from flask import Blueprint, jsonify, request, g
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# Ajoute le répertoire parent de api_v1 dans le path pour importer ml_feedback_saas
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
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from api_v1.utils import get_db, internal_error, bad_request
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from auth import jwt_required_middleware, plan_required
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ml_feedback_bp = Blueprint("v1_ml_feedback", __name__, url_prefix="/api/v1/ml/feedback")
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# Token admin interne — configurable via variable d'environnement
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ML_ADMIN_TOKEN = os.environ.get("ML_ADMIN_TOKEN", "")
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def _check_admin(req):
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"""Vérifie le token admin via header X-Admin-Token ou Authorization Bearer (plan pro)."""
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# 1. Token interne (scheduler/cron)
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admin_token = req.headers.get("X-Admin-Token", "").strip()
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if ML_ADMIN_TOKEN and admin_token == ML_ADMIN_TOKEN:
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return True, None
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# 2. Pas de token admin configuré → autoriser les utilisateurs "pro" authentifiés
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user = getattr(g, "current_user", None)
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if user and user.get("plan") == "pro":
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return True, None
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return False, jsonify({"error": "Accès admin requis", "code": 403}), 403
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@ml_feedback_bp.route("/run", methods=["POST"])
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@jwt_required_middleware
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def feedback_run():
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"""
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Déclenche le feedback loop ML pour une date donnée.
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---
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tags:
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- ML Feedback
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summary: Déclenche le feedback loop XGBoost (admin only)
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security:
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- Bearer: []
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- AdminToken: []
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parameters:
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- name: body
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in: body
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schema:
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type: object
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properties:
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date:
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type: string
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description: Date YYYY-MM-DD (défaut aujourd'hui)
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example: "2026-04-25"
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mode:
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type: string
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description: "run (défaut) ou backfill"
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enum: [run, backfill]
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example: run
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responses:
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200:
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description: Feedback loop exécuté avec succès
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400:
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description: Paramètre invalide
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403:
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description: Accès refusé
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500:
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description: Erreur interne
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"""
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# Vérification admin
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user = getattr(g, "current_user", None)
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admin_token = request.headers.get("X-Admin-Token", "").strip()
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is_admin = (ML_ADMIN_TOKEN and admin_token == ML_ADMIN_TOKEN) or (
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user and user.get("plan") == "pro"
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)
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if not is_admin:
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return jsonify({"error": "Accès admin requis", "code": 403}), 403
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body = request.get_json(silent=True) or {}
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date_str = body.get("date") or datetime.now().strftime("%Y-%m-%d")
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mode = body.get("mode", "run")
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# Validation date
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try:
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datetime.strptime(date_str, "%Y-%m-%d")
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except ValueError:
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return bad_request(f"Date invalide : {date_str}. Format attendu : YYYY-MM-DD")
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if mode not in ("run", "backfill"):
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return bad_request("mode doit être 'run' ou 'backfill'")
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try:
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import ml_feedback_saas
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if mode == "backfill":
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inseres, maj = ml_feedback_saas.backfill(date_str)
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total_inseres = inseres
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else:
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result = ml_feedback_saas.run(date_str)
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total_inseres = sum(result["inseres"].values())
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maj = result["maj"]
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return jsonify(
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{
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"status": "ok",
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"date": date_str,
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"mode": mode,
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"paris_inseres": total_inseres,
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"paris_mis_a_jour": maj,
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}
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), 200
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except Exception as e:
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return internal_error(str(e))
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@ml_feedback_bp.route("/stats", methods=["GET"])
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@jwt_required_middleware
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@plan_required("premium", "pro")
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def feedback_stats():
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"""
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Stats performances ML par stratégie.
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---
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tags:
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- ML Feedback
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summary: Stats paris ML par stratégie (premium+)
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security:
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- Bearer: []
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parameters:
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- name: date_debut
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in: query
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type: string
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description: Date de début YYYY-MM-DD
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- name: date_fin
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in: query
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type: string
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description: Date de fin YYYY-MM-DD
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responses:
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200:
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description: Stats par stratégie
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401:
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description: Token invalide
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403:
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description: Plan insuffisant (premium ou pro requis)
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"""
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date_debut = request.args.get("date_debut")
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date_fin = request.args.get("date_fin")
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# Validation optionnelle des dates
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for d_str, label in [(date_debut, "date_debut"), (date_fin, "date_fin")]:
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if d_str:
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try:
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datetime.strptime(d_str, "%Y-%m-%d")
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except ValueError:
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return bad_request(f"{label} invalide : {d_str}. Format : YYYY-MM-DD")
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conn = get_db()
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try:
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import ml_feedback_saas
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stats = ml_feedback_saas.get_feedback_stats(conn, date_debut, date_fin)
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return jsonify(
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{
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"status": "ok",
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"strategies": stats,
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"filters": {
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"date_debut": date_debut,
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"date_fin": date_fin,
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},
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"total_strategies": len(stats),
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}
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), 200
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except Exception as e:
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return internal_error(str(e))
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finally:
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conn.close()
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