Initial commit

hsbg_ai/backend/api/routes/learning.py

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+"""Routes API — Mode Apprentissage."""
+from fastapi import APIRouter, Depends, HTTPException
+from pydantic import BaseModel
+from sqlalchemy.ext.asyncio import AsyncSession
+from sqlalchemy import select, desc
+from typing import Literal
+from backend.database.db import get_db
+from backend.database.models import AIDecision, LearningFeedback
+
+router = APIRouter()
+_processor = None
+
+
+def _get_processor():
+    global _processor
+    if not _processor:
+        from backend.config.settings import get_settings
+        from backend.ai.learning.feedback_processor import FeedbackProcessor
+        _processor = FeedbackProcessor(get_settings())
+    return _processor
+
+
+class FeedbackIn(BaseModel):
+    decision_id: int
+    rating: Literal["good", "bad", "neutral"]
+    better_action: dict | None = None
+    comment: str | None = None
+
+
+@router.post("/feedback")
+async def submit_feedback(req: FeedbackIn, db: AsyncSession = Depends(get_db)):
+    """Soumet un retour utilisateur sur une décision IA."""
+    proc = _get_processor()
+    try:
+        fb = await proc.record_feedback(
+            db,
+            decision_id=req.decision_id,
+            rating=req.rating,
+            better_action=req.better_action,
+            comment=req.comment,
+        )
+        return {
+            "feedback_id": fb.id,
+            "rating": fb.rating,
+            "message": "Feedback enregistré — merci pour votre contribution!",
+        }
+    except ValueError as e:
+        raise HTTPException(404, str(e))
+
+
+@router.get("/stats")
+async def get_stats(db: AsyncSession = Depends(get_db)):
+    """Statistiques globales du système d'apprentissage."""
+    return await _get_processor().get_stats(db)
+
+
+@router.get("/decisions")
+async def get_decisions(
+    session_id: int | None = None,
+    limit: int = 20,
+    db: AsyncSession = Depends(get_db),
+):
+    """Historique des décisions IA avec leurs feedbacks."""
+    q = select(AIDecision).order_by(desc(AIDecision.created_at)).limit(limit)
+    if session_id:
+        q = q.where(AIDecision.session_id == session_id)
+    result = await db.execute(q)
+    decisions = result.scalars().all()
+    return [
+        {
+            "id": d.id,
+            "session_id": d.session_id,
+            "turn": d.turn,
+            "phase": d.phase,
+            "recommendation": d.recommendation,
+            "reasoning": d.reasoning,
+            "confidence": d.confidence,
+            "outcome_rating": d.outcome_rating,
+            "user_feedback": d.user_feedback,
+            "model_used": d.model_used,
+            "processing_ms": d.processing_ms,
+            "created_at": d.created_at.isoformat(),
+        }
+        for d in decisions
+    ]
+
+
+@router.post("/flush")
+async def flush_buffer():
+    """Force l'export du buffer d'apprentissage."""
+    await _get_processor().force_flush()
+    return {"status": "flushed"}