Update main.py

main.py

@@ -1,32 +1,19 @@
-import logging
-import sys
 import os
-import json
-import tempfile
-from datetime import datetime
-
-from fastapi import FastAPI, Request, HTTPException, UploadFile, File, BackgroundTasks, Query
+import logging
+from fastapi import FastAPI, Request, HTTPException
 from fastapi.responses import JSONResponse
-from fastapi.concurrency import run_in_threadpool
 from fastapi.middleware.cors import CORSMiddleware
-from pydantic import BaseModel, Field
-from typing import List, Dict, Any, Optional
-from bson import ObjectId
+from pydantic import BaseModel
 
-from src.models import load_all_models
-from src.services.cv_service import CVParsingService
-from src.services.analysis_service import AnalysisService
-from services.graph_service import GraphInterviewProcessor
+from src.services.graph_service import GraphInterviewProcessor
 
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-os.makedirs('/tmp/feedbacks', exist_ok=True)
-
 app = FastAPI(
-    title="AIrh Interview Assistant",
-    description="API pour l'analyse de CV et la simulation d'entretiens d'embauche avec analyse asynchrone.",
-    version="2.0.0",
+    title="Interview Simulation API",
+    description="API for interview simulations.",
+    version="1.0.0",
     docs_url="/docs",
     redoc_url="/redoc"
 )
@@ -39,88 +26,44 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-# --- Initialisation des services ---
-logger.info("Chargement des modèles et initialisation des services...")
-models = load_all_models()
-cv_service = CVParsingService(models)
-logger.info("Services initialisés.")
-
-
-# --- Définition des modèles Pydantic ---
-class Feedback(BaseModel):
-    status: str
-    feedback_data: Optional[Dict[str, Any]] = None
-
 class HealthCheck(BaseModel):
     status: str = "ok"
 
-# --- Endpoint de santé ---
 @app.get("/", response_model=HealthCheck, tags=["Status"])
 async def health_check():
     return HealthCheck()
 
-# --- Endpoint principal pour la simulation d'entretien ---
 @app.post("/simulate-interview/")
 async def simulate_interview(request: Request):
     """
-    Ce endpoint reçoit les données de l'entretien, instancie le processeur de graphe
-    et lance la conversation.
+    This endpoint receives the interview data, instantiates the graph processor
+    and starts the conversation.
     """
-    # CORRECTION : Récupérer l'instance du logger pour garantir sa disponibilité dans le scope de la fonction.
     logger = logging.getLogger(__name__)
     try:
         payload = await request.json()
-        
+
         if not all(k in payload for k in ["user_id", "job_offer_id", "cv_document", "job_offer"]):
-            raise HTTPException(status_code=400, detail="Données manquantes dans le payload (user_id, job_offer_id, cv_document, job_offer).")
-            
-        logger.info(f"Début de la simulation pour l'utilisateur : {payload['user_id']}")
-        
+            raise HTTPException(status_code=400, detail="Missing data in payload (user_id, job_offer_id, cv_document, job_offer).")
+
+        logger.info(f"Starting simulation for user: {payload['user_id']}")
+
         processor = GraphInterviewProcessor(payload)
         result = processor.invoke(payload.get("messages", []))
-        
+
         return JSONResponse(content=result)
 
     except ValueError as ve:
-        logger.error(f"Erreur de validation des données : {ve}", exc_info=True)
+        logger.error(f"Data validation error: {ve}", exc_info=True)
         return JSONResponse(content={"error": str(ve)}, status_code=400)
     except Exception as e:
-        logger.error(f"Erreur interne dans le endpoint simulate-interview: {e}", exc_info=True)
+        logger.error(f"Internal error in simulate-interview endpoint: {e}", exc_info=True)
         return JSONResponse(
-            content={"error": "Une erreur interne est survenue sur le serveur de l'assistant."},
+            content={"error": "An internal error occurred on the assistant's server."},
             status_code=500
         )
 
-# --- Endpoint pour l'analyse de CV ---
-@app.post("/parse-cv/", tags=["CV Parsing"])
-async def parse_cv(
-    file: UploadFile = File(...), 
-    user_id: str = Query(None, description="ID de l'utilisateur pour lier le CV")
-):
-    """
-    Analyse un fichier CV (PDF) et le stocke automatiquement dans MongoDB.
-    """
-    if file.content_type != "application/pdf":
-        raise HTTPException(status_code=400, detail="Fichier PDF requis")
-    
-    contents = await file.read()
-    
-    with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp:
-        tmp.write(contents)
-        tmp_path = tmp.name
-    
-    try:
-        result = await run_in_threadpool(cv_service.parse_cv, tmp_path, user_id)
-    finally:
-        if os.path.exists(tmp_path):
-            os.remove(tmp_path)
-            
-    if not result:
-        raise HTTPException(status_code=500, detail="Échec de l'extraction des données du CV.")
-        
-    return result
-
-# --- Démarrage de l'application (pour un test local) ---
 if __name__ == "__main__":
     import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=8000)
+    port = int(os.getenv("PORT", 8002)) # Use PORT environment variable, default to 8002
+    uvicorn.run(app, host="0.0.0.0", port=port)