import tempfile
import requests
import os
import logging
import json
from fastapi import FastAPI, UploadFile, File, HTTPException, BackgroundTasks
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

os.environ['HOME'] = '/tmp'
os.makedirs('/tmp/feedbacks', exist_ok=True)

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


try:
    from src.deep_learning_analyzer import MultiModelInterviewAnalyzer
    from src.rag_handler import get_rag_handler
    from src.crew.crew_pool import run_interview_analysis  
    
    analyzer_model = MultiModelInterviewAnalyzer()
    rag_handler_instance = get_rag_handler()
    MODELS_AVAILABLE = True
    logger.info("✅ Modèles d'analyse et RAG pré-chargés avec succès")
except Exception as e:
    logger.error(f"❌ Erreur lors du pré-chargement des modèles: {e}")
    MODELS_AVAILABLE = False
    analyzer_model = None
    rag_handler_instance = None
    run_interview_analysis = None


try:
    from src.cv_parsing_agents import CvParserAgent, create_fallback_cv_data
    CV_PARSING_AVAILABLE = True
    logger.info("✅ CV Parsing disponible")
except Exception as e:
    logger.error(f"❌ CV Parsing indisponible: {e}")
    CV_PARSING_AVAILABLE = False
    CvParserAgent = None
    create_fallback_cv_data = None

try:
    from src.interview_simulator.entretient_version_prod import InterviewProcessor
    INTERVIEW_AVAILABLE = True
    logger.info("✅ Interview Simulator disponible")
except Exception as e:
    logger.error(f"❌ Interview Simulator indisponible: {e}")
    INTERVIEW_AVAILABLE = False
    InterviewProcessor = None

try:
    from src.scoring_engine import ContextualScoringEngine
    SCORING_AVAILABLE = True
    logger.info("✅ Scoring Engine disponible")
except Exception as e:
    logger.error(f"❌ Scoring Engine indisponible: {e}")
    SCORING_AVAILABLE = False
    ContextualScoringEngine = None

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",
    docs_url="/docs",
    redoc_url="/redoc"
)

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

class InterviewRequest(BaseModel):
    user_id: str = Field(..., example="user_12345")
    job_offer_id: str = Field(..., example="job_offer_abcde")
    cv_document: Dict[str, Any]
    job_offer: Dict[str, Any]
    messages: List[Dict[str, Any]]
    conversation_history: List[Dict[str, Any]]

class Feedback(BaseModel):
    status: str
    feedback_data: Optional[Dict[str, Any]] = None

class HealthCheck(BaseModel):
    status: str = "ok"
    services: Dict[str, bool] = Field(default_factory=dict)
    message: str = "API AIrh fonctionnelle"

def analysis_in_background(user_id: str, conversation_history: list, job_description_text: str):
    """
    Fonction exécutée en arrière-plan pour analyser l'entretien
    et sauvegarder le résultat.
    """
    logger.info(f"Démarrage de l'analyse en arrière-plan pour l'utilisateur: {user_id}")
    try:
        if not MODELS_AVAILABLE:
            raise RuntimeError("Les modèles d'analyse ne sont pas disponibles.")
            
        report = run_interview_analysis(
            conversation_history,
            job_description_text,
            analyzer_model,
            rag_handler_instance
        )
        
        feedback_path = f"/tmp/feedbacks/{user_id}.json"
        with open(feedback_path, "w", encoding="utf-8") as f:
            json.dump({"status": "completed", "feedback_data": report}, f, ensure_ascii=False, indent=4)
        
        logger.info(f"✅ Analyse terminée et sauvegardée pour l'utilisateur: {user_id}")
    except Exception as e:
        logger.error(f"❌ Erreur durant l'analyse en arrière-plan pour {user_id}: {e}")
        feedback_path = f"/tmp/feedbacks/{user_id}.json"
        with open(feedback_path, "w", encoding="utf-8") as f:
            json.dump({"status": "error", "feedback_data": str(e)}, f, ensure_ascii=False, indent=4)

@app.get("/", response_model=HealthCheck, tags=["Status"])
async def health_check():
    """Health check de l'API."""
    services = {
        "models_loaded": MODELS_AVAILABLE,
        "cv_parsing": CV_PARSING_AVAILABLE,
        "interview_simulation": INTERVIEW_AVAILABLE,
        "scoring_engine": SCORING_AVAILABLE
    }
    return HealthCheck(services=services)

@app.post("/parse-cv/", tags=["CV Parsing"])
async def parse_cv(file: UploadFile = File(...)):
    """Analyse un CV PDF et extrait les informations structurées."""
    if not CV_PARSING_AVAILABLE:
        return create_fallback_cv_data() if create_fallback_cv_data else {"error": "Service de parsing indisponible"}
    
    if file.content_type != "application/pdf":
        raise HTTPException(status_code=400, detail="Fichier PDF requis")

    tmp_path = None
    try:
        contents = await file.read()
        with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp:
            tmp.write(contents)
            tmp_path = tmp.name
        
        cv_agent = CvParserAgent(pdf_path=tmp_path)
        parsed_data = await run_in_threadpool(cv_agent.process)
        
        if not parsed_data and create_fallback_cv_data:
            parsed_data = create_fallback_cv_data(tmp_path)
        
        if SCORING_AVAILABLE and ContextualScoringEngine and parsed_data:
            try:
                scoring_engine = ContextualScoringEngine(parsed_data)
                scored_data = await run_in_threadpool(scoring_engine.calculate_scores)
                if parsed_data.get("candidat"):
                    parsed_data["candidat"].update(scored_data)
            except Exception as e:
                logger.warning(f"Scoring échoué: {e}")
        
        return parsed_data
        
    except Exception as e:
        logger.error(f"Erreur parsing CV: {e}")
        if create_fallback_cv_data:
            return create_fallback_cv_data(tmp_path)
        raise HTTPException(status_code=500, detail=str(e))
    
    finally:
        if tmp_path and os.path.exists(tmp_path):
            os.remove(tmp_path)

@app.post("/simulate-interview/", tags=["Interview"])
async def simulate_interview(request: InterviewRequest, background_tasks: BackgroundTasks):
    """
    Gère une conversation d'entretien. Si la conversation se termine,
    lance une analyse en arrière-plan.
    """
    if not INTERVIEW_AVAILABLE or not MODELS_AVAILABLE:
        raise HTTPException(status_code=503, detail="Service de simulation ou modèles indisponibles")
    
    try:
        processor = InterviewProcessor(
            cv_document=request.cv_document,
            job_offer=request.job_offer,
            conversation_history=request.conversation_history
        )
        
        result = await run_in_threadpool(processor.run, messages=request.messages)
        
        response_content = result["messages"][-1].content
        
        # Déclencher l'analyse si l'entretien est terminé
        if "nous allons maintenant passer a l'analyse" in response_content.lower():
            logger.info(f"Fin d'entretien détectée pour {request.user_id}. Lancement de l'analyse en arrière-plan.")
            
            # Sauvegarder un statut initial
            feedback_path = f"/tmp/feedbacks/{request.user_id}.json"
            with open(feedback_path, "w", encoding="utf-8") as f:
                json.dump({"status": "processing"}, f, ensure_ascii=False, indent=4)
            
            job_description = request.job_offer.get('description', '')
            background_tasks.add_task(
                analysis_in_background,
                request.user_id,
                request.conversation_history + request.messages,
                job_description
            )
        
        return {"response": response_content}
        
    except Exception as e:
        logger.error(f"Erreur simulation entretien: {e}")
        raise HTTPException(status_code=500, detail=str(e))

@app.get("/get-feedback/{user_id}", response_model=Feedback, tags=["Analysis"])
async def get_feedback(user_id: str):
    """Récupère le résultat de l'analyse post-entretien."""
    feedback_path = f"/tmp/feedbacks/{user_id}.json"
    if not os.path.exists(feedback_path):
        raise HTTPException(status_code=404, detail="Feedback non trouvé ou non encore traité.")
    
    try:
        with open(feedback_path, "r", encoding="utf-8") as f:
            data = json.load(f)
        return Feedback(**data)
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Erreur à la lecture du feedback: {e}")

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=7860)