app/main.py

from fastapi import FastAPI, UploadFile, File, HTTPException, Request,Query
from app.voting import soft_voting
import io
from PIL import Image
from io import BytesIO
from pydantic import BaseModel
from typing import Union
from io import BytesIO
import base64
import logging
import logging
from app.model import load_models, predict_with_model
# Configuration de base du logger
logging.basicConfig(
    level=logging.DEBUG,  # DEBUG pour voir tous les logs (INFO, WARNING, ERROR, etc.)
    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
)

logger = logging.getLogger(__name__)

model_configs = load_models()

app = FastAPI()

# @app.post("/predict")
# async def predict(file: UploadFile = File(...)):
#     image_bytes = await file.read()
#     prediction = await soft_voting(image_bytes)
#     return {"prediction": int(prediction)}

class ImagePayload(BaseModel):
    image: str  # chaîne encodée en base64
@app.post("/predict")
async def predict(request: Request,
    file: UploadFile = File(None),
    payload: Union[ImagePayload, None] = None,
    mode: str = Query("single", enum=["single", "voting","automatic"], description="Mode de prédiction"),
    show_heatmap: bool = Query(False, description="Afficher la heatmap"),
    default_model: str = Query("efficientnetv2m", enum=["efficientnetv2m", "resnet50"], description="Model par défaut")
    ):
   
    logger.info("🔁 Requête reçue")
    logger.info(f"✅ Mode : {mode}")
    logger.info(f"✅ Default model : {default_model}")
    logger.info(f"✅ Show heatmap : {show_heatmap}")
    
    try:
        # Cas 1 : multipart avec fichier
        if file is not None:
            image_bytes = await file.read()
            logger.debug("✅ Image reçue via multipart :", file.filename, len(image_bytes), "octets")

        # Cas 2 : JSON base64
        elif await request.json():
            body = await request.json()
            if "image" not in body:
                raise HTTPException(status_code=422, detail="Champ 'image' manquant.")
            image_base64 = body["image"]
            image_bytes = base64.b64decode(image_base64)
            logger.debug("✅ Image décodée depuis base64 :", len(image_bytes), "octets")

        else:
            logger.info("⚠️ Aucune image reçue")
            raise HTTPException(status_code=400, detail="Format de requête non supporté.")

        # Appel de ta logique de prédiction
        logger.debug("🔍 Appel du vote multi-modèles...")
        prediction = await soft_voting(model_configs,image_bytes,mode,show_heatmap,default_model)

        # Pour l’instant : réponse simulée
        return prediction

    except Exception as e:
        logger.error("❌ Une erreur s'est produite", exc_info=True)
        raise HTTPException(status_code=500, detail=str(e))


@app.get("/health")
def health_check():
    return {"status": "ok"}