# api/extract.py
from fastapi import APIRouter, File, UploadFile, Form, HTTPException, Header
from services.face_service import get_face_service
import config
import logging
import numpy as np

router = APIRouter()
face_service = get_face_service()
logger = logging.getLogger(__name__)

@router.post("/extract")
async def extract_embedding(
    x_api_key: str = Header(...),
    files: list[UploadFile] = File(...)
):
    if x_api_key != config.Config.API_KEY:
        raise HTTPException(status_code=401, detail="Invalid API key")
    
    try:
        if len(files) == 0:
            raise HTTPException(status_code=400, detail="Please provide at least 1 image")
        
        if len(files) > 10:
            raise HTTPException(status_code=400, detail="Maximum 10 images allowed")
        
        embeddings = []
        
        for file in files:
            contents = await file.read()
            if len(contents) == 0:
                continue
            
            face_data, error = face_service.extract_face(contents)
            if face_data:
                embeddings.append(face_data['embedding'])
        
        if len(embeddings) == 0:
            return {
                "success": False,
                "message": "No valid faces detected",
                "embedding": None,
                "quality": 0
            }
        
        avg_embedding = np.mean(embeddings, axis=0)
        avg_embedding = avg_embedding / np.linalg.norm(avg_embedding)
        
        return {
            "success": True,
            "message": f"Embedding extracted from {len(embeddings)} faces",
            "embedding": avg_embedding.tolist(),
            "quality": 0.95,
            "faces_detected": len(embeddings)
        }
        
    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"Extract embedding error: {e}")
        raise HTTPException(status_code=500, detail=f"Extract failed: {str(e)}")