from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import cv2
from PIL import Image
import numpy as np
from io import BytesIO
import requests

app = FastAPI()

class ImageRequest(BaseModel):
    image_url: str

def buscar_existe(image_url):
    try:
        # Descargar la imagen desde la URL
        headers = {
            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
        }
        response = requests.get(image_url, headers=headers, timeout=30)
        response.raise_for_status()
        
        image = Image.open(BytesIO(response.content))
        
        # Convertir a RGB si es necesario
        if image.mode != 'RGB':
            image = image.convert('RGB')
            
        image = np.asarray(image)
        
        existe = "NO"
        print("Imagen shape: ", image.shape)
        
        # Usar cascada de detección facial
        face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
        
        # Convertir a escala de grises
        gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
            
        # Detectar rostros
        faces = face_cascade.detectMultiScale(
            gray, 
            scaleFactor=1.1, 
            minNeighbors=5, 
            minSize=(30, 30),
            flags=cv2.CASCADE_SCALE_IMAGE
        )
        
        if len(faces) > 0:
            existe = "SI"
            print(f"Se detectaron {len(faces)} rostro(s)")
        else:
            print("No se detectaron rostros")
            
        return existe
    except Exception as e:
        print(f"Error procesando imagen: {str(e)}")
        return "NO"

# Cambiar para aceptar JSON en el body
@app.post('/predict/')
async def predict_from_url(request: ImageRequest):
    try:
        print(f"Recibida URL: {request.image_url}")
        prediction = buscar_existe(request.image_url)
        return {"prediction": prediction}
    except Exception as e:
        print(f"Error en predict_from_url: {str(e)}")
        raise HTTPException(status_code=500, detail=str(e))

@app.get("/")
async def root():
    return {"message": "Servicio de detección de rostros funcionando"}

@app.get("/health")
async def health():
    return {"status": "OK"}

# Ruta alternativa usando query parameter (por si acaso)
@app.get('/predict_get/')
async def predict_get(image_url: str):
    try:
        prediction = buscar_existe(image_url)
        return {"prediction": prediction}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))