from PIL import Image
import cv2
import numpy as np
import io
import torchvision.transforms as T

def detect_face(image_bytes):
    nparr = np.frombuffer(image_bytes, np.uint8)
    img_np = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
    gray = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY)
    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
    return len(faces) > 0

def preprocess_image(image_bytes):
    image = Image.open(io.BytesIO(image_bytes)).convert('RGB')
    transform = T.Compose([
        T.Resize((224, 224)),
        T.ToTensor(),
    ])
    return transform(image).unsqueeze(0)