import cv2
import av
import asyncio
import numpy as np
import streamlit as st
from streamlit_webrtc import WebRtcMode, webrtc_streamer

# Fix asyncio event loop issue on Windows (this is useful for local testing on Windows)
if hasattr(asyncio, 'WindowsSelectorEventLoopPolicy'):
    asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())

# Load Haarcascade model
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

# Face detection function for WebRTC
def video_frame_callback(frame: av.VideoFrame) -> av.VideoFrame:
    # Convert the frame to a numpy array (OpenCV format)
    img = frame.to_ndarray(format="bgr24")
    
    # Convert image to grayscale
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    
    # Detect faces in the grayscale image
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
    
    # Draw rectangles around detected faces
    for (x, y, w, h) in faces:
        cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)

    # Return the processed frame
    return av.VideoFrame.from_ndarray(img, format="bgr24")

# Streamlit UI
st.title("Real-Time Face Detection")
st.write("Using OpenCV and Haar Cascade Model")

# WebRTC streamer (webrtc_streamer already handles async tasks internally)
webrtc_streamer(
    key="face-detection",
    mode=WebRtcMode.SENDRECV,
    rtc_configuration={"iceServers": [{"urls": ["stun:stun.l.google.com:19302"]}]},
    video_frame_callback=video_frame_callback,
    async_processing=True  # Keep this enabled for async processing
)