Update app.py

app.py

@@ -1,37 +1,44 @@
-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
-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:
-    img = frame.to_ndarray(format="bgr24")
-    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
-
-    for (x, y, w, h) in faces:
-        cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
-
-    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(
-    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
+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
 )