Update app.py

app.py

@@ -1,66 +1,57 @@
-import os
-import cv2
 import streamlit as st
+from streamlit_webrtc import webrtc_streamer, VideoTransformerBase
 import mediapipe as mp
-from PIL import Image
+import cv2
+import av
 
 # Initialize MediaPipe Hands
 mp_hands = mp.solutions.hands
 mp_drawing = mp.solutions.drawing_utils
 
-# Streamlit App Title
+class VideoProcessor(VideoTransformerBase):
+    def __init__(self):
+        # Initialize MediaPipe Hands
+        self.hands = mp_hands.Hands(
+            max_num_hands=2,
+            min_detection_confidence=0.5,
+            min_tracking_confidence=0.5,
+        )
+
+    def recv(self, frame):
+        img = frame.to_ndarray(format="bgr24")
+        img = cv2.flip(img, 1)  # Flip for a mirror effect
+        img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+        # Process the frame for hand landmarks
+        result = self.hands.process(img_rgb)
+
+        # Draw hand landmarks
+        if result.multi_hand_landmarks:
+            for hand_landmarks in result.multi_hand_landmarks:
+                mp_drawing.draw_landmarks(
+                    img, hand_landmarks, mp_hands.HAND_CONNECTIONS
+                )
+
+        return av.VideoFrame.from_ndarray(img, format="bgr24")
+
+# Streamlit UI
 st.title("Gesture & Hand Landmark Detection 🚀")
-st.write("This app uses MediaPipe and OpenCV to detect hand landmarks in real-time from your webcam.")
-
-# Start Webcam
-run_webcam = st.button("Start Webcam")
+st.write("This app uses MediaPipe and Streamlit to detect hand landmarks in real-time from your webcam.")
 
-# MediaPipe Hands configuration
-hands = mp_hands.Hands(
-    max_num_hands=2,
-    min_detection_confidence=0.5,
-    min_tracking_confidence=0.5,
+# WebRTC streamer for live video
+webrtc_streamer(
+    key="gesture-detection",
+    video_processor_factory=VideoProcessor,
+    rtc_configuration={"iceServers": [{"urls": ["stun:stun.l.google.com:19302"]}]},
 )
 
-# Webcam video feed
-if run_webcam:
-    stframe = st.empty()  # Placeholder for video frames
-    cap = cv2.VideoCapture(0)  # Open the first webcam
-    
-    if not cap.isOpened():
-        st.error("Unable to access the webcam. Please ensure it's connected and accessible.")
-    else:
-        while run_webcam:
-            ret, frame = cap.read()
-            if not ret:
-                st.error("Failed to grab frame from webcam.")
-                break
-
-            # Convert frame to RGB
-            frame = cv2.flip(frame, 1)  # Flip horizontally
-            frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-
-            # Process the frame with MediaPipe Hands
-            result = hands.process(frame_rgb)
-
-            # Draw hand landmarks
-            if result.multi_hand_landmarks:
-                for hand_landmarks in result.multi_hand_landmarks:
-                    mp_drawing.draw_landmarks(
-                        frame, 
-                        hand_landmarks, 
-                        mp_hands.HAND_CONNECTIONS,
-                        mp_drawing.DrawingSpec(color=(121, 22, 76), thickness=2, circle_radius=4),
-                        mp_drawing.DrawingSpec(color=(250, 44, 250), thickness=2, circle_radius=2),
-                    )
-
-            # Convert back to BGR for OpenCV display
-            frame_bgr = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
-
-            # Display frame in Streamlit
-            stframe.image(frame_bgr, channels="BGR", use_column_width=True)
-
-        cap.release()
-
-hands.close()
-st.write("Webcam stopped.")
+# Footer
+st.markdown(
+    """
+    <style>
+    .footer {text-align: center; font-size: 12px; color: grey; margin-top: 20px;}
+    </style>
+    <p class="footer"></p>
+    """,
+    unsafe_allow_html=True,
+)