Update app.py

app.py

@@ -1,74 +1,66 @@
 import os
+import cv2
 import streamlit as st
 import mediapipe as mp
-import cv2
-from streamlit_webrtc import webrtc_streamer, VideoTransformerBase
-import av
-import asyncio
-
-# Prevent asyncio event loop conflicts
-try:
-    asyncio.get_event_loop().run_until_complete(asyncio.sleep(0))
-except RuntimeError:
-    asyncio.set_event_loop(asyncio.new_event_loop())
+from PIL import Image
 
 # Initialize MediaPipe Hands
 mp_hands = mp.solutions.hands
 mp_drawing = mp.solutions.drawing_utils
 
-# Path to the gesture recognizer model
-model_path = "model/gesture_recognizer.task"
-if not os.path.exists(model_path):
-    st.error(f"Model file not found at {model_path}. Please upload it to the correct directory.")
-    st.stop()
+# Streamlit App Title
+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")
 
-class VideoProcessor(VideoTransformerBase):
-    def __init__(self):
-        # Initialize MediaPipe Hands
-        self.hands = mp_hands.Hands(
-            max_num_hands=2,
-            model_complexity=0,
-            min_detection_confidence=0.5,
-            min_tracking_confidence=0.5,
-        )
+# MediaPipe Hands configuration
+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)
+# 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
 
-        # Process the frame for hand landmarks
-        result = self.hands.process(img_rgb)
+            # Convert frame to RGB
+            frame = cv2.flip(frame, 1)  # Flip horizontally
+            frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
 
-        # 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
-                )
+            # Process the frame with MediaPipe Hands
+            result = hands.process(frame_rgb)
 
-        return av.VideoFrame.from_ndarray(img, format="bgr24")
+            # 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),
+                    )
 
-# Streamlit UI
-st.title("Gesture & Hand Landmark Detection 🚀")
-st.write("This app uses MediaPipe and Streamlit to detect hand landmarks in real-time.")
+            # Convert back to BGR for OpenCV display
+            frame_bgr = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
 
-# WebRTC streamer for live video
-webrtc_streamer(
-    key="gesture-detection",
-    video_processor_factory=VideoProcessor,
-    rtc_configuration={
-        "iceServers": [{"urls": ["stun:stun.l.google.com:19302"]}]
-    },
-)
+            # Display frame in Streamlit
+            stframe.image(frame_bgr, channels="BGR", use_column_width=True)
 
-# Footer
-st.markdown(
-    """
-    <style>
-    .footer {text-align: center; font-size: 12px; color: grey; margin-top: 20px;}
-    </style>
-    <p class="footer">Made with ❤️ using Streamlit & MediaPipe</p>
-    """,
-    unsafe_allow_html=True,
-)
+        cap.release()
+
+hands.close()
+st.write("Webcam stopped.")