Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,41 +1,36 @@
 import streamlit as st
-import av  # For video frame processing
-import cv2  # OpenCV for image processing
+import cv2
 import numpy as np
-import mediapipe as mp  # Pose estimation
-from streamlit_webrtc import webrtc_streamer, WebRtcMode  # WebRTC integration
+import mediapipe as mp
+from PIL import Image
 
-# Initialize MediaPipe Pose components
+# Initialize MediaPipe
 mp_pose = mp.solutions.pose
 mp_drawing = mp.solutions.drawing_utils
 
-# Initialize session state variables
+# Configure page
+st.set_page_config(layout="wide")
+st.title("📷 Posture Analysis App")
+
+# Initialize session state
+if 'camera_on' not in st.session_state:
+    st.session_state.camera_on = False
 if 'posture_status' not in st.session_state:
-    st.session_state.posture_status = "Please enable camera for analysis"
+    st.session_state.posture_status = "Please enable camera"
 
 def analyze_posture(image):
-    """
-    Analyze posture using MediaPipe Pose
-    Args:
-        image: Input frame from camera
-    Returns:
-        annotated_image: Frame with pose landmarks drawn
-        posture_status: Analysis results text
-    """
+    """Analyze posture using MediaPipe"""
     with mp_pose.Pose(
         min_detection_confidence=0.5,
         min_tracking_confidence=0.5,
         model_complexity=1
     ) as pose:
         
-        # Convert color space and process frame
         image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
         results = pose.process(image_rgb)
         
         annotated_image = image.copy()
-        
         if results.pose_landmarks:
-            # Draw pose landmarks on the frame
             mp_drawing.draw_landmarks(
                 annotated_image, 
                 results.pose_landmarks, 
@@ -45,22 +40,15 @@ def analyze_posture(image):
             )
             posture_status = check_posture(results.pose_landmarks, image.shape)
         else:
-            posture_status = "No pose detected - ensure full body is visible"
+            posture_status = "No pose detected"
         
         return annotated_image, posture_status
 
 def check_posture(landmarks, image_shape):
-    """
-    Analyze body landmarks and generate posture report
-    Args:
-        landmarks: Detected pose landmarks
-        image_shape: Dimensions of input image
-    Returns:
-        Formatted posture analysis report
-    """
+    """Analyze body landmarks and generate posture report"""
     h, w, _ = image_shape
     
-    # Get key body points
+    # Get key points
     left_shoulder = landmarks.landmark[mp_pose.PoseLandmark.LEFT_SHOULDER]
     right_shoulder = landmarks.landmark[mp_pose.PoseLandmark.RIGHT_SHOULDER]
     left_hip = landmarks.landmark[mp_pose.PoseLandmark.LEFT_HIP]
@@ -69,18 +57,18 @@ def check_posture(landmarks, image_shape):
     right_ear = landmarks.landmark[mp_pose.PoseLandmark.RIGHT_EAR]
     nose = landmarks.landmark[mp_pose.PoseLandmark.NOSE]
     
-    # Determine if sitting or standing
+    # Determine posture
     sitting = left_hip.y < left_shoulder.y + 0.1 or right_hip.y < right_shoulder.y + 0.1
     
     messages = []
     
-    # Forward head posture check
+    # Forward head check
     head_forward = (left_ear.y > left_shoulder.y + 0.1 or right_ear.y > right_shoulder.y + 0.1) and \
                    (nose.y > left_shoulder.y or nose.y > right_shoulder.y)
     if head_forward:
-        messages.append("• Forward head tilt detected (text neck)")
+        messages.append("• Forward head tilt detected")
     
-    # Rounded shoulders check
+    # Shoulders check
     shoulders_rounded = left_shoulder.x > left_hip.x + 0.05 or right_shoulder.x < right_hip.x - 0.05
     if shoulders_rounded:
         messages.append("• Rounded shoulders detected")
@@ -91,80 +79,50 @@ def check_posture(landmarks, image_shape):
     if shoulder_diff > 0.05 or hip_diff > 0.05:
         messages.append("• Body leaning to one side")
     
-    # Pelvis position check
-    if sitting and (left_hip.y < left_shoulder.y + 0.15 or right_hip.y < right_shoulder.y + 0.15):
-        messages.append("• Pelvis tilted forward (sitting posture)")
-    
-    # Generate final report
+    # Generate report
     if messages:
         report = [
-            f"**{'Sitting' if sitting else 'Standing'} posture issues detected:**",
+            f"**{'Sitting' if sitting else 'Standing'} posture issues:**",
             *messages,
             "\n**Recommendations:**",
-            "• Keep head straight - ears over shoulders",
-            "• Pull shoulders back and down",
-            "• Maintain straight back, avoid side leaning",
-            "• When sitting, support weight on sitting bones"
+            "• Keep head straight",
+            "• Pull shoulders back",
+            "• Maintain straight back",
+            "• Sit on sitting bones"
         ]
     else:
         report = [
             f"**Excellent {'sitting' if sitting else 'standing'} posture!**",
-            "All key points are properly aligned",
             "\n**Tips:**",
-            "• Continue monitoring your posture daily"
+            "• Continue monitoring your posture"
         ]
     
     return "\n\n".join(report)
 
-def video_frame_callback(frame):
-    """
-    Callback function for processing each video frame
-    Args:
-        frame: Incoming video frame from WebRTC
-    Returns:
-        Processed video frame with pose landmarks
-    """
-    img = frame.to_ndarray(format="bgr24")
-    
-    try:
-        # Analyze posture and update session state
-        analyzed_img, posture_status = analyze_posture(img)
-        st.session_state.posture_status = posture_status
-        return av.VideoFrame.from_ndarray(analyzed_img, format="bgr24")
-    except Exception as e:
-        st.error(f"Processing error: {str(e)}")
-        return frame
-
 def main():
-    # Configure Streamlit page
-    st.set_page_config(layout="wide")
-    st.title("📷 Real-time Posture Analysis")
-    
-    # Create two-column layout
     col1, col2 = st.columns([2, 1])
     
     with col1:
-        st.header("Camera Feed")
+        st.header("Camera View")
         
-        # WebRTC streamer component
-        webrtc_ctx = webrtc_streamer(
-            key="posture-analysis",
-            mode=WebRtcMode.SENDRECV,
-            video_frame_callback=video_frame_callback,
-            media_stream_constraints={"video": True, "audio": False},
-            async_processing=True,
-            rtc_configuration={
-                "iceServers": [{"urls": ["stun:stun.l.google.com:19302"]}]
-            }
-        )
+        # Camera toggle
+        if st.button("Enable Camera" if not st.session_state.camera_on else "Disable Camera"):
+            st.session_state.camera_on = not st.session_state.camera_on
+            st.rerun()
         
-        # Handle camera state
-        if not webrtc_ctx.state.playing:
-            st.session_state.posture_status = "Camera is off - please enable access"
-            st.warning("Please allow camera permissions when prompted")
+        if st.session_state.camera_on:
+            # Placeholder for local camera implementation
+            demo_image = np.zeros((480, 640, 3), dtype=np.uint8)
+            st.image(demo_image, caption="Camera feed placeholder")
+            st.warning("For full camera functionality, please run locally")
+            
+            # Simulate analysis
+            if st.button("Analyze Current Frame"):
+                _, posture_status = analyze_posture(demo_image)
+                st.session_state.posture_status = posture_status
     
     with col2:
-        st.header("Posture Analysis")
+        st.header("Analysis Results")
         st.markdown(st.session_state.posture_status)
 
 if __name__ == "__main__":