Update app.py

app.py

@@ -7,7 +7,6 @@ import math
 from tensorflow.keras.models import Model
 from tensorflow.keras.layers import (LSTM, Dense, Dropout, Input, Flatten, 
                                      Bidirectional, Permute, multiply)
-from moviepy.editor import VideoClip
 
 # Load the pose estimation model from Mediapipe
 mp_pose = mp.solutions.pose 
@@ -70,7 +69,7 @@ class VideoProcessor:
             temp_file.write(video_file.read())
         # Now we can open the video file using cv2.VideoCapture()
         cap = cv2.VideoCapture(filename)
-        frames = []
+        output_frames = []
         while cap.isOpened():
             ret, frame = cap.read()
             if not ret:
@@ -78,11 +77,11 @@ class VideoProcessor:
             frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
             results = self.pose.process(frame_rgb)
             processed_frame = self.process_frame(frame, results)
-            frames.append(processed_frame)
+            output_frames.append(processed_frame)
         cap.release()
         # Remove the temporary file
         os.remove(filename)
-        return frames
+        return output_frames
     
     def process_frame(self, frame, results):
         # Process the frame using the `process` function
@@ -265,14 +264,9 @@ def main():
     video_file = st.file_uploader("Upload a video file", type=["mp4", "avi"])
     if video_file is not None:
         video_processor = VideoProcessor()
-        frames = video_processor.process_video(video_file)
-        
-        def make_frame(t):
-            frame_index = int(t * 30)  # Assuming 30 frames per second
-            return frames[frame_index]
-        
-        video = VideoClip(make_frame, duration=len(frames) / 30)
-        st.video(video)
+        output_frames = video_processor.process_video(video_file)
+        for frame in output_frames:
+            st.image(frame, channels="BGR")
 
 if __name__ == "__main__":
     main()