Update app.py

app.py

@@ -18,80 +18,79 @@ loaded_model = loaded_model.to(device)
 loaded_model.eval()
 
 label_names = [
-    'Archery', 'BalanceBeam', 'BenchPress', 'ApplyEyeMakeup', 'BasketballDunk',
-    'BandMarching', 'BabyCrawling', 'ApplyLipstick', 'BaseballPitch', 'Basketball'
+   'Archery', 'BalanceBeam', 'BenchPress', 'ApplyEyeMakeup', 'BasketballDunk',
+   'BandMarching', 'BabyCrawling', 'ApplyLipstick', 'BaseballPitch', 'Basketball'
 ]
 
 def load_video(video_path):
-    try:
-        if not os.path.exists(video_path):
-            raise ValueError(f"Video file not found: {video_path}")
-            
-        video = EncodedVideo.from_path(video_path)
-        video_data = video.get_clip(start_sec=0, end_sec=video.duration)
-        return video_data['video']
-    except Exception as e:
-        raise ValueError(f"Error loading video: {str(e)}")
+   try:
+       if not os.path.exists(video_path):
+           raise ValueError(f"Video file not found: {video_path}")
+           
+       video = EncodedVideo.from_path(video_path)
+       video_data = video.get_clip(start_sec=0, end_sec=video.duration)
+       return video_data['video']
+   except Exception as e:
+       raise ValueError(f"Error loading video: {str(e)}")
 
 def preprocess_video(video_frames):
-    try:
-        transform_temporal = UniformTemporalSubsample(16)
-        video_frames = transform_temporal(video_frames)
-        video_frames = video_frames.float() / 255.0
-        if video_frames.shape[0] == 3:
-            video_frames = video_frames.permute(1, 0, 2, 3)
-        mean = torch.tensor([0.485, 0.456, 0.406])
-        std = torch.tensor([0.229, 0.224, 0.225])
-        for t in range(video_frames.shape[0]):
-            video_frames[t] = F_t.normalize(video_frames[t], mean, std)
-        video_frames = torch.stack([
-            F_t.resize(frame, [224, 224], antialias=True) 
-            for frame in video_frames
-        ])
-        video_frames = video_frames.unsqueeze(0)
-        return video_frames
-    except Exception as e:
-        raise ValueError(f"Error preprocessing video: {str(e)}")
+   try:
+       transform_temporal = UniformTemporalSubsample(16)
+       video_frames = transform_temporal(video_frames)
+       video_frames = video_frames.float() / 255.0
+       if video_frames.shape[0] == 3:
+           video_frames = video_frames.permute(1, 0, 2, 3)
+       mean = torch.tensor([0.485, 0.456, 0.406])
+       std = torch.tensor([0.229, 0.224, 0.225])
+       for t in range(video_frames.shape[0]):
+           video_frames[t] = F_t.normalize(video_frames[t], mean, std)
+       video_frames = torch.stack([
+           F_t.resize(frame, [224, 224], antialias=True) 
+           for frame in video_frames
+       ])
+       video_frames = video_frames.unsqueeze(0)
+       return video_frames
+   except Exception as e:
+       raise ValueError(f"Error preprocessing video: {str(e)}")
 
 def predict_video(video):
-    if video is None:
-        return "Please upload a video file."
-        
-    try:
-        video_data = load_video(video)
-        processed_video = preprocess_video(video_data)
-        processed_video = processed_video.to(device)
-        with torch.no_grad():
-            outputs = loaded_model(processed_video)
-            logits = outputs.logits
-            probabilities = F.softmax(logits, dim=-1)[0]
-            top_3 = torch.topk(probabilities, 3)
-        results = [
-            f"{label_names[idx.item()]}: {prob.item():.2%}"
-            for idx, prob in zip(top_3.indices, top_3.values)
-        ]
-        return "\n".join(results)
-    except Exception as e:
-        return f"Error processing video: {str(e)}"
+   if video is None:
+       return "Please upload a video file."
+       
+   try:
+       video_data = load_video(video)
+       processed_video = preprocess_video(video_data)
+       processed_video = processed_video.to(device)
+       with torch.no_grad():
+           outputs = loaded_model(processed_video)
+           logits = outputs.logits
+           probabilities = F.softmax(logits, dim=-1)[0]
+           top_3 = torch.topk(probabilities, 3)
+       results = [
+           f"{label_names[idx.item()]}: {prob.item():.2%}"
+           for idx, prob in zip(top_3.indices, top_3.values)
+       ]
+       return "\n".join(results)
+   except Exception as e:
+       return f"Error processing video: {str(e)}"
 
 iface = gr.Interface(
-    fn=predict_video,
-    inputs=gr.Video(label="Upload Video"),
-    outputs=gr.Textbox(label="Top 3 Predictions"),
-    title="Video Action Recognition",
-    description="Upload a video to classify the action being performed. The model will return the top 3 predictions.",
-    examples=[
-        ["test_video_1.avi"],
-        ["test_video_2.avi"],
-        ["test_video_3.avi"]
-    ],
-    cache_examples=True
+   fn=predict_video,
+   inputs=gr.Video(label="Upload Video"),
+   outputs=gr.Textbox(label="Top 3 Predictions"),
+   title="Video Action Recognition",
+   description="Upload a video to classify the action being performed. The model will return the top 3 predictions.",
+   examples=[
+       ["test_video_1.avi"],
+       ["test_video_2.avi"],
+       ["test_video_3.avi"]
+   ],
+   cache_examples=True
 )
 
 if __name__ == "__main__":
-    iface.launch(
-        debug=False,
-        server_name="0.0.0.0",
-        server_port=7860,
-        ssr=False
-    )
+   iface.launch(
+       debug=False,
+       server_name="0.0.0.0",
+       server_port=7860
+   )