Update app.py

app.py

@@ -9,19 +9,19 @@ model = torch.load(model_path, map_location=torch.device('cpu'))
 model.eval()
 
 # Define classes if not already defined
-classes = ['creatures',
- 'fish',
- 'jellyfish',
- 'penguin',
- 'puffin',
- 'shark',
- 'starfish',
- 'stingray'] # List of class labels
+classes = ['creatures', 'fish', 'jellyfish', 'penguin', 'puffin', 'shark', 'starfish', 'stingray'] # List of class labels
 
 # Define function for processing video
 def process_video(input_video):
+    if isinstance(input_video, str):
+        # This is the case when the input is a filename
+        input_video_path = input_video
+    else:
+        # This is the case when the input is a file object
+        input_video_path = input_video.name
+
     output_path = 'video_output.avi'
-    cap = cv2.VideoCapture(input_video.name)
+    cap = cv2.VideoCapture(input_video_path)
     fps = cap.get(cv2.CAP_PROP_FPS)
     fourcc = cv2.VideoWriter_fourcc(*'XVID')
     out = cv2.VideoWriter(output_path, fourcc, fps, (int(cap.get(3)), int(cap.get(4))))
@@ -33,7 +33,7 @@ def process_video(input_video):
         if not ret:
             break
 
-        img = torch.tensor(frame.transpose(2, 0, 1) / 255.0, dtype=torch.float32).to(device)
+        img = torch.tensor(frame.transpose(2, 0, 1) / 255.0, dtype=torch.float32)
         img = img.unsqueeze(0)
 
         with torch.no_grad():
@@ -59,8 +59,8 @@ def process_video(input_video):
 
     return output_path
 
-video_input = gr.Video(label="Input Video")
-processed_video = gr.Image(label="Processed Video")  # No 'outputs' submodule
+video_input = gr.inputs.Video(label="Input Video")
+processed_video = gr.outputs.Video(label="Processed Video")
 
 interface = gr.Interface(
     fn=process_video,