Update app.py

app.py

@@ -10,23 +10,31 @@ model = torch.jit.load(model_path, map_location=torch.device('cpu'))
 # Define the prediction function
 def predict(image):
     try:
+        print("Predict function called")  # Check if the function is being called
+
         # Preprocess image
         image = image.convert("RGB")
         input_tensor = torch.from_numpy(np.array(image)).permute(2, 0, 1).unsqueeze(0).float() / 255.0
 
+        print("Image preprocessed")  # Check if preprocessing is successful
+
         # Run the model
         with torch.no_grad():
             output = model(input_tensor)
+        
+        print("Model executed")  # Check if model execution is successful
 
         # Postprocess the output
         output_image = output.squeeze().permute(1, 2, 0).numpy()
         output_image = (output_image * 255).astype(np.uint8)
+        
+        print("Output generated")  # Check if postprocessing is successful
         return Image.fromarray(output_image)
 
     except Exception as e:
-        # Print the error for debugging
         print(f"Error during prediction: {str(e)}")
-        return None  # Return None if there is an error
+        return None
+
 
 # Gradio Interface
 iface = gr.Interface(