Fix Grad-CAM gradient issue and add debug logging

app.py

@@ -150,6 +150,8 @@ def predict(file_obj):
         # Convert to Tensor [Batch, Channel, Depth, Height, Width]
         inp = torch.from_numpy(img_data_normalized).float().unsqueeze(0).unsqueeze(0)
 
+        print(f"Input tensor shape: {inp.shape}, min: {inp.min()}, max: {inp.max()}")
+
         # Run Inference
         with torch.no_grad():
             outputs = model(inp)
@@ -162,9 +164,13 @@ def predict(file_obj):
             results.append([label, float(probs[i])])
 
         print(f"Prediction result: {results}")
+        print(f"Predicted class: {predicted_class} ({CLASS_LABELS[predicted_class]})")
 
         # Generate Grad-CAM heatmap for the predicted class
-        heatmap = grad_cam.generate_cam(inp, target_class=predicted_class)
+        # Need to create a new tensor that requires gradients
+        inp_grad = torch.from_numpy(img_data_normalized).float().unsqueeze(0).unsqueeze(0)
+        inp_grad.requires_grad = True
+        heatmap = grad_cam.generate_cam(inp_grad, target_class=predicted_class)
 
         # Resize heatmap back to original scan size
         heatmap_resized = resize(heatmap, original_shape, mode='constant', anti_aliasing=True).astype(np.float32)