Update app.py

app.py

@@ -148,6 +148,8 @@ def load_model(root_dir, model_name, model_file_name):
         model = Build_Custom_Model(model_name, NUM_CLASSES, pretrained=False).to(device)
     else:
         model = SEResNet50(spatial_dims=2, in_channels=1, num_classes=NUM_CLASSES).to(device)
+    print(os.path.join(root_dir, model_file_name))
+    print("=================================")
     model.load_state_dict(torch.load(os.path.join(root_dir, model_file_name),  map_location=device))
     model.eval()
     return model
@@ -177,21 +179,8 @@ if uploaded_ct_file is not None:
     with tempfile.NamedTemporaryFile(delete=False, suffix=".dcm") as temp_file:
         temp_file.write(uploaded_ct_file.getvalue())
 
-        # Debugging section to isolate the RuntimeError issue
-        try:
-            # Check if the temporary DICOM file is accessible and properly written
-            if not os.path.exists(temp_file.name) or os.path.getsize(temp_file.name) == 0:
-                print("Debugging: Temporary DICOM file is either missing or empty.")
-        
-            # Attempt to apply the evaluation transforms to the DICOM image
-            image_tensor = eval_transforms(temp_file.name).unsqueeze(0).to(device)
-        
-        except Exception as e:
-            print(f"Debugging: Exception caught while applying transform: {e}")
-            raise
-
-        # # Apply evaluation transforms to the DICOM image for model prediction
-        # image_tensor = eval_transforms(temp_file.name).unsqueeze(0).to(device)
+        # Apply evaluation transforms to the DICOM image for model prediction
+        image_tensor = eval_transforms(temp_file.name).unsqueeze(0).to(device)
 
         # Predict
         with torch.no_grad():