code cleanup

app.py

@@ -38,26 +38,26 @@ def visualize_instance_seg_mask(mask):
 def query_image(img):
     data = {"image": img}
     batch = preprocess(data)
-    
+
     network.eval()
     with torch.no_grad():
         pred = inference(batch['image'].unsqueeze(dim=0), network)
-        
+
     batch["pred"] = pred
     for k,v in batch["pred"].items():
         batch["pred"][k] = v.squeeze(dim=0)
-    
+
     batch = postprocess(batch)
-    
+
     result = visualize_instance_seg_mask(batch["type_map"].squeeze())
-    
+
     # Combine image
     result = batch["image"].permute(1, 2, 0).cpu().numpy() * 0.5 + result * 0.5
-    
+
     # Solve rotating problem
     result = np.fliplr(result)
     result = np.rot90(result, k=1)
-    
+
     return result
 
 # load Markdown file
@@ -66,11 +66,11 @@ with open('Description.md','r') as file:
 
 demo = gr.Interface(
     query_image, 
-    inputs=[gr.Image(type="filepath")], 
+    inputs=[gr.Image(type="filepath")],
     outputs="image",
     title="Medical Image Classification with MONAI - Pathology Nuclei Segmentation Classification",
     description = markdown_content,
-    examples=example_files
+    examples=example_files,
 )
 
 demo.queue(concurrency_count=20).launch()