Update app.py

app.py

@@ -214,7 +214,7 @@ def run_nnunet_predict(nifti_file):
     except subprocess.CalledProcessError as e:
         return f"Error: {e}"
 
-# Gradio Interfaceinterface = gr.Interface(
+# Define the interface
 interface = gr.Interface(
     fn=run_nnunet_predict,
     inputs=[
@@ -229,25 +229,27 @@ interface = gr.Interface(
     description="Upload a skull-stripped FLAIR image (.nii.gz) to generate a binary segmentation of multiple sclerosis lesions.",
 )
 
-# Add Markdown as a separate element
+# Markdown for citations
 markdown = gr.Markdown("""
 **If you find this tool useful, please consider citing:**
 
-- **Reference 1:** Machine Learning / Network Science  
-  P286 A Deep Learning-Based Pipeline for Longitudinal White Matter Lesion Segmentation Using Diverse FLAIR Images  
-  F. La Rosa, et al.  
-  *ACTRIMS Forum 2023 - Poster Presentations. Multiple Sclerosis Journal.* 2023;29(2_suppl):18-242.  
-  DOI: [10.1177/13524585231169437](https://doi.org/10.1177/13524585231169437)
+1. A Deep Learning-Based Pipeline for Longitudinal White Matter Lesion Segmentation Using Diverse FLAIR Images  
+   F. La Rosa, J. Dos Santos Silva, W. A. Mullins, H. Greenspan, J. F. Sumowski, D. S. Reich, & E. S. Beck.  
+   *ACTRIMS Forum 2023 - Poster Presentations. Multiple Sclerosis Journal.* 2023;29(2_suppl):18-242.  
+   DOI: [10.1177/13524585231169437](https://doi.org/10.1177/13524585231169437)
 
-- **Reference 2:** A Deep Learning Model for Generalized Use in Medical Imaging  
-  *Nature Methods*, 2020.  
-  DOI: [10.1038/s41592-020-01008-z](https://www.nature.com/articles/s41592-020-01008-z)
+2. nnU-Net: A Self-Configuring Method for Deep Learning-Based Biomedical Image Segmentation  
+   F. Isensee, P. F. Jaeger, S. A. Kohl, J. Petersen, & K. H. Maier-Hein.  
+   *Nature Methods.* 2021;18(2):203-211.  
+   DOI: [10.1038/s41592-020-01008-z](https://www.nature.com/articles/s41592-020-01008-z)
 """)
 
-# Use a Gradio Blocks layout to include the Markdown
+# Use Gradio Blocks for layout
 with gr.Blocks() as demo:
-    interface.render()
-    markdown.render()
+    with gr.Row():
+        interface.render()
+    with gr.Row():
+        markdown.render()
 
 # Debugging GPU environment
 if torch.cuda.is_available():
@@ -256,10 +258,7 @@ else:
     print("No GPU available. Falling back to CPU.")
     os.system("nvidia-smi")  # Check if NVIDIA tools are available
 
-# Download model files before launching the app
-download_model()
-
 # Launch the app
 if __name__ == "__main__":
-    interface.launch(share=True)
+    demo.launch(share=True)