Update app.py

app.py

@@ -227,23 +227,28 @@ interface = gr.Interface(
     ],
     title="FLAMeS: Multiple Sclerosis Lesion Segmentation",
     description="Upload a skull-stripped FLAIR image (.nii.gz) to generate a binary segmentation of multiple sclerosis lesions.",
-    examples=[
-        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 Rosa1, J. Dos Santos Silva1, W. A. Mullins2, H. Greenspan3, J. F. Sumowski1, D. S. Reich2, 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)
-        """)
-    ]
 )
 
+# Add Markdown as a separate element
+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)
+
+- **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)
+""")
+
+# Use a Gradio Blocks layout to include the Markdown
+with gr.Blocks() as demo:
+    interface.render()
+    markdown.render()
+
 # Debugging GPU environment
 if torch.cuda.is_available():
     print(f"GPU is available: {torch.cuda.get_device_name(0)}")