Update app.py

app.py

@@ -217,14 +217,31 @@ def run_nnunet_predict(nifti_file):
 # Gradio Interfaceinterface = gr.Interface(
 interface = gr.Interface(
     fn=run_nnunet_predict,
-    inputs=gr.File(label="Upload FLAIR Image (.nii.gz)"),
+    inputs=[
+        gr.File(label="Upload FLAIR Image (.nii.gz)")
+    ],
     outputs=[
         gr.File(label="Download Segmentation Mask"),
         gr.Image(label="Input: FLAIR image"),
         gr.Image(label="Output: Lesion Mask")
     ],
     title="FLAMeS: Multiple Sclerosis Lesion Segmentation",
-    description="Upload a skull-stripped FLAIR image (.nii.gz) to generate a binary segmentation of multiple sclerosis lesions."
+    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)
+        """)
+    ]
 )
 
 # Debugging GPU environment