reset output for example

app.py

@@ -211,6 +211,10 @@ if __name__ == "__main__":
             '''
         )
 
+        # download default datasets and assign paths
+        default_dataset_1_path = hf_hub_download(repo_id="minwoosun/uce-misc", filename="100_pbmcs_proc_subset.h5ad")
+        default_dataset_2_path = hf_hub_download(repo_id="minwoosun/uce-misc", filename="1k_pbmcs_proc_subset.h5ad")
+
         # Define Gradio inputs and outputs
         file_input = gr.File(label="Upload a .h5ad single cell gene expression file or select a default dataset below")
         # species_input = gr.Dropdown(choices=["human", "mouse"], label="Select species")
@@ -232,6 +236,10 @@ if __name__ == "__main__":
             image_output = gr.Image(type="numpy", label="UMAP of UCE Embeddings")
             file_output = gr.File(label="Download embeddings")
             pred_output = gr.File(label="Download predictions")
+
+            print(image_output)
+            print(file_output)
+            print(pred_output)
     
         # Add the components and link to the function
         run_button.click(
@@ -240,13 +248,10 @@ if __name__ == "__main__":
             outputs=[image_output, file_output, pred_output]
         )
 
-        default_dataset_1_path = hf_hub_download(repo_id="minwoosun/uce-misc", filename="100_pbmcs_proc_subset.h5ad")
-        default_dataset_2_path = hf_hub_download(repo_id="minwoosun/uce-misc", filename="1k_pbmcs_proc_subset.h5ad")
-
         examples = gr.Examples(
         	examples=[[default_dataset_1_path, "human", "PBMC 100 cells"],[default_dataset_2_path, "human", "PBMC 1000 cells"]],
         	inputs=[file_input, species_input, default_dataset_input],
-       #     outputs=[image_output, file_output, pred_output],
+            outputs=[image_output, file_output, pred_output],
         	fn=main,
         	cache_examples=True
         )