separate downloads from main function

app.py

@@ -35,7 +35,6 @@ def load_and_predict_with_classifier(x, model_path, output_path, save):
     
     return y_pred
 
-
 def plot_umap(adata):
 
     labels = pd.Categorical(adata.obs["cell_type"])
@@ -69,18 +68,13 @@ def plot_umap(adata):
 
     return img
 
-
 def toggle_file_input(default_dataset):
     if default_dataset != "None":
         return gr.update(interactive=False)  # Disable the file input if a default dataset is selected
     else:
         return gr.update(interactive=True)  # Enable the file input if no default dataset is selected
 
-
-def main(input_file_path, species, default_dataset):
-
-    BASE_PATH = '/home/user/app/UCE/'
- 
+def download_files(base_path):
     # clone and cd into UCE repo
     os.system('git clone https://github.com/minwoosun/UCE.git')
     os.chdir(BASE_PATH)
@@ -92,6 +86,23 @@ def main(input_file_path, species, default_dataset):
     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")
 
+    return default_dataset_1_path, default_dataset_2_path 
+
+def main(input_file_path, species, default_dataset, default_dataset_1_path, default_dataset_2_path):
+
+    BASE_PATH = '/home/user/app/UCE/'
+ 
+    # # clone and cd into UCE repo
+    # os.system('git clone https://github.com/minwoosun/UCE.git')
+    # os.chdir(BASE_PATH)
+    
+    # # Add the directory to the Python path
+    # sys.path.append(BASE_PATH)
+
+    # # Set default dataset path
+    # 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")
+
     # If the user selects a default dataset, use that instead of the uploaded file
     if default_dataset == "PBMC 100 cells":
         input_file_path = default_dataset_1_path
@@ -105,10 +116,6 @@ def main(input_file_path, species, default_dataset):
     from accelerate import Accelerator
 
     model_loc = 'minwoosun/uce-100m'
-
-    print(input_file_path)
-    print(BASE_PATH)
-    print(model_loc)
     
     # Construct the command
     command = [
@@ -156,6 +163,9 @@ def main(input_file_path, species, default_dataset):
 
 if __name__ == "__main__":   
 
+    BASE_PATH = '/home/user/app/UCE/'
+    default_dataset_1_path, default_dataset_2_path = download_files(BASE_PATH)
+
     with gr.Blocks() as demo:
         gr.Markdown(
             '''
@@ -218,32 +228,27 @@ 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(
             fn=main, 
-            inputs=[file_input, species_input, default_dataset_input], 
+            inputs=[file_input, species_input, default_dataset_input, default_dataset_1_path, default_dataset_2_path], 
             outputs=[image_output, file_output, pred_output]
         )
 
+        # # Examples section
+        # examples = [
+        #     ["", "human", "PBMC 100 cells"],
+        #     ["", "human", "PBMC 1000 cells"]
 
-        # Examples section
-        examples = [
-            [None, "human", "PBMC 100 cells"],
-            [None, "human", "PBMC 1000 cells"]
+        # ]
 
-        ]
-
-        gr.Examples(
-            fn=main,
-            examples=examples,
-            inputs=[file_input, species_input, default_dataset_input],
-            outputs=[image_output, file_output, pred_output],
-            cache_examples=True
-        )
+        # gr.Examples(
+        #     fn=main,
+        #     examples=examples,
+        #     inputs=[file_input, species_input, default_dataset_input],
+        #     outputs=[image_output, file_output, pred_output],
+        #     cache_examples=True
+        # )
 
     demo.launch()