Update app.py

app.py

@@ -1,5 +1,3 @@
-# --- START OF FINAL, POLISHED FILE app.py ---
-
 import gradio as gr
 import pandas as pd
 import plotly.express as px
@@ -156,33 +154,54 @@ with gr.Blocks(title="🤗 ModelVerse Explorer", fill_width=True, css=custom_css
         return gr.update(visible=choice == "Tag Filter"), gr.update(visible=choice == "Pipeline Filter")
     filter_choice_radio.change(fn=_toggle_filters_visibility, inputs=filter_choice_radio, outputs=[tag_filter_dropdown, pipeline_filter_dropdown])
 
-    def ui_load_data_controller(progress=gr.Progress()):
+    ## CHANGE: Renamed and modified ui_load_data_controller to also generate the initial plot
+    def load_and_generate_initial_plot(progress=gr.Progress()):
         progress(0, desc=f"Loading dataset '{HF_DATASET_ID}'...")
+        # --- Part 1: Data Loading ---
         try:
             current_df, load_success_flag, status_msg_from_load = load_models_data()
             if load_success_flag:
-                progress(0.9, desc="Processing data...")
+                progress(0.5, desc="Processing data...")
                 date_display = "Pre-processed (date unavailable)"
                 if 'data_download_timestamp' in current_df.columns and pd.notna(current_df['data_download_timestamp'].iloc[0]):
                     ts = pd.to_datetime(current_df['data_download_timestamp'].iloc[0], utc=True)
                     date_display = ts.strftime('%B %d, %Y, %H:%M:%S %Z')
                 param_count = (current_df['params'] > 0).sum() if 'params' in current_df.columns else 0
                 data_info_text = f"### Data Information\n- Source: `{HF_DATASET_ID}`\n- Status: {status_msg_from_load}\n- Total models loaded: {len(current_df):,}\n- Models with parameter counts: {param_count:,}\n- Data as of: {date_display}\n"
-                status_msg_ui = "Data loaded. Ready to generate plot."
             else:
                 data_info_text = f"### Data Load Failed\n- {status_msg_from_load}"
-                status_msg_ui = status_msg_from_load
         except Exception as e:
-            status_msg_ui = f"An unexpected error occurred: {str(e)}"
-            data_info_text = f"### Critical Error\n- {status_msg_ui}"
+            status_msg_from_load = f"An unexpected error occurred: {str(e)}"
+            data_info_text = f"### Critical Error\n- {status_msg_from_load}"
             load_success_flag = False
-            print(f"Critical error in ui_load_data_controller: {e}")
-        return current_df, load_success_flag, data_info_text, status_msg_ui
+            current_df = pd.DataFrame()
+            print(f"Critical error in load_and_generate_initial_plot: {e}")
+
+        # --- Part 2: Generate Initial Plot ---
+        # We call the existing plot generation function with the default values from the UI
+        progress(0.6, desc="Generating initial plot...")
+        # Get default values directly from the UI component definitions
+        default_metric = "downloads"
+        default_filter_type = "None"
+        default_tag = None
+        default_pipeline = None
+        default_param_indices = PARAM_CHOICES_DEFAULT_INDICES
+        default_k = 25
+        default_skip_orgs = "TheBloke,MaziyarPanahi,unsloth,modularai,Gensyn,bartowski"
+
+        # Reuse the existing controller function for plotting
+        initial_plot, initial_status = ui_generate_plot_controller(
+            default_metric, default_filter_type, default_tag, default_pipeline, 
+            default_param_indices, default_k, default_skip_orgs, current_df, progress
+        )
+        
+        # Return all the necessary updates for the UI
+        return current_df, load_success_flag, data_info_text, initial_status, initial_plot
 
     def ui_generate_plot_controller(metric_choice, filter_type, tag_choice, pipeline_choice, 
                                    param_range_indices, k_orgs, skip_orgs_input, df_current_models, progress=gr.Progress()):
         if df_current_models is None or df_current_models.empty:
-            return create_treemap(pd.DataFrame(), metric_choice, "Error: Model Data Not Loaded"), "Model data is not loaded."
+            return create_treemap(pd.DataFrame(), metric_choice, "Error: Model Data Not Loaded"), "Model data is not loaded. Cannot generate plot."
         
         progress(0.1, desc="Preparing data...")
         tag_to_use = tag_choice if filter_type == "Tag Filter" else None
@@ -208,10 +227,11 @@ with gr.Blocks(title="🤗 ModelVerse Explorer", fill_width=True, css=custom_css
             plot_stats_md = f"## Plot Statistics\n- **Models shown**: {total_items_in_plot:,}\n- **Total {metric_choice}**: {int(total_value_in_plot):,}"
         return plotly_fig, plot_stats_md
 
+    ## CHANGE: Updated demo.load to call the new function and to add plot_output to the outputs list
     demo.load(
-        fn=ui_load_data_controller, 
+        fn=load_and_generate_initial_plot, 
         inputs=[], 
-        outputs=[models_data_state, loading_complete_state, data_info_md, status_message_md]
+        outputs=[models_data_state, loading_complete_state, data_info_md, status_message_md, plot_output]
     )
 
     generate_plot_button.click(
@@ -223,6 +243,4 @@ with gr.Blocks(title="🤗 ModelVerse Explorer", fill_width=True, css=custom_css
 
 if __name__ == "__main__":
     print(f"Application starting...")
-    demo.queue().launch()
-
-# --- END OF FINAL, POLISHED FILE app.py ---
+    demo.queue().launch()