[data] fixing model

app.py

@@ -16,11 +16,22 @@ from src.display.css_html_js import custom_css
 ### Space initialisation
 
 
-# Load with multi-header and set the first column ('Model') as index
-LEADERBOARD_DF = pd.read_csv("leaderboard_data.csv", header=[0, 1], index_col=0)
-LEADERBOARD_DF.index.name = "Model" # Explicitly name the index
-# No need to astype(str) globally here, Gradio DataFrame handles it well.
-# If specific styling or type issues arise, it can be done selectively.
+# Load leaderboard data with multi-header, do not set index initially
+LEADERBOARD_DF_ORIGINAL = pd.read_csv("leaderboard_data.csv", header=[0, 1])
+
+# Function to prepare DataFrame for display (format headers, ensure Model column)
+def format_leaderboard_df_for_display(df_orig):
+    df_display = df_orig.copy()
+    new_columns = []
+    for col_tuple in df_display.columns:
+        if col_tuple == ('Model', 'Model'):
+            new_columns.append('Model')
+        else:
+            new_columns.append(f"{col_tuple[0]}\n{col_tuple[1]}")
+    df_display.columns = new_columns
+    return df_display
+
+LEADERBOARD_DF_DISPLAY_INIT = format_leaderboard_df_for_display(LEADERBOARD_DF_ORIGINAL)
 
 BIAS_DF = pd.read_csv("bias_evaluation_data.csv")
 BIAS_DF = BIAS_DF.astype(str).fillna("-")
@@ -37,15 +48,14 @@ with demo:
                 # Add the image display
                 gr.Image("table_snapshot.png", label="Original Table Snapshot", interactive=False)
                 
-                # Display the table with multi-level header
-                table_output = gr.DataFrame(value=LEADERBOARD_DF, label="Leaderboard Results", interactive=True, wrap=True)
+                # Initialize with the display-formatted DataFrame
+                table_output = gr.DataFrame(value=LEADERBOARD_DF_DISPLAY_INIT, label="Leaderboard Results", interactive=True, wrap=True)
                 
                 gr.Markdown("---")
                 gr.Markdown("### Display Options")
                 
-                # Removed column_selector
-
-                model_filter_choices = LEADERBOARD_DF.index.tolist()
+                # Filter choices from the original DataFrame's ('Model', 'Model') column
+                model_filter_choices = LEADERBOARD_DF_ORIGINAL[('Model', 'Model')].unique().tolist()
                 model_selector = gr.CheckboxGroup(
                     choices=model_filter_choices,
                     value=model_filter_choices, 
@@ -53,19 +63,21 @@ with demo:
                 )
 
                 def update_table(selected_models_from_filter):
+                    filtered_df_orig = LEADERBOARD_DF_ORIGINAL.copy()
                     if not selected_models_from_filter:
-                        # Return an empty DataFrame with the same index and columns
-                        filtered_df = LEADERBOARD_DF.iloc[0:0]
+                        # If no models selected, show an empty table structure (based on original for cols)
+                        filtered_df_orig = LEADERBOARD_DF_ORIGINAL[LEADERBOARD_DF_ORIGINAL[('Model', 'Model')].isin([])] # Empty but keeps structure
                     else:
-                        # Filter out any selected models not actually in the index to prevent errors
-                        valid_selected_models = [model for model in selected_models_from_filter if model in LEADERBOARD_DF.index]
+                        valid_selected_models = [model for model in selected_models_from_filter if model in model_filter_choices]
                         if not valid_selected_models:
-                             filtered_df = LEADERBOARD_DF.iloc[0:0]
+                            filtered_df_orig = LEADERBOARD_DF_ORIGINAL[LEADERBOARD_DF_ORIGINAL[('Model', 'Model')].isin([])]
                         else:
-                             filtered_df = LEADERBOARD_DF.loc[valid_selected_models]
-                    return gr.DataFrame.update(value=filtered_df)
+                            filtered_df_orig = LEADERBOARD_DF_ORIGINAL[LEADERBOARD_DF_ORIGINAL[('Model', 'Model')].isin(valid_selected_models)]
+                    
+                    # Format the filtered DataFrame for display
+                    df_to_display = format_leaderboard_df_for_display(filtered_df_orig)
+                    return gr.DataFrame.update(value=df_to_display)
 
-                # Event listener (only model_selector now)
                 model_selector.change(
                     fn=update_table,
                     inputs=[model_selector],