[update] info

app.py

@@ -1,5 +1,4 @@
 import gradio as gr
-from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
 import pandas as pd
 
 from src.about import (
@@ -16,33 +15,8 @@ from src.display.css_html_js import custom_css
 
 
 LEADERBOARD_DF = pd.read_csv("leaderboard_data.csv")
-
-
-def init_leaderboard(dataframe):
-    if dataframe is None or dataframe.empty:
-        raise ValueError("Leaderboard DataFrame is empty or None.")
-    
-    all_columns = dataframe.columns.tolist()
-    
-    datatypes = ['str'] * len(all_columns)
-    
-    default_selection = all_columns
-    search_columns = [all_columns[0]] if all_columns else []
-    
-    return Leaderboard(
-        value=dataframe,
-        datatype=datatypes,
-        select_columns=SelectColumns(
-            default_selection=default_selection,
-            cant_deselect=[],
-            label="Select Columns to Display:",
-        ),
-        search_columns=search_columns,
-        hide_columns=[],
-        filter_columns=[ColumnFilter(col, type="checkboxgroup", label=f"{col} types") for col in search_columns],
-        bool_checkboxgroup_label="Hide models",
-        interactive=False,
-    )
+# Ensure all data is treated as string initially for display consistency
+LEADERBOARD_DF = LEADERBOARD_DF.astype(str) 
 
 
 demo = gr.Blocks(css=custom_css)
@@ -51,10 +25,74 @@ with demo:
     gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
 
     with gr.Tabs(elem_classes="tab-buttons") as tabs:
-        with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
-            leaderboard = init_leaderboard(LEADERBOARD_DF)
+        with gr.TabItem("Unified performance evaluation of VLM captioners", elem_id="llm-benchmark-tab-table", id=0):
+            with gr.Column():
+                # 1. Display the table first
+                # Make DataFrame interactive for sorting
+                table_output = gr.DataFrame(value=LEADERBOARD_DF, label="Leaderboard Results", interactive=True, wrap=True)
+
+                gr.Markdown("---") # Visual separator
+
+                # 2. Controls below the table
+                gr.Markdown("### Display Options")
+
+                all_columns_list = LEADERBOARD_DF.columns.tolist()
+                column_selector = gr.CheckboxGroup(
+                    choices=all_columns_list,
+                    value=all_columns_list, # Initially, all columns are selected
+                    label="Select Columns to Display:"
+                )
+
+                # Assuming the first column is 'Model' for filtering
+                # If leaderboard_data.csv might be empty or have no columns, add checks
+                model_filter_col_name = ""
+                model_filter_choices = []
+                if not LEADERBOARD_DF.empty and LEADERBOARD_DF.columns.any():
+                    model_filter_col_name = LEADERBOARD_DF.columns[0]
+                    model_filter_choices = LEADERBOARD_DF[model_filter_col_name].astype(str).unique().tolist()
+                
+                model_selector = gr.CheckboxGroup(
+                    choices=model_filter_choices,
+                    value=model_filter_choices, # Initially, all models are selected
+                    label=f"Filter by {model_filter_col_name} types:" if model_filter_col_name else "Model Filter"
+                )
+
+                # Update function for the table
+                def update_table(selected_cols, selected_models_from_filter):
+                    temp_df = LEADERBOARD_DF.copy()
+                    
+                    # Filter by selected models (from the first column)
+                    if model_filter_col_name and selected_models_from_filter:
+                        temp_df = temp_df[temp_df[model_filter_col_name].isin(selected_models_from_filter)]
+                    elif model_filter_col_name and not selected_models_from_filter: # No models selected, show empty
+                         temp_df = pd.DataFrame(columns=LEADERBOARD_DF.columns)
+
+
+                    # Select display columns
+                    # Ensure selected_cols are valid columns present in the temp_df after filtering
+                    valid_selected_cols = [col for col in selected_cols if col in temp_df.columns]
+                    if not valid_selected_cols and not temp_df.empty : # If all columns are deselected, but df is not empty, show all original columns of filtered
+                        final_df = temp_df
+                    elif not valid_selected_cols and temp_df.empty: # if all columns deselected and df is empty
+                        final_df = pd.DataFrame(columns=selected_cols) # empty df with original column names
+                    else:
+                        final_df = temp_df[valid_selected_cols]
+                    
+                    return gr.DataFrame.update(value=final_df)
+
+                # Event listeners
+                column_selector.change(
+                    fn=update_table,
+                    inputs=[column_selector, model_selector],
+                    outputs=[table_output]
+                )
+                model_selector.change(
+                    fn=update_table,
+                    inputs=[column_selector, model_selector],
+                    outputs=[table_output]
+                )
 
-        with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2):
+        with gr.TabItem("📝 Bias-aware evaluation of VLM ", elem_id="llm-benchmark-tab-table", id=2):
             gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
 
     with gr.Row():

src/about.py

@@ -21,7 +21,7 @@ NUM_FEWSHOT = 0 # Change with your few shot
 
 
 # Your leaderboard name
-TITLE = """<h1 align="center" id="space-title">Demo leaderboard</h1>"""
+TITLE = """<h1 align="center" id="space-title">🪷 LOTUS: A Leaderboard for Detailed Image Captioning  from Quality to Societal Bias and User Preferences</h1>"""
 
 # What does your leaderboard evaluate?
 INTRODUCTION_TEXT = """