Update space

app.py

@@ -77,22 +77,29 @@ model_types = {
 }
 
 def filter_by_model_type(df, selected_types):
+    if not selected_types:  # If no types are selected, return an empty DataFrame
+        return df.head(0)
     selected_models = [model for type in selected_types for model in model_types[type]]
     return df[df['Method'].isin(selected_models)]
 
-css = """
-table > thead {
-    white-space: normal
-}
-
-table {
-    --cell-width-1: 250px
-}
+def format_dataframe(df, dataset):
+    columns = ['Method'] + [col for col in df.columns if dataset in col]
+    filtered_df = df[columns].copy()
+    filtered_df.columns = [col.split('_')[-1] if '_' in col else col for col in filtered_df.columns]
+    filtered_df = filtered_df.sort_values('MRR', ascending=False)
+    return filtered_df
 
-table > tbody > tr > td:nth-child(2) > div {
-    overflow-x: auto
-}
-"""
+def update_tables(selected_types):
+    filtered_df_full = filter_by_model_type(df_synthesized_full, selected_types)
+    filtered_df_10 = filter_by_model_type(df_synthesized_10, selected_types)
+    filtered_df_human = filter_by_model_type(df_human_generated, selected_types)
+    
+    outputs = []
+    for df in [filtered_df_full, filtered_df_10, filtered_df_human]:
+        for dataset in ['AMAZON', 'MAG', 'PRIME']:
+            outputs.append(format_dataframe(df, f"STARK-{dataset}"))
+    
+    return outputs
 
 with gr.Blocks(css=css) as demo:
     gr.Markdown("# Semi-structured Retrieval Benchmark (STaRK) Leaderboard")
@@ -109,66 +116,37 @@ with gr.Blocks(css=css) as demo:
     with gr.Tabs() as outer_tabs:
         with gr.TabItem("Synthesized (full)"):
             with gr.Tabs() as inner_tabs_synthesized_full:
-                for dataset in ['AMAZON', 'MAG', 'PRIME']:
+                syn_full_dfs = [gr.DataFrame(interactive=False) for _ in range(3)]
+                for df, dataset in zip(syn_full_dfs, ['AMAZON', 'MAG', 'PRIME']):
                     with gr.TabItem(dataset):
-                        gr.DataFrame(format_dataframe(df_synthesized_full, f"STARK-{dataset}"))
+                        df.render()
         
         with gr.TabItem("Synthesized (10%)"):
             with gr.Tabs() as inner_tabs_synthesized_10:
-                for dataset in ['AMAZON', 'MAG', 'PRIME']:
+                syn_10_dfs = [gr.DataFrame(interactive=False) for _ in range(3)]
+                for df, dataset in zip(syn_10_dfs, ['AMAZON', 'MAG', 'PRIME']):
                     with gr.TabItem(dataset):
-                        gr.DataFrame(format_dataframe(df_synthesized_10, f"STARK-{dataset}"))
+                        df.render()
         
         with gr.TabItem("Human-Generated"):
             with gr.Tabs() as inner_tabs_human:
-                for dataset in ['AMAZON', 'MAG', 'PRIME']:
+                human_dfs = [gr.DataFrame(interactive=False) for _ in range(3)]
+                for df, dataset in zip(human_dfs, ['AMAZON', 'MAG', 'PRIME']):
                     with gr.TabItem(dataset):
-                        gr.DataFrame(format_dataframe(df_human_generated, f"STARK-{dataset}"))
-    
-    def update_tables(selected_types):
-            filtered_df_full = filter_by_model_type(df_synthesized_full, selected_types)
-            filtered_df_10 = filter_by_model_type(df_synthesized_10, selected_types)
-            filtered_df_human = filter_by_model_type(df_human_generated, selected_types)
-            
-            outputs = []
-            for df in [filtered_df_full, filtered_df_10, filtered_df_human]:
-                for dataset in ['AMAZON', 'MAG', 'PRIME']:
-                    outputs.append(format_dataframe(df, f"STARK-{dataset}"))
-            
-            return outputs
-
-        # Create DataFrame components for each tab and dataset
-    dataframes = []
-    for _ in range(3):  # 3 main tabs
-        for _ in range(3):  # 3 datasets per tab
-            df_component = gr.DataFrame(visible=False)
-            dataframes.append(df_component)
+                        df.render()
 
-    # Update visibility and content of DataFrames based on selected tab
-    def update_visibility(tab):
-        visibilities = [False] * 9
-        start_index = tab * 3
-        visibilities[start_index:start_index+3] = [True, True, True]
-        return visibilities
-
-    outer_tabs.select(
-        update_visibility,
-        None,
-        dataframes
-    )
+    all_dfs = syn_full_dfs + syn_10_dfs + human_dfs
 
-    # Connect model type filter to update_tables function
     model_type_filter.change(
         update_tables,
         inputs=[model_type_filter],
-        outputs=dataframes
+        outputs=all_dfs
     )
 
-    # Initial load of tables
     demo.load(
         update_tables,
         inputs=[model_type_filter],
-        outputs=dataframes
+        outputs=all_dfs
     )
 
 demo.launch()