Update app.py

app.py

@@ -147,34 +147,30 @@ def filter_models(
     print(f"Initial df shape: {df.shape}")
     print(f"Initial df content:\n{df}")
 
-    if show_deleted:
-        filtered_df = df
-    else:
-        filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == True]
-    print(f"After deletion filter: {filtered_df.shape}")
-    print(f"After deletion filter content:\n{filtered_df}")
-
-    type_emoji = [t[0] for t in type_query]
-    filtered_df = filtered_df.loc[filtered_df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
+    filtered_df = df
+    
+    # type_emoji = [t[0] for t in type_query]
+    # filtered_df = filtered_df[filtered_df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
     print(f"After type filter: {filtered_df.shape}")
     print(f"After type filter content:\n{filtered_df}")
 
-    filtered_df = filtered_df.loc[filtered_df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])]
+    # Precision filterをコメントアウト
+    # filtered_df = filtered_df[filtered_df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])]
     print(f"After precision filter: {filtered_df.shape}")
     print(f"After precision filter content:\n{filtered_df}")
 
-    filtered_df = filtered_df.loc[filtered_df[AutoEvalColumn.add_special_tokens.name].isin(add_special_tokens_query)]
+    # filtered_df = filtered_df[filtered_df[AutoEvalColumn.add_special_tokens.name].isin(add_special_tokens_query)]
     print(f"After add_special_tokens filter: {filtered_df.shape}")
     print(f"After add_special_tokens filter content:\n{filtered_df}")
 
-    filtered_df = filtered_df.loc[filtered_df[AutoEvalColumn.num_few_shots.name].isin(num_few_shots_query)]
+    # filtered_df = filtered_df[filtered_df[AutoEvalColumn.num_few_shots.name].isin(num_few_shots_query)]
     print(f"After num_few_shots filter: {filtered_df.shape}")
     print(f"After num_few_shots filter content:\n{filtered_df}")
 
-    numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query]))
-    params_column = pd.to_numeric(filtered_df[AutoEvalColumn.params.name], errors="coerce")
-    mask = params_column.apply(lambda x: any(numeric_interval.contains(x)))
-    filtered_df = filtered_df.loc[mask]
+    # numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query]))
+    # params_column = pd.to_numeric(filtered_df[AutoEvalColumn.params.name], errors="coerce")
+    # mask = params_column.apply(lambda x: any(numeric_interval.contains(x)))
+    # filtered_df = filtered_df.loc[mask]
     print(f"After size filter: {filtered_df.shape}")
     print(f"After size filter content:\n{filtered_df}")
 
@@ -261,11 +257,10 @@ with demo:
                         elem_id="filter-columns-num-few-shots",
                     )
 
+            leaderboard_df_filtered = filter_models(leaderboard_df, [t.to_str(" : ") for t in ModelType], list(NUMERIC_INTERVALS.keys()), [i.value.name for i in Precision], [i.value.name for i in AddSpecialTokens], [i.value.name for i in NumFewShots], False, False, False)
+
             leaderboard_table = gr.components.Dataframe(
-                value=leaderboard_df[
-                    [c.name for c in fields(AutoEvalColumn) if c.never_hidden]
-                    + shown_columns.value
-                ],
+                value=leaderboard_df_filtered,
                 headers=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value,
                 datatype=TYPES,
                 elem_id="leaderboard-table",
@@ -273,9 +268,9 @@ with demo:
                 visible=True,
             )
             print("Leaderboard table initial value:")
-            print(leaderboard_table.value.head())
-            print(f"Leaderboard table shape: {leaderboard_table.value.shape}")
-
+            print(leaderboard_table.value)
+            print(f"Leaderboard table shape: {leaderboard_table.value.shape if isinstance(leaderboard_table.value, pd.DataFrame) else 'Not a DataFrame'}")
+            
             # Dummy leaderboard for handling the case when the user uses backspace key
             hidden_leaderboard_table_for_search = gr.components.Dataframe(
                 value=original_df[COLS],