Update app.py

app.py

@@ -129,8 +129,11 @@ def filter_queries(query: str, filtered_df: pd.DataFrame):
 def filter_models(
     df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, add_special_tokens_query: list, num_few_shots_query: list, show_deleted: bool, show_merges: bool, show_flagged: bool
 ) -> pd.DataFrame:
-    print(f"filter_models called with: type_query={type_query}, size_query={size_query}, precision_query={precision_query}")
-    print(f"Initial df shape: {df.shape}")
+    # filter_models関数の冒頭で
+    if 'T' in df.columns:
+        df = df.rename(columns={'T': 'Type_Symbol'})
+    elif 'Type_Symbol' not in df.columns:
+        df['Type_Symbol'] = '?'  
     
     # Show all models
     if show_deleted:
@@ -147,12 +150,21 @@ def filter_models(
     #    filtered_df = filtered_df[filtered_df[AutoEvalColumn.flagged.name] == False]
 
     type_emoji = [t[0] for t in type_query]
-    filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
-    if 'Unknown' not in precision_query:
-        precision_query.append('Unknown')
-    filtered_df = filtered_df.loc[filtered_df[AutoEvalColumn.precision.name].isin(precision_query)]
-    filtered_df = filtered_df.loc[filtered_df[AutoEvalColumn.add_special_tokens.name].isin(add_special_tokens_query + ["Unknown"])]
-    filtered_df = filtered_df.loc[filtered_df[AutoEvalColumn.num_few_shots.name].isin(num_few_shots_query + ["Unknown"])]
+    # type_emojiフィルタリングを条件付きで適用
+    if type_emoji:
+        filtered_df = filtered_df[filtered_df['Type_Symbol'].isin(type_emoji + ['?'])]
+    
+    # precisionフィルタリングを条件付きで適用
+    if precision_query:
+        filtered_df = filtered_df[filtered_df[AutoEvalColumn.precision.name].isin(precision_query + ['Unknown'])]
+    
+    # add_special_tokensフィルタリングを条件付きで適用
+    if add_special_tokens_query:
+        filtered_df = filtered_df[filtered_df[AutoEvalColumn.add_special_tokens.name].isin(add_special_tokens_query + ['Unknown'])]
+    
+    # num_few_shotsフィルタリングを条件付きで適用
+    if num_few_shots_query:
+        filtered_df = filtered_df[filtered_df[AutoEvalColumn.num_few_shots.name].isin(num_few_shots_query + ['Unknown'])]
     print(f"After num_few_shots filter: {filtered_df.shape}")
     
 
@@ -257,16 +269,13 @@ with demo:
 
             leaderboard_table = gr.components.Dataframe(
                 value=leaderboard_df[
-                    [c.name for c in fields(AutoEvalColumn) if c.never_hidden]
-                    + shown_columns.value
-                    # + [AutoEvalColumn.dummy.name]
+                    [c for c in COLS if c in leaderboard_df.columns]
                 ],
-                headers=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value,
-                datatype=TYPES,
+                headers=[c for c in COLS if c in leaderboard_df.columns],
+                datatype={c: str(leaderboard_df[c].dtype) for c in leaderboard_df.columns if c in COLS},
                 elem_id="leaderboard-table",
                 interactive=False,
                 visible=True,
-                #column_widths=["2%", "33%"] 
             )
             print(leaderboard_df.head())  # リーダーボードテーブルに渡される前のデータを確認