Update app.py

app.py

@@ -145,29 +145,30 @@ def filter_models(
 ) -> pd.DataFrame:
     print(f"Initial df shape: {df.shape}")
     print(f"Initial df columns: {df.columns}")
+    print(f"Initial df content:\n{df}")
     
     filtered_df = df
 
     # モデルタイプでフィルタリング
     type_emoji = [t.split()[0] for t in type_query]
-    filtered_df = filtered_df[filtered_df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
+    filtered_df = filtered_df[filtered_df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji + ["?"])]
     print(f"After type filter: {filtered_df.shape}")
 
     # Precisionでフィルタリング
-    filtered_df = filtered_df[filtered_df[AutoEvalColumn.precision.name].isin(precision_query + ["None", "Unknown"])]
+    filtered_df = filtered_df[filtered_df[AutoEvalColumn.precision.name].isin(precision_query + ["None", "Unknown", "?"])]
     print(f"After precision filter: {filtered_df.shape}")
 
     # Add Special Tokensでフィルタリング
-    filtered_df = filtered_df[filtered_df[AutoEvalColumn.add_special_tokens.name].isin(add_special_tokens_query + ["Unknown"])]
+    filtered_df = filtered_df[filtered_df[AutoEvalColumn.add_special_tokens.name].isin(add_special_tokens_query + ["Unknown", "?"])]
     print(f"After add_special_tokens filter: {filtered_df.shape}")
 
     # Num Few Shotsでフィルタリング
-    filtered_df = filtered_df[filtered_df[AutoEvalColumn.num_few_shots.name].isin(num_few_shots_query + ["Unknown"])]
+    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}")
 
     # モデルサイズでフィルタリング
     if "Unknown" in size_query:
-        size_mask = filtered_df[AutoEvalColumn.params.name].isna()
+        size_mask = filtered_df[AutoEvalColumn.params.name].isna() | (filtered_df[AutoEvalColumn.params.name] == "Unknown")
     else:
         numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query if s != "Unknown"]))
         params_column = pd.to_numeric(filtered_df[AutoEvalColumn.params.name], errors="coerce")
@@ -265,19 +266,19 @@ with demo:
             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)
 
             # 列名の重複を解消
-            leaderboard_df_filtered.columns = pd.io.parsers.ParserBase({'names':leaderboard_df_filtered.columns})._maybe_dedup_names(leaderboard_df_filtered.columns)
+            leaderboard_df_filtered.columns = pd.io.parsers.readers._maybe_dedup_names(leaderboard_df_filtered.columns)
             
             leaderboard_table = gr.components.Dataframe(
                 value=leaderboard_df_filtered,
                 headers=list(leaderboard_df_filtered.columns),
-                datatype={col: str(leaderboard_df_filtered[col].dtype) for col in leaderboard_df_filtered.columns},
+                datatype={col: "str" for col in leaderboard_df_filtered.columns},
                 elem_id="leaderboard-table",
                 interactive=False,
                 visible=True,
             )
             print("Leaderboard table initial value:")
             print(leaderboard_table.value)
-            print(f"Leaderboard table shape: {leaderboard_table.value.shape}")
+            print(f"Leaderboard table shape: {leaderboard_table.value.shape if isinstance(leaderboard_table.value, pd.DataFrame) else len(leaderboard_table.value)}")
             
             # Dummy leaderboard for handling the case when the user uses backspace key
             hidden_leaderboard_table_for_search = gr.components.Dataframe(