Update app.py

app.py

@@ -295,50 +295,6 @@ with demo:
 
             initial_columns = [c.name for c in fields(AutoEvalColumn) if c.never_hidden or c.displayed_by_default]
             leaderboard_df_filtered = select_columns(leaderboard_df, initial_columns)
-            
-            # 重複カラムの確認と削除
-            duplicate_columns = leaderboard_df_filtered.columns[leaderboard_df_filtered.columns.duplicated()]
-            if len(duplicate_columns) > 0:
-                print(f"Duplicate columns found: {duplicate_columns.tolist()}")
-                # 重複カラムを削除（最初の出現を保持）
-                leaderboard_df_filtered = leaderboard_df_filtered.loc[:, ~leaderboard_df_filtered.columns.duplicated()]
-                print("Duplicate columns have been removed.")
-            else:
-                print("No duplicate columns found.")
-
-            # 'T' カラムの欠損値を確認
-            missing_T = leaderboard_df_filtered['T'].isna().sum()
-            print(f"Number of rows with missing 'T': {missing_T}")
-            
-            # 'T' カラムが欠損している場合、埋める（ここでは空文字）
-            if missing_T > 0:
-                print("Filling missing 'T' values with empty strings.")
-                leaderboard_df_filtered['T'] = leaderboard_df_filtered['T'].fillna('')
-            
-            # データ型を定義
-            datatype_dict = {}
-            for col in leaderboard_df_filtered.columns:
-                if col == AutoEvalColumn.model.name:  # 'Model'
-                    datatype_dict[col] = "markdown"
-                elif col in TYPES:
-                    datatype_dict[col] = TYPES[col]
-                else:
-                    datatype_dict[col] = "str"  # デフォルトのデータ型
-
-            # 'T' カラムがすべてのレコードに存在するか確認
-            records = leaderboard_df_filtered.to_dict('records')
-            missing_T_in_records = [i for i, record in enumerate(records) if 'T' not in record]
-            print(f"Number of records missing 'T' key: {len(missing_T_in_records)}")
-            
-            if len(missing_T_in_records) > 0:
-                print("Records missing 'T' key:")
-                for i in missing_T_in_records[:5]:  # 最初の5件のみ表示
-                    print(f"Record {i}: {records[i]}")
-                # 欠損している場合、'T' キーを追加して空文字で埋める
-                for i in missing_T_in_records:
-                    records[i]['T'] = ''
-                # データフレームを更新
-                leaderboard_df_filtered = pd.DataFrame(records)
 
             
             leaderboard_df_filtered = leaderboard_df_filtered.rename(columns={'T': 'Type_'})
@@ -350,12 +306,6 @@ with demo:
             # 'Type_' カラムを文字列型に変換
             leaderboard_df_filtered['Type_'] = leaderboard_df_filtered['Type_'].astype(str)
             
-            # 'COLS' リストから 'T' と 'Model' を除外
-            if 'T' in COLS:
-                COLS.remove('T')
-            if 'Model' in COLS:
-                COLS.remove('Model')
-            
             # datatypeを準備
             datatype_dict = {}
             for col in leaderboard_df_filtered.columns:
@@ -365,7 +315,8 @@ with demo:
                     datatype_dict[col] = TYPES[col]
                 else:
                     datatype_dict[col] = "str"  # デフォルトのデータ型
-        
+
+            datatype_dict['Type_'] = "str"
             
             # 'Type_' が 'datatype_dict' に含まれているか確認
             if 'Type_' not in datatype_dict: