Update app.py

app.py

@@ -55,9 +55,6 @@ except Exception:
 LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
 original_df = LEADERBOARD_DF
 leaderboard_df = original_df.copy()
-print("Columns in COLS:", COLS)
-print("Columns in leaderboard_df:", leaderboard_df.columns.tolist())
-print("Data types:", leaderboard_df.dtypes.to_dict())
 (
     finished_eval_queue_df,
     running_eval_queue_df,
@@ -132,20 +129,12 @@ 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:
-    # 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:
         filtered_df = df
     else:  # Show only still on the hub models
         filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == True]
 
-    print(f"After deletion filter: {filtered_df.shape}")
-
     #if not show_merges:
     #    filtered_df = filtered_df[filtered_df[AutoEvalColumn.merged.name] == False]
 
@@ -153,36 +142,16 @@ def filter_models(
     #    filtered_df = filtered_df[filtered_df[AutoEvalColumn.flagged.name] == False]
 
     type_emoji = [t[0] for t in type_query]
-    # type_emojiフィルタリングを条件付きで適用
-    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
-    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
-    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
-    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}")
+    filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
+    filtered_df = filtered_df.loc[df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])]
+    filtered_df = filtered_df.loc[df[AutoEvalColumn.add_special_tokens.name].isin(add_special_tokens_query)]
+    filtered_df = filtered_df.loc[df[AutoEvalColumn.num_few_shots.name].isin(num_few_shots_query)]
     
 
     numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query]))
     params_column = pd.to_numeric(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("Filtered dataframe head:")
-    print(filtered_df.head())
-    print("Column names:")
-    print(filtered_df.columns.tolist())
-    print("Column data types:")
-    print(filtered_df.dtypes)
-    filtered_df = filtered_df.rename(columns={'T': 'Type_Symbol'})
-    print("Final filtered dataframe columns:")
-    print(filtered_df.columns.tolist())
-    print("Final filtered dataframe sample:")
-    print(filtered_df.head().to_dict('records'))
     return filtered_df
 
 leaderboard_df = 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)
@@ -268,13 +237,16 @@ with demo:
 
             leaderboard_table = gr.components.Dataframe(
                 value=leaderboard_df[
-                    [c for c in COLS if c in leaderboard_df.columns]
+                    [c.name for c in fields(AutoEvalColumn) if c.never_hidden]
+                    + shown_columns.value
+                    # + [AutoEvalColumn.dummy.name]
                 ],
-                headers=[c for c in COLS if c in leaderboard_df.columns],
-                datatype={c: str(t) for c, t in leaderboard_df.dtypes.items() if c in COLS},
+                headers=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value,
+                datatype=TYPES,
                 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