Update app.py

app.py

@@ -143,37 +143,41 @@ 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"filter_models called with: type_query={type_query}, size_query={size_query}, precision_query={precision_query}, add_special_tokens_query={add_special_tokens_query}, num_few_shots_query={num_few_shots_query}")
     print(f"Initial df shape: {df.shape}")
+    print(f"Initial df content:\n{df}")
 
-    # 各フィルタリング操作の後にprint文を追加
     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[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
+    filtered_df = filtered_df.loc[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[df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])]
-    # print(f"After precision filter: {filtered_df.shape}")
+    filtered_df = filtered_df.loc[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[df[AutoEvalColumn.add_special_tokens.name].isin(add_special_tokens_query)]
+    filtered_df = filtered_df.loc[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[df[AutoEvalColumn.num_few_shots.name].isin(num_few_shots_query)]
+    filtered_df = filtered_df.loc[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(df[AutoEvalColumn.params.name], errors="coerce")
+    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}")
 
-    print("Filtered dataframe head:")
-    print(filtered_df.head())
     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)
@@ -261,7 +265,7 @@ with demo:
                 value=leaderboard_df[
                     [c.name for c in fields(AutoEvalColumn) if c.never_hidden]
                     + shown_columns.value
-                ].to_dict('records'),  # DataFrameを辞書のリストに変換
+                ],
                 headers=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value,
                 datatype=TYPES,
                 elem_id="leaderboard-table",