Update app.py

app.py

@@ -53,6 +53,9 @@ except Exception:
     restart_space()
 
 LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
+print("Initial LEADERBOARD_DF:")
+print(LEADERBOARD_DF.head())
+print(f"LEADERBOARD_DF shape: {LEADERBOARD_DF.shape}")
 original_df = LEADERBOARD_DF
 leaderboard_df = original_df.copy()
 (
@@ -76,12 +79,23 @@ def update_table(
     show_flagged: bool,
     query: str,
 ):
+    print(f"Update table called with: type_query={type_query}, precision_query={precision_query}, size_query={size_query}")
+    print(f"hidden_df shape before filtering: {hidden_df.shape}")
+    
     filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, add_special_tokens_query, num_few_shots_query, show_deleted, show_merges, show_flagged)
+    print(f"filtered_df shape after filter_models: {filtered_df.shape}")
+    
     filtered_df = filter_queries(query, filtered_df)
+    print(f"filtered_df shape after filter_queries: {filtered_df.shape}")
+    
     print(f"Filter applied: query={query}, columns={columns}, type_query={type_query}, precision_query={precision_query}")
-    print(filtered_df.head())  # フィルタ後のデータを確認
+    print("Filtered dataframe head:")
+    print(filtered_df.head())
 
     df = select_columns(filtered_df, columns)
+    print(f"Final df shape: {df.shape}")
+    print("Final dataframe head:")
+    print(df.head())
     return df
 
 
@@ -129,12 +143,17 @@ 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}")
+    
     # 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]
 
@@ -143,15 +162,22 @@ def filter_models(
 
     type_emoji = [t[0] for t in type_query]
     filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
+    print(f"After type filter: {filtered_df.shape}")
     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[df[AutoEvalColumn.add_special_tokens.name].isin(add_special_tokens_query)]
+    print(f"After add_special_tokens filter: {filtered_df.shape}")
     filtered_df = filtered_df.loc[df[AutoEvalColumn.num_few_shots.name].isin(num_few_shots_query)]
+    print(f"After num_few_shots filter: {filtered_df.shape}")
     
 
     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())
     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)
@@ -248,7 +274,9 @@ with demo:
                 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
+            print("Leaderboard table initial value:")
+            print(leaderboard_table.value.head())
+            print(f"Leaderboard table shape: {leaderboard_table.value.shape}")
 
             # Dummy leaderboard for handling the case when the user uses backspace key
             hidden_leaderboard_table_for_search = gr.components.Dataframe(