Update app.py

app.py

@@ -53,11 +53,6 @@ except Exception:
     restart_space()
 
 LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
-print(f"Initial leaderboard_df shape: {LEADERBOARD_DF.shape}")
-print("Initial leaderboard_df columns:", LEADERBOARD_DF.columns.tolist())
-print("Initial leaderboard_df sample data:")
-print(LEADERBOARD_DF.head())
-
 original_df = LEADERBOARD_DF
 leaderboard_df = original_df.copy()
 (
@@ -81,34 +76,15 @@ def update_table(
     show_flagged: bool,
     query: str,
 ):
-    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
-    )
+    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)
     filtered_df = filter_queries(query, filtered_df)
-    
     print(f"Filter applied: query={query}, columns={columns}, type_query={type_query}, precision_query={precision_query}")
-    print(f"Filtered DataFrame shape: {filtered_df.shape}")
-    
-    if not filtered_df.empty:
-        print(filtered_df.head())  # フィルタ後のデータを確認
-    else:
-        print("Filtered DataFrame is empty.")
-    
+    print(filtered_df.head())  # フィルタ後のデータを確認
+
     df = select_columns(filtered_df, columns)
-    print(f"DataFrame after selecting columns: {df.shape}")
-    
-    if not df.empty:
-        print(df.head())  # 選択後のデータを確認
-    else:
-        print("DataFrame after selecting columns is empty.")
-    
     return df
 
 
-
-
 def load_query(request: gr.Request):  # triggered only once at startup => read query parameter if it exists
     query = request.query_params.get("query") or ""
     return query, query # return one for the "search_bar", one for a hidden component that triggers a reload only if value has changed
@@ -151,54 +127,33 @@ 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
+    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:
-    # 初期状態
-    print(f"Initial DataFrame shape: {df.shape}")
-    
-    # Show deleted models フィルタ
+    # Show all models
     if show_deleted:
         filtered_df = df
-        print("Show deleted models: ON")
-    else:
+    else:  # Show only still on the hub models
         filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == True]
-        print(f"After filtering deleted models: {filtered_df.shape}")
-    
-    # Model type フィルタ
+
+    #if not show_merges:
+    #    filtered_df = filtered_df[filtered_df[AutoEvalColumn.merged.name] == False]
+
+    #if not show_flagged:
+    #    filtered_df = filtered_df[filtered_df[AutoEvalColumn.flagged.name] == False]
+
     type_emoji = [t[0] for t in type_query]
-    filtered_df = filtered_df[filtered_df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
-    print(f"After filtering by model type: {filtered_df.shape}")
-    
-    # Precision フィルタ（"Unknown" を含める）
-    filtered_df = filtered_df[filtered_df[AutoEvalColumn.precision.name].isin(precision_query + ["None", "Unknown"])]
-    print(f"After filtering by precision (including 'Unknown'): {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)]
     
-    # Add Special Tokens フィルタ
-    filtered_df = filtered_df[filtered_df[AutoEvalColumn.add_special_tokens.name].isin(add_special_tokens_query)]
-    print(f"After filtering by add_special_tokens: {filtered_df.shape}")
-    
-    # Num Few Shots フィルタ
-    filtered_df = filtered_df[filtered_df[AutoEvalColumn.num_few_shots.name].isin(num_few_shots_query)]
-    print(f"After filtering by num_few_shots: {filtered_df.shape}")
-    
-    # Model size フィルタ
+
     numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query]))
-    params_column = pd.to_numeric(filtered_df[AutoEvalColumn.params.name], errors="coerce")
+    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[mask]
-    print(f"After filtering by model size: {filtered_df.shape}")
-    
+    filtered_df = filtered_df.loc[mask]
     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)
 
 demo = gr.Blocks(css=custom_css)