Apply black to app.py

app.py

@@ -41,14 +41,24 @@ def restart_space():
 try:
     print(EVAL_REQUESTS_PATH)
     snapshot_download(
-        repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
+        repo_id=QUEUE_REPO,
+        local_dir=EVAL_REQUESTS_PATH,
+        repo_type="dataset",
+        tqdm_class=None,
+        etag_timeout=30,
+        token=TOKEN,
     )
 except Exception:
     restart_space()
 try:
     print(EVAL_RESULTS_PATH)
     snapshot_download(
-        repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
+        repo_id=RESULTS_REPO,
+        local_dir=EVAL_RESULTS_PATH,
+        repo_type="dataset",
+        tqdm_class=None,
+        etag_timeout=30,
+        token=TOKEN,
     )
 except Exception:
     restart_space()
@@ -78,16 +88,30 @@ 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"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)
+    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(
+        f"Filter applied: query={query}, columns={columns}, type_query={type_query}, precision_query={precision_query}"
+    )
     print("Filtered dataframe head:")
     print(filtered_df.head())
 
@@ -100,7 +124,10 @@ def update_table(
 
 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
+    return (
+        query,
+        query,
+    )  # return one for the "search_bar", one for a hidden component that triggers a reload only if value has changed
 
 
 def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame:
@@ -118,10 +145,11 @@ def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame:
 #     ]
 #     return filtered_df
 
+
 def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
     always_here_cols = [
         AutoEvalColumn.model_type_symbol.name,  # 'T'
-        AutoEvalColumn.model.name,              # 'Model'
+        AutoEvalColumn.model.name,  # 'Model'
     ]
 
     # 'always_here_cols' を 'columns' から除外して重複を避ける
@@ -137,8 +165,14 @@ def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
             seen.add(c)
 
     # 'Model' カラムにリンクを含む形式で再構築
-    if 'Model' in df.columns:
-        df['Model'] = df['Model'].apply(lambda x: f'[{x.split(">")[-2].split("<")[0]}]({x.split("href=")[1].split(chr(34))[1]})' if isinstance(x, str) and 'href=' in x else x)
+    if "Model" in df.columns:
+        df["Model"] = df["Model"].apply(
+            lambda x: (
+                f'[{x.split(">")[-2].split("<")[0]}]({x.split("href=")[1].split(chr(34))[1]})'
+                if isinstance(x, str) and "href=" in x
+                else x
+            )
+        )
 
     # フィルタリングされたカラムでデータフレームを作成
     filtered_df = df[unique_columns]
@@ -166,7 +200,15 @@ 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:
     print(f"Initial df shape: {df.shape}")
     print(f"Initial df content:\n{df}")
@@ -174,34 +216,38 @@ def filter_models(
     filtered_df = df
 
     # Model Type フィルタリング
-    type_column = 'T' if 'T' in df.columns else 'Type_'
+    type_column = "T" if "T" in df.columns else "Type_"
     type_emoji = [t.split()[0] for t in type_query]
     filtered_df = df[df[type_column].isin(type_emoji)]
     print(f"After type filter: {filtered_df.shape}")
 
     # Precision フィルタリング
-    filtered_df = filtered_df[filtered_df['Precision'].isin(precision_query + ['Unknown', '?'])]
+    filtered_df = filtered_df[filtered_df["Precision"].isin(precision_query + ["Unknown", "?"])]
     print(f"After precision filter: {filtered_df.shape}")
 
     # Model Size フィルタリング
-    if 'Unknown' in size_query:
-        size_mask = filtered_df['#Params (B)'].isna() | (filtered_df['#Params (B)'] == 0)
+    if "Unknown" in size_query:
+        size_mask = filtered_df["#Params (B)"].isna() | (filtered_df["#Params (B)"] == 0)
     else:
-        size_mask = filtered_df['#Params (B)'].apply(lambda x: any(x in NUMERIC_INTERVALS[s] for s in size_query if s != 'Unknown'))
+        size_mask = filtered_df["#Params (B)"].apply(
+            lambda x: any(x in NUMERIC_INTERVALS[s] for s in size_query if s != "Unknown")
+        )
     filtered_df = filtered_df[size_mask]
     print(f"After size filter: {filtered_df.shape}")
 
     # Add Special Tokens フィルタリング
-    filtered_df = filtered_df[filtered_df['Add Special Tokens'].isin(add_special_tokens_query + ['Unknown', '?'])]
+    filtered_df = filtered_df[filtered_df["Add Special Tokens"].isin(add_special_tokens_query + ["Unknown", "?"])]
     print(f"After add_special_tokens filter: {filtered_df.shape}")
 
     # Num Few Shots フィルタリング
-    filtered_df = filtered_df[filtered_df['Few-shot'].astype(str).isin([str(x) for x in num_few_shots_query] + ['Unknown', '?'])]
+    filtered_df = filtered_df[
+        filtered_df["Few-shot"].astype(str).isin([str(x) for x in num_few_shots_query] + ["Unknown", "?"])
+    ]
     print(f"After num_few_shots filter: {filtered_df.shape}")
 
     # Show deleted models フィルタリング
     if not show_deleted:
-        filtered_df = filtered_df[filtered_df['Available on the hub'] == True]
+        filtered_df = filtered_df[filtered_df["Available on the hub"] == True]
     print(f"After show_deleted filter: {filtered_df.shape}")
 
     print("Filtered dataframe head:")
@@ -211,9 +257,29 @@ def filter_models(
 
 # Prepare leaderboard dataframes
 
-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)
+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,
+)
 
-leaderboard_df_filtered = 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)
+leaderboard_df_filtered = 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,
+)
 # 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_filtered, initial_columns)
 
@@ -227,17 +293,23 @@ leaderboard_df_filtered = filter_models(leaderboard_df, [t.to_str(" : ") for t i
 # )
 
 # DataFrameの初期化部分のみを修正
-initial_columns = ['T'] + [c.name for c in fields(AutoEvalColumn) if (c.never_hidden or c.displayed_by_default) and c.name != 'T']
+initial_columns = ["T"] + [
+    c.name for c in fields(AutoEvalColumn) if (c.never_hidden or c.displayed_by_default) and c.name != "T"
+]
 leaderboard_df_filtered = select_columns(leaderboard_df, initial_columns)
 
 # Model列のリンク形式を修正
-leaderboard_df_filtered['Model'] = leaderboard_df_filtered['Model'].apply(
-    lambda x: f'[{x.split(">")[-2].split("<")[0]}]({x.split("href=")[1].split(chr(34))[1]})' if isinstance(x, str) and 'href=' in x else x
+leaderboard_df_filtered["Model"] = leaderboard_df_filtered["Model"].apply(
+    lambda x: (
+        f'[{x.split(">")[-2].split("<")[0]}]({x.split("href=")[1].split(chr(34))[1]})'
+        if isinstance(x, str) and "href=" in x
+        else x
+    )
 )
 
 # 数値データを文字列に変換
 for col in leaderboard_df_filtered.columns:
-    if col not in ['T', 'Model']:
+    if col not in ["T", "Model"]:
         leaderboard_df_filtered[col] = leaderboard_df_filtered[col].astype(str)
 
 # Leaderboard demo
@@ -252,9 +324,7 @@ with gr.Blocks() as demo_leaderboard:
             )
             shown_columns = gr.CheckboxGroup(
                 choices=[
-                    c.name
-                    for c in fields(AutoEvalColumn)
-                    if not c.hidden and not c.never_hidden# and not c.dummy
+                    c.name for c in fields(AutoEvalColumn) if not c.hidden and not c.never_hidden  # and not c.dummy
                 ],
                 value=[
                     c.name
@@ -468,7 +538,7 @@ with gr.Blocks() as demo_submission:
             add_special_tokens,
         ],
         submission_result,
-        )
+    )
 
 # Main demo