Update app.py

app.py

@@ -143,37 +143,43 @@ 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}, 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}")
 
     filtered_df = df
-    
-    # type_emoji = [t[0] for t in type_query]
-    # filtered_df = filtered_df[filtered_df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
+
+    # Model Type フィルタリング
+    type_emoji = [t.split()[0] for t in type_query]
+    filtered_df = filtered_df[filtered_df['T'].isin(type_emoji)]
     print(f"After type filter: {filtered_df.shape}")
-    print(f"After type filter content:\n{filtered_df}")
 
-    # Precision filterをコメントアウト
-    # filtered_df = filtered_df[filtered_df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])]
+    # Precision フィルタリング
+    filtered_df = filtered_df[filtered_df['Precision'].isin(precision_query + ['Unknown', '?'])]
     print(f"After precision filter: {filtered_df.shape}")
-    print(f"After precision filter content:\n{filtered_df}")
 
-    # filtered_df = filtered_df[filtered_df[AutoEvalColumn.add_special_tokens.name].isin(add_special_tokens_query)]
+    # 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)
+    else:
+        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', '?'])]
     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[filtered_df[AutoEvalColumn.num_few_shots.name].isin(num_few_shots_query)]
+    # 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', '?'])]
     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(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}")
+    # 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]
+    print(f"After show_deleted 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)