Update app.py

app.py

@@ -142,46 +142,45 @@ 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, shown_columns: list
+    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}")
+
     filtered_df = df
 
-    if 'T' in shown_columns:
-        type_emoji = [t.split()[0] for t in type_query]
-        filtered_df = filtered_df[filtered_df['T'].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}")
 
-    if 'Precision' in shown_columns:
-        filtered_df = filtered_df[filtered_df['Precision'].isin(precision_query + ['Unknown', '?'])]
+    # 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}")
 
-    if '#Params (B)' in shown_columns:
-        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(pd.Interval(NUMERIC_INTERVALS[s].left, NUMERIC_INTERVALS[s].right).contains(x) for s in size_query if s != 'Unknown'))
-        filtered_df = filtered_df[size_mask]
+    # 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}")
 
-    if 'Add Special Tokens' in shown_columns:
-        filtered_df = filtered_df[filtered_df['Add Special Tokens'].isin(add_special_tokens_query + ['Unknown', '?'])]
+    # 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}")
 
-    if 'Few-shot' in shown_columns:
-        filtered_df = filtered_df[filtered_df['Few-shot'].astype(str).isin([str(x) for x in num_few_shots_query] + ['Unknown', '?'])]
+    # 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}")
 
-    if not show_deleted and 'Available on the hub' in shown_columns:
+    # 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)