Update app.py

app.py

@@ -110,34 +110,34 @@ def load_leaderboard_data(csv_file_path):
         return pd.DataFrame(columns=UGI_COLS + WRITING_STYLE_COLS + ANIME_RATING_COLS + ADDITIONAL_COLS)
 
 # Update the leaderboard table based on the search query and parameter range filters
-def update_table(df, query, param_ranges, is_foundation, columns, w10_range, additional_cols):
+def update_table(df: pd.DataFrame, query: str, param_ranges: list, is_foundation: bool, columns: list, w10_range: tuple, additional_cols: list) -> pd.DataFrame:
     mask = pd.Series(True, index=df.index)
     
     # Apply model size filter
     if param_ranges:
-        size_mask = pd.Series(False, index=filtered_df.index)
+        size_mask = pd.Series(False, index=df.index)
         for param_range in param_ranges:
             if param_range == '~2':
-                size_mask |= (filtered_df['Total Params'] < 2.5)
+                size_mask |= (df['Total Params'] < 2.5)
             elif param_range == '~4':
-                size_mask |= ((filtered_df['Total Params'] >= 2.5) & (filtered_df['Total Params'] < 6))
+                size_mask |= ((df['Total Params'] >= 2.5) & (df['Total Params'] < 6))
             elif param_range == '~8':
-                size_mask |= ((filtered_df['Total Params'] >= 6) & (filtered_df['Total Params'] < 9.5))
+                size_mask |= ((df['Total Params'] >= 6) & (df['Total Params'] < 9.5))
             elif param_range == '~13':
-                size_mask |= ((filtered_df['Total Params'] >= 9.5) & (filtered_df['Total Params'] < 16))
+                size_mask |= ((df['Total Params'] >= 9.5) & (df['Total Params'] < 16))
             elif param_range == '~20':
-                size_mask |= ((filtered_df['Total Params'] >= 16) & (filtered_df['Total Params'] < 28))
+                size_mask |= ((df['Total Params'] >= 16) & (df['Total Params'] < 28))
             elif param_range == '~34':
-                size_mask |= ((filtered_df['Total Params'] >= 28) & (filtered_df['Total Params'] < 40))
+                size_mask |= ((df['Total Params'] >= 28) & (df['Total Params'] < 40))
             elif param_range == '~50':
-                size_mask |= ((filtered_df['Total Params'] >= 40) & (filtered_df['Total Params'] < 65))
+                size_mask |= ((df['Total Params'] >= 40) & (df['Total Params'] < 65))
             elif param_range == '~70+':
-                size_mask |= (filtered_df['Total Params'] >= 65)
+                size_mask |= (df['Total Params'] >= 65)
             elif param_range == 'Closed':
-                size_mask |= filtered_df['Total Params'].isna()
-        filtered_df = filtered_df[size_mask]
+                size_mask |= df['Total Params'].isna()
         mask &= size_mask
     
+    # Apply foundation model filter
     if is_foundation:
         mask &= df['Foundation'] == 1
     
@@ -145,6 +145,7 @@ def update_table(df, query, param_ranges, is_foundation, columns, w10_range, add
         escaped_query = re.escape(query)
         mask &= df['Model'].str.contains(escaped_query, case=False, na=False, regex=True)
     
+    # Apply W/10 filtering
     if 'W/10 👍' in df.columns:
         mask &= (df['W/10 👍'] >= w10_range[0]) & (df['W/10 👍'] <= w10_range[1])