Update app.py

app.py

@@ -68,9 +68,9 @@ leaderboard_df = original_df.copy()
 # Searching and filtering
 def update_table(
     hidden_df: pd.DataFrame,
-    columns: list,
+    shown_columns: list,
     type_query: list,
-    precision_query: str,
+    precision_query: list,
     size_query: list,
     add_special_tokens_query: list,
     num_few_shots_query: list,
@@ -82,21 +82,20 @@ def update_table(
     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, shown_columns)
     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}")
+    if query:
+        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={shown_columns}, type_query={type_query}, precision_query={precision_query}")
     print("Filtered dataframe head:")
     print(filtered_df.head())
 
-    df = select_columns(filtered_df, columns)
-    print(f"Final df shape: {df.shape}")
-    print("Final dataframe head:")
-    print(df.head())
-    return df
+    return select_columns(filtered_df, shown_columns)
 
 
 def load_query(request: gr.Request):  # triggered only once at startup => read query parameter if it exists
@@ -141,45 +140,46 @@ 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, shown_columns: list
 ) -> pd.DataFrame:
     print(f"Initial df shape: {df.shape}")
-    print(f"Initial df content:\n{df}")
-
+    
     filtered_df = df
 
-    # 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)]
+    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)]
     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', '?'])]
+    if 'Precision' in shown_columns:
+        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)
-    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]
+    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]
     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', '?'])]
+    if 'Add Special Tokens' in shown_columns:
+        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', '?'])]
+    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', '?'])]
     print(f"After num_few_shots filter: {filtered_df.shape}")
 
-    # Show deleted models フィルタリング
-    if not show_deleted:
+    if not show_deleted and 'Available on the hub' in shown_columns:
         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)