Support filtering by search strings and columns at the same time. (#4)

- Support filtering by search strings and columns at the same time. (df10b19d6e4187b27cde2efc16e5e888fd4ba543)

app.py

@@ -146,27 +146,23 @@ def refresh(agg: str = "max"):
 
 
 # Function to update the table based on search query
-def update_table(search_query):
+def filter_and_search(cols: list[str], search_query: str):
     df = leaderboard_df
-    if search_query:
+    if len(search_query) > 0:
         search_terms = search_query.split(";")
         search_terms = [term.strip().lower() for term in search_terms]
         pattern = "|".join(search_terms)
         df = df[df["Model"].str.lower().str.contains(pattern, regex=True)]
         # Drop any columns which are all NaN
         df = df.dropna(how="all", axis=1)
-    return df
-
-
-def filter_columns(cols):
-    index_cols = list(leaderboard_df.columns[:1])
-    new_cols = index_cols + cols
-    df = leaderboard_df
-    df = df.copy()[new_cols]
-    # Drop rows with NaN values
-    df = df.copy().dropna(how="all", axis=0, subset=[c for c in df.columns if c in cols])
-    # Recompute average
-    df.insert(loc=1, column="Average", value=df.mean(axis=1, numeric_only=True))
+    if len(cols) > 0:
+        index_cols = list(leaderboard_df.columns[:1])
+        new_cols = index_cols + cols
+        df = df.copy()[new_cols]
+        # Drop rows with NaN values
+        df = df.copy().dropna(how="all", axis=0, subset=[c for c in df.columns if c in cols])
+        # Recompute average
+        df.insert(loc=1, column="Average", value=df.mean(axis=1, numeric_only=True))
     return df
 
 
@@ -201,9 +197,9 @@ with demo:
         with gr.Row():
             refresh_button = gr.Button("Refresh")
 
-    cols_bar.change(filter_columns, inputs=[cols_bar], outputs=[leaderboard_table])
+    cols_bar.change(filter_and_search, inputs=[cols_bar, search_bar], outputs=[leaderboard_table])
     agg.change(refresh, inputs=[agg], outputs=[leaderboard_table])
-    search_bar.submit(update_table, inputs=[search_bar], outputs=[leaderboard_table])
+    search_bar.submit(filter_and_search, inputs=[cols_bar, search_bar], outputs=[leaderboard_table])
     refresh_button.click(refresh, inputs=[], outputs=[leaderboard_table])
 
 demo.launch()