Fixed the leaderboard filtering functionality. Modified filter_models() function in app.py/

app.py

@@ -94,11 +94,14 @@ def filter_models(
     df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, show_deleted: bool
 ) -> pd.DataFrame:
     # Show all models
-    if show_deleted:
-        filtered_df = df
-    else:  # Show only still on the hub models
-        filtered_df = df[df[utils.AutoEvalColumn.still_on_hub.name]]
-
+    filtered_df = df
+    # if show_deleted:
+    #   filtered_df = df
+    # else:  # Show only still on the hub models
+        # filtered_df = df[df[utils.AutoEvalColumn.still_on_hub.name]]
+
+    filtered_df = df
+    
     type_emoji = [t[0] for t in type_query]
     filtered_df = filtered_df.loc[df[utils.AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
     filtered_df = filtered_df.loc[df[utils.AutoEvalColumn.precision.name].isin(precision_query + ["None"])]

src/populate.py

@@ -13,7 +13,7 @@ def get_leaderboard_df(results_path: str, requests_path: str, cols: list, benchm
     all_data_json = [v.to_dict() for v in raw_data]
 
     df = pd.DataFrame.from_records(all_data_json)
-    df = df.sort_values(by=[utils.AutoEvalColumn.accuracy.name], ascending=False)
+    df = df.sort_values(by=[utils.AutoEvalColumn.hallucination_rate.name], ascending=True)
     df = df[cols].round(decimals=2)
 
     # filter out if any of the benchmarks have not been produced