Update space

src/populate.py

@@ -18,15 +18,16 @@ def get_model_leaderboard_df(results_path: str, requests_path: str="", cols: lis
     df = pd.DataFrame.from_records(all_data_json)
     
     df = df[benchmark_cols]
-    df = df.dropna(subset=benchmark_cols)
+    print(df.head())
     
-    if rank_col:
+    if rank_col: # if there is one col in rank_col, sort by that column and remove NaN values
+        df = df.dropna(subset=benchmark_cols)
         df = df.sort_values(by=[rank_col[0]], ascending=True)
     else: # when rank_col is empty, sort by averaging all the benchmarks, except the first one
-        avg_rank = df.iloc[:, 1:].mean(axis=1)
+        avg_rank = df.iloc[:, 1:].mean(axis=1) # we'll skip NaN, instrad of deleting the whole row
         df["Average Rank"] = avg_rank
         df = df.sort_values(by=["Average Rank"], ascending=True)
-        
+
     
     # df = df.sort_values(by=[AutoEvalColumn.score.name], ascending=True)
     # df[AutoEvalColumn.rank.name] = df[AutoEvalColumn.score.name].rank(ascending=True, method="min")
@@ -42,7 +43,7 @@ def get_model_leaderboard_df(results_path: str, requests_path: str="", cols: lis
     #         df = df[cols].round(decimals=2)
 
     # filter out if any of the benchmarks have not been produced
-    df = df[has_no_nan_values(df, benchmark_cols)]
+    # df = df[has_no_nan_values(df, benchmark_cols)]
     return df