[Add] average to df

app.py

@@ -104,8 +104,13 @@ abs_path = Path(__file__).parent
 persian_df = jsonl_to_dataframe(str(abs_path / "leaderboard_persian.jsonl"))
 base_df = jsonl_to_dataframe(str(abs_path / "leaderboard_base.jsonl"))
 
-persian_df = persian_df.append(compute_averages(persian_df), ignore_index=True)
-base_df = base_df.append(compute_averages(base_df), ignore_index=True)
+
+all_columns = ["Average ⬆️", "Precision", "#Params (B)", "Part Multiple Choice", "ARC Easy", "ARC Challenging", "MMLU Pro", "GSM8k Persian", "Multiple Choice Persian"]
+columns_to_average = ["Part Multiple Choice", "ARC Easy", "ARC Challenging", "MMLU Pro", "GSM8k Persian", "Multiple Choice Persian"]
+
+
+persian_df = persian_df.append(compute_averages(persian_df, columns_to_average), ignore_index=True)
+base_df = base_df.append(compute_averages(base_df, columns_to_average), ignore_index=True)
 
 with gr.Blocks(css=custom_css) as demo:
     gr.Markdown("""
@@ -118,7 +123,7 @@ with gr.Blocks(css=custom_css) as demo:
                 """)
         Leaderboard(
         value=persian_df,
-        select_columns=["Average ⬆️", "Precision", "#Params (B)", "Part Multiple Choice", "ARC Easy", "ARC Challenging", "MMLU Pro", "GSM8k Persian", "Multiple Choice Persian"],
+        select_columns=all_columns,
         search_columns=["model_name_for_query"],
         hide_columns=["model_name_for_query",],
         filter_columns=["Precision", "#Params (B)"],
@@ -129,7 +134,7 @@ with gr.Blocks(css=custom_css) as demo:
                 """)
         Leaderboard(
         value=base_df,
-        select_columns=["Average ⬆️", "Precision", "#Params (B)", "Part Multiple Choice", "ARC Easy", "ARC Challenging", "MMLU Pro", "GSM8k Persian", "Multiple Choice Persian"],
+        select_columns=all_columns,
         search_columns=["model_name_for_query"],
         hide_columns=["model_name_for_query",],
         filter_columns=["Precision", "#Params (B)"],

utils.py

@@ -151,7 +151,7 @@ def jsonl_to_dataframe(input_file):
     return pd.DataFrame(data)
 
 
-def compute_averages(df):
-    average_metrics = df.mean(numeric_only=True).to_dict()
-    average_metrics["Model"] = "Average ⬆️"
-    return average_metrics
+def compute_averages(df, columns_to_average):
+    average_metrics = {col: df[col].mean() for col in columns_to_average if col in df}
+    average_metrics["Model"] = "Average ⬆️"  # Label the row as "Average"
+    return average_metrics