test

app.py

@@ -2,7 +2,7 @@ import gradio as gr
 from gradio_leaderboard import Leaderboard, SelectColumns, ColumnFilter
 from pathlib import Path
 
-from utils import LLM_BENCHMARKS_ABOUT_TEXT, LLM_BENCHMARKS_SUBMIT_TEXT, custom_css, jsonl_to_dataframe, add_average_column_to_df, apply_markdown_format_for_columns, submit, PART_LOGO
+from utils import LLM_BENCHMARKS_ABOUT_TEXT, LLM_BENCHMARKS_SUBMIT_TEXT, custom_css, jsonl_to_dataframe, add_average_column_to_df, apply_markdown_format_for_columns, submit, PART_LOGO, sort_dataframe_by_column
 
 
 
@@ -15,10 +15,12 @@ leaderboard_df = jsonl_to_dataframe(str(abs_path / "leaderboard_data.jsonl"))
 all_columns = ["Model", "Average Accuracy", "Precision", "#Params (B)", "Part Multiple Choice", "ARC Easy", "ARC Challenge", "MMLU Pro", "GSM8k Persian", "Multiple Choice Persian"]
 columns_to_average = ["Part Multiple Choice", "ARC Easy", "ARC Challenge", "MMLU Pro", "GSM8k Persian", "Multiple Choice Persian"]
 
+average_column_name = "Average Accuracy"
 
-leaderboard_df = add_average_column_to_df(leaderboard_df, columns_to_average, index=3, average_column_name="Average Accuracy")
+leaderboard_df = add_average_column_to_df(leaderboard_df, columns_to_average, index=3, average_column_name=average_column_name)
 
 leaderboard_df = apply_markdown_format_for_columns(df=leaderboard_df, model_column_name="Model")
+leaderboard_df = sort_dataframe_by_column(leaderboard_df, column_name=average_column_name)
 
 columns_data_type = ["markdown" for i in range(len(leaderboard_df.columns))]
 # "str", "number", "bool", "date", "markdown"

utils.py

@@ -191,6 +191,12 @@ def jsonl_to_dataframe(input_file):
     return pd.DataFrame(data)
 
 
+def sort_dataframe_by_column(df, column_name):
+    if column_name not in df.columns:
+        raise ValueError(f"Column '{column_name}' does not exist in the DataFrame.")
+    return df.sort_values(by=column_name, ascending=True).reset_index(drop=True)
+
+
 def add_average_column_to_df(df,columns_to_average, index=3, average_column_name="Average Accuracy"):
     average_column = df[columns_to_average].mean(axis=1)
     df.insert(index, average_column_name, average_column)