app.py

import ast
import argparse
import glob
import pickle

import gradio as gr
import numpy as np
import pandas as pd
def model_hyperlink(model_name, link):
    return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'
def load_leaderboard_table_csv(filename, add_hyperlink=True):
    lines = open(filename).readlines()
    heads = [v.strip() for v in lines[0].split(",")]
    rows = []
    for i in range(1, len(lines)):
        row = [v.strip() for v in lines[i].split(",")]
        for j in range(len(heads)):
            item = {}
            for h, v in zip(heads, row):
                if "Score" in h:
                    item[h] = float(v)
                elif h != "Model" and h != "Parameters (B)" and h != "Repo" and h != "Quantization" and h != "Link":
                    item[h] = int(v)
                else:
                    item[h] = v
            if add_hyperlink:
                item["Repo"] = model_hyperlink(item["Repo"], item["Link"])
        rows.append(item)
    return rows

def get_arena_table(model_table_df):
    # sort by rating
    model_table_df = model_table_df.sort_values(by=["Final Score"], ascending=False)
    values = []
    for i in range(len(model_table_df)):
        row = []
        model_key = model_table_df.index[i]
        model_name = model_table_df["Model"].values[model_key]
        # rank
        row.append(i + 1)
        # model display name
        row.append(model_name)

        row.append(
            model_table_df["Parameters (B)"].values[model_key]
        )
        row.append(
            model_table_df["Repo"].values[model_key]
        )
        row.append(
            model_table_df["Quantization"].values[model_key]
        )
        row.append(
            model_table_df["Final Score"].values[model_key]
        )
        row.append(
            model_table_df["Strict Prompt Score"].values[model_key]
        )
        row.append(
            model_table_df["Strict Inst Score"].values[model_key]
        )
        row.append(
            model_table_df["Loose Prompt Score"].values[model_key]
        )
        row.append(
            model_table_df["Loose Inst Score"].values[model_key]
        )
        values.append(row)
    return values

def build_leaderboard_tab(leaderboard_table_file, show_plot=False):
    if leaderboard_table_file:
        data = load_leaderboard_table_csv(leaderboard_table_file)
        model_table_df = pd.DataFrame(data)
        md_head = f"""
        # 🏆 IFEval Leaderboard
        """
        gr.Markdown(md_head, elem_id="leaderboard_markdown")
        with gr.Tabs() as tabs:
            # arena table
            arena_table_vals = get_arena_table(model_table_df)
            with gr.Tab("IFEval", id=0):
                md = "Leaderboard for various Large Language Models measured with IFEval benchmark.\n\n[IFEval](https://github.com/google-research/google-research/tree/master/instruction_following_eval) is a straightforward and easy-to-reproduce evaluation benchmark. It focuses on a set of \"verifiable instructions\" such as \"write in more than 400 words\" and \"mention the keyword of AI at least 3 times\". We identified 25 types of those verifiable instructions and constructed around 500 prompts, with each prompt containing one or more verifiable instructions. \n\nTest ran with `lm-evaluation-harness`. Raw results can be found in the `results` directory. Made by [Kristian Polso](https://polso.info)."
                gr.Markdown(md, elem_id="leaderboard_markdown")
                gr.Dataframe(
                    headers=[
                        "Rank",
                        "Model",
                        "Parameters (B)",
                        "Repo",
                        "Quantization",
                        "Final Score",
                        "Strict Prompt Score",
                        "Strict Inst Score",
                        "Loose Prompt Score",
                        "Loose Inst Score"
                    ],
                    datatype=[
                        "number",
                        "str",
                        "number",
                        "markdown",
                        "str",
                        "number",
                        "number",
                        "number",
                        "number",
                        "number"
                    ],
                    value=arena_table_vals,
                    elem_id="arena_leaderboard_dataframe",
                    height=700,
                    column_widths=[50, 150, 100, 150, 100, 100, 100, 100, 100, 100],
                    wrap=True,
                )

    else:
        pass

def build_demo(leaderboard_table_file):
    text_size = gr.themes.sizes.text_lg

    with gr.Blocks(
        title="IFEval Leaderboard",
        theme=gr.themes.Base(text_size=text_size),
    ) as demo:
        leader_components = build_leaderboard_tab(
            leaderboard_table_file, show_plot=True
        )
    return demo

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--share", action="store_true")
    parser.add_argument("--IFEval_file", type=str, default="./IFEval.csv")
    args = parser.parse_args()

    demo = build_demo(args.IFEval_file)
    demo.launch()