app.py

import json
from pathlib import Path

import gradio as gr
import pandas as pd

TITLE = """<h1 align="center" id="space-title">LLM Leaderboard for H4 Models</h1>"""

DESCRIPTION = f"""
Evaluation of H4 and community models across a diverse range of benchmarks from [LightEval](https://github.com/huggingface/lighteval). All scores are reported as accuracy.
"""


def get_leaderboard_df(merge_values: bool = True):
    filepaths = list(Path("eval_results").rglob("*.json"))

    # Parse filepaths to get unique models
    models = set()
    for filepath in filepaths:
        path_parts = Path(filepath).parts
        model_revision = "_".join(path_parts[1:4])
        models.add(model_revision)

    # Initialize DataFrame
    df = pd.DataFrame(index=list(models))

    # Extract data from each file and populate the DataFrame
    for filepath in filepaths:
        path_parts = Path(filepath).parts
        date = filepath.stem.split("_")[-1][:-3].split("T")[0]
        model_revision = "_".join(path_parts[1:4]) + "_" + date
        task = path_parts[4].capitalize()
        df.loc[model_revision, "Date"] = date

        with open(filepath, "r") as file:
            data = json.load(file)
            first_result_key = next(iter(data["results"]))  # gets the first key in 'results'
            # TruthfulQA has two metrics, so we need to pick the `mc2` one that's reported on the leaderboard
            if task.lower() == "truthfulqa":
                value = data["results"][first_result_key]["truthfulqa_mc2"]
            # IFEval has several metrics but we report just the prompt-loose-acc one
            elif task.lower() == "ifeval":
                value = data["results"][first_result_key]["prompt_level_loose_acc"]
            # MMLU has several metrics but we report just the average one
            elif task.lower() == "mmlu":
                value = [v["acc"] for k, v in data["results"].items() if "_average" in k.lower()][0]
            # HellaSwag and ARC reports acc_norm
            elif task.lower() in ["hellaswag", "arc"]:
                value = data["results"][first_result_key]["acc_norm"]
            # BBH has several metrics but we report just the average one
            elif task.lower() == "bbh":
                if "all" in data["results"]:
                    value = data["results"]["all"]["acc"]
                else:
                    value = -100
            # AGIEval reports acc_norm
            elif task.lower() == "agieval":
                value = data["results"]["all"]["acc_norm"]
            # MATH reports qem
            elif task.lower() == "math":
                value = data["results"]["all"]["qem"]
            # MINIMATH reports qem
            elif task.lower() == "mini_math":
                value = data["results"]["all"]["qem"]
            else:
                first_metric_key = next(
                    iter(data["results"][first_result_key])
                )  # gets the first key in the first result
                value = data["results"][first_result_key][first_metric_key]  # gets the value of the first metric
            df.loc[model_revision, task] = value

    # Put IFEval / BBH / AGIEval in first columns
    ifeval_col = df.pop("Ifeval")
    df.insert(1, "Ifeval", ifeval_col)
    bbh_col = df.pop("Bbh")
    df.insert(2, "Bbh", bbh_col)
    agieval_col = df.pop("Agieval")
    df.insert(3, "Agieval", agieval_col)
    # Drop rows where every entry is NaN
    df = df.dropna(how="all", axis=0, subset=[c for c in df.columns if c != "Date"])
    df.insert(loc=1, column="Average", value=df.mean(axis=1, numeric_only=True))
    # Convert all values to percentage
    df[df.select_dtypes(include=["number"]).columns] *= 100.0
    df = df.sort_values(by=["Average"], ascending=False)
    df = df.reset_index().rename(columns={"index": "Model"}).round(2)
    # Strip off date from model name
    df["Model"] = df["Model"].apply(lambda x: x.rsplit("_", 1)[0])

    if merge_values:
        merged_df = df.drop(["Date", "Average"], axis=1).groupby("Model").max().reset_index()
        merged_df.insert(loc=0, column="Average", value=merged_df.mean(axis=1, numeric_only=True))
        df = df[["Model", "Date"]].merge(merged_df, on="Model", how="left")
        df.drop_duplicates(subset=["Model"], inplace=True)
        df = df.sort_values(by=["Average"], ascending=False).round(2)
    return df


def refresh(merge_values: bool = True):
    return get_leaderboard_df(merge_values)


# Function to update the table based on search query
def update_table(search_query):
    df = get_leaderboard_df()
    if search_query:
        search_terms = search_query.split(";")
        search_terms = [term.strip().lower() for term in search_terms]
        pattern = "|".join(search_terms)
        df = df[df["Model"].str.lower().str.contains(pattern, regex=True)]
    return df


leaderboard_df = get_leaderboard_df()

demo = gr.Blocks()

with demo:
    gr.HTML(TITLE)
    with gr.Column():
        gr.Markdown(DESCRIPTION, elem_classes="markdown-text")
        with gr.Row():
            search_bar = gr.Textbox(placeholder="Search for your model...", show_label=False)
            merge_values = gr.Checkbox(
                value=True,
                label="Merge evals",
                info="Merge evals for the same model. If there are duplicates, we display the largest one.",
            )
        with gr.Group():
            leaderboard_df = get_leaderboard_df()
            leaderboard_table = gr.Dataframe(value=leaderboard_df, wrap=True, height=1000)
        with gr.Row():
            refresh_button = gr.Button("Refresh")

    merge_values.change(refresh, inputs=[merge_values], outputs=[leaderboard_table])
    search_bar.submit(update_table, inputs=[search_bar], outputs=[leaderboard_table])
    refresh_button.click(refresh, inputs=[], outputs=[leaderboard_table])

demo.launch()