app.py

import gradio as gr
import json
from huggingface_hub import HfApi
import pandas as pd
import os


def compute_df():
    api = HfApi(token=os.environ.get("HF_API_KEY"))
    # download all files in https://huggingface.co/illuin-cde/baselines
    files = [f for f in api.list_repo_files("illuin-cde/baselines") if f.startswith("metrics")]
    print(files)

    metrics = []
    for file in files:
        result_path = api.hf_hub_download("illuin-cde/baselines", filename=file)
        with open(result_path, "r") as f:
            dic = json.load(f)
            dic.update(dic["metrics"])
            del dic["metrics"]
            metrics.append(dic)

    df = pd.DataFrame(metrics)
    df = df[["model", "dataset", "split", "is_contextual", "ndcg_at_1", "ndcg_at_5", "ndcg_at_10", "ndcg_at_100"]]
    df["model"] = df["model"].apply(lambda x: x.split("/")[-1])
    df["dataset"] = df["dataset"].apply(lambda x: x.split("/")[-1])
    # round all numeric columns
    df = df.round(3)

    # sort by ndcg_at_5
    df = df.sort_values("ndcg_at_5", ascending=False)

    # gradio display
    gradio_df = gr.Dataframe(df)
    return gradio_df

# refresh button and precompute
gr.Interface(fn=compute_df, title="Results Leaderboard", inputs=None, outputs="dataframe").launch()