# app.py

import gradio as gr
import pandas as pd

# CSS for layout styling
css = """
table > thead {
    white-space: normal
}
table {
    --cell-width-1: 250px
}
table > tbody > tr > td:nth-child(2) > div {
    overflow-x: auto
}
.filter-checkbox-group {
    max-width: max-content;
}
"""

# Load dataset
def load_data():
    # load dataset from csv file
    df = pd.read_csv("results.csv")
    return df

df = load_data()

with gr.Blocks() as demo:
    gr.Markdown("# In-Context Learning Embedding and Reranker Benchmark (ICLERB) Leaderboard")
    gr.Markdown("## Introduction\nIn-Context Learning Embedding and Reranker Benchmark (ICLERB) is a benchmark to evaluate embedding and reranker models used to retrieve documents for In-Context Learning (ICL). The methodology is described in this [paper](https://arxiv.org/abs/2411.18947). ")
    gr.Markdown("## Leaderboard")
    gr.Dataframe(df)
    gr.Markdown("## Replicating results\nThe code used to generate these results will be shared on Github soon.")
    gr.Markdown("## Citation\nTo use this data in your research, please cite the following [paper](https://arxiv.org/abs/2411.18947):")
    gr.Markdown("<pre>@article{iclerb,title={ICLERB: In-Context Learning Embedding and Reranker Benchmark},\nauthor={Al Ghossein, Marie and Contal, Emile and Robicquet, Alexandre},\njournal={arXiv preprint arXiv:2411.18947},\nyear={2024}}</pre>")
    gr.Markdown("## Acknowledgements\nICLERB was developed at [Crossing Minds](https://www.crossingminds.com/iclerb) by:")
    gr.Markdown("- [Marie Al Ghossein](https://www.linkedin.com/in/mariealghossein/)")
    gr.Markdown("- [Emile Contal](https://www.linkedin.com/in/emile-contal-72837652/)")
    gr.Markdown("- [Alexandre Robicquet](https://www.linkedin.com/in/alexandrerobicquet/)")
    
demo.launch()