app.py

# import gradio as gr
# import pandas as pd

# df = pd.read_csv('FuseReviews_leaderboard.csv')

# headline = """# FuseReviews Leaderboard

# When submitting your results to the leaderboard, please make sure it is in a csv file with a single column "predictions". Make sure the predictions align to the testset order. Please send your predictions to [this mail](mailto:[email protected]).
# Please include in your email 1) a name for your model, 2) your team name (including your affiliation), and optionally, 3) a github repo or paper link.
# """
# demo = gr.Blocks()
# with demo:
#     with gr.Row():
#         gr.Markdown(headline)

#     with gr.Column():
#         leaderboard_df = gr.components.DataFrame(
#             value=df,
#             datatype=["markdown", "number", "number", "number"]
#         )

# demo.launch()

import gradio as gr
import pandas as pd

# df = pd.read_table("visit_bench_leaderboard.tsv")
df = pd.read_table('visitbench_leaderboard_Single~Image_Nov072023.tsv')

headline = """# VisIT-Bench Leaderboard
To submit your results to the leaderboard, you can run our auto-evaluation code, following the instructions [here](https://github.com/Hritikbansal/visit_bench_sandbox). Once you are happy with the results, you can send to [this mail](mailto:[email protected]).
Please include in your email 1) a name for your model, 2) your team name (including your affiliation), and optionally, 3) a github repo or paper link. Please also attach your predictions: you can add a "predictions" column to [this csv](https://huggingface.co/datasets/mlfoundations/VisIT-Bench/raw/main/test/metadata.csv).
"""
demo = gr.Blocks()
with demo:
    with gr.Row():
        gr.Markdown(headline)

    with gr.Column():
        leaderboard_df = gr.components.DataFrame(
            value=df,
            datatype=["markdown", "markdown", "number", "number", "number"]
        )

demo.launch()