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# 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()
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