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import pandas as pd
import gradio as gr
import csv
import json
import os
import shutil
from huggingface_hub import Repository
HF_TOKEN = os.environ.get("HUGGINGFACE_TOKEN")
MODEL_INFO = [
"Model",
"Avg",
"Visual Quality",
"Temporal Consistency",
"Dynamic Degree",
"Text-to-Video Alignment",
]
DATA_TITILE_TYPE = ['markdown', 'number', 'number', 'number', 'number', 'number',]
SUBMISSION_NAME = "VideoScore-Leaderboard"
SUBMISSION_URL = os.path.join("https://huggingface.co/datasets/hexuan21/", SUBMISSION_NAME)
CSV_DIR = "./VideoScore-Leaderboard/results.csv"
COLUMN_NAMES = MODEL_INFO
LEADERBORAD_INTRODUCTION = """# VideoScore Leaderboard
"""
TABLE_INTRODUCTION = """
"""
LEADERBORAD_INFO = """
We list the information of the used datasets as follows:<br>
"""
CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
CITATION_BUTTON_TEXT = r"""@inproceedings{hendrycks2021measuring,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Hendrycks, Dan and Burns, Collin and Kadavath, Saurav and Arora, Akul and Basart, Steven and Tang, Eric and Song, Dawn and Steinhardt, Jacob},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021}
}
}"""
SUBMIT_INTRODUCTION = """# Submit on Science Leaderboard Introduction
## ⚠ Please note that you need to submit the json file with following format:
```json
{
"Model": "[NAME]",
"Repo": "https://huggingface.co/[MODEL_NAME]"
"TheoremQA": 50,
"MATH": 50,
"GSM": 50,
"GPQA": 50,
"MMLU-STEM": 50
}
```
After submitting, you can click the "Refresh" button to see the updated leaderboard(it may takes few seconds).
"""
def get_df():
repo = Repository(local_dir=SUBMISSION_NAME, clone_from=SUBMISSION_URL, use_auth_token=HF_TOKEN)
repo.git_pull()
df = pd.read_csv(CSV_DIR)
df['Model'] = df['Model'].apply(lambda x: f"[{x.split(']')[0][1:]}]({x.split('(')[1][:-1]})")
df['Avg'] = df[["Visual Quality",
"Temporal Consistency",
"Dynamic Degree",
"Text-to-Video Alignment",]].mean(axis=1).round(2)
df = df.sort_values(by=['Avg'], ascending=False)
return df[COLUMN_NAMES]
def refresh_data():
return get_df() |