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import gradio as gr
import json
import pandas as pd
from urllib.request import urlopen
from urllib.error import URLError
import re
from datetime import datetime
CITATION_BUTTON_TEXT = r"""@misc{2023opencompass,
title={OpenCompass: A Universal Evaluation Platform for Foundation Models},
author={OpenCompass Contributors},
howpublished = {\url{https://github.com/open-compass/opencompass}},
year={2023}
}"""
CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
DATA_URL_BASE = "http://opencompass.oss-cn-shanghai.aliyuncs.com/dev-assets/hf-research/"
def findfile():
model_meta_info = 'model-meta-info'
results_sum = 'hf-academic'
url = f"{DATA_URL_BASE}{model_meta_info}.json"
response = urlopen(url)
model_info = json.loads(response.read().decode('utf-8'))
url = f"{DATA_URL_BASE}{results_sum}.json"
response = urlopen(url)
results = json.loads(response.read().decode('utf-8'))
return model_info, results
MAIN_LEADERBOARD_DESCRIPTION = """## Compass Academic Leaderboard
--WIP--
"""
Initial_title = 'Compass Academic Leaderboard'
def create_interface():
model_info, results = findfile()
with gr.Blocks() as demo:
title_comp = gr.Markdown(Initial_title)
gr.Markdown(MAIN_LEADERBOARD_DESCRIPTION)
with gr.Tabs(elem_classes='tab-buttons') as tabs:
with gr.TabItem('Results', elem_id='main', id=0):
# math_main_tab(results)
pass
with gr.TabItem('Predictions', elem_id='notmain', id=1):
# dataset_tab(results, structs[i], dataset)
pass
return demo
# model_info, results = findfile()
# breakpoint()
if __name__ == '__main__':
demo = create_interface()
demo.queue()
demo.launch(server_name='0.0.0.0')
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