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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"
head_style = """
<style>
@media (min-width: 1536px)
{
.gradio-container {
min-width: var(--size-full) !important;
}
}
</style>
"""
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
model_info, results = findfile()
MAIN_LEADERBOARD_DESCRIPTION = """## Compass Academic Leaderboard
--WIP--
"""
Initial_title = 'Compass Academic Leaderboard'
def make_results_tab(model_info, results):
models_list, datasets_list = [], []
for i in model_info:
models_list.append([i['abbr'], i['display_name']])
for i in results.keys():
datasets_list.append(i)
result_list = []
index = 0
for model in models_list:
this_result = {}
this_result['Index'] = index
this_result['Model Name'] = model[1]
index += 1
for dataset in datasets_list:
this_result[dataset] = results[dataset][model[0]]
result_list.append(this_result)
df = pd.DataFrame(result_list)
return df
def calculate_column_widths(df):
column_widths = []
for column in df.columns:
header_length = len(str(column))
max_content_length = df[column].astype(str).map(len).max()
width = max(header_length * 10, max_content_length * 8) + 20
width = max(160, min(400, width))
column_widths.append(width)
return column_widths
def show_results_tab(df):
def filter_df(model_name):
newdf = make_results_tab(model_info, results)
default_val = 'Input the Model Name'
if model_name != default_val:
method_names = [x.split('</a>')[0].split('>')[-1].lower() for x in newdf['Model Name']]
flag = [model_name.lower() in name for name in method_names]
newdf['TEMP'] = flag
newdf = newdf[newdf['TEMP'] == True]
newdf.pop('TEMP')
return newdf
with gr.Row():
model_name = gr.Textbox(
value='Input the Model Name',
label='Search Model Name',
interactive=True
)
with gr.Column():
table = gr.DataFrame(
value=df,
interactive=False,
wrap=False,
column_widths=calculate_column_widths(df),
)
model_name.submit(
fn=filter_df,
inputs=model_name,
outputs=table
)
def create_interface():
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):
df = make_results_tab(model_info, results)
show_results_tab(df)
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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