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from __future__ import annotations
import gradio as gr
import numpy as np
import pandas as pd
import requests
from huggingface_hub.hf_api import SpaceInfo
SHEET_ID = '1L7AHpWMVU_kZVLcsk8H2FTizgzeVxWPDoBxw7K8KHXw'
SHEET_NAME = 'model'
csv_url = f'https://docs.google.com/spreadsheets/d/{SHEET_ID}/gviz/tq?tqx=out:csv&sheet={SHEET_NAME}'
class ModelList:
def __init__(self):
self.table = pd.read_csv(csv_url)
self.table = self.table.astype({'Year':'string'})
self._preprocess_table()
self.table_header = '''
<tr>
<td width="15%">Name</td>
<td width="10%">Year Published</td>
<td width="10%">Source</td>
<td width="30%">About</td>
<td width="10%">Task</td>
<td width="15%">Training Data Type</td>
<td width="10%">Publication</td>
</tr>'''
def _preprocess_table(self) -> None:
self.table['name_lowercase'] = self.table['Name'].str.lower()
rows = []
for row in self.table.itertuples():
source = f'<a href="{row.Source}" target="_blank">Link</a>' if isinstance(
row.Source, str) else ''
paper = f'<a href="{row.Paper}" target="_blank">Link</a>' if isinstance(
row.Source, str) else ''
row = f'''
<tr>
<td>{row.Name}</td>
<td>{row.Year}</td>
<td>{source}</td>
<td>{row.About}</td>
<td>{row.task}</td>
<td>{row.data}</td>
<td>{paper}</td>
</tr>'''
rows.append(row)
self.table['html_table_content'] = rows
def render(self, search_query: str,
case_sensitive: bool,
filter_names: list[str],
data_types: list[str]) -> tuple[int, str]:
df = self.table
if search_query:
if case_sensitive:
df = df[df.name.str.contains(search_query)]
else:
df = df[df.name_lowercase.str.contains(search_query.lower())]
df = self.filter_table(df, filter_names, data_types)
result = self.to_html(df, self.table_header)
return result
@staticmethod
def filter_table(df: pd.DataFrame, filter_names: list[str], data_types: list[str]) -> pd.DataFrame:
df = df.loc[df.task.isin(set(filter_names))]
df = df.loc[df.data.isin(set(data_types))]
return df
@staticmethod
def to_html(df: pd.DataFrame, table_header: str) -> str:
table_data = ''.join(df.html_table_content)
html = f'''
<table>
{table_header}
{table_data}
</table>'''
return html
model_list = ModelList()
with gr.Blocks() as demo:
with gr.Row():
gr.Image(value="RAII.svg",scale=1,show_download_button=False,show_share_button=False,show_label=False,height=100,container=False)
gr.Markdown("# Models for Healthcare Teams")
search_box = gr.Textbox(label='Search Name',placeholder='You can search for titles with regular expressions. e.g. (?<!sur)face',max_lines=1)
case_sensitive = gr.Checkbox(label='Case Sensitive')
filter_names1 = gr.CheckboxGroup(choices=['NLP','Computer Vision', 'Multi-Model'], value=['NLP','Computer Vision', 'Multi-Model'], label='Task')
data_type_names1 = ['Biomedical Corpus','Scientific Corpus','Clinical Corpus','Image','Mixed']
data_types1 = gr.CheckboxGroup(choices=data_type_names1, value=data_type_names1, label='Training Data Type')
search_button = gr.Button('Search')
table = gr.HTML(show_label=False)
demo.load(fn=model_list.render, inputs=[search_box, case_sensitive, filter_names1, data_types1,],outputs=[table,])
search_box.submit(fn=model_list.render, inputs=[search_box, case_sensitive, filter_names1, data_types1,], outputs=[table,])
search_button.click(fn=model_list.render, inputs=[search_box, case_sensitive, filter_names1, data_types1,], outputs=[table,])
demo.queue()
demo.launch(share=False)
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