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from __future__ import annotations
import numpy as np
import pandas as pd
import requests
from huggingface_hub.hf_api import SpaceInfo
class PaperList:
def __init__(self):
self.organization_name = 'ICML2022'
self.table = pd.read_csv('papers.csv')
self._preprcess_table()
self.table_header = '''
<tr>
<td width="50%">Paper</td>
<td width="26%">Authors</td>
<td width="4%">pdf</td>
<td width="4%">arXiv</td>
<td width="4%">GitHub</td>
<td width="4%">HF Spaces</td>
<td width="4%">HF Models</td>
<td width="4%">HF Datasets</td>
</tr>'''
@staticmethod
def load_space_info(author: str) -> list[SpaceInfo]:
path = 'https://huggingface.co/api/spaces'
r = requests.get(path, params={'author': author})
d = r.json()
return [SpaceInfo(**x) for x in d]
def add_spaces_to_table(self, organization_name: str,
df: pd.DataFrame) -> pd.DataFrame:
spaces = self.load_space_info(organization_name)
name2space = {
s.id.split('/')[1].lower(): f'https://huggingface.co/spaces/{s.id}'
for s in spaces
}
df['hf_space'] = df.loc[:, ['hf_space', 'github']].apply(
lambda x: x[0] if isinstance(x[0], str) else name2space.get(
x[1].split('/')[-1].lower()
if isinstance(x[1], str) else '', np.nan),
axis=1)
return df
def _preprcess_table(self) -> None:
self.table = self.add_spaces_to_table(self.organization_name,
self.table)
self.table['title_lowercase'] = self.table.title.str.lower()
rows = []
for row in self.table.itertuples():
paper = f'<a href="{row.url}" target="_blank">{row.title}</a>'
pdf = f'<a href="{row.pdf}" target="_blank">pdf</a>'
arxiv = f'<a href="{row.arxiv}" target="_blank">arXiv</a>' if isinstance(
row.arxiv, str) else ''
github = f'<a href="{row.github}" target="_blank">GitHub</a>' if isinstance(
row.github, str) else ''
hf_space = f'<a href="{row.hf_space}" target="_blank">Space</a>' if isinstance(
row.hf_space, str) else ''
hf_model = f'<a href="{row.hf_model}" target="_blank">Model</a>' if isinstance(
row.hf_model, str) else ''
hf_dataset = f'<a href="{row.hf_dataset}" target="_blank">Dataset</a>' if isinstance(
row.hf_dataset, str) else ''
row = f'''
<tr>
<td>{paper}</td>
<td>{row.authors}</td>
<td>{pdf}</td>
<td>{arxiv}</td>
<td>{github}</td>
<td>{hf_space}</td>
<td>{hf_model}</td>
<td>{hf_dataset}</td>
</tr>'''
rows.append(row)
self.table['html_table_content'] = rows
def render(self, search_query: str, case_sensitive: bool,
filter_names: list[str]) -> tuple[int, str]:
df = self.add_spaces_to_table(self.organization_name, self.table)
if search_query:
if case_sensitive:
df = df[df.title.str.contains(search_query)]
else:
df = df[df.title_lowercase.str.contains(search_query.lower())]
has_arxiv = 'arXiv' in filter_names
has_github = 'GitHub' in filter_names
has_hf_space = 'HF Space' in filter_names
has_hf_model = 'HF Model' in filter_names
has_hf_dataset = 'HF Dataset' in filter_names
df = self.filter_table(df, has_arxiv, has_github, has_hf_space,
has_hf_model, has_hf_dataset)
return len(df), self.to_html(df, self.table_header)
@staticmethod
def filter_table(df: pd.DataFrame, has_arxiv: bool, has_github: bool,
has_hf_space: bool, has_hf_model: bool,
has_hf_dataset: bool) -> pd.DataFrame:
if has_arxiv:
df = df[~df.arxiv.isna()]
if has_github:
df = df[~df.github.isna()]
if has_hf_space:
df = df[~df.hf_space.isna()]
if has_hf_model:
df = df[~df.hf_model.isna()]
if has_hf_dataset:
df = df[~df.hf_dataset.isna()]
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
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