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from __future__ import annotations
import pandas as pd
class PaperList:
def __init__(self):
self.organization_name = 'ICML2023'
self.table = pd.read_csv('papers.csv')
self._preprocess_table()
self.table_header = '''
<tr>
<td width="40%">Title</td>
<td width="28%">Authors</td>
<td width="5%">arXiv</td>
<td width="5%">GitHub</td>
<td width="7%">Paper pages</td>
<td width="5%">Spaces</td>
<td width="5%">Models</td>
<td width="5%">Datasets</td>
</tr>'''
def _preprocess_table(self) -> None:
self.table['title_lowercase'] = self.table.title.str.lower()
rows = []
for row in self.table.itertuples():
title = f'{row.title}'
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_paper = f'<a href="{row.hf_paper}" target="_blank">Paper page</a>' if isinstance(
row.hf_paper, 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>{title}</td>
<td>{row.authors}</td>
<td>{arxiv}</td>
<td>{github}</td>
<td>{hf_paper}</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.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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