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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_json("papers.json").fillna("")
claim_info = pd.read_csv("claim_info.csv", dtype={"arxiv_id": str, "n_authors": int, "n_linked_authors": int})
self.table = pd.merge(self.table, claim_info, on="arxiv_id", how="left")
self.table[["n_authors", "n_linked_authors"]] = (
self.table[["n_authors", "n_linked_authors"]].fillna(-1).astype(int)
)
self._preprocess_table()
self.table_header = """
<tr>
<td width="38%">Title</td>
<td width="20%">Authors</td>
<td width="5%">Type</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>
<td width="5%">Claimed</td>
</tr>"""
def _preprocess_table(self) -> None:
self.table["title_lowercase"] = self.table.title.str.lower()
self.table["arxiv"] = self.table.arxiv_id.apply(lambda x: f"https://arxiv.org/abs/{x}" if x else "")
self.table["hf_paper"] = self.table.arxiv_id.apply(lambda x: f"https://huggingface.co/papers/{x}" if x else "")
self.table["authors"] = self.table.authors.apply(lambda x: ", ".join(x))
rows = []
for row in self.table.itertuples():
title = f'<a href="{row.url}" target="_blank">{row.title}</a>'
arxiv = f'<a href="{row.arxiv}" target="_blank">arXiv</a>' if row.arxiv else ""
github = f'<a href="{row.github}" target="_blank">GitHub</a>' if row.github else ""
hf_paper = f'<a href="{row.hf_paper}" target="_blank">Paper page</a>' if row.hf_paper else ""
hf_space = f'<a href="{row.hf_space}" target="_blank">Space</a>' if row.hf_space else ""
hf_model = f'<a href="{row.hf_model}" target="_blank">Model</a>' if row.hf_model else ""
hf_dataset = f'<a href="{row.hf_dataset}" target="_blank">Dataset</a>' if row.hf_dataset else ""
author_linked = "✅" if row.n_linked_authors > 0 else ""
n_linked_authors = "" if row.n_linked_authors == -1 else row.n_linked_authors
n_authors = "" if row.n_authors == -1 else row.n_authors
claimed_paper = "" if n_linked_authors == "" else f"{n_linked_authors}/{n_authors} {author_linked}"
row = f"""
<tr>
<td>{title}</td>
<td>{row.authors}</td>
<td>{row.type}</td>
<td>{arxiv}</td>
<td>{github}</td>
<td>{hf_paper}</td>
<td>{hf_space}</td>
<td>{hf_model}</td>
<td>{hf_dataset}</td>
<td>{claimed_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],
presentation_type: str,
) -> tuple[str, str]:
df = self.table
if presentation_type != "(ALL)":
df = df[df.type == presentation_type.lower()]
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 = "Space" in filter_names
has_hf_model = "Model" in filter_names
has_hf_dataset = "Dataset" in filter_names
df = self.filter_table(df, has_arxiv, has_github, has_hf_space, has_hf_model, has_hf_dataset)
n_claimed = len(df[df.n_linked_authors > 0])
return f"{len(df)} ({n_claimed} claimed)", 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 != ""]
if has_github:
df = df[df.github != ""]
if has_hf_space:
df = df[df.hf_space != ""]
if has_hf_model:
df = df[df.hf_model != ""]
if has_hf_dataset:
df = df[df.hf_dataset != ""]
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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