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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) | |
| 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 | |
| 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 | |