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import operator |
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import datasets |
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import pandas as pd |
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from huggingface_hub import HfApi |
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from math import isnan |
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api = HfApi() |
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class PaperList: |
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COLUMN_INFO = [ |
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["ID", "str"], |
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["Title", "str"], |
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["Authors", "str"], |
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["Paper page", "markdown"], |
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["GitHub", "markdown"], |
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["Spaces", "markdown"], |
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["Models", "markdown"], |
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["Datasets", "markdown"], |
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] |
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def __init__(self): |
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self.df_raw = self.get_df() |
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self.df_prettified = self.prettify(self.df_raw) |
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@staticmethod |
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def get_df() -> pd.DataFrame: |
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df = datasets.load_dataset("CVPR2024/CVPR2024-papers", split="train").to_pandas() |
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df["paper_page"] = df["arxiv_id"].apply( |
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lambda arxiv_id: f"https://huggingface.co/papers/{arxiv_id}" if not isnan(arxiv_id) else "" |
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) |
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return df |
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@staticmethod |
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def create_link(text: str, url: str) -> str: |
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return f'<a href="{url}" target="_blank">{text}</a>' |
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@staticmethod |
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def prettify(df: pd.DataFrame) -> pd.DataFrame: |
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rows = [] |
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for _, row in df.iterrows(): |
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new_row = { |
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"ID": row["id"], |
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"Title": row["title"], |
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"Authors": row["authors"], |
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"Paper page": PaperList.create_link(row["arxiv_id"], row["paper_page"]) if not isnan(row["arxiv_id"]) else " ", |
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"GitHub": "\n".join([PaperList.create_link("GitHub", url) for url in row["GitHub"]] if row["GitHub"]!="[]" else " "), |
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"Spaces": "\n".join( |
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[ |
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PaperList.create_link(repo_id, f"https://huggingface.co/spaces/{repo_id}") |
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for repo_id in row["Space"] |
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] if row["Space"] != "[]" else [" "]), |
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"Models": "\n".join( |
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[PaperList.create_link(repo_id, f"https://huggingface.co/{repo_id}") for repo_id in row["Model"]] |
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if row["Model"] != "[]" else [" "]) , |
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"Datasets": "\n".join( |
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[ |
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PaperList.create_link(repo_id, f"https://huggingface.co/datasets/{repo_id}") |
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for repo_id in row["Dataset"] |
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] if row["Dataset"] != "[]" else [" "] |
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), |
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} |
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rows.append(new_row) |
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return pd.DataFrame(rows, columns=PaperList.get_column_names()) |
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@staticmethod |
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def get_column_names(): |
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return list(map(operator.itemgetter(0), PaperList.COLUMN_INFO)) |
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def get_column_datatypes(self, column_names: list[str]) -> list[str]: |
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mapping = dict(self.COLUMN_INFO) |
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return [mapping[name] for name in column_names] |
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def search( |
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self, |
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title_search_query: str, |
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author_search_query: str, |
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) -> pd.DataFrame: |
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df = self.df_raw.copy() |
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df = df[df["title"].str.contains(title_search_query, case=False)] |
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df = df[df["authors"].str.contains(author_search_query, case=False)] |
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return self.prettify(df) |
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