app.py

#!/usr/bin/env python

import gradio as gr
import pandas as pd

from papers import PaperList

DESCRIPTION = "# ICLR 2024 Papers"

TUTORIAL = """\
#### Tutorial for claiming the ICLR 2024 papers

1. Find your paper in the table below.
2. Click on the Paper page link in the table.
3. Click on your name in the corresponding Paper page.
4. Click “claim authorship”.
    - This will redirect you to the Papers section of your Settings.
5. Confirm the request in the redirected page.

The admin team will validate your request soon. Once confirmed, the Paper page will show as verified.

If you need further assistance, see the guide [here](https://huggingface.co/docs/hub/paper-pages#claiming-authorship-to-a-paper).

If your paper is not yet indexed on Hugging Face, you can index it by following this [guide](https://huggingface.co/docs/hub/paper-pages#can-i-have-a-paper-page-even-if-i-have-no-modeldatasetspace) and open a PR with [this Space](https://huggingface.co/spaces/ICLR2024/update-ICLR2024-papers) to add your Paper page to this Space.

#### Tutorial for creating a PR

To add data to the table below, please use [this Space](https://huggingface.co/spaces/ICLR2024/update-ICLR2024-papers) to create a PR.
"""


paper_list = PaperList()

DEFAULT_COLUMNS = [
    "Title",
    "Type",
    "Paper page",
    "👍",
    "💬",
    "OpenReview",
    "Project page",
    "GitHub",
    "Spaces",
    "Models",
]


def update_num_papers(df: pd.DataFrame) -> str:
    if "claimed" in df.columns:
        return f"{len(df)} / {len(paper_list.df_raw)} ({len(df[df['claimed'].str.contains('✅')])} claimed)"
    else:
        return f"{len(df)} / {len(paper_list.df_raw)}"


def update_df(
    title_search_query: str,
    abstract_search_query: str,
    max_num_to_retrieve: int,
    filter_names: list,
    presentation_type: str,
    column_names: list[str],
) -> pd.DataFrame:
    return gr.DataFrame(
        value=paper_list.search(
            title_search_query,
            abstract_search_query,
            max_num_to_retrieve,
            filter_names,
            presentation_type,
            column_names,
        ),
        datatype=paper_list.get_column_datatypes(column_names),
    )


with gr.Blocks(css="style.css") as demo:
    gr.Markdown(DESCRIPTION)
    with gr.Accordion(label="Tutorial", open=True):
        gr.Markdown(TUTORIAL)
    with gr.Group():
        search_title = gr.Textbox(label="Search title")
        with gr.Row():
            with gr.Column(scale=4):
                search_abstract = gr.Textbox(
                    label="Search abstract",
                    info="The result may not be accurate as the abstract does not contain all the information.",
                )
            with gr.Column(scale=1):
                max_num_to_retrieve = gr.Slider(
                    label="Max number to retrieve",
                    info="This is used only for search on abstracts.",
                    minimum=1,
                    maximum=len(paper_list.df_raw),
                    step=1,
                    value=100,
                )

        filter_names = gr.CheckboxGroup(
            label="Filter",
            choices=[
                "Paper page",
                "GitHub",
                "Space",
                "Model",
                "Dataset",
            ],
        )
        presentation_type = gr.Radio(
            label="Presentation Type",
            choices=["(ALL)", "Oral", "Spotlight Poster", "Poster"],
            value="(ALL)",
        )
        column_names = gr.CheckboxGroup(label="Columns", choices=paper_list.get_column_names(), value=DEFAULT_COLUMNS)

    num_papers = gr.Textbox(
        label="Number of papers", value=update_num_papers(paper_list.df_prettified), interactive=False
    )
    df = gr.Dataframe(
        value=paper_list.df_prettified,
        datatype=paper_list.get_column_datatypes(paper_list.get_column_names()),
        type="pandas",
        row_count=(0, "dynamic"),
        interactive=False,
        height=1000,
        elem_id="table",
        wrap=True,
    )

    inputs = [
        search_title,
        search_abstract,
        max_num_to_retrieve,
        filter_names,
        presentation_type,
        column_names,
    ]
    gr.on(
        triggers=[
            search_title.submit,
            search_abstract.submit,
            filter_names.input,
            presentation_type.input,
            column_names.input,
        ],
        fn=update_df,
        inputs=inputs,
        outputs=df,
        api_name=False,
    ).then(
        fn=update_num_papers,
        inputs=df,
        outputs=num_papers,
        queue=False,
        api_name=False,
    )
    demo.load(
        fn=update_df,
        inputs=inputs,
        outputs=df,
        api_name=False,
    ).then(
        fn=update_num_papers,
        inputs=df,
        outputs=num_papers,
        queue=False,
        api_name=False,
    )

if __name__ == "__main__":
    demo.queue(api_open=False).launch(show_api=False)