#!/usr/bin/env python

import datetime

import gradio as gr
import pandas as pd
from apscheduler.schedulers.background import BackgroundScheduler
from gradio_calendar import Calendar

from papers import PaperList, get_df

DESCRIPTION = "# [Daily Papers](https://huggingface.co/papers)"

FOOT_NOTE = """\
Related useful Spaces:
- [Semantic Scholar Paper Recommender](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) by [davanstrien](https://huggingface.co/davanstrien)
- [ArXiv CS RAG](https://huggingface.co/spaces/bishmoy/Arxiv-CS-RAG) by [bishmoy](https://huggingface.co/bishmoy)
- [Paper Q&A](https://huggingface.co/spaces/chansung/paper_qa) by [chansung](https://huggingface.co/chansung)
- [dailypapershackernews](https://huggingface.co/spaces/akhaliq/dailypapershackernews) by [akhaliq](https://huggingface.co/akhaliq)
"""


paper_list = PaperList(get_df())


def update_paper_list() -> None:
    global paper_list
    paper_list = PaperList(get_df())


scheduler = BackgroundScheduler()
scheduler.add_job(func=update_paper_list, trigger="cron", hour="*", timezone="UTC", misfire_grace_time=60)
scheduler.start()


def update_df() -> gr.Dataframe:
    return gr.Dataframe(value=paper_list.df_prettified)


def update_num_papers(df: pd.DataFrame) -> str:
    return f"{len(df)} / {len(paper_list.df_raw)}"


def search(
    start_date: datetime.datetime,
    end_date: datetime.datetime,
    search_title: str,
    search_abstract: str,
    max_num_to_retrieve: int,
) -> pd.DataFrame:
    return paper_list.search(start_date, end_date, search_title, search_abstract, max_num_to_retrieve)


with gr.Blocks(css_paths="style.css") as demo:
    gr.Markdown(DESCRIPTION)
    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,
                )
        with gr.Row():
            start_date = Calendar(label="Start date", type="datetime", value="2023-05-05")
            end_date = Calendar(label="End date", type="datetime")

    num_papers = gr.Textbox(label="Number of papers", value=update_num_papers(paper_list.df_raw), interactive=False)
    df = gr.Dataframe(
        value=paper_list.df_prettified,
        datatype=paper_list.column_datatype,
        type="pandas",
        interactive=False,
        max_height=1000,
        elem_id="table",
        column_widths=["10%", "10%", "60%", "10%", "5%", "5%"],
        wrap=True,
    )

    gr.Markdown(FOOT_NOTE)

    gr.on(
        triggers=[start_date.change, end_date.change, search_title.submit, search_abstract.submit],
        fn=search,
        inputs=[start_date, end_date, search_title, search_abstract, max_num_to_retrieve],
        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,
        outputs=df,
        queue=False,
        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)