import arxiv
import gradio as gr
import pandas as pd
from apscheduler.schedulers.background import BackgroundScheduler
from cachetools import TTLCache, cached
from setfit import SetFitModel
from tqdm.auto import tqdm
import os

os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"

CACHE_TIME = 60 * 60 * 12  # 12 hours
MAX_RESULTS = 1_000


@cached(cache=TTLCache(maxsize=10, ttl=CACHE_TIME))
def get_arxiv_result():
    search = arxiv.Search(
        query="ti:dataset AND abs:machine learning",
        max_results=MAX_RESULTS,
        sort_by=arxiv.SortCriterion.SubmittedDate,
    )
    return [
        {
            "title": result.title,
            "abstract": result.summary,
            "url": result.entry_id,
            "category": result.primary_category,
            "updated": result.updated,
        }
        for result in tqdm(search.results(), total=MAX_RESULTS)
    ]


def load_model():
    return SetFitModel.from_pretrained("librarian-bots/is_new_dataset_t`eacher_model")


def format_row_for_model(row):
    return f"TITLE: {row['title']} \n\nABSTRACT: {row['abstract']}"


int2label = {0: "new_dataset", 1: "not_new_dataset"}


def get_predictions(data: list[dict], model=None, batch_size=64):
    if model is None:
        model = load_model()
    predictions = []
    for i in tqdm(range(0, len(data), batch_size)):
        batch = data[i : i + batch_size]
        text_inputs = [format_row_for_model(row) for row in batch]
        batch_predictions = model.predict_proba(text_inputs)
        for j, row in enumerate(batch):
            prediction = batch_predictions[j]
            row["prediction"] = int2label[int(prediction.argmax())]
            row["probability"] = float(prediction.max())
            predictions.append(row)
    return predictions


def create_markdown(row):
    title = row["title"]
    abstract = row["abstract"]
    arxiv_id = row["arxiv_id"]
    hub_paper_url = f"https://huggingface.co/papers/{arxiv_id}"
    updated = row["updated"]
    updated = updated.strftime("%Y-%m-%d")
    broad_category = row["broad_category"]
    category = row["category"]
    return f""" <h2> {title} </h2> Updated: {updated} 
    | Category: {broad_category}  | Subcategory: {category} |
\n\n{abstract}
\n\n [Hugging Face Papers page]({hub_paper_url})
    """


@cached(cache=TTLCache(maxsize=10, ttl=CACHE_TIME))
def prepare_data():
    print("Downloading arxiv results...")
    arxiv_results = get_arxiv_result()
    print("loading model...")
    model = load_model()
    print("Making predictions...")
    predictions = get_predictions(arxiv_results, model=model)
    df = pd.DataFrame(predictions)
    df.loc[:, "arxiv_id"] = df["url"].str.extract(r"(\d+\.\d+)")
    df.loc[:, "broad_category"] = df["category"].str.split(".").str[0]
    df.loc[:, "markdown"] = df.apply(create_markdown, axis=1)
    return df


all_possible_arxiv_categories = sorted(prepare_data().category.unique().tolist())
broad_categories = sorted(prepare_data().broad_category.unique().tolist())


def create_markdown_summary(categories=None, new_only=True, narrow_categories=None):
    df = prepare_data()
    if new_only:
        df = df[df["prediction"] == "new_dataset"]
    if narrow_categories is not None:
        df = df[df["category"].isin(narrow_categories)]
    if categories is not None and not narrow_categories:
        df = df[df["broad_category"].isin(categories)]
    number_of_results = len(df)
    results = (
        "<h1 style='text-align: center'> arXiv papers related to datasets</h1> \n\n"
    )
    results += f"Number of results: {number_of_results}\n\n"
    results += "\n\n<br>".join(df["markdown"].tolist())
    return results


scheduler = BackgroundScheduler()
scheduler.add_job(prepare_data, "cron", hour=3, minute=30)
scheduler.start()

description = """This Space shows recent papers on arXiv that are *likely* to be papers introducing new datasets related to machine learning. \n\n
The Space works by:
- searching for papers on arXiv with the term `dataset` in the title + "machine learning" in the abstract
- passing the abstract and title of the papers to a machine learning model that predicts if the paper is introducing a new dataset or not
 
This Space is a WIP in progress. The model is not perfect, and the search query is not perfect. If you have  suggestions for how to improve this Space, please open a Discussion.\n\n"""


with gr.Blocks() as demo:
    gr.Markdown(
        "<h1 style='text-align: center'>  &#x2728;New Datasets in Machine Learning "
        " &#x2728; </h1>"
    )
    gr.Markdown(description)
    with gr.Row():
        broad_categories = gr.Dropdown(
            choices=broad_categories,
            label="Broad arXiv Category",
            multiselect=True,
            value="cs",
            size="sm",
        )
    with gr.Accordion("Advanced Options", open=False):
        gr.Markdown(
            "Narrow by arXiv categories. **Note** this will take precedence over the"
            " broad category selection."
        )
        narrow_categories = gr.Dropdown(
            choices=all_possible_arxiv_categories,
            value=None,
            multiselect=True,
            label="Narrow arXiv Category",
        )
        gr.ClearButton(narrow_categories, "Clear Narrow Categories", size="sm")
    with gr.Row():
        new_only = gr.Checkbox(True, label="New Datasets Only", size="sm")
    results = gr.Markdown(create_markdown_summary())
    broad_categories.change(
        create_markdown_summary,
        inputs=[broad_categories, new_only, narrow_categories],
        outputs=results,
    )
    narrow_categories.change(
        create_markdown_summary,
        inputs=[broad_categories, new_only, narrow_categories],
        outputs=results,
    )
    new_only.select(
        create_markdown_summary,
        [broad_categories, new_only, narrow_categories],
        results,
    )

demo.launch()