from typing import List, Tuple

import gradio as gr
import pandas as pd
from textacy.extract.keyterms.sgrank import sgrank as keywords

from examples.common import NLP, IDF, NEWS_EXAMPLES


def process(text: str) -> pd.DataFrame:
    doc = NLP(text)
    terms: List[Tuple[str, float]] = keywords(doc, topn=10, include_pos=("NOUN", "PROPN"), idf=IDF, ngrams=(1, 2, 3))
    term_set = [t for t, _ in terms]
    return pd.DataFrame([{"Keyphrase": term, "Score": prob}
                         for term, prob in terms
                         if all(other == term or term not in other for other in term_set)])


demo = gr.Interface(
    fn=process,
    inputs=gr.Textbox(value=NEWS_EXAMPLES[0], lines=10, label="Input text", show_label=True),
    outputs=gr.DataFrame(label="Keywords", show_label=False, max_cols=2, max_rows=10),
    examples=NEWS_EXAMPLES,
    # cache_examples=True,
)