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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, | |
) | |