app.py

import streamlit as st

from paraphraser import get_key_sentences, ParaphraseModel

paraphraser = ParaphraseModel()


# Add a model selector to the sidebar:
#model = st.sidebar.selectbox(
#    'Select Model',
#    ('T5-base', 'DistilT5-base', 'T5-small')
#)

top_n = st.sidebar.slider('Top_n', 1, 20, 5)
diversity = st.sidebar.slider('Diversity', 0.0, 1.0, 0.6)


top_k = st.sidebar.slider('Top_K', 100, 300, 168)
top_p = st.sidebar.slider('Top_P', 0.0, 1.0, 0.95)

st.header("Bullet-point Summarization")
#st.write(f'Model in use: {model}')

txt = st.text_area('Text to analyze', )

if len(txt) >= 1:
    key_sentences = get_key_sentences(txt, top_n=top_n, diversity=('mmr', diversity))
    sentences = []
    for i in sorted(key_sentences):
        sentences.append(key_sentences[i])

    paraphrased_sentences = paraphraser(sentences, top_k=top_k, top_p=top_p, num_sequences=1)
else:
    sentences = []
    paraphrased_sentences = []
    
st.header('Extracted Key Sentences')
st.write(sentences)

st.header('Paraphrase results')
st.write(paraphrased_sentences)