app.py

import streamlit as st
from transformers import pipeline
@st.cache(allow_output_mutation=True)
def summarize_model():
    model = pipeline("summarization")
    return model
summ = summarize_model()
st.title("Summarize Your Text")
st.subheader("Paste any article in the text area below and click on the 'Summarize Text' button to get the summarized textual data")
st.subheader("This application is using HuggingFace's transformers pre-trained model for text summarization.")
sentence = st.text_area('Paste your copied data here...', height=100)
button = st.button("Summarize Text")
max_lengthy = st.sidebar.slider('Maximum summary length (words)', min_value=30, max_value=700, value=100, step=10)
num_beamer = st.sidebar.slider('Speed vs quality of Summary (1 is fastest but less accurate)', min_value=1, max_value=8, value=4, step=1)
with st.spinner("Summarizing..."):
    if button and sentence:
        summary = summ(sentence, max_length = max_lengthy, min_length = 50, num_beams=num_beamer, do_sample=True,early_stopping=True, repetition_penalty=1.5, length_penalty=1.5)[0]
        st.write(summary['summary_text'])