app.py

import streamlit as st
from transformers import pipeline

@st.cache(allow_output_mutation=True)
def load_summarizer():
    model = pipeline("summarization", model="google/bigbird-pegasus-large-bigpatent")
    return model

summarizer = load_summarizer()
st.title("Patent Text Summarizer")
sentence = st.text_area('Please paste your Patent Text :', height=300)
button = st.button("Summarize")

max = st.sidebar.slider('Select max', 50, 500, step=10, value=200)
min = st.sidebar.slider('Select min', 10, 150, step=10, value=100)
#do_sample = st.sidebar.checkbox("Do sample", value=False)
with st.spinner("Generating Patent Summary.."):
    if button and sentence:
        #chunks = generate_chunks(sentence)
        res = summarizer(sentence,
                         max_length=max, 
                         min_length=min, 
                         #do_sample=do_sample
                         )
        text = ' '.join([summ['summary_text'] for summ in res])
        # st.write(result[0]['summary_text'])
        st.write(text)