AmitPandit175/Text_Summarization

1import streamlit as st2from transformers import BartTokenizer, BartForConditionalGeneration3 4# @st.cache_resource5# def load_model():6#     model = BartForConditionalGeneration.from_pretrained("./models/bart")7#     tokenizer = BartTokenizer.from_pretrained("./models/bart")8#     return model, tokenizer9 10@st.cache_resource11def load_model():12    model = BartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn")13    tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn")14    return model, tokenizer15 16 17st.title("Text Summarization")18text = st.text_area("Enter your long text:")19 20if st.button("Summarize"):21    model, tokenizer = load_model()22    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)23    summary_ids = model.generate(inputs["input_ids"], max_length=128, num_beams=4)24    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)25    st.subheader("Summary:")26    st.write(summary)27