app.py

import streamlit as st
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "facebook/bart-large-cnn"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

def main():
    st.title("Text Summarization App")

    text = st.text_area('Enter the text to summarize:', '')

    if st.button('Summarize'):
        if text:
            input_ids = tokenizer(text, return_tensors="pt", padding=True, truncation=True).input_ids
            with torch.no_grad():
                output_ids = model.generate(input_ids)

            summary = tokenizer.decode(output_ids[0], skip_special_tokens=True)

            st.subheader("Summary:")
            st.write(summary)
        else:
            st.warning("Please enter some text to summarize.")

if __name__ == "__main__":
    main()