app.py

import streamlit as st
from transformers import BartTokenizer, TFBartForConditionalGeneration

# Load the model and tokenizer
model_path = 'facebook/bart-large-cnn'
tokenizer_path = 'facebook/bart-large-cnn'

tokenizer = BartTokenizer.from_pretrained(tokenizer_path)
model = TFBartForConditionalGeneration.from_pretrained(model_path)

def summarize_text(text):
    inputs = tokenizer.encode('summarize: ' + text, return_tensors='tf', max_length=1024, truncation=True)
    summary_ids = model.generate(
        inputs,
        max_length=150,
        min_length=40,
        length_penalty=2.0,
        num_beams=4,
        early_stopping=True
    )
    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
    return summary

st.title("Text Summarization")
text = st.text_area("Enter text to summarize", height=200)

if st.button("Summarize"):
    summary = summarize_text(text)
    st.write("Summary:")
    st.write(summary)