app.py

import streamlit as st
from transformers import BartTokenizer, BartForSequenceClassification

def summarize_text(data, max_length):
    model_name = "facebook/bart-large-cnn"
    model = BartForSequenceClassification.from_pretrained(model_name)
    tokenizer = BartTokenizer.from_pretrained(model_name)

    inputs = tokenizer(data, max_length=max_length, return_tensors="pt", truncation=True)
    summary_ids = model.generate(inputs["input_ids"])

    summary_text = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
    return summary_text

def main():
    st.title("Data Summarization")

    data = st.text_area("Enter your Data", "")
    max_length = st.slider("Summary Length", 20, 1000, 200)

    if st.button("Submit"):
        if not data:
            st.warning("Please enter some text for summarization.")
        else:
            summary = summarize_text(data, max_length)
            st.text_area("Your Summary", summary, height=200)

if __name__ == "__main__":
    main()