app.py

import streamlit as st
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# Load model and tokenizer
@st.cache_resource()
def load_model():
    tokenizer = AutoTokenizer.from_pretrained("google/pegasus-xsum")
    model = AutoModelForSeq2SeqLM.from_pretrained("google/pegasus-xsum")
    return tokenizer, model

tokenizer, model = load_model()

st.title("Text Summarization with Pegasus-XSum")
st.write("Enter text below and get a summarized version using the Pegasus model.")

# User input
text_input = st.text_area("Enter text to summarize:", "")

if st.button("Summarize"):
    if text_input:
        # Tokenize input
        inputs = tokenizer(text_input, return_tensors="pt", truncation=True, max_length=512)
        
        # Generate summary
        summary_ids = model.generate(**inputs, max_length=60, min_length=10, length_penalty=2.0, num_beams=4)
        summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
        
        # Display summary
        st.subheader("Summary:")
        st.write(summary)
    else:
        st.warning("Please enter some text to summarize.")