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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.")
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