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# streamlit_app.py
import streamlit as st
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
st.set_page_config(page_title="Text Summarizer", layout="centered")
st.title("📝 Text Summarization App")
input_text = st.text_area("Enter text to summarize", height=200)
@st.cache_resource
def load_model():
model = AutoModelForSeq2SeqLM.from_pretrained("google/pegasus-cnn_dailymail")
tokenizer = AutoTokenizer.from_pretrained("google/pegasus-cnn_dailymail")
return model, tokenizer
if st.button("Summarize"):
if input_text.strip() == "":
st.warning("Please enter some text.")
else:
try:
with st.spinner("Summarizing..."):
model, tokenizer = load_model()
inputs = tokenizer(input_text, return_tensors="pt", truncation=True)
summary_ids = model.generate(
inputs["input_ids"],
max_length=100,
min_length=30,
length_penalty=2.0,
num_beams=4,
early_stopping=True
)
summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
st.subheader("Summary")
st.success(summary)
except Exception as e:
st.error(f"Error: {e}")
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