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import streamlit as st
from transformers import pipeline
# Function to load the model
@st.cache_data
def load_model():
summarizer = pipeline("text2text-generation", model="pk248/pegasus-samsum")
return summarizer
# Load the model
model = load_model()
# Streamlit UI
st.title("Text Summarization App")
st.write("This app uses the pk248/pegasus-samsum model to summarize text.")
# Text input
user_input = st.text_area("Enter text to summarize:", height=200)
# Summarize button
if st.button("Summarize"):
if user_input:
# Model inference
summary = model(user_input, max_length=1000, min_length=5, length_penalty=2.0)
st.write(summary[0]["generated_text"])
else:
st.write("Please enter some text to summarize.")
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