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import streamlit as st
from transformers import pipeline
def main():
st.title("Text Summarization")
# Initialize the summarizer pipeline with a more powerful model
summarizer = pipeline(
task="summarization",
model="facebook/bart-large-cnn", # Consider using a larger model
min_length=50,
max_length=150,
truncation=True,
)
# User input
input_text = st.text_area("Enter the text you want to summarize:", height=200)
# Summarize button
if st.button("Summarize"):
if input_text:
# Split the text into smaller chunks if it's too long
max_input_length = 1024 # BART can handle up to 1024 tokens
input_chunks = [input_text[i:i+max_input_length] for i in range(0, len(input_text), max_input_length)]
# Generate the summary for each chunk and combine them
summary = ""
for chunk in input_chunks:
output = summarizer(chunk, max_length=150, min_length=50, do_sample=False)
summary += output[0]['summary_text'] + " "
# Display the summary as bullet points
st.subheader("Summary:")
bullet_points = summary.split(". ")
for point in bullet_points:
if point: # Ensure that empty strings are not included
st.write(f"- {point.strip()}")
else:
st.warning("Please enter text to summarize.")
if __name__ == "__main__":
main()
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