app.py

import streamlit as st
from transformers import pipeline

# Load the summarization model from Hugging Face
summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6", revision="a4f8f3e")


# Streamlit app
def main():
    # Set the title and description of the app
    st.title("Text Summarizer App")
    st.write(
        "Enter a piece of text, select the length of the summary, and get a concise summary!"
    )

    # Input text box for user input
    input_text = st.text_area("Enter your text here:")

    # Length selector for summary
    summary_length = st.slider("Select summary length:", min_value=50, max_value=500, value=150, step=50)

    # Check if the user has entered any text
    if st.button("Generate Summary"):
        if not input_text:
            st.warning("Please enter some text.")
        else:
            # Generate summary using the Hugging Face summarization model
            summary = summarizer(input_text, max_length=summary_length, min_length=50, length_penalty=2.0, num_beams=4)
            st.subheader("Summary:")
            st.write(summary[0]["summary_text"])

# Run the app
if __name__ == "__main__":
    main()