import streamlit as st
from transformers import pipeline

# Load summarization model
@st.cache_resource
def load_summarizer():
    summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
    return summarizer

# Summarize input text
def summarize_text(input_text, max_length=130, min_length=30):
    summarizer = load_summarizer()
    
    summary = summarizer(input_text, max_length=max_length, min_length=min_length, do_sample=False)
    
    return summary[0]['summary_text']

# Streamlit app interface
def main():
    st.title("Text Summarization App")

    # Input box for text to summarize
    input_text = st.text_area("Enter the text you want to summarize:", "Paste your article or text here...")

    # Slider for max and min length of the summary
    max_length = st.slider("Maximum length of summary:", min_value=50, max_value=300, value=130)
    min_length = st.slider("Minimum length of summary:", min_value=20, max_value=100, value=30)

    # Button to summarize
    if st.button("Summarize"):
        with st.spinner("Summarizing..."):
            summary = summarize_text(input_text, max_length=max_length, min_length=min_length)
            st.subheader("Summary")
            st.write(summary)

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