import streamlit as st
from transformers import pipeline
from newspaper import Article

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

summarizer = load_summarizer()

# Streamlit UI
st.title("📰 Real-Time Content Summarizer")
st.write("Paste a URL or enter text to get a summary!")

# Input option
option = st.radio("Choose input type:", ("URL", "Manual Text"))

if option == "URL":
    url = st.text_input("Enter the article URL:")
    if st.button("Summarize") and url:
        try:
            article = Article(url)
            article.download()
            article.parse()
            text = article.text
            summary = summarizer(text, max_length=150, min_length=50, do_sample=False)[0]['summary_text']
            st.subheader("Summary:")
            st.write(summary)
        except Exception as e:
            st.error("Error fetching the article. Please check the URL.")

elif option == "Manual Text":
    text_input = st.text_area("Enter text to summarize:")
    if st.button("Summarize") and text_input:
        summary = summarizer(text_input, max_length=150, min_length=50, do_sample=False)[0]['summary_text']
        st.subheader("Summary:")
        st.write(summary)

st.write("Made with ❤️ using Streamlit & Hugging Face Transformers")