app.py

import streamlit as st
import transformers
import keybert
from transformers import pipeline
from keybert import KeyBERT

# Load summarizer and keyword model
summarizer_bart = pipeline("summarization", model="facebook/bart-large-cnn")
kw_model = KeyBERT()

# Title
st.markdown("<h1 style='text-align: center;'>Text Summarization App</h1>", unsafe_allow_html=True)

# Modes
mode = st.radio("Modes", ["Paragraph", "Bullet Points", "Custom"], horizontal=True)

# Summary length
length = st.slider("Summary Length", 1, 2, 1, label_visibility="collapsed")
summary_type = "Short" if length == 1 else "Long"
st.write(f"Summary Length: **{summary_type}**")

# Layout
col1, col2 = st.columns(2)

with col1:
    st.markdown("### Paste your text below:")
    user_input = st.text_area("", height=300, placeholder="Paste job description or any paragraph...")
    
    if user_input.strip():
        # Extract keywords using KeyBERT
        keywords = [kw[0] for kw in kw_model.extract_keywords(user_input, top_n=5)]
        selected_keywords = st.multiselect("Select Keywords", keywords, default=keywords)
        st.markdown(f"**{len(user_input.split())} words**")

        if st.button("Summarize", use_container_width=True):
            summary = summarizer_bart(user_input, max_length=150 if summary_type == "Short" else 250,
                                      min_length=40, do_sample=False)[0]['summary_text']
            st.session_state['summary'] = summary
    else:
        st.warning("Please enter some text.")

with col2:
    st.markdown("### Summary")
    if 'summary' in st.session_state:
        st.success(st.session_state['summary'])
        st.markdown(f"1 sentence • {len(st.session_state['summary'].split())} words")
        st.button("Paraphrase Summary")
        st.download_button("📥 Download Summary", st.session_state['summary'], file_name="summary.txt")
    else:
        st.info("Your summary will appear here after clicking **Summarize**.")