import streamlit as st
import PyPDF2
from extractive_model import summarize_with_textrank
from abstractive_model import summarize_with_bart
from keyword_extraction import extract_keywords
from keyphrase_extraction import extract_sentences_with_obligations
#from blanc import BlancHelp

# Set page to wide mode
st.set_page_config(layout="wide")

# Function to handle file upload and return its content
def load_pdf(file):
    pdf_reader = PyPDF2.PdfReader(file)
    pdf_text = ""
    for page_num in range(len(pdf_reader.pages)):
        pdf_text += pdf_reader.pages[page_num].extract_text() or ""
    return pdf_text

# Main app
def main():
    st.title("Terms of Service Summarizer")

    # Layout: 3 columns
    col1, col2, col3 = st.columns([1, 3, 2], gap="large")

    # Left column: Radio buttons for summarizer choice
    with col1:
        radio_options = ['Abstractive', 'Extractive', 'Keyword Extraction', 'Keyphrase Extraction']
        help_text = "Abstractive: Abstractive summarization generates a summary that may contain words not present in the original text. " \
                "It uses a fine-tuned model on BART-large-CNN.<br>" \
                "Extractive: Extractive summarization selects and extracts sentences or phrases directly from the original text to create a summary using the TextRank algorithm.<br>" \
                "Keyword Extraction: Keyword extraction identifies and extracts important keywords or terms from the text using the Rake algorithm. " \
                "These keywords can be used for various purposes such as content analysis and SEO.<br>" \
                "Keyphrase Extraction: Keyphrase extraction is similar to keyword extraction but focuses on identifying multi-word phrases or expressions that are significant in the text using the Rake algorithm."
        
        radio_selection = st.radio("Choose type of summarizer:", radio_options, help=help_text)
                                   
    # Middle column: Text input and File uploader
    with col2:
        user_input = st.text_area("Enter your text here:")
        uploaded_file = st.file_uploader("Upload a PDF", type="pdf")
        if st.button("Summarize"):
            if uploaded_file and user_input:
                st.warning("Please provide either text input or a PDF file, not both.")
                return
            elif uploaded_file:
                # Extract text from PDF
                file_content = load_pdf(uploaded_file)
                st.write("PDF uploaded successfully.")
            elif user_input:
                file_content = user_input
            else:
                st.warning("Please upload a PDF or enter some text to summarize.")
                return

            # Perform extractive summarization
            if radio_selection == "Extractive":
                summary = summarize_with_textrank(file_content)
                st.session_state.summary = summary

            # Perform extractive summarization
            if radio_selection == "Abstractive":
                summary = summarize_with_bart(file_content)
                st.session_state.summary = summary

            # Perform Keyword Extraction
            if radio_selection == "Keyword Extraction":
                summary = extract_keywords(file_content)
                st.session_state.summary = summary

            # Perform Keyphrase Extraction
            if radio_selection == "Keyphrase Extraction":
                summary = extract_sentences_with_obligations(file_content)
                st.session_state.summary = summary

    # Right column: Displaying text after pressing 'Summarize'
    with col3:
        st.write("Summary:")
        if 'summary' in st.session_state:
            st.write(st.session_state.summary)

if __name__ == "__main__":
    main()