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| 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 summarization generates a summary that may contain words not present in the original text. It uses a fine-tuned model on BART-large-CNN.", | |
| "Extractive summarization selects and extracts sentences or phrases directly from the original text to create a summary using the TextRank algorithm.", | |
| "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.", | |
| "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() | |