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| import streamlit as st | |
| import re | |
| import PyPDF2 | |
| import matplotlib.pyplot as plt | |
| import io | |
| from wordcloud import WordCloud | |
| from PIL import Image | |
| from rouge import Rouge | |
| from datasets import load_dataset | |
| from extractive_summarization import summarize_with_textrank, summarize_with_lsa | |
| from abstractive_summarization import summarize_with_bart_cnn, summarize_with_bart_ft, summarize_with_led, summarize_with_t5 | |
| from keyword_extraction import extract_keywords | |
| from keyphrase_extraction import extract_sentences_with_obligations | |
| from hybrid_summarization import summarize_hybrid | |
| #-------------------------------------------------------------------# | |
| # Load in ToS-Summaries dataset | |
| dataset = load_dataset("EE21/ToS-Summaries") | |
| # Extract titles or identifiers for the ToS | |
| tos_titles = [f"Document {i}" for i in range(len(dataset['train']))] | |
| # 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 = ["Hybrid (RAKE + BART Fine-tuned)", "Abstractive (LongT5)", "Abstractive (LED)", 'Abstractive (BART Fine-tuned)', "Abstractive (BART-large-CNN)", 'Extractive (TextRank)', | |
| "Extractive (Latent Semantic Analysis)", 'Keyphrase Extraction (RAKE)', 'Keyword Extraction (RAKE)'] | |
| 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 a text") | |
| uploaded_file = st.file_uploader("Upload a PDF", type="pdf") | |
| # Dropdown for selecting the document | |
| tos_selection_index = st.selectbox("Select a Terms of Service Document", range(len(tos_titles)), format_func=lambda x: tos_titles[x]) | |
| if st.button("Summarize"): | |
| if uploaded_file and user_input and tos_selection_index: | |
| 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 | |
| elif tos_selection_index is not None: | |
| file_content = dataset['train'][tos_selection_index]['plain_text'] | |
| else: | |
| st.warning("Please upload a PDF, enter some text, or select a document to summarize.") | |
| return | |
| # Perform hybrid summarization | |
| if radio_selection == "Hybrid (RAKE + BART Fine-tuned)": | |
| summary = summarize_hybrid(file_content) | |
| st.session_state.summary = summary | |
| # Perform extractive summarization | |
| if radio_selection == "Extractive (TextRank)": | |
| summary = summarize_with_textrank(file_content) | |
| st.session_state.summary = summary | |
| # Perform extractive summarization | |
| if radio_selection == "Extractive (Latent Semantic Analysis)": | |
| summary = summarize_with_lsa(file_content) | |
| st.session_state.summary = summary | |
| # Perform abstractive summarization | |
| if radio_selection == "Abstractive (BART Fine-tuned)": | |
| summary = summarize_with_bart_ft(file_content) | |
| st.session_state.summary = summary | |
| # Perform abstractive summarization | |
| if radio_selection == "Abstractive (BART-large-CNN)": | |
| summary = summarize_with_bart_cnn(file_content) | |
| st.session_state.summary = summary | |
| # Perform abstractive summarization | |
| if radio_selection == "Abstractive (LongT5)": | |
| summary = summarize_with_t5(file_content) | |
| st.session_state.summary = summary | |
| # Perform abstractive summarization | |
| if radio_selection == "Abstractive (LED)": | |
| summary = summarize_with_led(file_content) | |
| st.session_state.summary = summary | |
| # Perform Keyword Extraction | |
| if radio_selection == "Keyword Extraction (RAKE)": | |
| summary = extract_keywords(file_content) | |
| st.session_state.summary = summary | |
| # Perform Keyphrase Extraction | |
| if radio_selection == "Keyphrase Extraction (RAKE)": | |
| 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) | |
| # Generate and display word cloud | |
| wordcloud = WordCloud(width=800, height=400, background_color='white', max_words=20).generate(st.session_state.summary) | |
| # Convert to PIL Image | |
| image = wordcloud.to_image() | |
| # Convert PIL Image to bytes | |
| buf = io.BytesIO() | |
| image.save(buf, format='PNG') | |
| byte_im = buf.getvalue() | |
| st.image(byte_im, caption='Word Cloud of Summary', use_column_width=True) | |
| # Check if no PDF or text input is provided and a ToS document is selected | |
| if not uploaded_file and not user_input and tos_selection_index is not None and 'summary' in dataset['train'][tos_selection_index]: | |
| # Fetch the reference summary | |
| reference_summary = dataset['train'][tos_selection_index]['summary'] | |
| # Calculate ROUGE scores | |
| rouge = Rouge() | |
| scores = rouge.get_scores(st.session_state.summary, reference_summary) | |
| # Display ROUGE scores as styled text | |
| col1, col2, col3 = st.columns(3) | |
| with col1: | |
| st.markdown(f"<p style='text-align: center; color: black; border: 1px solid #cccccc; padding: 5px; border-radius: 4px;'>ROUGE-1: {scores[0]['rouge-1']['f']:.4f}</p>", unsafe_allow_html=True) | |
| with col2: | |
| st.markdown(f"<p style='text-align: center; color: black; border: 1px solid #cccccc; padding: 5px; border-radius: 4px;'>ROUGE-2: {scores[0]['rouge-2']['f']:.4f}</p>", unsafe_allow_html=True) | |
| with col3: | |
| st.markdown(f"<p style='text-align: center; color: black; border: 1px solid #cccccc; padding: 5px; border-radius: 4px;'>ROUGE-L: {scores[0]['rouge-l']['f']:.4f}</p>", unsafe_allow_html=True) | |
| if __name__ == "__main__": | |
| main() | |