Spaces:
Runtime error
Runtime error
| # app.py | |
| import streamlit as st | |
| from extractive import preprocess_text, get_sentence_embeddings, build_semantic_graph, apply_textrank, generate_summary | |
| from abstractive import abstractive_summary | |
| from utils import extract_named_entities | |
| from transformers import AutoTokenizer, AutoModel | |
| # Load pre-trained BERT model and tokenizer | |
| model_name = "dmis-lab/biobert-base-cased-v1.2" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModel.from_pretrained(model_name) | |
| # Streamlit app layout | |
| st.title("Hybrid Summarization App") | |
| st.write("Upload text files for multi-document summarization or enter text manually for single-document summarization.") | |
| # Multi-document summarization | |
| st.header("Multi-Document Summarization") | |
| uploaded_files = st.file_uploader("Upload text files", type="txt", accept_multiple_files=True) | |
| if uploaded_files: | |
| texts = [file.read().decode("utf-8") for file in uploaded_files] | |
| # Perform extractive summarization for each document | |
| extractive_summaries = [] | |
| for text in texts: | |
| sentences = preprocess_text(text) | |
| embeddings = get_sentence_embeddings(sentences, model, tokenizer) | |
| graph = build_semantic_graph(embeddings) | |
| ranked_sentences = apply_textrank(graph, sentences) | |
| ext_summary = generate_summary(ranked_sentences, sentences, max_length_ratio=0.5) | |
| extractive_summaries.append(ext_summary) | |
| # Combine extractive summaries for multi-document summarization | |
| combined_extractive_summary = " ".join(extractive_summaries) | |
| st.write("Combined Extractive Summary:", combined_extractive_summary) | |
| # Extract named entities from the combined summary | |
| entities = extract_named_entities(combined_extractive_summary) | |
| st.write("Named Entities:", entities) | |
| # Choose summary length ratio for abstractive summarization | |
| abs_ratio_option = st.selectbox("Choose abstractive summary length ratio", ("1/2", "1/3", "1/4")) | |
| abs_ratio = {"1/2": 0.5, "1/3": 0.33, "1/4": 0.25}[abs_ratio_option] | |
| # Perform abstractive summarization | |
| combined_input = combined_extractive_summary + " " + ' '.join([ent[0] for ent in entities]) | |
| abs_summary = abstractive_summary(combined_input, max_length_ratio=abs_ratio, min_length_ratio=abs_ratio/2) | |
| st.write("Abstractive Summary:", abs_summary) | |
| # Single-document summarization | |
| st.header("Single-Document Summarization") | |
| text_input = st.text_area("Enter text here") | |
| if text_input: | |
| # Extract named entities | |
| entities = extract_named_entities(text_input) | |
| st.write("Named Entities:", entities) | |
| # Perform extractive summarization | |
| sentences = preprocess_text(text_input) | |
| embeddings = get_sentence_embeddings(sentences, model, tokenizer) | |
| graph = build_semantic_graph(embeddings) | |
| ranked_sentences = apply_textrank(graph, sentences) | |
| ext_summary = generate_summary(ranked_sentences, sentences, max_length_ratio=0.5) | |
| st.write("Extractive Summary:", ext_summary) | |
| # Choose summary length ratio for abstractive summarization | |
| abs_ratio_option = st.selectbox("Choose abstractive summary length ratio", ("1/2", "1/3", "1/4")) | |
| abs_ratio = {"1/2": 0.5, "1/3": 0.33, "1/4": 0.25}[abs_ratio_option] | |
| # Perform abstractive summarization | |
| combined_input = ext_summary + " " + ' '.join([ent[0] for ent in entities]) | |
| abs_summary = abstractive_summary(combined_input, max_length_ratio=abs_ratio, min_length_ratio=abs_ratio/2) | |
| st.write("Abstractive Summary:", abs_summary) | |