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# 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)
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