import nltk import validators import streamlit as st from transformers import AutoTokenizer, pipeline # local modules from extractive_summarizer.model_processors import Summarizer from utils import ( clean_text, fetch_article_text, preprocess_text_for_abstractive_summarization, read_text_from_file, ) if __name__ == "__main__": # --------------------------------- # Main Application # --------------------------------- st.title("Text Summarizer 📝") summarize_type = st.sidebar.selectbox( "Summarization type", options=["Extractive", "Abstractive"] ) # --------------------------- # SETUP & Constants nltk.download("punkt") abs_tokenizer_name = "facebook/bart-large-cnn" abs_model_name = "facebook/bart-large-cnn" abs_tokenizer = AutoTokenizer.from_pretrained(abs_tokenizer_name) abs_max_length = 130 abs_min_length = 30 # --------------------------- inp_text = st.text_input("Enter text or a url here") st.subheader("----- OR -----") uploaded_file = st.file_uploader( "Upload a .txt, .pdf, .word file for summarization" ) is_url = validators.url(inp_text) if is_url: # complete text, chunks to summarize (list of sentences for long docs) text, clean_txt = fetch_article_text(url=inp_text) elif uploaded_file: clean_txt = read_text_from_file(uploaded_file) else: clean_txt = clean_text(inp_text) # view summarized text (expander) with st.expander("View input text"): if is_url: st.write(clean_txt[0]) else: st.write(clean_txt) summarize = st.button("Summarize") # called on toggle button [summarize] if summarize: if summarize_type == "Extractive": if is_url: text_to_summarize = " ".join([txt for txt in clean_txt]) else: text_to_summarize = clean_txt # extractive summarizer with st.spinner( text="Creating extractive summary. This might take a few seconds ..." ): ext_model = Summarizer() summarized_text = ext_model(text_to_summarize, num_sentences=6) elif summarize_type == "Abstractive": with st.spinner( text="Creating abstractive summary. This might take a few seconds ..." ): text_to_summarize = clean_txt abs_summarizer = pipeline( "summarization", model=abs_model_name, tokenizer=abs_tokenizer_name ) if is_url is False: # list of chunks text_to_summarize = preprocess_text_for_abstractive_summarization( tokenizer=abs_tokenizer, text=clean_txt ) tmp_sum = abs_summarizer( text_to_summarize, max_length=abs_max_length, min_length=abs_min_length, do_sample=False, ) summarized_text = " ".join([summ["summary_text"] for summ in tmp_sum]) # final summarized output st.subheader("Summarized text") st.info(summarized_text)