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| 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.markdown( | |
| "<h3 style='text-align: center; color: green;'>OR</h3>", | |
| unsafe_allow_html=True, | |
| ) | |
| uploaded_file = st.file_uploader( | |
| "Upload a .txt, .pdf, .docx 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) | |
| clean_txt = clean_text(clean_txt) | |
| 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) | |