app.py

import torch
import nltk
import validators
import streamlit as st
from transformers import pipeline, T5Tokenizer

# local modules
from extractive_summarizer.model_processors import Summarizer
from src.utils import clean_text, fetch_article_text
from src.abstractive_summarizer import (
    abstractive_summarizer,
    preprocess_text_for_abstractive_summarization,
)

# # abstractive summarizer model
# @st.cache()
# def load_abs_model():
#     tokenizer = T5Tokenizer.from_pretrained("t5-base")
#     model = T5ForConditionalGeneration.from_pretrained("t5-base")
#     return tokenizer, model


if __name__ == "__main__":
    # ---------------------------------
    # Main Application
    # ---------------------------------
    st.title("Text Summarizer 📝")
    summarize_type = st.sidebar.selectbox(
        "Summarization type", options=["Extractive", "Abstractive"]
    )
    # ---------------------------
    # SETUP
    nltk.download("punkt")
    abs_tokenizer_name = "t5-base"
    abs_model_name = "t5-base"
    abs_tokenizer = T5Tokenizer.from_pretrained(abs_tokenizer_name)
    # ---------------------------

    inp_text = st.text_input("Enter text or a url here")

    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)
    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])
            # 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")
                if not is_url:
                    # list of chunks
                    text_to_summarize = preprocess_text_for_abstractive_summarization(
                        tokenizer=abs_tokenizer, text=clean_txt
                    )
                tmp_sum = abs_summarizer(text_to_summarize, 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)