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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])
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 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)
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