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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"]
)
st.markdown(
"Enter a text or a url to get a concise summary of the article while conserving the overall meaning. This app supports text in the following formats:"
)
st.markdown(
"""- Raw text in text box
- URL of article/news to be summarized
- .txt, .pdf, .docx file formats"""
)
st.markdown("---")
# ---------------------------
# 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 = 90
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=5)
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)
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