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import streamlit as st
from transformers import pipeline
from qa.qa import parse_file
from transformers import AutoTokenizer
from typing import Text, Union
@st.cache_resource
def summarization_model(
model_name:str="facebook/bart-large-cnn",
custom_tokenizer:Union[AutoTokenizer, bool]=False
):
summarizer = pipeline(
model=model_name,
tokenizer=model_name if custom_tokenizer==False else custom_tokenizer,
task="summarization"
)
return summarizer
@st.cache_data
def split_string_into_token_chunks(s:Text, _tokenizer:AutoTokenizer, chunk_size:int):
# Tokenize the entire string
token_ids = _tokenizer.encode(s)
# Split the token ids into chunks of the desired size
chunks = [token_ids[i:i+chunk_size] for i in range(0, len(token_ids), chunk_size)]
# Decode each chunk back into a string
return [_tokenizer.decode(chunk) for chunk in chunks]
def summarization_main():
st.markdown("<h2 style='text-align: center'>Text Summarization</h2>", unsafe_allow_html=True)
st.markdown("<h3 style='text-align: left'><b>What is text summarization about?<b></h3>", unsafe_allow_html=True)
st.write("""
Text summarization is common NLP task concerned with producing a shorter version of a given text while preserving the important information
contained in such text
""")
OPTION_1 = "I want to input some text"
OPTION_2 = "I want to upload a file"
option = st.radio("How would you like to start? Choose an option below", [OPTION_1, OPTION_2])
# greenlight to summarize
text_is_given = False
if option == OPTION_1:
sample_text = ""
text = st.text_area(
"Input a text in English (10,000 characters max)",
value=sample_text,
max_chars=10_000,
height=330)
# toggle text is given greenlight
if text != sample_text:
text_is_given = not text_is_given
elif option == OPTION_2:
uploaded_file = st.file_uploader(
"Upload a pdf, docx, or txt file (scanned documents not supported)",
type=["pdf", "docx", "txt"],
help="Scanned documents are not supported yet 🥲"
)
if uploaded_file is not None:
# parse the file using custom parsers and build a concatenation for the summarizer
text = " ".join(parse_file(uploaded_file))
# toggle text is given greenlight
text_is_given = not text_is_given
if text_is_given:
# minimal number of words in the summary
min_length, max_length = 30, 200
user_max_length = max_length
# user_max_lenght = st.slider(
# label="Maximal number of tokens in the summary",
# min_value=min_length,
# max_value=max_length,
# value=150,
# step=10,
# )
summarizer_downloaded = False
# loading the tokenizer to split the input document into feasible chunks
model_name = "facebook/bart-large-cnn"
tokenizer = AutoTokenizer.from_pretrained(model_name)
# the maximum number of tokens the model can handle depends on the model - accounting for tokens added by tokenizer
chunk_size = int(0.88*tokenizer.model_max_length)
# loading the summarization model considered
with st.spinner(text="Loading summarization model..."):
summarizer = summarization_model(model_name=model_name)
summarizer_downloaded = True
if summarizer_downloaded:
button = st.button("Summarize!")
if button:
with st.spinner(text="Summarizing text..."):
# summarizing each chunk of the input text to avoid exceeding the maximum number of tokens
summary = ""
chunks = split_string_into_token_chunks(text, tokenizer, chunk_size)
for chunk in chunks:
chunk_summary = summarizer(chunk, max_length=user_max_length, min_length=min_length)
summary += "\n" + chunk_summary[0]["summary_text"]
st.markdown("<h3 style='text-align: left'><b>Summary<b></h3>", unsafe_allow_html=True)
print(summary)
st.write(summary)
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