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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 | |
| 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 | |
| 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) | |