app.py

from os import write
import pandas as pd
import base64
from typing import Sequence
import streamlit as st

from models import create_nest_sentences, load_summary_model, summarizer_gen, load_model, classifier_zero
from utils import plot_result, plot_dual_bar_chart, examples_load, example_long_text_load
import json


summarizer = load_summary_model()   
classifier = load_model()
ex_text, ex_license, ex_labels = examples_load()
ex_long_text = example_long_text_load()


# if __name__ == '__main__':
st.header("Summzarization & Multi-label Classification for Long Text")
st.write("This app summarizes and then classifies your long text with multiple labels (_Please allow for a minimum of 30secs to load results_).")
st.write("Inputs: User enters their own custom text and labels")
st.write("Outputs: A summary of the text, pre and post summary label likelihood percentages and a downloadable csv of the results")

with st.form(key='my_form'):
    example_text = ex_long_text #ex_text
    display_text = "[Excerpt from Project Gutenberg: Frankenstein]\n" + example_text + "\n\n" + ex_license
    text_input = st.text_area("Input any text you want to summaryize & classify here (keep in mind very long text will take a while to process):", display_text)

    if text_input == display_text:
        text_input = example_text

    labels = st.text_input('Possible labels (comma-separated):',ex_labels, max_chars=1000)
    labels = list(set([x.strip() for x in labels.strip().split(',') if len(x.strip()) > 0]))
    submit_button = st.form_submit_button(label='Submit')

if submit_button:
    if len(labels) == 0:
        st.write('Enter some text and at least one possible topic to see predictions.')

    # For each body of text, create text chunks of a certain token size required for the transformer
    nested_sentences = create_nest_sentences(document = text_input, token_max_length = 1024)

    summary = []
    st.markdown("### Text Chunk & Summaries")
    st.markdown("Breaks up the original text into sections with complete sentences totaling \
        less than 1024 tokens, a requirement for the summarizer.")

    # For each chunk of sentences (within the token max), generate a summary
    for n in range(0, len(nested_sentences)):
        text_chunk = " ".join(map(str, nested_sentences[n]))
        st.markdown(f"###### Chunk {n+1}/{len(nested_sentences)}" )
        st.markdown(text_chunk)

        chunk_summary = summarizer_gen(summarizer, sequence=text_chunk, maximum_tokens = 300, minimum_tokens = 20)
        summary.append(chunk_summary) 
        st.markdown("###### Partial Summary")
        st.markdown(chunk_summary)
        # Combine all the summaries into a list and compress into one document, again
        final_summary = " \n".join(list(summary))

    # final_summary = summarizer_gen(summarizer, sequence=text_input, maximum_tokens = 30, minimum_tokens = 100)
    st.markdown("### Combined Summary")
    st.markdown(final_summary)

    topics, scores = classifier_zero(classifier, sequence=final_summary, labels=labels, multi_class=True)
    # st.markdown("### Top Label Predictions: Combined Summary")
    # plot_result(topics[::-1][:], scores[::-1][:])
    # st.markdown("### Download Data")
    data = pd.DataFrame({'label': topics, 'scores_from_summary': scores})
    # st.dataframe(data)
    # coded_data = base64.b64encode(data.to_csv(index = False). encode ()).decode()
    # st.markdown(
    #     f'<a href="data:file/csv;base64, {coded_data}" download = "data.csv">Download Data</a>',
    #     unsafe_allow_html = True
    #     )

    st.markdown("### Top Label Predictions: Summary & Full Text")
    topics_ex_text, scores_ex_text = classifier_zero(classifier, sequence=example_text, labels=labels, multi_class=True)
    plot_dual_bar_chart(topics, scores, topics_ex_text, scores_ex_text)

    data_ex_text = pd.DataFrame({'label': topics_ex_text, 'scores_from_full_text': scores_ex_text})
    data2 = pd.merge(data, data_ex_text, on = ['label'])
    st.markdown("### Data Table")
    
    coded_data = base64.b64encode(data2.to_csv(index = False). encode ()).decode()
    st.markdown(
        f'<a href="data:file/csv;base64, {coded_data}" download = "data.csv">Click here to download the data</a>',
        unsafe_allow_html = True
        )
    st.dataframe(data2)