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# Reference: https://huggingface.co/spaces/team-zero-shot-nli/zero-shot-nli/blob/main/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
# from utils import plot_result, examples_load, example_long_text_load, to_excel
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 (_output includes partial summaries, \
bar chart and data table of labels with confidence scores as well as a data download link_).")
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
# minimum_tokens = 30
# maximum_tokens = 100
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)
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