Clean up notes

app.py

@@ -1,4 +1,3 @@
-# Reference: https://huggingface.co/spaces/team-zero-shot-nli/zero-shot-nli/blob/main/app.py
 
 from os import write
 import pandas as pd
@@ -8,7 +7,6 @@ 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
 
 
@@ -31,8 +29,6 @@ if __name__ == '__main__':
         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')
@@ -41,8 +37,6 @@ if __name__ == '__main__':
         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)
 
@@ -69,21 +63,17 @@ if __name__ == '__main__':
         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)

models.py

@@ -33,7 +33,6 @@ def load_summary_model():
     summarizer = pipeline(task='summarization', model=model_name)
     return summarizer
 
-
 # def load_summary_model():
 #     model_name = "facebook/bart-large-mnli"
 #     tokenizer = BartTokenizer.from_pretrained(model_name)
@@ -41,7 +40,6 @@ def load_summary_model():
 #     summarizer = pipeline(task='summarization', model=model, tokenizer=tokenizer, framework='pt')
 #     return summarizer
 
-
 def summarizer_gen(summarizer, sequence:str, maximum_tokens:int, minimum_tokens:int):
 	output = summarizer(sequence, num_beams=4, max_length=maximum_tokens, min_length=minimum_tokens, do_sample=False)
 	return output[0].get('summary_text')