Update app.py

app.py

@@ -1,15 +1,14 @@
 import streamlit as st
-import torch
 from transformers import pipeline
 import spacy
 from spacy import displacy
 import plotly.express as px
 import numpy as np
 
-st.set_page_config(page_title="NIU NLP Prototype")
+st.set_page_config(page_title="NLP Prototype")
 
 st.title("Natural Language Processing Prototype")
-st.write("_This web application is intended for educational use, please do not upload any classified, proprietary, or sensitive information._")
+st.write("_This web application is intended for educational use, please do not upload any sensitive information._")
 st.subheader("__Which natural language processing task would you like to try?__")
 st.write("- __Sentiment Analysis:__ Identifying whether a piece of text has a positive or negative sentiment.")
 st.write("- __Named Entity Recognition:__ Identifying all geopolitical entities, organizations, people, locations, or dates in a body of text.")
@@ -67,7 +66,6 @@ with st.spinner(text="Please wait for the models to load. This should take appro
 if option == 'Text Classification':
     cat1 = st.text_input('Enter each possible category name (separated by a comma). Maximum 5 categories.')
     text = st.text_area('Enter Text Below:', height=200)
-    #uploaded_file = st.file_uploader("Choose a file", type=['txt'])
     submit = st.button('Generate')
     if submit:
         st.subheader("Classification Results:")
@@ -82,18 +80,16 @@ if option == 'Text Summarization':
     max_lengthy = st.slider('Maximum summary length (words)', min_value=30, max_value=150, value=60, step=10)
     num_beamer = st.slider('Speed vs quality of summary (1 is fastest)', min_value=1, max_value=8, value=4, step=1)
     text = st.text_area('Enter Text Below (maximum 800 words):', height=300) 
-    #uploaded_file = st.file_uploader("Choose a file", type=['txt'])
     submit = st.button('Generate')  
     if submit:
         st.subheader("Summary:")
         with st.spinner(text="This may take a moment..."):
             summWords = sum2(text, max_length=max_lengthy, min_length=15, num_beams=num_beamer, do_sample=True, early_stopping=True, repetition_penalty=1.5, length_penalty=1.5)
-        text2 =summWords[0]["summary_text"] #re.sub(r'\s([?.!"](?:\s|$))', r'\1', )
+        text2 =summWords[0]["summary_text"] 
         st.write(text2)
 
 if option == 'Sentiment Analysis':
     text = st.text_area('Enter Text Below:', height=200)
-    #uploaded_file = st.file_uploader("Choose a file", type=['txt'])
     submit = st.button('Generate')
     if submit:
         st.subheader("Sentiment:")
@@ -104,7 +100,6 @@ if option == 'Sentiment Analysis':
 
 if option == 'Named Entity Recognition':
     text = st.text_area('Enter Text Below:', height=300)
-    #uploaded_file = st.file_uploader("Choose a file", type=['txt'])
     submit = st.button('Generate')
     if submit:    
         entities = []