OOlajide commited on
Commit
b7d66a6
1 Parent(s): 6c15309

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +7 -8
app.py CHANGED
@@ -1,4 +1,3 @@
1
- import torch
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  import streamlit as st
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  from transformers import pipeline
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@@ -6,11 +5,11 @@ st.set_page_config(page_title="Common NLP Tasks")
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  st.title("Common NLP Tasks")
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  st.subheader("Use the menu on the left to select a NLP task to do (click on > if closed).")
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- expander = st.sidebar.expander('About')
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  expander.write("This web app allows you to perform common Natural Language Processing tasks, select a task below to get started.")
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- st.sidebar.header('What will you like to do?')
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- option = st.sidebar.radio('', ['Extractive question answering', 'Text summarization', 'Text generation', 'Sentiment analysis'])
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  @st.cache(show_spinner=False, allow_output_mutation=True)
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  def question_model():
@@ -33,7 +32,7 @@ def sentiment_model():
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  sentiment_analysis = pipeline("sentiment-analysis")
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  return sentiment_analysis
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- if option == 'Extractive question answering':
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  st.markdown("<h2 style='text-align: center; color:red;'>Extract answer from text</h2>", unsafe_allow_html=True)
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  source = st.radio("How would you like to start? Choose an option below", ["I want to input some text", "I want to upload a file"])
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  if source == "I want to input some text":
@@ -46,7 +45,7 @@ if option == 'Extractive question answering':
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  question_answerer = question_model()
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  with st.spinner(text="Getting answer..."):
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  answer = question_answerer(context=context, question=question)
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- st.write(answer["answer"])
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  elif source == "I want to upload a file":
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  uploaded_file = st.file_uploader("Choose a .txt file to upload", type=["txt"])
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  question = st.text_input(label='Enter your question')
@@ -54,8 +53,8 @@ if option == 'Extractive question answering':
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  if button:
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  question_answerer = question_model()
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  with st.spinner(text="Getting answer..."):
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- answer = question_answerer(context=context, question=question)
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- st.write(answer["answer"])
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  elif option == 'Text summarization':
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  st.markdown("<h2 style='text-align: center; color:red;'>Summarize text</h2>", unsafe_allow_html=True)
 
 
1
  import streamlit as st
2
  from transformers import pipeline
3
 
 
5
  st.title("Common NLP Tasks")
6
  st.subheader("Use the menu on the left to select a NLP task to do (click on > if closed).")
7
 
8
+ expander = st.sidebar.expander("About")
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  expander.write("This web app allows you to perform common Natural Language Processing tasks, select a task below to get started.")
10
 
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+ st.sidebar.header("What will you like to do?")
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+ option = st.sidebar.radio("", ["Extractive question answering", "Text summarization", "Text generation", "Sentiment analysis"])
13
 
14
  @st.cache(show_spinner=False, allow_output_mutation=True)
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  def question_model():
 
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  sentiment_analysis = pipeline("sentiment-analysis")
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  return sentiment_analysis
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+ if option == "Extractive question answering":
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  st.markdown("<h2 style='text-align: center; color:red;'>Extract answer from text</h2>", unsafe_allow_html=True)
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  source = st.radio("How would you like to start? Choose an option below", ["I want to input some text", "I want to upload a file"])
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  if source == "I want to input some text":
 
45
  question_answerer = question_model()
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  with st.spinner(text="Getting answer..."):
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  answer = question_answerer(context=context, question=question)
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+ st.write(f"Answer: {answer["answer"]}")
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  elif source == "I want to upload a file":
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  uploaded_file = st.file_uploader("Choose a .txt file to upload", type=["txt"])
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  question = st.text_input(label='Enter your question')
 
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  if button:
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  question_answerer = question_model()
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  with st.spinner(text="Getting answer..."):
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+ answer = question_answerer(context=uploaded_file, question=question)
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+ st.write(f"Answer: {answer["answer"]}")
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59
  elif option == 'Text summarization':
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  st.markdown("<h2 style='text-align: center; color:red;'>Summarize text</h2>", unsafe_allow_html=True)