app.py

import streamlit as st
from transformers import AutoTokenizer, AutoModelForQuestionAnswering, pipeline

st.title('Question-Answering NLU')

st.sidebar.title('Navigation')
menu = st.sidebar.radio("", options=["Introduction", "Parsing NLU data into SQuAD 2.0", "Generating Questions", "Training",
                                     "Evaluation"], index=0)


if menu == "Introduction":

    st.markdown('''

Question Answering NLU (QANLU) is an approach that maps the NLU task into question answering, 
leveraging pre-trained question-answering models to perform well on few-shot settings. Instead of 
training an intent classifier or a slot tagger, for example, we can ask the model intent- and 
slot-related questions in natural language: 

```
Context : I'm looking for a cheap flight to Boston.

Question: Is the user looking to book a flight?
Answer  : Yes

Question: Is the user asking about departure time?
Answer  : No

Question: What price is the user looking for?
Answer  : cheap

Question: Where is the user flying from?
Answer  : (empty)
```

Thus, by asking questions for each intent and slot in natural language, we can effectively construct an NLU hypothesis. For more details,
please read the paper: 
[Language model is all you need: Natural language understanding as question answering](https://assets.amazon.science/33/ea/800419b24a09876601d8ab99bfb9/language-model-is-all-you-need-natural-language-understanding-as-question-answering.pdf).

In this Space, we will see how to transform [MATIS++](https://github.com/amazon-research/multiatis) 
NLU data (e.g. utterances and intent / slot annotations) into [SQuAD 2.0 format](https://rajpurkar.github.io/SQuAD-explorer/explore/v2.0/dev/)
question-answering data that can be used by QANLU. MATIS++ includes
the original English version of ATIS and a translation into eight languages: German, Spanish, French, 
Japanese, Hindi, Portuguese, Turkish, and Chinese. 

''')

elif menu == "Evaluation":
    st.header('QANLU Evaluation')
    tokenizer = AutoTokenizer.from_pretrained("AmazonScience/qanlu", use_auth_token=True)

    model = AutoModelForQuestionAnswering.from_pretrained("AmazonScience/qanlu", use_auth_token=True)

    qa_pipeline = pipeline('question-answering', model=model, tokenizer=tokenizer)
    
    context = st.text_input(
        'Please enter the context:',
        value="I want a cheap flight to Boston."
    )
    question = st.text_input(
        'Please enter the question:',
        value="What is the destination?"
    )


    qa_input = {
      'context': 'Yes. No. ' + context,
      'question': question
    }

    if st.button('Ask QANLU'):
        answer = qa_pipeline(qa_input)
        st.write(answer)