app.py

import streamlit as st
from transformers import pipeline

x = st.slider('Select a value')
st.write(x, 'squared is', x * x)

question_answerer = pipeline("question-answering")

context = r""" Extractive Question Answering is the task of extracting an answer from a text given a question. 
An example of a question answering dataset is the SQuAD dataset, which is entirely based on that task. 
If you would like to fine-tune a model on a SQuAD task, you may leverage the 
examples/pytorch/question-answering/run_squad.py script."""
question = "What is extractive question answering?" #"What is a good example of a question answering dataset?"
result = question_answerer(question=question, context=context)
answer = result['answer']
score = round(result['score'], 4)
span = f"start: {result['start']}, end: {result['end']}"

st.write(answer)
st.write(f"score: {score}")
st.write(f"span: {span}")