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import streamlit as st
# TODO: improve layout (columns, sidebar, forms)
# st.set_page_config(layout='wide')
st.title('Question answering help desk application')
##########################################################
st.subheader('1. A simple question')
##########################################################
WIKI_URL = 'https://en.wikipedia.org/w/api.php'
WIKI_QUERY = "?format=json&action=query&prop=extracts&explaintext=1"
WIKI_BERT = "&titles=BERT_(language_model)"
WIKI_METHOD = 'GET'
response = req.request(WIKI_METHOD, f'{WIKI_URL}{WIKI_QUERY}{WIKI_BERT}')
resp_json = json.loads(response.content.decode("utf-8"))
wiki_bert = resp_json['query']['pages']['62026514']['extract']
paragraph = wiki_bert
written_passage = st.text_area(
'Paragraph used for QA (you can also edit, or copy/paste new content)',
paragraph,
height=250
)
if written_passage:
paragraph = written_passage
question = 'How many languages does bert understand?'
written_question = st.text_input(
'Question used for QA (you can also edit, and experiment with the answers)',
question
)
if written_question:
question = written_question
QA_URL = "https://api-inference.huggingface.co/models/deepset/roberta-base-squad2"
QA_METHOD = 'POST'
if st.button('Run QA inference (get answer prediction)'):
if paragraph and question:
inputs = {'question': question, 'context': paragraph}
payload = json.dumps(inputs)
prediction = req.request(QA_METHOD, QA_URL, data=payload)
answer = json.loads(prediction.content.decode("utf-8"))
answer_span = answer["answer"]
answer_score = answer["score"]
st.write(f'Answer: **{answer_span}**')
start_par = max(0, answer["start"]-86)
stop_para = min(answer["end"]+90, len(paragraph))
answer_context = paragraph[start_par:stop_para].replace(answer_span, f'**{answer_span}**')
st.write(f'Answer context (and score): ... _{answer_context}_ ... (score: {format(answer_score, ".3f")})')
st.write(f'Answer JSON: ')
st.write(answer)
else:
st.write('Write some passage of text and a question')
st.stop()
"""
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}")
""" |