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import json
import streamlit as st
import requests as req
# 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() |