|
import json; import streamlit as st; import requests as req; from transformers import pipeline |
|
WIKI_URL = 'https://en.wikipedia.org/w/api.php'; WIKI_BERT = "&titles=BERT_(language_model)" |
|
WIKI_QUERY = "?format=json&action=query&prop=extracts&explaintext=1"; WIKI_METHOD = 'GET' |
|
DPR_MODEL = "deepset/roberta-base-squad2" |
|
pipe_exqa = pipeline("question-answering", model=DPR_MODEL) |
|
st.title('Question Answering example') |
|
st.subheader('1. A simple question (extractive, closed domain)') |
|
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 |
|
par_text = 'Paragraph used for QA (you can also edit, or copy/paste new content)' |
|
written_passage = st.text_area(par_text, paragraph, height=250) |
|
if written_passage: |
|
paragraph = written_passage |
|
question = 'How many attention heads does Bert have?' |
|
query_text = 'Question used for QA (you can also edit, and experiment with the answers)' |
|
written_question = st.text_input(query_text, question) |
|
if written_question: |
|
question = written_question |
|
if st.button('Run QA inference (get answer prediction)'): |
|
if paragraph and question: |
|
try: |
|
qa_result = pipe_exqa(question=question, context=paragraph) |
|
except Exception as e: |
|
qa_result = str(e) |
|
|
|
if "answer" in qa_result.keys(): |
|
answer_span, answer_score = qa_result["answer"], qa_result["score"] |
|
st.write(f'Answer: **{answer_span}**') |
|
start_par, stop_para = max(0, qa_result["start"]-86), min(qa_result["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(qa_result) |
|
else: |
|
st.write('Write some passage of text and a question'); st.stop() |
|
|
