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import json; import streamlit as st; import requests as req; from transformers import pipeline |
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WIKI_URL = 'https://en.wikipedia.org/w/api.php'; WIKI_BERT = "&titles=BERT_(language_model)" |
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WIKI_QUERY = "?format=json&action=query&prop=extracts&explaintext=1"; WIKI_METHOD = 'GET' |
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pipe_exqa = pipeline("question-answering") |
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st.title('Question Answering example') |
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st.subheader('1. A simple question (extractive, closed domain)') |
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response = req.request(WIKI_METHOD, f'{WIKI_URL}{WIKI_QUERY}{WIKI_BERT}') |
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resp_json = json.loads(response.content.decode("utf-8")) |
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wiki_bert = resp_json['query']['pages']['62026514']['extract'] |
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paragraph = wiki_bert |
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par_text = 'Paragraph used for QA (you can also edit, or copy/paste new content)' |
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written_passage = st.text_area(par_text, paragraph, height=250) |
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if written_passage: |
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paragraph = written_passage |
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question = 'How many attention heads does Bert have?' |
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query_text = 'Question used for QA (you can also edit, and experiment with the answers)' |
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written_question = st.text_input(query_text, question) |
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if written_question: |
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question = written_question |
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QA_URL = "https://api-inference.huggingface.co/models/deepset/roberta-base-squad2"; QA_METHOD = 'POST' |
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if st.button('Run QA inference (get answer prediction)'): |
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if paragraph and question: |
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inputs = {'question': question, 'context': paragraph} |
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payload = json.dumps(inputs) |
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prediction = req.request(QA_METHOD, QA_URL, data=payload) |
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answer = json.loads(prediction.content.decode("utf-8")) |
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answer_dict = dict(answer) |
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print(answer_dict) |
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if "answer" in answer_dict.keys(): |
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answer_span, answer_score = answer_dict["answer"], answer_dict["score"] |
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st.write(f'Answer: **{answer_span}**') |
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start_par, stop_para = max(0, answer_dict["start"]-86), min(answer_dict["end"]+90, len(paragraph)) |
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answer_context = paragraph[start_par:stop_para].replace(answer_span, f'**{answer_span}**') |
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st.write(f'Answer context (and score): ... _{answer_context}_ ... (score: {format(answer_score, ".3f")})') |
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st.write(f'Answer JSON: '); st.write(answer) |
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else: |
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try: |
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qa_result = pipe_exqa(question=question, context=paragraph) |
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except Exception as e: |
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qa_result = str(e) |
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if "answer" in qa_result.keys(): |
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answer_span, answer_score = qa_result["answer"], qa_result["score"] |
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st.write(f'Answer: **{answer_span}**') |
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start_par, stop_para = max(0, qa_result["start"]-86), min(qa_result["end"]+90, len(paragraph)) |
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answer_context = paragraph[start_par:stop_para].replace(answer_span, f'**{answer_span}**') |
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st.write(f'Answer context (and score): ... _{answer_context}_ ... (score: {format(answer_score, ".3f")})') |
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st.write(f'Answer JSON: '); st.write(qa_result) |
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else: |
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st.write('Write some passage of text and a question'); st.stop() |
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