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()