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import os; import json; import requests
import streamlit as st; from transformers import pipeline
ES_URL = os.environ.get("ES_URL")
question = 'What is the capital of Netherlands?'
query_text = 'Query used for search or question answering (you can also edit, and experiment with the anwers)'
written_question = st.text_input(query_text, question)
if written_question:
question = written_question
if st.button('Run semantic question answering'):
if question:
try:
url = f"{ES_URL}/document/_search?pretty"
# payload = json.dumps({"query":{"match":{"content":"moldova"}}})
payload = json.dumps({"query": {
"more_like_this": { "like": question, # "What is the capital city of Netherlands?"
"fields": ["content"], "min_term_freq": 1.9, "min_doc_freq": 4, "max_query_terms": 50
}}})
headers = {'Content-Type': 'application/json'}
response = requests.request("GET", url, headers=headers, data=payload)
kws_result = response.json() # print(response.text)
except Exception as e:
qa_result = str(e)
top_5_hits = kws_result['hits']['hits'][:5] # print("First 5 results:")
top_5_text = [{'text': hit['_source']['content'][:500],
'confidence': hit['_score']} for hit in top_5_hits ]
top_5_para = [hit['_source']['content'][:5000] for hit in top_5_hits]
DPR_MODEL = "deepset/roberta-base-squad2" #, model="distilbert-base-cased-distilled-squad"
pipe_exqa = pipeline("question-answering", model=DPR_MODEL)
qa_results = [pipe_exqa(question=question, context=paragraph) for paragraph in top_5_para]
for i, qa_result in enumerate(qa_results):
if "answer" in qa_result.keys():
answer_span, answer_score = qa_result["answer"], qa_result["score"]
st.write(f'Answer: **{answer_span}**')
paragraph = top_5_para[i]
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}_ ...')
st.write(f'(answer confidence: {format(answer_score, ".3f")})')
st.write(f'Answers JSON: '); st.write(qa_results)
for i, doc_hit in enumerate(top_5_text):
st.subheader(f'Search result #{i+1} (and score):')
st.write(f'<em>{doc_hit["text"]}...</em>', unsafe_allow_html = True)
st.markdown(f'> (*confidence score*: **{format(doc_hit["confidence"], ".3f")}**)')
st.write(f'Search results JSON: '); st.write(top_5_text)
else:
st.write('Write a query to submit your keyword search'); st.stop()
# question_similarity = [ (hit['_score'], hit['_source']['content'][:200])
# for hit in result_first_two_hits ] # print(question_similarity)
if st.button('Run keyword search'):
if question:
try:
url = f"{ES_URL}/document/_search?pretty"
# payload = json.dumps({"query":{"match":{"content":"moldova"}}})
payload = json.dumps({"query": {
"more_like_this": { "like": question, # "What is the capital city of Netherlands?"
"fields": ["content"], "min_term_freq": 1.9, "min_doc_freq": 4, "max_query_terms": 50
}}})
headers = {'Content-Type': 'application/json'}
response = requests.request("GET", url, headers=headers, data=payload)
kws_result = response.json() # print(response.text)
# qa_result = pipe_exqa(question=question, context=paragraph)
except Exception as e:
qa_result = str(e)
top_5_hits = kws_result['hits']['hits'][:5] # print("First 5 results:")
top_5_text = [{'text': hit['_source']['content'][:500],
'confidence': hit['_score']} for hit in top_5_hits ]
for i, doc_hit in enumerate(top_5_text):
st.subheader(f'Search result #{i+1} (and score):')
st.write(f'<em>{doc_hit["text"]}...</em>', unsafe_allow_html = True)
st.markdown(f'> (*confidence score*: **{format(doc_hit["confidence"], ".3f")}**)')
st.write(f'Answer JSON: '); st.write(top_5_text) # st.write(qa_result)
else:
st.write('Write a query to submit your keyword search'); st.stop()
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