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import os
import numpy as np
import streamlit as st
from elasticsearch import Elasticsearch
from embedders.LatinBERT import LatinBERT
from embedders.labse import LaBSE
if 'models' not in st.session_state:
st.session_state['models'] = dict(
LaBSE=LaBSE(),
LatinBERT=LatinBERT(bertPath="./embedders/latin_bert/latin_bert", tokenizerPath="./embedders/tokenizer/latin.subword.encoder")
)
verify_certs=True
host = os.environ["ELASTIC_HOST"]
user_pass = os.environ["ELASTIC_AUTH"].split(":")
es = Elasticsearch(host, basic_auth=user_pass, verify_certs=verify_certs)
def searchCloseSentence(document, startNumber, numCloseSentence=3):
queryPrevious = {
"bool": {
"must": [{
"term": {
"document": document
}
}, {
"range": {
"number": {
"gte": startNumber - numCloseSentence,
"lt": startNumber,
}
}
}
]
}
}
queryNext = {
"bool": {
"must": [{
"term": {
"document": document
}
}, {
"range": {
"number": {
"lte": startNumber+3,
"gt": startNumber,
}
}
}
]
}
}
previous = es.search(
index="sentences",
query=queryPrevious
)
nexts = es.search(
index="sentences",
query=queryNext
)
previous_hits = sorted(previous["hits"]["hits"], key=lambda e: e["_source"]["number"])
previous_context = "".join([r["_source"]["sentence"] for r in previous_hits])
subsequent_hits = sorted(nexts["hits"]["hits"], key=lambda e: e["_source"]["number"])
subsequent_context = "".join([r["_source"]["sentence"] for r in subsequent_hits])
document_name_results = es.search(
index="documents",
query={
"bool": {
"must": [{
"term": {
"id": document
}
}
]
}
}
)
document_name_data = document_name_results["hits"]["hits"][0]["_source"]
document_name = f"{document_name_data['title']} - {document_name_data['author']}"
return document_name, previous_context, subsequent_context
def prepareResults(results):
results = results['hits']['hits']
#string_results = []
for sentence in results:
text = sentence['_source']['sentence']
score = sentence['_score']
document = sentence['_source']['document']
number = sentence['_source']['number']
document_name, previous_context, subsequent_context = searchCloseSentence(document, number, 3)
string_result = f"#### {document_name} (score: {score:.2f})\n{previous_context} **{text}** {subsequent_context}"
#string_results.append(string_result)
results_placeholder.markdown(string_result)
#return string_results
def search():
if query == "":
return
results_placeholder.markdown(f"Searching with {model_name} query={query}")
status_indicator.write(f"Computing query embeddings...")
query_vector = None
embeddingType = None
if model_name in ["LaBSE", "LatinBERT"]:
query_vector = st.session_state['models'][model_name](query)[0, :].numpy().tolist()
embeddingType = "labse_embedding" if model_name == "LaBSE" else "latinBERT_embedding"
elif model_name in ["LaBSE-LatinBERT-Mean","LaBSE-LatinBERT-CONCAT"]:
query_vector_labse = st.session_state['models']['LaBSE'](query)[0, :].numpy().tolist()
query_vector_latinBERT = st.session_state['models']['LatinBERT'](query)[0, :].numpy().tolist()
if model_name == "LaBSE-LatinBERT-Mean":
query_vector = np.mean([query_vector_labse, query_vector_latinBERT], axis=0).tolist()
embeddingType = "mean_embedding"
elif model_name == "LaBSE-LatinBERT-CONCAT":
query_vector = query_vector_latinBERT + query_vector_labse
embeddingType = "concat_embedding"
script = {
"source": f"cosineSimilarity(params.query_vector, '{embeddingType}') + 1.0",
"params": {"query_vector": query_vector}
}
status_indicator.write(f"Preparing the script for search...")
results = es.search(
index='sentences',
query={
"script_score": {
"query": {"match_all": {}},
"script": script
}
},
size=limit
)
status_indicator.write(f"Prettifying the results ...")
prepareResults(results)
st.header("Serica Intelligent Search")
st.write("This is a fork of this repo(https://huggingface.co/spaces/galatolo/serica-intelligent-search)")
st.write("Perform an intelligent search using a Sentence Embedding Transformer model on the SERICA database")
model_name = st.selectbox("Model", ["LaBSE", "LatinBERT", "LaBSE-LatinBERT-Mean", "LaBSE-LatinBERT-CONCAT"])
limit = st.number_input("Number of results (sentences) ", 25)
query = st.text_input("Query", value="")
status_indicator = st.empty()
do_search = st.button("Search")
results_placeholder = st.container()
if do_search:
search()
#do_search(model_name, query, limit, results_placeholder, status_indicator)
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