app.py

from flask import Flask,request,jsonify,Blueprint,session,render_template,Response
import spacy
import numpy as np
from sentence_transformers import SentenceTransformer,util
from elasticsearch import Elasticsearch
#bp = Blueprint('bp', __name__)   

app=Flask(__name__)



def similarity(model,vertical):
    vertical_list = ["Mortgage","Real estate","Retail","Insurance","Human_Resource","Pharmaceutical"]
    word_embeddings = model.encode(vertical_list, convert_to_tensor=True)
    user_embedding = model.encode(vertical, convert_to_tensor=True)
    cosine_similarities = util.pytorch_cos_sim(user_embedding, word_embeddings)
    most_similar_word_index = np.argmax(cosine_similarities)
    most_similar_word = vertical_list[most_similar_word_index]
    return most_similar_word



def get_entities(text):
    ner_model=spacy.load("spacy_ner_model/en_pipeline/en_pipeline-0.0.0")
    sim_model=SentenceTransformer("sentence_transformer_model")

    doc = ner_model(text)

    city = None
    vertical = None
    zip_code = None
    state = None
    state_code = None

    entity_variables = {
        'CITY': 'city',
        'VERTICAL': 'vertical',
        'ZIPCODE': 'zip_code',
        'STATE': 'state',
        'STATE_CODE': 'state_code'
    }

    entity_values = {var: None for var in entity_variables.values()}

    for ent in doc.ents:
        label = ent.label_
        if label in entity_variables:
            var_name = entity_variables[label]
            var_value = ent.text
            if var_name == 'vertical':
                if entity_values[var_name] is None or len(var_value) < len(entity_values[var_name]):
                    entity_values[var_name] = var_value
            else:
                entity_values[var_name] = var_value


    city = entity_values['city']
    if entity_values['vertical']:
        vertical=similarity(sim_model,entity_values['vertical'])
    else:
        vertical = None
    zip_code = entity_values['zip_code']
    state = entity_values['state']
    state_code = entity_values['state_code']


    should_clauses = []
    fields_with_values = []
    fields = [
        ("city", city),
        ("vertical", vertical),
        ("zip_code", zip_code),
        ("state", state),
        ("state_code", state_code)
    ]

    for field, value in fields:
        if value is not None:
            should_clause = {
                "match": {
                    field: value
                }
            }
            should_clauses.append(should_clause)
            fields_with_values.append(field)
            fields_with_values.append("name")

    query = {
        'query': {
            "bool": {
                "must": should_clauses
            }
            
        }
    }

    return query,fields_with_values



@app.route('/search',methods=["GET"])
def search():
    text = request.args.get("text")
    #text=request.get_json()["text"]

    #http://127.0.0.1:5000/search?text=give%20me%20best%20loan%20agents%20in%20Chicago
    query,fields_with_values=get_entities(text)
    ELASTIC_PASSWORD = "tILF0omcl27kdZ2G7sFZ3SGf"
    es = Elasticsearch(["https://elasticsearch.preprod.experience.com:443"], basic_auth=("elastic_app_user", ELASTIC_PASSWORD), verify_certs= True)
    results = es.search(index='profiles_phase2', body=query, track_total_hits=True, source=fields_with_values)
    l=[]
    for hit in results['hits']['hits']:
        source = hit['_source']
        l.append(source)
    print("hai")
    return jsonify(l)




if __name__=="__main__":
    with app.app_context():
        #app.register_blueprint(bp)
        app.run()