review-automation-server/nlu_job/nlu_utils.py

from django.conf import settings
from requests import post
import json

nlu_server_url = settings.NLU_SERVER_URI


def model_inference(text):
    url = nlu_server_url + '/model/parse'
    payload = {'text': text}
    headers = {'content-type': 'application/json'}

    response = post(url, data=json.dumps(payload), headers=headers)
    if response.status_code == 200:
        return response.json()
    return response


def is_a_name(name):
    '''
    function that decide whether it is a person name or not
    :param -> a string usually reviewer name:
    :return -> a boolean True/False:
    '''

    response = model_inference(name.title())
    entities = response.get('entities')
    if not entities:
        return False
    entity = entities[0]
    if entity.get('entity') == 'PERSON':
        return True
    else:
        return False


def analyze_inference(response):
    '''
    response has four property
    ['intent', 'entities', 'intent_ranking', 'text']
    we took all intents that has more than 10% of intent confident.
    all the intents that has bellow confidence has been omitted.
    :param response:
    :return: dictionary with key of intent and value of it's confident.
    '''

    res_intents = response.get('intent_ranking')
    intents = {}
    for intent in res_intents:
        key = intent.get('name')
        values = intent.get('confidence')
        if values > 0.1:
            intents[key] = int(values*100)

    return intents