review-automation-server/review/model_pred_report.py

import json
import requests
import csv
from difflib import SequenceMatcher
from django.utils import timezone

from .models import Review, CustomReply


# constants
nlu_server_url = 'http://localhost:5005'
replies = CustomReply.objects.all()


def model_inference(text):
    url = nlu_server_url + '/model/parse'
    payload = {'text': text}
    headers = {'content-type': 'application/json'}
    response = requests.post(url, data=json.dumps(payload), headers=headers)
    if response.status_code == 200:
        res = response.json()
    intents_rankings = res.get('intent_ranking')
    intents = []
    for intent in intents_rankings:
        if intent.get('confidence') > 0.2:
            intents.append(intent['name'])

    return intents


def get_review_actual_intent(review):
    actual_reply = review.reply.replied_text
    for c_r in replies:
        replied_text = c_r.reply
        similarity = SequenceMatcher(None, actual_reply, replied_text).ratio()
        if similarity > 0.7:
            return c_r.reply_category
    return None