# -*- coding: iso-8859-1 -*-
import random
import json
import pickle
import numpy as np

import nltk
from nltk.stem import WordNetLemmatizer

from tensorflow.keras.models import load_model

# Vorlage Code von NeuralNines YT Channel (https://www.youtube.com/watch?v=1lwddP0KUEg&t=1022s)


class Osiris_Sprache:
    """Osiris Sprach Tools

    Enth�lt alle Funktionen zur Handhabung und Verarbeitung von Input Messages

    Returns:
        None
    """

    lematizer: WordNetLemmatizer
    intents: json

    words: None
    classes: None
    model: None

    def __init__(self):
        self.lematizer = WordNetLemmatizer()
        self.intents = json.loads(open('intents.json').read())

        self.words = pickle.load(open('words.pkl', 'rb'))
        self.classes = pickle.load(open('classes.pkl', 'rb'))
        self.model = load_model('osiris_sprachmodel.h5')

    def clean_up_sentence(self, sentence):
        sentence_words = nltk.word_tokenize(sentence)
        sentence_words = [self.lematizer.lemmatize(
            word) for word in sentence_words]
        return sentence_words

    def bag_of_words(self, sentence):
        sentence_words = self.clean_up_sentence(sentence)
        bag = [0] * len(self.words)
        for w in sentence_words:
            for i, word in enumerate(self.words):
                if word == w:
                    bag[i] = 1
        return np.array(bag)

    def predict_class(self, sentence):
        bagOfWords = self.bag_of_words(sentence)
        res = self.model.predict(np.array([bagOfWords]))[0]
        ERROR_THRESHOLD = 0.25
        results = [[i, r] for i, r in enumerate(res) if r > ERROR_THRESHOLD]

        results.sort(key=lambda x: x[1], reverse=True)
        return_list = []
        for r in results:
            return_list.append(
                {'intent': self.classes[r[0]], 'probability': str(r[1])})
        return return_list

    def get_response(self, intent_list):
        tag = intent_list[0]['intent']
        list_of_intents = self.intents['intents']
        for intent in list_of_intents:
            if intent['tag'] == tag:
                result = random.choice(i['response'])
                break
        return result

    #print('Du kannst jetzt mit mir Reden!')

# while True:
#    message = input("")
#    ints = predict_class(message)
#    res = get_response(ints, intents)
#    print(res)