# -*- 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 Sequential
from tensorflow.keras.layers import Dense, Activation, Dropout
from tensorflow.keras.optimizers import SGD

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


def train_osiris():
    lemmatizer = WordNetLemmatizer()
    intents = json.loads(open('intents.json').read())

    words = []
    classes = []
    documents = []
    ignore_letters = ['?', '!', ',', '.']

    for intent in intents['intents']:
        for pattern in intent['pattern']:
            word_list = nltk.word_tokenize(pattern)
            words.extend(word_list)
            documents.append((word_list, intent["tag"]))

            if intent['tag'] not in classes:
                classes.append(intent['tag'])

    words = [lemmatizer.lemmatize(word)
             for word in words if word not in ignore_letters]
    words = sorted(set(words))

    classes = sorted(set(classes))

    pickle.dump(words, open('words.pkl', 'wb'))
    pickle.dump(classes, open('classes.pkl', 'wb'))

    training = []
    output_empty = [0] * len(classes)

    for document in documents:
        bag = []
        word_patterns = document[0]
        word_patterns = [lemmatizer.lemmatize(
            word.lower()) for word in word_patterns]
        for word in words:
            bag.append(1) if word in word_patterns else bag.append(0)

        output_row = list(output_empty)
        output_row[classes.index(document[1])] = 1

        training.append([bag, output_row])

    random.shuffle(training)
    training = np.array(training)

    train_x = list(training[:, 0])
    train_y = list(training[:, 1])

    model = Sequential()
    model.add(Dense(128, input_shape=(len(train_x[0]),), activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(64, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(len(train_y[0]), activation='softmax'))

    sgd = SGD(learning_rate=0.01, decay=1e-6, momentum=0.9, nesterov=True)
    model.compile(loss='categorical_crossentropy',
                  optimizer=sgd, metrics=['accuracy'])

    hist = model.fit(np.array(train_x), np.array(train_y),
                     epochs=200, batch_size=5, verbose=1)
    model.save('osiris_sprachmodel.h5', hist)
    print('Sprachmodel für Osiris erstellt!')