import random
import json
import pickle

import nltk
nltk.download('punkt')
nltk.download('wordnet')
nltk.download('omw-1.4')
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

import numpy as np

lemmatizer = WordNetLemmatizer()

intents = json.loads(open("intents.json").read())

words = []
classes = []
documents = []

ignore_letters = ["?", "!", ".", ","]

for intent in intents["intents"]:
    for pattern in intent["patterns"]:
        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'))

dataset = []
template = [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(template)
    output_row[classes.index(document[1])] = 1
    dataset.append([bag, output_row])

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

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

model = Sequential()
model.add(Dense(256, input_shape=(len(train_x[0]),),
                activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(len(train_y[0]), activation='softmax'))


sgd = SGD(learning_rate=0.01,
          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("chatbot_model.h5", hist)
print("Done!")