import numpy as np
from keras.models import Sequential
from keras.layers import Embedding, LSTM, Dense, Dropout
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.utils import to_categorical

#Подготовка текста, получение данных для тренировки модели
def tokenize(filename):
    with open(filename, encoding='utf-8') as file:
        text = file.read()

    tokenizer = Tokenizer()
    tokenizer.fit_on_texts([text])
    uniq_words_amount = len(tokenizer.word_index) + 1

    sequences = []
    list_token = tokenizer.texts_to_sequences([text])[0]
    for i in range(1, len(list_token)):
        sequences.append(list_token[:i + 1])

    max_seq_length = max([len(x) for x in sequences])
    sequences = pad_sequences(sequences, maxlen=max_seq_length)

    x, y = sequences[:, :-1], sequences[:, -1]
    y = to_categorical(y, num_classes=uniq_words_amount)

    return max_seq_length, uniq_words_amount, tokenizer, x, y

#Создание и тренировка модели
def train_model(max_seq_length, uniq_words_amount, x, y, epochs):
    model = Sequential()
    model.add(Embedding(uniq_words_amount, 128, input_length=max_seq_length-1))
    model.add(LSTM(152, return_sequences=False))
    model.add(Dropout(0.2))
    model.add(Dense(uniq_words_amount, activation='softmax'))
    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
    model.fit(x, y, epochs=epochs, verbose=1)
    return model

#Генерация текста
def generate_text(text, tokenizer, model, max_seq_length):
    i = 0
    while(i < 100):
        list_token = tokenizer.texts_to_sequences([text])[0]
        token_list = pad_sequences([list_token], maxlen=max_seq_length-1, padding='pre')
        predict = model.predict(token_list)
        predict_index = np.argmax(predict, axis=-1)
        word = tokenizer.index_word.get(predict_index[0])
        text += " " + word
        i = i+1
    return text

msl, uwa, tokenizer, x, y = tokenize("text_rus.txt")
model = train_model(msl, uwa, x, y, 140)
print("Rus: ", generate_text("Кофе со специями", tokenizer, model, msl))

msl, uwa, tokenizer, x, y = tokenize("text_eng.txt")
model = train_model(msl, uwa, x, y, 140)
print("Eng: ", generate_text("The spiced coffee", tokenizer, model, msl))