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

# Чтение текста из файла
# with open('russian.txt', 'r', encoding='utf-8') as file:
#     text = file.read()
with open('english.txt', 'r', encoding='utf-8') as file:
    text = file.read()

# Обучение Tokenizer на тексте
tokenizer = Tokenizer(char_level=True)
tokenizer.fit_on_texts([text])
sequences = tokenizer.texts_to_sequences([text])[0]

# Создание x, y последовательностей
X_data, y_data = [], []
seq_length = 10
for i in range(seq_length, len(sequences)):
    sequence = sequences[i - seq_length:i]
    target = sequences[i]
    X_data.append(sequence)
    y_data.append(target)

# Преобразование в массивы
X_mass = pad_sequences(X_data, maxlen=seq_length)
y_mass = np.array(y_data)

# Создание модели
vocab_size = len(tokenizer.word_index) + 1
model = Sequential()
model.add(LSTM(256, input_shape=(seq_length, 1), return_sequences=True))
model.add(LSTM(128, input_shape=(seq_length, 1)))
model.add(Dense(vocab_size, activation='softmax'))

# Компиляция
model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

# Обучение
model.fit(X_mass, y_mass, epochs=100, verbose=1)

# Функция генерации
def generate_text(_text, gen_length):
    generated_text = _text
    for _ in range(gen_length):
        seq = tokenizer.texts_to_sequences([_text])[0]
        seq = pad_sequences([seq], maxlen=seq_length)
        prediction = model.predict(seq)[0]
        predicted_index = np.argmax(prediction)
        predicted_char = tokenizer.index_word[predicted_index]
        generated_text += predicted_char
        _text += predicted_char
        _text = _text[1:]
    return generated_text

# Генерация текста
# _text = "Она сверкала"
_text = "It sparkled and smoked"
generate_text = generate_text(_text, 250)
print(generate_text)