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


# Чтение из файла
def load_text(file_path):
    with open(file_path, encoding='utf-8') as file:
        return file.read()


# Создание токенайзера и последовательностей на основе входного текста
def create_tokenizer_and_sequences(text):
    tokenizer = Tokenizer()
    tokenizer.fit_on_texts([text])
    total_words = len(tokenizer.word_index) + 1
    input_sequences = []
    for line in text.split('\n'):
        token_list = tokenizer.texts_to_sequences([line])[0]
        for i in range(1, len(token_list)):
            n_gram_sequence = token_list[:i + 1]
            input_sequences.append(n_gram_sequence)
    max_sequence_length = max([len(x) for x in input_sequences])
    input_sequences = pad_sequences(input_sequences, maxlen=max_sequence_length, padding='pre')
    predictors, labels = input_sequences[:, :-1], input_sequences[:, -1]
    return tokenizer, total_words, predictors, labels, max_sequence_length


# Создание и обучение модели
def create_and_train_model(total_words, max_sequence_length, predictors, labels):
    model = Sequential()
    model.add(Embedding(total_words, 256, input_length=max_sequence_length - 1))
    model.add(LSTM(units=1024))
    model.add(Dense(total_words, activation='softmax'))
    model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    model.fit(predictors, labels, epochs=100, verbose=1, batch_size=64)
    return model


# Генерация текста
def generate_text(seed_text, next_words, model, max_sequence_length, tokenizer):
    for _ in range(next_words):
        token_list = tokenizer.texts_to_sequences([seed_text])[0]
        token_list = pad_sequences([token_list], maxlen=max_sequence_length - 1, padding='pre')
        predicted = np.argmax(model.predict(token_list), axis=-1)
        output_word = ""
        for word, index in tokenizer.word_index.items():
            if index == predicted:
                output_word = word
                break
        seed_text += " " + output_word
    return seed_text


# Использование ранее определенных функций
eng_text = load_text('public/text/eng.txt')
rus_text = load_text('public/text/rus.txt')

tokenizer_eng, total_words_eng, predictors_eng, labels_eng, max_seq_len_eng = create_tokenizer_and_sequences(eng_text)
tokenizer_rus, total_words_rus, predictors_rus, labels_rus, max_seq_len_rus = create_tokenizer_and_sequences(rus_text)

model_eng = create_and_train_model(total_words_eng, max_seq_len_eng, predictors_eng, labels_eng)
model_rus = create_and_train_model(total_words_rus, max_seq_len_rus, predictors_rus, labels_rus)

print(generate_text("\"Event Horizon\"", 50, model_eng, max_seq_len_eng, tokenizer_eng))
print(generate_text("\"Горизонт событий\"", 50, model_rus, max_seq_len_rus, tokenizer_rus))