from tensorflow.python.keras.utils.np_utils import to_categorical
from utils import SEQ_LEN, N_EPOCHS, BATCH_SIZE, PREDICTION_LEN
from data import prepare_text, generate_text, read_text, peek_random_seed
from model import Model

import numpy as np

if __name__ == '__main__':
    text = read_text('en.txt')

    vocab = sorted(set(text))

    n_vocab = len(vocab)

    ids = dict((c, i) for i, c in enumerate(vocab))
    chars = dict((i, c) for i, c in enumerate(vocab))

    dataX, dataY = prepare_text(text=text, ids=ids, seq_length=SEQ_LEN)

    n_patterns = len(dataX)

    X = np.reshape(dataX, (n_patterns, SEQ_LEN, 1))

    X = X / float(n_vocab)

    y = to_categorical(dataY)

    model = Model(X, y)
    model.compile_model()
    model.fit_model(X, y, BATCH_SIZE, N_EPOCHS)

    pattern = dataX[peek_random_seed(dataX)]

    prediction = generate_text(model, pattern, chars, n_vocab, PREDICTION_LEN)

    print("\nPREDICTION: ", prediction)