#!/usr/bin/python3.6
# -*- coding: utf-8 -*-

from __future__ import print_function
from keras.callbacks import LambdaCallback
from keras.models import Sequential
from keras.models import load_model
from keras.layers import Dense, Activation
from keras.layers import LSTM
from keras.optimizers import RMSprop
from keras.utils.data_utils import get_file
import numpy as np
import random
import sys
import io
import os
import warnings

argvs = sys.argv
argc = len(argvs)
if argc != 2:
    quit()

path = get_file('ginga.txt', origin='https://www.terra.jp.net/dysd/keras/ginga.txt')
with io.open(path, encoding='utf-8') as f:
    text = f.read().lower()

chars = sorted(list(set(text)))
char_indices = dict((c, i) for i, c in enumerate(chars))
indices_char = dict((i, c) for i, c in enumerate(chars))

# cut the text in semi-redundant sequences of maxlen characters
maxlen = 40

# load the model
model = load_model('ginga_300.h5')

def sample(preds, temperature=1.0):
    # helper function to sample an index from a probability array
    preds = np.asarray(preds).astype('float64')
    preds = np.log(preds) / temperature
    exp_preds = np.exp(preds)
    preds = exp_preds / np.sum(exp_preds)
    probas = np.random.multinomial(1, preds, 1)
    return np.argmax(probas)

def on_epoch_end(epoch):
    # Function invoked at end of each epoch. Prints generated text.
    for diversity in [0.5]:

        generated = ''
        sentence = argvs[1]
        generated += sentence
        sentence = '\n' * maxlen + sentence
        sentence = sentence[len(sentence)-maxlen-1:len(sentence)-1]

        for i in range(800):
            x_pred = np.zeros((1, maxlen, len(chars)))
            for t, char in enumerate(sentence):
                x_pred[0, t, char_indices[char]] = 1.

            preds = model.predict(x_pred, verbose=0)[0]
            next_index = sample(preds, diversity)
            next_char = indices_char[next_index]

            generated += next_char

            # print(sentence, ':', next_char)
            if len(sentence)<maxlen:
                sentence += next_char
            else:
                sentence = sentence[1:] + next_char

        print(generated)

on_epoch_end(60)