import sys
import numpy
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import LSTM
from keras.utils import np_utils
from keras.models import load_model

# load ascii text and covert to lowercase
filename = "/home/prometej/Workspaces/PythonWorkspace/chatbot-unk/wiki_merged"
raw_text = open(filename).read()
raw_text = raw_text.lower()

# create mapping of unique chars to integers, and a reverse mapping
chars = sorted(list(set(raw_text)))
char_to_int = dict((c, i) for i, c in enumerate(chars))
int_to_char = dict((i, c) for i, c in enumerate(chars))

# summarize the loaded data
n_chars = len(raw_text)
n_vocab = len(chars)
print("Total Characters: ", n_chars)
print("Total Vocab: ", n_vocab)

# prepare the dataset of input to output pairs encoded as integers
seq_length = 100
dataX = []
dataY = []

for i in range(0, n_chars - seq_length, 1):
	seq_in = raw_text[i:i + seq_length]
	seq_out = raw_text[i + seq_length]
	dataX.append([char_to_int[char] for char in seq_in])
	dataY.append(char_to_int[seq_out])
n_patterns = len(dataX)

print("Total Patterns: ", n_patterns)

# reshape X to be [samples, time steps, features]
X = numpy.reshape(dataX, (n_patterns, seq_length, 1))

# normalize
X = X / float(n_vocab)

model = load_model('/home/prometej/Workspaces/PythonWorkspace/project_chatbot/LSTM_model.h5')

# pick a random seed
start = numpy.random.randint(0, len(dataX)-1)
pattern = dataX[start]
print("\n\nTest sequence:")
print("\"", ''.join([int_to_char[value] for value in pattern]), "\"\n")


# generate characters
for i in range(100):
	x = numpy.reshape(pattern, (1, len(pattern), 1))
	x = x / float(n_vocab)
	prediction = model.predict(x, verbose=0)
	index = numpy.argmax(prediction)
	result = int_to_char[index]
	seq_in = [int_to_char[value] for value in pattern]
	sys.stdout.write(result)
	pattern.append(index)
	pattern = pattern[1:len(pattern)]

print("\nDone.")
