simulate_bot_GAN.py

# -*- coding: utf-8 -*-

__author__ = 'Oswaldo Ludwig'
__version__ = '1.01'

from keras.layers import Input, Embedding, LSTM, Dense, RepeatVector, Dropout, merge
from keras.optimizers import Adam 
from keras.models import Model
from keras.models import Sequential
from keras.layers import Activation, Dense
from keras.preprocessing import sequence

import keras.backend as K
import numpy as np
np.random.seed(1234)  # for reproducibility
import cPickle
import theano
import os.path
import sys
import nltk
import re
import time

from keras.utils import plot_model

word_embedding_size = 100
sentence_embedding_size = 300
dictionary_size = 7000
maxlen_input = 50
maxlen_input_consist = 40
dense_size = 1500

questions_file = 'context_simple'
vocabulary_file = 'vocabulary_movie'
weights_file = 'my_model_weights_bot.h5'
unknown_token = 'something'
file_generated_context = 'generated_context'
file_generated_answer = 'generated_answer'
name_of_computer = 'john'
name = 'john'
depth_of_thinking = 2
threshold_max = 0.65
threshold_min = 0.50
pressure = 4.
bidirec = 0


def greedy_decoder(input):

    flag = 0
    prob = 1
    ans_partial = np.zeros((1,maxlen_input))
    ans_partial[0, -1] = 2  #  the index of the symbol BOS (begin of sentence)
    for k in range(maxlen_input - 1):
        ye = model2.predict([input, ans_partial])
        yel = ye[0,:]
        p = np.max(yel)
        mp = np.argmax(ye)
        ans_partial[0, 0:-1] = ans_partial[0, 1:]
        ans_partial[0, -1] = mp
        if mp == 3:  #  the index of the symbol EOS (end of sentence)
            flag = 1
        if flag == 0:    
            prob = prob * p * 1.05
    text = ''
    for k in ans_partial[0]:
        k = k.astype(int)
        if k < (dictionary_size-2):
            w = vocabulary[k]
            text = text + w[0] + ' '
    return(text, prob)
    
    
def preprocess(raw_word, name):
    
    if raw_word == '':
        return ' . '
    
    l1 = ['won’t','won\'t','wouldn’t','wouldn\'t','’m', '’re', '’ve', '’ll', '’s','’d', 'n’t', '\'m', '\'re', '\'ve', '\'ll', '\'s', '\'d', 'can\'t', 'n\'t', 'B: ', 'A: ', ',', ';', '.', '?', '!', ':', '. ?', ',   .', '. ,', 'EOS', 'BOS', 'eos', 'bos']
    l2 = ['will not','will not','would not','would not',' am', ' are', ' have', ' will', ' is', ' had', ' not', ' am', ' are', ' have', ' will', ' is', ' had', 'can not', ' not', '', '', ' ,', ' ;', ' .', ' ?', ' !', ' :', '? ', '.', ',', '', '', '', '']
    l3 = ['-', '_', ' *', ' /', '* ', '/ ', '\"', ' \\"', '\\ ', '--', '...', '. . .']
    l4 = ['jeffrey','fred','benjamin','paula','walter','rachel','andy','helen','harrington','kathy','ronnie','carl','annie','cole','ike','milo','cole','rick','johnny','loretta','cornelius','claire','romeo','casey','johnson','rudy','stanzi','cosgrove','wolfi','kevin','paulie','cindy','paulie','enzo','mikey','i\97','davis','jeffrey','norman','johnson','dolores','tom','brian','bruce','john','laurie','stella','dignan','elaine','jack','christ','george','frank','mary','amon','david','tom','joe','paul','sam','charlie','bob','marry','walter','james','jimmy','michael','rose','jim','peter','nick','eddie','johnny','jake','ted','mike','billy','louis','ed','jerry','alex','charles','tommy','bobby','betty','sid','dave','jeffrey','jeff','marty','richard','otis','gale','fred','bill','jones','smith','mickey']    

    raw_word = raw_word.lower()
    raw_word = raw_word.replace(', ' + name_of_computer, '')
    raw_word = raw_word.replace(name_of_computer + ' ,', '')

    for j, term in enumerate(l1):
        raw_word = raw_word.replace(term,l2[j])
        
    for term in l3:
        raw_word = raw_word.replace(term,' ')
    
    for term in l4:
        raw_word = raw_word.replace(', ' + term, ', ' + name)
        raw_word = raw_word.replace(' ' + term + ' ,' ,' ' + name + ' ,')
        raw_word = raw_word.replace('i am ' + term, 'i am ' + name_of_computer)
        raw_word = raw_word.replace('my name is' + term, 'my name is ' + name_of_computer)
    
    for j in range(30):
        raw_word = raw_word.replace('. .', '')
        raw_word = raw_word.replace('.  .', '')
        raw_word = raw_word.replace('..', '')
       
    for j in range(5):
        raw_word = raw_word.replace('  ', ' ')
        
    if raw_word[-1] <>  '!' and raw_word[-1] <> '?' and raw_word[-1] <> '.' and raw_word[-2:] <>  '! ' and raw_word[-2:] <> '? ' and raw_word[-2:] <> '. ':
        raw_word = raw_word + ' .'
    
    if raw_word == ' !' or raw_word == ' ?' or raw_word == ' .' or raw_word == ' ! ' or raw_word == ' ? ' or raw_word == ' . ':
        raw_word = 'what ?'
    
    return raw_word


def tokenize(sentences):

    # Tokenizing the sentences into words:
    tokenized_sentences = nltk.word_tokenize(sentences.decode('utf-8'))
    index_to_word = [x[0] for x in vocabulary]
    word_to_index = dict([(w,i) for i,w in enumerate(index_to_word)])
    tokenized_sentences = [w if w in word_to_index else unknown_token for w in tokenized_sentences]
    X = np.asarray([word_to_index[w] for w in tokenized_sentences])
    s = X.size
    Q = np.zeros((1,maxlen_input))
    if s < (maxlen_input + 1):
        Q[0,- s:] = X
    else:
        Q[0,:] = X[- maxlen_input:]
    
    return Q

# Open files to save the conversation for further training:
qf = open(file_generated_context, 'w')
af = open(file_generated_answer, 'w')

print('Starting the model...')

# *******************************************************************
# Keras model of the chatbot + discriminator: 
# *******************************************************************

ad = Adam(lr=0.00005) 

input_context = Input(shape=(maxlen_input,), dtype='int32', name='input context')
input_answer = Input(shape=(maxlen_input,), dtype='int32', name='input answer')

if bidirec == 1:
    LSTM_encoder_bot = Bidirectional(LSTM(sentence_embedding_size, init= 'lecun_uniform', name = 'encoder bot'))
else:
    LSTM_encoder_bot = LSTM(sentence_embedding_size, init= 'lecun_uniform', name = 'encoder bot')
    
LSTM_decoder_bot = LSTM(sentence_embedding_size, init= 'lecun_uniform', name = 'decoder bot')

if bidirec == 1:
    LSTM_encoder_discriminator = Bidirectional(LSTM(sentence_embedding_size, init= 'lecun_uniform'), trainable=False, name = 'encoder discriminator')
else:
    LSTM_encoder_discriminator = LSTM(sentence_embedding_size, init= 'lecun_uniform', trainable=False, name = 'encoder discriminator')
    
LSTM_decoder_discriminator = LSTM(sentence_embedding_size, init= 'lecun_uniform', trainable=False, name = 'decoder discriminator')

if os.path.isfile(weights_file):
    Shared_Embedding = Embedding(output_dim=word_embedding_size, input_dim=dictionary_size, input_length=maxlen_input, trainable=False, name = 'shared')
else:
    Shared_Embedding = Embedding(output_dim=word_embedding_size, input_dim=dictionary_size, weights=[embedding_matrix], input_length=maxlen_input, trainable=False, name = 'shared')

word_embedding_context = Shared_Embedding(input_context)
context_embedding_bot = LSTM_encoder_bot(word_embedding_context)

word_embedding_answer = Shared_Embedding(input_answer)
answer_embedding_bot = LSTM_decoder_bot(word_embedding_answer)
context_embedding_discriminator = LSTM_encoder_discriminator(word_embedding_context)
answer_embedding_discriminator = LSTM_decoder_discriminator(word_embedding_answer)
merge_layer = merge([context_embedding_bot, answer_embedding_bot], mode='concat', concat_axis=1, name = 'concatenation bot')
out = Dense(dictionary_size/2, activation="relu", name = 'bot')(merge_layer)
out = Dense(dictionary_size, activation="softmax", name = 'decision bot')(out)
loss = merge([context_embedding_discriminator, answer_embedding_discriminator, out], mode='concat', concat_axis=1, name = 'concatenation discriminator')
loss = Dense(1, activation="sigmoid", trainable=False, name = 'discriminator output')(loss)
model = Model(input=[input_context, input_answer], output = [loss])


if os.path.isfile(weights_file):
    print('loading the bot weights...')
    model.load_weights(weights_file)

# *******************************************************************
# Only the model of the chatbot:
# *******************************************************************

input_context2 = Input(shape=(maxlen_input,), dtype='int32', name='input context')
input_answer2 = Input(shape=(maxlen_input,), dtype='int32', name='input answer')

if bidirec == 1:
    LSTM_encoder_bot2 = Bidirectional(LSTM(sentence_embedding_size, init= 'lecun_uniform', weights=model.layers[3].get_weights(), name = 'encoder bot'))
else:
    LSTM_encoder_bot2 = LSTM(sentence_embedding_size, init= 'lecun_uniform', weights=model.layers[3].get_weights(), name = 'encoder bot')
    
LSTM_decoder_bot2 = LSTM(sentence_embedding_size, init= 'lecun_uniform', weights=model.layers[4].get_weights(), name = 'decoder bot')

Shared_Embedding2 = Embedding(output_dim=word_embedding_size, input_dim=dictionary_size, input_length=maxlen_input, trainable=False, weights=model.layers[2].get_weights(), name = 'shared')

word_embedding_context2 = Shared_Embedding2(input_context2)
context_embedding_bot2 = LSTM_encoder_bot2(word_embedding_context2)

word_embedding_answer2 = Shared_Embedding2(input_answer2)
answer_embedding_bot2 = LSTM_decoder_bot2(word_embedding_answer2)
merge_layer2 = merge([context_embedding_bot2, answer_embedding_bot2], mode='concat', concat_axis=1, name = 'concatenation bot')
out2 = Dense(dictionary_size/2, activation="relu", weights=model.layers[6].get_weights(), name = 'bot')(merge_layer2)
out2 = Dense(dictionary_size, activation="softmax", weights=model.layers[9].get_weights(), name = 'decision bot')(out2)

model2 = Model(input=[input_context2, input_answer2], output = [out2])

# Loading the data:
vocabulary = cPickle.load(open(vocabulary_file, 'rb'))
index_to_word = [x[0] for x in vocabulary]
word_to_index = dict([(w,i) for i,w in enumerate(index_to_word)])

# Loading the context file:
print ("Reading the context data...")
q = open(questions_file, 'r')
questions = q.read()
questions = [p for p in questions.split('\n')]

print(questions[0])

for text in questions:
    
    que = preprocess(text, name_of_computer)
    Q = tokenize(que)
    predout, prob = greedy_decoder(Q[0:1])
    start_index = predout.find('EOS')
    text_context = preprocess(predout[0:start_index], name)
    # append generated text_context:
    qf.write(text_context + ' \n')
    print('context: %s'%text_context)
    que = preprocess(text_context, name_of_computer)
    Q = tokenize(que)
    predout, prob = greedy_decoder(Q[0:1])
    start_index = predout.find('EOS')
    text_answer = preprocess(predout[0:start_index], name)
    # append generated answer:
    af.write(text_answer + ' \n')
    print('answer: %s'%text_answer)   

qf.close()
af.close()