text: 
  # coding: utf-8
  
  # In[1]:
  
  
  # things we need for NLP
  import nltk
  from nltk.stem import SnowballStemmer
  stemmer = SnowballStemmer("spanish")
  
  # things we need for Tensorflow
  import numpy as np
  import tflearn
  import tensorflow as tf
  import random
  import pickle
  import json
  # In[2]:
  
  
  # restore all of our data structures
  
  data = pickle.load( open( "c:\\Users\\Lucs\Desktop\\ChatPy\\training_data", "rb" ) )
  words = data['words']
  classes = data['classes']
  train_x = data['train_x']
  train_y = data['train_y']
  
  # import our chat-bot intents file
  with open('c:\\Users\\Lucs\Desktop\\ChatPy\\intents_esp.json') as json_data:
      intents = json.load(json_data)
  
  
  # In[3]:
  
  # Build neural network
  tf.reset_default_graph()
  net = tflearn.input_data(shape=[None, len(train_x[0])]) #Capa entrada
  net = tflearn.fully_connected(net, 20)
  net = tflearn.fully_connected(net, 10)
  net = tflearn.fully_connected(net, len(train_y[0]), activation='softmax') #Capa salida
  net = tflearn.regression(net)
  
  # Define model and setup tensorboard
  model = tflearn.DNN(net, tensorboard_dir='c:\\Users\\Lucs\Desktop\\ChatPy\\tflearn_logs')
  
  
  # In[4]:
  
  
  def clean_up_sentence(sentence):
      # tokenize the pattern
      sentence_words = nltk.word_tokenize(sentence)
      # stem each word
      sentence_words = [stemmer.stem(word.lower()) for word in sentence_words]
      return sentence_words
  
  # return bag of words array: 0 or 1 for each word in the bag that exists in the sentence
  def bow(sentence, words, show_details=False):
      # tokenize the pattern
      sentence_words = clean_up_sentence(sentence)
      # bag of words
      bag = [0]*len(words)  
      for s in sentence_words:
          for i,w in enumerate(words):
              if w == s: 
                  bag[i] = 1
                  if show_details:
                      print ("found in bag: %s" % w)
  
      return(np.array(bag))
  
  
  # In[5]:
  
  
  # load our saved model
  model.load('c:\\Users\\Lucs\Desktop\\ChatPy\\model.tflearn')
  
  
  # In[6]:
  
  
  # create a data structure to hold user context
  context = {}
  
  ERROR_THRESHOLD = 0.25
  def classify(sentence):
      # generate probabilities from the model
      results = model.predict([bow(sentence, words)])[0]
      # filter out predictions below a threshold
      results = [[i,r] for i,r in enumerate(results) if r>ERROR_THRESHOLD]
      # sort by strength of probability
      results.sort(key=lambda x: x[1], reverse=True)
      return_list = []
      for r in results:
          return_list.append((classes[r[0]], r[1]))
      # return tuple of intent and probability
      return return_list
  
  def response(sentence, userID='123', show_details=False):
      results = classify(sentence)
      # if we have a classification then find the matching intent tag
      if results:
          # loop as long as there are matches to process
          while results:
              for i in intents['intents']:
                  # find a tag matching the first result
                  if i['tag'] == results[0][0]:
                      # set context for this intent if necessary
                      if 'context_set' in i:
                          if show_details: print ('context:', i['context_set'])
                          context[userID] = i['context_set']
  
                      # check if this intent is contextual and applies to this user's conversation
                      if not 'context_filter' in i or (userID in context and 'context_filter' in i and i['context_filter'] == context[userID]):
                          if show_details: print ('tag:', i['tag'])
                          # a random response from the intent
                          return random.choice(i['responses'])
  
              results.pop(0)
  
  
  # In[7]:
  
  
  #response('is your shop open today?')
  
  
  # In[9]:
  
  
  #Ingles
  #response('do you take cash?')
  
  #response('what kind of mopeds do you rent?')
  
  #response('Bye Bye')
  
  #response('Goodbye')
  
  #response('Good day')
  
  #response('How are you?')
  
  #response('Could you help me?')
  
  #response('rent a moped')
  
  #response('today')
  
  
  # In[11]:
  
  
  #Espaniol
  
  #response("Hola")
  
  #response("Hol")
  
  #response("Como estas?")
  
  #response("Que horarios?")
  
  #response("Horario?")
  
  