Chatbot-application.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# things we need for NLP\n",
    "import nltk\n",
    "from nltk.stem.lancaster import LancasterStemmer\n",
    "stemmer = LancasterStemmer()\n",
    "\n",
    "# things we need for Tensorflow\n",
    "import numpy as np\n",
    "import tflearn\n",
    "import tensorflow as tf\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import our chat-bot intents file\n",
    "import json\n",
    "with open('intent.json.txt') as json_data:\n",
    "    intents = json.load(json_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27 documents\n",
      "9 classes ['goodbye', 'greeting', 'hours', 'mopeds', 'opentoday', 'payments', 'rental', 'thanks', 'today']\n",
      "48 unique stemmed words [\"'d\", \"'s\", 'a', 'acceiv', 'anyon', 'ar', 'bye', 'can', 'card', 'cash', 'credit', 'day', 'do', 'doe', 'good', 'goodby', 'hav', 'hello', 'help', 'hi', 'hour', 'how', 'i', 'is', 'kind', 'lat', 'lik', 'mastercard', 'mop', 'of', 'on', 'op', 'rent', 'see', 'tak', 'thank', 'that', 'ther', 'thi', 'to', 'today', 'we', 'what', 'when', 'which', 'work', 'yo', 'you']\n"
     ]
    }
   ],
   "source": [
    "words = []\n",
    "classes = []\n",
    "documents = []\n",
    "ignore_words = ['?']\n",
    "# loop through each sentence in our intents patterns\n",
    "for intent in intents['intents']:\n",
    "    for pattern in intent['patterns']:\n",
    "        # tokenize each word in the sentence\n",
    "        w = nltk.word_tokenize(pattern)\n",
    "        # add to our words list\n",
    "        words.extend(w)\n",
    "        # add to documents in our corpus\n",
    "        documents.append((w, intent['tag']))\n",
    "        # add to our classes list\n",
    "        if intent['tag'] not in classes:\n",
    "            classes.append(intent['tag'])\n",
    "\n",
    "# stem and lower each word and remove duplicates\n",
    "words = [stemmer.stem(w.lower()) for w in words if w not in ignore_words]\n",
    "words = sorted(list(set(words)))\n",
    "\n",
    "# remove duplicates\n",
    "classes = sorted(list(set(classes)))\n",
    "\n",
    "print (len(documents), \"documents\")\n",
    "print (len(classes), \"classes\", classes)\n",
    "print (len(words), \"unique stemmed words\", words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create our training data\n",
    "training = []\n",
    "output = []\n",
    "# create an empty array for our output\n",
    "output_empty = [0] * len(classes)\n",
    "\n",
    "# training set, bag of words for each sentence\n",
    "for doc in documents:\n",
    "    # initialize our bag of words\n",
    "    bag = []\n",
    "    # list of tokenized words for the pattern\n",
    "    pattern_words = doc[0]\n",
    "    # stem each word\n",
    "    pattern_words = [stemmer.stem(word.lower()) for word in pattern_words]\n",
    "    # create our bag of words array\n",
    "    for w in words:\n",
    "        bag.append(1) if w in pattern_words else bag.append(0)\n",
    "\n",
    "    # output is a '0' for each tag and '1' for current tag\n",
    "    output_row = list(output_empty)\n",
    "    output_row[classes.index(doc[1])] = 1\n",
    "\n",
    "    training.append([bag, output_row])\n",
    "\n",
    "# shuffle our features and turn into np.array\n",
    "random.shuffle(training)\n",
    "training = np.array(training)\n",
    "\n",
    "# create train and test lists\n",
    "train_x = list(training[:,0])\n",
    "train_y = list(training[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Step: 3999  | total loss: \u001b[1m\u001b[32m0.02843\u001b[0m\u001b[0m | time: 0.029s\n",
      "| Adam | epoch: 1000 | loss: 0.02843 - acc: 0.9999 -- iter: 24/27\n",
      "Training Step: 4000  | total loss: \u001b[1m\u001b[32m0.02839\u001b[0m\u001b[0m | time: 0.040s\n",
      "| Adam | epoch: 1000 | loss: 0.02839 - acc: 0.9999 -- iter: 27/27\n",
      "--\n",
      "INFO:tensorflow:D:\\data-science-ml-foundation-10\\model.tflearn is not in all_model_checkpoint_paths. Manually adding it.\n"
     ]
    }
   ],
   "source": [
    "# reset underlying graph data\n",
    "tf.reset_default_graph()\n",
    "# Build neural network\n",
    "net = tflearn.input_data(shape=[None, len(train_x[0])])\n",
    "net = tflearn.fully_connected(net, 8)\n",
    "net = tflearn.fully_connected(net, 8)\n",
    "net = tflearn.fully_connected(net, len(train_y[0]), activation='softmax')\n",
    "net = tflearn.regression(net)\n",
    "\n",
    "# Define model and setup tensorboard\n",
    "model = tflearn.DNN(net, tensorboard_dir='tflearn_logs')\n",
    "# Start training (apply gradient descent algorithm)\n",
    "model.fit(train_x, train_y, n_epoch=1000, batch_size=8, show_metric=True)\n",
    "model.save('model.tflearn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# save all of our data structures\n",
    "import pickle\n",
    "pickle.dump( {'words':words, 'classes':classes, 'train_x':train_x, 'train_y':train_y}, open( \"training_data\", \"wb\" ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# restore all of our data structures\n",
    "import pickle\n",
    "data = pickle.load( open( \"training_data\", \"rb\" ) )\n",
    "words = data['words']\n",
    "classes = data['classes']\n",
    "train_x = data['train_x']\n",
    "train_y = data['train_y']\n",
    "\n",
    "# import our chat-bot intents file\n",
    "import json\n",
    "with open(r'intent.json.txt') as json_data:\n",
    "    intents = json.load(json_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# load our saved model\n",
    "model.load('./model.tflearn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def clean_up_sentence(sentence):\n",
    "    # tokenize the pattern\n",
    "    sentence_words = nltk.word_tokenize(sentence)\n",
    "    # stem each word\n",
    "    sentence_words = [stemmer.stem(word.lower()) for word in sentence_words]\n",
    "    return sentence_words\n",
    "\n",
    "# return bag of words array: 0 or 1 for each word in the bag that exists in the sentence\n",
    "def bow(sentence, words, show_details=False):\n",
    "    # tokenize the pattern\n",
    "    sentence_words = clean_up_sentence(sentence)\n",
    "    # bag of words\n",
    "    bag = [0]*len(words)  \n",
    "    for s in sentence_words:\n",
    "        for i,w in enumerate(words):\n",
    "            if w == s: \n",
    "                bag[i] = 1\n",
    "                if show_details:\n",
    "                    print (\"found in bag: %s\" % w)\n",
    "\n",
    "    return(np.array(bag))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ERROR_THRESHOLD = 0.25\n",
    "def classify(sentence):\n",
    "    # generate probabilities from the model\n",
    "    results = model.predict([bow(sentence, words)])[0]\n",
    "    # filter out predictions below a threshold\n",
    "    results = [[i,r] for i,r in enumerate(results) if r>ERROR_THRESHOLD]\n",
    "    # sort by strength of probability\n",
    "    results.sort(key=lambda x: x[1], reverse=True)\n",
    "    return_list = []\n",
    "    for r in results:\n",
    "        return_list.append((classes[r[0]], r[1]))\n",
    "    # return tuple of intent and probability\n",
    "    return return_list\n",
    "\n",
    "def response(sentence, userID='123', show_details=False):\n",
    "    results = classify(sentence)\n",
    "    # if we have a classification then find the matching intent tag\n",
    "    if results:\n",
    "        # loop as long as there are matches to process\n",
    "        while results:\n",
    "            for i in intents['intents']:\n",
    "                # find a tag matching the first result\n",
    "                if i['tag'] == results[0][0]:\n",
    "                    # a random response from the intent\n",
    "                    return print(random.choice(i['responses']))\n",
    "\n",
    "            results.pop(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We're open every day from 9am-9pm\n"
     ]
    }
   ],
   "source": [
    "response('is your shop open today?')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('opentoday', 0.94616896)]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classify('is your shop open today?')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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