CBT_ChatBot/CBT_ChatBot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "su3gTjl0Jkmf",
    "outputId": "59c20d13-8be8-4d2b-af68-2133bb818dc9"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /home/paritosh/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to /home/paritosh/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n",
      "[nltk_data] Downloading package omw-1.4 to /home/paritosh/nltk_data...\n",
      "[nltk_data]   Package omw-1.4 is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('punkt')\n",
    "nltk.download('wordnet')\n",
    "nltk.download('omw-1.4')\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "import json\n",
    "import pickle\n",
    "\n",
    "import numpy as np\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation, Dropout\n",
    "from tensorflow.keras.optimizers import SGD\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "USFDymoFUxO6"
   },
   "outputs": [],
   "source": [
    "words=[]\n",
    "classes = []\n",
    "documents = []\n",
    "ignore_words = ['?', '!']\n",
    "data_file = open('intents.json').read()\n",
    "intents = json.loads(data_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "KCZHP2uRVOna"
   },
   "outputs": [],
   "source": [
    "for intent in intents['intents']:\n",
    "    for pattern in intent['patterns']:\n",
    "\n",
    "        # take each word and tokenize it\n",
    "        w = nltk.word_tokenize(pattern)\n",
    "        words.extend(w)\n",
    "        # adding documents\n",
    "        documents.append((w, intent['tag']))\n",
    "\n",
    "        # adding classes to our class list\n",
    "        if intent['tag'] not in classes:\n",
    "            classes.append(intent['tag'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-tyu4EMwVSNP",
    "outputId": "acdf0766-834c-4a7a-dc1f-7287f1390f71"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "69 documents\n",
      "18 classes ['academic', 'adverse_drug', 'blood_pressure', 'blood_pressure_search', 'cultural', 'goodbye', 'greeting', 'hospital_search', 'how_you_feel', 'negative_core_beliefs', 'nfeel', 'options', 'pfeel', 'pharmacy_search', 'postive_core_beliefs', 'religious', 'thanks', 'why_you_feel']\n",
      "116 unique lemmatized words [\"'s\", ',', 'a', 'academic', 'adverse', 'all', 'always', 'anxious', 'anyone', 'are', 'awesome', 'be', 'behavior', 'blood', 'by', 'bye', 'can', 'causing', 'chatting', 'check', 'confident', 'could', 'crush', 'cultural', 'culture', 'data', 'day', 'depressed', 'detail', 'do', 'dont', 'drug', 'enough', 'entry', 'find', 'for', 'give', 'god', 'good', 'goodbye', 'happen', 'have', 'hello', 'help', 'helpful', 'helping', 'hey', 'hi', 'history', 'hola', 'hospital', 'how', 'i', 'id', 'is', 'job', 'later', 'learning', 'life', 'like', 'list', 'load', 'locate', 'log', 'looking', 'lookup', 'make', 'management', 'me', 'module', 'nearby', 'next', 'nice', 'no', 'not', 'of', 'offered', 'open', 'patient', 'people', 'pharmacy', 'pressure', 'provide', 'reaction', 'related', 'religion', 'result', 'roomate', 'search', 'searching', 'see', 'show', 'study', 'suitable', 'support', 'task', 'thank', 'thanks', 'that', 'them', 'there', 'thing', 'till', 'time', 'to', 'transfer', 'up', 'want', 'what', 'when', 'which', 'with', 'worthless', 'worthy', 'yes', 'you']\n"
     ]
    }
   ],
   "source": [
    "words = [lemmatizer.lemmatize(w.lower()) for w in words if w not in ignore_words]\n",
    "words = sorted(list(set(words)))\n",
    "\n",
    "classes = sorted(list(set(classes)))\n",
    "\n",
    "print (len(documents), \"documents\")\n",
    "\n",
    "print (len(classes), \"classes\", classes)\n",
    "\n",
    "print (len(words), \"unique lemmatized words\", words)\n",
    "\n",
    "\n",
    "pickle.dump(words,open('words.pkl','wb'))\n",
    "pickle.dump(classes,open('classes.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DqMg8vCjVVJS",
    "outputId": "fbfe053b-bedf-47b1-a7ab-7b45a2825ab6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data created\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-6-1a891a7a8859>:22: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  training = np.array(training)\n"
     ]
    }
   ],
   "source": [
    "# initializing training data\n",
    "training = []\n",
    "output_empty = [0] * len(classes)\n",
    "for doc in documents:\n",
    "    # initializing bag of words\n",
    "    bag = []\n",
    "    # list of tokenized words for the pattern\n",
    "    pattern_words = doc[0]\n",
    "    # lemmatize each word - create base word, in attempt to represent related words\n",
    "    pattern_words = [lemmatizer.lemmatize(word.lower()) for word in pattern_words]\n",
    "    # create our bag of words array with 1, if word match found in current pattern\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 (for each pattern)\n",
    "    output_row = list(output_empty)\n",
    "    output_row[classes.index(doc[1])] = 1\n",
    "\n",
    "    training.append([bag, output_row])\n",
    "# shuffle our features and turn into np.array\n",
    "random.shuffle(training)\n",
    "training = np.array(training)\n",
    "# create train and test lists. X - patterns, Y - intents\n",
    "train_x = list(training[:,0])\n",
    "train_y = list(training[:,1])\n",
    "print(\"Training data created\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uBhKl6NuVmwX",
    "outputId": "f5dcc984-5ed8-46f1-9570-1755ac5ad3a1"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/paritosh/anaconda3/lib/python3.8/site-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(SGD, self).__init__(name, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch 115/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1444 - accuracy: 0.9710\n",
      "Epoch 116/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0785 - accuracy: 1.0000\n",
      "Epoch 117/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0894 - accuracy: 0.9710\n",
      "Epoch 118/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0263 - accuracy: 1.0000\n",
      "Epoch 119/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1005 - accuracy: 0.9855\n",
      "Epoch 120/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0867 - accuracy: 0.9855\n",
      "Epoch 121/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1407 - accuracy: 0.9710\n",
      "Epoch 122/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1189 - accuracy: 0.9565\n",
      "Epoch 123/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1049 - accuracy: 0.9710\n",
      "Epoch 124/200\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1029 - accuracy: 0.9855\n",
      "Epoch 125/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1409 - accuracy: 0.9710\n",
      "Epoch 126/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0400 - accuracy: 1.0000\n",
      "Epoch 127/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0819 - accuracy: 0.9710\n",
      "Epoch 128/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0662 - accuracy: 1.0000\n",
      "Epoch 129/200\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0595 - accuracy: 0.9710\n",
      "Epoch 130/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0435 - accuracy: 1.0000\n",
      "Epoch 131/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1079 - accuracy: 0.9710\n",
      "Epoch 132/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0713 - accuracy: 0.9855\n",
      "Epoch 133/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0444 - accuracy: 1.0000\n",
      "Epoch 134/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0664 - accuracy: 0.9855\n",
      "Epoch 135/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1158 - accuracy: 0.9855\n",
      "Epoch 136/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0871 - accuracy: 0.9855\n",
      "Epoch 137/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0367 - accuracy: 0.9855\n",
      "Epoch 138/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1511 - accuracy: 0.9275\n",
      "Epoch 139/200\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0394 - accuracy: 1.0000\n",
      "Epoch 140/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1116 - accuracy: 0.9855\n",
      "Epoch 141/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0620 - accuracy: 0.9855\n",
      "Epoch 142/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1279 - accuracy: 0.9420\n",
      "Epoch 143/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0974 - accuracy: 0.9855\n",
      "Epoch 144/200\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0391 - accuracy: 1.0000\n",
      "Epoch 145/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0664 - accuracy: 0.9710\n",
      "Epoch 146/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0823 - accuracy: 0.9855\n",
      "Epoch 147/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0390 - accuracy: 1.0000\n",
      "Epoch 148/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1164 - accuracy: 0.9420\n",
      "Epoch 149/200\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0892 - accuracy: 0.9710\n",
      "Epoch 150/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0580 - accuracy: 1.0000\n",
      "Epoch 151/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0459 - accuracy: 1.0000\n",
      "Epoch 152/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0795 - accuracy: 0.9855\n",
      "Epoch 153/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0799 - accuracy: 0.9855\n",
      "Epoch 154/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0609 - accuracy: 1.0000\n",
      "Epoch 155/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1144 - accuracy: 0.9565\n",
      "Epoch 156/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1170 - accuracy: 0.9710\n",
      "Epoch 157/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0304 - accuracy: 1.0000\n",
      "Epoch 158/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0325 - accuracy: 1.0000\n",
      "Epoch 159/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0326 - accuracy: 1.0000\n",
      "Epoch 160/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0099 - accuracy: 1.0000\n",
      "Epoch 161/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0467 - accuracy: 0.9855\n",
      "Epoch 162/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0336 - accuracy: 1.0000\n",
      "Epoch 163/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0331 - accuracy: 1.0000\n",
      "Epoch 164/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1116 - accuracy: 0.9710\n",
      "Epoch 165/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0281 - accuracy: 1.0000\n",
      "Epoch 166/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0619 - accuracy: 0.9855\n",
      "Epoch 167/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0443 - accuracy: 0.9855\n",
      "Epoch 168/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0504 - accuracy: 0.9855\n",
      "Epoch 169/200\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0560 - accuracy: 0.9710\n",
      "Epoch 170/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0577 - accuracy: 0.9855\n",
      "Epoch 171/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0308 - accuracy: 0.9855\n",
      "Epoch 172/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0441 - accuracy: 0.9855\n",
      "Epoch 173/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0255 - accuracy: 1.0000\n",
      "Epoch 174/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0449 - accuracy: 0.9855\n",
      "Epoch 175/200\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0485 - accuracy: 1.0000\n",
      "Epoch 176/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0333 - accuracy: 0.9855\n",
      "Epoch 177/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0659 - accuracy: 1.0000\n",
      "Epoch 178/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0652 - accuracy: 0.9710\n",
      "Epoch 179/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0334 - accuracy: 1.0000\n",
      "Epoch 180/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0512 - accuracy: 0.9855\n",
      "Epoch 181/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0538 - accuracy: 1.0000\n",
      "Epoch 182/200\n",
      "14/14 [==============================] - 0s 1ms/step - loss: 0.0401 - accuracy: 1.0000\n",
      "Epoch 183/200\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0718 - accuracy: 0.9710\n",
      "Epoch 184/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1591 - accuracy: 0.9420\n",
      "Epoch 185/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0693 - accuracy: 0.9855\n",
      "Epoch 186/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0570 - accuracy: 0.9855\n",
      "Epoch 187/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0706 - accuracy: 0.9710\n",
      "Epoch 188/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0353 - accuracy: 1.0000\n",
      "Epoch 189/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0215 - accuracy: 1.0000\n",
      "Epoch 190/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0296 - accuracy: 1.0000\n",
      "Epoch 191/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0714 - accuracy: 0.9710\n",
      "Epoch 192/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1141 - accuracy: 0.9710\n",
      "Epoch 193/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0295 - accuracy: 1.0000\n",
      "Epoch 194/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0376 - accuracy: 0.9855\n",
      "Epoch 195/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0607 - accuracy: 0.9710\n",
      "Epoch 196/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0297 - accuracy: 1.0000\n",
      "Epoch 197/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0878 - accuracy: 0.9855\n",
      "Epoch 198/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0410 - accuracy: 0.9855\n",
      "Epoch 199/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0481 - accuracy: 0.9855\n",
      "Epoch 200/200\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0946 - accuracy: 0.9565\n",
      "model created\n"
     ]
    }
   ],
   "source": [
    "# Create model - 3 layers. First layer 128 neurons, second layer 64 neurons and 3rd output layer contains number of neurons\n",
    "# equal to number of intents to predict output intent with softmax\n",
    "model = Sequential()\n",
    "model.add(Dense(128, input_shape=(len(train_x[0]),), activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(64, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(len(train_y[0]), activation='softmax'))\n",
    "\n",
    "# Compile model. Stochastic gradient descent with Nesterov accelerated gradient gives good results for this model\n",
    "sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)\n",
    "model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])\n",
    "\n",
    "#fitting and saving the model\n",
    "hist = model.fit(np.array(train_x), np.array(train_y), epochs=200, batch_size=5, verbose=1)\n",
    "model.save('chatbot_model.h5', hist)\n",
    "\n",
    "print(\"model created\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "X4NPGz76WDFE"
   },
   "outputs": [],
   "source": [
    "from keras.models import load_model\n",
    "model = load_model('chatbot_model.h5')\n",
    "import json\n",
    "import random\n",
    "intents = json.loads(open('intents.json').read())\n",
    "words = pickle.load(open('words.pkl','rb'))\n",
    "classes = pickle.load(open('classes.pkl','rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "2kHrfU8AWrE6"
   },
   "outputs": [],
   "source": [
    "def clean_up_sentence(sentence):\n",
    "    sentence_words = nltk.word_tokenize(sentence)\n",
    "    sentence_words = [lemmatizer.lemmatize(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",
    "\n",
    "def bow(sentence, words, show_details=True):\n",
    "    # tokenize the pattern\n",
    "    sentence_words = clean_up_sentence(sentence)\n",
    "    # bag of words - matrix of N words, vocabulary matrix\n",
    "    bag = [0]*len(words)\n",
    "    for s in sentence_words:\n",
    "        for i,w in enumerate(words):\n",
    "            if w == s:\n",
    "                # assign 1 if current word is in the vocabulary position\n",
    "                bag[i] = 1\n",
    "                if show_details:\n",
    "                    print (\"found in bag: %s\" % w)\n",
    "    return(np.array(bag))\n",
    "\n",
    "def predict_class(sentence, model):\n",
    "    # filter out predictions below a threshold\n",
    "    p = bow(sentence, words,show_details=False)\n",
    "    res = model.predict(np.array([p]))[0]\n",
    "    ERROR_THRESHOLD = 0.25\n",
    "    results = [[i,r] for i,r in enumerate(res) 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({\"intent\": classes[r[0]], \"probability\": str(r[1])})\n",
    "    return return_list\n",
    "\n",
    "def getResponse(ints, intents_json):\n",
    "    tag = ints[0]['intent']\n",
    "    list_of_intents = intents_json['intents']\n",
    "    for i in list_of_intents:\n",
    "        if(i['tag']== tag):\n",
    "            result = random.choice(i['responses'])\n",
    "            break\n",
    "    return result\n",
    "\n",
    "def chatbot_response(msg):\n",
    "    ints = predict_class(msg, model)\n",
    "    res = getResponse(ints, intents)\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 356
    },
    "id": "NspAyMeVWrxZ",
    "outputId": "72edd64f-3b2f-4e96-c26a-157047bb33bb"
   },
   "outputs": [],
   "source": [
    "#Creating GUI with tkinter\n",
    "import tkinter\n",
    "from tkinter import *\n",
    "\n",
    "\n",
    "def send():\n",
    "    msg = EntryBox.get(\"1.0\",'end-1c').strip()\n",
    "    EntryBox.delete(\"0.0\",END)\n",
    "\n",
    "    if msg != '':\n",
    "        ChatLog.config(state=NORMAL)\n",
    "        ChatLog.insert(END, \"You: \" + msg + '\\n\\n')\n",
    "        ChatLog.config(foreground=\"#442265\", font=(\"Verdana\", 12 ))\n",
    "\n",
    "        res = chatbot_response(msg)\n",
    "        ChatLog.insert(END, \"Bot: \" + res + '\\n\\n')\n",
    "\n",
    "        ChatLog.config(state=DISABLED)\n",
    "        ChatLog.yview(END)\n",
    "\n",
    "\n",
    "base = Tk()\n",
    "base.title(\"Aishika\")\n",
    "base.geometry(\"400x500\")\n",
    "base.resizable(width=FALSE, height=FALSE)\n",
    "\n",
    "#Create Chat window\n",
    "ChatLog = Text(base, bd=0, bg=\"white\", height=\"8\", width=\"50\", font=\"Arial\",)\n",
    "\n",
    "ChatLog.config(state=DISABLED)\n",
    "\n",
    "#Bind scrollbar to Chat window\n",
    "scrollbar = Scrollbar(base, command=ChatLog.yview, cursor=\"heart\")\n",
    "ChatLog['yscrollcommand'] = scrollbar.set\n",
    "\n",
    "#Create Button to send message\n",
    "SendButton = Button(base, font=(\"Verdana\",12,'bold'), text=\"Send\", width=\"12\", height=5,\n",
    "                    bd=0, bg=\"#32de97\", activebackground=\"#3c9d9b\",fg='#ffffff',\n",
    "                    command= send )\n",
    "\n",
    "#Create the box to enter message\n",
    "EntryBox = Text(base, bd=0, bg=\"white\",width=\"29\", height=\"5\", font=\"Arial\")\n",
    "#EntryBox.bind(\"<Return>\", send)\n",
    "\n",
    "\n",
    "#Place all components on the screen\n",
    "scrollbar.place(x=376,y=6, height=386)\n",
    "ChatLog.place(x=6,y=6, height=386, width=370)\n",
    "EntryBox.place(x=128, y=401, height=90, width=265)\n",
    "SendButton.place(x=6, y=401, height=90)\n",
    "\n",
    "base.mainloop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Gc-ZDqLNWr12"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "CBT_ChatBot.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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