Chatbot2_New.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "a2fca897",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package omw-1.4 to C:\\Users\\tirthadagr8\n",
      "[nltk_data]     ghosh\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package omw-1.4 is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('omw-1.4')\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "import json\n",
    "import pickle\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\n",
    "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": 69,
   "id": "bd75b2f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "for intent in intents['intents']:\n",
    "    for pattern in intent['patterns']:\n",
    "#tokenize each word\n",
    "        w = nltk.word_tokenize(pattern)\n",
    "        words.extend(w)\n",
    "#add documents in the 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'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "fa8c1ae5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "47 documents\n",
      "9 classes ['adverse_drug', 'blood_pressure', 'blood_pressure_search', 'goodbye', 'greeting', 'hospital_search', 'options', 'pharmacy_search', 'thanks']\n",
      "88 unique lemmatized words [\"'s\", ',', 'a', 'adverse', 'all', 'anyone', 'are', 'awesome', 'be', 'behavior', 'blood', 'by', 'bye', 'can', 'causing', 'chatting', 'check', 'could', 'data', 'day', 'detail', 'do', 'dont', 'drug', 'entry', 'find', 'for', 'give', 'good', 'goodbye', 'have', 'hello', 'help', 'helpful', 'helping', 'hey', 'hi', 'history', 'hola', 'hospital', 'how', 'i', 'id', 'is', 'later', 'list', 'load', 'locate', 'log', 'looking', 'lookup', 'management', 'me', 'module', 'nearby', 'next', 'nice', 'of', 'offered', 'open', 'patient', 'pharmacy', 'pressure', 'provide', 'reaction', 'related', 'result', 'search', 'searching', 'see', 'show', 'suitable', 'support', 'task', 'thank', 'thanks', 'that', 'there', 'till', 'time', 'to', 'transfer', 'up', 'want', 'what', 'which', 'with', 'you']\n"
     ]
    }
   ],
   "source": [
    "# lemmatize, lower each word and remove duplicates\n",
    "words = [lemmatizer.lemmatize(w.lower()) for w in words if w not in ignore_words]\n",
    "words = sorted(list(set(words)))\n",
    "# sort classes\n",
    "classes = sorted(list(set(classes)))\n",
    "# documents = combination between patterns and intents\n",
    "print (len(documents), \"documents\")\n",
    "# classes = intents\n",
    "print (len(classes), \"classes\", classes)\n",
    "# words = all words, vocabulary\n",
    "print (len(words), \"unique lemmatized words\", words)\n",
    "pickle.dump(words,open('words.pkl','wb'))\n",
    "pickle.dump(classes,open('classes.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "783950ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data created\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-71-98bf8d2847b6>: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": [
    "# create our training data\n",
    "training = []\n",
    "# create an empty array for our output\n",
    "output_empty = [0] * len(classes)\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",
    "# 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",
    "# 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",
    "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": 72,
   "id": "13274c3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "1/1 [==============================] - 0s 8ms/step - loss: 1.0729e-06 - accuracy: 1.0000\n",
      "Epoch 128/200\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 1.1921e-07 - accuracy: 1.0000\n",
      "Epoch 129/200\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n",
      "Epoch 130/200\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n",
      "Epoch 131/200\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n",
      "Epoch 132/200\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 6.1989e-06 - accuracy: 1.0000\n",
      "Epoch 133/200\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.0275 - accuracy: 1.0000\n",
      "Epoch 134/200\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 4.7325e-05 - accuracy: 1.0000\n",
      "Epoch 135/200\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 8.3446e-06 - accuracy: 1.0000\n",
      "Epoch 136/200\n",
      "1/1 [==============================] - 0s 11ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n",
      "Epoch 137/200\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 3.1991e-04 - accuracy: 1.0000\n",
      "Epoch 138/200\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n",
      "Epoch 139/200\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 3.0040e-05 - accuracy: 1.0000\n",
      "Epoch 140/200\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n",
      "Epoch 141/200\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 2.3842e-07 - accuracy: 1.0000\n",
      "Epoch 142/200\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n",
      "Epoch 143/200\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0018 - accuracy: 1.0000\n",
      "Epoch 144/200\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n",
      "Epoch 145/200\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n",
      "Epoch 146/200\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.0023 - accuracy: 1.0000\n",
      "Epoch 147/200\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n",
      "Epoch 148/200\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 1.1921e-07 - accuracy: 1.0000\n",
      "Epoch 149/200\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 1.1551e-04 - accuracy: 1.0000\n",
      "Epoch 150/200\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 0.0811 - accuracy: 1.0000\n",
      "Epoch 151/200\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n",
      "Epoch 152/200\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 7.1526e-07 - accuracy: 1.0000\n",
      "Epoch 153/200\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 1.3828e-05 - accuracy: 1.0000\n",
      "Epoch 154/200\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 2.1815e-05 - accuracy: 1.0000\n",
      "Epoch 155/200\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n",
      "Epoch 156/200\n",
      "1/1 [==============================] - 0s 11ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n",
      "Epoch 157/200\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 1.6093e-05 - accuracy: 1.0000\n",
      "Epoch 158/200\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n",
      "Epoch 159/200\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 8.3446e-07 - accuracy: 1.0000\n",
      "Epoch 160/200\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 5.9605e-07 - accuracy: 1.0000\n",
      "Epoch 161/200\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n",
      "Epoch 162/200\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n",
      "Epoch 163/200\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 4.7684e-07 - accuracy: 1.0000\n",
      "Epoch 164/200\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 4.7684e-07 - accuracy: 1.0000\n",
      "Epoch 165/200\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 8.3446e-07 - accuracy: 1.0000\n",
      "Epoch 166/200\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 9.8133e-04 - accuracy: 1.0000\n",
      "Epoch 167/200\n",
      "1/1 [==============================] - 0s 11ms/step - loss: 3.2186e-06 - accuracy: 1.0000\n",
      "Epoch 168/200\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n",
      "Epoch 169/200\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 8.3446e-07 - accuracy: 1.0000\n",
      "Epoch 170/200\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n",
      "Epoch 171/200\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 2.3842e-07 - accuracy: 1.0000\n",
      "Epoch 172/200\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 2.8610e-06 - accuracy: 1.0000\n",
      "Epoch 173/200\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n",
      "Epoch 174/200\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n",
      "Epoch 175/200\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n",
      "Epoch 176/200\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n",
      "Epoch 177/200\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n",
      "Epoch 178/200\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 2.9774e-04 - accuracy: 1.0000\n",
      "Epoch 179/200\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.1014 - accuracy: 1.0000\n",
      "Epoch 180/200\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n",
      "Epoch 181/200\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 1.4983e-04 - accuracy: 1.0000\n",
      "Epoch 182/200\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 1.5378e-05 - accuracy: 1.0000\n",
      "Epoch 183/200\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.3389 - accuracy: 1.0000\n",
      "Epoch 184/200\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n",
      "Epoch 185/200\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n",
      "Epoch 186/200\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 2.3842e-07 - accuracy: 1.0000\n",
      "Epoch 187/200\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 2.0121e-04 - accuracy: 1.0000\n",
      "Epoch 188/200\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 3.9165e-04 - accuracy: 1.0000\n",
      "Epoch 189/200\n",
      "1/1 [==============================] - 0s 11ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n",
      "Epoch 190/200\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 1.1921e-07 - accuracy: 1.0000\n",
      "Epoch 191/200\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n",
      "Epoch 192/200\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n",
      "Epoch 193/200\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n",
      "Epoch 194/200\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 1.0967e-05 - accuracy: 1.0000\n",
      "Epoch 195/200\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 2.2650e-06 - accuracy: 1.0000\n",
      "Epoch 196/200\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n",
      "Epoch 197/200\n",
      "1/1 [==============================] - 0s 11ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n",
      "Epoch 198/200\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 2.3842e-07 - accuracy: 1.0000\n",
      "Epoch 199/200\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n",
      "Epoch 200/200\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.0000e+00 - accuracy: 1.0000\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",
    "# 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",
    "#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",
    "print(\"model created\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "102eeb86",
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "import pickle\n",
    "import numpy as np\n",
    "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": 75,
   "id": "b9f98bd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_up_sentence(sentence):\n",
    "# tokenize the pattern - split words into array\n",
    "    sentence_words = nltk.word_tokenize(sentence)\n",
    "# stem each word - create short form for word\n",
    "    sentence_words = [lemmatizer.lemmatize(word.lower()) for word in sentence_words]\n",
    "    return sentence_words\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=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",
    "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "cc0ecfcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "def chatbot_response(text):\n",
    "    ints = predict_class(text, model)\n",
    "    res = getResponse(ints, intents)\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "34256673",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import tkinter\n",
    "tkinter._test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "c7a07365",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating GUI with tkinter\n",
    "import tkinter \n",
    "from tkinter import*\n",
    "\n",
    "def send():\n",
    "    msg = EntryBox.get(\"1.0\",'end-1c').strip()\n",
    "    EntryBox.delete(\"0.0\",END)\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",
    "        res = chatbot_response(msg)\n",
    "        ChatLog.insert(END, \"Bot: \" + res + '\\n\\n')\n",
    "        ChatLog.config(state=DISABLED)\n",
    "        ChatLog.yview(END)\n",
    "    \n",
    "    \n",
    "base = Tk()\n",
    "base.title(\"Hello\")\n",
    "base.geometry(\"400x500\")\n",
    "base.resizable(width=FALSE, height=FALSE)\n",
    "#Create Chat window\n",
    "ChatLog = Text(base, bd=0, bg=\"white\", height=\"8\", width=\"50\", font=\"Arial\",)\n",
    "ChatLog.config(state=DISABLED)\n",
    "#Bind scrollbar to Chat window\n",
    "scrollbar = Scrollbar(base, command=ChatLog.yview, cursor=\"heart\")\n",
    "ChatLog['yscrollcommand'] = scrollbar.set\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",
    "#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",
    "#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",
    "base.mainloop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8122f00a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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