training (1).ipynb

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     "text": [
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     C:\\Users\\CATE\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation, Dropout\n",
    "from keras.optimizers import SGD\n",
    "import json\n",
    "import nltk\n",
    "import nltk\n",
    "nltk.download('wordnet')\n",
    "import pickle\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "import random\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'C:\\\\Users\\\\CATE\\\\Documents\\\\Life Coach\\\\data\\\\intents.json'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[4], line 6\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnltk\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mstem\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m WordNetLemmatizer\n\u001b[0;32m      5\u001b[0m \u001b[38;5;66;03m# Load intents from JSON file\u001b[39;00m\n\u001b[1;32m----> 6\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mC:\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mUsers\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mCATE\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mDocuments\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mLife Coach\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mintents.json\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m file:\n\u001b[0;32m      7\u001b[0m     intents_data \u001b[38;5;241m=\u001b[39m file\u001b[38;5;241m.\u001b[39mread()\n\u001b[0;32m      9\u001b[0m \u001b[38;5;66;03m# Parse JSON data into a Python dictionary\u001b[39;00m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\IPython\\core\\interactiveshell.py:310\u001b[0m, in \u001b[0;36m_modified_open\u001b[1;34m(file, *args, **kwargs)\u001b[0m\n\u001b[0;32m    303\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m file \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m}:\n\u001b[0;32m    304\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    305\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIPython won\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt let you open fd=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfile\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m by default \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    306\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mas it is likely to crash IPython. If you know what you are doing, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    307\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myou can use builtins\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m open.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    308\u001b[0m     )\n\u001b[1;32m--> 310\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m io_open(file, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'C:\\\\Users\\\\CATE\\\\Documents\\\\Life Coach\\\\data\\\\intents.json'"
     ]
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   "source": [
    "import json\n",
    "import nltk\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "\n",
    "# Load intents from JSON file\n",
    "with open(r'C:\\Users\\CATE\\Documents\\Life Coach\\data\\intent.json') as file:\n",
    "    intents_data = file.read()\n",
    "\n",
    "# Parse JSON data into a Python dictionary\n",
    "intents = json.loads(intents_data)\n",
    "\n",
    "# Initialize lemmatizer\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "\n",
    "# Initialize lists\n",
    "words = []\n",
    "classes = []\n",
    "documents = []\n",
    "ignore_words = ['?', '!']\n",
    "\n",
    "# Iterate over intents\n",
    "for intent in intents['intents']:\n",
    "    # Iterate over patterns in the current intent\n",
    "    for pattern in intent['patterns']:\n",
    "        # Tokenize each word\n",
    "        w = nltk.word_tokenize(pattern)\n",
    "        words.extend(w)\n",
    "        # Add documents to the corpus\n",
    "        documents.append((w, intent['tag']))\n",
    "        # Add tag to classes list\n",
    "        if intent['tag'] not in classes:\n",
    "            classes.append(intent['tag'])\n",
    "\n",
    "# Lemmatize words 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",
    "\n",
    "# Sort classes\n",
    "classes = sorted(list(set(classes)))\n",
    "\n",
    "# Print information\n",
    "print(len(documents), \"documents\")\n",
    "print(len(classes), \"classes\", classes)\n",
    "print(len(words), \"unique lemmatized words\", words)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\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",
    "    \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])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Shuffle features\n",
    "random.shuffle(training)\n",
    "\n",
    "# Separate features (patterns) and intents\n",
    "train_x = [pattern for pattern, intent in training]\n",
    "train_y = [intent for pattern, intent in training]\n",
    "\n",
    "# Convert to numpy arrays\n",
    "train_x = np.array(train_x)\n",
    "train_y = np.array(train_y)\n",
    "\n",
    "print(\"Training data created\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile model. Stochastic gradient descent with Nesterov accelerated gradient gives good results for this model\n",
    "sgd = SGD(learning_rate=0.01, decay=1e-6, momentum=0.9, nesterov=True)\n",
    "model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#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('model.h5', hist)\n",
    "print(\"model created\")"
   ]
  }
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