chatbot_song_recommender_system.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "chatbot song recommender system.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Srishti20022/Music-me-Chatbot_song_recommendor_system-/blob/main/chatbot_song_recommender_system.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TJem_sS9FqbP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2f07de25-ca40-4742-829c-148482c9eaa0"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "KLtRqLAGEkLg",
        "outputId": "80bf8b33-fba7-47da-b4fb-fcf991ecfc01"
      },
      "source": [
        "!pip install anvil-uplink"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting anvil-uplink\n",
            "  Downloading anvil_uplink-0.3.39-py2.py3-none-any.whl (62 kB)\n",
            "\u001b[?25l\r\u001b[K     |█████▎                          | 10 kB 25.7 MB/s eta 0:00:01\r\u001b[K     |██████████▌                     | 20 kB 32.8 MB/s eta 0:00:01\r\u001b[K     |███████████████▊                | 30 kB 16.6 MB/s eta 0:00:01\r\u001b[K     |█████████████████████           | 40 kB 12.1 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▎     | 51 kB 5.5 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▌| 61 kB 5.7 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 62 kB 823 kB/s \n",
            "\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from anvil-uplink) (1.15.0)\n",
            "Collecting argparse\n",
            "  Downloading argparse-1.4.0-py2.py3-none-any.whl (23 kB)\n",
            "Collecting ws4py\n",
            "  Downloading ws4py-0.5.1.tar.gz (51 kB)\n",
            "\u001b[K     |████████████████████████████████| 51 kB 201 kB/s \n",
            "\u001b[?25hRequirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from anvil-uplink) (0.16.0)\n",
            "Building wheels for collected packages: ws4py\n",
            "  Building wheel for ws4py (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ws4py: filename=ws4py-0.5.1-py3-none-any.whl size=45230 sha256=4ba90ee32343ea29a2a3f32438560190994977570ce49e35126bf3ba385315dc\n",
            "  Stored in directory: /root/.cache/pip/wheels/29/ea/7d/3410aa0aa0e4402ead9a7a97ab2214804887e0f5c2b76f0c96\n",
            "Successfully built ws4py\n",
            "Installing collected packages: ws4py, argparse, anvil-uplink\n",
            "Successfully installed anvil-uplink-0.3.39 argparse-1.4.0 ws4py-0.5.1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "argparse",
                  "google"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ba5bes9x5g0p",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5a6505d6-3833-4ffa-c3a6-39dfa7dd50a7"
      },
      "source": [
        "import anvil.server\n",
        "\n",
        "anvil.server.connect(uplink_key)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Connecting to wss://anvil.works/uplink\n",
            "Anvil websocket open\n",
            "Connected to \"Default environment (dev)\" as SERVER\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O6kGcc4Qz58h"
      },
      "source": [
        "# pip install tensorflow keras pickle nltk\n",
        "from google.colab import drive\n",
        "import numpy as np\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Activation, Dropout\n",
        "# from keras.optimizers import SGD\n",
        "from tensorflow.keras.optimizers import SGD\n",
        "import random\n",
        "import nltk\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "lemmatizer = WordNetLemmatizer()\n",
        "import json\n",
        "import pickle\n",
        "intents_file = open('/content/drive/MyDrive/Colab Notebooks/intents.json').read()\n",
        "intents = json.loads(intents_file)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MOlYh5OC1FE-",
        "outputId": "2412209f-37e3-4eb0-91b5-e22e99ccf9fa"
      },
      "source": [
        "import nltk\n",
        "nltk.download('punkt')\n",
        "words=[]\n",
        "classes = []\n",
        "documents = []\n",
        "ignore_letters = ['!', '?', ',', '.']\n",
        "for intent in intents['intents']:\n",
        "    for pattern in intent['patterns']:\n",
        "        #tokenize each word\n",
        "        word = nltk.word_tokenize(pattern)\n",
        "        words.extend(word)\n",
        "        #add documents in the corpus\n",
        "        documents.append((word, intent['tag']))\n",
        "        # add to our classes list\n",
        "        if intent['tag'] not in classes:\n",
        "            classes.append(intent['tag'])\n",
        "print(documents)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[(['hi'], 'greeting'), (['hello'], 'greeting'), (['whats', 'up'], 'greeting'), (['sup'], 'greeting'), (['is', 'anyone', 'there'], 'greeting'), (['whats', 'good'], 'greeting'), (['hey'], 'greeting'), (['bye'], 'goodbye'), (['cya'], 'goodbye'), (['see', 'you', 'later'], 'goodbye'), (['goodbye'], 'goodbye'), (['im', 'leaving'], 'goodbye'), (['have', 'a', 'good', 'day'], 'goodbye'), (['how', 'old', 'are', 'you'], 'age'), (['what', 'is', 'your', 'age'], 'age'), (['thanks'], 'thanks'), (['thank', 'you'], 'thanks'), (['thankyou'], 'thanks'), (['ty'], 'thanks'), (['I', 'owe', 'you', 'one'], 'thanks'), (['whats', 'is', 'your', 'name'], 'name'), (['whats', 'your', 'name'], 'name'), (['whats', 'should', 'I', 'call', 'you'], 'name'), (['how', 'should', 'I', 'address', 'you'], 'name'), (['Yes', 'it', 'does'], 'sky_net_yes'), (['Yeah'], 'sky_net_yes'), (['Haha', 'yep'], 'sky_net_yes'), (['yes'], 'sky_net_yes'), (['Indeed'], 'sky_net_yes'), (['Yup'], 'sky_net_yes'), (['Just', 'like', 'the', 'terminator'], 'sky_net_yes'), (['no'], 'sky_net_no'), (['nah'], 'sky_net_no'), (['not', 'really'], 'sky_net_no'), (['thats', 'scary'], 'sky_net_no'), (['singularity'], 'sky_net_no'), (['how', 'are', 'you'], 'how_are_you'), (['how', 'are', 'you', 'doing'], 'how_are_you'), (['what', 'is', 'going', 'on'], 'how_are_you'), (['I', 'am', 'doing', 'great'], 'doing_great'), (['I', 'am', 'well'], 'doing_great'), (['Im', 'great'], 'doing_great'), (['awesome'], 'doing_great'), (['happy'], 'doing_great'), (['better'], 'doing_great'), (['not', 'great'], 'doing_badly'), (['not', 'well'], 'doing_badly'), (['not', 'good'], 'doing_badly'), (['bad'], 'doing_badly'), (['badly'], 'doing_badly'), (['terrible'], 'doing_badly'), (['horrible'], 'doing_badly'), (['awful'], 'doing_badly'), (['sad'], 'doing_badly'), (['wait', 'you', 'watch', 'Netflix'], 'netflix'), (['how', 'do', 'you', 'watch', 'Netflix'], 'netflix'), (['Netflix'], 'netflix'), (['how', 'can', 'you', 'run'], 'quick_run'), (['how', 'do', 'you', 'run'], 'quick_run'), (['how', 'run'], 'quick_run'), (['why', 'run'], 'quick_run'), (['run'], 'quick_run'), (['you', 'real'], 'real_bot'), (['you', 'human'], 'real_bot'), (['you', 'robot'], 'real_bot'), (['you', 'alive'], 'real_bot'), (['you', 'sentient'], 'real_bot'), (['you', 'conscious'], 'real_bot'), (['tell', 'me', 'joke'], 'joke'), (['got', 'any', 'good', 'jokes'], 'joke'), (['got', 'jokes'], 'joke'), (['can', 'you', 'tell', 'joke'], 'joke'), (['tell', 'joke'], 'joke'), (['haha'], 'good_joke'), (['that', 'was', 'funny'], 'good_joke'), (['very', 'funny'], 'good_joke'), (['good', 'one'], 'good_joke'), (['bad', 'joke'], 'bad_joke'), (['trash', 'joke'], 'bad_joke'), (['terrible'], 'bad_joke'), (['not', 'funny'], 'bad_joke'), (['I', 'hate', 'you'], 'hate'), (['you', 'stupid'], 'hate'), (['you', 'dumb'], 'hate'), (['you', 'mean'], 'hate'), (['you', 'my', 'friend'], 'like'), (['I', 'like', 'you'], 'like'), (['I', 'love', 'you'], 'like'), (['you', 'cool'], 'like'), (['you', 'are', 'chill'], 'like'), (['whats', 'favorite', 'show'], 'favorite_show'), (['favorite', 'tv', 'show'], 'favorite_show'), (['Whats', 'favorite', 'movie'], 'favorite_movie'), (['whats', 'favorite', 'film'], 'favorite_movie'), (['best', 'movie'], 'favorite_movie'), (['your', 'favorite', 'movie'], 'favorite_movie'), (['whats', 'favorite', 'movie'], 'favorite_movie'), (['What', 'think', 'about'], 'your_thoughts'), (['What', 'your', 'thoughts'], 'your_thoughts')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fQmkYrce2APb",
        "outputId": "8b759637-0e5b-4cdc-f4d2-b2f8989c5fb3"
      },
      "source": [
        "import nltk\n",
        "nltk.download('wordnet')\n",
        "# create the training data\n",
        "training = []\n",
        "# create empty array for the output\n",
        "output_empty = [0] * len(classes)\n",
        "# training set, bag of words for every sentence\n",
        "for doc in documents:\n",
        "    # initializing bag of words\n",
        "\n",
        "    bag = []\n",
        "    # list of tokenized words for the pattern\n",
        "    word_patterns = doc[0]\n",
        "    # lemmatize each word - create base word, in attempt to represent related words\n",
        "    word_patterns = [lemmatizer.lemmatize(word.lower()) for word in word_patterns]\n",
        "    # create the bag of words array with 1, if word is found in current pattern\n",
        "    for word in words:\n",
        "        bag.append(1) if word in word_patterns 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 the features and make numpy array\n",
        "random.shuffle(training)\n",
        "training = np.array(training)\n",
        "# create training and testing lists. X - patterns, Y - intents\n",
        "train_x = list(training[:,0])\n",
        "train_y = list(training[:,1])\n",
        "print(\"Training data is created\")"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/wordnet.zip.\n",
            "Training data is created\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:25: 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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N5SaLoZJ2kpo",
        "outputId": "d110b936-1ad2-4ea1-c422-5dd1baed6f5c"
      },
      "source": [
        "# deep neural networds model\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",
        "# Compiling model. SGD 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",
        "#Training 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 is created\")"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/optimizer_v2.py:356: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  \"The `lr` argument is deprecated, use `learning_rate` instead.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "20/20 [==============================] - 1s 2ms/step - loss: 3.0831 - accuracy: 0.0707\n",
            "Epoch 2/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 2.8746 - accuracy: 0.0808\n",
            "Epoch 3/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 2.8169 - accuracy: 0.1818\n",
            "Epoch 4/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 2.6969 - accuracy: 0.2626\n",
            "Epoch 5/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 2.6612 - accuracy: 0.2020\n",
            "Epoch 6/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 2.5733 - accuracy: 0.1919\n",
            "Epoch 7/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 2.3531 - accuracy: 0.2525\n",
            "Epoch 8/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 2.2559 - accuracy: 0.3333\n",
            "Epoch 9/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 2.2536 - accuracy: 0.3333\n",
            "Epoch 10/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 2.2195 - accuracy: 0.2929\n",
            "Epoch 11/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 1.9786 - accuracy: 0.3131\n",
            "Epoch 12/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 1.9616 - accuracy: 0.4444\n",
            "Epoch 13/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 1.7797 - accuracy: 0.4242\n",
            "Epoch 14/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 1.7140 - accuracy: 0.4141\n",
            "Epoch 15/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 1.9798 - accuracy: 0.4141\n",
            "Epoch 16/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 1.7023 - accuracy: 0.4343\n",
            "Epoch 17/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 1.7264 - accuracy: 0.4545\n",
            "Epoch 18/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 1.5838 - accuracy: 0.4343\n",
            "Epoch 19/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 1.6309 - accuracy: 0.4545\n",
            "Epoch 20/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 1.6902 - accuracy: 0.4141\n",
            "Epoch 21/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 1.5834 - accuracy: 0.4747\n",
            "Epoch 22/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 1.4780 - accuracy: 0.5354\n",
            "Epoch 23/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 1.5284 - accuracy: 0.5253\n",
            "Epoch 24/200\n",
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            "Epoch 25/200\n",
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            "Epoch 26/200\n",
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            "Epoch 27/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 1.3617 - accuracy: 0.5758\n",
            "Epoch 28/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 1.2256 - accuracy: 0.5960\n",
            "Epoch 29/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 1.3481 - accuracy: 0.5556\n",
            "Epoch 30/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 1.3179 - accuracy: 0.5960\n",
            "Epoch 31/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 1.1740 - accuracy: 0.5657\n",
            "Epoch 32/200\n",
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            "Epoch 34/200\n",
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            "Epoch 38/200\n",
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            "Epoch 39/200\n",
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            "Epoch 40/200\n",
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            "Epoch 41/200\n",
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            "Epoch 42/200\n",
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            "Epoch 53/200\n",
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            "Epoch 54/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.7121 - accuracy: 0.7273\n",
            "Epoch 55/200\n",
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            "Epoch 56/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.8239 - accuracy: 0.6869\n",
            "Epoch 57/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.7132 - accuracy: 0.8081\n",
            "Epoch 58/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.6695 - accuracy: 0.7879\n",
            "Epoch 59/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.7563 - accuracy: 0.7374\n",
            "Epoch 60/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.7566 - accuracy: 0.7778\n",
            "Epoch 61/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.7840 - accuracy: 0.7273\n",
            "Epoch 62/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.7457 - accuracy: 0.7374\n",
            "Epoch 63/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.8732 - accuracy: 0.6970\n",
            "Epoch 64/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.7415 - accuracy: 0.7172\n",
            "Epoch 65/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.6295 - accuracy: 0.7778\n",
            "Epoch 66/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.7864 - accuracy: 0.7071\n",
            "Epoch 67/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.6703 - accuracy: 0.7576\n",
            "Epoch 68/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.5871 - accuracy: 0.8081\n",
            "Epoch 69/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.7459 - accuracy: 0.7374\n",
            "Epoch 70/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.6288 - accuracy: 0.7778\n",
            "Epoch 71/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5966 - accuracy: 0.8081\n",
            "Epoch 72/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5830 - accuracy: 0.7980\n",
            "Epoch 73/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.5919 - accuracy: 0.7677\n",
            "Epoch 74/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.7052 - accuracy: 0.7475\n",
            "Epoch 75/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.6731 - accuracy: 0.7576\n",
            "Epoch 76/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.6234 - accuracy: 0.7677\n",
            "Epoch 77/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.6792 - accuracy: 0.7576\n",
            "Epoch 78/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.7430 - accuracy: 0.7677\n",
            "Epoch 79/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5632 - accuracy: 0.8182\n",
            "Epoch 80/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.6510 - accuracy: 0.7576\n",
            "Epoch 81/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5607 - accuracy: 0.8182\n",
            "Epoch 82/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.6138 - accuracy: 0.7576\n",
            "Epoch 83/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.6247 - accuracy: 0.7475\n",
            "Epoch 84/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.6343 - accuracy: 0.7980\n",
            "Epoch 85/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.7009 - accuracy: 0.7475\n",
            "Epoch 86/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5540 - accuracy: 0.8283\n",
            "Epoch 87/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.5549 - accuracy: 0.7879\n",
            "Epoch 88/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.6504 - accuracy: 0.7475\n",
            "Epoch 89/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5817 - accuracy: 0.7879\n",
            "Epoch 90/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.5699 - accuracy: 0.7778\n",
            "Epoch 91/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.6480 - accuracy: 0.7879\n",
            "Epoch 92/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.6164 - accuracy: 0.7576\n",
            "Epoch 93/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.5841 - accuracy: 0.7778\n",
            "Epoch 94/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5073 - accuracy: 0.8384\n",
            "Epoch 95/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.7140 - accuracy: 0.7879\n",
            "Epoch 96/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5821 - accuracy: 0.7576\n",
            "Epoch 97/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.7431 - accuracy: 0.7677\n",
            "Epoch 98/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.6127 - accuracy: 0.7374\n",
            "Epoch 99/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.5968 - accuracy: 0.7778\n",
            "Epoch 100/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5640 - accuracy: 0.7778\n",
            "Epoch 101/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4929 - accuracy: 0.7980\n",
            "Epoch 102/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.6127 - accuracy: 0.7778\n",
            "Epoch 103/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.7001 - accuracy: 0.7475\n",
            "Epoch 104/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5070 - accuracy: 0.7778\n",
            "Epoch 105/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.6076 - accuracy: 0.7475\n",
            "Epoch 106/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.6417 - accuracy: 0.7778\n",
            "Epoch 107/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.6856 - accuracy: 0.7475\n",
            "Epoch 108/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5751 - accuracy: 0.8081\n",
            "Epoch 109/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5055 - accuracy: 0.8586\n",
            "Epoch 110/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4217 - accuracy: 0.8687\n",
            "Epoch 111/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.7444 - accuracy: 0.8081\n",
            "Epoch 112/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.5597 - accuracy: 0.8081\n",
            "Epoch 113/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.5320 - accuracy: 0.7980\n",
            "Epoch 114/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4723 - accuracy: 0.8081\n",
            "Epoch 115/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.6494 - accuracy: 0.7778\n",
            "Epoch 116/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5777 - accuracy: 0.7374\n",
            "Epoch 117/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.6223 - accuracy: 0.7677\n",
            "Epoch 118/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.5249 - accuracy: 0.8283\n",
            "Epoch 119/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5812 - accuracy: 0.7778\n",
            "Epoch 120/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.6147 - accuracy: 0.8283\n",
            "Epoch 121/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.5939 - accuracy: 0.7879\n",
            "Epoch 122/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5251 - accuracy: 0.8081\n",
            "Epoch 123/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5815 - accuracy: 0.7980\n",
            "Epoch 124/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5483 - accuracy: 0.7677\n",
            "Epoch 125/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.4989 - accuracy: 0.7879\n",
            "Epoch 126/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.4687 - accuracy: 0.8182\n",
            "Epoch 127/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.5909 - accuracy: 0.7677\n",
            "Epoch 128/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.6075 - accuracy: 0.8081\n",
            "Epoch 129/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5377 - accuracy: 0.8384\n",
            "Epoch 130/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.5024 - accuracy: 0.7980\n",
            "Epoch 131/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.4855 - accuracy: 0.8283\n",
            "Epoch 132/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.6719 - accuracy: 0.7374\n",
            "Epoch 133/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.4238 - accuracy: 0.8384\n",
            "Epoch 134/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5654 - accuracy: 0.7677\n",
            "Epoch 135/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.5228 - accuracy: 0.7879\n",
            "Epoch 136/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4684 - accuracy: 0.7980\n",
            "Epoch 137/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.4296 - accuracy: 0.8384\n",
            "Epoch 138/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5613 - accuracy: 0.7879\n",
            "Epoch 139/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4625 - accuracy: 0.8384\n",
            "Epoch 140/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.4929 - accuracy: 0.8586\n",
            "Epoch 141/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.7027 - accuracy: 0.7778\n",
            "Epoch 142/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.4502 - accuracy: 0.7980\n",
            "Epoch 143/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.4463 - accuracy: 0.8182\n",
            "Epoch 144/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4746 - accuracy: 0.8283\n",
            "Epoch 145/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4465 - accuracy: 0.7980\n",
            "Epoch 146/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.6268 - accuracy: 0.7879\n",
            "Epoch 147/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5696 - accuracy: 0.7980\n",
            "Epoch 148/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5256 - accuracy: 0.8384\n",
            "Epoch 149/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.4918 - accuracy: 0.8081\n",
            "Epoch 150/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5842 - accuracy: 0.7980\n",
            "Epoch 151/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4848 - accuracy: 0.7879\n",
            "Epoch 152/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5876 - accuracy: 0.8182\n",
            "Epoch 153/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4631 - accuracy: 0.8485\n",
            "Epoch 154/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5111 - accuracy: 0.7980\n",
            "Epoch 155/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.6339 - accuracy: 0.7980\n",
            "Epoch 156/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5776 - accuracy: 0.8283\n",
            "Epoch 157/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5682 - accuracy: 0.7980\n",
            "Epoch 158/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4414 - accuracy: 0.8283\n",
            "Epoch 159/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5804 - accuracy: 0.7879\n",
            "Epoch 160/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4475 - accuracy: 0.7879\n",
            "Epoch 161/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4349 - accuracy: 0.8485\n",
            "Epoch 162/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4666 - accuracy: 0.8485\n",
            "Epoch 163/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4251 - accuracy: 0.8182\n",
            "Epoch 164/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.4263 - accuracy: 0.8182\n",
            "Epoch 165/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3933 - accuracy: 0.8283\n",
            "Epoch 166/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4358 - accuracy: 0.7980\n",
            "Epoch 167/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5169 - accuracy: 0.7980\n",
            "Epoch 168/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4899 - accuracy: 0.8283\n",
            "Epoch 169/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4148 - accuracy: 0.8687\n",
            "Epoch 170/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4275 - accuracy: 0.8283\n",
            "Epoch 171/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4259 - accuracy: 0.8687\n",
            "Epoch 172/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4785 - accuracy: 0.8283\n",
            "Epoch 173/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5425 - accuracy: 0.7980\n",
            "Epoch 174/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5881 - accuracy: 0.7980\n",
            "Epoch 175/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4591 - accuracy: 0.8283\n",
            "Epoch 176/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4477 - accuracy: 0.8384\n",
            "Epoch 177/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4548 - accuracy: 0.8283\n",
            "Epoch 178/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.6482 - accuracy: 0.7778\n",
            "Epoch 179/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4115 - accuracy: 0.8081\n",
            "Epoch 180/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5584 - accuracy: 0.8283\n",
            "Epoch 181/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5775 - accuracy: 0.7778\n",
            "Epoch 182/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4712 - accuracy: 0.8485\n",
            "Epoch 183/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.5195 - accuracy: 0.8283\n",
            "Epoch 184/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5118 - accuracy: 0.8081\n",
            "Epoch 185/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5307 - accuracy: 0.7879\n",
            "Epoch 186/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.6057 - accuracy: 0.8081\n",
            "Epoch 187/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3904 - accuracy: 0.8687\n",
            "Epoch 188/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.5449 - accuracy: 0.8081\n",
            "Epoch 189/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3702 - accuracy: 0.8384\n",
            "Epoch 190/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5413 - accuracy: 0.8081\n",
            "Epoch 191/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4780 - accuracy: 0.8485\n",
            "Epoch 192/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.4283 - accuracy: 0.8182\n",
            "Epoch 193/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3972 - accuracy: 0.8687\n",
            "Epoch 194/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4311 - accuracy: 0.7778\n",
            "Epoch 195/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.4990 - accuracy: 0.8283\n",
            "Epoch 196/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5920 - accuracy: 0.7879\n",
            "Epoch 197/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3970 - accuracy: 0.8182\n",
            "Epoch 198/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.4668 - accuracy: 0.8384\n",
            "Epoch 199/200\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.5232 - accuracy: 0.8081\n",
            "Epoch 200/200\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.5063 - accuracy: 0.8182\n",
            "model is created\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "chIZ8fT222Lo"
      },
      "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('/content/drive/MyDrive/Colab Notebooks/intents.json').read())\n",
        "words = pickle.load(open('/content/drive/MyDrive/Colab Notebooks/words.pkl','rb'))\n",
        "classes = pickle.load(open('/content/drive/MyDrive/Colab Notebooks/classes.pkl','rb'))\n",
        "def clean_up_sentence(sentence):\n",
        "    # tokenize the pattern - splitting words into array\n",
        "    sentence_words = nltk.word_tokenize(sentence)\n",
        "    # stemming every word - reducing to base form\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 words that exist in sentence\n",
        "def bag_of_words(sentence, words, show_details=True):\n",
        "    # tokenizing patterns\n",
        "    sentence_words = clean_up_sentence(sentence)\n",
        "    # bag of words - vocabulary matrix\n",
        "    bag = [0]*len(words)  \n",
        "    for s in sentence_words:\n",
        "        for i,word in enumerate(words):\n",
        "            if word == 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\" % word)\n",
        "    return(np.array(bag))\n",
        "def predict_class(sentence):\n",
        "    # filter below  threshold predictions\n",
        "    p = bag_of_words(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",
        "    # sorting strength 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",
        "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",
        "#Creating tkinter GUI\n",
        "# import tkinter\n",
        "# from tkinter import *\n",
        "# def send():\n",
        "#     msg = EntryBox.get(\"1.0\",'end-1c').strip()\n",
        "#     EntryBox.delete(\"0.0\",END)\n",
        "#     if msg != '':\n",
        "#         ChatBox.config(state=NORMAL)\n",
        "#         ChatBox.insert(END, \"You: \" + msg + '\\n\\n')\n",
        "#         ChatBox.config(foreground=\"#446665\", font=(\"Verdana\", 12 )) \n",
        "#         ints = predict_class(msg)\n",
        "#         res = getResponse(ints, intents)\n",
        "#         ChatBox.insert(END, \"Bot: \" + res + '\\n\\n')           \n",
        "#         ChatBox.config(state=DISABLED)\n",
        "#         ChatBox.yview(END)\n",
        "# root = Tk()\n",
        "# root.title(\"Chatbot\")\n",
        "# root.geometry(\"400x500\")\n",
        "# root.resizable(width=FALSE, height=FALSE)\n",
        "# #Create Chat window\n",
        "# ChatBox = Text(root, bd=0, bg=\"white\", height=\"8\", width=\"50\", font=\"Arial\",)\n",
        "# ChatBox.config(state=DISABLED)\n",
        "# #Bind scrollbar to Chat window\n",
        "# scrollbar = Scrollbar(root, command=ChatBox.yview, cursor=\"heart\")\n",
        "# ChatBox['yscrollcommand'] = scrollbar.set\n",
        "# #Create Button to send message\n",
        "# SendButton = Button(root, font=(\"Verdana\",12,'bold'), text=\"Send\", width=\"12\", height=5,\n",
        "#                     bd=0, bg=\"#f9a602\", activebackground=\"#3c9d9b\",fg='#000000',\n",
        "#                     command= send )\n",
        "# #Create the box to enter message\n",
        "# EntryBox = Text(root, 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",
        "# ChatBox.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",
        "# root.mainloop()"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "naiGxGhK7wgu",
        "outputId": "61413d55-8a64-4a87-f3d3-20d775c32f16"
      },
      "source": [
        "import numpy as np\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Activation, Dropout\n",
        "# from keras.optimizers import SGD\n",
        "from tensorflow.keras.optimizers import SGD\n",
        "import random\n",
        "\n",
        "import nltk\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "lemmatizer = WordNetLemmatizer()\n",
        "import json\n",
        "import pickle\n",
        "\n",
        "words=[]\n",
        "classes = []\n",
        "documents = []\n",
        "ignore_letters = ['!', '?', ',', '.']\n",
        "intents_file = open('/content/drive/MyDrive/Colab Notebooks/intents.json').read()\n",
        "intents = json.loads(intents_file)\n",
        "\n",
        "for intent in intents['intents']:\n",
        "    for pattern in intent['patterns']:\n",
        "        #tokenize each word\n",
        "        word = nltk.word_tokenize(pattern)\n",
        "        words.extend(word)\n",
        "        #add documents in the corpus\n",
        "        documents.append((word, intent['tag']))\n",
        "        # add to our classes list\n",
        "        if intent['tag'] not in classes:\n",
        "            classes.append(intent['tag'])\n",
        "print(documents)\n",
        "# lemmaztize and lower each word and remove duplicates\n",
        "words = [lemmatizer.lemmatize(w.lower()) for w in words if w not in ignore_letters]\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",
        "\n",
        "pickle.dump(words,open('words.pkl','wb'))\n",
        "pickle.dump(classes,open('classes.pkl','wb'))\n",
        "\n",
        "# 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 word in words:\n",
        "        bag.append(1) if word 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\")\n",
        "\n",
        "# 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=172, batch_size=5, verbose=1)\n",
        "model.save('chatbot_model.h5', hist)\n",
        "\n",
        "print(\"model created\")"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[(['hi'], 'greeting'), (['hello'], 'greeting'), (['whats', 'up'], 'greeting'), (['sup'], 'greeting'), (['is', 'anyone', 'there'], 'greeting'), (['whats', 'good'], 'greeting'), (['hey'], 'greeting'), (['bye'], 'goodbye'), (['cya'], 'goodbye'), (['see', 'you', 'later'], 'goodbye'), (['goodbye'], 'goodbye'), (['im', 'leaving'], 'goodbye'), (['have', 'a', 'good', 'day'], 'goodbye'), (['how', 'old', 'are', 'you'], 'age'), (['what', 'is', 'your', 'age'], 'age'), (['thanks'], 'thanks'), (['thank', 'you'], 'thanks'), (['thankyou'], 'thanks'), (['ty'], 'thanks'), (['I', 'owe', 'you', 'one'], 'thanks'), (['whats', 'is', 'your', 'name'], 'name'), (['whats', 'your', 'name'], 'name'), (['whats', 'should', 'I', 'call', 'you'], 'name'), (['how', 'should', 'I', 'address', 'you'], 'name'), (['Yes', 'it', 'does'], 'sky_net_yes'), (['Yeah'], 'sky_net_yes'), (['Haha', 'yep'], 'sky_net_yes'), (['yes'], 'sky_net_yes'), (['Indeed'], 'sky_net_yes'), (['Yup'], 'sky_net_yes'), (['Just', 'like', 'the', 'terminator'], 'sky_net_yes'), (['no'], 'sky_net_no'), (['nah'], 'sky_net_no'), (['not', 'really'], 'sky_net_no'), (['thats', 'scary'], 'sky_net_no'), (['singularity'], 'sky_net_no'), (['how', 'are', 'you'], 'how_are_you'), (['how', 'are', 'you', 'doing'], 'how_are_you'), (['what', 'is', 'going', 'on'], 'how_are_you'), (['I', 'am', 'doing', 'great'], 'doing_great'), (['I', 'am', 'well'], 'doing_great'), (['Im', 'great'], 'doing_great'), (['awesome'], 'doing_great'), (['happy'], 'doing_great'), (['better'], 'doing_great'), (['not', 'great'], 'doing_badly'), (['not', 'well'], 'doing_badly'), (['not', 'good'], 'doing_badly'), (['bad'], 'doing_badly'), (['badly'], 'doing_badly'), (['terrible'], 'doing_badly'), (['horrible'], 'doing_badly'), (['awful'], 'doing_badly'), (['sad'], 'doing_badly'), (['wait', 'you', 'watch', 'Netflix'], 'netflix'), (['how', 'do', 'you', 'watch', 'Netflix'], 'netflix'), (['Netflix'], 'netflix'), (['how', 'can', 'you', 'run'], 'quick_run'), (['how', 'do', 'you', 'run'], 'quick_run'), (['how', 'run'], 'quick_run'), (['why', 'run'], 'quick_run'), (['run'], 'quick_run'), (['you', 'real'], 'real_bot'), (['you', 'human'], 'real_bot'), (['you', 'robot'], 'real_bot'), (['you', 'alive'], 'real_bot'), (['you', 'sentient'], 'real_bot'), (['you', 'conscious'], 'real_bot'), (['tell', 'me', 'joke'], 'joke'), (['got', 'any', 'good', 'jokes'], 'joke'), (['got', 'jokes'], 'joke'), (['can', 'you', 'tell', 'joke'], 'joke'), (['tell', 'joke'], 'joke'), (['haha'], 'good_joke'), (['that', 'was', 'funny'], 'good_joke'), (['very', 'funny'], 'good_joke'), (['good', 'one'], 'good_joke'), (['bad', 'joke'], 'bad_joke'), (['trash', 'joke'], 'bad_joke'), (['terrible'], 'bad_joke'), (['not', 'funny'], 'bad_joke'), (['I', 'hate', 'you'], 'hate'), (['you', 'stupid'], 'hate'), (['you', 'dumb'], 'hate'), (['you', 'mean'], 'hate'), (['you', 'my', 'friend'], 'like'), (['I', 'like', 'you'], 'like'), (['I', 'love', 'you'], 'like'), (['you', 'cool'], 'like'), (['you', 'are', 'chill'], 'like'), (['whats', 'favorite', 'show'], 'favorite_show'), (['favorite', 'tv', 'show'], 'favorite_show'), (['Whats', 'favorite', 'movie'], 'favorite_movie'), (['whats', 'favorite', 'film'], 'favorite_movie'), (['best', 'movie'], 'favorite_movie'), (['your', 'favorite', 'movie'], 'favorite_movie'), (['whats', 'favorite', 'movie'], 'favorite_movie'), (['What', 'think', 'about'], 'your_thoughts'), (['What', 'your', 'thoughts'], 'your_thoughts')]\n",
            "99 documents\n",
            "21 classes ['age', 'bad_joke', 'doing_badly', 'doing_great', 'favorite_movie', 'favorite_show', 'good_joke', 'goodbye', 'greeting', 'hate', 'how_are_you', 'joke', 'like', 'name', 'netflix', 'quick_run', 'real_bot', 'sky_net_no', 'sky_net_yes', 'thanks', 'your_thoughts']\n",
            "113 unique lemmatized words ['a', 'about', 'address', 'age', 'alive', 'am', 'any', 'anyone', 'are', 'awesome', 'awful', 'bad', 'badly', 'best', 'better', 'bye', 'call', 'can', 'chill', 'conscious', 'cool', 'cya', 'day', 'do', 'doe', 'doing', 'dumb', 'favorite', 'film', 'friend', 'funny', 'going', 'good', 'goodbye', 'got', 'great', 'haha', 'happy', 'hate', 'have', 'hello', 'hey', 'hi', 'horrible', 'how', 'human', 'i', 'im', 'indeed', 'is', 'it', 'joke', 'just', 'later', 'leaving', 'like', 'love', 'me', 'mean', 'movie', 'my', 'nah', 'name', 'netflix', 'no', 'not', 'old', 'on', 'one', 'owe', 'real', 'really', 'robot', 'run', 'sad', 'scary', 'see', 'sentient', 'should', 'show', 'singularity', 'stupid', 'sup', 'tell', 'terminator', 'terrible', 'thank', 'thanks', 'thankyou', 'that', 'thats', 'the', 'there', 'think', 'thought', 'trash', 'tv', 'ty', 'up', 'very', 'wa', 'wait', 'watch', 'well', 'what', 'whats', 'why', 'yeah', 'yep', 'yes', 'you', 'your', 'yup']\n",
            "Training data created\n",
            "Epoch 1/172\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:70: 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",
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/optimizer_v2.py:356: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  \"The `lr` argument is deprecated, use `learning_rate` instead.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "20/20 [==============================] - 0s 2ms/step - loss: 3.0846 - accuracy: 0.0000e+00\n",
            "Epoch 2/172\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 3.0208 - accuracy: 0.0909\n",
            "Epoch 3/172\n",
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            "Epoch 5/172\n",
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            "Epoch 7/172\n",
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            "Epoch 8/172\n",
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            "Epoch 9/172\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 2.5020 - accuracy: 0.3333\n",
            "Epoch 10/172\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 2.5003 - accuracy: 0.3131\n",
            "Epoch 11/172\n",
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            "Epoch 12/172\n",
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            "Epoch 15/172\n",
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            "Epoch 16/172\n",
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            "Epoch 18/172\n",
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            "Epoch 20/172\n",
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            "Epoch 21/172\n",
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            "Epoch 22/172\n",
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            "Epoch 23/172\n",
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            "20/20 [==============================] - 0s 2ms/step - loss: 0.2823 - accuracy: 0.9394\n",
            "Epoch 75/172\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2616 - accuracy: 0.9192\n",
            "Epoch 76/172\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.2604 - accuracy: 0.9394\n",
            "Epoch 77/172\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.2315 - accuracy: 0.9293\n",
            "Epoch 78/172\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.3404 - accuracy: 0.8990\n",
            "Epoch 79/172\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.1912 - accuracy: 0.9697\n",
            "Epoch 80/172\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.2440 - accuracy: 0.9192\n",
            "Epoch 81/172\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2304 - accuracy: 0.9293\n",
            "Epoch 82/172\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.1611 - accuracy: 0.9394\n",
            "Epoch 83/172\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.2257 - accuracy: 0.8990\n",
            "Epoch 84/172\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.1868 - accuracy: 0.9495\n",
            "Epoch 85/172\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.2380 - accuracy: 0.9293\n",
            "Epoch 86/172\n",
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            "Epoch 87/172\n",
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            "Epoch 157/172\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.0647 - accuracy: 1.0000\n",
            "Epoch 158/172\n",
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            "Epoch 159/172\n",
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            "Epoch 160/172\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.0641 - accuracy: 0.9798\n",
            "Epoch 161/172\n",
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            "Epoch 162/172\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.1287 - accuracy: 0.9394\n",
            "Epoch 163/172\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.0995 - accuracy: 0.9697\n",
            "Epoch 164/172\n",
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            "Epoch 165/172\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.0915 - accuracy: 0.9697\n",
            "Epoch 166/172\n",
            "20/20 [==============================] - 0s 1ms/step - loss: 0.1580 - accuracy: 0.9495\n",
            "Epoch 167/172\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.1350 - accuracy: 0.9596\n",
            "Epoch 168/172\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.1525 - accuracy: 0.9293\n",
            "Epoch 169/172\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.0706 - accuracy: 0.9798\n",
            "Epoch 170/172\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.0539 - accuracy: 1.0000\n",
            "Epoch 171/172\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.1624 - accuracy: 0.9192\n",
            "Epoch 172/172\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.0582 - accuracy: 0.9798\n",
            "model created\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_eClH25jEfc4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eef8b5e9-2cd0-49ee-ca41-ae32693aea3b"
      },
      "source": [
        "!pip install ibm_watson"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting ibm_watson\n",
            "  Downloading ibm-watson-5.2.3.tar.gz (406 kB)\n",
            "\u001b[?25l\r\u001b[K     |▉                               | 10 kB 21.7 MB/s eta 0:00:01\r\u001b[K     |█▋                              | 20 kB 28.8 MB/s eta 0:00:01\r\u001b[K     |██▍                             | 30 kB 12.7 MB/s eta 0:00:01\r\u001b[K     |███▏                            | 40 kB 9.4 MB/s eta 0:00:01\r\u001b[K     |████                            | 51 kB 5.5 MB/s eta 0:00:01\r\u001b[K     |████▉                           | 61 kB 6.0 MB/s eta 0:00:01\r\u001b[K     |█████▋                          | 71 kB 6.2 MB/s eta 0:00:01\r\u001b[K     |██████▍                         | 81 kB 6.6 MB/s eta 0:00:01\r\u001b[K     |███████▎                        | 92 kB 4.9 MB/s eta 0:00:01\r\u001b[K     |████████                        | 102 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████▉                       | 112 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████▋                      | 122 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████▌                     | 133 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████▎                    | 143 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████                    | 153 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████▉                   | 163 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████████▊                  | 174 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████████▌                 | 184 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████████▎                | 194 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████████                | 204 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████               | 215 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████▊              | 225 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████▌             | 235 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████▎            | 245 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████▏           | 256 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████████           | 266 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████████▊          | 276 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▌         | 286 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████████▍        | 296 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████████▏       | 307 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████       | 317 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▊      | 327 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▋     | 337 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▍    | 348 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▏   | 358 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████   | 368 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▉  | 378 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▋ | 389 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▍| 399 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 406 kB 5.4 MB/s \n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: python-dateutil>=2.5.3 in /usr/local/lib/python3.7/dist-packages (from ibm_watson) (2.8.2)\n",
            "Collecting ibm-cloud-sdk-core==3.*,>=3.3.6\n",
            "  Downloading ibm-cloud-sdk-core-3.11.3.tar.gz (45 kB)\n",
            "\u001b[K     |████████████████████████████████| 45 kB 3.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests<3.0,>=2.0 in /usr/local/lib/python3.7/dist-packages (from ibm_watson) (2.23.0)\n",
            "Collecting websocket-client==1.1.0\n",
            "  Downloading websocket_client-1.1.0-py2.py3-none-any.whl (68 kB)\n",
            "\u001b[K     |████████████████████████████████| 68 kB 7.0 MB/s \n",
            "\u001b[?25hCollecting PyJWT<3.0.0,>=2.0.1\n",
            "  Downloading PyJWT-2.1.0-py3-none-any.whl (16 kB)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.5.3->ibm_watson) (1.15.0)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0,>=2.0->ibm_watson) (2021.5.30)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0,>=2.0->ibm_watson) (1.24.3)\n",
            "Building wheels for collected packages: ibm-watson, ibm-cloud-sdk-core\n",
            "  Building wheel for ibm-watson (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ibm-watson: filename=ibm_watson-5.2.3-py3-none-any.whl size=403336 sha256=57b1024f909b4d609bb72679b9e97ef07b88e56a0891372df8d8f3ea3c643d41\n",
            "  Stored in directory: /root/.cache/pip/wheels/93/bc/4e/50facb1865141c8ee96092137b8e2f8495f04c1555f5ae5b36\n",
            "  Building wheel for ibm-cloud-sdk-core (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ibm-cloud-sdk-core: filename=ibm_cloud_sdk_core-3.11.3-py3-none-any.whl size=74751 sha256=998d7af65a5a7527918daf76e8363a5cb40d1d169cb83c3b6ac02d0c19b98464\n",
            "  Stored in directory: /root/.cache/pip/wheels/93/85/b2/7c2428617824a175293f7957d4a20b4975eb0ddbef52800006\n",
            "Successfully built ibm-watson ibm-cloud-sdk-core\n",
            "Installing collected packages: PyJWT, websocket-client, ibm-cloud-sdk-core, ibm-watson\n",
            "Successfully installed PyJWT-2.1.0 ibm-cloud-sdk-core-3.11.3 ibm-watson-5.2.3 websocket-client-1.1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OWBZbYdIEnVH"
      },
      "source": [
        "import anvil.server"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "283kbj3jFIu6"
      },
      "source": [
        "anvil.server.connect(uplink_key)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "INk-3PU0rqA0"
      },
      "source": [
        "import json\n",
        "from ibm_watson import ToneAnalyzerV3\n",
        "from ibm_cloud_sdk_core.authenticators import IAMAuthenticator"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yLZKraGLpUoq"
      },
      "source": [
        "msg = list()\n",
        "text = str()"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jY-qqSlY8ifi"
      },
      "source": [
        "@anvil.server.callable\n",
        "def responsed(msg1):\n",
        "    msg.append(msg1)\n",
        "    ints = predict_class(msg1)\n",
        "    res = getResponse(ints, intents)\n",
        "    return res"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "deibzvJ8WtXw"
      },
      "source": [
        "# # IBM Tone analyzer\n",
        "# import json\n",
        "# from ibm_watson import ToneAnalyzerV3\n",
        "# from ibm_cloud_sdk_core.authenticators import IAMAuthenticator\n",
        "\n",
        "# authenticator = IAMAuthenticator(api_key)\n",
        "# tone_analyzer = ToneAnalyzerV3(\n",
        "#     version='2017-09-21',\n",
        "#     authenticator=authenticator\n",
        "# )\n",
        "\n",
        "# tone_analyzer.set_service_url(https://api.eu-gb.tone-analyzer.watson.cloud.ibm.com/instances/api_key)\n",
        "# text = \" \"\n",
        "# for i in msg:\n",
        "#     text = text+i\n",
        "# tone_analysis = tone_analyzer.tone(\n",
        "#     {'text': text},\n",
        "#     content_type='application/json'\n",
        "# ).get_result()\n",
        "# print(json.dumps(tone_analysis, indent=2))"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "StHSsHWQk20Q"
      },
      "source": [
        "@anvil.server.callable\n",
        "def song_emotion():\n",
        "    authenticator = IAMAuthenticator(api_key)\n",
        "    tone_analyzer = ToneAnalyzerV3(\n",
        "        version='2017-09-21',\n",
        "        authenticator=authenticator\n",
        "    \n",
        "    )\n",
        "\n",
        "    tone_analyzer.set_service_url(https://api.eu-gb.tone-analyzer.watson.cloud.ibm.com/instances/api_key)\n",
        "    # text = \"\"\n",
        "    # for i in msg:\n",
        "    #     text = text+i\n",
        "    len1 = len(msg)\n",
        "    tone_analysis = tone_analyzer.tone(\n",
        "        {'text': msg[len1-1]+\" \"+msg[len1-2]+\" \"+msg[len1-3]+\" \"+msg[len1-4]+\" \"+msg[len1-5]},\n",
        "        content_type='application/json'\n",
        "    ).get_result()\n",
        "    dic1 = dict()\n",
        "    emotion=tone_analysis[\"document_tone\"][\"tones\"][0][\"tone_name\"]\n",
        "    dic1['emotion'] = emotion\n",
        "    import requests\n",
        "\n",
        "    url=f\"http://ws.audioscrobbler.com/2.0/?method=tag.gettoptracks&tag={emotion}&api_key={api_key}&format=json&limit=10\"\n",
        "    response = requests.get(url)\n",
        "    payload = response.json()\n",
        "    for i in range(10):\n",
        "        r=payload['tracks']['track'][i]\n",
        "        dic1[r['name']] = r['url']\n",
        "    return dic1"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zt38OzQXt_Lz"
      },
      "source": [
        "# print(msg)\n",
        "# # print(text)\n",
        "# print(len(msg))"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q18XH1RWpAXV"
      },
      "source": [
        "# SONG RECOMMENDATION"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8N4bfBmXnBkm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e876ab41-e148-438a-84e5-323b98a6793d"
      },
      "source": [
        "import requests\n",
        "\n",
        "url=f\"http://ws.audioscrobbler.com/2.0/?method=tag.gettoptracks&tag=happy&api_key={api_key}&format=json&limit=5\"\n",
        "response = requests.get(url)\n",
        "payload = response.json()\n",
        "# for i in range(4):\n",
        "r=payload['tracks']['track'][0]\n",
        "# print(r['url'])\n",
        "print(payload)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'tracks': {'track': [{'name': 'Pumped Up Kicks', 'duration': '236', 'mbid': '816b3284-5f24-4f3a-9554-750e0bf5d060', 'url': 'https://www.last.fm/music/Foster+the+People/_/Pumped+Up+Kicks', 'streamable': {'#text': '0', 'fulltrack': '0'}, 'artist': {'name': 'Foster the People', 'mbid': 'e0e1a584-dd0a-4bd1-88d1-c4c62895039d', 'url': 'https://www.last.fm/music/Foster+the+People'}, 'image': [{'#text': 'https://lastfm.freetls.fastly.net/i/u/34s/2a96cbd8b46e442fc41c2b86b821562f.png', 'size': 'small'}, {'#text': 'https://lastfm.freetls.fastly.net/i/u/64s/2a96cbd8b46e442fc41c2b86b821562f.png', 'size': 'medium'}, {'#text': 'https://lastfm.freetls.fastly.net/i/u/174s/2a96cbd8b46e442fc41c2b86b821562f.png', 'size': 'large'}, {'#text': 'https://lastfm.freetls.fastly.net/i/u/300x300/2a96cbd8b46e442fc41c2b86b821562f.png', 'size': 'extralarge'}], '@attr': {'rank': '1'}}, {'name': \"I'm Yours\", 'duration': '242', 'mbid': 'a5a2330e-2fff-4601-a715-6e68a8e98fbf', 'url': 'https://www.last.fm/music/Jason+Mraz/_/I%27m+Yours', 'streamable': {'#text': '0', 'fulltrack': '0'}, 'artist': {'name': 'Jason Mraz', 'mbid': '82eb8936-7bf6-4577-8320-a2639465206d', 'url': 'https://www.last.fm/music/Jason+Mraz'}, 'image': [{'#text': 'https://lastfm.freetls.fastly.net/i/u/34s/2a96cbd8b46e442fc41c2b86b821562f.png', 'size': 'small'}, {'#text': 'https://lastfm.freetls.fastly.net/i/u/64s/2a96cbd8b46e442fc41c2b86b821562f.png', 'size': 'medium'}, {'#text': 'https://lastfm.freetls.fastly.net/i/u/174s/2a96cbd8b46e442fc41c2b86b821562f.png', 'size': 'large'}, {'#text': 'https://lastfm.freetls.fastly.net/i/u/300x300/2a96cbd8b46e442fc41c2b86b821562f.png', 'size': 'extralarge'}], '@attr': {'rank': '2'}}, {'name': 'Dog Days Are Over', 'duration': '270', 'mbid': '772ea437-45b3-4868-b3c0-b80526ef0fa3', 'url': 'https://www.last.fm/music/Florence+%252B+the+Machine/_/Dog+Days+Are+Over', 'streamable': {'#text': '0', 'fulltrack': '0'}, 'artist': {'name': 'Florence + the Machine', 'mbid': '5fee3020-513b-48c2-b1f7-4681b01db0c6', 'url': 'https://www.last.fm/music/Florence+%252B+the+Machine'}, 'image': [{'#text': 'https://lastfm.freetls.fastly.net/i/u/34s/2a96cbd8b46e442fc41c2b86b821562f.png', 'size': 'small'}, {'#text': 'https://lastfm.freetls.fastly.net/i/u/64s/2a96cbd8b46e442fc41c2b86b821562f.png', 'size': 'medium'}, {'#text': 'https://lastfm.freetls.fastly.net/i/u/174s/2a96cbd8b46e442fc41c2b86b821562f.png', 'size': 'large'}, {'#text': 'https://lastfm.freetls.fastly.net/i/u/300x300/2a96cbd8b46e442fc41c2b86b821562f.png', 'size': 'extralarge'}], '@attr': {'rank': '3'}}, {'name': 'A-Punk', 'duration': '136', 'mbid': '047db898-cedf-48a0-afc2-5b686c9b8840', 'url': 'https://www.last.fm/music/Vampire+Weekend/_/A-Punk', 'streamable': {'#text': '0', 'fulltrack': '0'}, 'artist': {'name': 'Vampire Weekend', 'mbid': 'af37c51c-0790-4a29-b995-456f98a6b8c9', 'url': 'https://www.last.fm/music/Vampire+Weekend'}, 'image': [{'#text': 'https://lastfm.freetls.fastly.net/i/u/34s/2a96cbd8b46e442fc41c2b86b821562f.png', 'size': 'small'}, {'#text': 'https://lastfm.freetls.fastly.net/i/u/64s/2a96cbd8b46e442fc41c2b86b821562f.png', 'size': 'medium'}, {'#text': 'https://lastfm.freetls.fastly.net/i/u/174s/2a96cbd8b46e442fc41c2b86b821562f.png', 'size': 'large'}, {'#text': 'https://lastfm.freetls.fastly.net/i/u/300x300/2a96cbd8b46e442fc41c2b86b821562f.png', 'size': 'extralarge'}], '@attr': {'rank': '4'}}, {'name': \"Friday I'm in Love\", 'duration': '214', 'mbid': '8d9104c6-7d8e-460b-8c3d-2d797f79953d', 'url': 'https://www.last.fm/music/The+Cure/_/Friday+I%27m+in+Love', 'streamable': {'#text': '0', 'fulltrack': '0'}, 'artist': {'name': 'The Cure', 'mbid': '69ee3720-a7cb-4402-b48d-a02c366f2bcf', 'url': 'https://www.last.fm/music/The+Cure'}, 'image': [{'#text': 'https://lastfm.freetls.fastly.net/i/u/34s/2a96cbd8b46e442fc41c2b86b821562f.png', 'size': 'small'}, {'#text': 'https://lastfm.freetls.fastly.net/i/u/64s/2a96cbd8b46e442fc41c2b86b821562f.png', 'size': 'medium'}, {'#text': 'https://lastfm.freetls.fastly.net/i/u/174s/2a96cbd8b46e442fc41c2b86b821562f.png', 'size': 'large'}, {'#text': 'https://lastfm.freetls.fastly.net/i/u/300x300/2a96cbd8b46e442fc41c2b86b821562f.png', 'size': 'extralarge'}], '@attr': {'rank': '5'}}], '@attr': {'tag': 'happy', 'page': '1', 'perPage': '5', 'totalPages': '4502', 'total': '22510'}}}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zio9sPymm7kF"
      },
      "source": [
        "# print(dic1)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZCECIQiBEDpr",
        "outputId": "96c9b4c4-2384-4635-e679-77859a1688e9"
      },
      "source": [
        "print(\"Chatbot : Hey there, Wassup ?\")\n",
        "# responded function takes text of user and returns chatbot output\n",
        "for i in range(5):\n",
        "    m = input(\"User : \")\n",
        "    res = responsed(m)\n",
        "    print(\"Chatbot : \"+res)\n",
        "ans = song_emotion()\n",
        "print(\"Emotion : \"+ans['emotion'])"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Chatbot : Hey there, Wassup ?\n",
            "User : Today is my birthday, what is your age?\n",
            "Chatbot : I'm a robot I dont have an age...\n",
            "User : oh! I am in a good mood today, how are you\n",
            "Chatbot : I've never been better, how are you?\n",
            "User : I am good, you are nice\n",
            "Chatbot : Youre pretty cool yourself!\n",
            "User : Thank you, what is your name\n",
            "Chatbot : I dont have a name yet but I was thinking maybe Alex. That has a nice ring to it dont you think?\n",
            "User : yes\n",
            "Chatbot : Yep, I like how it sounds. I got it from the Terminator.\n",
            "Emotion : Joy\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O4yla2WTHiGz",
        "outputId": "515ace4c-c65e-4532-b687-61bce13656da"
      },
      "source": [
        "# song_emotion function would return dictionary consisting of emotion and recommended songs\n",
        "ans = song_emotion()\n",
        "print(\"Emotion : \"+ans['emotion'])\n",
        "ans.pop('emotion')\n",
        "lst = list(ans.keys())\n",
        "print(\"Song Recommendations : \")\n",
        "for i in range(10):\n",
        "    print(\"Song_name : \"+lst[i])\n",
        "    print(\"Song_URL : \"+ans[lst[i]])\n"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Emotion : Joy\n",
            "Song Recommendations : \n",
            "Song_name : Butter\n",
            "Song_URL : https://www.last.fm/music/BTS/_/Butter\n",
            "Song_name : You And Your Heart\n",
            "Song_URL : https://www.last.fm/music/Jack+Johnson/_/You+And+Your+Heart\n",
            "Song_name : Yoshimi Battles The Pink Robots Part 1\n",
            "Song_URL : https://www.last.fm/music/The+Flaming+Lips/_/Yoshimi+Battles+The+Pink+Robots+Part+1\n",
            "Song_name : Come on! Feel the Illinoise!\n",
            "Song_URL : https://www.last.fm/music/Sufjan+Stevens/_/Come+on%21+Feel+the+Illinoise%21\n",
            "Song_name : praise you - radio edit\n",
            "Song_URL : https://www.last.fm/music/Fatboy+Slim/_/praise+you+-+radio+edit\n",
            "Song_name : Let's Go\n",
            "Song_URL : https://www.last.fm/music/Matt+&+Kim/_/Let%27s+Go\n",
            "Song_name : Old Old Fashioned\n",
            "Song_URL : https://www.last.fm/music/Frightened+Rabbit/_/Old+Old+Fashioned\n",
            "Song_name : Lake Shore Drive\n",
            "Song_URL : https://www.last.fm/music/Aliotta+Haynes+Jeremiah/_/Lake+Shore+Drive\n",
            "Song_name : My Little Town\n",
            "Song_URL : https://www.last.fm/music/Simon+&+Garfunkel/_/My+Little+Town\n",
            "Song_name : Esa Noche\n",
            "Song_URL : https://www.last.fm/music/Caf%C3%A9+Tacvba/_/Esa+Noche\n"
          ]
        }
      ]
    }
  ]
}