chatbot.ipynb

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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q SpeechRecognition"
      ],
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        },
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          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m32.8/32.8 MB\u001b[0m \u001b[31m37.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
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    {
      "cell_type": "code",
      "source": [
        "#impport library\n",
        "import json\n",
        "import nltk\n",
        "import time\n",
        "import random\n",
        "import string\n",
        "import pickle\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from gtts import  gTTS\n",
        "from io import BytesIO\n",
        "import tensorflow as tf\n",
        "import IPython.display as ipd\n",
        "import speech_recognition as sr \n",
        "import matplotlib.pyplot as plt\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from tensorflow.keras.models import Model\n",
        "from keras.utils.vis_utils import plot_model\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.layers import Input, Embedding, LSTM\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.layers import Flatten, Dense, GlobalMaxPool1D"
      ],
      "metadata": {
        "id": "AVIho8DWJ6oN"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#package tokenizer\n",
        "nltk.download('punkt')\n",
        "#package Lemmatization\n",
        "nltk.download('wordnet')\n",
        "#package multigual wordnet data\n",
        "nltk.download('omw-1.4')"
      ],
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        },
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      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n",
            "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
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          "metadata": {},
          "execution_count": 13
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    {
      "cell_type": "code",
      "source": [
        "# Importing the dataset\n",
        "with open('/content/drive/MyDrive/Dataset/anotasi/kb.json') as content:\n",
        "  data1 = json.load(content)\n",
        "\n",
        "# Mendapatkan semua data ke dalam list\n",
        "tags = [] # data tag\n",
        "inputs = [] # data input atau pattern\n",
        "responses = {} # data respon\n",
        "words = [] # Data kata \n",
        "classes = [] # Data Kelas atau Tag\n",
        "documents = [] # Data Kalimat Dokumen\n",
        "ignore_words = ['?', '!'] # Mengabaikan tanda spesial karakter\n",
        "\n",
        "for intent in data1['intents']:\n",
        "  responses[intent['tag']]=intent['responses']\n",
        "  for lines in intent['patterns']:\n",
        "    inputs.append(lines)\n",
        "    tags.append(intent['tag'])\n",
        "    for pattern in intent['patterns']:\n",
        "      w = nltk.word_tokenize(pattern)\n",
        "      words.extend(w)\n",
        "      documents.append((w, intent['tag']))\n",
        "      # add to our classes list\n",
        "      if intent['tag'] not in classes:\n",
        "        classes.append(intent['tag'])\n",
        "\n",
        "# Konversi data json ke dalam dataframe\n",
        "data = pd.DataFrame({\"patterns\":inputs, \"tags\":tags})"
      ],
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        "id": "9TiF1sHXL2Rl"
      },
      "execution_count": 14,
      "outputs": []
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      "source": [
        "data.head()"
      ],
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              "  patterns      tags\n",
              "0    hello  greeting\n",
              "1      hai  greeting\n",
              "2     helo  greeting\n",
              "3       hi  greeting\n",
              "4       hy  greeting"
            ],
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              "      <th></th>\n",
              "      <th>patterns</th>\n",
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              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>hello</td>\n",
              "      <td>greeting</td>\n",
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Removing Punctuations (Menghilangkan Punktuasi)\n",
        "data['patterns'] = data['patterns'].apply(lambda wrd:[ltrs.lower() for ltrs in wrd if ltrs not in string.punctuation])\n",
        "data['patterns'] = data['patterns'].apply(lambda wrd: ''.join(wrd))"
      ],
      "metadata": {
        "id": "N2A6Y8bwMNAr"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lemmatizer = WordNetLemmatizer()\n",
        "words = [lemmatizer.lemmatize(w.lower()) for w in words if w not in ignore_words]\n",
        "words = sorted(list(set(words)))\n",
        "\n",
        "print (len(words), \"unique lemmatized words\", words)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WAtp-hIzMdk9",
        "outputId": "f39528e9-540f-45fa-a567-0b172328bc24"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9 unique lemmatized words ['apa', 'hai', 'hello', 'helo', 'hi', 'hy', 'itu', 'panbot', 'siapa']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# sort classes\n",
        "classes = sorted(list(set(classes)))\n",
        "print (len(classes), \"classes\", classes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y6hdz9EQMlsY",
        "outputId": "0c677532-c75d-497a-a5d7-53c0e6fdbd41"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2 classes ['greeting', 'panbot']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# documents = combination between patterns and intents\n",
        "print (len(documents), \"documents\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r21gVw6gMhIo",
        "outputId": "09e5b2d4-e1f1-416d-b684-60fa52a53f84"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "29 documents\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Tokenize the data (Tokenisasi Data)\n",
        "tokenizer = Tokenizer(num_words=2000)\n",
        "tokenizer.fit_on_texts(data['patterns'])\n",
        "train = tokenizer.texts_to_sequences(data['patterns'])\n",
        "train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OyUlZbpTMsgu",
        "outputId": "7de8eacd-1428-445d-c25f-dc11f8f3cf44"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[2], [3], [4], [5], [6], [7, 8, 1], [9, 1]]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Apply padding \n",
        "x_train = pad_sequences(train)\n",
        "print(x_train) # Padding Sequences"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E3C5c64hMviG",
        "outputId": "afccd1ea-cba3-4ec7-8fce-52ac7aa2474c"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0 0 2]\n",
            " [0 0 3]\n",
            " [0 0 4]\n",
            " [0 0 5]\n",
            " [0 0 6]\n",
            " [7 8 1]\n",
            " [0 9 1]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Encoding the outputs \n",
        "le = LabelEncoder()\n",
        "y_train = le.fit_transform(data['tags'])\n",
        "print(y_train) #Label Encodings"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "22944h7uM54a",
        "outputId": "47043305-3d5a-4f4a-909a-69559d15abc9"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 0 0 0 0 1 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# input length\n",
        "input_shape = x_train.shape[1]\n",
        "print(input_shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tacMYEKLM9c-",
        "outputId": "359a00ae-7a87-410e-f0ba-6f83244a9868"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# define vocabulary\n",
        "vocabulary = len(tokenizer.word_index)\n",
        "print(\"number of unique words : \", vocabulary)\n",
        "\n",
        "# output length\n",
        "output_length = le.classes_.shape[0]\n",
        "print(\"output length: \", output_length)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NizIOt5fNA1J",
        "outputId": "877ec59b-8a5d-461f-eb28-c0a506a4d920"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of unique words :  9\n",
            "output length:  2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "pickle.dump(words, open('/content/drive/MyDrive/Dataset/model/words.pkl','wb'))\n",
        "pickle.dump(classes, open('/content/drive/MyDrive/Dataset/model/classes.pkl','wb'))"
      ],
      "metadata": {
        "id": "0fX8UmmaNDm0"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pickle.dump(le, open('/content/drive/MyDrive/Dataset/model/le.pkl','wb'))\n",
        "pickle.dump(tokenizer, open('/content/drive/MyDrive/Dataset/model/tokenizers.pkl','wb'))\n"
      ],
      "metadata": {
        "id": "WNS8yeTvO-Ns"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Neural Network Model"
      ],
      "metadata": {
        "id": "CvwHspmZPLTp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating the model (Membuat Modeling)\n",
        "i = Input(shape=(input_shape,))\n",
        "x = Embedding(vocabulary+1,10)(i) # Layer Embedding\n",
        "x = LSTM(10, return_sequences=True)(x) # Layer Long Short Term Memory\n",
        "x = Flatten()(x) # Layer Flatten\n",
        "x = Dense(output_length, activation=\"softmax\")(x) # Layer Dense\n",
        "model  = Model(i,x)\n",
        "\n",
        "# Compiling the model (Kompilasi Model)\n",
        "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer='adam', metrics=['accuracy'])\n"
      ],
      "metadata": {
        "id": "TjQOgbdwPJAd"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualization Plot Architecture Model (Visualisasi Plot Arsitektur Model)\n",
        "plot_model(model, to_file='/content/model_plot.png', show_shapes=True, show_layer_names=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "id": "41iu5ubnPWes",
        "outputId": "40e7b636-2775-4119-93af-02e0161619f7"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1UFNnySlP1Xi",
        "outputId": "05ad008c-85db-4a58-bda1-e18c09d83d4b"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 3)]               0         \n",
            "                                                                 \n",
            " embedding (Embedding)       (None, 3, 10)             100       \n",
            "                                                                 \n",
            " lstm (LSTM)                 (None, 3, 10)             840       \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 30)                0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 2)                 62        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,002\n",
            "Trainable params: 1,002\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Training the model (Latih model data sampai 300 kali)\n",
        "train = model.fit(x_train, y_train, epochs=300)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eujG3gN5P40f",
        "outputId": "385567a7-6510-4295-9af5-8887d9b2599b"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/300\n",
            "1/1 [==============================] - 0s 21ms/step - loss: 0.0024 - accuracy: 1.0000\n",
            "Epoch 2/300\n",
            "1/1 [==============================] - 0s 18ms/step - loss: 0.0024 - accuracy: 1.0000\n",
            "Epoch 3/300\n",
            "1/1 [==============================] - 0s 19ms/step - loss: 0.0024 - accuracy: 1.0000\n",
            "Epoch 4/300\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 0.0024 - accuracy: 1.0000\n",
            "Epoch 5/300\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 0.0023 - accuracy: 1.0000\n",
            "Epoch 6/300\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.0023 - accuracy: 1.0000\n",
            "Epoch 7/300\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.0023 - accuracy: 1.0000\n",
            "Epoch 8/300\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 0.0023 - accuracy: 1.0000\n",
            "Epoch 9/300\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.0023 - accuracy: 1.0000\n",
            "Epoch 10/300\n",
            "1/1 [==============================] - 0s 18ms/step - loss: 0.0023 - accuracy: 1.0000\n",
            "Epoch 11/300\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 0.0023 - accuracy: 1.0000\n",
            "Epoch 12/300\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 0.0023 - accuracy: 1.0000\n",
            "Epoch 13/300\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.0023 - accuracy: 1.0000\n",
            "Epoch 14/300\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.0022 - accuracy: 1.0000\n",
            "Epoch 15/300\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 0.0022 - accuracy: 1.0000\n",
            "Epoch 16/300\n",
            "1/1 [==============================] - 0s 19ms/step - loss: 0.0022 - accuracy: 1.0000\n",
            "Epoch 17/300\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 0.0022 - accuracy: 1.0000\n",
            "Epoch 18/300\n",
            "1/1 [==============================] - 0s 18ms/step - loss: 0.0022 - accuracy: 1.0000\n",
            "Epoch 19/300\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 0.0022 - accuracy: 1.0000\n",
            "Epoch 20/300\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 0.0022 - accuracy: 1.0000\n",
            "Epoch 21/300\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 0.0022 - accuracy: 1.0000\n",
            "Epoch 22/300\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 0.0022 - accuracy: 1.0000\n",
            "Epoch 23/300\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.0022 - accuracy: 1.0000\n",
            "Epoch 24/300\n",
            "1/1 [==============================] - 0s 18ms/step - loss: 0.0021 - accuracy: 1.0000\n",
            "Epoch 25/300\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.0021 - accuracy: 1.0000\n",
            "Epoch 26/300\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.0021 - accuracy: 1.0000\n",
            "Epoch 27/300\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.0021 - accuracy: 1.0000\n",
            "Epoch 28/300\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.0021 - accuracy: 1.0000\n",
            "Epoch 29/300\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 0.0021 - accuracy: 1.0000\n",
            "Epoch 30/300\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.0021 - accuracy: 1.0000\n",
            "Epoch 31/300\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 0.0021 - accuracy: 1.0000\n",
            "Epoch 32/300\n",
            "1/1 [==============================] - 0s 22ms/step - loss: 0.0021 - accuracy: 1.0000\n",
            "Epoch 33/300\n",
            "1/1 [==============================] - 0s 21ms/step - loss: 0.0021 - accuracy: 1.0000\n",
            "Epoch 34/300\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.0020 - accuracy: 1.0000\n",
            "Epoch 35/300\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.0020 - accuracy: 1.0000\n",
            "Epoch 36/300\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 0.0020 - accuracy: 1.0000\n",
            "Epoch 37/300\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 0.0020 - accuracy: 1.0000\n",
            "Epoch 38/300\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 0.0020 - accuracy: 1.0000\n",
            "Epoch 39/300\n",
            "1/1 [==============================] - 0s 20ms/step - loss: 0.0020 - accuracy: 1.0000\n",
            "Epoch 40/300\n",
            "1/1 [==============================] - 0s 19ms/step - loss: 0.0020 - accuracy: 1.0000\n",
            "Epoch 41/300\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 0.0020 - accuracy: 1.0000\n",
            "Epoch 42/300\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.0020 - accuracy: 1.0000\n",
            "Epoch 43/300\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 0.0020 - accuracy: 1.0000\n",
            "Epoch 44/300\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 0.0020 - accuracy: 1.0000\n",
            "Epoch 45/300\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 0.0020 - accuracy: 1.0000\n",
            "Epoch 46/300\n",
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            "1/1 [==============================] - 0s 15ms/step - loss: 9.8973e-04 - accuracy: 1.0000\n",
            "Epoch 230/300\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 9.8663e-04 - accuracy: 1.0000\n",
            "Epoch 231/300\n",
            "1/1 [==============================] - 0s 18ms/step - loss: 9.8359e-04 - accuracy: 1.0000\n",
            "Epoch 232/300\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 9.8055e-04 - accuracy: 1.0000\n",
            "Epoch 233/300\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 9.7751e-04 - accuracy: 1.0000\n",
            "Epoch 234/300\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 9.7448e-04 - accuracy: 1.0000\n",
            "Epoch 235/300\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 9.7151e-04 - accuracy: 1.0000\n",
            "Epoch 236/300\n",
            "1/1 [==============================] - 0s 18ms/step - loss: 9.6847e-04 - accuracy: 1.0000\n",
            "Epoch 237/300\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 9.6551e-04 - accuracy: 1.0000\n",
            "Epoch 238/300\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 9.6255e-04 - accuracy: 1.0000\n",
            "Epoch 239/300\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 9.5961e-04 - accuracy: 1.0000\n",
            "Epoch 240/300\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 9.5667e-04 - accuracy: 1.0000\n",
            "Epoch 241/300\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 9.5377e-04 - accuracy: 1.0000\n",
            "Epoch 242/300\n",
            "1/1 [==============================] - 0s 19ms/step - loss: 9.5083e-04 - accuracy: 1.0000\n",
            "Epoch 243/300\n",
            "1/1 [==============================] - 0s 19ms/step - loss: 9.4796e-04 - accuracy: 1.0000\n",
            "Epoch 244/300\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 9.4507e-04 - accuracy: 1.0000\n",
            "Epoch 245/300\n",
            "1/1 [==============================] - 0s 19ms/step - loss: 9.4220e-04 - accuracy: 1.0000\n",
            "Epoch 246/300\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 9.3934e-04 - accuracy: 1.0000\n",
            "Epoch 247/300\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 9.3649e-04 - accuracy: 1.0000\n",
            "Epoch 248/300\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 9.3365e-04 - accuracy: 1.0000\n",
            "Epoch 249/300\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 9.3084e-04 - accuracy: 1.0000\n",
            "Epoch 250/300\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 9.2804e-04 - accuracy: 1.0000\n",
            "Epoch 251/300\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 9.2525e-04 - accuracy: 1.0000\n",
            "Epoch 252/300\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 9.2246e-04 - accuracy: 1.0000\n",
            "Epoch 253/300\n",
            "1/1 [==============================] - 0s 18ms/step - loss: 9.1971e-04 - accuracy: 1.0000\n",
            "Epoch 254/300\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 9.1696e-04 - accuracy: 1.0000\n",
            "Epoch 255/300\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 9.1420e-04 - accuracy: 1.0000\n",
            "Epoch 256/300\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 9.1150e-04 - accuracy: 1.0000\n",
            "Epoch 257/300\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 9.0877e-04 - accuracy: 1.0000\n",
            "Epoch 258/300\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 9.0606e-04 - accuracy: 1.0000\n",
            "Epoch 259/300\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 9.0338e-04 - accuracy: 1.0000\n",
            "Epoch 260/300\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 9.0066e-04 - accuracy: 1.0000\n",
            "Epoch 261/300\n",
            "1/1 [==============================] - 0s 18ms/step - loss: 8.9802e-04 - accuracy: 1.0000\n",
            "Epoch 262/300\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 8.9536e-04 - accuracy: 1.0000\n",
            "Epoch 263/300\n",
            "1/1 [==============================] - 0s 19ms/step - loss: 8.9270e-04 - accuracy: 1.0000\n",
            "Epoch 264/300\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 8.9009e-04 - accuracy: 1.0000\n",
            "Epoch 265/300\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 8.8745e-04 - accuracy: 1.0000\n",
            "Epoch 266/300\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 8.8487e-04 - accuracy: 1.0000\n",
            "Epoch 267/300\n",
            "1/1 [==============================] - 0s 18ms/step - loss: 8.8227e-04 - accuracy: 1.0000\n",
            "Epoch 268/300\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 8.7969e-04 - accuracy: 1.0000\n",
            "Epoch 269/300\n",
            "1/1 [==============================] - 0s 19ms/step - loss: 8.7708e-04 - accuracy: 1.0000\n",
            "Epoch 270/300\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 8.7452e-04 - accuracy: 1.0000\n",
            "Epoch 271/300\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 8.7197e-04 - accuracy: 1.0000\n",
            "Epoch 272/300\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 8.6944e-04 - accuracy: 1.0000\n",
            "Epoch 273/300\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 8.6687e-04 - accuracy: 1.0000\n",
            "Epoch 274/300\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 8.6437e-04 - accuracy: 1.0000\n",
            "Epoch 275/300\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 8.6186e-04 - accuracy: 1.0000\n",
            "Epoch 276/300\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 8.5937e-04 - accuracy: 1.0000\n",
            "Epoch 277/300\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 8.5687e-04 - accuracy: 1.0000\n",
            "Epoch 278/300\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 8.5443e-04 - accuracy: 1.0000\n",
            "Epoch 279/300\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 8.5195e-04 - accuracy: 1.0000\n",
            "Epoch 280/300\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 8.4951e-04 - accuracy: 1.0000\n",
            "Epoch 281/300\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 8.4703e-04 - accuracy: 1.0000\n",
            "Epoch 282/300\n",
            "1/1 [==============================] - 0s 19ms/step - loss: 8.4460e-04 - accuracy: 1.0000\n",
            "Epoch 283/300\n",
            "1/1 [==============================] - 0s 18ms/step - loss: 8.4221e-04 - accuracy: 1.0000\n",
            "Epoch 284/300\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 8.3977e-04 - accuracy: 1.0000\n",
            "Epoch 285/300\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 8.3736e-04 - accuracy: 1.0000\n",
            "Epoch 286/300\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 8.3498e-04 - accuracy: 1.0000\n",
            "Epoch 287/300\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 8.3257e-04 - accuracy: 1.0000\n",
            "Epoch 288/300\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 8.3024e-04 - accuracy: 1.0000\n",
            "Epoch 289/300\n",
            "1/1 [==============================] - 0s 18ms/step - loss: 8.2784e-04 - accuracy: 1.0000\n",
            "Epoch 290/300\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 8.2553e-04 - accuracy: 1.0000\n",
            "Epoch 291/300\n",
            "1/1 [==============================] - 0s 18ms/step - loss: 8.2318e-04 - accuracy: 1.0000\n",
            "Epoch 292/300\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 8.2085e-04 - accuracy: 1.0000\n",
            "Epoch 293/300\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 8.1851e-04 - accuracy: 1.0000\n",
            "Epoch 294/300\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 8.1621e-04 - accuracy: 1.0000\n",
            "Epoch 295/300\n",
            "1/1 [==============================] - 0s 19ms/step - loss: 8.1389e-04 - accuracy: 1.0000\n",
            "Epoch 296/300\n",
            "1/1 [==============================] - 0s 18ms/step - loss: 8.1162e-04 - accuracy: 1.0000\n",
            "Epoch 297/300\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 8.0933e-04 - accuracy: 1.0000\n",
            "Epoch 298/300\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 8.0705e-04 - accuracy: 1.0000\n",
            "Epoch 299/300\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 8.0479e-04 - accuracy: 1.0000\n",
            "Epoch 300/300\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 8.0253e-04 - accuracy: 1.0000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model Analyst"
      ],
      "metadata": {
        "id": "pIFTYYNfQHcy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting model Accuracy and Loss (Visualisasi Plot Hasil Akurasi dan Loss)\n",
        "# Plot Akurasi\n",
        "plt.figure(figsize=(14, 5))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(train.history['accuracy'],label='Training Set Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('Accuracy')\n",
        "# Plot Loss\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(train.history['loss'],label='Training Set Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Loss')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "NG1sWFBrQB1r",
        "outputId": "1bcacc14-91f6-44c0-8f8d-06053b37e97d"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Testing chatbot"
      ],
      "metadata": {
        "id": "Wi2vTVDdQhMq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Membuat Input Chat\n",
        "while True:\n",
        "  texts_p = []\n",
        "  prediction_input = input('👨‍🦰 Kamu : ')\n",
        "  \n",
        "  # Menghapus punktuasi dan konversi ke huruf kecil\n",
        "  prediction_input = [letters.lower() for letters in prediction_input if letters not in string.punctuation]\n",
        "  prediction_input = ''.join(prediction_input)\n",
        "  texts_p.append(prediction_input)\n",
        "\n",
        "  # Tokenisasi dan Padding\n",
        "  prediction_input = tokenizer.texts_to_sequences(texts_p)\n",
        "  prediction_input = np.array(prediction_input).reshape(-1)\n",
        "  prediction_input = pad_sequences([prediction_input],input_shape)\n",
        "\n",
        "  # Mendapatkan hasil keluaran pada model \n",
        "  output = model.predict(prediction_input)\n",
        "  output = output.argmax()\n",
        "\n",
        "  # Menemukan respon sesuai data tag dan memainkan voice bot\n",
        "  response_tag = le.inverse_transform([output])[0]\n",
        "  print(\"🤖 PanBot : \", random.choice(responses[response_tag]))\n",
        "  tts = gTTS(random.choice(responses[response_tag]), lang='id')\n",
        "  # Simpan model voice bot ke dalam Google Drive\n",
        "  tts.save('/content/drive/MyDrive/Dataset/model/KadekBot.wav')\n",
        "  time.sleep(0.08)\n",
        "  # Load model voice bot from Google Drive\n",
        "  ipd.display(ipd.Audio('/content/drive/MyDrive/Dataset/model/KadekBot.wav', autoplay=False))\n",
        "  print(\"=\"*60 + \"\\n\")\n",
        "  # Tambahkan respon 'goodbye' agar bot bisa berhenti\n",
        "  if response_tag == \"goodbye\":\n",
        "    break"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "meEmkxdeQefJ",
        "outputId": "b2a21b0c-28e9-49fd-ef81-220d332590a1"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "👨‍🦰 Kamu : hy\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "🤖 PanBot :  Hallo Panbot datang, apa kabarmu hari ini ?\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ],
            "text/html": [
              "\n",
              "                <audio  controls=\"controls\" >\n",
              "                    <source 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\" type=\"audio/x-wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "============================================================\n",
            "\n",
            "👨‍🦰 Kamu : googbye\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "🤖 PanBot :  Hai! Panbot disini , mau tanya  tentang apa nih ?\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ],
            "text/html": [
              "\n",
              "                <audio  controls=\"controls\" >\n",
              "                    <source 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\" type=\"audio/x-wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "============================================================\n",
            "\n",
            "👨‍🦰 Kamu : goodbye\n",
            "1/1 [==============================] - 0s 55ms/step\n",
            "🤖 PanBot :  Hallo Panbot datang, apa kabarmu hari ini ?\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ],
            "text/html": [
              "\n",
              "                <audio  controls=\"controls\" >\n",
              "                    <source 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\" type=\"audio/x-wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "============================================================\n",
            "\n",
            "👨‍🦰 Kamu : google itu apa \n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "🤖 PanBot :  panbot adalah platfrom chatbot untuk meminta informasi\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ],
            "text/html": [
              "\n",
              "                <audio  controls=\"controls\" >\n",
              "                    <source 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\" type=\"audio/x-wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "============================================================\n",
            "\n",
            "👨‍🦰 Kamu : bye\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "🤖 PanBot :  Hallo Panbot datang, apa kabarmu hari ini ?\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ],
            "text/html": [
              "\n",
              "                <audio  controls=\"controls\" >\n",
              "                    <source 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\" type=\"audio/x-wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "============================================================\n",
            "\n",
            "👨‍🦰 Kamu : goodbye\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "🤖 PanBot :  Hai! Panbot disini , mau tanya  tentang apa nih ?\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ],
            "text/html": [
              "\n",
              "                <audio  controls=\"controls\" >\n",
              "                    <source 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type=\"audio/x-wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "============================================================\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "ERROR:root:Internal Python error in the inspect module.\n",
            "Below is the traceback from this internal error.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3553, in run_code\n",
            "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
            "  File \"<ipython-input-46-171dfbbc1c3b>\", line 4, in <cell line: 2>\n",
            "    prediction_input = input('👨‍🦰 Kamu : ')\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\", line 851, in raw_input\n",
            "    return self._input_request(str(prompt),\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\", line 895, in _input_request\n",
            "    raise KeyboardInterrupt(\"Interrupted by user\") from None\n",
            "KeyboardInterrupt: Interrupted by user\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 2099, in showtraceback\n",
            "    stb = value._render_traceback_()\n",
            "AttributeError: 'KeyboardInterrupt' object has no attribute '_render_traceback_'\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 1101, in get_records\n",
            "    return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 248, in wrapped\n",
            "    return f(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 281, in _fixed_getinnerframes\n",
            "    records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n",
            "  File \"/usr/lib/python3.10/inspect.py\", line 1662, in getinnerframes\n",
            "    frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n",
            "  File \"/usr/lib/python3.10/inspect.py\", line 1624, in getframeinfo\n",
            "    lines, lnum = findsource(frame)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 170, in findsource\n",
            "    file = getsourcefile(object) or getfile(object)\n",
            "  File \"/usr/lib/python3.10/inspect.py\", line 829, in getsourcefile\n",
            "    module = getmodule(object, filename)\n",
            "  File \"/usr/lib/python3.10/inspect.py\", line 869, in getmodule\n",
            "    if ismodule(module) and hasattr(module, '__file__'):\n",
            "KeyboardInterrupt\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "    \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n",
            "\u001b[0;32m<ipython-input-46-171dfbbc1c3b>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m   \u001b[0mtexts_p\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m   \u001b[0mprediction_input\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'👨‍🦰 Kamu : '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36mraw_input\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m    850\u001b[0m             )\n\u001b[0;32m--> 851\u001b[0;31m         return self._input_request(str(prompt),\n\u001b[0m\u001b[1;32m    852\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_ident\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m    894\u001b[0m                 \u001b[0;31m# re-raise KeyboardInterrupt, to truncate traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 895\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Interrupted by user\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    896\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: Interrupted by user",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mshowtraceback\u001b[0;34m(self, exc_tuple, filename, tb_offset, exception_only, running_compiled_code)\u001b[0m\n\u001b[1;32m   2098\u001b[0m                         \u001b[0;31m# in the engines. This should return a list of strings.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2099\u001b[0;31m                         \u001b[0mstb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_render_traceback_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2100\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'KeyboardInterrupt' object has no attribute '_render_traceback_'",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "    \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mshowtraceback\u001b[0;34m(self, exc_tuple, filename, tb_offset, exception_only, running_compiled_code)\u001b[0m\n\u001b[1;32m   2099\u001b[0m                         \u001b[0mstb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_render_traceback_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2100\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2101\u001b[0;31m                         stb = self.InteractiveTB.structured_traceback(etype,\n\u001b[0m\u001b[1;32m   2102\u001b[0m                                             value, tb, tb_offset=tb_offset)\n\u001b[1;32m   2103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mstructured_traceback\u001b[0;34m(self, etype, value, tb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m   1365\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1366\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1367\u001b[0;31m         return FormattedTB.structured_traceback(\n\u001b[0m\u001b[1;32m   1368\u001b[0m             self, etype, value, tb, tb_offset, number_of_lines_of_context)\n\u001b[1;32m   1369\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mstructured_traceback\u001b[0;34m(self, etype, value, tb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m   1265\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose_modes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1266\u001b[0m             \u001b[0;31m# Verbose modes need a full traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1267\u001b[0;31m             return VerboseTB.structured_traceback(\n\u001b[0m\u001b[1;32m   1268\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0metype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb_offset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumber_of_lines_of_context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1269\u001b[0m             )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mstructured_traceback\u001b[0;34m(self, etype, evalue, etb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m   1122\u001b[0m         \u001b[0;34m\"\"\"Return a nice text document describing the traceback.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1124\u001b[0;31m         formatted_exception = self.format_exception_as_a_whole(etype, evalue, etb, number_of_lines_of_context,\n\u001b[0m\u001b[1;32m   1125\u001b[0m                                                                tb_offset)\n\u001b[1;32m   1126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mformat_exception_as_a_whole\u001b[0;34m(self, etype, evalue, etb, number_of_lines_of_context, tb_offset)\u001b[0m\n\u001b[1;32m   1080\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1081\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1082\u001b[0;31m         \u001b[0mlast_unique\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecursion_repeat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfind_recursion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morig_etype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1083\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1084\u001b[0m         \u001b[0mframes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat_records\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecords\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlast_unique\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecursion_repeat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mfind_recursion\u001b[0;34m(etype, value, records)\u001b[0m\n\u001b[1;32m    380\u001b[0m     \u001b[0;31m# first frame (from in to out) that looks different.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    381\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_recursion_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0metype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 382\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    383\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    384\u001b[0m     \u001b[0;31m# Select filename, lineno, func_name to track frames with\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: object of type 'NoneType' has no len()"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Simpan model dalam bentuk format file .h5 atau .pkl (pickle)\n",
        "model.save('chat_model.h5')\n",
        "\n",
        "print('Model Created Successfully!')"
      ],
      "metadata": {
        "id": "ujwbcHGnSVI4"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}