JW_chatbot.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "JW_chatbot.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOWJHywVmFPkesoBXTGL4Yx",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JaewonLee0217/JW_chatbot/blob/main/JW_chatbot.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EIONZ7HosAD6"
      },
      "source": [
        "import json \r\n",
        "import numpy as np \r\n",
        "import tensorflow as tf\r\n",
        "from tensorflow import keras\r\n",
        "from tensorflow.keras.models import Sequential\r\n",
        "from tensorflow.keras.layers import Dense, Embedding, GlobalAveragePooling1D\r\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\r\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\r\n",
        "from sklearn.preprocessing import LabelEncoder"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S0la99nWsL84"
      },
      "source": [
        "with open('intents.json') as file:\r\n",
        "  data = json.load(file)\r\n",
        "  #데이터 구조는 패턴, 리스폰스, 태그로 구성되어 있고, \r\n",
        "\r\n",
        "  training_sentences = [] # 실제 어구 \r\n",
        "  training_labels = [] #태그 정보들 \r\n",
        "  labels = [] # 이 문장이 인사인지 도움인지 뭐를 뜻하는 지 태그 정보 \r\n",
        "  responses = [] # 패턴에 대한 반응\r\n",
        "\r\n",
        "\r\n",
        "#data proprecessing -> 각 리스트에 정보 분배 \r\n",
        "for intent in data['intents']:\r\n",
        "  for pattern in intent['patterns']:\r\n",
        "    training_sentences.append(pattern)\r\n",
        "    training_labels.append(intent['tag'])\r\n",
        "  responses.append(intent['responses'])\r\n",
        "\r\n",
        "\r\n",
        "# 라벨 리스트에 태그 정보들 기록 \r\n",
        "  if intent['tag'] not in labels:\r\n",
        "    labels.append(intent['tag'])\r\n",
        "\r\n",
        "#라벨 리스트에 담은 길이가 클래스의 총 개수로 정해준다.\r\n",
        "num_classes = len(labels)"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7xCRpwWDtEAy"
      },
      "source": [
        "#인코딩 실행 sklearn의 전처리 라이브러리 불러오서 각 패턴에 대한 태그들,\r\n",
        "# 뭐라뭐라 말했을 떄 그 말에 대한 단어들을 모아둔 training_labels 에서 인코더를 fit 시키고 \r\n",
        "# 그것을 가주고 숫자 형태로 transforming 시켜 준다. \r\n",
        "lbl_encoder = LabelEncoder()\r\n",
        "lbl_encoder.fit(training_labels)\r\n",
        "training_labels = lbl_encoder.transform(training_labels)"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XyKE4lnEBvR4",
        "outputId": "aaef70b4-c05e-4b5f-c2dd-73d6c5c17701"
      },
      "source": [
        "training_labels"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4, 4, 4, 4, 4, 3, 3, 3, 7, 7, 7, 7, 0, 0, 0, 6, 6, 6, 5, 5, 5, 5,\n",
              "       5, 5, 5, 2, 2, 2, 2, 2, 1, 1, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eMWR43QjtZDG"
      },
      "source": [
        "vocab_size = 1000\r\n",
        "embedding_dim = 16\r\n",
        "max_len = 20\r\n",
        "oov_token = \"<OOV>\"\r\n",
        "\r\n",
        "#tokenizer는 텐서플로우의 keras라이브러리 꺼 사용. \r\n",
        "tokenizer = Tokenizer(num_words=vocab_size, oov_token=oov_token)\r\n",
        "#여기서 문장에서 각 토큰 들 짤라주고 \r\n",
        "tokenizer.fit_on_texts(training_sentences)\r\n",
        "\r\n",
        "#짜른 토큰들을 word to index화 시킨다. \r\n",
        "word_index = tokenizer.word_index\r\n",
        "sequences = tokenizer.texts_to_sequences(training_sentences)\r\n",
        "\r\n",
        "# 그럼 sequence에는 각 단어 문장의 단어마다 숫자로 표현되어 있지만 길이가 \r\n",
        "# 서로 다르므로 제로 패딩을 통해서 각 문장마다 길이 동일하게 만들어주고 트레이닝을 진행해야 한다\r\n",
        "padded_sequences = pad_sequences(sequences, truncating='post', maxlen=max_len)\r\n"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YbQbFKQltbnn",
        "outputId": "8132f9ee-68a5-4e49-e4ac-a23c5a20c358"
      },
      "source": [
        "#모델 훈련\r\n",
        "#모델을 먼저 텐서플로우 케라스 모델에서 세퀀셜가져오고\r\n",
        "\r\n",
        "\r\n",
        "#keras.layer 라이브러리에서 임베딩함수로 인풋이 들어갈 부분을 만들어준다. \r\n",
        "model = Sequential()\r\n",
        "model.add(Embedding(vocab_size, embedding_dim, input_length=max_len))\r\n",
        "model.add(GlobalAveragePooling1D())\r\n",
        "model.add(Dense(16, activation='relu'))\r\n",
        "model.add(Dense(16, activation='relu'))\r\n",
        "model.add(Dense(num_classes, activation='softmax'))\r\n",
        "\r\n",
        "model.compile(loss='sparse_categorical_crossentropy', \r\n",
        "              optimizer='adam', metrics=['accuracy'])\r\n",
        "\r\n",
        "model.summary()"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_2 (Embedding)      (None, 20, 16)            16000     \n",
            "_________________________________________________________________\n",
            "global_average_pooling1d_2 ( (None, 16)                0         \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 16)                272       \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 16)                272       \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 8)                 136       \n",
            "=================================================================\n",
            "Total params: 16,680\n",
            "Trainable params: 16,680\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rDmuB7Hww7al",
        "outputId": "e8ef811f-f480-4e9b-afb6-cd05971704e3"
      },
      "source": [
        "epochs = 490\r\n",
        "history = model.fit(padded_sequences, np.array(training_labels), epochs=epochs)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/490\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 2.0787 - accuracy: 0.1225\n",
            "Epoch 2/490\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 2.0776 - accuracy: 0.1225\n",
            "Epoch 3/490\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 2.0768 - accuracy: 0.2449\n",
            "Epoch 4/490\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 2.0762 - accuracy: 0.2756\n",
            "Epoch 5/490\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 2.0755 - accuracy: 0.3368\n",
            "Epoch 6/490\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 2.0750 - accuracy: 0.2756\n",
            "Epoch 7/490\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 2.0744 - accuracy: 0.2449\n",
            "Epoch 8/490\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 2.0739 - accuracy: 0.2143\n",
            "Epoch 9/490\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 2.0734 - accuracy: 0.2652\n",
            "Epoch 10/490\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 2.0723 - accuracy: 0.3368\n",
            "Epoch 11/490\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 2.0719 - accuracy: 0.2143\n",
            "Epoch 12/490\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 2.0716 - accuracy: 0.2039\n",
            "Epoch 13/490\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 2.0711 - accuracy: 0.2039\n",
            "Epoch 14/490\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 2.0704 - accuracy: 0.2143\n",
            "Epoch 15/490\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 2.0699 - accuracy: 0.2039\n",
            "Epoch 16/490\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 2.0691 - accuracy: 0.2143\n",
            "Epoch 17/490\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 2.0684 - accuracy: 0.2143\n",
            "Epoch 18/490\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 2.0676 - accuracy: 0.2143\n",
            "Epoch 19/490\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 2.0669 - accuracy: 0.2039\n",
            "Epoch 20/490\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 2.0655 - accuracy: 0.2143\n",
            "Epoch 21/490\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 2.0646 - accuracy: 0.2143\n",
            "Epoch 22/490\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 2.0647 - accuracy: 0.2039\n",
            "Epoch 23/490\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 2.0641 - accuracy: 0.2039\n",
            "Epoch 24/490\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 2.0627 - accuracy: 0.2143\n",
            "Epoch 25/490\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 2.0626 - accuracy: 0.2039\n",
            "Epoch 26/490\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 2.0615 - accuracy: 0.2143\n",
            "Epoch 27/490\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 2.0612 - accuracy: 0.2039\n",
            "Epoch 28/490\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 2.0603 - accuracy: 0.2039\n",
            "Epoch 29/490\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 2.0580 - accuracy: 0.2143\n",
            "Epoch 30/490\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 2.0575 - accuracy: 0.2143\n",
            "Epoch 31/490\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 2.0566 - accuracy: 0.2143\n",
            "Epoch 32/490\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 2.0547 - accuracy: 0.2143\n",
            "Epoch 33/490\n",
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            "Epoch 34/490\n",
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            "Epoch 35/490\n",
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            "Epoch 38/490\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 2.0532 - accuracy: 0.2039\n",
            "Epoch 39/490\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 2.0500 - accuracy: 0.2143\n",
            "Epoch 40/490\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 2.0508 - accuracy: 0.2143\n",
            "Epoch 41/490\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 2.0480 - accuracy: 0.2143\n",
            "Epoch 42/490\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 2.0500 - accuracy: 0.2143\n",
            "Epoch 43/490\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 2.0495 - accuracy: 0.2143\n",
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            "Epoch 55/490\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 2.0455 - accuracy: 0.2039\n",
            "Epoch 56/490\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 2.0448 - accuracy: 0.2039\n",
            "Epoch 57/490\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 2.0399 - accuracy: 0.2143\n",
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            "2/2 [==============================] - 0s 11ms/step - loss: 2.0404 - accuracy: 0.2143\n",
            "Epoch 60/490\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 2.0410 - accuracy: 0.2143\n",
            "Epoch 61/490\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 2.0370 - accuracy: 0.2143\n",
            "Epoch 62/490\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 2.0379 - accuracy: 0.2143\n",
            "Epoch 63/490\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 2.0362 - accuracy: 0.2143\n",
            "Epoch 64/490\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 2.0393 - accuracy: 0.2143\n",
            "Epoch 65/490\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 2.0393 - accuracy: 0.2143\n",
            "Epoch 66/490\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 2.0358 - accuracy: 0.2143\n",
            "Epoch 67/490\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 2.0386 - accuracy: 0.2143\n",
            "Epoch 68/490\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 2.0385 - accuracy: 0.2143\n",
            "Epoch 69/490\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 2.0361 - accuracy: 0.2143\n",
            "Epoch 70/490\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 2.0357 - accuracy: 0.2143\n",
            "Epoch 71/490\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 2.0376 - accuracy: 0.2143\n",
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            "2/2 [==============================] - 0s 12ms/step - loss: 2.0375 - accuracy: 0.2143\n",
            "Epoch 73/490\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 2.0356 - accuracy: 0.2143\n",
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            "2/2 [==============================] - 0s 9ms/step - loss: 2.0376 - accuracy: 0.2039\n",
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            "2/2 [==============================] - 0s 12ms/step - loss: 2.0333 - accuracy: 0.2143\n",
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            "2/2 [==============================] - 0s 12ms/step - loss: 2.0352 - accuracy: 0.2039\n",
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            "2/2 [==============================] - 0s 11ms/step - loss: 2.0344 - accuracy: 0.2039\n",
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            "2/2 [==============================] - 0s 12ms/step - loss: 2.0312 - accuracy: 0.2143\n",
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            "2/2 [==============================] - 0s 16ms/step - loss: 2.0296 - accuracy: 0.2143\n",
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            "2/2 [==============================] - 0s 12ms/step - loss: 2.0275 - accuracy: 0.2039\n",
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            "2/2 [==============================] - 0s 14ms/step - loss: 2.0236 - accuracy: 0.2143\n",
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            "2/2 [==============================] - 0s 15ms/step - loss: 2.0220 - accuracy: 0.2143\n",
            "Epoch 90/490\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 2.0213 - accuracy: 0.2143\n",
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            "2/2 [==============================] - 0s 12ms/step - loss: 2.0201 - accuracy: 0.2143\n",
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            "2/2 [==============================] - 0s 17ms/step - loss: 2.0236 - accuracy: 0.2039\n",
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            "2/2 [==============================] - 0s 9ms/step - loss: 2.0165 - accuracy: 0.2143\n",
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            "Epoch 97/490\n",
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            "Epoch 100/490\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 2.0141 - accuracy: 0.2143\n",
            "Epoch 101/490\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 2.0153 - accuracy: 0.2143\n",
            "Epoch 102/490\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 2.0145 - accuracy: 0.2143\n",
            "Epoch 103/490\n",
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            "2/2 [==============================] - 0s 12ms/step - loss: 0.8045 - accuracy: 0.9388\n",
            "Epoch 458/490\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 0.8099 - accuracy: 0.9388\n",
            "Epoch 459/490\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 0.7976 - accuracy: 0.9388\n",
            "Epoch 460/490\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 0.8056 - accuracy: 0.9081\n",
            "Epoch 461/490\n",
            "2/2 [==============================] - 0s 18ms/step - loss: 0.8049 - accuracy: 0.9081\n",
            "Epoch 462/490\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 0.8006 - accuracy: 0.8775\n",
            "Epoch 463/490\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.7991 - accuracy: 0.8469\n",
            "Epoch 464/490\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.8017 - accuracy: 0.8469\n",
            "Epoch 465/490\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 0.7875 - accuracy: 0.8775\n",
            "Epoch 466/490\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.7837 - accuracy: 0.8469\n",
            "Epoch 467/490\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.7737 - accuracy: 0.8775\n",
            "Epoch 468/490\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 0.7640 - accuracy: 0.9081\n",
            "Epoch 469/490\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.7554 - accuracy: 0.9186\n",
            "Epoch 470/490\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.7553 - accuracy: 0.9388\n",
            "Epoch 471/490\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.7529 - accuracy: 0.9388\n",
            "Epoch 472/490\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.7421 - accuracy: 0.9388\n",
            "Epoch 473/490\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.7392 - accuracy: 0.9388\n",
            "Epoch 474/490\n",
            "2/2 [==============================] - 0s 18ms/step - loss: 0.7349 - accuracy: 0.9388\n",
            "Epoch 475/490\n",
            "2/2 [==============================] - 0s 17ms/step - loss: 0.7436 - accuracy: 0.9388\n",
            "Epoch 476/490\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.7339 - accuracy: 0.9388\n",
            "Epoch 477/490\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 0.7384 - accuracy: 0.9388\n",
            "Epoch 478/490\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.7234 - accuracy: 0.9388\n",
            "Epoch 479/490\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.7194 - accuracy: 0.9388\n",
            "Epoch 480/490\n",
            "2/2 [==============================] - 0s 19ms/step - loss: 0.7174 - accuracy: 0.9388\n",
            "Epoch 481/490\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 0.7229 - accuracy: 0.9388\n",
            "Epoch 482/490\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.7201 - accuracy: 0.9388\n",
            "Epoch 483/490\n",
            "2/2 [==============================] - 0s 17ms/step - loss: 0.7186 - accuracy: 0.8775\n",
            "Epoch 484/490\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.7000 - accuracy: 0.8879\n",
            "Epoch 485/490\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.7052 - accuracy: 0.8775\n",
            "Epoch 486/490\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.6966 - accuracy: 0.9388\n",
            "Epoch 487/490\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.6869 - accuracy: 0.9388\n",
            "Epoch 488/490\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.6918 - accuracy: 0.9388\n",
            "Epoch 489/490\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 0.6793 - accuracy: 0.9388\n",
            "Epoch 490/490\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.6715 - accuracy: 0.9388\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HMDagg5B7JqT"
      },
      "source": [
        "최종 loss: 0.6715 - accuracy: 0.9388\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SpFkzqoJ7SE6",
        "outputId": "f3da4f76-7357-4f4a-ffd4-76cb0f775a22"
      },
      "source": [
        "# to save the trained model\r\n",
        "model.save(\"JW_model_2\")\r\n",
        "\r\n",
        "import pickle\r\n",
        "\r\n",
        "# to save the fitted tokenizer\r\n",
        "with open('tokenizer.pickle', 'wb') as handle:\r\n",
        "    pickle.dump(tokenizer, handle, protocol=pickle.HIGHEST_PROTOCOL)\r\n",
        "    \r\n",
        "# to save the fitted label encoder\r\n",
        "with open('label_encoder.pickle', 'wb') as ecn_file:\r\n",
        "    pickle.dump(lbl_encoder, ecn_file, protocol=pickle.HIGHEST_PROTOCOL)"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: JW_model_2/assets\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ayoD19Lt8KK8"
      },
      "source": [
        "pip install colorama\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 900
        },
        "id": "5A-disAv77PR",
        "outputId": "3ce96462-ad90-4dde-ec8d-0b527a61e00b"
      },
      "source": [
        "\r\n",
        "import json \r\n",
        "import numpy as np\r\n",
        "from tensorflow import keras\r\n",
        "from sklearn.preprocessing import LabelEncoder\r\n",
        "\r\n",
        "import colorama \r\n",
        "colorama.init()\r\n",
        "from colorama import Fore, Style, Back\r\n",
        "\r\n",
        "import random\r\n",
        "import pickle\r\n",
        "\r\n",
        "with open(\"intents.json\") as file:\r\n",
        "    data = json.load(file)\r\n",
        "\r\n",
        "\r\n",
        "def chat():\r\n",
        "    # load trained model\r\n",
        "    model = keras.models.load_model('chat_model')\r\n",
        "\r\n",
        "    # load tokenizer object\r\n",
        "    with open('tokenizer.pickle', 'rb') as handle:\r\n",
        "        tokenizer = pickle.load(handle)\r\n",
        "\r\n",
        "    # load label encoder object\r\n",
        "    with open('label_encoder.pickle', 'rb') as enc:\r\n",
        "        lbl_encoder = pickle.load(enc)\r\n",
        "\r\n",
        "    # parameters\r\n",
        "    max_len = 20\r\n",
        "    \r\n",
        "    while True:\r\n",
        "        print(Fore.LIGHTBLUE_EX + \"User: \" + Style.RESET_ALL, end=\"\")\r\n",
        "        inp = input()\r\n",
        "        if inp.lower() == \"quit\":\r\n",
        "            break\r\n",
        "\r\n",
        "        result = model.predict(keras.preprocessing.sequence.pad_sequences(tokenizer.texts_to_sequences([inp]),\r\n",
        "                                             truncating='post', maxlen=max_len))\r\n",
        "        #print('result : ',result)\r\n",
        "        tag = lbl_encoder.inverse_transform([np.argmax(result)])\r\n",
        "        #print('tag : ',tag)\r\n",
        "\r\n",
        "        for i in data['intents']:\r\n",
        "            if i['tag'] == tag:\r\n",
        "                print(Fore.GREEN + \"ChatBot:\" + Style.RESET_ALL , np.random.choice(i['responses']))\r\n",
        "\r\n",
        "        # print(Fore.GREEN + \"ChatBot:\" + Style.RESET_ALL,random.choice(responses))\r\n",
        "\r\n",
        "print(Fore.YELLOW + \"Start messaging with the bot (type quit to stop)!\" + Style.RESET_ALL)\r\n",
        "chat()"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Start messaging with the bot (type quit to stop)!\n",
            "User: Hi\n",
            "WARNING:tensorflow:7 out of the last 14 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fa75cdfcd90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "result :  [[0.03010814 0.01078339 0.03460111 0.08598869 0.6074504  0.00819192\n",
            "  0.0414453  0.18143098]]\n",
            "tag :  ['greeting']\n",
            "ChatBot: Hi there\n",
            "User: Who are you?\n",
            "result :  [[0.2224784  0.00356181 0.00604405 0.17680888 0.2193965  0.00768735\n",
            "  0.31941083 0.04461214]]\n",
            "tag :  ['name']\n",
            "ChatBot: You can call me Joana.\n",
            "User: What are you?\n",
            "result :  [[0.21921374 0.00356596 0.00594932 0.17610598 0.21728979 0.00751528\n",
            "  0.32617855 0.04418132]]\n",
            "tag :  ['name']\n",
            "ChatBot: I'm Joana!\n",
            "User: "
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.6/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    728\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 729\u001b[0;31m                 \u001b[0mident\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdin_socket\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\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    730\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/usr/local/lib/python3.6/dist-packages/jupyter_client/session.py\u001b[0m in \u001b[0;36mrecv\u001b[0;34m(self, socket, mode, content, copy)\u001b[0m\n\u001b[1;32m    802\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 803\u001b[0;31m             \u001b[0mmsg_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_multipart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\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    804\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mzmq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mZMQError\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;32m/usr/local/lib/python3.6/dist-packages/zmq/sugar/socket.py\u001b[0m in \u001b[0;36mrecv_multipart\u001b[0;34m(self, flags, copy, track)\u001b[0m\n\u001b[1;32m    582\u001b[0m         \"\"\"\n\u001b[0;32m--> 583\u001b[0;31m         \u001b[0mparts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mflags\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrack\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrack\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    584\u001b[0m         \u001b[0;31m# have first part already, only loop while more to receive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.recv\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.recv\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket._recv_copy\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/zmq/backend/cython/checkrc.pxd\u001b[0m in \u001b[0;36mzmq.backend.cython.checkrc._check_rc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-62-cfffc5f24074>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mYELLOW\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"Start messaging with the bot (type quit to stop)!\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mStyle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRESET_ALL\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 52\u001b[0;31m \u001b[0mchat\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[0;32m<ipython-input-62-cfffc5f24074>\u001b[0m in \u001b[0;36mchat\u001b[0;34m()\u001b[0m\n\u001b[1;32m     33\u001b[0m     \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLIGHTBLUE_EX\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"User: \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mStyle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRESET_ALL\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\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---> 35\u001b[0;31m         \u001b[0minp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\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     36\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"quit\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m             \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36mraw_input\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m    702\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[1;32m    703\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_header\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 704\u001b[0;31m             \u001b[0mpassword\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\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    705\u001b[0m         )\n\u001b[1;32m    706\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/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    732\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    733\u001b[0m                 \u001b[0;31m# re-raise KeyboardInterrupt, to truncate traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 734\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    735\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    736\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xFdZteup8Yoy"
      },
      "source": [
        "for i in data['intents']:\r\n",
        "  print(i['tag'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "CaVC7Zs899q0",
        "outputId": "5e912bd2-5ea9-46cd-869f-b4da41d7ba2e"
      },
      "source": [
        "labels[5]\r\n"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'help'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HwpMYasDCcvh"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}