Your_Type.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Your-Type",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xp4Qqf4LP3Fm"
      },
      "source": [
        "## 연구\n",
        "K-mean은 값들의 평균을 이용하는 알고리즘이기에 수치형 데이터에 더 적합하다.  \n",
        "범주형 데이터를 군집화 하기위해서 K-Prototype 알고리즘을 써봐도 될 것 같다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aQlxx-1GKKRF",
        "outputId": "5f40beda-e1a1-42e4-835e-4379d56597cf"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "question = [\"1\", \"2\", \"3\", \"4\", \"5\"]\n",
        "form = [\n",
        "    [0,1,1,0,1]\n",
        "]\n",
        "\n",
        "forms = np.random.randint(0,4,(40, 10)) / 3\n",
        "forms"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.66666667, 0.33333333, 1.        , 0.        , 0.66666667,\n",
              "        0.66666667, 0.        , 0.        , 0.        , 0.        ],\n",
              "       [0.33333333, 0.33333333, 1.        , 0.33333333, 1.        ,\n",
              "        1.        , 1.        , 0.33333333, 0.        , 0.        ],\n",
              "       [0.33333333, 0.66666667, 0.33333333, 0.66666667, 0.        ,\n",
              "        0.33333333, 1.        , 0.66666667, 1.        , 0.66666667],\n",
              "       [0.66666667, 0.        , 0.        , 0.66666667, 0.66666667,\n",
              "        0.        , 0.33333333, 0.        , 1.        , 0.66666667],\n",
              "       [0.66666667, 0.66666667, 0.33333333, 0.33333333, 0.66666667,\n",
              "        0.66666667, 0.66666667, 0.66666667, 0.33333333, 1.        ],\n",
              "       [0.33333333, 0.        , 1.        , 0.33333333, 0.        ,\n",
              "        0.66666667, 0.66666667, 0.        , 0.66666667, 0.66666667],\n",
              "       [0.        , 0.33333333, 0.33333333, 0.33333333, 1.        ,\n",
              "        0.66666667, 0.33333333, 0.66666667, 0.66666667, 0.33333333],\n",
              "       [1.        , 0.        , 0.66666667, 0.33333333, 1.        ,\n",
              "        0.33333333, 1.        , 0.33333333, 0.        , 0.        ],\n",
              "       [0.        , 0.33333333, 1.        , 1.        , 0.33333333,\n",
              "        0.33333333, 0.        , 0.        , 0.        , 0.        ],\n",
              "       [1.        , 0.33333333, 0.        , 0.66666667, 0.        ,\n",
              "        0.        , 0.66666667, 0.66666667, 0.        , 0.66666667],\n",
              "       [1.        , 1.        , 0.        , 0.33333333, 0.33333333,\n",
              "        0.        , 0.33333333, 0.        , 0.33333333, 0.        ],\n",
              "       [0.66666667, 1.        , 1.        , 0.33333333, 0.        ,\n",
              "        0.33333333, 1.        , 0.        , 0.66666667, 1.        ],\n",
              "       [0.66666667, 0.        , 0.        , 0.        , 0.33333333,\n",
              "        0.33333333, 1.        , 0.        , 0.66666667, 0.33333333],\n",
              "       [1.        , 1.        , 0.66666667, 0.33333333, 1.        ,\n",
              "        1.        , 0.66666667, 1.        , 0.66666667, 0.66666667],\n",
              "       [0.        , 0.33333333, 0.        , 1.        , 0.33333333,\n",
              "        0.        , 0.33333333, 0.33333333, 1.        , 1.        ],\n",
              "       [0.66666667, 1.        , 0.        , 1.        , 0.33333333,\n",
              "        1.        , 0.33333333, 1.        , 0.66666667, 0.66666667],\n",
              "       [1.        , 0.66666667, 1.        , 1.        , 0.66666667,\n",
              "        0.        , 0.66666667, 0.        , 0.33333333, 0.33333333],\n",
              "       [0.        , 0.        , 0.66666667, 0.        , 0.33333333,\n",
              "        0.        , 0.66666667, 0.66666667, 0.66666667, 1.        ],\n",
              "       [0.33333333, 0.33333333, 1.        , 0.66666667, 0.66666667,\n",
              "        0.33333333, 0.66666667, 0.33333333, 0.33333333, 1.        ],\n",
              "       [0.66666667, 1.        , 1.        , 1.        , 0.        ,\n",
              "        0.33333333, 0.        , 0.33333333, 0.        , 1.        ],\n",
              "       [0.66666667, 0.66666667, 0.        , 0.33333333, 0.66666667,\n",
              "        0.        , 0.66666667, 0.66666667, 1.        , 0.        ],\n",
              "       [0.33333333, 0.33333333, 0.33333333, 0.        , 1.        ,\n",
              "        0.66666667, 1.        , 0.        , 1.        , 1.        ],\n",
              "       [0.66666667, 0.66666667, 1.        , 1.        , 0.66666667,\n",
              "        0.        , 0.66666667, 1.        , 0.33333333, 0.        ],\n",
              "       [0.33333333, 0.        , 0.66666667, 1.        , 0.        ,\n",
              "        1.        , 1.        , 0.66666667, 0.66666667, 0.66666667],\n",
              "       [0.66666667, 0.        , 1.        , 0.        , 0.        ,\n",
              "        1.        , 1.        , 0.66666667, 1.        , 1.        ],\n",
              "       [0.33333333, 0.33333333, 0.        , 0.        , 0.66666667,\n",
              "        0.        , 0.33333333, 0.        , 0.66666667, 0.66666667],\n",
              "       [0.33333333, 0.66666667, 0.66666667, 0.66666667, 0.33333333,\n",
              "        0.        , 0.66666667, 0.33333333, 0.33333333, 0.        ],\n",
              "       [0.        , 0.33333333, 0.        , 0.66666667, 1.        ,\n",
              "        0.        , 0.        , 1.        , 0.        , 0.        ],\n",
              "       [0.33333333, 0.66666667, 0.        , 0.66666667, 1.        ,\n",
              "        0.        , 0.        , 0.33333333, 0.        , 0.        ],\n",
              "       [1.        , 0.66666667, 1.        , 0.        , 0.33333333,\n",
              "        0.66666667, 0.66666667, 0.66666667, 0.        , 1.        ],\n",
              "       [1.        , 0.        , 0.        , 0.66666667, 0.33333333,\n",
              "        0.33333333, 0.33333333, 0.66666667, 1.        , 1.        ],\n",
              "       [0.66666667, 1.        , 0.66666667, 0.33333333, 1.        ,\n",
              "        0.        , 1.        , 0.66666667, 0.        , 0.        ],\n",
              "       [0.33333333, 0.66666667, 0.33333333, 0.66666667, 0.        ,\n",
              "        0.33333333, 1.        , 0.33333333, 1.        , 0.        ],\n",
              "       [0.66666667, 1.        , 0.66666667, 0.        , 0.        ,\n",
              "        1.        , 1.        , 0.66666667, 0.        , 0.66666667],\n",
              "       [0.66666667, 0.33333333, 0.        , 0.66666667, 0.        ,\n",
              "        0.66666667, 0.33333333, 1.        , 0.66666667, 0.66666667],\n",
              "       [1.        , 0.        , 1.        , 0.66666667, 0.33333333,\n",
              "        0.33333333, 0.33333333, 1.        , 0.        , 0.33333333],\n",
              "       [0.33333333, 0.33333333, 1.        , 1.        , 0.        ,\n",
              "        0.33333333, 0.33333333, 0.        , 1.        , 0.33333333],\n",
              "       [1.        , 0.        , 0.33333333, 0.33333333, 0.        ,\n",
              "        0.66666667, 0.66666667, 1.        , 0.        , 1.        ],\n",
              "       [0.        , 1.        , 0.        , 0.33333333, 0.66666667,\n",
              "        1.        , 0.        , 0.        , 0.        , 0.        ],\n",
              "       [0.        , 1.        , 0.33333333, 0.33333333, 0.66666667,\n",
              "        1.        , 0.        , 0.66666667, 1.        , 0.33333333]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "FzHAPgBaNOBH",
        "outputId": "18e2785a-b4a8-4951-fc3f-e8ece86c2680"
      },
      "source": [
        "# 2D로 만들어서 시각화 하기\n",
        "from sklearn.decomposition import PCA\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "pca = PCA(n_components=2)\n",
        "form_2d = pca.fit_transform(forms)\n",
        "plt.scatter(form_2d.T[0], form_2d.T[1])\n",
        "plt.show()\n",
        "print(pca.explained_variance_ratio_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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l+Gql9uffFBGNiGgMDblA0cnzvFSPg7tBPrPNTtpGEBHndzsm6XlJCyLiYPJF/0KX8/4UuA+4LiIebXvu8dLEK5K+A3x2Wrm3P7rmorPf1EYAnuel7Iq8AM2JcpvHien3iO20VUMjwLpkex1wb+cJkk4BfgDc3tkonAQPJIlW+8ITKfNTW0Wdo95OXNUmcZuozeMzd+5ksXu5DZxaNToneLF0GnAXcAbwDPCRiDgkqQFcFRGflPQx4DvA7rZLr4iInZK20Wo4FrAzueYPk/3eRqMRzWbzhPNtVhZVuoNeuXHbhCWccbNOnuGblz6TtCMiGselpwkEg+JAYFY+Szbc170RMTE8exY/2fCBXPJTR90CgecaMrNcTKVtww3hg+FAYGa5mKjNo1OZG8LLzHMNmVku2heuGT18BPHm/uZlbggvOwcCM8tNezfIKjWEl50DgZkNhFczKw63EZiZ1ZwDgZlZzblqyArF9cbH+L2wvDgQWGF4TYVj/F5Ynlw1ZJk70ZXSPO3yMX4vLE8uEVim0tzJetrlY/xeWJ5cIrBMpbmTLdOaCv1eH7pM74WVnwOBZSrNnWxZpl3OYwnJsrwXVg0OBJapNHeyZVlTIY/6+7K8F1YNbiOwTKVdKa0Mo03zqr8vw3th1eASgWWqDneyrr+3qklVIpA0F7gTWAz8mtYKZS9OcN7rwK5k9zcRsTpJXwJsAU4DdgB/HxGvpsmTDV7V72S9PrRVTdoSwQbgoYhYCjyU7E/kSEQsTx6r29K/CtwUEW8HXgSuTJkfs76rQ6nH6iXtmsVPAe+PiIPJQvSPRMRxt0WS/hARb+lIEzAGvC0ijkr6S+CLEXHRZL/XS1WamU1fv5aqnB8RB5Pt54D5Xc47VVJT0qOS1iZppwGHI+Josn8A6HpLJWl98hzNsbGxlNk2M7Nxk7YRSHoQeNsEh65r34mIkNSteHFmRIxKOgvYJmkX8NJ0MhoRm4BN0CoRTOdaMzPrbtJAEBHndzsm6XlJC9qqhl7o8hyjyc/9kh4BVgD/BcyWdFJSKlgIZDs808zMJpW2amgEWJdsrwPu7TxB0hxJM5PtecBKYE+0GiceBi7tdb2ZmfVX2gFlG4G7JF0JPAN8BEBSA7gqIj4JvAP4lqQ3aAWejRGxJ7n+c8AWSV8GHgO+nTI/1oXntjezblL1GhoU9xqans4ZQaHV791dHs3qpVuvIU8xUQO95sYZdCBwScVs8BwIaqCoc9t7FS6zYvBcQzVQ1LlxvAqXWTE4ENRAUee2L2pJxaxuHAhqoKhz4xS1pGJWN24jqIkizgjqWTzNisGBwAZmPDCVodeQezdZlTkQWE/9/gIsYkmlk3s3WdW5jcC6ymOR9jJw7yarOgcC68pfgC3u3WRV50BgXfkLsMW9m6zqHAisK38BthR1HIZZVhwIrCt/AbYUdRyGWVbca8i6KlP3zn4rQ+8msxPlQGA9+QvQrPpcNWRmVnMOBGZmNZcqEEiaK+kBSXuTn3MmOOevJe1se/yfpLXJsdsk/art2PI0+TEzs+lL20awAXgoIjZK2pDsf679hIh4GFgOrcAB7AP+u+2UayLi7pT5MLMMeW6leklbNbQG2JxsbwbWTnL+pcCPIuLllL/XzPrEU4vUT9pAMD8iDibbzwHzJzn/MuCOjrSvSHpc0k2SZna7UNJ6SU1JzbGxsRRZNrNePLVI/UwaCCQ9KOmJCR5r2s+LiACix/MsAJYBW9uSrwXOAd4NzKWjWqnj+TdFRCMiGkNDQ5Nl28xOkKcWqZ9J2wgi4vxuxyQ9L2lBRBxMvuhf6PFUHwF+EBGvtT33eGniFUnfAT47xXybWZ+cPnsWoxN86ddtapE6SVs1NAKsS7bXAff2OPdyOqqFkuCBJNFqX3giZX7MLCVPLVI/aQPBRuACSXuB85N9JDUk3TJ+kqTFwCLgfzqu/09Ju4BdwDzgyynzY2YpeW6l+lGrar9cGo1GNJvNQWfDcuBujGbZkbQjIhqd6Z5ryArLS0Sa5cNTTFhhuRujWT5cIrDCcjfGYnD1XPW5RGCF5RXSBs+jjOvBgcAKy90YB8/Vc/XgqiErLK+QNniunqsHBwIrNK+QNlgeZVwPrhoys65cPVcPLhGYWVeunqsHBwIz68nVc9XnqiEzs5pzIDAzqzkHAjOzmnMgMDOrOQcCM7OaK+V6BJLGgGcGnY8u5gG/G3QmclbH1wz1fN1+zeV2ZkQct+h7KQNBkUlqTrTwQ5XV8TVDPV+3X3M1uWrIzKzmHAjMzGrOgSB7mwadgQGo42uGer5uv+YKchuBmVnNuURgZlZzDgRmZjXnQJCSpL+VtFvSG5K6djGTtErSU5L2SdqQZx6zJmmupAck7U1+zuly3uuSdiaPkbzzmYXJPjdJMyXdmRz/X0mL889ltqbwmq+QNNb22X5yEPnMkqRbJb0g6YkuxyXpG8l78rikd+adx35yIEjvCeAS4MfdTpA0A7gZuBg4F7hc0rn5ZK8vNgAPRcRS4KFkfyJHImJ58lidX/ayMcXP7UrgxYh4O3AT8NV8c5mtafyt3tn22d6Sayb74zZgVY/jFwNLk8d64Js55Ck3DgQpRcSTETHZSt7nAfsiYn9EvApsAdb0P3d9swbYnGxvBtYOMC/9NJXPrf29uBv4oCTlmMesVe1vdUoi4sfAoR6nrAFuj5ZHgdmSFuSTu/5zIMjHMPBs2/6BJK2s5kfEwWT7OWB+l/NOldSU9KikMgaLqXxufzwnIo4CLwGn5ZK7/pjq3+qHkyqSuyUtyidrA1W1/+E38QplUyDpQeBtExy6LiLuzTs/eej1mtt3IiIkdeuDfGZEjEo6C9gmaVdEPJ11Xi13PwTuiIhXJP0DrRLRBwacJ0vBgWAKIuL8lE8xCrTfNS1M0gqr12uW9LykBRFxMCkev9DlOUaTn/slPQKsAMoUCKbyuY2fc0DSScBbgd/nk72+mPQ1R0T767sF+FoO+Rq00v0PT4erhvKxHVgqaYmkU4DLgFL2okmMAOuS7XXAcaUiSXMkzUy25wErgT255TAbU/nc2t+LS4FtUe5RmpO+5o668dXAkznmb1BGgI8nvYfeC7zUVj1afhHhR4oH8De06gtfAZ4HtibppwP3t533IeCXtO6Irxt0vlO+5tNo9RbaCzwIzE3SG8AtyfZfAbuAXyQ/rxx0vk/wtR73uQHXA6uT7VOB7wH7gJ8DZw06zzm85huA3cln+zBwzqDznMFrvgM4CLyW/D9fCVwFXJUcF63eVE8nf8+NQec5y4enmDAzqzlXDZmZ1ZwDgZlZzTkQmJnVnAOBmVnNORCYmdWcA4GZWc05EJiZ1dz/A1eJ4WLKwKGyAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "[0.20773124 0.16715466]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WORu6RmNMSDt",
        "outputId": "39008091-715f-4efc-c7a6-81c1ba69bf40"
      },
      "source": [
        "from sklearn.cluster import KMeans\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# create model and prediction\n",
        "model = KMeans(n_clusters=4, algorithm='auto')\n",
        "model.fit(forms)\n",
        "print(model.cluster_centers_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.42424242 0.45454545 0.09090909 0.36363636 0.60606061 0.36363636\n",
            "  0.42424242 0.27272727 0.75757576 0.48484848]\n",
            " [0.54545455 0.45454545 0.72727273 0.60606061 0.72727273 0.24242424\n",
            "  0.48484848 0.45454545 0.09090909 0.06060606]\n",
            " [0.37037037 0.33333333 0.77777778 0.51851852 0.11111111 0.48148148\n",
            "  0.81481481 0.37037037 0.77777778 0.7037037 ]\n",
            " [0.81481481 0.66666667 0.44444444 0.48148148 0.25925926 0.66666667\n",
            "  0.55555556 0.77777778 0.25925926 0.81481481]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "FmDjldunIxKp",
        "outputId": "d2ba273d-c9f7-4e79-a0bd-2952727f4f91"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "pca_data = pd.DataFrame(form_2d, columns=['PCA_x', 'PCA_y'])\n",
        "km_data = pd.DataFrame(model.predict(forms), columns=['predict'])\n",
        "\n",
        "plt.scatter(pca_data['PCA_x'], pca_data['PCA_y'], c=km_data['predict'], alpha=0.5)\n",
        "centers = pd.DataFrame(pca.transform(model.cluster_centers_), columns=['PCA_x', 'PCA_y'])\n",
        "\n",
        "plt.scatter(centers['PCA_x'], centers['PCA_y'], s=50, marker='D', c='r')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "17F0R4nDflQM",
        "outputId": "4b6cbf77-1227-452c-de71-c6d6566f833e"
      },
      "source": [
        "def process(form):\n",
        "    return int(model.predict(np.array([form])/4)[0])\n",
        "process([0,1,2,3,2,1,2,1,2,1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WSPxIQGTjYfZ",
        "outputId": "90e84d3e-a7a8-4067-8f7e-9f86c26a2282"
      },
      "source": [
        "!pip install flask\n",
        "!pip install flask-ngrok\n",
        "!pip install flask_cors"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: flask-ngrok in /usr/local/lib/python3.6/dist-packages (0.0.25)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from flask-ngrok) (2.23.0)\n",
            "Requirement already satisfied: Flask>=0.8 in /usr/local/lib/python3.6/dist-packages (from flask-ngrok) (1.1.2)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->flask-ngrok) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->flask-ngrok) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->flask-ngrok) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->flask-ngrok) (2020.6.20)\n",
            "Requirement already satisfied: Jinja2>=2.10.1 in /usr/local/lib/python3.6/dist-packages (from Flask>=0.8->flask-ngrok) (2.11.2)\n",
            "Requirement already satisfied: itsdangerous>=0.24 in /usr/local/lib/python3.6/dist-packages (from Flask>=0.8->flask-ngrok) (1.1.0)\n",
            "Requirement already satisfied: Werkzeug>=0.15 in /usr/local/lib/python3.6/dist-packages (from Flask>=0.8->flask-ngrok) (1.0.1)\n",
            "Requirement already satisfied: click>=5.1 in /usr/local/lib/python3.6/dist-packages (from Flask>=0.8->flask-ngrok) (7.1.2)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.6/dist-packages (from Jinja2>=2.10.1->Flask>=0.8->flask-ngrok) (1.1.1)\n",
            "Collecting flask_cors\n",
            "  Downloading https://files.pythonhosted.org/packages/69/7f/d0aeaaafb5c3c76c8d2141dbe2d4f6dca5d6c31872d4e5349768c1958abc/Flask_Cors-3.0.9-py2.py3-none-any.whl\n",
            "Requirement already satisfied: Six in /usr/local/lib/python3.6/dist-packages (from flask_cors) (1.15.0)\n",
            "Requirement already satisfied: Flask>=0.9 in /usr/local/lib/python3.6/dist-packages (from flask_cors) (1.1.2)\n",
            "Requirement already satisfied: Werkzeug>=0.15 in /usr/local/lib/python3.6/dist-packages (from Flask>=0.9->flask_cors) (1.0.1)\n",
            "Requirement already satisfied: Jinja2>=2.10.1 in /usr/local/lib/python3.6/dist-packages (from Flask>=0.9->flask_cors) (2.11.2)\n",
            "Requirement already satisfied: itsdangerous>=0.24 in /usr/local/lib/python3.6/dist-packages (from Flask>=0.9->flask_cors) (1.1.0)\n",
            "Requirement already satisfied: click>=5.1 in /usr/local/lib/python3.6/dist-packages (from Flask>=0.9->flask_cors) (7.1.2)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.6/dist-packages (from Jinja2>=2.10.1->Flask>=0.9->flask_cors) (1.1.1)\n",
            "Installing collected packages: flask-cors\n",
            "Successfully installed flask-cors-3.0.9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jBbZKzazja1G",
        "outputId": "6db652c4-c4af-4283-9ca3-157b0d9fb549"
      },
      "source": [
        "from flask_ngrok import run_with_ngrok\n",
        "from flask import Flask, request, redirect, url_for, render_template, jsonify\n",
        "from flask_cors import CORS, cross_origin\n",
        "\n",
        "# 유효성 검증 로직 추가해야함\n",
        "\n",
        "app = Flask(__name__)\n",
        "run_with_ngrok(app)\n",
        "\n",
        "app.config['CORS_HEADERS'] = 'Content-Type'\n",
        "\n",
        "CORS(app)\n",
        "CORS(app, resources={r'*': {'origins': '*'}})\n",
        "\n",
        "@app.route(\"/\")\n",
        "def index():\n",
        "    # return render_template('index.html')\n",
        "    return \"<h1>Running Flask on Google Colab!</h1>\"\n",
        "\n",
        "@app.route(\"/form\", methods=['GET', 'POST'])\n",
        "def form():\n",
        "    if request.method == 'GET':\n",
        "        return \"<h1>form PAGE</h1>\"\n",
        "    elif request.method == 'POST':\n",
        "        answer = request.json['answer']\n",
        "        print(answer)\n",
        "        if answer != None:\n",
        "            utype = process(answer)\n",
        "            return jsonify({'type': utype})\n",
        "\n",
        "@app.route(\"/report/<utype>\", methods=['GET', 'POST'])\n",
        "def report(utype):\n",
        "    if request.method == 'GET':\n",
        "        return \"<h1>form PAGE</h1>\"\n",
        "    elif request.method == 'POST':\n",
        "        return jsonify({\n",
        "            'type': utype,\n",
        "            'name': 'test type'\n",
        "        })\n",
        "\n",
        "@app.route(\"/types\")\n",
        "def types():\n",
        "    if request.method == 'GET':\n",
        "        return \"<h1>타입 전체보기</h1>\"\n",
        "\n",
        "@app.route(\"/types/<utype>\")\n",
        "def types_type(utype):\n",
        "    if request.method == 'GET':\n",
        "        return \"<h1>타입 상세보기</h1>\"\n",
        "\n",
        "app.run()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " * Serving Flask app \"__main__\" (lazy loading)\n",
            " * Environment: production\n",
            "\u001b[31m   WARNING: This is a development server. Do not use it in a production deployment.\u001b[0m\n",
            "\u001b[2m   Use a production WSGI server instead.\u001b[0m\n",
            " * Debug mode: off\n",
            " * Running on http://587b529df178.ngrok.io\n",
            " * Traffic stats available on http://127.0.0.1:4040\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "127.0.0.1 - - [18/Nov/2020 21:23:29] \"\u001b[37mOPTIONS /form HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [18/Nov/2020 21:23:29] \"\u001b[37mPOST /form HTTP/1.1\u001b[0m\" 200 -\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "127.0.0.1 - - [18/Nov/2020 21:23:39] \"\u001b[37mGET / HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [18/Nov/2020 21:23:58] \"\u001b[37mOPTIONS /form HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [18/Nov/2020 21:23:59] \"\u001b[37mPOST /form HTTP/1.1\u001b[0m\" 200 -\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "127.0.0.1 - - [18/Nov/2020 21:23:59] \"\u001b[37mGET /report/0 HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [18/Nov/2020 21:24:00] \"\u001b[33mGET /favicon.ico HTTP/1.1\u001b[0m\" 404 -\n",
            "127.0.0.1 - - [18/Nov/2020 21:24:02] \"\u001b[37mGET /report/0 HTTP/1.1\u001b[0m\" 200 -\n"
          ],
          "name": "stderr"
        }
      ]
    }
  ]
}