Waste_Image_Classification_Prediction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Pljd2rp66tsV"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from glob import glob\n",
        "import sklearn.metrics as metrics\n",
        "import tensorflow as tf\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import Flatten, Dense\n",
        "from tensorflow.keras.applications import VGG16\n",
        "from tensorflow.keras.preprocessing.image import load_img, img_to_array"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classes = ['Aluminium', 'Carton', 'Glass', 'Organic Waste', 'Other Plastics', 'Paper and Cardboard', 'Plastic', 'Textiles', 'Wood']\n",
        "num_class = 9\n"
      ],
      "metadata": {
        "id": "zm5G8RGL9cih"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Sud-rN4-TY8",
        "outputId": "b70da581-3ecd-4141-93dd-f3c1a0da47cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "img_folder_path = \"/gdrive/My Drive/IOT/data\""
      ],
      "metadata": {
        "id": "cydLi2wb-YYc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir train"
      ],
      "metadata": {
        "id": "m7HxWcLS_mgw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /gdrive/My Drive/IOT/data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ppLaCe8FObCH",
        "outputId": "97d8dceb-39fd-4042-e8de-da0ee02a8655"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/gdrive/My Drive/IOT/data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cp *.jpg /content/train/"
      ],
      "metadata": {
        "id": "WaVu0cxWOlb4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/train/\n",
        "!ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iEKfMRnpOwtH",
        "outputId": "edca7ce5-69e9-45dd-a764-db35da11f12e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/train\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = []\n",
        "train = \"/content/train\"\n",
        "import os\n",
        "for filename in os.listdir(train):\n",
        "    if filename.endswith(\"jpg\"):\n",
        "        # Your code comes here such as\n",
        "        print(filename)\n",
        "        data.append(filename)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UB2bpWE0_n0N",
        "outputId": "6a161590-d387-4564-d096-d40239bd3a8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w1xSAUOVAGjB",
        "outputId": "2a74481d-199a-42e4-d0cb-bde1c7c0550b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(data))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "85xMw5qAAKLt",
        "outputId": "3e1713f0-a58c-4350-90bc-fc48a24d5648"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "323\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from PIL import Image\n",
        "\n",
        "model = tf.keras.models.load_model('/gdrive/My Drive/IOT/IOT_Waste_Model.h5')\n",
        "predictions = []\n",
        "\n",
        "for filename in os.listdir(train):\n",
        "    img = Image.open(os.path.join(train, filename))\n",
        "    i = \"/content/train/\"+filename\n",
        "    print(i)\n",
        "    img = tf.keras.preprocessing.image.load_img(i, target_size=(256, 256))\n",
        "    img_array = tf.keras.preprocessing.image.img_to_array(img)\n",
        "    img_array = tf.expand_dims(img_array, 0)\n",
        "    pred = model.predict(img_array)\n",
        "    class_label = np.argmax(pred)\n",
        "    predictions.append(class_label)\n",
        "    img.close()\n",
        "\n",
        "print(predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0JYq1XffANgd",
        "outputId": "688854a1-d644-4df9-8fc5-55a86dce95db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " print(predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TBW0eOglQDwO",
        "outputId": "132e5d5e-81a4-4f14-f22f-d17334af8cfe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[6, 5, 4, 7, 5, 5, 6, 5, 7, 5, 5, 6, 6, 7, 7, 5, 7, 6, 6, 6, 7, 6, 7, 6, 5, 6, 5, 5, 6, 7, 6, 5, 7, 6, 5, 6, 6, 5, 5, 6, 6, 7, 5, 7, 5, 5, 5, 5, 2, 7, 6, 5, 6, 5, 7, 6, 7, 6, 7, 6, 7, 6, 6, 5, 6, 6, 7, 7, 4, 6, 7, 7, 5, 6, 6, 7, 6, 5, 6, 6, 7, 6, 7, 7, 5, 5, 7, 6, 6, 7, 6, 7, 7, 7, 6, 5, 5, 6, 6, 6, 7, 6, 7, 5, 7, 7, 7, 6, 7, 5, 7, 6, 6, 6, 5, 6, 7, 5, 6, 7, 5, 7, 6, 6, 6, 7, 7, 6, 4, 6, 4, 7, 6, 7, 3, 7, 5, 7, 6, 5, 6, 6, 5, 5, 5, 7, 6, 6, 6, 6, 6, 5, 6, 7, 7, 6, 5, 6, 2, 7, 6, 6, 7, 7, 5, 5, 6, 6, 7, 6, 4, 7, 6, 7, 7, 7, 6, 6, 5, 6, 6, 7, 6, 7, 7, 6, 6, 6, 7, 7, 7, 5, 6, 5, 6, 6, 5, 6, 6, 6, 5, 7, 7, 5, 5, 6, 7, 7, 6, 5, 5, 7, 6, 6, 5, 5, 5, 5, 6, 6, 5, 7, 6, 5, 6, 5, 5, 5, 6, 7, 6, 7, 7, 6, 6, 6, 6, 6, 6, 5, 7, 6, 6, 5, 6, 6, 7, 5, 6, 5, 7, 7, 6, 6, 6, 5, 6, 6, 7, 7, 5, 6, 6, 7, 4, 7, 6, 6, 7, 5, 5, 6, 7, 6, 2, 5, 6, 6, 7, 6, 6, 6, 7, 6, 7, 6, 6, 6, 5, 6, 5, 5, 7, 5, 7, 6, 5, 7, 7, 5, 7, 6, 7, 7, 5, 7, 6, 7, 5, 6, 5, 6, 6, 6, 6, 5, 6, 5, 4, 6, 5, 7, 6]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " print(len(predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p984PutGWkoD",
        "outputId": "f7ee225b-35c6-4504-ae73-8b7a01eefe95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "323\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_arr = np.array(predictions)"
      ],
      "metadata": {
        "id": "tLAUvuw1WM3m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "filename_arr=np.array(data)"
      ],
      "metadata": {
        "id": "DEUxEneiWfBy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# concatenate the arrays column-wise\n",
        "output = np.column_stack((filename_arr, pred_arr))\n",
        "print(output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "auEcWzktWjXR",
        "outputId": "9872e0c6-1396-475c-f98a-99bc35fcb20d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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            " ['12.843405-80.154573_117.jpg' '6']]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.DataFrame({'classes': predictions})\n",
        "\n",
        "# create a dictionary to map integer values to categorical strings\n",
        "classes_mapping = {1: 'Aluminium', 2: 'Carton', 3: 'Glass', 4:'Organic Waste', 5:'Other Plastics', 6:'Paper and Cardboard',\n",
        "                        7:'Plastic', 8:'Textiles', 9:'Wood'}\n",
        "\n",
        "# use the map() function to apply the mapping to the dataframe column\n",
        "df['classes_mapping'] = df['classes'].map(classes_mapping)\n",
        "\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FwulUDJSXIom",
        "outputId": "710fb8bf-47ad-46f4-be09-50b65294868f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     classes      classes_mapping\n",
            "0          6  Paper and Cardboard\n",
            "1          5       Other Plastics\n",
            "2          4        Organic Waste\n",
            "3          7              Plastic\n",
            "4          5       Other Plastics\n",
            "..       ...                  ...\n",
            "318        4        Organic Waste\n",
            "319        6  Paper and Cardboard\n",
            "320        5       Other Plastics\n",
            "321        7              Plastic\n",
            "322        6  Paper and Cardboard\n",
            "\n",
            "[323 rows x 2 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_arr = df['classes_mapping'].to_numpy()"
      ],
      "metadata": {
        "id": "-c-sQF0ZZfrh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "out = np.column_stack((output, class_arr))\n",
        "print(out)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ceNpEu3_Z3z4",
        "outputId": "74756f55-a3b1-4423-a490-9908fe91b832"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[['12.843405-80.154573_136.jpg' '6' 'Paper and Cardboard']\n",
            " ['12.843405-80.154573_105.jpg' '5' 'Other Plastics']\n",
            " ['12.843405-80.154573_184.jpg' '4' 'Organic Waste']\n",
            " ['12.841837-80.155035_9.jpg' '7' 'Plastic']\n",
            " ['12.843405-80.154573_45.jpg' '5' 'Other Plastics']\n",
            " ['12.843405-80.154573_38.jpg' '5' 'Other Plastics']\n",
            " ['12.843405-80.154573_138.jpg' '6' 'Paper and Cardboard']\n",
            " ['12.843405-80.154573_90.jpg' '5' 'Other Plastics']\n",
            " ['12.841837-80.155035_19.jpg' '7' 'Plastic']\n",
            " ['12.843405-80.154573_111.jpg' '5' 'Other Plastics']\n",
            " ['12.843405-80.154573_49.jpg' '5' 'Other Plastics']\n",
            " ['12.843405-80.154573_171.jpg' '6' 'Paper and Cardboard']\n",
            " ['12.843405-80.154573_196.jpg' '6' 'Paper and Cardboard']\n",
            " ['12.841837-80.155035_68.jpg' '7' 'Plastic']\n",
            " ['12.841837-80.155035_28.jpg' '7' 'Plastic']\n",
            " ['12.843405-80.154573_60.jpg' '5' 'Other Plastics']\n",
            " ['12.841837-80.155035_33.jpg' '7' 'Plastic']\n",
            " ['12.843405-80.154573_139.jpg' '6' 'Paper and Cardboard']\n",
            " ['12.843405-80.154573_126.jpg' '6' 'Paper and Cardboard']\n",
            " ['12.843405-80.154573_177.jpg' '6' 'Paper and Cardboard']\n",
            " ['12.841837-80.155035_12.jpg' '7' 'Plastic']\n",
            " ['12.843405-80.154573_141.jpg' '6' 'Paper and Cardboard']\n",
            " ['12.841837-80.155035_47.jpg' '7' 'Plastic']\n",
            " ['12.843405-80.154573_39.jpg' '6' 'Paper and Cardboard']\n",
            " ['12.843405-80.154573_76.jpg' '5' 'Other Plastics']\n",
            " ['12.843405-80.154573_123.jpg' '6' 'Paper and Cardboard']\n",
            " ['12.843405-80.154573_61.jpg' '5' 'Other Plastics']\n",
            " ['12.843405-80.154573_95.jpg' '5' 'Other Plastics']\n",
            " ['12.841837-80.155035_83.jpg' '6' 'Paper and Cardboard']\n",
            " ['12.841837-80.155035_63.jpg' '7' 'Plastic']\n",
            " ['12.843405-80.154573_225.jpg' '6' 'Paper and Cardboard']\n",
            " ['12.843405-80.154573_58.jpg' '5' 'Other Plastics']\n",
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            " ['12.841837-80.155035_24.jpg' '7' 'Plastic']\n",
            " ['12.843405-80.154573_207.jpg' '5' 'Other Plastics']\n",
            " ['12.841837-80.155035_52.jpg' '7' 'Plastic']\n",
            " ['12.843405-80.154573_116.jpg' '6' 'Paper and Cardboard']\n",
            " ['12.843405-80.154573_71.jpg' '5' 'Other Plastics']\n",
            " ['12.841837-80.155035_18.jpg' '7' 'Plastic']\n",
            " ['12.841837-80.155035_4.jpg' '7' 'Plastic']\n",
            " ['12.843405-80.154573_83.jpg' '5' 'Other Plastics']\n",
            " ['12.841837-80.155035_3.jpg' '7' 'Plastic']\n",
            " ['12.843405-80.154573_19.jpg' '6' 'Paper and Cardboard']\n",
            " ['12.841837-80.155035_78.jpg' '7' 'Plastic']\n",
            " ['12.841837-80.155035_35.jpg' '7' 'Plastic']\n",
            " ['12.843405-80.154573_217.jpg' '5' 'Other Plastics']\n",
            " ['12.841837-80.155035_89.jpg' '7' 'Plastic']\n",
            " ['12.843405-80.154573_127.jpg' '6' 'Paper and Cardboard']\n",
            " ['12.841837-80.155035_70.jpg' '7' 'Plastic']\n",
            " ['12.843405-80.154573_89.jpg' '5' 'Other Plastics']\n",
            " ['12.843405-80.154573_204.jpg' '6' 'Paper and Cardboard']\n",
            " ['12.843405-80.154573_70.jpg' '5' 'Other Plastics']\n",
            " ['12.843405-80.154573_173.jpg' '6' 'Paper and Cardboard']\n",
            " ['12.843405-80.154573_146.jpg' '6' 'Paper and Cardboard']\n",
            " ['12.843405-80.154573_153.jpg' '6' 'Paper and Cardboard']\n",
            " ['12.843405-80.154573_29.jpg' '6' 'Paper and Cardboard']\n",
            " ['12.843405-80.154573_73.jpg' '5' 'Other Plastics']\n",
            " ['12.843405-80.154573_216.jpg' '6' 'Paper and Cardboard']\n",
            " ['12.843405-80.154573_97.jpg' '5' 'Other Plastics']\n",
            " ['12.843405-80.154573_224.jpg' '4' 'Organic Waste']\n",
            " ['12.843405-80.154573_223.jpg' '6' 'Paper and Cardboard']\n",
            " ['12.843405-80.154573_113.jpg' '5' 'Other Plastics']\n",
            " ['12.841837-80.155035_93.jpg' '7' 'Plastic']\n",
            " ['12.843405-80.154573_117.jpg' '6' 'Paper and Cardboard']]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "out_1 = pd.DataFrame(out)"
      ],
      "metadata": {
        "id": "hKRpqm6_bQXh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(out_1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mCQ1q9UdbchH",
        "outputId": "212458c0-bf2e-4361-c45e-ff275d5c38bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                               0  1                    2\n",
            "0    12.843405-80.154573_136.jpg  6  Paper and Cardboard\n",
            "1    12.843405-80.154573_105.jpg  5       Other Plastics\n",
            "2    12.843405-80.154573_184.jpg  4        Organic Waste\n",
            "3      12.841837-80.155035_9.jpg  7              Plastic\n",
            "4     12.843405-80.154573_45.jpg  5       Other Plastics\n",
            "..                           ... ..                  ...\n",
            "318  12.843405-80.154573_224.jpg  4        Organic Waste\n",
            "319  12.843405-80.154573_223.jpg  6  Paper and Cardboard\n",
            "320  12.843405-80.154573_113.jpg  5       Other Plastics\n",
            "321   12.841837-80.155035_93.jpg  7              Plastic\n",
            "322  12.843405-80.154573_117.jpg  6  Paper and Cardboard\n",
            "\n",
            "[323 rows x 3 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "out_1.set_axis([\"Image Name\", \"Prediction\", \"Waste Type\"],\n",
        "                    axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "02lNfMQAg2kQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(out_1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "08IOpBUig9w_",
        "outputId": "fc3c9546-6f21-4c4a-cedb-739cd5557d61"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                      Image Name Prediction           Waste Type\n",
            "0    12.843405-80.154573_136.jpg          6  Paper and Cardboard\n",
            "1    12.843405-80.154573_105.jpg          5       Other Plastics\n",
            "2    12.843405-80.154573_184.jpg          4        Organic Waste\n",
            "3      12.841837-80.155035_9.jpg          7              Plastic\n",
            "4     12.843405-80.154573_45.jpg          5       Other Plastics\n",
            "..                           ...        ...                  ...\n",
            "318  12.843405-80.154573_224.jpg          4        Organic Waste\n",
            "319  12.843405-80.154573_223.jpg          6  Paper and Cardboard\n",
            "320  12.843405-80.154573_113.jpg          5       Other Plastics\n",
            "321   12.841837-80.155035_93.jpg          7              Plastic\n",
            "322  12.843405-80.154573_117.jpg          6  Paper and Cardboard\n",
            "\n",
            "[323 rows x 3 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "out_1.to_csv('output.csv', encoding = 'utf-8-sig')\n",
        "files.download('output.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "LfEiJUyFaFIy",
        "outputId": "21ea20ba-2752-47a0-e189-d9f4ba0c7a1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_e9cb789e-f090-4c05-8f6b-3836cc6818de\", \"output.csv\", 15524)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "epSnb-hoeuw3"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}