{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ArovaSyams/deep_learning_image_classifier/blob/main/dicoding_submission_image_classifier.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tIA49NgRozMg"
      },
      "source": [
        " # Dicoding Submission Image Classifier with TensorFlow\n",
        " - Nama: Wifqo Arova Syams  \n",
        " - Email: wifqoarova17@gmail.com\n",
        " - Id Dicoding: Wifqo Arova Syams  \n",
        "  \n",
        " submission is an artificial neural network program using TensorFlow.   This program should be able to recognize the shape of a hand forming scissors, stone, or paper."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "install external library for split the folder"
      ],
      "metadata": {
        "id": "PA6O5GAbwmp_"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zzuFkhiXvV5B",
        "outputId": "c2097690-3e74-4646-b567-9a80a7baf073"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting split-folders\n",
            "  Downloading split_folders-0.5.1-py3-none-any.whl (8.4 kB)\n",
            "Installing collected packages: split-folders\n",
            "Successfully installed split-folders-0.5.1\n"
          ]
        }
      ],
      "source": [
        "# download splitfolders library\n",
        "!pip install split-folders"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Import the necessary library"
      ],
      "metadata": {
        "id": "El19qpjSw0sb"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 129,
      "metadata": {
        "id": "1Z4hhbnNoi13"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import zipfile, os\n",
        "import splitfolders\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "import numpy as np\n",
        "from google.colab import files\n",
        "from tensorflow.keras.preprocessing import image\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Download the dataset from wget provided by dicoding"
      ],
      "metadata": {
        "id": "_GUDpGwmw4gu"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BUHPvW3Tp61q",
        "outputId": "2c555541-952a-4d49-daed-de9fa26c2446"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2023-11-10 11:32:42--  https://github.com/dicodingacademy/assets/releases/download/release/rockpaperscissors.zip\n",
            "Resolving github.com (github.com)... 140.82.112.4\n",
            "Connecting to github.com (github.com)|140.82.112.4|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/391417272/7eb836f2-695b-4a46-9c78-b65867166957?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20231110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20231110T113242Z&X-Amz-Expires=300&X-Amz-Signature=3a555dbdc51a9ca09f93a44f97d41f94a61e81231143a97134f3e7b892a2caff&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=391417272&response-content-disposition=attachment%3B%20filename%3Drockpaperscissors.zip&response-content-type=application%2Foctet-stream [following]\n",
            "--2023-11-10 11:32:42--  https://objects.githubusercontent.com/github-production-release-asset-2e65be/391417272/7eb836f2-695b-4a46-9c78-b65867166957?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20231110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20231110T113242Z&X-Amz-Expires=300&X-Amz-Signature=3a555dbdc51a9ca09f93a44f97d41f94a61e81231143a97134f3e7b892a2caff&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=391417272&response-content-disposition=attachment%3B%20filename%3Drockpaperscissors.zip&response-content-type=application%2Foctet-stream\n",
            "Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 322873683 (308M) [application/octet-stream]\n",
            "Saving to: ‘/tmp/rockpaperscissors.zip’\n",
            "\n",
            "/tmp/rockpapersciss 100%[===================>] 307.92M  25.7MB/s    in 5.2s    \n",
            "\n",
            "2023-11-10 11:32:48 (59.6 MB/s) - ‘/tmp/rockpaperscissors.zip’ saved [322873683/322873683]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# download data from wget\n",
        "!wget --no-check-certificate \\\n",
        "    https://github.com/dicodingacademy/assets/releases/download/release/rockpaperscissors.zip \\\n",
        "    -O /tmp/rockpaperscissors.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Extract the data that has been downloaded and place it in tmp directory"
      ],
      "metadata": {
        "id": "82waaGbQxBF0"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2IBSqhLgqYkC"
      },
      "outputs": [],
      "source": [
        "# extract the data that we download\n",
        "local_zip = \"/tmp/rockpaperscissors.zip\"\n",
        "zip_ref = zipfile.ZipFile(local_zip, \"r\")\n",
        "zip_ref.extractall(\"/tmp\")\n",
        "zip_ref.close()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Split directory to train directory and validation directory, and then make the copy with name rps_splitted"
      ],
      "metadata": {
        "id": "QjFKxCGvxJ8h"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fWRE7ICyxIG_",
        "outputId": "cec64c72-67ec-464c-dbd1-441c988240b2"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Copying files: 2189 files [00:00, 2935.37 files/s]\n"
          ]
        }
      ],
      "source": [
        "# split folder to train, val dir\n",
        "splitfolders.ratio(\"/tmp/rockpaperscissors\", output=\"/tmp/rps_splitted\", seed=1337, ratio=(.6, .4))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This step we will divide the directory for train and validation diectory"
      ],
      "metadata": {
        "id": "HIQFewiVxV3p"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xulBuOQEqx01"
      },
      "outputs": [],
      "source": [
        "# divide directory for train and validation dir\n",
        "base_dir = \"/tmp/rps_splitted\"\n",
        "train_dir = os.path.join(base_dir, \"train\")\n",
        "validation_dir = os.path.join(base_dir, \"val\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "we will remove unnecesary folder and file that not will be use for training"
      ],
      "metadata": {
        "id": "surbtxmJxcug"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ncNMzFynBECS"
      },
      "outputs": [],
      "source": [
        "# remove useless dir from train and validation dir\n",
        "os.rmdir(\"/tmp/rps_splitted/train/rps-cv-images\")\n",
        "os.remove(\"/tmp/rps_splitted/val/rps-cv-images/README_rpc-cv-images.txt\")\n",
        "os.rmdir(\"/tmp/rps_splitted/val/rps-cv-images\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Use Image Data Generator to make Image Augmentation with all writed bellow for make more variant about data."
      ],
      "metadata": {
        "id": "uPjwG3sqxs0_"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MtVPeGBuwstL"
      },
      "outputs": [],
      "source": [
        "# use imagedatagenerator for preparing train and validation data\n",
        "train_dategen = ImageDataGenerator(\n",
        "    rescale=1./255,\n",
        "    rotation_range=30,\n",
        "    horizontal_flip=True,\n",
        "    vertical_flip=True,\n",
        "    brightness_range=[0.4,1.5],\n",
        "    shear_range=0.4,\n",
        "    fill_mode=\"nearest\",\n",
        "    width_shift_range=0.2,\n",
        "    height_shift_range=0.2,\n",
        "    zoom_range=0.3\n",
        ")\n",
        "\n",
        "test_datagen = ImageDataGenerator(rescale=1./255)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prepare training data and validation data from the image dataset loaded in memory via flow_from_directory function"
      ],
      "metadata": {
        "id": "z6i2Nwd6yG_d"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 115,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DaEJPLBtzJxz",
        "outputId": "0fadc157-de73-41c7-b996-95a9f38f1106"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 1312 images belonging to 3 classes.\n",
            "Found 876 images belonging to 3 classes.\n"
          ]
        }
      ],
      "source": [
        "# prepare training and validation data from the image dataset loaded in memory via the flow() function.\n",
        "train_generator = train_dategen.flow_from_directory(\n",
        "    train_dir,\n",
        "    target_size=(150, 150),\n",
        "    batch_size=32,\n",
        "    subset=\"training\",\n",
        "    class_mode=\"categorical\"\n",
        ")\n",
        "\n",
        "validation_generator = test_datagen.flow_from_directory(\n",
        "    validation_dir,\n",
        "    target_size=(150, 150),\n",
        "    batch_size=32,\n",
        "    class_mode=\"categorical\"\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here we will make the Sequential model that have 5 convolution and max pooling, and 3 hidden layer"
      ],
      "metadata": {
        "id": "lHB5YtU4yWRy"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 133,
      "metadata": {
        "id": "90Ibuprl20Y8"
      },
      "outputs": [],
      "source": [
        "# make model with 5 layer convolution and max pooling, and 3 hidden layer\n",
        "model = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Conv2D(32, (3, 3), activation=\"relu\", input_shape=(150, 150, 3)),\n",
        "    tf.keras.layers.MaxPooling2D(2, 2,),\n",
        "    tf.keras.layers.Conv2D(64, (3, 3), activation=\"relu\"),\n",
        "    tf.keras.layers.MaxPooling2D(2, 2,),\n",
        "    tf.keras.layers.Conv2D(128, (3, 3), activation=\"relu\"),\n",
        "    tf.keras.layers.MaxPooling2D(2, 2,),\n",
        "    tf.keras.layers.Conv2D(512, (3, 3), activation=\"relu\"),\n",
        "    tf.keras.layers.MaxPooling2D(2, 2,),\n",
        "    tf.keras.layers.Conv2D(1024, (3, 3), activation=\"relu\"),\n",
        "    tf.keras.layers.MaxPooling2D(2, 2,),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(1024, activation=\"relu\"),\n",
        "    tf.keras.layers.Dense(1024, activation=\"relu\"),\n",
        "    tf.keras.layers.Dense(1024, activation=\"relu\"),\n",
        "    tf.keras.layers.Dense(3, activation=\"sigmoid\")\n",
        "])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Summarize the model for make sure it go as planned"
      ],
      "metadata": {
        "id": "88nX7BaZyput"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 117,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HvEisoaTAooG",
        "outputId": "d3a5204c-429a-4454-eb3b-50253cf5f2a8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_13\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_60 (Conv2D)          (None, 148, 148, 32)      896       \n",
            "                                                                 \n",
            " max_pooling2d_60 (MaxPooli  (None, 74, 74, 32)        0         \n",
            " ng2D)                                                           \n",
            "                                                                 \n",
            " conv2d_61 (Conv2D)          (None, 72, 72, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_61 (MaxPooli  (None, 36, 36, 64)        0         \n",
            " ng2D)                                                           \n",
            "                                                                 \n",
            " conv2d_62 (Conv2D)          (None, 34, 34, 128)       73856     \n",
            "                                                                 \n",
            " max_pooling2d_62 (MaxPooli  (None, 17, 17, 128)       0         \n",
            " ng2D)                                                           \n",
            "                                                                 \n",
            " conv2d_63 (Conv2D)          (None, 15, 15, 512)       590336    \n",
            "                                                                 \n",
            " max_pooling2d_63 (MaxPooli  (None, 7, 7, 512)         0         \n",
            " ng2D)                                                           \n",
            "                                                                 \n",
            " conv2d_64 (Conv2D)          (None, 5, 5, 1024)        4719616   \n",
            "                                                                 \n",
            " max_pooling2d_64 (MaxPooli  (None, 2, 2, 1024)        0         \n",
            " ng2D)                                                           \n",
            "                                                                 \n",
            " flatten_14 (Flatten)        (None, 4096)              0         \n",
            "                                                                 \n",
            " dense_46 (Dense)            (None, 1024)              4195328   \n",
            "                                                                 \n",
            " dense_47 (Dense)            (None, 1024)              1049600   \n",
            "                                                                 \n",
            " dense_48 (Dense)            (None, 1024)              1049600   \n",
            "                                                                 \n",
            " dense_49 (Dense)            (None, 3)                 3075      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 11700803 (44.64 MB)\n",
            "Trainable params: 11700803 (44.64 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here we will compile the model with SGD Optimizer and categorical crossentropy loss function"
      ],
      "metadata": {
        "id": "o8RT6MkqyvDS"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 118,
      "metadata": {
        "id": "N2aNZNceKwsI"
      },
      "outputs": [],
      "source": [
        "# compile model with 'SGD'  Optimizer and categorical crosseentropy function loss\n",
        "model.compile(\n",
        "    loss=\"categorical_crossentropy\",\n",
        "    optimizer=tf.keras.optimizers.SGD(),\n",
        "    metrics=[\"accuracy\"]\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this step we start to train the model with 15 epoch and 20 step each epoch for training data and 5 steps for validation data"
      ],
      "metadata": {
        "id": "HsAFGDYKy5ad"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 123,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CW-r94UvMy2f",
        "outputId": "7ddf7947-3cc2-446d-ff0a-7e17100918a6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "20/20 [==============================] - 64s 3s/step - loss: 0.9464 - accuracy: 0.6547 - val_loss: 0.8582 - val_accuracy: 0.7750\n",
            "Epoch 2/15\n",
            "20/20 [==============================] - 63s 3s/step - loss: 0.9154 - accuracy: 0.6047 - val_loss: 0.8569 - val_accuracy: 0.6625\n",
            "Epoch 3/15\n",
            "20/20 [==============================] - 64s 3s/step - loss: 0.8804 - accuracy: 0.5938 - val_loss: 0.6897 - val_accuracy: 0.8875\n",
            "Epoch 4/15\n",
            "20/20 [==============================] - 63s 3s/step - loss: 0.7699 - accuracy: 0.7109 - val_loss: 0.9702 - val_accuracy: 0.6313\n",
            "Epoch 5/15\n",
            "20/20 [==============================] - 59s 3s/step - loss: 0.8217 - accuracy: 0.6625 - val_loss: 0.5888 - val_accuracy: 0.8000\n",
            "Epoch 6/15\n",
            "20/20 [==============================] - 61s 3s/step - loss: 0.6154 - accuracy: 0.7812 - val_loss: 0.4460 - val_accuracy: 0.8313\n",
            "Epoch 7/15\n",
            "20/20 [==============================] - 60s 3s/step - loss: 0.5584 - accuracy: 0.7906 - val_loss: 0.3781 - val_accuracy: 0.9187\n",
            "Epoch 8/15\n",
            "20/20 [==============================] - 60s 3s/step - loss: 0.4761 - accuracy: 0.8281 - val_loss: 0.4223 - val_accuracy: 0.8375\n",
            "20/20 [==============================] - 60s 3s/step - loss: 0.4558 - accuracy: 0.8391 - val_loss: 0.4216 - val_accuracy: 0.8438\n",
            "Epoch 10/15\n",
            "20/20 [==============================] - 65s 3s/step - loss: 0.3854 - accuracy: 0.8687 - val_loss: 0.3257 - val_accuracy: 0.9000\n",
            "Epoch 11/15\n",
            "20/20 [==============================] - 61s 3s/step - loss: 0.3072 - accuracy: 0.8969 - val_loss: 0.2723 - val_accuracy: 0.9000\n",
            "Epoch 12/15\n",
            "20/20 [==============================] - 61s 3s/step - loss: 0.3637 - accuracy: 0.8625 - val_loss: 0.4339 - val_accuracy: 0.8313\n",
            "Epoch 13/15\n",
            "20/20 [==============================] - 60s 3s/step - loss: 0.3059 - accuracy: 0.8875 - val_loss: 0.2650 - val_accuracy: 0.9187\n",
            "Epoch 14/15\n",
            "20/20 [==============================] - 59s 3s/step - loss: 0.2201 - accuracy: 0.9250 - val_loss: 0.2675 - val_accuracy: 0.9125\n",
            "Epoch 15/15\n",
            "20/20 [==============================] - 60s 3s/step - loss: 0.1914 - accuracy: 0.9266 - val_loss: 0.2188 - val_accuracy: 0.9312\n"
          ]
        }
      ],
      "source": [
        "# train model with fit()\n",
        "model_accuracy = model.fit(\n",
        "    train_generator,\n",
        "    steps_per_epoch=20,\n",
        "    epochs=15,\n",
        "    validation_data=validation_generator,\n",
        "    validation_steps=5\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see here the accuracy of the model is high enough with ratio 0.93"
      ],
      "metadata": {
        "id": "YYr3jHyVzGo7"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 125,
      "metadata": {
        "id": "XcVCQj8NN84L",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "72d92f05-7202-4902-d093-7c43a8adb679"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.926562488079071"
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ],
      "source": [
        "# test for train accuracy\n",
        "model_accuracy.history[\"accuracy\"][-1]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "the validation accuracy have the high ratio that is 0.93"
      ],
      "metadata": {
        "id": "bvraDZZnzTO9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# test for validation accuracy\n",
        "model_accuracy.history[\"val_accuracy\"][-1]"
      ],
      "metadata": {
        "id": "ysfvSK4Vswou",
        "outputId": "e76597a3-c363-477f-ef15-cf3f9b15e221",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9312499761581421"
            ]
          },
          "metadata": {},
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this process we try for predict the new data"
      ],
      "metadata": {
        "id": "xg155TcazZyD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# predict for the new data\n",
        "uploaded = files.upload()\n",
        "\n",
        "for fn in uploaded.keys():\n",
        "\n",
        "    # predicting for image\n",
        "    path = fn\n",
        "    img = image.load_img(path, target_size=(150, 150))\n",
        "\n",
        "    imgplot = plt.imshow(img)\n",
        "    x = image.img_to_array(img)\n",
        "    x = np.expand_dims(x, axis=0)\n",
        "    images = np.vstack([x])\n",
        "\n",
        "    classes = model.predict(images, batch_size=10)\n",
        "    print(fn)\n",
        "\n",
        "    if classes[0][0]==1:\n",
        "        print('Paper')\n",
        "    elif classes[0][1]==1:\n",
        "        print('Rock')\n",
        "    elif classes[0][2]==1:\n",
        "        print('Scissor')"
      ],
      "metadata": {
        "id": "lXB654f7uAig",
        "outputId": "4205ad8d-95ee-4787-a042-75941ee2691e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-c7e214eb-c092-4f09-9f14-0d37517568e2\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-c7e214eb-c092-4f09-9f14-0d37517568e2\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving sYwtAuLI5zLIEfJI.png to sYwtAuLI5zLIEfJI.png\n",
            "1/1 [==============================] - 0s 51ms/step\n",
            "sYwtAuLI5zLIEfJI.png\n",
            "Scissor\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPkD8wlb7WFsVrmQ3b85yf0",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}