GuntingBatuKertas.ipynb

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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "fUQ4CmpmFpeG"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Dicoding: Rock Scissor Paper\n",
        "- Nama: Wahyu Ikbal Maulana\n",
        "- Email: wahyuikbal777@gmail.com\n",
        "- Id Dicoding: wahyudesu"
      ],
      "metadata": {
        "id": "ATHV9b719EGp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Import library"
      ],
      "metadata": {
        "id": "h1smvdHk3TLv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras import layers, models\n",
        "from keras.preprocessing import image\n",
        "\n",
        "from google.colab import files"
      ],
      "metadata": {
        "id": "RRIx_HjB3sE7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load dataset"
      ],
      "metadata": {
        "id": "RAZcAI2o3W0C"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JtejYqFn-HfY",
        "outputId": "45271bc8-cbeb-4cfc-cc9b-05488d18999a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-12-20 12:53:17--  https://github.com/dicodingacademy/assets/releases/download/release/rockpaperscissors.zip\n",
            "Resolving github.com (github.com)... 140.82.112.3\n",
            "Connecting to github.com (github.com)|140.82.112.3|: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%2F20231220%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20231220T125317Z&X-Amz-Expires=300&X-Amz-Signature=87175f8ed6a52a898b35699f54a8408a7e30fdae2356c0c423ab3669da35a71d&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",
            "--2023-12-20 12:53:17--  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%2F20231220%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20231220T125317Z&X-Amz-Expires=300&X-Amz-Signature=87175f8ed6a52a898b35699f54a8408a7e30fdae2356c0c423ab3669da35a71d&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: ‘rockpaperscissors.zip’\n",
            "\n",
            "rockpaperscissors.z 100%[===================>] 307.92M   147MB/s    in 2.1s    \n",
            "\n",
            "2023-12-20 12:53:19 (147 MB/s) - ‘rockpaperscissors.zip’ saved [322873683/322873683]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget --no-check-certificate \\\n",
        "        https://github.com/dicodingacademy/assets/releases/download/release/rockpaperscissors.zip -O rockpaperscissors.zip"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import zipfile,os\n",
        "local_zip = '/content/rockpaperscissors.zip'\n",
        "zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
        "zip_ref.extractall('/content')\n",
        "zip_ref.close()\n",
        "\n",
        "directory = '/content/rockpaperscissors/rps-cv-images'\n",
        "directory"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "q10OZYlM_XN0",
        "outputId": "73dff1bf-d015-4fb0-ae2f-8cb4e4fb9804"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/rockpaperscissors/rps-cv-images'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Applying Data Augmentation"
      ],
      "metadata": {
        "id": "Zm6sBZQAFO70"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "datagen = ImageDataGenerator(\n",
        "                    rescale=1./255,\n",
        "                    rotation_range=20,\n",
        "                    horizontal_flip=True,\n",
        "                    vertical_flip=True,\n",
        "                    shear_range = 0.2,\n",
        "                    fill_mode = 'nearest',\n",
        "                    validation_split = 0.4\n",
        "                    )"
      ],
      "metadata": {
        "id": "BdvLy5mC9YNi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training set & Validation set"
      ],
      "metadata": {
        "id": "PbWGFemlFVjd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Data generator for training the model\n",
        "train_generator = datagen.flow_from_directory(\n",
        "    directory,\n",
        "    target_size=(100, 150),     # Chosen target image size\n",
        "    class_mode=\"categorical\",   # Image data classified into multiple classes\n",
        "    subset='training'           # Fetching training data from directory\n",
        ")\n",
        "\n",
        "# Data generator for validating the model\n",
        "val_generator = datagen.flow_from_directory(\n",
        "    directory,\n",
        "    target_size=(100, 150),     # Chosen target image size\n",
        "    class_mode=\"categorical\",   # Image data classified into multiple classes\n",
        "    subset='validation'         # Fetching validation data from directory\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p7JhoSKt-kzc",
        "outputId": "6e989851-7ae7-4346-e1b4-165b4789a767"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 1314 images belonging to 3 classes.\n",
            "Found 874 images belonging to 3 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_generator.class_indices"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2g2v3OfWFpLn",
        "outputId": "a35a16d4-b2c8-41b6-b821-af8cdad30c20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'paper': 0, 'rock': 1, 'scissors': 2}"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Defining the CNN Model\n"
      ],
      "metadata": {
        "id": "HxW9EaxkFeO7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.models.Sequential([\n",
        "    # Creating a sequential model\n",
        "    # The input shape is an image with size 150x150 and 3 color channels\n",
        "    tf.keras.layers.Conv2D(16, (3,3), activation='relu', input_shape=(100, 150, 3)),  # First convolutional layer\n",
        "    tf.keras.layers.MaxPooling2D(2, 2),\n",
        "\n",
        "    tf.keras.layers.Conv2D(32, (3,3), activation='relu'),                             # Second convolutional layer\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "\n",
        "    tf.keras.layers.Conv2D(32, (3,3), activation='relu'),                             # Third convolutional layer\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "\n",
        "    tf.keras.layers.Flatten(),                                                        # Input layer for the Deep Learning Neural Network\n",
        "    tf.keras.layers.Dropout(0.5),\n",
        "\n",
        "    tf.keras.layers.Dense(128, activation='relu'),                                   # First hidden layer with 128 neurons\n",
        "\n",
        "    tf.keras.layers.Dense(512, activation='relu'),                                   # Second hidden layer with 512 neurons\n",
        "\n",
        "    tf.keras.layers.Dense(3, activation='softmax')                                   # Output layer\n",
        "])\n",
        "\n",
        "model.summary()\n",
        "Model: \"Sequential\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "26ynn_5RnTTT",
        "outputId": "571db310-5b04-4fef-e508-1b1a69a32b5a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_3 (Conv2D)           (None, 98, 148, 16)       448       \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPoolin  (None, 49, 74, 16)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_4 (Conv2D)           (None, 47, 72, 32)        4640      \n",
            "                                                                 \n",
            " max_pooling2d_4 (MaxPoolin  (None, 23, 36, 32)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_5 (Conv2D)           (None, 21, 34, 32)        9248      \n",
            "                                                                 \n",
            " max_pooling2d_5 (MaxPoolin  (None, 10, 17, 32)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " flatten_1 (Flatten)         (None, 5440)              0         \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 5440)              0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 128)               696448    \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 512)               66048     \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 3)                 1539      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 778371 (2.97 MB)\n",
            "Trainable params: 778371 (2.97 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Compiling the model"
      ],
      "metadata": {
        "id": "6vHNSpftFlfD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(\n",
        "    loss = 'categorical_crossentropy',\n",
        "    optimizer = tf.optimizers.Adam(),\n",
        "    metrics = ['accuracy']\n",
        ")"
      ],
      "metadata": {
        "id": "GXZ5tzBTIDVb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(\n",
        "      train_generator,\n",
        "      steps_per_epoch=25,  # Number of batches to be executed in each epoch\n",
        "      epochs=21,  # Increase the number of epochs if the model accuracy is not optimal yet\n",
        "      validation_data=val_generator,  # Display the accuracy during validation data testing\n",
        "      validation_steps=5,  # Number of batches to be executed in each epoch of validation\n",
        "      verbose=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MoXuYXcLo_8R",
        "outputId": "c71c04dd-0da5-4f07-ecc7-b5dea2b72645"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/21\n",
            "25/25 [==============================] - 16s 578ms/step - loss: 1.0882 - accuracy: 0.3883 - val_loss: 0.9412 - val_accuracy: 0.5688\n",
            "Epoch 2/21\n",
            "25/25 [==============================] - 16s 634ms/step - loss: 0.6814 - accuracy: 0.7403 - val_loss: 0.4776 - val_accuracy: 0.8188\n",
            "Epoch 3/21\n",
            "25/25 [==============================] - 15s 596ms/step - loss: 0.4205 - accuracy: 0.8390 - val_loss: 0.3427 - val_accuracy: 0.8813\n",
            "Epoch 4/21\n",
            "25/25 [==============================] - 17s 668ms/step - loss: 0.3425 - accuracy: 0.8750 - val_loss: 0.2510 - val_accuracy: 0.9062\n",
            "Epoch 5/21\n",
            "25/25 [==============================] - 15s 612ms/step - loss: 0.2755 - accuracy: 0.9038 - val_loss: 0.2343 - val_accuracy: 0.9125\n",
            "Epoch 6/21\n",
            "25/25 [==============================] - 15s 614ms/step - loss: 0.2595 - accuracy: 0.9100 - val_loss: 0.1426 - val_accuracy: 0.9438\n",
            "Epoch 7/21\n",
            "25/25 [==============================] - 15s 613ms/step - loss: 0.2024 - accuracy: 0.9287 - val_loss: 0.2141 - val_accuracy: 0.9312\n",
            "Epoch 8/21\n",
            "25/25 [==============================] - 15s 567ms/step - loss: 0.2036 - accuracy: 0.9225 - val_loss: 0.2436 - val_accuracy: 0.9125\n",
            "Epoch 9/21\n",
            "25/25 [==============================] - 14s 549ms/step - loss: 0.2088 - accuracy: 0.9273 - val_loss: 0.2786 - val_accuracy: 0.9312\n",
            "Epoch 10/21\n",
            "25/25 [==============================] - 15s 594ms/step - loss: 0.1603 - accuracy: 0.9545 - val_loss: 0.2627 - val_accuracy: 0.9187\n",
            "Epoch 11/21\n",
            "25/25 [==============================] - 15s 581ms/step - loss: 0.2099 - accuracy: 0.9200 - val_loss: 0.1795 - val_accuracy: 0.9500\n",
            "Epoch 12/21\n",
            "25/25 [==============================] - 16s 641ms/step - loss: 0.2679 - accuracy: 0.9026 - val_loss: 0.2041 - val_accuracy: 0.9312\n",
            "Epoch 13/21\n",
            "25/25 [==============================] - 16s 653ms/step - loss: 0.1838 - accuracy: 0.9350 - val_loss: 0.2344 - val_accuracy: 0.9438\n",
            "Epoch 14/21\n",
            "25/25 [==============================] - 14s 574ms/step - loss: 0.1498 - accuracy: 0.9416 - val_loss: 0.2488 - val_accuracy: 0.9250\n",
            "Epoch 15/21\n",
            "25/25 [==============================] - 15s 594ms/step - loss: 0.1897 - accuracy: 0.9299 - val_loss: 0.1664 - val_accuracy: 0.9625\n",
            "Epoch 16/21\n",
            "25/25 [==============================] - 14s 578ms/step - loss: 0.1650 - accuracy: 0.9390 - val_loss: 0.1661 - val_accuracy: 0.9250\n",
            "Epoch 17/21\n",
            "25/25 [==============================] - 15s 597ms/step - loss: 0.1744 - accuracy: 0.9468 - val_loss: 0.1181 - val_accuracy: 0.9750\n",
            "Epoch 18/21\n",
            "25/25 [==============================] - 14s 556ms/step - loss: 0.1204 - accuracy: 0.9636 - val_loss: 0.1711 - val_accuracy: 0.9375\n",
            "Epoch 19/21\n",
            "25/25 [==============================] - 15s 611ms/step - loss: 0.1506 - accuracy: 0.9488 - val_loss: 0.1619 - val_accuracy: 0.9438\n",
            "Epoch 20/21\n",
            "25/25 [==============================] - 15s 594ms/step - loss: 0.0915 - accuracy: 0.9662 - val_loss: 0.1050 - val_accuracy: 0.9688\n",
            "Epoch 21/21\n",
            "25/25 [==============================] - 14s 574ms/step - loss: 0.1024 - accuracy: 0.9638 - val_loss: 0.1281 - val_accuracy: 0.9750\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Training and Validation Accuracy Plots"
      ],
      "metadata": {
        "id": "yHIrllns4ZTM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['accuracy'], label='accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label = 'val_accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([0.5, 1])\n",
        "plt.legend(loc='lower right')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "BsF6-tVS4VdE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "bf79ee51-7468-4754-8fff-ea8f03923564"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Image Prediction"
      ],
      "metadata": {
        "id": "fUQ4CmpmFpeG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "uploaded = files.upload()\n",
        "\n",
        "for fn in uploaded.keys():\n",
        "    path = fn\n",
        "    img = image.load_img(path, target_size=(100,150))\n",
        "    imgplot = plt.imshow(img)\n",
        "    x = image.img_to_array(img)\n",
        "    x = np.expand_dims(x, axis=0)\n",
        "\n",
        "    images = np.vstack([x])\n",
        "    classes = model.predict(images, batch_size=10)\n",
        "\n",
        "    if classes[0,0]!=0:\n",
        "        print('image ('+fn+') is paper')\n",
        "    elif classes[0,1]!=0:\n",
        "        print('image ('+fn+') is rock')\n",
        "    elif classes[0,2]!=0:\n",
        "        print('image ('+fn+') is scissor')\n",
        "    else:\n",
        "        print('unknown')"
      ],
      "metadata": {
        "id": "VDh3jmib7VcC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "22e96385-1a30-426e-ce2a-15610eaa9194"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-8f575330-d377-468a-baaf-0a3655e875b3\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-8f575330-d377-468a-baaf-0a3655e875b3\">\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 00nKV8oHuTGi20gq.png to 00nKV8oHuTGi20gq (1).png\n",
            "1/1 [==============================] - 0s 107ms/step\n",
            "image (00nKV8oHuTGi20gq (1).png) is rock\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}