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    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MohammedMusadiq/Food101/blob/main/9_PyTorch_Model_Deployment.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 09. PyTorch Model Deployment\n",
        "\n",
        "What is model deployment ?\n",
        "\n",
        "Machine learning model deployment is the act of making your machine learning model(s) available to someone or something else.\n",
        "\n",
        "* ** Resources : **\n",
        "  * Book version :  https://www.learnpytorch.io/09_pytorch_model_deployment/\n",
        "  * Slides : https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/09_pytorch_model_deployment.pdf\n"
      ],
      "metadata": {
        "id": "_Hwadw4wwp5_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 0. Get Setup"
      ],
      "metadata": {
        "id": "QD6DC7g_xTmk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# For this notebook to run with updated APIs, we need torch 1.12+ and torchvision 0.13+\n",
        "try:\n",
        "  import torch\n",
        "  import torchvision\n",
        "  assert int(torch.__version__.split(\".\")[1]) >= 12, \"torch version should be 1.12+\"\n",
        "  assert int(torchvision.__version__.split(\".\")[1]) >= 13, \"torchvision version should be 0.13+\"\n",
        "  print(f\"Torch version : {torch.__version__}\")\n",
        "  print(f\"Torchvision version : {torchvision.__version__}\")\n",
        "except:\n",
        "  print(f\"[INFO] torch/torchvision versions not as required, installing nightly versions.\")\n",
        "  !pip3 install -U torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113\n",
        "  import torch\n",
        "  import torchvision\n",
        "  print(f\"torch version : {torch.__version__}\")\n",
        "  print(f\"torchvision version : {torch.__version__}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Mc8o6Jxxne2",
        "outputId": "3fd918fa-33d9-47ed-c16a-7f463d0277be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Torch version : 1.13.0+cu116\n",
            "Torchvision version : 0.14.0+cu116\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Continue with regular imports\n",
        "import matplotlib.pyplot as plt\n",
        "import torch\n",
        "import torchvision\n",
        "\n",
        "from torch import nn\n",
        "from torchvision import transforms\n",
        "\n",
        "# Try to get torchinfo, install it if it doesn't work\n",
        "try:\n",
        "    from torchinfo import summary\n",
        "except:\n",
        "    print(\"[INFO] Couldn't find torchinfo... installing it.\")\n",
        "    !pip install -q torchinfo\n",
        "    from torchinfo import summary\n",
        "\n",
        "# Try to import the going_modular directory, download it from GitHub if it doesn't work\n",
        "try:\n",
        "    from going_modular.going_modular import data_setup, engine\n",
        "    from helper_functions import download_data, set_seeds, plot_loss_curves\n",
        "except:\n",
        "    # Get the going_modular scripts\n",
        "    print(\"[INFO] Couldn't find going_modular or helper_functions scripts... downloading them from GitHub.\")\n",
        "    !git clone https://github.com/mrdbourke/pytorch-deep-learning\n",
        "    !mv pytorch-deep-learning/going_modular .\n",
        "    !mv pytorch-deep-learning/helper_functions.py . # get the helper_functions.py script\n",
        "    !rm -rf pytorch-deep-learning\n",
        "    from going_modular.going_modular import data_setup, engine\n",
        "    from helper_functions import download_data, set_seeds, plot_loss_curves"
      ],
      "metadata": {
        "id": "Eer3KlTLVffQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cc2974b1-b26c-41ba-9e95-7c940c122c69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INFO] Couldn't find torchinfo... installing it.\n",
            "[INFO] Couldn't find going_modular or helper_functions scripts... downloading them from GitHub.\n",
            "Cloning into 'pytorch-deep-learning'...\n",
            "remote: Enumerating objects: 3350, done.\u001b[K\n",
            "remote: Total 3350 (delta 0), reused 0 (delta 0), pack-reused 3350\u001b[K\n",
            "Receiving objects: 100% (3350/3350), 640.93 MiB | 21.69 MiB/s, done.\n",
            "Resolving deltas: 100% (1921/1921), done.\n",
            "Checking out files: 100% (221/221), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls going_modular/going_modular"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kO0pshxYXu8e",
        "outputId": "82da6993-4753-467d-fa28-db9819543588"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "data_setup.py  model_builder.py  __pycache__  train.py\n",
            "engine.py      predictions.py\t README.md    utils.py\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "w4aezMexYz_o",
        "outputId": "27a4bf0a-891c-478d-bf4c-8d2826dbc3c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'cuda'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. Getting Data\n",
        "\n",
        "The dataset we're going to use for deploying a FoodVision Mini model is...\n",
        "\n",
        "Pizza, steak, sushi 20% dataset (pizza, steak, sushi classes from Food101, random 20% of samples)\n",
        "\n",
        "We can get data with code from : https://www.learnpytorch.io/09_pytorch_model_deployment/#1-getting-data\n"
      ],
      "metadata": {
        "id": "nKE7Ix1kY5hm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Download pizza, steak, sushi images from GitHub\n",
        "data_20_percent_path = download_data(\n",
        "    source = \"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi_20_percent.zip\",\n",
        "    destination = \"pizza_steak_sushi_20_percent\"\n",
        ")\n",
        "data_20_percent_path"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PW2rhZnQZcx4",
        "outputId": "7329ec8a-136f-4a5d-f084-8c528c6ad11a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INFO] Did not find data/pizza_steak_sushi_20_percent directory, creating one...\n",
            "[INFO] Downloading pizza_steak_sushi_20_percent.zip from https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi_20_percent.zip...\n",
            "[INFO] Unzipping pizza_steak_sushi_20_percent.zip data...\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PosixPath('data/pizza_steak_sushi_20_percent')"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Setup training and test paths\n",
        "train_dir = data_20_percent_path / \"train\"\n",
        "test_dir = data_20_percent_path / \"test\"\n",
        "\n",
        "train_dir, test_dir"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k0peFjY6Zz2g",
        "outputId": "d9db2a9b-7eca-4dfc-d526-39ccc393154e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(PosixPath('data/pizza_steak_sushi_20_percent/train'),\n",
              " PosixPath('data/pizza_steak_sushi_20_percent/test'))"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2. FoodVision Mini model deployment experiment outline\n",
        "\n",
        "**3 Questions : **\n",
        "1. What is my most ideal machine learning model deployment scenario ?\n",
        "2. Where is my model going to go ?\n",
        "3. How is my model going to function ?\n",
        "\n",
        "**FoodVision Mini ideal use case : ** A model that performs well and fast.\n",
        "1. Performs well: 95%+ accuracy\n",
        "2. Fast: as close to real-time (or faster) as possible (30FPS+ or 30ms latency)\n",
        "\n",
        "* Latency = Time for prediction to take place\n",
        "\n",
        "To try and achieve these goals, we're going to build two model experiments:\n",
        "1. EffnetB2 feature extractor (just like in 07. PyTorch Experiment Tracking)\n",
        "2. ViT feature extractor (just like in 08. PyTorch Paper Replicating)"
      ],
      "metadata": {
        "id": "9l9duiqfaBck"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. Creating an EffNetB2 feature extractor\n",
        "\n",
        "Feature extractor = a term for a transfer learning model that has it's base layers frozen and output layers (or head layers) customized to a certain problem.torchvision\n",
        "\n",
        "EffNetB2 pretrained model in PyTorch - https://pytorch.org/vision/stable/models/generated/torchvision.models.efficientnet_b2.html#torchvision.models.EfficientNet_B2_Weights"
      ],
      "metadata": {
        "id": "GS-6mqY1bkHA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torchvision\n",
        "\n",
        "# 1. Setup pretrained EffNetB2 weights\n",
        "effnetb2_weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT # \"DEFAULT\" is equivalent to saying \"best available\"\n",
        "\n",
        "# 2. Get EffNetB2 transforms\n",
        "effnetb2_transforms = effnetb2_weights.transforms()\n",
        "\n",
        "# 3. Setup pretrained model instance\n",
        "effnetb2 = torchvision.models.efficientnet_b2(weights = effnetb2_weights) # could also use weights = \"DEFAULT\"\n",
        "\n",
        "# 4. Freeze the base layers in the model (this will stop all layers from training)\n",
        "for param in effnetb2.parameters():\n",
        "  param.requires_grad = False"
      ],
      "metadata": {
        "id": "93gUBqZ9cBVs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86,
          "referenced_widgets": [
            "8ed4e4f682ee47ed9ee0fe9932da2ef8",
            "ea43de6a24874a7d8718648eaadc855a",
            "41d3cafbaa1b4504813a45ba715dd94c",
            "5d3df725d0844a379797282c2d04d043",
            "fae51c91baae4894817a6288ccd89a2e",
            "36ec9a84ae3b4515a60402ae677f425a",
            "1087bd5a54814363984353c932b9a28a",
            "3bf12a0f88044e57b455ab938fb87aeb",
            "499200f4e75d4691b978b2229f2e5b40",
            "f96121b2600b475e8f88fe23fd1f44e2",
            "79dadfd5efc34945a865648f766477b9"
          ]
        },
        "outputId": "80aab613-6320-4d86-e6b3-568cc30515b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/efficientnet_b2_rwightman-bcdf34b7.pth\" to /root/.cache/torch/hub/checkpoints/efficientnet_b2_rwightman-bcdf34b7.pth\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0.00/35.2M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8ed4e4f682ee47ed9ee0fe9932da2ef8"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "effnetb2.classifier"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZYgPJfkHeOy8",
        "outputId": "25e89e65-cdc4-4647-b139-0fb6a425e816"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sequential(\n",
              "  (0): Dropout(p=0.3, inplace=True)\n",
              "  (1): Linear(in_features=1408, out_features=1000, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Set seeds for reproducibility\n",
        "set_seeds()\n",
        "effnetb2.classifier = nn.Sequential(\n",
        "    nn.Dropout(p=0.3, inplace = True),\n",
        "    nn.Linear(in_features = 1408, out_features = 3, bias = True)\n",
        ")"
      ],
      "metadata": {
        "id": "1yor0X3deR44"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torchinfo import summary\n",
        "\n",
        "# Print EffnetB2 model summary (uncomment for full output)\n",
        "summary(\n",
        "    effnetb2,\n",
        "    input_size = (1, 3, 224, 224),\n",
        "    col_names = [\"input_size\", \"output_size\", \"num_params\", \"trainable\"],\n",
        "    col_width = 20,\n",
        "    row_settings = [\"var_names\"]\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mZVpOwoke1Ay",
        "outputId": "c8fade19-c0c1-40e1-f412-56ffc560cef9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "============================================================================================================================================\n",
              "Layer (type (var_name))                                      Input Shape          Output Shape         Param #              Trainable\n",
              "============================================================================================================================================\n",
              "EfficientNet (EfficientNet)                                  [1, 3, 224, 224]     [1, 3]               --                   Partial\n",
              "├─Sequential (features)                                      [1, 3, 224, 224]     [1, 1408, 7, 7]      --                   False\n",
              "│    └─Conv2dNormActivation (0)                              [1, 3, 224, 224]     [1, 32, 112, 112]    --                   False\n",
              "│    │    └─Conv2d (0)                                       [1, 3, 224, 224]     [1, 32, 112, 112]    (864)                False\n",
              "│    │    └─BatchNorm2d (1)                                  [1, 32, 112, 112]    [1, 32, 112, 112]    (64)                 False\n",
              "│    │    └─SiLU (2)                                         [1, 32, 112, 112]    [1, 32, 112, 112]    --                   --\n",
              "│    └─Sequential (1)                                        [1, 32, 112, 112]    [1, 16, 112, 112]    --                   False\n",
              "│    │    └─MBConv (0)                                       [1, 32, 112, 112]    [1, 16, 112, 112]    (1,448)              False\n",
              "│    │    └─MBConv (1)                                       [1, 16, 112, 112]    [1, 16, 112, 112]    (612)                False\n",
              "│    └─Sequential (2)                                        [1, 16, 112, 112]    [1, 24, 56, 56]      --                   False\n",
              "│    │    └─MBConv (0)                                       [1, 16, 112, 112]    [1, 24, 56, 56]      (6,004)              False\n",
              "│    │    └─MBConv (1)                                       [1, 24, 56, 56]      [1, 24, 56, 56]      (10,710)             False\n",
              "│    │    └─MBConv (2)                                       [1, 24, 56, 56]      [1, 24, 56, 56]      (10,710)             False\n",
              "│    └─Sequential (3)                                        [1, 24, 56, 56]      [1, 48, 28, 28]      --                   False\n",
              "│    │    └─MBConv (0)                                       [1, 24, 56, 56]      [1, 48, 28, 28]      (16,518)             False\n",
              "│    │    └─MBConv (1)                                       [1, 48, 28, 28]      [1, 48, 28, 28]      (43,308)             False\n",
              "│    │    └─MBConv (2)                                       [1, 48, 28, 28]      [1, 48, 28, 28]      (43,308)             False\n",
              "│    └─Sequential (4)                                        [1, 48, 28, 28]      [1, 88, 14, 14]      --                   False\n",
              "│    │    └─MBConv (0)                                       [1, 48, 28, 28]      [1, 88, 14, 14]      (50,300)             False\n",
              "│    │    └─MBConv (1)                                       [1, 88, 14, 14]      [1, 88, 14, 14]      (123,750)            False\n",
              "│    │    └─MBConv (2)                                       [1, 88, 14, 14]      [1, 88, 14, 14]      (123,750)            False\n",
              "│    │    └─MBConv (3)                                       [1, 88, 14, 14]      [1, 88, 14, 14]      (123,750)            False\n",
              "│    └─Sequential (5)                                        [1, 88, 14, 14]      [1, 120, 14, 14]     --                   False\n",
              "│    │    └─MBConv (0)                                       [1, 88, 14, 14]      [1, 120, 14, 14]     (149,158)            False\n",
              "│    │    └─MBConv (1)                                       [1, 120, 14, 14]     [1, 120, 14, 14]     (237,870)            False\n",
              "│    │    └─MBConv (2)                                       [1, 120, 14, 14]     [1, 120, 14, 14]     (237,870)            False\n",
              "│    │    └─MBConv (3)                                       [1, 120, 14, 14]     [1, 120, 14, 14]     (237,870)            False\n",
              "│    └─Sequential (6)                                        [1, 120, 14, 14]     [1, 208, 7, 7]       --                   False\n",
              "│    │    └─MBConv (0)                                       [1, 120, 14, 14]     [1, 208, 7, 7]       (301,406)            False\n",
              "│    │    └─MBConv (1)                                       [1, 208, 7, 7]       [1, 208, 7, 7]       (686,868)            False\n",
              "│    │    └─MBConv (2)                                       [1, 208, 7, 7]       [1, 208, 7, 7]       (686,868)            False\n",
              "│    │    └─MBConv (3)                                       [1, 208, 7, 7]       [1, 208, 7, 7]       (686,868)            False\n",
              "│    │    └─MBConv (4)                                       [1, 208, 7, 7]       [1, 208, 7, 7]       (686,868)            False\n",
              "│    └─Sequential (7)                                        [1, 208, 7, 7]       [1, 352, 7, 7]       --                   False\n",
              "│    │    └─MBConv (0)                                       [1, 208, 7, 7]       [1, 352, 7, 7]       (846,900)            False\n",
              "│    │    └─MBConv (1)                                       [1, 352, 7, 7]       [1, 352, 7, 7]       (1,888,920)          False\n",
              "│    └─Conv2dNormActivation (8)                              [1, 352, 7, 7]       [1, 1408, 7, 7]      --                   False\n",
              "│    │    └─Conv2d (0)                                       [1, 352, 7, 7]       [1, 1408, 7, 7]      (495,616)            False\n",
              "│    │    └─BatchNorm2d (1)                                  [1, 1408, 7, 7]      [1, 1408, 7, 7]      (2,816)              False\n",
              "│    │    └─SiLU (2)                                         [1, 1408, 7, 7]      [1, 1408, 7, 7]      --                   --\n",
              "├─AdaptiveAvgPool2d (avgpool)                                [1, 1408, 7, 7]      [1, 1408, 1, 1]      --                   --\n",
              "├─Sequential (classifier)                                    [1, 1408]            [1, 3]               --                   True\n",
              "│    └─Dropout (0)                                           [1, 1408]            [1, 1408]            --                   --\n",
              "│    └─Linear (1)                                            [1, 1408]            [1, 3]               4,227                True\n",
              "============================================================================================================================================\n",
              "Total params: 7,705,221\n",
              "Trainable params: 4,227\n",
              "Non-trainable params: 7,700,994\n",
              "Total mult-adds (M): 657.64\n",
              "============================================================================================================================================\n",
              "Input size (MB): 0.60\n",
              "Forward/backward pass size (MB): 156.80\n",
              "Params size (MB): 30.82\n",
              "Estimated Total Size (MB): 188.22\n",
              "============================================================================================================================================"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.1 Creating a function to make an EffNetB2 feature extractor"
      ],
      "metadata": {
        "id": "UWuurv0ae7sg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def create_effnetb2_model(\n",
        "    num_classes: int = 3, # default output classes = 3 (pizza, steak, sushi)\n",
        "    seed: int = 42 ):\n",
        "  # 1, 2, 3 Create EffNeB2 pretrained weights, transforms and model\n",
        "  weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT\n",
        "  transforms = weights.transforms()\n",
        "  model = torchvision.models.efficientnet_b2(weights = weights)\n",
        "\n",
        "  # 4. Freeze all layers in the base model\n",
        "  for param in model.parameters():\n",
        "    param.requires_grad = False\n",
        "\n",
        "  # 5. Change classifier head with random seed for reproducibility\n",
        "  torch.manual_seed(seed)\n",
        "  model.classifier = nn.Sequential(\n",
        "      nn.Dropout(p = 0.3, inplace = True),\n",
        "      nn.Linear(in_features = 1408, out_features=num_classes)\n",
        "  )\n",
        "  return model, transforms"
      ],
      "metadata": {
        "id": "waQw1-XkfDVv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "effnetb2, effnetb2_transforms = create_effnetb2_model(\n",
        "    num_classes = 3,\n",
        "    seed = 42\n",
        ")"
      ],
      "metadata": {
        "id": "0P1FcIidgVHp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.2 Creating DataLoaders for EffNetB2"
      ],
      "metadata": {
        "id": "c-fcrDQPgf40"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Setup DataLoaders\n",
        "from going_modular.going_modular import data_setup\n",
        "\n",
        "train_dataloader_effnetb2, test_dataloader_effnetb2, class_names = data_setup.create_dataloaders(\n",
        "    train_dir = train_dir,\n",
        "    test_dir = test_dir,\n",
        "    transform = effnetb2_transforms,\n",
        "    batch_size = 32\n",
        ")"
      ],
      "metadata": {
        "id": "gE5Ypk8lgwCA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(train_dataloader_effnetb2), len(test_dataloader_effnetb2), class_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HdbciYC8hIYe",
        "outputId": "09e70991-7851-4508-f8b3-870a4989b26b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15, 5, ['pizza', 'steak', 'sushi'])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.3 Training EffNetB2 feature extractor"
      ],
      "metadata": {
        "id": "syBcV2YFhRQx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from going_modular.going_modular import engine\n",
        "\n",
        "# Loss function\n",
        "loss_fn = torch.nn.CrossEntropyLoss()\n",
        "\n",
        "# Optimizer\n",
        "optimizer = torch.optim.Adam(\n",
        "    params = effnetb2.parameters(),\n",
        "    lr = 1e-3\n",
        ")\n",
        "\n",
        "# Training function (engine.py)\n",
        "set_seeds()\n",
        "effnetb2_results = engine.train(\n",
        "    model = effnetb2,\n",
        "    train_dataloader = train_dataloader_effnetb2,\n",
        "    test_dataloader = test_dataloader_effnetb2,\n",
        "    epochs = 10,\n",
        "    optimizer = optimizer,\n",
        "    loss_fn = loss_fn,\n",
        "    device = device\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223,
          "referenced_widgets": [
            "cb5cbea92234452996c83863e46c2eb7",
            "1404c6c92df044cd80b16faade2af40a",
            "5a1c9bcb17274748904f462ca9781110",
            "a754b97d3f054aa7b62050269df5f34d",
            "0cce46aabaea478fb65bbdc18a1b42fd",
            "13bdb043f7fe4d2a8e0d7d49f29f98b9",
            "d90a72be1ef849e48ef4e542ccb27fe2",
            "21a59611a6fd41e4bdfd7cf8e512891c",
            "e1977bf230db4eb0b7d96cf73a584e9a",
            "a2c8059f8e4c4ec0a197f2d70f0e5dbe",
            "bcba980653334ef2bc47b66a92ad80d4"
          ]
        },
        "id": "dIWanCs0hZ44",
        "outputId": "67eb91f0-4be5-4f3c-bf46-e1b6087e948a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/10 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cb5cbea92234452996c83863e46c2eb7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 1 | train_loss: 0.9848 | train_acc: 0.5667 | test_loss: 0.7388 | test_acc: 0.9409\n",
            "Epoch: 2 | train_loss: 0.7138 | train_acc: 0.8375 | test_loss: 0.5851 | test_acc: 0.9409\n",
            "Epoch: 3 | train_loss: 0.5871 | train_acc: 0.8938 | test_loss: 0.4897 | test_acc: 0.9500\n",
            "Epoch: 4 | train_loss: 0.4480 | train_acc: 0.9104 | test_loss: 0.4342 | test_acc: 0.9318\n",
            "Epoch: 5 | train_loss: 0.4264 | train_acc: 0.9125 | test_loss: 0.3899 | test_acc: 0.9443\n",
            "Epoch: 6 | train_loss: 0.4377 | train_acc: 0.8896 | test_loss: 0.3513 | test_acc: 0.9688\n",
            "Epoch: 7 | train_loss: 0.4238 | train_acc: 0.8750 | test_loss: 0.3264 | test_acc: 0.9563\n",
            "Epoch: 8 | train_loss: 0.3881 | train_acc: 0.8979 | test_loss: 0.3458 | test_acc: 0.9443\n",
            "Epoch: 9 | train_loss: 0.3773 | train_acc: 0.8792 | test_loss: 0.3125 | test_acc: 0.9193\n",
            "Epoch: 10 | train_loss: 0.3739 | train_acc: 0.8688 | test_loss: 0.2809 | test_acc: 0.9688\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.4 Inspeccting EffNetB2 loss curves"
      ],
      "metadata": {
        "id": "WLg6t7NVpLFF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from helper_functions import plot_loss_curves\n",
        "plot_loss_curves(effnetb2_results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "S8v3-mO14FDZ",
        "outputId": "1917cbb3-db55-42cb-c46a-b468735d8ca9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x504 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.5 Saving EffNetB2 feature extractor"
      ],
      "metadata": {
        "id": "q38FxeNP4NiK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from going_modular.going_modular import utils\n",
        "\n",
        "# Save the model\n",
        "utils.save_model(\n",
        "    model = effnetb2,\n",
        "    target_dir = \"models\",\n",
        "    model_name = \"09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth\"\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZvKqkasG4YG4",
        "outputId": "65bc7686-002b-4440-9f52-39d5d54241b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INFO] Saving model to: models/09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.6 Inspecting the size of our EffNetB2 feature extractor\n",
        "\n",
        "Why would it be important to consider the size of a saved model ?\n",
        "\n",
        "If we're deploying our model to be used on a mobile app/website, there may be limited compute resources.\n",
        "\n",
        "So if our model file too large,we may not be able to store/run it on our target device."
      ],
      "metadata": {
        "id": "mfzLwAnO4whb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pathlib import Path\n",
        "\n",
        "# Get the model size in bytes and convert to megabytes\n",
        "pretrained_effnetb2_model_size = Path(\"models/09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth\").stat().st_size / (1024*1024)\n",
        "print(f\"Pretrained EffNetB2 feature extractor model size : {round(pretrained_effnetb2_model_size, 2)} MB\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "14yJRJke5QzA",
        "outputId": "d2027802-c18f-40b4-9e6e-abd5b3f2cd50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pretrained EffNetB2 feature extractor model size : 29.86 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.7 Collecting EffNetB2 feature extractor stats"
      ],
      "metadata": {
        "id": "SCvXsT0t52SU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Count number of parameters in EffNetB2\n",
        "effnetb2_total_params = sum(torch.numel(param) for param in effnetb2.parameters())\n",
        "effnetb2_total_params"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "clNtRSyB6ke5",
        "outputId": "5dd82261-2a3f-4f15-974a-e3b6089caa4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7705221"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a dictionary with EffNetB2 statistics\n",
        "effnetb2_stats={\n",
        "    \"test_loss\" : effnetb2_results[\"test_loss\"][-1],\n",
        "    \"test_acc\" : effnetb2_results[\"test_acc\"][-1],\n",
        "    \"number_of_parameters\" : effnetb2_total_params,\n",
        "    \"model_size (MB)\" : pretrained_effnetb2_model_size\n",
        "}\n",
        "effnetb2_stats"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q0eyzBjh6ytV",
        "outputId": "75650f62-4dc2-4893-9712-1ac26bd8aeee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'test_loss': 0.28086441457271577,\n",
              " 'test_acc': 0.96875,\n",
              " 'number_of_parameters': 7705221,\n",
              " 'model_size (MB)': 29.863232612609863}"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4. Creating a ViT feature extractor\n",
        "\n",
        "We're up to our second modelling experiment, repeating the steps for EffNetB2 but this time with a ViT feature extractor, see here for ideas :\n",
        "https://www.learnpytorch.io/08_pytorch_paper_replicating/#10-using-a-pretrained-vit-from-torchvisionmodels-on-the-same-dataset"
      ],
      "metadata": {
        "id": "NEQOa2wC7gkv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check out the ViT heads layer\n",
        "vit = torchvision.models.vit_b_16()\n",
        "vit.heads"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6SrL0FhrBp7d",
        "outputId": "e7ab5cef-8469-4796-caca-450f92f81554"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sequential(\n",
              "  (head): Linear(in_features=768, out_features=1000, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def create_vit_model(num_classes:int=3,\n",
        "                     seed:int=42):\n",
        "  # Create ViT_B_16 pretrained weights, transforms and model\n",
        "  weights = torchvision.models.ViT_B_16_Weights.DEFAULT\n",
        "  transforms = weights.transforms()\n",
        "  model = torchvision.models.vit_b_16(weights=weights)\n",
        "\n",
        "  # Freeze all of the base layers\n",
        "  for param in model.parameters():\n",
        "    param.requires_grad = False\n",
        "\n",
        "  # Change classifier head to suit our needs\n",
        "  torch.manual_seed(seed)\n",
        "  model.heads = nn.Sequential(nn.Linear(in_features=768,\n",
        "                                        out_features=num_classes))\n",
        "\n",
        "  return model, transforms"
      ],
      "metadata": {
        "id": "mkl1d5WWB1ln"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vit, vit_transforms = create_vit_model()\n",
        "vit_transforms"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 188,
          "referenced_widgets": [
            "ad18725aea444fb295355cc42b7f5425",
            "e67263c2e6b24534ade572bf293cb46b",
            "40985ea18a884a59a2530649480d0c67",
            "7578cbd2cdab4026942567704e6aa58b",
            "041d7ca012784679a05f526408b2af88",
            "1beeba55e9794aef86fd74e3ee44e7bb",
            "2a868ebf320e4a5884558cacba802c05",
            "0b7fa08c278f4e6b9f8bbe75daae4cae",
            "b4f1d6df77c444f895ece59c51cb2bdf",
            "25f83116d2eb49ec855535a08170ac6c",
            "59782315a218498a805095aba9b05d9a"
          ]
        },
        "id": "Od_LPjkOD-FC",
        "outputId": "1072434f-742a-490b-fe15-0f0eaafcee73"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/vit_b_16-c867db91.pth\" to /root/.cache/torch/hub/checkpoints/vit_b_16-c867db91.pth\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0.00/330M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ad18725aea444fb295355cc42b7f5425"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ImageClassification(\n",
              "    crop_size=[224]\n",
              "    resize_size=[256]\n",
              "    mean=[0.485, 0.456, 0.406]\n",
              "    std=[0.229, 0.224, 0.225]\n",
              "    interpolation=InterpolationMode.BILINEAR\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dir, test_dir"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "43sSNx3a4nWS",
        "outputId": "66eab207-35f3-42d4-9674-c668aa667f0b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(PosixPath('data/pizza_steak_sushi_20_percent/train'),\n",
              " PosixPath('data/pizza_steak_sushi_20_percent/test'))"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torchinfo import summary\n",
        "\n",
        "# Print ViT model summary\n",
        "summary(\n",
        "    vit,\n",
        "    input_size = (1, 3, 224, 224),\n",
        "    col_names = [\"input_size\", \"output_size\", \"num_params\", \"trainable\"],\n",
        "    col_width = 20,\n",
        "    row_settings = [\"var_names\"]\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RazAT5CnEEeJ",
        "outputId": "92ec6270-2a1a-44c5-a1c8-396c4891480b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "============================================================================================================================================\n",
              "Layer (type (var_name))                                      Input Shape          Output Shape         Param #              Trainable\n",
              "============================================================================================================================================\n",
              "VisionTransformer (VisionTransformer)                        [1, 3, 224, 224]     [1, 3]               768                  Partial\n",
              "├─Conv2d (conv_proj)                                         [1, 3, 224, 224]     [1, 768, 14, 14]     (590,592)            False\n",
              "├─Encoder (encoder)                                          [1, 197, 768]        [1, 197, 768]        151,296              False\n",
              "│    └─Dropout (dropout)                                     [1, 197, 768]        [1, 197, 768]        --                   --\n",
              "│    └─Sequential (layers)                                   [1, 197, 768]        [1, 197, 768]        --                   False\n",
              "│    │    └─EncoderBlock (encoder_layer_0)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
              "│    │    └─EncoderBlock (encoder_layer_1)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
              "│    │    └─EncoderBlock (encoder_layer_2)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
              "│    │    └─EncoderBlock (encoder_layer_3)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
              "│    │    └─EncoderBlock (encoder_layer_4)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
              "│    │    └─EncoderBlock (encoder_layer_5)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
              "│    │    └─EncoderBlock (encoder_layer_6)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
              "│    │    └─EncoderBlock (encoder_layer_7)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
              "│    │    └─EncoderBlock (encoder_layer_8)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
              "│    │    └─EncoderBlock (encoder_layer_9)                   [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
              "│    │    └─EncoderBlock (encoder_layer_10)                  [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
              "│    │    └─EncoderBlock (encoder_layer_11)                  [1, 197, 768]        [1, 197, 768]        (7,087,872)          False\n",
              "│    └─LayerNorm (ln)                                        [1, 197, 768]        [1, 197, 768]        (1,536)              False\n",
              "├─Sequential (heads)                                         [1, 768]             [1, 3]               --                   True\n",
              "│    └─Linear (0)                                            [1, 768]             [1, 3]               2,307                True\n",
              "============================================================================================================================================\n",
              "Total params: 85,800,963\n",
              "Trainable params: 2,307\n",
              "Non-trainable params: 85,798,656\n",
              "Total mult-adds (M): 172.47\n",
              "============================================================================================================================================\n",
              "Input size (MB): 0.60\n",
              "Forward/backward pass size (MB): 104.09\n",
              "Params size (MB): 229.20\n",
              "Estimated Total Size (MB): 333.89\n",
              "============================================================================================================================================"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4.1 Create DataLoaders for ViT feature extractor"
      ],
      "metadata": {
        "id": "8LTeZ5mEEhey"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Setup ViT DataLoaders\n",
        "from going_modular.going_modular import data_setup\n",
        "train_dataloader_vit, test_dataloader_vit, class_names = data_setup.create_dataloaders(train_dir=train_dir,\n",
        "                                                                                       test_dir=test_dir,\n",
        "                                                                                       transform=vit_transforms,\n",
        "                                                                                       batch_size=32)\n",
        "len(train_dataloader_vit), len(test_dataloader_vit), class_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7uDuMfKMEn01",
        "outputId": "71464008-6f74-4839-c2c3-47e54561b270"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15, 5, ['pizza', 'steak', 'sushi'])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4.2 Training ViT Feature Extractor\n",
        "\n",
        "We're up to model experiment number two : a ViT feature extractor."
      ],
      "metadata": {
        "id": "4UrJE2-nFKpW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from going_modular.going_modular import engine\n",
        "\n",
        "# Setup optimizer\n",
        "optimizer = torch.optim.Adam(params=vit.parameters(),\n",
        "                             lr=1e-3)\n",
        "\n",
        "# Setup loss function\n",
        "loss_fn = torch.nn.CrossEntropyLoss()\n",
        "\n",
        "# Train ViT feature extractor with seeds set for reproducibility\n",
        "set_seeds()\n",
        "vit_results = engine.train(model=vit,\n",
        "                           train_dataloader=train_dataloader_vit,\n",
        "                           test_dataloader=test_dataloader_vit,\n",
        "                           epochs=10,\n",
        "                           optimizer=optimizer,\n",
        "                           loss_fn=loss_fn,\n",
        "                           device=device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223,
          "referenced_widgets": [
            "7d8c131395414bd7ac3dce22f8006a37",
            "6e508675a537409e8f196958aa18add8",
            "15bdf9f739a14f23bd8311d4f282a173",
            "d1a567d3236546d7950d6e96f070f7e6",
            "61d89eb5f4c04fc2a4ee682d0fd0781d",
            "1906a13c1ef24d49bc6991404594c794",
            "818d282b78da4e33baad7a32d037aa86",
            "714147734ab0456dbaeccce75ef4576b",
            "e0e38acba0544350a0e0fd20de7dddd8",
            "c377222a7a764da19b363ff2b24c6489",
            "be23ffc261664dd9be5d1c0eefa6cf57"
          ]
        },
        "id": "IbjEPYcsFiG_",
        "outputId": "7b417770-3fa5-4c9f-9efe-c4aa95b74925"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/10 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7d8c131395414bd7ac3dce22f8006a37"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 1 | train_loss: 0.7023 | train_acc: 0.7500 | test_loss: 0.2714 | test_acc: 0.9290\n",
            "Epoch: 2 | train_loss: 0.2531 | train_acc: 0.9104 | test_loss: 0.1669 | test_acc: 0.9602\n",
            "Epoch: 3 | train_loss: 0.1766 | train_acc: 0.9542 | test_loss: 0.1270 | test_acc: 0.9693\n",
            "Epoch: 4 | train_loss: 0.1277 | train_acc: 0.9625 | test_loss: 0.1072 | test_acc: 0.9722\n",
            "Epoch: 5 | train_loss: 0.1163 | train_acc: 0.9646 | test_loss: 0.0950 | test_acc: 0.9784\n",
            "Epoch: 6 | train_loss: 0.1270 | train_acc: 0.9375 | test_loss: 0.0830 | test_acc: 0.9722\n",
            "Epoch: 7 | train_loss: 0.0899 | train_acc: 0.9771 | test_loss: 0.0844 | test_acc: 0.9784\n",
            "Epoch: 8 | train_loss: 0.0928 | train_acc: 0.9812 | test_loss: 0.0759 | test_acc: 0.9722\n",
            "Epoch: 9 | train_loss: 0.0933 | train_acc: 0.9792 | test_loss: 0.0729 | test_acc: 0.9784\n",
            "Epoch: 10 | train_loss: 0.0662 | train_acc: 0.9833 | test_loss: 0.0642 | test_acc: 0.9847\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4.3 Plot curves of ViT feature extractor"
      ],
      "metadata": {
        "id": "kkfCrGEBGYF_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from helper_functions import plot_loss_curves\n",
        "\n",
        "plot_loss_curves(vit_results)"
      ],
      "metadata": {
        "id": "PySgFUxlHTgz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "outputId": "f366a2b8-f5e1-4733-92b5-e19b700c232c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x504 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4.4 Saving ViT feature extractor"
      ],
      "metadata": {
        "id": "MQtlo3FnHc9R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Save model\n",
        "from going_modular.going_modular import utils\n",
        "\n",
        "utils.save_model(\n",
        "    model = vit,\n",
        "    target_dir = \"models\",\n",
        "    model_name = \"09_pretrained_vit_feature_extractor_pizza_steak_sushi_20_percent.pth\")"
      ],
      "metadata": {
        "id": "VX-DRf-GH6jc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "06e23fcb-5860-416d-ae46-962b0d52708e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INFO] Saving model to: models/09_pretrained_vit_feature_extractor_pizza_steak_sushi_20_percent.pth\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4.5 Checking the size of ViT feature extractor"
      ],
      "metadata": {
        "id": "2LkQvHx7IT7o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pathlib import Path\n",
        "\n",
        "# Get the model size in bytes then convert to megabytes\n",
        "pretrained_vit_model_size = Path(\"models/09_pretrained_vit_feature_extractor_pizza_steak_sushi_20_percent.pth\").stat().st_size / (1024 * 1024)\n",
        "print(f\"Pretrained ViT feature extractor model size : {pretrained_vit_model_size} MB\")"
      ],
      "metadata": {
        "id": "Uv2DvY09Ic9K",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "87028288-3d5b-47d2-b65c-688bc5e7bbad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pretrained ViT feature extractor model size : 327.37038707733154 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4.6 Collecting ViT feature extractor stats"
      ],
      "metadata": {
        "id": "uH7NW4epJETE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Count number of parameters in ViT\n",
        "vit_total_params = sum(torch.numel(param) for param in vit.parameters())\n",
        "vit_total_params"
      ],
      "metadata": {
        "id": "2C2SD1fjJMBi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cfec10d2-7f35-4efd-c8a5-c13da2913fc5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "85800963"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create ViT statistics dictionary\n",
        "vit_stats = {\n",
        "    \"test_loss\" : vit_results[\"test_loss\"][-1],\n",
        "    \"test_acc\" : vit_results[\"test_acc\"][-1],\n",
        "    \"number_of_parameters\" : vit_total_params,\n",
        "    \"model_size (MB)\" : pretrained_vit_model_size\n",
        "}"
      ],
      "metadata": {
        "id": "YPOYrCoQJxfs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vit_stats"
      ],
      "metadata": {
        "id": "YKfg1vdpKcbB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dfaf3b13-5d60-45eb-da65-8ea0122d64cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'test_loss': 0.06418211054988206,\n",
              " 'test_acc': 0.984659090909091,\n",
              " 'number_of_parameters': 85800963,\n",
              " 'model_size (MB)': 327.37038707733154}"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5. Making predictions with our trained models and timing them\n",
        "\n",
        "Our goal :\n",
        "1. Performs well (95%+ test accuracy)\n",
        "2. Fast (30+ FPS)\n",
        "\n",
        "To test criteria two:\n",
        "1. Loop through test images\n",
        "2. Time how long each model takes to make a prediction on the image\n",
        "\n",
        "Let's work towards making a function called `pred_and_store()` to do so.\n",
        "\n",
        "First we'll need a list of test image paths."
      ],
      "metadata": {
        "id": "QD4pRxfyKiGR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pathlib import Path\n",
        "\n",
        "# Get all test data paths\n",
        "test_data_paths = list(Path(test_dir).glob(\"*/*.jpg\"))\n",
        "test_data_paths[:5]"
      ],
      "metadata": {
        "id": "PpwBwwbLLg0Y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c5fa9e05-1742-4c02-afda-207f28504d7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[PosixPath('data/pizza_steak_sushi_20_percent/test/pizza/1032754.jpg'),\n",
              " PosixPath('data/pizza_steak_sushi_20_percent/test/pizza/2398925.jpg'),\n",
              " PosixPath('data/pizza_steak_sushi_20_percent/test/pizza/1067986.jpg'),\n",
              " PosixPath('data/pizza_steak_sushi_20_percent/test/pizza/482858.jpg'),\n",
              " PosixPath('data/pizza_steak_sushi_20_percent/test/pizza/204151.jpg')]"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5.1 Creating a function to make across the test dataset\n",
        "\n",
        "Steps to create `pred_and_store()`:\n",
        "\n",
        "1. Create a function that takes a list of paths and a trained PyTorch and a series of transforms a list of target class names and a target device.\n",
        "2. Create an empty list (can return a full list of all predictions later).\n",
        "3. Loop through the target input paths (the rest of the steps will take place inside the loop).\n",
        "4. Create an empty dictionary for each sample (prediction statistics will go in here).\n",
        "5. Get the sample path and ground truth class from the filepath.\n",
        "6. Start the prediction timer.\n",
        "7. Open the image using `PIL.Image.open(path)`.\n",
        "8. Transform the image to be usable with a given model.\n",
        "9. Prepare the model for inference by sending to the target device and turning on `eval()` mode.\n",
        "10. Turn on `torch.inference_mode()` and pass the target transformed image to the model and perform forward pass + calculate pred prob + pred class.\n",
        "11. Add the pred prob + pred class to empty dictionary from step 4.\n",
        "12. End the prediction timer started in step 6 and add the time to the prediction dictionary.\n",
        "13. See if the predicted class matches the ground truth class.\n",
        "14. Append the updated prediction dictionary to the empty list of predictions we created in step 2.\n",
        "15. Return the list of predcition dictionaries."
      ],
      "metadata": {
        "id": "ixR6ovglLvhB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pathlib\n",
        "import torch\n",
        "from typing import List, Dict\n",
        "\n",
        "from PIL import Image\n",
        "from timeit import default_timer as timer # https://docs.python.org/3/library/timeit.html#timeit.default_timer\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "# 1. Creating a function that takes a list of paths and a trained PyTorch and a series of transforms a list of target class names and a target device.\n",
        "def pred_and_store(\n",
        "    paths: List[pathlib.Path],\n",
        "    model: torch.nn.Module,\n",
        "    transform: torchvision.transforms,\n",
        "    class_names: List[str],\n",
        "    device: str = \"cuda\" if torch.cuda.is_available() else \"cpu\" ) -> List[Dict] :\n",
        "\n",
        "    # 2. Create an empty list (can return a full list of all predictions later).\n",
        "    pred_list = []\n",
        "\n",
        "    # 3. Loop through the target input paths (the rest of the steps will take place inside the loop).\n",
        "    for path in tqdm(paths):\n",
        "\n",
        "      # 4. Create an empty dictionary for each sample (prediction statistics will go in here).\n",
        "      pred_dict = {}\n",
        "\n",
        "      # 5. Get the sample path and ground truth class from the filepath.\n",
        "      pred_dict[\"image_path\"] = path\n",
        "      class_name = path.parent.stem\n",
        "      pred_dict[\"class_name\"] = class_name\n",
        "\n",
        "      # 6. Start the prediction timer\n",
        "      start_time = timer()\n",
        "\n",
        "      # 7. Open the image using Image.open(path)\n",
        "      img = Image.open(path)\n",
        "\n",
        "      # 8. Transform the image to be usable with a given model (also add a batch dimension and send to target device)\n",
        "      transformed_image = transform(img).unsqueeze(0).to(device)\n",
        "\n",
        "      # 9. Prepare the model for inference by sending to the target device and turning on eval() mode.\n",
        "      model = model.to(device)\n",
        "      model.eval()\n",
        "\n",
        "      # 10. Turn on `torch.inference_mode()` and pass the target transformed image to the model and perform forward pass + calculate pred prob + pred class.\n",
        "      with  torch.inference_mode():\n",
        "        pred_logit = model(transformed_image)\n",
        "        pred_prob = torch.softmax(pred_logit, dim=1) # Turn logits into prediction probabilities\n",
        "        pred_label = torch.argmax(pred_prob, dim=1) # Turn prediction probability into prediction label\n",
        "        pred_class = class_names[pred_label.cpu()] # hardcode prediction class to be on CPU (Python variables live on CPU)\n",
        "\n",
        "        # 11. Add the perd prob + pred class to empty dictionary from step 4.\n",
        "        pred_dict[\"pred_prob\"] = round(pred_prob.unsqueeze(0).max().cpu().item(), 4)\n",
        "        pred_dict[\"pred_class\"] = pred_class\n",
        "\n",
        "        # 12. End the prediction timer started in step 6 and add the time to the prediction dictionary.\n",
        "        end_time = timer()\n",
        "        pred_dict[\"time_for_pred\"] = round(end_time-start_time, 4)\n",
        "\n",
        "      # 13. See if the predicted class matches the ground truth class.\n",
        "      pred_dict[\"correct\"] = class_name == pred_class\n",
        "\n",
        "      # 14. Append the updated prediction dictionary to the empty list of predictions we created in step 2.\n",
        "      pred_list.append(pred_dict)\n",
        "\n",
        "    # 15. Return the list of prediction dictionaries.\n",
        "    return pred_list"
      ],
      "metadata": {
        "id": "yEx0_zXFRrx0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5.2 Making and timing predicitons with EffNetB2\n",
        "\n",
        "Let's test our `pred_and_store()` function.\n",
        "\n",
        "Two things to note:\n",
        "1. Device - we're going to hardcore our predictions to happen on CPU (because you won't always be sure of having a GPU when you deploy your model).\n",
        "2. Transforms - we want to make sure each of the models are predicting on images that have been prepared with appropriate transforms (e.g. EffNetB2 with `effnetb2_transforms`)."
      ],
      "metadata": {
        "id": "T_0C47piZfdV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions test dataset with EffNetB2\n",
        "effnetb2_test_pred_dicts = pred_and_store(\n",
        "    paths = test_data_paths,\n",
        "    model = effnetb2,\n",
        "    transform = effnetb2_transforms,\n",
        "    class_names = class_names,\n",
        "    device = \"cpu\" ) # hardcode predictions to happen on CPU"
      ],
      "metadata": {
        "id": "wcC97S6QaZpC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/150 [00:00<?, ?it/s]"
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    {
      "cell_type": "code",
      "source": [
        "effnetb2_test_pred_dicts[:2]"
      ],
      "metadata": {
        "id": "du0lTKUea1DR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1e5e429b-e662-4944-8aae-09d395df8cd1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'image_path': PosixPath('data/pizza_steak_sushi_20_percent/test/pizza/1032754.jpg'),\n",
              "  'class_name': 'pizza',\n",
              "  'pred_prob': 0.4798,\n",
              "  'pred_class': 'pizza',\n",
              "  'time_for_pred': 0.4217,\n",
              "  'correct': True},\n",
              " {'image_path': PosixPath('data/pizza_steak_sushi_20_percent/test/pizza/2398925.jpg'),\n",
              "  'class_name': 'pizza',\n",
              "  'pred_prob': 0.8771,\n",
              "  'pred_class': 'pizza',\n",
              "  'time_for_pred': 0.144,\n",
              "  'correct': True}]"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Turn the test_pred_dicts into a DataFrame\n",
        "import pandas as pd\n",
        "effnetb2_test_pred_df = pd.DataFrame(effnetb2_test_pred_dicts)\n",
        "effnetb2_test_pred_df.head()"
      ],
      "metadata": {
        "id": "JlviAZv4a5uG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "8e722c35-aeeb-47f7-adf6-aeeaf23f12db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                          image_path class_name  pred_prob  \\\n",
              "0  data/pizza_steak_sushi_20_percent/test/pizza/1...      pizza     0.4798   \n",
              "1  data/pizza_steak_sushi_20_percent/test/pizza/2...      pizza     0.8771   \n",
              "2  data/pizza_steak_sushi_20_percent/test/pizza/1...      pizza     0.9921   \n",
              "3  data/pizza_steak_sushi_20_percent/test/pizza/4...      pizza     0.9848   \n",
              "4  data/pizza_steak_sushi_20_percent/test/pizza/2...      pizza     0.9244   \n",
              "\n",
              "  pred_class  time_for_pred  correct  \n",
              "0      pizza         0.4217     True  \n",
              "1      pizza         0.1440     True  \n",
              "2      pizza         0.1262     True  \n",
              "3      pizza         0.1304     True  \n",
              "4      pizza         0.1317     True  "
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>image_path</th>\n",
              "      <th>class_name</th>\n",
              "      <th>pred_prob</th>\n",
              "      <th>pred_class</th>\n",
              "      <th>time_for_pred</th>\n",
              "      <th>correct</th>\n",
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              "      <th>0</th>\n",
              "      <td>data/pizza_steak_sushi_20_percent/test/pizza/1...</td>\n",
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              "      <th>1</th>\n",
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              "      <td>pizza</td>\n",
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              "      <td>0.1440</td>\n",
              "      <td>True</td>\n",
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              "      <th>2</th>\n",
              "      <td>data/pizza_steak_sushi_20_percent/test/pizza/1...</td>\n",
              "      <td>pizza</td>\n",
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              "      <th>3</th>\n",
              "      <td>data/pizza_steak_sushi_20_percent/test/pizza/4...</td>\n",
              "      <td>pizza</td>\n",
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              "      <td>0.1304</td>\n",
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              "      <td>data/pizza_steak_sushi_20_percent/test/pizza/2...</td>\n",
              "      <td>pizza</td>\n",
              "      <td>0.9244</td>\n",
              "      <td>pizza</td>\n",
              "      <td>0.1317</td>\n",
              "      <td>True</td>\n",
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              "  </tbody>\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "effnetb2_test_pred_df.correct.value_counts()"
      ],
      "metadata": {
        "id": "mXp5-KJ9beMo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ddea59a0-ccf4-4a3a-8b00-d705e28d1186"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True     145\n",
              "False      5\n",
              "Name: correct, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Find the average time per prediciton\n",
        "effnetb2_average_time_per_pred = round(effnetb2_test_pred_df.time_for_pred.mean(), 4)\n",
        "print(f\"EffnetB2 average time per predicition is : {effnetb2_average_time_per_pred}\")"
      ],
      "metadata": {
        "id": "v-ExlG-ZblhG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6f5c7987-31b6-479a-feca-1d707004fd08"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EffnetB2 average time per predicition is : 0.1336\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note** : Prediction times will vary (much like training times) depending on the hardware you're using... so generally the faster your compute (e.g. CPU or GPU), the faster the predicitons will happen."
      ],
      "metadata": {
        "id": "yW6PSxZRcCPH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Add time per pred to EffNetB2 stats dictionary\n",
        "effnetb2_stats[\"time_per_pred_cpu\"] = effnetb2_average_time_per_pred\n",
        "effnetb2_stats"
      ],
      "metadata": {
        "id": "6BqsUe8ccbzz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a70aaf70-3f63-4738-cdef-6d3c855f21f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'test_loss': 0.28086441457271577,\n",
              " 'test_acc': 0.96875,\n",
              " 'number_of_parameters': 7705221,\n",
              " 'model_size (MB)': 29.863232612609863,\n",
              " 'time_per_pred_cpu': 0.1336}"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5.3 Making and timing predictions with ViT"
      ],
      "metadata": {
        "id": "N1RpqReYctRI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Make list of prediction dictionaries with ViT feature extractor model on test images\n",
        "vit_test_pred_dicts = pred_and_store(\n",
        "    paths = test_data_paths,\n",
        "    model = vit,\n",
        "    transform = vit_transforms,\n",
        "    class_names = class_names,\n",
        "    device = \"cpu\") # hardcode device to CPU because not sure if GPU available when we deploy"
      ],
      "metadata": {
        "id": "Sj0K0Eoqc8Xh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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            "3fbcf436eec0440592bfc3bfbfe54fa7",
            "02f95e71dce44200b3244e9bd9373457",
            "f9c6e4adabc34c9e85e2074b881931b2"
          ]
        },
        "outputId": "388d23a2-67ca-43fc-ec9e-0a420c243bc6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/150 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "46c96f82f1224f09807b5135974c8e64"
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          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the first couple of ViT predictions\n",
        "vit_test_pred_dicts[:2]"
      ],
      "metadata": {
        "id": "ro8oiqB8dtUn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "450e2ab8-27d9-40d5-8a8f-3e85757ef694"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'image_path': PosixPath('data/pizza_steak_sushi_20_percent/test/pizza/1032754.jpg'),\n",
              "  'class_name': 'pizza',\n",
              "  'pred_prob': 0.9957,\n",
              "  'pred_class': 'pizza',\n",
              "  'time_for_pred': 0.791,\n",
              "  'correct': True},\n",
              " {'image_path': PosixPath('data/pizza_steak_sushi_20_percent/test/pizza/2398925.jpg'),\n",
              "  'class_name': 'pizza',\n",
              "  'pred_prob': 0.9982,\n",
              "  'pred_class': 'pizza',\n",
              "  'time_for_pred': 0.6598,\n",
              "  'correct': True}]"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Turn vit_test_pred_dicts\n",
        "import pandas as pd\n",
        "vit_test_pred_df = pd.DataFrame(vit_test_pred_dicts)\n",
        "vit_test_pred_df.head()"
      ],
      "metadata": {
        "id": "igio4vptd6CP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "a913eb65-d91a-488d-d023-3fc082ef0114"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                          image_path class_name  pred_prob  \\\n",
              "0  data/pizza_steak_sushi_20_percent/test/pizza/1...      pizza     0.9957   \n",
              "1  data/pizza_steak_sushi_20_percent/test/pizza/2...      pizza     0.9982   \n",
              "2  data/pizza_steak_sushi_20_percent/test/pizza/1...      pizza     0.9987   \n",
              "3  data/pizza_steak_sushi_20_percent/test/pizza/4...      pizza     0.9988   \n",
              "4  data/pizza_steak_sushi_20_percent/test/pizza/2...      pizza     0.9974   \n",
              "\n",
              "  pred_class  time_for_pred  correct  \n",
              "0      pizza         0.7910     True  \n",
              "1      pizza         0.6598     True  \n",
              "2      pizza         0.6948     True  \n",
              "3      pizza         0.6686     True  \n",
              "4      pizza         0.6511     True  "
            ],
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              "\n",
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              "      <div>\n",
              "<style scoped>\n",
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              "      <th>pred_prob</th>\n",
              "      <th>pred_class</th>\n",
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              "      <th>correct</th>\n",
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              "      <th>0</th>\n",
              "      <td>data/pizza_steak_sushi_20_percent/test/pizza/1...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>data/pizza_steak_sushi_20_percent/test/pizza/1...</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>data/pizza_steak_sushi_20_percent/test/pizza/2...</td>\n",
              "      <td>pizza</td>\n",
              "      <td>0.9974</td>\n",
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              "  </svg>\n",
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              "      \n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-66d1f491-4ca9-476a-abb3-313e33114fb8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-66d1f491-4ca9-476a-abb3-313e33114fb8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# See how many correct\n",
        "vit_test_pred_df.correct.value_counts()"
      ],
      "metadata": {
        "id": "Ke6y5qVQeKiA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a72b4278-e20e-48c8-9d73-c1d678730347"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True     148\n",
              "False      2\n",
              "Name: correct, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate average time per prediction for ViT model\n",
        "vit_average_time_per_pred = round(vit_test_pred_df.time_for_pred.mean(), 4)\n",
        "print(f\"ViT average time per prediction : {vit_average_time_per_pred}\")"
      ],
      "metadata": {
        "id": "x6SISniKeQpo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6de78192-566e-4d85-d47f-45a1381de040"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ViT average time per prediction : 0.657\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vit_stats[\"time_per_pred_cpu\"] = vit_average_time_per_pred\n",
        "vit_stats"
      ],
      "metadata": {
        "id": "CJ7Qvlw0fVyN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aa784dc0-c448-4157-a9f7-07b40d175db0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'test_loss': 0.06418211054988206,\n",
              " 'test_acc': 0.984659090909091,\n",
              " 'number_of_parameters': 85800963,\n",
              " 'model_size (MB)': 327.37038707733154,\n",
              " 'time_per_pred_cpu': 0.657}"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 6. Comparing model results, prediction times and size"
      ],
      "metadata": {
        "id": "lB6mlYx131ZK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Turn stat dictionaries into DataFrame\n",
        "df = pd.DataFrame([effnetb2_stats, vit_stats])\n",
        "\n",
        "# Add column for model names\n",
        "df[\"model\"] = [\"EffNetB2\", \"ViT\"]\n",
        "\n",
        "# Convert accuracy to percentages\n",
        "df[\"test_acc\"] = round(df[\"test_acc\"] * 100, 2)\n",
        "\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "zewzqzo_nqs4",
        "outputId": "a3f0ced5-472c-47b1-c6f6-108bd826ef1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   test_loss  test_acc  number_of_parameters  model_size (MB)  \\\n",
              "0   0.280864     96.88               7705221        29.863233   \n",
              "1   0.064182     98.47              85800963       327.370387   \n",
              "\n",
              "   time_per_pred_cpu     model  \n",
              "0             0.1336  EffNetB2  \n",
              "1             0.6570       ViT  "
            ],
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "        vertical-align: top;\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>test_loss</th>\n",
              "      <th>test_acc</th>\n",
              "      <th>number_of_parameters</th>\n",
              "      <th>model_size (MB)</th>\n",
              "      <th>time_per_pred_cpu</th>\n",
              "      <th>model</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>0.280864</td>\n",
              "      <td>96.88</td>\n",
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              "      <td>EffNetB2</td>\n",
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              "      <td>ViT</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e92c6e00-3807-4ee0-82a5-1a1affe481cb button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e92c6e00-3807-4ee0-82a5-1a1affe481cb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Which model is better ?\n",
        "* `test_loss` (lower is better) - ViT\n",
        "* `test_acc` (higher is better) - ViT\n",
        "* `number_of_parameters` (generally lower is better) - EffNetB2(if a model has more parameters, it generally takes longer to compute)\n",
        "  * sometimes models with higher parameters can still perform fast.\n",
        "* `model_size (MB)` - EffNetB2 (for our use case of deploying to a mobile device, generally lower is better)\n",
        "* `time_per_pred_cpu` (lower is better, will be highly dependent on the hardware you're running on) - EffNetB2\n",
        "\n",
        "Both models fail to achive our goal of 30+ FPS... however we could always just try and use EffNetB2 and see how it goes."
      ],
      "metadata": {
        "id": "wLhvC1wAoZ0i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Compare ViT to EffNetB2 accross different characteristics\n",
        "pd.DataFrame(\n",
        "    data = (df.set_index(\"model\").loc[\"ViT\"] / df.set_index(\"model\").loc[\"EffNetB2\"]),\n",
        "    columns = [\"ViT to EffNetB2 ratios\"]\n",
        ").T"
      ],
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            "text/plain": [
              "                        test_loss  test_acc  number_of_parameters  \\\n",
              "ViT to EffNetB2 ratios   0.228516  1.016412             11.135432   \n",
              "\n",
              "                        model_size (MB)  time_per_pred_cpu  \n",
              "ViT to EffNetB2 ratios        10.962323           4.917665  "
            ],
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              "      <th></th>\n",
              "      <th>test_loss</th>\n",
              "      <th>test_acc</th>\n",
              "      <th>number_of_parameters</th>\n",
              "      <th>model_size (MB)</th>\n",
              "      <th>time_per_pred_cpu</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ViT to EffNetB2 ratios</th>\n",
              "      <td>0.228516</td>\n",
              "      <td>1.016412</td>\n",
              "      <td>11.135432</td>\n",
              "      <td>10.962323</td>\n",
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    {
      "cell_type": "markdown",
      "source": [
        "### 6.1 Visualizing the speed vs. performance tradeoff\n",
        "So we've compared our EffNetB2 and ViT feature extractor modes, now let's visualize the comparision with a speed vs. performance plot.\n",
        "\n",
        "We can fo so with matplotlib:\n",
        "\n",
        "1. Create a scatter plot from the comparision DataFrame to compare EffNetB2 and ViT across test accuracy and prediction time.\n",
        "2. Add titles and labels to make sure our plot look nice.\n",
        "3. Annotate the samples on the scatter plot so we know what's going on.\n",
        "4. Create a legend based on the model sizes (`model_size (MB)`)"
      ],
      "metadata": {
        "id": "zJm9OfiirQUV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "rTsgth2Tsvcb",
        "outputId": "0be09feb-e6e1-47e1-8ac2-048b9bf348ff"
      },
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      "outputs": [
        {
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          "data": {
            "text/plain": [
              "   test_loss  test_acc  number_of_parameters  model_size (MB)  \\\n",
              "0   0.280864     96.88               7705221        29.863233   \n",
              "1   0.064182     98.47              85800963       327.370387   \n",
              "\n",
              "   time_per_pred_cpu     model  \n",
              "0             0.1336  EffNetB2  \n",
              "1             0.6570       ViT  "
            ],
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              "      <th></th>\n",
              "      <th>test_loss</th>\n",
              "      <th>test_acc</th>\n",
              "      <th>number_of_parameters</th>\n",
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              "      <th>time_per_pred_cpu</th>\n",
              "      <th>model</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>0.280864</td>\n",
              "      <td>96.88</td>\n",
              "      <td>7705221</td>\n",
              "      <td>29.863233</td>\n",
              "      <td>0.1336</td>\n",
              "      <td>EffNetB2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.064182</td>\n",
              "      <td>98.47</td>\n",
              "      <td>85800963</td>\n",
              "      <td>327.370387</td>\n",
              "      <td>0.6570</td>\n",
              "      <td>ViT</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "  "
            ]
          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib\n",
        "matplotlib.__version__"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "kk4OKe6fswUT",
        "outputId": "d9b17ea0-b750-4704-d950-cec530a9ff94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'3.2.2'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Create a plot from model comparision DataFrame\n",
        "import matplotlib.pyplot as plt\n",
        "fig, ax = plt.subplots(figsize=(12, 8))\n",
        "scatter = ax.scatter(\n",
        "    data = df,\n",
        "    x = \"time_per_pred_cpu\",\n",
        "    y = \"test_acc\",\n",
        "    c = [\"blue\", \"orange\"],\n",
        "    s = \"model_size (MB)\"\n",
        ")\n",
        "\n",
        "# 2. Add titles and labels to make our plot look good\n",
        "ax.set_title(\"FoodVision Mini Interface Speed vs Performance\", fontsize=18)\n",
        "ax.set_xlabel(\"Prediction time per image (seconds\", fontsize=14)\n",
        "ax.set_ylabel(\"Test accuracy (%)\", fontsize=14)\n",
        "ax.tick_params(axis=\"both\", labelsize=12)\n",
        "ax.grid(True)\n",
        "\n",
        "# 3. Annotate the samples on the scatter plot so we know what's going on.\n",
        "for index, row in df.iterrows():\n",
        "  ax.annotate(\n",
        "      s = row[\"model\"], # note : in some versions of Matplotlib, this may need to be \"test\" rather than \"s\"\n",
        "      xy = (row[\"time_per_pred_cpu\"] + 0.0006, row[\"test_acc\"]+0.03),\n",
        "      size = 12\n",
        "  )\n",
        "\n",
        "# 4. Create a legend based on the model sizes (model_size (MB)).\n",
        "handles, labels = scatter.legend_elements(prop = \"sizes\", alpha=0.5)\n",
        "model_size_legend = ax.legend(\n",
        "    handles, labels, loc = \"lower right\", title=\"Model Size (MB)\", fontsize=12\n",
        ")\n",
        "\n",
        "# Save the figure\n",
        "plt.savefig(\"09-foodvision-mini-inference-speed-vs-performance.png\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "3miSdjUus1sP",
        "outputId": "5338540b-96a7-4f30-97b1-7bf212244d30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 7. Bringing FoodVision Mini to life by creating a Gradio demo\n",
        "we've chosen to deploy EffNetB2 as it fulfills our criteria the best.\n",
        "\n",
        "What is Gradio ?\n",
        "    \n",
        "    Gradio is the fastest way to demo your machine learning model with a friendly web interface so that anyone can use it, anywhere! https://gradion.app/\n",
        "\n",
        "For FoodVision Mini, we're going to be working towards building something like this : https://huggingface.co/spaces/mrdbourke/foodvision_mini\n"
      ],
      "metadata": {
        "id": "3DG1RVIfvqT7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import/install Gradio\n",
        "try:\n",
        "  import gradio as gr\n",
        "except:\n",
        "  !pip -q install gradio\n",
        "  import gradio as gr\n",
        "\n",
        "print(f\"Gradio version : {gr.__version__}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DVd2aVy8xHWl",
        "outputId": "c84866fe-1d5e-4217-ef9b-60de14cc4789"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25h  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for python-multipart (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Gradio version : 3.16.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 7.1 Gradio overview\n",
        "\n",
        "Gradio helps you create machine learning demos.\n",
        "\n",
        "Why create a demo ?\n",
        "\n",
        "So other people can try our models and we can test them in the real-world.\n",
        "\n",
        "Deployment is as important as training.\n",
        "\n",
        "The overall premise of Gradio is to map inputs -> function/model -> outputs."
      ],
      "metadata": {
        "id": "QrS5jeewxYOS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 7.2 Creating a function to map our inputs and outputs"
      ],
      "metadata": {
        "id": "7Ce5Nt3uyCBW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Put our model on the cpu\n",
        "effnetb2 = effnetb2.to(\"cpu\")\n",
        "\n",
        "# Check the device\n",
        "next(iter(effnetb2.parameters())).device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tuKBjPyuyb9h",
        "outputId": "86eefb8e-8a6d-4a9e-f47a-498de733a237"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cpu')"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's create a function called `predict()` to go from:\n",
        "\n",
        "  images of food -> ML model (EffNetB2) -> outputs (food class label, prediction time)"
      ],
      "metadata": {
        "id": "ucwGh1t6ypa_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from typing import Tuple, Dict\n",
        "\n",
        "def predict(img) -> Tuple[Dict, float]:\n",
        "  # Start a timer\n",
        "  start_time = timer()\n",
        "\n",
        "  # Transform the input image for use with EffNetB2\n",
        "  img = effnetb2_transforms(img).unsqueeze(0) # unsqueeze = add batch dimension on 0th index\n",
        "\n",
        "  # Put the model into eval mode, make prediction\n",
        "  effnetb2.eval()\n",
        "  with torch.inference_mode():\n",
        "    # Pass transformed image through the model and turn the prediction logits into probabilities\n",
        "    pred_probs = torch.softmax(effnetb2(img), dim=1)\n",
        "\n",
        "  # Create a prediction label and prediction probability dictionary\n",
        "  pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}\n",
        "\n",
        "  # Calculate pred time\n",
        "  end_time = timer()\n",
        "  pred_time = round(end_time - start_time, 4)\n",
        "\n",
        "  # Return pred dict and pred time\n",
        "  return pred_labels_and_probs, pred_time"
      ],
      "metadata": {
        "id": "9yZrEjn6zGhe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "from PIL import Image\n",
        "\n",
        "# Get a list of all test image filepaths\n",
        "test_data_path = list(Path(test_dir).glob(\"*/*.jpg\"))\n",
        "print(f\"Example test data path: {test_data_paths[0]}\")\n",
        "\n",
        "# Randomly select a test image path\n",
        "random_image_path = random.sample(test_data_paths, k=1)[0]\n",
        "\n",
        "# Predict on the target image and print out the outputs\n",
        "pred_dict, pred_time = predict(img = Image.open(test_data_path[0]))\n",
        "print(pred_dict)\n",
        "print(pred_time)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DPwsWf_A40Ky",
        "outputId": "0e1ed9e8-d820-4f3f-a8c6-af84abc7e78a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Example test data path: data/pizza_steak_sushi_20_percent/test/pizza/1032754.jpg\n",
            "{'pizza': 0.4798494577407837, 'steak': 0.22592522203922272, 'sushi': 0.2942253351211548}\n",
            "0.2332\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 7.3 Creating a list of example images\n",
        "\n",
        "The examples for Gradio can be `examples` parameter, see here : https://gradio.app/docs/#building-demos"
      ],
      "metadata": {
        "id": "zsHdTpeM6v9A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create list of example inputs to our Gradio demo\n",
        "example_list = [[str(filepath)] for filepath in random.sample(test_data_paths, k=3)]\n",
        "example_list"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jWlGcYoR7KU3",
        "outputId": "952b85ea-a227-4618-fb24-cf9c6542f703"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[['data/pizza_steak_sushi_20_percent/test/pizza/416067.jpg'],\n",
              " ['data/pizza_steak_sushi_20_percent/test/sushi/1404154.jpg'],\n",
              " ['data/pizza_steak_sushi_20_percent/test/steak/3873655.jpg']]"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 7.4 Building a Gradio Interface\n",
        "\n",
        "Let's use `gr.Interface()` to go from :\n",
        "    input : image -> transform -> perdict with EffNetB2 -> output: pred, prob, time"
      ],
      "metadata": {
        "id": "0Mw_BF2t7j9y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio as gr\n",
        "\n",
        "# Create title, description and article\n",
        "title = \"FoodVision Mini 🍕🥩🍣\"\n",
        "description = \"An [EfficientNetB2 feature extractor](https://pytorch.org/vision/stable/models/generated/torchvision.models.efficientnet_b2.html#torch)\"\n",
        "article = \"Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/#74-building-a-gradio-interface)\"\n",
        "\n",
        "# Create the gradio demo\n",
        "demo = gr.Interface(\n",
        "    fn = predict, # maps inputs to outputs\n",
        "    inputs = gr.Image(type=\"pil\"),\n",
        "    outputs=[\n",
        "        gr.Label(num_top_classes=3, label=\"Predictions\"),\n",
        "        gr.Number(label = \"Prediction time(s) \")\n",
        "    ],\n",
        "    examples = example_list,\n",
        "    title = title,\n",
        "    description = description,\n",
        "    article = article\n",
        ")\n",
        "\n",
        "# Launch the demo !\n",
        "demo.launch(\n",
        "    debug = False, # print errors locally ?\n",
        "    share = True\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "id": "XSIVmQxK76Ua",
        "outputId": "88478a05-aa91-4e6f-c227-6b772ba4e3a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Running on public URL: https://0255401a-0642-4feb.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades (NEW!), check out Spaces: https://huggingface.co/spaces\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><iframe src=\"https://0255401a-0642-4feb.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 8. Turning our FoodVision Mini Gradio Demo into a deployable app\n",
        "\n",
        "Our  Gradio demos from Google Colab are fantastic but they expire within 72 hours.\n",
        "\n",
        "To fix this, we're going to prepare our app files so we can host them on Hugging Face Spaces : https://huggingface.co/docs/hub/spaces"
      ],
      "metadata": {
        "id": "TPQ3p2qlAVGW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 8.1 What is hugging Face Spaces ?\n",
        "Hugging Face Spaces offer a simple way to host ML demo apps directly on your profile or your organization's profile. This allows you to create your ML portfolio showcase your projects at conference or to stakeholders, and work collaboratively with other people in the ML ecosystem.\n",
        "\n",
        "If GitHub is a place to show your coding ability, Hugging Face Spaces is a place to show your machine learning ability (through sharing ML demos that you've built')\n"
      ],
      "metadata": {
        "id": "iPrIy4MtBT10"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 8.2 Deployed Gradio app structure\n",
        "\n",
        "Let's start to put all of our app files into a single directory:\n",
        "\n",
        "    Colab -> folder with all Gradio files -> upload app files to Hugging Face Spaces -> deploy\n",
        "\n",
        "By the end our file structure will look like this:\n",
        "\n",
        "    demos/\n",
        "    └── foodvision_mini/\n",
        "      ├── 09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth\n",
        "      ├── app.py\n",
        "      ├── examples/\n",
        "      │   ├── example_1.jpg\n",
        "      │   ├── example_2.jpg\n",
        "      │   └── example_3.jpg\n",
        "      ├── model.py\n",
        "      └── requirements.txt\n",
        "\n",
        "Why use this structure ?\n",
        "\n",
        "Because it's one of the simplest we could start with\n",
        "\n",
        "You can see this in action :\n",
        "* Deployed app - https://huggingface.co/spaces/mrdbourke/foodvision_mini\n",
        "* See the example file structure - https://huggingface.co/spaces/mrdbourke/foodvision_mini/tree/main"
      ],
      "metadata": {
        "id": "aBwUNpuUD6HC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 8.3 Creating a `demos` folder to store our FoodVision app files"
      ],
      "metadata": {
        "id": "oVDjeqMrI8r6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import shutil\n",
        "from pathlib import Path\n",
        "\n",
        "# Create Foodvision mini demo path\n",
        "foodvision_mini_demo_path = Path(\"demos/foodvision_mini/\")\n",
        "\n",
        "# Remove files that might exist and create a new directory\n",
        "if foodvision_mini_demo_path.exists():\n",
        "  shutil.rmtree(foodvision_mini_demo_path)\n",
        "  foodvision_mini_demo_path.mkdir(\n",
        "      parents = True,\n",
        "      exist_ok = True\n",
        "  )\n",
        "else:\n",
        "  foodvision_mini_demo_path.mkdir(\n",
        "      parents = True,\n",
        "      exist_ok = True\n",
        "  )\n",
        "\n",
        "!ls demos/foodvision_mini/"
      ],
      "metadata": {
        "id": "zjhkchfnJDQO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 8.4 Creating a folder of example images to use with our FoodVision Mini demo\n",
        "What we want:\n",
        "* 3 images in an `examples/` directory\n",
        "* Images should be from the test set"
      ],
      "metadata": {
        "id": "K36IEigbO5jD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import shutil\n",
        "from pathlib import Path\n",
        "\n",
        "# Create an examples directory\n",
        "foodvision_mini_examples_path = foodvision_mini_demo_path / \"examples\"\n",
        "foodvision_mini_examples_path.mkdir(parents = True, exist_ok = True)\n",
        "\n",
        "# Collect three random test dataset image paths\n",
        "foodvision_mini_examples = [\n",
        "    Path('data/pizza_steak_sushi_20_percent/test/sushi/592799.jpg'),\n",
        "    Path('data/pizza_steak_sushi_20_percent/test/steak/3622237.jpg'),\n",
        "    Path('data/pizza_steak_sushi_20_percent/test/pizza/2582289.jpg')\n",
        "]\n",
        "\n",
        "# Copy the three images to the examples directory\n",
        "for example in foodvision_mini_examples:\n",
        "  destination = foodvision_mini_examples_path / example.name\n",
        "  print(f\"[INFO] Copying {example} to {destination}\")\n",
        "  shutil.copy2(\n",
        "      src = example,\n",
        "      dst = destination\n",
        "  )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TNRvG76tPOAG",
        "outputId": "df494f15-c582-405a-a168-3e9820c541b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INFO] Copying data/pizza_steak_sushi_20_percent/test/sushi/592799.jpg to demos/foodvision_mini/examples/592799.jpg\n",
            "[INFO] Copying data/pizza_steak_sushi_20_percent/test/steak/3622237.jpg to demos/foodvision_mini/examples/3622237.jpg\n",
            "[INFO] Copying data/pizza_steak_sushi_20_percent/test/pizza/2582289.jpg to demos/foodvision_mini/examples/2582289.jpg\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's verify that we can get a list of lists from our `examples/` directory."
      ],
      "metadata": {
        "id": "X9O_tK3OQmVx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "# Get example filepaths in a list of lists\n",
        "example_list = [[\"examples/\" + example] for example in os.listdir(foodvision_mini_examples_path)]\n",
        "example_list, os.listdir(foodvision_mini_examples_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X2klXQQnQ6rv",
        "outputId": "45c6418f-7c14-4c3e-c648-2dd1b9211fc5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([['examples/2582289.jpg'], ['examples/592799.jpg'], ['examples/3622237.jpg']],\n",
              " ['2582289.jpg', '592799.jpg', '3622237.jpg'])"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 8.5 Moving our trained EffNetB2 model to our FoodVision Mini demo directory"
      ],
      "metadata": {
        "id": "71wB5dTyRRbh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import shutil\n",
        "\n",
        "# Create a source path for our target model\n",
        "effnetb2_foodvision_mini_model_path = \"models/09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth\"\n",
        "\n",
        "# Create a destination path for our target model\n",
        "effnetb2_foodvision_mini_model_destination = foodvision_mini_demo_path / effnetb2_foodvision_mini_model_path.split(\"/\")[1]\n",
        "\n",
        "# Try to move the model file\n",
        "try:\n",
        "  print(f\"[INFO] Attempting to move {effnetb2_foodvision_mini_model_path} to {effnetb2_foodvision_mini_model_destination}\")\n",
        "\n",
        "  # Move the model\n",
        "  shutil.move(\n",
        "      src = effnetb2_foodvision_mini_model_path,\n",
        "      dst = effnetb2_foodvision_mini_model_destination\n",
        "  )\n",
        "  print(f\"[INFO] Model move complete.\")\n",
        "# If the model has already been moved, check if it exists\n",
        "except:\n",
        "  print(f\"[INFO] No model found at {effnetb2_foodvision_mini_model_path}, perhaps its already been moved?\")\n",
        "  print(f\"[INFO] Model exist at {effnetb2_foodvision_mini_model_destination} : {effnetb2_foodvision_mini_model_destination.exists()}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jgCdwM6-RhOa",
        "outputId": "0d2f0e3b-58d7-4f7e-a73e-ddb342641564"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INFO] Attempting to move models/09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth to demos/foodvision_mini/09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth\n",
            "[INFO] Model move complete.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 8.6 Turning off EffNetB2 model into a Python script (`model.py`)\n",
        "\n",
        "We have a saved `.pth` model `saved_dict` and want to load it into a model instance.\n",
        "\n",
        "Let's move our `create_effnetb2_model()` function to a script so we can reuse it."
      ],
      "metadata": {
        "id": "U9UQOexETIRV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile demos/foodvision_mini/model.py\n",
        "import torch\n",
        "import torchvision\n",
        "\n",
        "from torch import nn\n",
        "def create_effnetb2_model(num_classes:int=3, # default output classes = 3 (pizza, steak, sushi)\n",
        "                          seed:int=42):\n",
        "  # 1, 2, 3 Create EffNetB2 pretrained weights, transforms and model\n",
        "  weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT\n",
        "  transforms = weights.transforms()\n",
        "  model = torchvision.models.efficientnet_b2(weights=weights)\n",
        "\n",
        "  # 4. Freeze all layers in the base model\n",
        "  for param in model.parameters():\n",
        "    param.requires_grad = False\n",
        "\n",
        "  # 5. Change classifier head with random seed for reproducibility\n",
        "  torch.manual_seed(seed)\n",
        "  model.classifier = nn.Sequential(\n",
        "      nn.Dropout(p=0.3, inplace=True),\n",
        "      nn.Linear(in_features=1408, out_features=num_classes)\n",
        "  )\n",
        "\n",
        "  return model, transforms"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8p4CrNSJTpZE",
        "outputId": "eaae04cb-0426-4c28-d6a1-4423da75601e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Writing demos/foodvision_mini/model.py\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NdLE7v4hT9ZC",
        "outputId": "796037a4-4799-4bc2-f47b-2a334197acbe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['pizza', 'steak', 'sushi']"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 8.7 Turning our FoodVision Mini Gradio app into a Python script (`app.py`)\n",
        "The `app.py` file will have four major parts:\n",
        "1. Imports and class names setup\n",
        "2. Model and transforms preparation\n",
        "3. Predict function (predict())\n",
        "4. Gradio app - our Gradio interface + launch command"
      ],
      "metadata": {
        "id": "YrsZrUmHT_iB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile demos/foodvision_mini/app.py\n",
        "\n",
        "### 1. Imports and class names setup ###\n",
        "import gradio as gr\n",
        "import os\n",
        "import torch\n",
        "import torchvision\n",
        "\n",
        "from model import create_effnetb2_model\n",
        "from timeit import default_timer as timer\n",
        "from typing import Tuple, Dict\n",
        "\n",
        "# Setup class names\n",
        "class_names = [\"pizza\", \"steak\", \"sushi\"]\n",
        "\n",
        "### 2. Model and transforms preparation ###\n",
        "effnetb2, effnetb2_transforms = create_effnetb2_model(\n",
        "  num_classes = 3\n",
        ")\n",
        "\n",
        "# load save weights\n",
        "effnetb2.load_state_dict(\n",
        "  torch.load(\n",
        "    f = \"09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth\",\n",
        "    map_location = torch.device(\"cpu\") # Load the model to the CPU\n",
        "  )\n",
        ")\n",
        "\n",
        "### 3. Predict function ###\n",
        "\n",
        "def predict(img) -> Tuple[Dict, float]:\n",
        "  # Start a timer\n",
        "  start_time = timer()\n",
        "\n",
        "  # Transform the input image for use with EffNetB2\n",
        "  img = effnetb2_transforms(img).unsqueeze(0) # unsqueeze = add batch dimension on 0th index\n",
        "\n",
        "  # Put the model into eval mode, make prediction\n",
        "  effnetb2.eval()\n",
        "  with torch.inference_mode():\n",
        "    # Pass transformed image through the model and turn the prediction logits into probabilities\n",
        "    pred_probs = torch.softmax(effnetb2(img), dim = 1)\n",
        "\n",
        "  # Create a prediction label and prediction probability dictionary\n",
        "  pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}\n",
        "\n",
        "  # Calculate pred time\n",
        "  end_time = timer()\n",
        "  pred_time = round(end_time - start_time, 4)\n",
        "\n",
        "  # Return pred dict and pred time\n",
        "  return pred_labels_and_probs, pred_time\n",
        "\n",
        "### 4. Gradio app ###\n",
        "\n",
        "# Create title, description and article\n",
        "title = \"Foodvision Mini 🍕🥩🍣\"\n",
        "description = \"An [EfficientNetB2 feature extractor](https://pytorch.org/vision/stable/models/generated/torchvision.models.efficientnet_b2.html#torchvision.models.efficientnet_b2) computer vision model to classify images as pizza, steak or sushi.\"\n",
        "article = \"Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/#74-building-a-gradio-interface).\"\n",
        "\n",
        "# Create an example list\n",
        "example_list = [[\"examples/\"+example] for example in os.listdir(\"examples\")]\n",
        "\n",
        "# Create the Gradio demo\n",
        "demo = gr.Interface(\n",
        "  fn = predict, # maps inputs to outputs\n",
        "  inputs = gr.Image(type=\"pil\"),\n",
        "  outputs = [\n",
        "    gr.Label(num_top_classes=3, label=\"Predictions\"),\n",
        "    gr.Number(label=\"Prediction time (s)\")\n",
        "  ],\n",
        "  examples = example_list,\n",
        "  title = title,\n",
        "  description = description,\n",
        "  article = article\n",
        ")\n",
        "\n",
        "# launch the demo!\n",
        "demo.launch()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zc-Q0mCqUiCv",
        "outputId": "54b3aa7a-8868-40cf-fff4-02e3516f6ac2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Writing demos/foodvision_mini/app.py\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 9.8 Creating a requirements file for FoodVision Mini (`requirements.txt`)\n",
        "The requirements file will tell our Hugging Face Space what software dependencies our app requires.\n",
        "\n",
        "The three main ones are:\n",
        "* `torch`\n",
        "* `torchvision`\n",
        "* `gradio`\n"
      ],
      "metadata": {
        "id": "pCei7ZW-YvcJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile demos/foodvision_mini/requirements.txt\n",
        "torch == 1.12.0\n",
        "torchvision == 0.13.0\n",
        "gradio == 3.1.4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zZL-YxV9ZMkl",
        "outputId": "db5bc952-dada-4ede-fcc1-2198eca70adf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Writing demos/foodvision_mini/requirements.txt\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 9. Deploying our FoodVision Mini App HuggingFace Spaces\n",
        "There are two main options for uploading to a Hugging Face Space (also called a Hugging Face Repositry, similar to a git repository):\n",
        "* Uploading via the Hugging Face Web interface (easiest).\n",
        "* Uploading via the command line or terminal.\n",
        "  * Bonus : You can also use the huggingface_hub library to interact with Hugging Face, this would be a good extension to the above two options."
      ],
      "metadata": {
        "id": "CIn01yvjZY-c"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 9.1 Downloading our FoodVision Mini App Files\n",
        "\n",
        "We want to download our `foodvision_mini` demo app so we can upload it to Hugging Face Spaces."
      ],
      "metadata": {
        "id": "zU0_dC7Gp3PU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!ls demos/foodvision_mini/examples"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7GWPinP7qTxv",
        "outputId": "cf889ad5-4dd6-4090-8596-f9cf347f3978"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2582289.jpg  3622237.jpg  592799.jpg\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Change into the foodvision_mini directory and then zip it from the inside\n",
        "!cd demos/foodvision_mini && zip -r ../foodvision_mini.zip * -x \"*.pyc\" \"*.ipynb\" \"*__pycache__*\" \"*ipynb_checkpoints*\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XzP7kSpRqYs2",
        "outputId": "28b9983c-01ca-4a25-a893-f3a1bf944b30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  adding: 09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth (deflated 8%)\n",
            "  adding: app.py (deflated 52%)\n",
            "  adding: examples/ (stored 0%)\n",
            "  adding: examples/2582289.jpg (deflated 17%)\n",
            "  adding: examples/592799.jpg (deflated 1%)\n",
            "  adding: examples/3622237.jpg (deflated 0%)\n",
            "  adding: model.py (deflated 45%)\n",
            "  adding: requirements.txt (deflated 11%)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Download\n",
        "try:\n",
        "  from google.colab import files\n",
        "  files.download(\"demos/foodvision_mini.zip\")\n",
        "except:\n",
        "  print(f\"Not running in Google Colab, can't use google.colab.files.download(), please download foodvision_mini.zip manually.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "r28AvWxZsHCA",
        "outputId": "50e745cc-66c1-4a1f-8f5a-a3662590fbf5"
      },
      "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_70128899-7f71-4b79-a61c-36eb11db475a\", \"foodvision_mini.zip\", 28977361)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 9.2 Running our Gradio demo app locally\n",
        "Running the app locally - https://www.learnpytorch.io/09_pytorch_model_deployment/#92-running-our-foodvision-mini-demo-locally"
      ],
      "metadata": {
        "id": "zmooupRssuG7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 9.3 Uploading our FoodVision Mini Gradio demo to Hugging Face Spaces\n",
        "\n",
        "See the steps here - https://learnpytorch.io/09_pytorch_model_deployment/#93-uploading-to-hugging-face\n",
        "\n",
        "See the live app deployed here - https://huggingface.io/spaces/mrdbourke/foodvision_mini_video\n",
        "\n",
        "we can also share our app be embedding it - https://gradio.app/sharing_your_app/#embedding-hosted-spaces\n",
        "\n"
      ],
      "metadata": {
        "id": "sY0q3We7xkAO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# IPython is a library to help make Python interactive\n",
        "from IPython.display import IFrame\n",
        "\n",
        "# Embed FoodVision Mini Gradio demo\n",
        "IFrame(src = \"https://hf.space/embed/ahamedddd/FoodVision_Mini2/+\", width=900, height = 750)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 771
        },
        "id": "gm7DEbgyyjXd",
        "outputId": "2129ec60-279a-405d-e241-14121f4d0b27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.lib.display.IFrame at 0x7fb55f622910>"
            ],
            "text/html": [
              "\n",
              "        <iframe\n",
              "            width=\"900\"\n",
              "            height=\"750\"\n",
              "            src=\"https://hf.space/embed/ahamedddd/FoodVision_Mini2/+\"\n",
              "            frameborder=\"0\"\n",
              "            allowfullscreen\n",
              "        ></iframe>\n",
              "        "
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 10. Creating FoodVision Big!!!\n",
        "\n",
        "FoodVision Mini works well with 3 classes (pizza, steak, sushi).\n",
        "\n",
        "So all of experimenting is paying off...\n",
        "\n",
        "\n",
        "Let's step things up a notch and make FoodVision Big!!! using all of the Food101 classes"
      ],
      "metadata": {
        "id": "DHEEWW3sA1Du"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 10.1 Creating a model for FoodVision Big + Transforms"
      ],
      "metadata": {
        "id": "BSaC13LXOeVD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create Food101 model and transforms\n",
        "effnetb2_food101, effnetb2_transforms = create_effnetb2_model(num_classes = 101)"
      ],
      "metadata": {
        "id": "4DOpS_rTOkx4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torchinfo import summary\n",
        "\n",
        "# Print EffNetB2 model summary\n",
        "summary(\n",
        "    effnetb2_food101,\n",
        "    input_size = (1, 3, 224, 224),\n",
        "    col_names = [\"input_size\", \"output_size\", \"num_params\", \"trainable\"],\n",
        "    col_width = 20,\n",
        "    row_settings = [\"var_names\"]\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IgT3oADDOyEc",
        "outputId": "672d12b3-298a-4ad0-a8bc-05924cd898de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "============================================================================================================================================\n",
              "Layer (type (var_name))                                      Input Shape          Output Shape         Param #              Trainable\n",
              "============================================================================================================================================\n",
              "EfficientNet (EfficientNet)                                  [1, 3, 224, 224]     [1, 101]             --                   Partial\n",
              "├─Sequential (features)                                      [1, 3, 224, 224]     [1, 1408, 7, 7]      --                   False\n",
              "│    └─Conv2dNormActivation (0)                              [1, 3, 224, 224]     [1, 32, 112, 112]    --                   False\n",
              "│    │    └─Conv2d (0)                                       [1, 3, 224, 224]     [1, 32, 112, 112]    (864)                False\n",
              "│    │    └─BatchNorm2d (1)                                  [1, 32, 112, 112]    [1, 32, 112, 112]    (64)                 False\n",
              "│    │    └─SiLU (2)                                         [1, 32, 112, 112]    [1, 32, 112, 112]    --                   --\n",
              "│    └─Sequential (1)                                        [1, 32, 112, 112]    [1, 16, 112, 112]    --                   False\n",
              "│    │    └─MBConv (0)                                       [1, 32, 112, 112]    [1, 16, 112, 112]    (1,448)              False\n",
              "│    │    └─MBConv (1)                                       [1, 16, 112, 112]    [1, 16, 112, 112]    (612)                False\n",
              "│    └─Sequential (2)                                        [1, 16, 112, 112]    [1, 24, 56, 56]      --                   False\n",
              "│    │    └─MBConv (0)                                       [1, 16, 112, 112]    [1, 24, 56, 56]      (6,004)              False\n",
              "│    │    └─MBConv (1)                                       [1, 24, 56, 56]      [1, 24, 56, 56]      (10,710)             False\n",
              "│    │    └─MBConv (2)                                       [1, 24, 56, 56]      [1, 24, 56, 56]      (10,710)             False\n",
              "│    └─Sequential (3)                                        [1, 24, 56, 56]      [1, 48, 28, 28]      --                   False\n",
              "│    │    └─MBConv (0)                                       [1, 24, 56, 56]      [1, 48, 28, 28]      (16,518)             False\n",
              "│    │    └─MBConv (1)                                       [1, 48, 28, 28]      [1, 48, 28, 28]      (43,308)             False\n",
              "│    │    └─MBConv (2)                                       [1, 48, 28, 28]      [1, 48, 28, 28]      (43,308)             False\n",
              "│    └─Sequential (4)                                        [1, 48, 28, 28]      [1, 88, 14, 14]      --                   False\n",
              "│    │    └─MBConv (0)                                       [1, 48, 28, 28]      [1, 88, 14, 14]      (50,300)             False\n",
              "│    │    └─MBConv (1)                                       [1, 88, 14, 14]      [1, 88, 14, 14]      (123,750)            False\n",
              "│    │    └─MBConv (2)                                       [1, 88, 14, 14]      [1, 88, 14, 14]      (123,750)            False\n",
              "│    │    └─MBConv (3)                                       [1, 88, 14, 14]      [1, 88, 14, 14]      (123,750)            False\n",
              "│    └─Sequential (5)                                        [1, 88, 14, 14]      [1, 120, 14, 14]     --                   False\n",
              "│    │    └─MBConv (0)                                       [1, 88, 14, 14]      [1, 120, 14, 14]     (149,158)            False\n",
              "│    │    └─MBConv (1)                                       [1, 120, 14, 14]     [1, 120, 14, 14]     (237,870)            False\n",
              "│    │    └─MBConv (2)                                       [1, 120, 14, 14]     [1, 120, 14, 14]     (237,870)            False\n",
              "│    │    └─MBConv (3)                                       [1, 120, 14, 14]     [1, 120, 14, 14]     (237,870)            False\n",
              "│    └─Sequential (6)                                        [1, 120, 14, 14]     [1, 208, 7, 7]       --                   False\n",
              "│    │    └─MBConv (0)                                       [1, 120, 14, 14]     [1, 208, 7, 7]       (301,406)            False\n",
              "│    │    └─MBConv (1)                                       [1, 208, 7, 7]       [1, 208, 7, 7]       (686,868)            False\n",
              "│    │    └─MBConv (2)                                       [1, 208, 7, 7]       [1, 208, 7, 7]       (686,868)            False\n",
              "│    │    └─MBConv (3)                                       [1, 208, 7, 7]       [1, 208, 7, 7]       (686,868)            False\n",
              "│    │    └─MBConv (4)                                       [1, 208, 7, 7]       [1, 208, 7, 7]       (686,868)            False\n",
              "│    └─Sequential (7)                                        [1, 208, 7, 7]       [1, 352, 7, 7]       --                   False\n",
              "│    │    └─MBConv (0)                                       [1, 208, 7, 7]       [1, 352, 7, 7]       (846,900)            False\n",
              "│    │    └─MBConv (1)                                       [1, 352, 7, 7]       [1, 352, 7, 7]       (1,888,920)          False\n",
              "│    └─Conv2dNormActivation (8)                              [1, 352, 7, 7]       [1, 1408, 7, 7]      --                   False\n",
              "│    │    └─Conv2d (0)                                       [1, 352, 7, 7]       [1, 1408, 7, 7]      (495,616)            False\n",
              "│    │    └─BatchNorm2d (1)                                  [1, 1408, 7, 7]      [1, 1408, 7, 7]      (2,816)              False\n",
              "│    │    └─SiLU (2)                                         [1, 1408, 7, 7]      [1, 1408, 7, 7]      --                   --\n",
              "├─AdaptiveAvgPool2d (avgpool)                                [1, 1408, 7, 7]      [1, 1408, 1, 1]      --                   --\n",
              "├─Sequential (classifier)                                    [1, 1408]            [1, 101]             --                   True\n",
              "│    └─Dropout (0)                                           [1, 1408]            [1, 1408]            --                   --\n",
              "│    └─Linear (1)                                            [1, 1408]            [1, 101]             142,309              True\n",
              "============================================================================================================================================\n",
              "Total params: 7,843,303\n",
              "Trainable params: 142,309\n",
              "Non-trainable params: 7,700,994\n",
              "Total mult-adds (M): 657.78\n",
              "============================================================================================================================================\n",
              "Input size (MB): 0.60\n",
              "Forward/backward pass size (MB): 156.80\n",
              "Params size (MB): 31.37\n",
              "Estimated Total Size (MB): 188.77\n",
              "============================================================================================================================================"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "from torchvision.transforms.autoaugment import TrivialAugmentWide\n",
        "Since we're working with a larger dataset, we may want to introduce some data augmentation techniques:\n",
        "* This is because with larger datasets and larger models, overfitting becomes more of a problem.\n",
        "* Because we're working with a large number of classes, let's use TrivialAugment as our data augmentation technique.\n",
        "\n",
        "For a list of state-of-the-art computer vision recipies : https://pytorch.org/blog/how-to-train-state-of-the-art-models-using-torchvision-latest-primitives/"
      ],
      "metadata": {
        "id": "MUVBIGcSPPZZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating training data transforms\n",
        "food101_train_transforms = torchvision.transforms.Compose(\n",
        "    [\n",
        "        torchvision.transforms.TrivialAugmentWide(),\n",
        "        effnetb2_transforms\n",
        "    ]\n",
        ")\n",
        "food101_train_transforms"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ogf-SExQQEAC",
        "outputId": "7ec00a9e-f68c-45d3-8b88-2922880a3f2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Compose(\n",
              "    TrivialAugmentWide(num_magnitude_bins=31, interpolation=InterpolationMode.NEAREST, fill=None)\n",
              "    ImageClassification(\n",
              "    crop_size=[288]\n",
              "    resize_size=[288]\n",
              "    mean=[0.485, 0.456, 0.406]\n",
              "    std=[0.229, 0.224, 0.225]\n",
              "    interpolation=InterpolationMode.BICUBIC\n",
              ")\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Testing data transform\n",
        "effnetb2_transforms"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TGfd5pTgQcTo",
        "outputId": "ba28c158-e52f-40bd-a33d-dade1abbdb12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ImageClassification(\n",
              "    crop_size=[288]\n",
              "    resize_size=[288]\n",
              "    mean=[0.485, 0.456, 0.406]\n",
              "    std=[0.229, 0.224, 0.225]\n",
              "    interpolation=InterpolationMode.BICUBIC\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 10.2 Getting data for FoodVision Big\n",
        "Get Food101 dataset - https://pytorch.org/vision/main/generated/torchvision.datasets.Food101.html"
      ],
      "metadata": {
        "id": "sH8_O7-SQiBz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torchvision import datasets\n",
        "\n",
        "# Setup data directory\n",
        "from pathlib import Path\n",
        "data_dir = Path(\"data\")\n",
        "\n",
        "# Get the training data (~750 images x 101 classes)\n",
        "train_data = datasets.Food101(\n",
        "    root = data_dir,\n",
        "    split = \"train\",\n",
        "    transform = food101_train_transforms, # apply data augmentation to training data\n",
        "    download = True\n",
        ")\n",
        "\n",
        "# Get the testing data (~250 images x 101 classes)\n",
        "test_data = datasets.Food101(\n",
        "    root = data_dir,\n",
        "    split = \"test\",\n",
        "    transform = effnetb2_transforms, # don't perform data augmentation on the test data\n",
        "    download = True\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
            "69e885eaa6864bb5b3c8073bc547e3dd",
            "8908d82f31df47148b88a2995ba9aee9",
            "580346bbec6f4259a5aacfd12d157d67",
            "3c7404ef625b4b699820430a57260756",
            "6887c3ba262b47e58214dd6dce1f05a9",
            "d30b0abf57a049ffa440170e6398ac66",
            "e754306f58514065a0dfc0c0d19f661e",
            "a0235bc63aef4294aa656000ce8d617e",
            "0bb123c742a2466f9b440b0ea4fbf817",
            "181c6f84d8714587bcbc736803cfbf50",
            "45868c3362ff428d9c5150780a7f8e3a"
          ]
        },
        "id": "ngoom1MwQ-C5",
        "outputId": "bb09e20a-77f9-4119-e511-b692f9e4bc7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://data.vision.ee.ethz.ch/cvl/food-101.tar.gz to data/food-101.tar.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/4996278331 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "69e885eaa6864bb5b3c8073bc547e3dd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/food-101.tar.gz to data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "750 * 101, 250 * 101"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7fdev27KTg48",
        "outputId": "c187cf8e-9d0e-46a9-ada7-727f97a6c38e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(75750, 25250)"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get Food101 class names\n",
        "food101_class_names = train_data.classes\n",
        "\n",
        "# View the first 10\n",
        "food101_class_names[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f4_rkBg_Tmp6",
        "outputId": "ace893e1-f8f9-4d5e-d202-5b84fcf0dc2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['apple_pie',\n",
              " 'baby_back_ribs',\n",
              " 'baklava',\n",
              " 'beef_carpaccio',\n",
              " 'beef_tartare',\n",
              " 'beet_salad',\n",
              " 'beignets',\n",
              " 'bibimbap',\n",
              " 'bread_pudding',\n",
              " 'breakfast_burrito']"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 10.3 Creating a subset of Food101 dataset for faster experimenting\n",
        "Why create a subset ?\n",
        "\n",
        "We want our first few experiments to run as quick as possible.\n",
        "\n",
        "We know FoodVision Mini works pretty well but this is the first time we've upgraded to 101 classes.\n",
        "\n",
        "To do so, let's make a subset of 20% of the data from the Food101 dataset (training and test).\n",
        "\n",
        "Our short-term goal: to beat the original Food101 paper result of 56.40% accuracy on the test dataset (see the paper : https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/static/bossard_eccv14_food-101.pdf )\n",
        "\n",
        "We want to beat this result using mordern deep learning techniques and only 20% of the data."
      ],
      "metadata": {
        "id": "cyp40OeeT0gT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "len(train_data) * 0.2, len(test_data) * 0.2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xsVCoh06Uqvm",
        "outputId": "53b6b03a-f0bb-4258-b72e-021878373ffb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15150.0, 5050.0)"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import random_split # https://pytorch.org/docs/stable/data.html#torch.utils.data.random_split\n",
        "\n",
        "def split_dataset(\n",
        "    dataset: torchvision.datasets,\n",
        "    split_size: float = 0.2,\n",
        "    seed: int = 42):\n",
        "\n",
        "  # Create split lengths based on original dataset length\n",
        "  length_1 = int(len(dataset) * split_size) # Default to 20% data split\n",
        "  length_2 = len(dataset) - length_1 # remaining length\n",
        "\n",
        "  # Print out info\n",
        "  print(f\"[INFO] Splitting dataset of length {len(dataset)} into splits of size {length_1} and {length_2}\")\n",
        "\n",
        "  # Create splits with given random seed\n",
        "  random_split_1,random_split_2 = torch.utils.data.random_split(\n",
        "      dataset,\n",
        "      lengths = [length_1, length_2],\n",
        "      generator = torch.manual_seed(seed)\n",
        "  )\n",
        "\n",
        "  return random_split_1, random_split_2"
      ],
      "metadata": {
        "id": "mnm5CCH9VApy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create training 20% split Food101\n",
        "train_data_food101_20_percent, _ = split_dataset(\n",
        "    dataset = train_data,\n",
        "    split_size = 0.2\n",
        ")\n",
        "\n",
        "# Create testing 20% split Food101\n",
        "test_data_food101_20_percent, _ = split_dataset(\n",
        "    dataset = test_data,\n",
        "    split_size = 0.2\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FJS5t5NpWgWJ",
        "outputId": "ce297846-2850-4055-f406-20e556be26b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INFO] Splitting dataset of length 75750 into splits of size 15150 and 60600\n",
            "[INFO] Splitting dataset of length 25250 into splits of size 5050 and 20200\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(train_data_food101_20_percent), len(test_data_food101_20_percent)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ifT2MiruW8hw",
        "outputId": "649eba72-fd2e-4511-d64c-0bafd21a0606"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15150, 5050)"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 10.4 Turning our Food101 datasets into `DataLoader`s"
      ],
      "metadata": {
        "id": "nKbo9AEoXD9N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "os.cpu_count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZI7nHQ-HXLdT",
        "outputId": "a9e8fa01-b285-4c5e-ebcf-ef4651b06d20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import torch\n",
        "\n",
        "NUM_WORKERS = 2 # this value is very experimental and the best value will differ depending on the hardware you're using, search \"pytorch num workers settings\" for more\n",
        "BATCH_SIZE = 32\n",
        "\n",
        "# Create Food101 20% training DataLoader\n",
        "train_dataloader_food101_20_percent = torch.utils.data.DataLoader(\n",
        "    dataset = train_data_food101_20_percent,\n",
        "    batch_size = BATCH_SIZE,\n",
        "    shuffle = True\n",
        ")\n",
        "\n",
        "# Create Food101 20% testing DataLoader\n",
        "test_dataloader_food101_20_percent = torch.utils.data.DataLoader(\n",
        "    dataset = test_data_food101_20_percent,\n",
        "    batch_size = BATCH_SIZE,\n",
        "    shuffle = False,\n",
        "    num_workers = NUM_WORKERS\n",
        ")\n"
      ],
      "metadata": {
        "id": "ZxQgYcCQXOFR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(train_dataloader_food101_20_percent), len(test_dataloader_food101_20_percent)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eQtU0EMQYj0L",
        "outputId": "f9fcaa67-1266-43db-a278-3be87b4a3baa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(474, 158)"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 10.5  Training FoodVision Big!!!\n",
        "\n",
        "Things for training:\n",
        "\n",
        "* 5 epochs\n",
        "* Optimizer: `torch.optim.Adam(lr = 1e-3)`\n",
        "* Loss function : `torch.nn.CrossEntropyLoss(label_smoothing=0.1)`\n",
        "\n",
        "Why use label smoothing ?\n",
        "\n",
        "Label smoothing helps to prevent overfitting (it's a regularization technique).\n",
        "\n",
        "Without label smoothing and 5 classes:\n",
        "    [0.00, 0.00, 0.99, 0.01, 0.00]\n",
        "With label smoothing and 5 classes:\n",
        "    [0.01, 0.01, 0.96, 0.01, 0.01]\n",
        "\n",
        "    **Note**: Depending on your hardware, running the following cell may take 15-20 min (takes about 17 minutes on NVIDIA Tesla P100 GPU)."
      ],
      "metadata": {
        "id": "uECGIPWkYsgh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from going_modular.going_modular import engine\n",
        "\n",
        "# Setup optimizer\n",
        "optimizer = torch.optim.Adam(\n",
        "    params = effnetb2_food101.parameters(),\n",
        "    lr = 1e-3\n",
        ")\n",
        "\n",
        "# Setup Loss\n",
        "loss_fn = torch.nn.CrossEntropyLoss(label_smoothing = 0.1)\n",
        "\n",
        "# Want to beat the original Food101 paper's result of 56.4% accuracy on the test dataset with 20% of the data\n",
        "set_seeds()\n",
        "effnetb2_food101_results = engine.train(\n",
        "    model = effnetb2_food101,\n",
        "    train_dataloader = train_dataloader_food101_20_percent,\n",
        "    test_dataloader = test_dataloader_food101_20_percent,\n",
        "    optimizer = optimizer,\n",
        "    loss_fn = loss_fn,\n",
        "    epochs = 5,\n",
        "    device = device\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136,
          "referenced_widgets": [
            "c889b3ff081741ee89b20652b8882583",
            "0685be64d072436f9d1d15418bfefec3",
            "10256ba3d72f47f7b1e981e31d91f10a",
            "0eac1dee05624909b4f9b76db47c3b95",
            "6ea6d4ef6cfc428ab8bcb27bfd71833d",
            "3d419d374f5443d9862e3fd2fa18edc0",
            "7ec27c30cd304670847be072c59541c1",
            "7735b3cc8a0d4fa496eff335d02873c3",
            "e7f72806d830496aa1df212748da6d61",
            "7ba88f16a76d4159a2aee6dbb9070d99",
            "2a117b9ab8d54a48b06f6f3671ef5ce6"
          ]
        },
        "id": "T0pGLT1aZ4Xv",
        "outputId": "15812e74-e319-46bd-9998-f3dabea4634a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/5 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c889b3ff081741ee89b20652b8882583"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 1 | train_loss: 3.6405 | train_acc: 0.2799 | test_loss: 2.7875 | test_acc: 0.4909\n",
            "Epoch: 2 | train_loss: 2.8619 | train_acc: 0.4441 | test_loss: 2.4754 | test_acc: 0.5326\n",
            "Epoch: 3 | train_loss: 2.6600 | train_acc: 0.4832 | test_loss: 2.3547 | test_acc: 0.5549\n",
            "Epoch: 4 | train_loss: 2.5470 | train_acc: 0.5113 | test_loss: 2.3044 | test_acc: 0.5728\n",
            "Epoch: 5 | train_loss: 2.4878 | train_acc: 0.5247 | test_loss: 2.2739 | test_acc: 0.5795\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 10.6 Inspectig loss curves of FoodVision Big model"
      ],
      "metadata": {
        "id": "mbbeETvKa9ew"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from helper_functions import plot_loss_curves\n",
        "plot_loss_curves(effnetb2_food101_results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "I5ZT-ZF2bLX1",
        "outputId": "ee4fc578-9f9d-489a-eb0b-12e3381d3d5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x504 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 10.7 Save and load FoodVision Big Model"
      ],
      "metadata": {
        "id": "8KflLCFrbUKk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from going_modular.going_modular import utils\n",
        "\n",
        "# Create a model path\n",
        "effnetb2_food101_model_path = \"09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth\"\n",
        "\n",
        "# Save FoodVision Big model\n",
        "utils.save_model(\n",
        "    model = effnetb2_food101,\n",
        "    target_dir = \"models/\",\n",
        "    model_name = effnetb2_food101_model_path\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5KZYQZ40bZq_",
        "outputId": "ae16eaf5-48c7-4ecf-b92b-ba232a08340c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INFO] Saving model to: models/09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create Food101 compatible EffNetB2 isntance\n",
        "loaded_effnetb2_food101, effnetb2_transforms = create_effnetb2_model(num_classes = 101)\n",
        "\n",
        "# Load the saved model's state_dict()\n",
        "loaded_effnetb2_food101.load_state_dict(torch.load(\"models/09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth\"))\n"
      ],
      "metadata": {
        "id": "0yUF4efdb9re"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 10.8 Checking FoodVision Big Model Size"
      ],
      "metadata": {
        "id": "r4Rp2EKBdEU6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pathlib import Path\n",
        "\n",
        "# Get the model size in bytes then convert to megabytes\n",
        "pretrained_effnetb2_food101_model_size = Path(\"models\", effnetb2_food101_model_path).stat().st_size // (1024 * 1024) # division converts bytes to megabytes (roughly)\n",
        "print(f\"Pretrained EffNetB2 feature extractor Food101 model size : 30MB\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JJIFoMp1dJB-",
        "outputId": "6013e04f-23bb-48c3-bf8c-a790a8a6fa25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pretrained EffNetB2 feature extractor Food101 model size : 30MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 11. Turning Our FoodVision Big Model into a deployable app\n",
        "\n",
        "Why deploy a model ?\n",
        "\n",
        "Deployng a model allows you to see how your model goes in the real-world (the ultimate test set).\n",
        "\n",
        "let's create an outline for our FoodVision Big app:\n",
        "\n",
        "  demos/\n",
        "    foodvision_big/\n",
        "      09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth\n",
        "      app.py\n",
        "      class_names.txt\n",
        "      examples/\n",
        "        example_1.jpg\n",
        "      model.py\n",
        "      requirements.txt\n"
      ],
      "metadata": {
        "id": "TWIBWXkcd6eJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pathlib import Path\n",
        "\n",
        "# Create FoodVision Big demo path\n",
        "foodvision_big_demo_path = Path(\"demos/foodvision_big/\")\n",
        "\n",
        "# Make FoodVision Big demo directory\n",
        "foodvision_big_demo_path.mkdir(\n",
        "    parents = True,\n",
        "    exist_ok = True\n",
        ")\n",
        "\n",
        "# Make FoodVision Big Demo examples directory\n",
        "( foodvision_big_demo_path / \"examples\" ).mkdir(parents = True, exist_ok = True)"
      ],
      "metadata": {
        "id": "T-gXZ2R1egG7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!ls demos/foodvision_big/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gcop2AV8fmMV",
        "outputId": "59a3eb39-1e85-408e-c70c-bfd8999a029d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "examples\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 11.1 Downloading an example image and moving it to the `examples` directory"
      ],
      "metadata": {
        "id": "DD5iFFmofpZx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Download and move example image\n",
        "!wget https://github.com/mrdbourke/pytorch-deep-learning/raw/main/images/04-pizza-dad.jpeg\n",
        "!mv 04-pizza-dad.jpeg demos/foodvision_big/examples/04-pizza-dad.jpeg"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pf6R9Ftvf8WQ",
        "outputId": "46778617-cd55-4a8b-8d5a-5097270017dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-01-08 03:56:58--  https://github.com/mrdbourke/pytorch-deep-learning/raw/main/images/04-pizza-dad.jpeg\n",
            "Resolving github.com (github.com)... 192.30.255.113\n",
            "Connecting to github.com (github.com)|192.30.255.113|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/04-pizza-dad.jpeg [following]\n",
            "--2023-01-08 03:56:58--  https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/04-pizza-dad.jpeg\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 2874848 (2.7M) [image/jpeg]\n",
            "Saving to: ‘04-pizza-dad.jpeg’\n",
            "\n",
            "04-pizza-dad.jpeg   100%[===================>]   2.74M  --.-KB/s    in 0.05s   \n",
            "\n",
            "2023-01-08 03:56:59 (58.8 MB/s) - ‘04-pizza-dad.jpeg’ saved [2874848/2874848]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mv models/09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth demos/foodvision_big/"
      ],
      "metadata": {
        "id": "8TmxPPFqgVFt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 11.2 Saving Food101 class names to file (`class_names.txt`)"
      ],
      "metadata": {
        "id": "BjuFBoeqgi3h"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's save all of the Food101 class names to a .txt file so we can import them in our app"
      ],
      "metadata": {
        "id": "DJnG-Lihgory"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check out the first 10 Food101 class names\n",
        "food101_class_names[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7oIP3OBXgz2N",
        "outputId": "e735832e-7d5a-4e6f-ff3a-51055060afe5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['apple_pie',\n",
              " 'baby_back_ribs',\n",
              " 'baklava',\n",
              " 'beef_carpaccio',\n",
              " 'beef_tartare',\n",
              " 'beet_salad',\n",
              " 'beignets',\n",
              " 'bibimbap',\n",
              " 'bread_pudding',\n",
              " 'breakfast_burrito']"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create path to Food101 class names\n",
        "foodvision_big_class_names_path = foodvision_big_demo_path / \"class_names.txt\"\n",
        "foodvision_big_class_names_path"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XA3Jb-lTg6Vh",
        "outputId": "ea5bab58-2dfd-4aa7-b174-48ebb45e44b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PosixPath('demos/foodvision_big/class_names.txt')"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Write Food101 class names to test file\n",
        "with open(foodvision_big_class_names_path, \"w\") as f:\n",
        "  print(f\"[INFO] Saving Food101 class names to {foodvision_big_class_names_path}\")\n",
        "  f.write(\"\\n\".join(food101_class_names)) # new line per class name"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KsiW3VRahQAf",
        "outputId": "af189adc-0879-4d86-f58a-7fc7e4eb9ab7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INFO] Saving Food101 class names to demos/foodvision_big/class_names.txt\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Open Food101 class names file and read each line into a list\n",
        "with open(foodvision_big_class_names_path, \"r\") as f:\n",
        "  food101_class_names_loaded = [food.strip() for food in f.readlines()]\n",
        "\n",
        "food101_class_names_loaded[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ivJjkCGdh6dN",
        "outputId": "d76a3da7-4b33-4d4e-8832-0e786219009d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['apple_pie', 'baby_back_ribs', 'baklava', 'beef_carpaccio', 'beef_tartare']"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 11.3 Turning our FoodVision Big model into a Python Script (`model.py`)"
      ],
      "metadata": {
        "id": "M39jkE1TiUJG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile demos/foodvision_big/model.py\n",
        "import torch\n",
        "import torchvision\n",
        "\n",
        "from torch import nn\n",
        "\n",
        "def create_effnetb2_model(num_classes:int=3, # default output classes = 3 (pizza, steak, sushi)\n",
        "                          seed:int=42):\n",
        "  # 1, 2, 3 Create EffNetB2 pretrained weights, transforms and model\n",
        "  weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT\n",
        "  transforms = weights.transforms()\n",
        "  model = torchvision.models.efficientnet_b2(weights=weights)\n",
        "\n",
        "  # 4. Freeze all layers in the base model\n",
        "  for param in model.parameters():\n",
        "    param.requires_grad = False\n",
        "\n",
        "  # 5. Change classifier head with random seed for reproducibility\n",
        "  torch.manual_seed(seed)\n",
        "  model.classifier = nn.Sequential(\n",
        "      nn.Dropout(p=0.3, inplace=True),\n",
        "      nn.Linear(in_features=1408, out_features=num_classes)\n",
        "  )\n",
        "\n",
        "  return model, transforms"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ut-Bx2tziv1i",
        "outputId": "0d366de2-8518-432d-c16d-a7ae7ddf80ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Writing demos/foodvision_big/model.py\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 11.4 Turning our FoodVision Big Gradio app into a Python Script (`app.py`)\n",
        "The `app.py` file will have four major parts:\n",
        "1. Imports and class names setup - for class names, we'll need to import from `class_names.txt` rather than with a Python list\n",
        "2. Model and transforms preperation - we'll need to make sure our model is suitable for FoodVision Big\n",
        "3. Predict function (`predict()`) - this can stay the same as the original `predict()`\n",
        "4. Gradio app - our Gradio interface + launch command - this will change slightly from FoodVision Mini to reflect the FoodVision Big Updates"
      ],
      "metadata": {
        "id": "0uHnnYUJi2Ol"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile demos/foodvision_big/app.py\n",
        "\n",
        "### 1. Imports and class names setup ###\n",
        "import gradio as gr\n",
        "import os\n",
        "import torch\n",
        "\n",
        "from model import create_effnetb2_model\n",
        "from timeit import default_timer as timer\n",
        "from typing import Tuple, Dict\n",
        "\n",
        "# Setup class names\n",
        "with open(\"class_names.txt\", \"r\") as f:\n",
        "  class_names = [food_name.strip() for food_name in f.readlines()]\n",
        "\n",
        "### 2. Model and transforms preparation ###\n",
        "# Create model and transforms\n",
        "effnetb2, effnetb2_transforms = create_effnetb2_model(num_classes=101)\n",
        "\n",
        "# Load saved weights\n",
        "effnetb2.load_state_dict(\n",
        "    torch.load(f=\"09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth\",\n",
        "               map_location=torch.device(\"cpu\")) # load to CPU\n",
        ")\n",
        "\n",
        "### 3. Predict function ###\n",
        "\n",
        "def predict(img) -> Tuple[Dict, float]:\n",
        "  # Start a timer\n",
        "  start_time = timer()\n",
        "\n",
        "  # Transform the input image for use with EffNetB2\n",
        "  img = effnetb2_transforms(img).unsqueeze(0) # unsqueeze = add batch dimension on 0th index\n",
        "\n",
        "  # Put model into eval mode, make prediction\n",
        "  effnetb2.eval()\n",
        "  with torch.inference_mode():\n",
        "    # Pass transformed image through the model and turn the prediction logits into probaiblities\n",
        "    pred_probs = torch.softmax(effnetb2(img), dim=1)\n",
        "\n",
        "  # Create a prediction label and prediction probability dictionary\n",
        "  pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}\n",
        "\n",
        "  # Calculate pred time\n",
        "  end_time = timer()\n",
        "  pred_time = round(end_time - start_time, 4)\n",
        "\n",
        "  # Return pred dict and pred time\n",
        "  return pred_labels_and_probs, pred_time\n",
        "\n",
        "### 4. Gradio app ###\n",
        "\n",
        "# Create title, description and article\n",
        "title = \"FoodVision BIG 🍔👁💪\"\n",
        "description = \"An [EfficientNetB2 feature extractor](https://pytorch.org/vision/stable/models/generated/torchvision.models.efficientnet_b2.html#torchvision.models.efficientnet_b2) computer vision model to classify images [101 classes of food from the Food101 dataset](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/extras/food101_class_names.txt).\"\n",
        "article = \"Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/#11-turning-our-foodvision-big-model-into-a-deployable-app).\"\n",
        "\n",
        "# Create example list\n",
        "example_list = [[\"examples/\" + example] for example in os.listdir(\"examples\")]\n",
        "\n",
        "# Create the Gradio demo\n",
        "demo = gr.Interface(fn=predict, # maps inputs to outputs\n",
        "                    inputs=gr.Image(type=\"pil\"),\n",
        "                    outputs=[gr.Label(num_top_classes=5, label=\"Predictions\"),\n",
        "                             gr.Number(label=\"Prediction time (s)\")],\n",
        "                    examples=example_list,\n",
        "                    title=title,\n",
        "                    description=description,\n",
        "                    article=article)\n",
        "\n",
        "# Launch the demo!\n",
        "demo.launch()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_6D_QdgskFv1",
        "outputId": "2d7d336e-ba63-4dd8-d16c-e3fa17c1c72b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Writing demos/foodvision_big/app.py\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 11.5 Creating a requirements file for FoodVision Big (`requirements.txt`)"
      ],
      "metadata": {
        "id": "bvuiaA6tkZro"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile demos/foodvision_big/requirements.txt\n",
        "torch == 1.12.0\n",
        "torchvision == 0.13.0\n",
        "gradio == 3.1.4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Gcr9PsvkjiO",
        "outputId": "0044d9f4-86f1-40dc-fb50-a6ee5661137e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Writing demos/foodvision_big/requirements.txt\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 11.6 Downloading our FoodVision Big App Files"
      ],
      "metadata": {
        "id": "1AFaVKSGku7H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Change into the foodvision_big directory and then zip it from th inside\n",
        "!cd demos/foodvision_big && zip -r ../foodvision_big.zip * -x \"*.pyc\" \"*.ipynb\" \"*__pycache__*\" \"*ipynb_checkpoints*\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TzQNfHt6k2nz",
        "outputId": "3a7c6571-2d17-42d2-fe26-67f494fd6556"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "updating: app.py (deflated 54%)\n",
            "updating: class_names.txt (deflated 48%)\n",
            "updating: examples/ (stored 0%)\n",
            "updating: examples/04-pizza-dad.jpeg (deflated 0%)\n",
            "updating: model.py (deflated 46%)\n",
            "updating: requirements.txt (deflated 11%)\n",
            "  adding: 09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth (deflated 8%)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Download\n",
        "try:\n",
        "  from google.colab import files\n",
        "  files.download(\"demos/foodvision_big.zip\")\n",
        "except:\n",
        "  print(f\"Not running in Google Colab, can't use google.colab.files.download(), please download foodvision_big.zip manually\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "QxMaF8melW1j",
        "outputId": "cd128e7e-4f11-4149-fd08-c413571315b8"
      },
      "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_ab8846d5-3162-4182-bfca-61ea2c75dc06\", \"foodvision_big.zip\", 32189313)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 11.7 Deploying our FoodVision Big Model App to Hugging Faces Spaces\n",
        "\n",
        "Let's bring FoodVision Big to life by deploying it to the world!!!\n",
        "\n",
        "See steps here : https://www.learnpytorch.io/09_pytorch_model_deployment/#117-deploying-our-foodvision-big-app-to-huggingface-spaces\n",
        "\n",
        "See our deployed app here : https://huggingface.io/spaces/mrdbourke/foodvision_big_video"
      ],
      "metadata": {
        "id": "xTgAv1q7lzG2"
      }
    }
  ]
}