Model_Training.ipynb

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      "source": [],
      "metadata": {
        "id": "cpSvvy3Fj-gh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "import zipfile\n",
        "import pathlib\n",
        "import os\n",
        "import shutil\n",
        "\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tarfile\n",
        "\n",
        "try:\n",
        "    from PIL import Image\n",
        "except:\n",
        "    print (f'[INFO] Couldn\\'t find pillow installed...... Installing it')\n",
        "    !pip3 install -q pillow\n",
        "    from PIL import Image\n",
        "\n",
        "#importing machine learning moudules and libraries\n",
        "\n",
        "try:\n",
        "    import torch\n",
        "    import torchvision\n",
        "    assert int(torch.__version__.split(\".\")[1])>=12\n",
        "    assert int(torch.__version__.split(\".\")[1])>=13\n",
        "    print(f'torch version: {torch.__version__}')\n",
        "    print(f\" torchvision version: {torchvision.__version__}\")\n",
        "except:\n",
        "  print(f\"torch version not up to date....... Installing updated version \\n\")\n",
        "  print(f\"torchvision version not up to date........ Installing the updated version\")\n",
        "  !pip3 install -U torch, torchvision, torchaudion --extra-index-url https://download.pytorch.org/whl/cu112\n",
        "  import torch\n",
        "  import torchvision\n",
        "  print(f\"torch version: {torch.__version__}\")\n",
        "  print(f\"torchvision version: {torchvision.__version__}\")\n",
        "\n",
        "from torchvision import datasets, transforms\n",
        "from torch.utils import tensorboard\n",
        "from torch.utils.data import DataLoader\n",
        "from torch import nn\n",
        "\n",
        "try:\n",
        "    from torchinfo import summary\n",
        "except:\n",
        "    print(f'[INFO] torchinfo not installed....installing')\n",
        "    !pip install -q torchinfo\n",
        "    from torchinfo import summary\n"
      ],
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        }
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      "execution_count": null,
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          "text": [
            "torch version not up to date....... Installing updated version \n",
            "\n",
            "torchvision version not up to date........ Installing the updated version\n",
            "\u001b[31mERROR: Invalid requirement: 'torch,'\u001b[0m\u001b[31m\n",
            "\u001b[0mtorch version: 2.1.0+cu118\n",
            "torchvision version: 0.16.0+cu118\n",
            "[INFO] torchinfo not installed....installing\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get training data\n",
        "data_download = pathlib.Path(\"/content/data\")\n",
        "train_data = datasets.Food101(root=data_download,\n",
        "                              split=\"train\",\n",
        "                              # transform=transforms.ToTensor(),\n",
        "                              download=True)\n",
        "\n",
        "# Get testing data\n",
        "test_data = datasets.Food101(root=data_download,\n",
        "                             split=\"test\",\n",
        "                             # transform=transforms.ToTensor(),\n",
        "                             download=True)"
      ],
      "metadata": {
        "id": "T_nvy2N4j_KR",
        "outputId": "a2e64d98-b224-4934-cb50-0da6b1b5f0b1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://data.vision.ee.ethz.ch/cvl/food-101.tar.gz to /content/data/food-101.tar.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 4996278331/4996278331 [11:00<00:00, 7570056.32it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting /content/data/food-101.tar.gz to /content/data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get random 10% of training images\n",
        "import random\n",
        "\n",
        "# Setup data paths\n",
        "data_path1 = data_download / \"food-101\" / \"images\"\n",
        "target_classes = [\"pizza\", \"steak\", \"sushi\", \"tacos\"]\n",
        "\n",
        "# Change amount of data to get (e.g. 0.1 = random 10%, 0.2 = random 20%)\n",
        "amount_to_get = 0.2\n",
        "\n",
        "# Create function to separate a random amount of data\n",
        "def get_subset(image_path=data_path1,\n",
        "               data_splits=[\"train\", \"test\"],\n",
        "               target_classes=[\"pizza\", \"steak\", \"sushi\",\"tacos\"],\n",
        "               amount=0.1,\n",
        "               seed=42):\n",
        "    random.seed(42)\n",
        "    label_splits = {}\n",
        "\n",
        "    # Get labels\n",
        "    for data_split in data_splits:\n",
        "        print(f\"[INFO] Creating image split for: {data_split}...\")\n",
        "        label_path = data_download / \"food-101\" / \"meta\" / f\"{data_split}.txt\"\n",
        "        with open(label_path, \"r\") as f:\n",
        "            labels = [line.strip(\"\\n\") for line in f.readlines() if line.split(\"/\")[0] in target_classes]\n",
        "\n",
        "        # Get random subset of target classes image ID's\n",
        "        number_to_sample = round(amount * len(labels))\n",
        "        print(f\"[INFO] Getting random subset of {number_to_sample} images for {data_split}...\")\n",
        "        sampled_images = random.sample(labels, k=number_to_sample)\n",
        "\n",
        "        # Apply full paths\n",
        "        image_paths = [pathlib.Path(str(image_path / sample_image) + \".jpg\") for sample_image in sampled_images]\n",
        "        label_splits[data_split] = image_paths\n",
        "    return label_splits\n",
        "\n",
        "label_splits = get_subset(amount=amount_to_get)"
      ],
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        "id": "qEVJrs67j_T6",
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        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INFO] Creating image split for: train...\n",
            "[INFO] Getting random subset of 600 images for train...\n",
            "[INFO] Creating image split for: test...\n",
            "[INFO] Getting random subset of 200 images for test...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create target directory path\n",
        "target_dir_name = f\"/content/data/pizza_steak_sushi_tacos_{str(int(amount_to_get*100))}_percent\"\n",
        "print(f\"Creating directory: '{target_dir_name}'\")\n",
        "\n",
        "# Setup the directories\n",
        "target_dir = pathlib.Path(target_dir_name)\n",
        "\n",
        "# Make the directories\n",
        "target_dir.mkdir(parents=True, exist_ok=True)"
      ],
      "metadata": {
        "id": "PFITfcU6j_U_",
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        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Creating directory: '/content/data/pizza_steak_sushi_tacos_20_percent'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for image_split in label_splits.keys():\n",
        "    for image_path in label_splits[str(image_split)]:\n",
        "        dest_dir = target_dir / image_split / image_path.parent.stem / image_path.name\n",
        "        if not dest_dir.parent.is_dir():\n",
        "            dest_dir.parent.mkdir(parents=True, exist_ok=True)\n",
        "        print(f\"[INFO] Copying {image_path} to {dest_dir}...\")\n",
        "        shutil.copy2(image_path, dest_dir)"
      ],
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            "[INFO] Copying /content/data/food-101/images/tacos/2746697.jpg to /content/data/pizza_steak_sushi_tacos_20_percent/train/tacos/2746697.jpg...\n",
            "[INFO] Copying /content/data/food-101/images/pizza/3269634.jpg to /content/data/pizza_steak_sushi_tacos_20_percent/train/pizza/3269634.jpg...\n",
            "[INFO] Copying /content/data/food-101/images/pizza/1524655.jpg to /content/data/pizza_steak_sushi_tacos_20_percent/train/pizza/1524655.jpg...\n",
            "[INFO] Copying /content/data/food-101/images/steak/2825100.jpg to /content/data/pizza_steak_sushi_tacos_20_percent/train/steak/2825100.jpg...\n",
            "[INFO] Copying /content/data/food-101/images/steak/225990.jpg to /content/data/pizza_steak_sushi_tacos_20_percent/train/steak/225990.jpg...\n",
            "[INFO] Copying /content/data/food-101/images/steak/1839481.jpg to /content/data/pizza_steak_sushi_tacos_20_percent/train/steak/1839481.jpg...\n",
            "[INFO] Copying /content/data/food-101/images/pizza/38349.jpg to /content/data/pizza_steak_sushi_tacos_20_percent/train/pizza/38349.jpg...\n",
            "[INFO] Copying /content/data/food-101/images/pizza/3018077.jpg to /content/data/pizza_steak_sushi_tacos_20_percent/train/pizza/3018077.jpg...\n",
            "[INFO] Copying /content/data/food-101/images/tacos/3587203.jpg to /content/data/pizza_steak_sushi_tacos_20_percent/train/tacos/3587203.jpg...\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Setup train and testing paths\n",
        "train_dir = target_dir / \"train\" #path to only the training folder\n",
        "test_dir = target_dir / \"test\"   #path to only the test folder\n",
        "\n",
        "train_dir, test_dir"
      ],
      "metadata": {
        "id": "gMeKnUkxj_ZE",
        "outputId": "bf0abf79-112b-48a3-a199-2e44372e15c9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(PosixPath('/content/data/pizza_steak_sushi_tacos_20_percent/train'),\n",
              " PosixPath('/content/data/pizza_steak_sushi_tacos_20_percent/test'))"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_path = data_download  #path to all the data\n",
        "image_path = target_dir    #path to folder containing train and test folder.\n",
        "image_path"
      ],
      "metadata": {
        "id": "AMrcwTZnj_a4",
        "outputId": "11b48441-61ad-462c-c170-fd30b0a20276",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PosixPath('/content/data/pizza_steak_sushi_tacos_20_percent')"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\""
      ],
      "metadata": {
        "id": "J2bmI84Bj_cz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#creating ViT model\n",
        "\n",
        "def create_vit_model(num_classes: int = 4,\n",
        "                     seed: int = 42):\n",
        "  ''' Creates a ViT-B/16 feature extractor model and transforms.\n",
        "\n",
        "  Args:\n",
        "      num_classes (int, optional): number of target classes. Defaults to 4.\n",
        "      seed (int, optional): random seed value for output layer. Defaults to 42.\n",
        "\n",
        "  Returns:\n",
        "          model (torch.nn.Module): ViT-B/16 feature extractor model.\n",
        "          transforms (torchvision.transforms): ViT-B/16 image transforms.\n",
        "  '''\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",
        "  #freezing all layers of the model\n",
        "  for param in model.parameters():\n",
        "      param.requires_grad = False\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": "mK6sjbK7j_e4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#creating the ViT model\n",
        "vit, vit_transforms = create_vit_model()"
      ],
      "metadata": {
        "id": "9SY-q7UTj_gt",
        "outputId": "090b842c-10d2-4ad4-fe8c-54315f674bb9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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",
            "100%|██████████| 330M/330M [00:05<00:00, 69.0MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "summary(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": {
        "id": "yY-IvspYky90",
        "outputId": "eb7c9188-c278-4a6f-eed0-e4bd3046510b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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, 4]               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, 4]               --                   True\n",
              "│    └─Linear (0)                                            [1, 768]             [1, 4]               3,076                True\n",
              "============================================================================================================================================\n",
              "Total params: 85,801,732\n",
              "Trainable params: 3,076\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): 229.21\n",
              "Estimated Total Size (MB): 333.89\n",
              "============================================================================================================================================"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#creating a dataloader object\n",
        "#notice that the return annotation is extracted directly from the docstring.Tuple[DataLoader, DataLoader, List[str]]:\n",
        "NUM_WORKERS = os.cpu_count()\n",
        "def create_dataloaders(\n",
        "    train_dir: str,\n",
        "    test_dir: str,\n",
        "    transform: transforms.Compose,\n",
        "    batch_size: int,\n",
        "    num_workers: int = NUM_WORKERS):\n",
        "  ''' Creates training and testing DataLoaders.\n",
        "\n",
        "  Takes in a training directory and testing directory path and turns\n",
        "  them into a Pytorch Dataset Object, then to pytorch Dataloaders\n",
        "\n",
        "  Args:\n",
        "      train_dir: Path object to training directory.\n",
        "      test_dir:  Path object to test directory.\n",
        "      transform: transformation to perform on data objects\n",
        "      num_workers: An integer for number of workers per DataLoader.\n",
        "\n",
        "  Returns:\n",
        "      A tuple of (train_dataloader, test_dataloader, class_names).\n",
        "      Where class_names is a list of the target classes.\n",
        "      Example uasge:\n",
        "        train_dataloader, test_dataloader, class_names =\\\n",
        "        = create_dataloaders(train_dir = path/ to/train_dir,\n",
        "                              test_dir = path/to/test_dir,\n",
        "                              transform = torchvision.transfroms,\n",
        "                              batch_size = 32,\n",
        "                              num_workers = 4 )\n",
        "\n",
        "  '''\n",
        "  #Using ImageFolder() to create a dataset object\n",
        "  train_data = datasets.ImageFolder(train_dir, transform = transform)\n",
        "  test_data = datasets.ImageFolder(test_dir, transform = transform)\n",
        "\n",
        "  #Get class names\n",
        "  class_names = train_data.classes #remember that ImageFolder maps class-to-idx\n",
        "\n",
        "  #Turn images into dataloaders\n",
        "  train_dataloader = DataLoader(train_data,\n",
        "                                batch_size = batch_size,\n",
        "                                shuffle = True,\n",
        "                                num_workers =num_workers,\n",
        "                                pin_memory = True)\n",
        "  test_dataloader = DataLoader(test_data,\n",
        "                               batch_size = batch_size,\n",
        "                               shuffle = False,\n",
        "                               num_workers = num_workers,\n",
        "                               pin_memory = True)\n",
        "\n",
        "  return train_dataloader, test_dataloader, class_names\n",
        "\n"
      ],
      "metadata": {
        "id": "phkAirddkzAh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# creating dataloaders\n",
        "train_dataloader_vit, test_dataloader_vit, class_names = create_dataloaders(train_dir,\n",
        "                                                                            test_dir,\n",
        "                                                                            vit_transforms,\n",
        "                                                                            batch_size = 32)"
      ],
      "metadata": {
        "id": "qP0auUxKkzC6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# define the train and test functions\n",
        "from tqdm.auto import tqdm\n",
        "from typing import Dict, List, Tuple\n",
        "\n",
        "def train_step(model: torch.nn.Module,\n",
        "               dataloader: torch.utils.data.DataLoader,\n",
        "               loss_fn: torch.nn.Module,\n",
        "               optimizer: torch.optim.Optimizer,\n",
        "               device: torch.device) -> Tuple[float, float]:\n",
        "  \"\"\"Trains a PyTorch model for a single epoch.\n",
        "\n",
        "  Turns a target PyTorch model to training mode and then\n",
        "  runs through all of the required training steps (forward\n",
        "  pass, loss calculation, optimizer step).\n",
        "\n",
        "  Args:\n",
        "    model: A PyTorch model to be trained.\n",
        "    dataloader: A DataLoader instance for the model to be trained on.\n",
        "    loss_fn: A PyTorch loss function to minimize.\n",
        "    optimizer: A PyTorch optimizer to help minimize the loss function.\n",
        "    device: A target device to compute on (e.g. \"cuda\" or \"cpu\").\n",
        "\n",
        "  Returns:\n",
        "    A tuple of training loss and training accuracy metrics.\n",
        "    In the form (train_loss, train_accuracy). For example:\n",
        "\n",
        "    (0.1112, 0.8743)\n",
        "  \"\"\"\n",
        "  # Put model in train mode\n",
        "  model.train()\n",
        "\n",
        "  # Setup train loss and train accuracy values\n",
        "  train_loss, train_acc = 0, 0\n",
        "\n",
        "  # Loop through data loader data batches\n",
        "  for batch, (X, y) in enumerate(dataloader):\n",
        "      # Send data to target device\n",
        "      X, y = X.to(device), y.to(device)\n",
        "\n",
        "      # 1. Forward pass\n",
        "      y_pred = model(X)\n",
        "\n",
        "      # 2. Calculate  and accumulate loss\n",
        "      loss = loss_fn(y_pred, y)\n",
        "      train_loss += loss.item()\n",
        "\n",
        "      # 3. Optimizer zero grad\n",
        "      optimizer.zero_grad()\n",
        "\n",
        "      # 4. Loss backward\n",
        "      loss.backward()\n",
        "\n",
        "      # 5. Optimizer step\n",
        "      optimizer.step()\n",
        "\n",
        "      # Calculate and accumulate accuracy metric across all batches\n",
        "      y_pred_class = torch.argmax(torch.softmax(y_pred, dim=1), dim=1)\n",
        "      train_acc += (y_pred_class == y).sum().item()/len(y_pred)\n",
        "\n",
        "  # Adjust metrics to get average loss and accuracy per batch\n",
        "  train_loss = train_loss / len(dataloader)\n",
        "  train_acc = train_acc / len(dataloader)\n",
        "  return train_loss, train_acc\n",
        "\n",
        "def test_step(model: torch.nn.Module,\n",
        "              dataloader: torch.utils.data.DataLoader,\n",
        "              loss_fn: torch.nn.Module,\n",
        "              device: torch.device) -> Tuple[float, float]:\n",
        "  \"\"\"Tests a PyTorch model for a single epoch.\n",
        "\n",
        "  Turns a target PyTorch model to \"eval\" mode and then performs\n",
        "  a forward pass on a testing dataset.\n",
        "\n",
        "  Args:\n",
        "    model: A PyTorch model to be tested.\n",
        "    dataloader: A DataLoader instance for the model to be tested on.\n",
        "    loss_fn: A PyTorch loss function to calculate loss on the test data.\n",
        "    device: A target device to compute on (e.g. \"cuda\" or \"cpu\").\n",
        "\n",
        "  Returns:\n",
        "    A tuple of testing loss and testing accuracy metrics.\n",
        "    In the form (test_loss, test_accuracy). For example:\n",
        "\n",
        "    (0.0223, 0.8985)\n",
        "  \"\"\"\n",
        "  # Put model in eval mode\n",
        "  model.eval()\n",
        "\n",
        "  # Setup test loss and test accuracy values\n",
        "  test_loss, test_acc = 0, 0\n",
        "\n",
        "  # Turn on inference context manager\n",
        "  with torch.inference_mode():\n",
        "      # Loop through DataLoader batches\n",
        "      for batch, (X, y) in enumerate(dataloader):\n",
        "          # Send data to target device\n",
        "          X, y = X.to(device), y.to(device)\n",
        "\n",
        "          # 1. Forward pass\n",
        "          test_pred_logits = model(X)\n",
        "\n",
        "          # 2. Calculate and accumulate loss\n",
        "          loss = loss_fn(test_pred_logits, y)\n",
        "          test_loss += loss.item()\n",
        "\n",
        "          # Calculate and accumulate accuracy\n",
        "          test_pred_labels = test_pred_logits.argmax(dim=1)\n",
        "          test_acc += ((test_pred_labels == y).sum().item()/len(test_pred_labels))\n",
        "\n",
        "  # Adjust metrics to get average loss and accuracy per batch\n",
        "  test_loss = test_loss / len(dataloader)\n",
        "  test_acc = test_acc / len(dataloader)\n",
        "  return test_loss, test_acc\n",
        "\n",
        "def train(model: torch.nn.Module,\n",
        "          train_dataloader: torch.utils.data.DataLoader,\n",
        "          test_dataloader: torch.utils.data.DataLoader,\n",
        "          optimizer: torch.optim.Optimizer,\n",
        "          loss_fn: torch.nn.Module,\n",
        "          epochs: int,\n",
        "          device: torch.device) -> Dict[str, List]:\n",
        "  \"\"\"Trains and tests a PyTorch model.\n",
        "\n",
        "  Passes a target PyTorch models through train_step() and test_step()\n",
        "  functions for a number of epochs, training and testing the model\n",
        "  in the same epoch loop.\n",
        "\n",
        "  Calculates, prints and stores evaluation metrics throughout.\n",
        "\n",
        "  Args:\n",
        "    model: A PyTorch model to be trained and tested.\n",
        "    train_dataloader: A DataLoader instance for the model to be trained on.\n",
        "    test_dataloader: A DataLoader instance for the model to be tested on.\n",
        "    optimizer: A PyTorch optimizer to help minimize the loss function.\n",
        "    loss_fn: A PyTorch loss function to calculate loss on both datasets.\n",
        "    epochs: An integer indicating how many epochs to train for.\n",
        "    device: A target device to compute on (e.g. \"cuda\" or \"cpu\").\n",
        "\n",
        "  Returns:\n",
        "    A dictionary of training and testing loss as well as training and\n",
        "    testing accuracy metrics. Each metric has a value in a list for\n",
        "    each epoch.\n",
        "    In the form: {train_loss: [...],\n",
        "                  train_acc: [...],\n",
        "                  test_loss: [...],\n",
        "                  test_acc: [...]}\n",
        "    For example if training for epochs=2:\n",
        "                 {train_loss: [2.0616, 1.0537],\n",
        "                  train_acc: [0.3945, 0.3945],\n",
        "                  test_loss: [1.2641, 1.5706],\n",
        "                  test_acc: [0.3400, 0.2973]}\n",
        "  \"\"\"\n",
        "  # Create empty results dictionary\n",
        "  results = {\"train_loss\": [],\n",
        "      \"train_acc\": [],\n",
        "      \"test_loss\": [],\n",
        "      \"test_acc\": []\n",
        "  }\n",
        "\n",
        "  # Loop through training and testing steps for a number of epochs\n",
        "  for epoch in tqdm(range(epochs)):\n",
        "      train_loss, train_acc = train_step(model=model,\n",
        "                                          dataloader=train_dataloader,\n",
        "                                          loss_fn=loss_fn,\n",
        "                                          optimizer=optimizer,\n",
        "                                          device=device)\n",
        "      test_loss, test_acc = test_step(model=model,\n",
        "          dataloader=test_dataloader,\n",
        "          loss_fn=loss_fn,\n",
        "          device=device)\n",
        "\n",
        "      # Print out what's happening\n",
        "      print(\n",
        "          f\"Epoch: {epoch+1} | \"\n",
        "          f\"train_loss: {train_loss:.4f} | \"\n",
        "          f\"train_acc: {train_acc:.4f} | \"\n",
        "          f\"test_loss: {test_loss:.4f} | \"\n",
        "          f\"test_acc: {test_acc:.4f}\"\n",
        "      )\n",
        "\n",
        "      # Update results dictionary\n",
        "      results[\"train_loss\"].append(train_loss)\n",
        "      results[\"train_acc\"].append(train_acc)\n",
        "      results[\"test_loss\"].append(test_loss)\n",
        "      results[\"test_acc\"].append(test_acc)\n",
        "\n",
        "  # Return the filled results at the end of the epochs\n",
        "  return results"
      ],
      "metadata": {
        "id": "_7_NufamkzFC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#define a seed function\n",
        "\n",
        "def set_seeds(seed: int=42):\n",
        "  torch.manual_seed(seed)\n",
        "  torch.cuda.manual_seed(seed)"
      ],
      "metadata": {
        "id": "uP6kSCxkkzG-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#train our model\n",
        "\n",
        "#setup the optimizer\n",
        "optimizer = torch.optim.Adam(params = vit.parameters(),\n",
        "                             lr = 1e-3)\n",
        "\n",
        "#setup the loss function\n",
        "loss_fn = torch.nn.CrossEntropyLoss()\n",
        "\n",
        "set_seeds()\n",
        "vit_results = 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)\n"
      ],
      "metadata": {
        "id": "iQ55Q32slEMv",
        "outputId": "f15518b4-f6b4-4794-f278-d3397ebb9c5f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223,
          "referenced_widgets": [
            "544cbc32b9bb43cc9b79bc0b33b650fc",
            "70cd77acff324033b12ccc23f670da65",
            "0c9ede53453143c898b005fd06233735",
            "1bfa0a4a45c244c5b17301f82946d514",
            "56729a592f4e4653b8c93a6da4575979",
            "8df137fe5b944d3797e863e8d1d98335",
            "015c0648e7f0477cb44cc1affd83e1f6",
            "0b0b9f6ad75e4e17971d55c562dd8b8b",
            "93af85872b1042f2a1f90aa3f1b0e56e",
            "52cdc762e79d4d09af36741156e4073b",
            "b8bcc27ae3784e3ca5a9bc15a8dd7139"
          ]
        }
      },
      "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": "544cbc32b9bb43cc9b79bc0b33b650fc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 1 | train_loss: 0.7575 | train_acc: 0.7845 | test_loss: 0.3838 | test_acc: 0.9107\n",
            "Epoch: 2 | train_loss: 0.2778 | train_acc: 0.9260 | test_loss: 0.2705 | test_acc: 0.9241\n",
            "Epoch: 3 | train_loss: 0.2019 | train_acc: 0.9375 | test_loss: 0.2402 | test_acc: 0.9196\n",
            "Epoch: 4 | train_loss: 0.1662 | train_acc: 0.9430 | test_loss: 0.2190 | test_acc: 0.9196\n",
            "Epoch: 5 | train_loss: 0.1393 | train_acc: 0.9600 | test_loss: 0.2089 | test_acc: 0.9152\n",
            "Epoch: 6 | train_loss: 0.1194 | train_acc: 0.9720 | test_loss: 0.2026 | test_acc: 0.9241\n",
            "Epoch: 7 | train_loss: 0.1048 | train_acc: 0.9764 | test_loss: 0.1961 | test_acc: 0.9241\n",
            "Epoch: 8 | train_loss: 0.0904 | train_acc: 0.9830 | test_loss: 0.1902 | test_acc: 0.9286\n",
            "Epoch: 9 | train_loss: 0.0793 | train_acc: 0.9868 | test_loss: 0.1878 | test_acc: 0.9330\n",
            "Epoch: 10 | train_loss: 0.0707 | train_acc: 0.9934 | test_loss: 0.1846 | test_acc: 0.9330\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# define a function to save the model\n",
        "\n",
        "def save_model(model: torch.nn.Module,\n",
        "               target_dir: str,\n",
        "               model_name: str):\n",
        "  \"\"\"Saves a PyTorch model to a target directory.\n",
        "\n",
        "  Args:\n",
        "    model: A target PyTorch model to save.\n",
        "    target_dir: A directory for saving the model to.\n",
        "    model_name: A filename for the saved model. Should include\n",
        "      either \".pth\" or \".pt\" as the file extension.\n",
        "\n",
        "  Example usage:\n",
        "    save_model(model=model_0,\n",
        "               target_dir=\"models\",\n",
        "               model_name=\"05_going_modular_tingvgg_model.pth\")\n",
        "  \"\"\"\n",
        "  #create a target directory\n",
        "  target_dir_path = pathlib.Path(target_dir)\n",
        "  target_dir_path.mkdir(parents=True,\n",
        "                        exist_ok = True)\n",
        "\n",
        "  #create model save path\n",
        "  assert model_name.endswith(\".pth\") or model_name.endswith(\".pt\"), \"model_name should end with '.pt' or '.pth'\"\n",
        "  model_save_path = target_dir_path / model_name\n",
        "\n",
        "  #saving the model state_dict()\n",
        "  print(f\"[INFO] Saving model to: {model_save_path}\")\n",
        "  torch.save(obj = model.state_dict(),\n",
        "             f=model_save_path)"
      ],
      "metadata": {
        "id": "yDGgfHfAlEPr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "save_model(model = vit,\n",
        "           target_dir = 'models',\n",
        "           model_name = \"pretrained_vit_feature_extractor_pizza_steak_sushi_tacos_20_percent.pth\")"
      ],
      "metadata": {
        "id": "G9HNW58MnQUm",
        "outputId": "e8c596c8-a7ae-445a-9dcd-a6f64ef74c83",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INFO] Saving model to: models/pretrained_vit_feature_extractor_pizza_steak_sushi_tacos_20_percent.pth\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vit.to(\"cpu\")\n",
        "\n",
        "#check the device\n",
        "next(iter(vit.parameters())).device"
      ],
      "metadata": {
        "id": "q6butC3AlERn",
        "outputId": "7e47f0b5-c147-4a08-e852-daed090600c7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cpu')"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from timeit import default_timer as timer"
      ],
      "metadata": {
        "id": "ahjMOggVlETs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#create a gradio predict function\n",
        "def predict(img) -> Tuple[Dict, float]:\n",
        "  \"\"\"Transforms and performs a prediction on img and returns pretiction and time taken.\n",
        "  \"\"\"\n",
        "  #start the timer\n",
        "  start_time = timer()\n",
        "\n",
        "  # Transform the target image and add a batch dimension\n",
        "  img = vit_transforms(img).unsqueeze(0)\n",
        "\n",
        "  #put model into evaluation mode and turn on inference mode\n",
        "  vit.eval()\n",
        "\n",
        "  with torch.inference_mode():\n",
        "    #pass the transformed image through the model and turn the prediction logits into predicted probabilitieis\n",
        "    pred_probs = torch.softmax(vit(img), dim = 1)\n",
        "\n",
        "  #create a predicition label and prediction probability dictionary for each prediction class\n",
        "  #this is the required format for gradio's output parameters\n",
        "  pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}\n",
        "\n",
        "  #calculate the prediction time\n",
        "  pred_time = round(timer()- start_time, 5)\n",
        "\n",
        "  #Return the predicted dictionary and predicted time\n",
        "  return pred_labels_and_probs, pred_time\n"
      ],
      "metadata": {
        "id": "c2cx9R3ulEVM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "#create a list of examples as required by gradio\n",
        "test_data_paths = list(pathlib.Path(test_dir).glob(\"*/*.jpg\"))\n",
        "examples_list = [[str(filepath)] for filepath in random.sample(test_data_paths, k=3)]\n",
        "examples_list"
      ],
      "metadata": {
        "id": "NZdj2FbtnhHt",
        "outputId": "2356b9b4-bf33-491d-85f0-7d725d53de07",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[['/content/data/pizza_steak_sushi_tacos_20_percent/test/steak/2510026.jpg'],\n",
              " ['/content/data/pizza_steak_sushi_tacos_20_percent/test/steak/3873283.jpg'],\n",
              " ['/content/data/pizza_steak_sushi_tacos_20_percent/test/sushi/3196729.jpg']]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Build the Gradio Interface\n",
        "\n",
        "import gradio as gr\n",
        "#create title, description and article strings\n",
        "title = 'FoodClassifier Mini'\n",
        "description = \"A Vision Transformer feature extractor computer vision model to classify images of food as pizza, steak, sushi or tacos.\"\n",
        "article = \"Created by [Emerald Henry](https://henrii1.github.io).\"\n",
        "\n",
        "# Create the Gradio demo\n",
        "demo = gr.Interface(fn=predict,\n",
        "                    inputs = gr.Image(type=\"pil\"),\n",
        "                    outputs = [gr.Label(num_top_classes=4, label=\"Predictions\"),\n",
        "                               gr.Number(label = \"Prediction time (s)\")],\n",
        "                    examples = examples_list,\n",
        "                    title = title,\n",
        "                    description = description,\n",
        "                    article = article)\n",
        "\n",
        "# Launch the demo!\n",
        "\n",
        "demo.launch(debug=False,\n",
        "            share=True)"
      ],
      "metadata": {
        "id": "cw3uZ7PdlEYq",
        "outputId": "ae4652ab-a161-4a17-c834-a28e240664cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 611
        }
      },
      "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://f4bd1803cc39607f73.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><iframe src=\"https://f4bd1803cc39607f73.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": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZK3d9K39fBVl"
      },
      "outputs": [],
      "source": [
        "#!pip install -q gradio"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#import gradio as gr"
      ],
      "metadata": {
        "id": "hWVbVmyKfDEM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#creating the app in hugging space\n",
        "#creating a demos folder to store out food vision mini app files\n",
        "\n",
        "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 already exist there and create new directory\n",
        "\n",
        "if foodvision_mini_demo_path.exists():\n",
        "  shutil.rmtree(foodvision_mini_demo_path)\n",
        "  foodvision_mini_demo_path.mkdir(parents = True,\n",
        "                                  exist_ok = True)\n",
        "else:\n",
        "  foodvision_mini_demo_path.mkdir(parents = True,\n",
        "                                  exist_ok = True)\n",
        "\n",
        "!ls demos/foodvision_mini/"
      ],
      "metadata": {
        "id": "EkFwZL7XlX-e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#creating a folder of examples\n",
        "\n",
        "#create an example directory\n",
        "foodvision_mini_examples_path = foodvision_mini_demo_path / \"examples\"\n",
        "foodvision_mini_examples_path.mkdir(parents = True,\n",
        "                                    exist_ok = True)\n",
        "\n",
        "#collect three random dataset image paths\n",
        "foodvision_mini_examples = [Path('data/pizza_steak_sushi_tacos_20_percent/test/sushi/1016390.jpg'),\n",
        "                            Path('data/pizza_steak_sushi_tacos_20_percent/test/steak/1064847.jpg'),\n",
        "                            Path('data/pizza_steak_sushi_tacos_20_percent/test/pizza/1106738.jpg')]\n",
        "\n",
        "#copy the three random images to the examples directory\n",
        "for example in foodvision_mini_examples:\n",
        "  destination = foodvision_mini_examples_path / example.name\n",
        "  shutil.copy2(src = example, dst = destination)"
      ],
      "metadata": {
        "id": "WUaY-cEElYBF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#moving the model to our foodvision directory\n",
        "vit_foodvision_mini_model_path = 'models/pretrained_vit_feature_extractor_pizza_steak_sushi_tacos_20_percent.pth'\n",
        "\n",
        "#create a destination path for our target model\n",
        "vit_foodvision_mini_model_destination = foodvision_mini_demo_path /vit_foodvision_mini_model_path.split(\"/\")[1]\n",
        "\n",
        "\n",
        "#Try to move the file\n",
        "\n",
        "try:\n",
        "  print(f\"[INFO] Attempting to move {vit_foodvision_mini_model_path} to {vit_foodvision_mini_model_destination}\")\n",
        "  #move the file\n",
        "  shutil.move(src=vit_foodvision_mini_model_path,\n",
        "              dst = vit_foodvision_mini_model_destination)\n",
        "  print(f\"[INFO] Model move complete.\")\n",
        "\n",
        "except:\n",
        "    print(f\"[INFO] No model found at {vit_foodvision_mini_model_path}, perhaps its already been moved?\")\n",
        "    print(f\"[INFO] Model exists at {vit_foodvision_mini_model_destination}: {vit_foodvision_mini_model_destination.exists()}\")\n",
        "\n"
      ],
      "metadata": {
        "id": "cjwpPk2ylYDR",
        "outputId": "0e7f1191-6324-4249-f29b-fe0b2aa324ee",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INFO] Attempting to move models/pretrained_vit_feature_extractor_pizza_steak_sushi_tacos_20_percent.pth to demos/foodvision_mini/pretrained_vit_feature_extractor_pizza_steak_sushi_tacos_20_percent.pth\n",
            "[INFO] Model move complete.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile demos/foodvision_mini/model.py\n",
        "import torch\n",
        "import torchvision\n",
        "\n",
        "from torch import nn\n",
        "\n",
        "def create_vit_model1(num_classes: int = 4,\n",
        "                      seed: int =42)\n",
        "  \"\"\"Creates an ViT feature extractor model and transforms.\n",
        "\n",
        "  Args:\n",
        "        num_classes (int, optional): number of classes in the classifier head.\n",
        "            Defaults to 4.\n",
        "        seed (int, optional): random seed value. Defaults to 42.\n",
        "\n",
        "  Returns:\n",
        "        model (torch.nn.Module): ViT feature extractor model.\n",
        "        transforms (torchvision.transforms): ViT image transforms.\n",
        "  \"\"\"\n",
        "  #create vit pretrained weights and 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 layers in base model\n",
        "  for param in model.parameters():\n",
        "      param.requires_grad = False\n",
        "\n",
        "  #Change classifier head with random seed for reproducibility\n",
        "  torch.manual_seed(seed)\n",
        "  model.classifier = nn.Sequential(nn.Dropout(p=0.3, inplace = True),\n",
        "                                    nn.Linear(in_features = 1408, out_features = num_classes))\n",
        "  return model, transforms"
      ],
      "metadata": {
        "id": "dslfuekklYGu",
        "outputId": "428ac4b4-8343-4820-bc83-059fd67eba31",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Writing demos/foodvision_mini/model.py\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#turn gradio into written file\n",
        "%%writefile demos/foodvision_mini/app.py\n",
        "#import moudules\n",
        "import gradio as gr\n",
        "import os\n",
        "import torch\n",
        "\n",
        "from model import create_vit_model1\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\", \"Tacos\"]\n",
        "\n",
        "#model and transform preparation\n",
        "\n",
        "#create model\n",
        "vit, vit_transforms1 = create_vit_model1(num_classes = 4)\n",
        "\n",
        "#load saved weights\n",
        "vit.load_state_dict(torch.load(f= 'models/pretrained_vit_feature_extractor_pizza_steak_sushi_tacos_20_percent.pth',\n",
        "                                map_location = torch.device(\"cpu\") )\n",
        "\n",
        ")\n",
        "\n",
        "#predict function\n",
        "def predict(img) -> Tuple[Dict, float]:\n",
        "  \"\"\"Transforms and performs a prediction on img and returns prediction and time taken.\n",
        "  \"\"\"\n",
        "  # start the timer\n",
        "  start_time = timer()\n",
        "\n",
        "  #Transform the target image and add a batch dimension\n",
        "  img = vit_transforms1(img).unsqueeze(0)\n",
        "\n",
        "  #put model into evaluation mode and turn on inference model\n",
        "  vit.eval()\n",
        "  with torch.inference_mode():\n",
        "    #pass the transformed image throug the model and turn prediction to probabilites\n",
        "    pred_probs = torch.softmax(vit(img), dim = 1)\n",
        "\n",
        "  #create a prediction label and prediction probability dic for each class, as required by gradio\n",
        "  pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i] ) for i in range(len(class_names))}\n",
        "\n",
        "  #Calculate the prediction time\n",
        "  pred_time = round(timer() - start_time, 5)\n",
        "\n",
        "  return pred_labels_and_probs, pred_time\n",
        "\n",
        "\n",
        "\n",
        "#Gradio App\n",
        "\n",
        "#create title, description and article strings\n",
        "\n",
        "title = \"FoodClassifier Mini\"\n",
        "description = \"A ViT feature extractor computer vision model to classify images of food as pizza, steak, sushi or Tacos\"\n",
        "article = created by [Emerald Henry](https://henrii1.github.io)\n",
        "\n",
        "example_list = [[\"examples/\" + example] for example in os.listdir(\"examples\")]\n",
        "\n",
        "#create the gradio demo\n",
        "\n",
        "demo = gr. Interface(fn= predict,\n",
        "                      inputs = gr.Image(type = \"pil,\n",
        "                      outpus = [gr.Label(num_top_classes = 4, label='Preciction,\n",
        "                                gr.Number(label = \"Prediction time (s,\n",
        "                      examples = example_list,\n",
        "                      title = title,\n",
        "                      description = description,\n",
        "                      article = article)\n",
        "demo.launch()\n"
      ],
      "metadata": {
        "id": "BUDVPtdwqimG",
        "outputId": "bd42a424-a387-4251-efc3-90ae314f1a96",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Writing demos/foodvision_mini/app.py\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile demos/foodvision_mini/requirement.txt\n",
        "\n",
        "torch==1.12.0\n",
        "torchvision == 0.13.0\n",
        "gradio == 3.1.4"
      ],
      "metadata": {
        "id": "ODaDxdqQqioY",
        "outputId": "851bb86b-501f-4081-8d6d-a9af87ca8fa1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Writing demos/foodvision_mini/requirement.txt\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "zip -r ../foodvision_mini.zip * -x \"*.pyc\" \"*.ipynb\" \"*__pycache__*\" \"*ipynb_checkpoints*\"\n"
      ],
      "metadata": {
        "id": "ymr4WLLhqiql",
        "outputId": "a4c87e70-9a79-4ed8-af58-121299245bf3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-38-15d467c6e404>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    zip -r ../foodvision_mini.zip * -x \"*.pyc\" \"*.ipynb\" \"*__pycache__*\" \"*ipynb_checkpoints*\"\u001b[0m\n\u001b[0m            ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#download the file were working with\n",
        "\n",
        "!cd demos/foodvision_mini && zip -r ../foodvision_mini.zip * -x \"*.pyc\" \"*.ipynb\" \"*__pycache__*\" \"*ipynb_checkpoints*\"\n",
        "\n",
        "# Download the zipped Food vision mini app.\n",
        "\n",
        "try:\n",
        "  from google.colab import files\n",
        "  files.download(\"demos/foodvision_mini.zip\")\n",
        "except:\n",
        "  print(\"Not running in Google Colab, can't use google.colab.files.download(), please download manually\")"
      ],
      "metadata": {
        "id": "EpArW_lWqit5",
        "outputId": "e77bc6ff-35e4-4257-93c4-ccf9c41f03ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  adding: app.py (deflated 55%)\n",
            "  adding: examples/ (stored 0%)\n",
            "  adding: examples/1106738.jpg (deflated 0%)\n",
            "  adding: examples/1064847.jpg (deflated 0%)\n",
            "  adding: examples/1016390.jpg (deflated 1%)\n",
            "  adding: model.py (deflated 54%)\n",
            "  adding: pretrained_vit_feature_extractor_pizza_steak_sushi_tacos_20_percent.pth (deflated 7%)\n",
            "  adding: requirement.txt (deflated 9%)\n"
          ]
        },
        {
          "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_f692928a-0578-42b0-abe7-e20a91e0e49e\", \"foodvision_mini.zip\", 319290794)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "J4D_eE3QlYWY"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}