Project V1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "V28",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Nightskull100/Dress-Type-Detection-using-CNN/blob/main/Project V1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torchvision import datasets, transforms\n",
        "import matplotlib.pyplot as plt\n",
        "from collections import OrderedDict\n",
        "\n",
        "# Download training and testing data\n",
        "transform = transforms.Compose([transforms.ToTensor(),\n",
        "                                transforms.Normalize((0.5,), (0.5,))])\n",
        "train_ds = datasets.FashionMNIST('F_MNIST_data', download=True, train=True, transform=transform)\n",
        "test_ds = datasets.FashionMNIST('F_MNIST_data', download=True, train=False, transform=transform)"
      ],
      "metadata": {
        "id": "kL0Q0EuQR14p",
        "outputId": "d4fcbba2-121d-4a17-ab31-5ed61580c908",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to F_MNIST_data/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 26421880/26421880 [00:01<00:00, 13578113.29it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting F_MNIST_data/FashionMNIST/raw/train-images-idx3-ubyte.gz to F_MNIST_data/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to F_MNIST_data/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 29515/29515 [00:00<00:00, 276980.13it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting F_MNIST_data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to F_MNIST_data/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to F_MNIST_data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 4422102/4422102 [00:00<00:00, 5109192.26it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting F_MNIST_data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to F_MNIST_data/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to F_MNIST_data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 5148/5148 [00:00<00:00, 10156292.09it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting F_MNIST_data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to F_MNIST_data/FashionMNIST/raw\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# split train set into training (80%) and validation set (20%)\n",
        "train_num = len(train_ds)\n",
        "indices = list(range(train_num))\n",
        "np.random.shuffle(indices)\n",
        "split = int(np.floor(0.2 * train_num))\n",
        "val_idx, train_idx = indices[:split], indices[split:]\n",
        "len(val_idx), len(train_idx)"
      ],
      "metadata": {
        "id": "Ehg6iBCqSWT-",
        "outputId": "6a8f533a-9f8c-4e56-eb20-44cb04998218",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(12000, 48000)"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prepare dataloaders\n",
        "train_sampler = torch.utils.data.sampler.SubsetRandomSampler(train_idx)\n",
        "train_dl = torch.utils.data.DataLoader(train_ds, batch_size=64, sampler=train_sampler)\n",
        "val_sampler = torch.utils.data.sampler.SubsetRandomSampler(val_idx)\n",
        "val_dl = torch.utils.data.DataLoader(train_ds, batch_size=64, sampler=val_sampler)\n",
        "test_dl = torch.utils.data.DataLoader(test_ds, batch_size=64, shuffle=True)"
      ],
      "metadata": {
        "id": "g3N0Vop0SgUz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "image, label = next(iter(train_dl))\n",
        "print(image[0].shape, label.shape)\n",
        "desc = ['T-shirt/top','Trouser','Pullover','Dress','Coat','Sandal','Shirt','Sneaker','Bag','Ankle Boot']\n",
        "print(desc[label[0].item()])\n",
        "plt.imshow(image[0].numpy().squeeze(), cmap='gray');"
      ],
      "metadata": {
        "id": "Ml5oimfySmsV",
        "outputId": "1eda8f3d-59fc-4c3b-b802-cba6898e2a13",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([1, 28, 28]) torch.Size([64])\n",
            "Ankle Boot\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def network():\n",
        "    model = nn.Sequential(OrderedDict([('fc1', nn.Linear(784, 128)),\n",
        "                                       ('relu1', nn.ReLU()),\n",
        "                                       ('drop1', nn.Dropout(0.25)),\n",
        "                                       ('fc2', nn.Linear(128, 64)),\n",
        "                                       ('relu2', nn.ReLU()),\n",
        "                                       ('drop1', nn.Dropout(0.25)),\n",
        "                                       ('output', nn.Linear(64, 10)),\n",
        "                                       ('logsoftmax', nn.LogSoftmax(dim=1))]))\n",
        "    # Use GPU if available\n",
        "    device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "    model = model.to(device)\n",
        "\n",
        "    # define the criterion and optimizer\n",
        "    loss_fn = nn.NLLLoss()\n",
        "    optimizer = optim.Adam(model.parameters(), lr=0.003)\n",
        "\n",
        "    return model, loss_fn, optimizer, device"
      ],
      "metadata": {
        "id": "ozDXsioWSy0u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model, loss_fn, optimizer, device = network()\n",
        "print(model)"
      ],
      "metadata": {
        "id": "1hR54siHTDuk",
        "outputId": "ff55e761-1020-487f-c596-cc8822315076",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sequential(\n",
            "  (fc1): Linear(in_features=784, out_features=128, bias=True)\n",
            "  (relu1): ReLU()\n",
            "  (drop1): Dropout(p=0.25, inplace=False)\n",
            "  (fc2): Linear(in_features=128, out_features=64, bias=True)\n",
            "  (relu2): ReLU()\n",
            "  (output): Linear(in_features=64, out_features=10, bias=True)\n",
            "  (logsoftmax): LogSoftmax(dim=1)\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def train_validate(model, loss_fn, optimizer, trainloader, testloader, device, n_epochs=25):\n",
        "    train_losses = []\n",
        "    test_losses = []\n",
        "    for epoch in range(n_epochs):\n",
        "        # Set mode to training - Dropouts will be used here\n",
        "        model.train()\n",
        "        train_epoch_loss = 0\n",
        "        for images, labels in trainloader:\n",
        "            images, labels = images.to(device), labels.to(device)\n",
        "            # flatten the images to batch_size x 784\n",
        "            images = images.view(images.shape[0], -1)\n",
        "            # forward pass\n",
        "            outputs = model(images)\n",
        "            # backpropogation\n",
        "            train_batch_loss = loss_fn(outputs, labels)\n",
        "            optimizer.zero_grad()\n",
        "            train_batch_loss.backward()\n",
        "            # Weight updates\n",
        "            optimizer.step()\n",
        "            train_epoch_loss += train_batch_loss.item()\n",
        "        else:\n",
        "            # One epoch of training complete\n",
        "            # calculate average training epoch loss\n",
        "            train_epoch_loss = train_epoch_loss/len(trainloader)\n",
        "\n",
        "            # Now Validate on testset\n",
        "            with torch.no_grad():\n",
        "                test_epoch_acc = 0\n",
        "                test_epoch_loss = 0\n",
        "                # Set mode to eval - Dropouts will NOT be used here\n",
        "                model.eval()\n",
        "                for images, labels in testloader:\n",
        "                    images, labels = images.to(device), labels.to(device)\n",
        "                    # flatten images to batch_size x 784\n",
        "                    images = images.view(images.shape[0], -1)\n",
        "                    # make predictions\n",
        "                    test_outputs = model(images)\n",
        "                    # calculate test loss\n",
        "                    test_batch_loss = loss_fn(test_outputs, labels)\n",
        "                    test_epoch_loss += test_batch_loss\n",
        "\n",
        "                    # get probabilities, extract the class associated with highest probability\n",
        "                    proba = torch.exp(test_outputs)\n",
        "                    _, pred_labels = proba.topk(1, dim=1)\n",
        "\n",
        "                    # compare actual labels and predicted labels\n",
        "                    result = pred_labels == labels.view(pred_labels.shape)\n",
        "                    batch_acc = torch.mean(result.type(torch.FloatTensor))\n",
        "                    test_epoch_acc += batch_acc.item()\n",
        "                else:\n",
        "                    # One epoch of training and validation done\n",
        "                    # calculate average testing epoch loss\n",
        "                    test_epoch_loss = test_epoch_loss/len(testloader)\n",
        "                    # calculate accuracy as correct_pred/total_samples\n",
        "                    test_epoch_acc = test_epoch_acc/len(testloader)\n",
        "                    # save epoch losses for plotting\n",
        "                    train_losses.append(train_epoch_loss)\n",
        "                    test_losses.append(test_epoch_loss)\n",
        "                    # print stats for this epoch\n",
        "                    print(f'Epoch: {epoch} -> train_loss: {train_epoch_loss:.19f}, val_loss: {test_epoch_loss:.19f}, ',\n",
        "                          f'val_acc: {test_epoch_acc*100:.2f}%')\n",
        "    # Finally plot losses\n",
        "    plt.plot(train_losses, label='train-loss')\n",
        "    plt.plot(test_losses, label='val-loss')\n",
        "    plt.legend()\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "KiWjAmxXTWz-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train and validate\n",
        "train_validate(model, loss_fn, optimizer, train_dl, val_dl, device)"
      ],
      "metadata": {
        "id": "fOvSgk_4Tfv3",
        "outputId": "224bbb5c-ca23-4ec7-9fbf-ae58a1ddcce7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 864
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0 -> train_loss: 0.5688990403413772823, val_loss: 0.4267962574958801270,  val_acc: 84.33%\n",
            "Epoch: 1 -> train_loss: 0.4584171397884686527, val_loss: 0.4239452183246612549,  val_acc: 84.19%\n",
            "Epoch: 2 -> train_loss: 0.4243147429029147100, val_loss: 0.3911571502685546875,  val_acc: 85.70%\n",
            "Epoch: 3 -> train_loss: 0.4035343632698059069, val_loss: 0.3909696638584136963,  val_acc: 85.31%\n",
            "Epoch: 4 -> train_loss: 0.3900834276477495588, val_loss: 0.3862922787666320801,  val_acc: 86.09%\n",
            "Epoch: 5 -> train_loss: 0.3783403584559758559, val_loss: 0.3543260097503662109,  val_acc: 86.84%\n",
            "Epoch: 6 -> train_loss: 0.3689573630094528101, val_loss: 0.3688744604587554932,  val_acc: 86.43%\n",
            "Epoch: 7 -> train_loss: 0.3585608521302541263, val_loss: 0.3623376786708831787,  val_acc: 86.64%\n",
            "Epoch: 8 -> train_loss: 0.3580319956441720408, val_loss: 0.3374220132827758789,  val_acc: 87.37%\n",
            "Epoch: 9 -> train_loss: 0.3441609425942103173, val_loss: 0.3434705436229705811,  val_acc: 87.12%\n",
            "Epoch: 10 -> train_loss: 0.3417473329404989713, val_loss: 0.3636229634284973145,  val_acc: 86.74%\n",
            "Epoch: 11 -> train_loss: 0.3359168258011341335, val_loss: 0.3492007553577423096,  val_acc: 87.43%\n",
            "Epoch: 12 -> train_loss: 0.3356773256262143290, val_loss: 0.3363957703113555908,  val_acc: 87.59%\n",
            "Epoch: 13 -> train_loss: 0.3278974480628967236, val_loss: 0.3285931050777435303,  val_acc: 87.79%\n",
            "Epoch: 14 -> train_loss: 0.3209892046650250630, val_loss: 0.3480418622493743896,  val_acc: 87.46%\n",
            "Epoch: 15 -> train_loss: 0.3223945088287194505, val_loss: 0.3490113317966461182,  val_acc: 87.32%\n",
            "Epoch: 16 -> train_loss: 0.3219262594779332409, val_loss: 0.3426370024681091309,  val_acc: 87.48%\n",
            "Epoch: 17 -> train_loss: 0.3140170510808626969, val_loss: 0.3408143520355224609,  val_acc: 87.75%\n",
            "Epoch: 18 -> train_loss: 0.3102177990476290281, val_loss: 0.3609493672847747803,  val_acc: 86.72%\n",
            "Epoch: 19 -> train_loss: 0.3093387784957885578, val_loss: 0.3443438410758972168,  val_acc: 87.67%\n",
            "Epoch: 20 -> train_loss: 0.3078756541212399611, val_loss: 0.3342774808406829834,  val_acc: 87.95%\n",
            "Epoch: 21 -> train_loss: 0.3060808049937089326, val_loss: 0.3427028656005859375,  val_acc: 87.76%\n",
            "Epoch: 22 -> train_loss: 0.3003152553538481206, val_loss: 0.3297471404075622559,  val_acc: 88.41%\n",
            "Epoch: 23 -> train_loss: 0.2990515535076459508, val_loss: 0.3365467786788940430,  val_acc: 87.78%\n",
            "Epoch: 24 -> train_loss: 0.2951882797976335127, val_loss: 0.3327170908451080322,  val_acc: 88.17%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format = 'retina'\n",
        "\n",
        "# Test out the network!\n",
        "dataiter = iter(test_dl)\n",
        "images, labels = dataiter.__next__()\n",
        "images, labels = images.to(device), labels.to(device)\n",
        "index = 49\n",
        "img, label = images[index], labels[index]\n",
        "# Convert 2D image to 1D vector\n",
        "img = img.view(img.shape[0], -1)\n",
        "\n",
        "# Calculate the class probabilities (softmax) for img\n",
        "proba = torch.exp(model(img))\n",
        "\n",
        "# Plot the image and probabilities\n",
        "desc = ['T-shirt/top','Trouser','Pullover','Dress','Coat','Sandal','Shirt','Sneaker','Bag','Ankle Boot']\n",
        "fig, (ax1, ax2) =  plt.subplots(figsize=(13, 6), nrows=1, ncols=2)\n",
        "ax1.axis('off')\n",
        "ax1.imshow(images[index].cpu().numpy().squeeze())\n",
        "ax1.set_title(desc[label.item()])\n",
        "ax2.bar(range(10), proba.detach().cpu().numpy().squeeze())\n",
        "ax2.set_xticks(range(10))\n",
        "ax2.set_xticklabels(desc, size='small')\n",
        "ax2.set_title('Predicted Probabilities')\n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "id": "6kbumTwcViZ1",
        "outputId": "ef45df4d-454d-422c-ec77-b5c8236eb8af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 604
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1300x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "image/png": {
              "width": 1246,
              "height": 587
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "from PIL import Image\n",
        "import io\n",
        "import torch\n",
        "import matplotlib.pyplot as plt\n",
        "from torchvision import transforms\n",
        "\n",
        "# Function to preprocess the image from URL\n",
        "def preprocess_image_from_url(image_url):\n",
        "    transform = transforms.Compose([\n",
        "        transforms.Grayscale(num_output_channels=1),  # Convert to grayscale\n",
        "        transforms.Resize((28, 28)),                 # Resize to 28x28 pixels\n",
        "        transforms.ToTensor(),                      # Convert image to tensor\n",
        "        transforms.Normalize((0.5,), (0.5,))        # Normalize the tensor\n",
        "    ])\n",
        "\n",
        "    # Fetch the image from the URL\n",
        "    try:\n",
        "        response = requests.get(image_url)\n",
        "        response.raise_for_status()  # Check for HTTP errors\n",
        "        image = Image.open(io.BytesIO(response.content)).convert('RGB')\n",
        "    except requests.RequestException as e:\n",
        "        print(f\"Error fetching image: {e}\")\n",
        "        return None\n",
        "    except IOError as e:\n",
        "        print(f\"Error opening image: {e}\")\n",
        "        return None\n",
        "\n",
        "    # Apply the transform to the image\n",
        "    image_tensor = transform(image)\n",
        "\n",
        "    # Add a batch dimension (needed for model input)\n",
        "    image_tensor = image_tensor.unsqueeze(0)  # Shape: [1, 1, 28, 28]\n",
        "\n",
        "    return image_tensor\n",
        "\n",
        "# Get the image URL from the user\n",
        "image_url = input(\"Enter the image URL: \")\n",
        "\n",
        "# Preprocess the image\n",
        "image_tensor = preprocess_image_from_url(image_url)\n",
        "if image_tensor is None:\n",
        "    print(\"Failed to preprocess image.\")\n",
        "else:\n",
        "        # Predict the class and probabilities\n",
        "        with torch.no_grad():\n",
        "            model.eval()  # Set the model to evaluation mode\n",
        "            image_tensor = image_tensor.to(device)\n",
        "            image_tensor = image_tensor.view(image_tensor.shape[0], -1)\n",
        "            proba = torch.exp(model(image_tensor))\n",
        "            predicted_class = torch.argmax(proba, dim=1).item()\n",
        "\n",
        "        # Plot the image and probabilities\n",
        "        desc = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
        "                'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle Boot']\n",
        "\n",
        "        fig, (ax1, ax2) = plt.subplots(figsize=(13, 6), nrows=1, ncols=2)\n",
        "\n",
        "        # Display the image\n",
        "        ax1.imshow(image_tensor.squeeze().cpu().numpy().reshape(28, 28), cmap='gray')\n",
        "        ax1.axis('off')\n",
        "        ax1.set_title(desc[predicted_class])\n",
        "\n",
        "        # Display the probabilities\n",
        "        ax2.bar(range(10), proba.squeeze().cpu().numpy())\n",
        "        ax2.set_xticks(range(10))\n",
        "        ax2.set_xticklabels(desc, size='small')\n",
        "        ax2.set_title('Predicted Probabilities')\n",
        "\n",
        "        plt.tight_layout()\n",
        "        plt.show()\n"
      ],
      "metadata": {
        "id": "0pAPYTJAVrgk",
        "outputId": "11f21ab1-82fe-40d4-9fbc-7dacac982b9e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter the image URL: https://img.freepik.com/free-photo/white-tshirt-template-wooden-background_1409-4097.jpg?t=st=1723303527~exp=1723307127~hmac=f4bef4209d1cc5adb20612d6689ecbd122fd37393d1bcd2b6272ae50ff7d4334&w=740\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1300x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "image/png": {
              "width": 1246,
              "height": 587
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "from PIL import Image\n",
        "import torch\n",
        "import matplotlib.pyplot as plt\n",
        "from torchvision import transforms\n",
        "\n",
        "# This will prompt you to upload a file from your local machine\n",
        "uploaded = files.upload()\n",
        "\n",
        "# Get the filename of the uploaded image\n",
        "image_path = next(iter(uploaded))\n",
        "\n",
        "# Function to preprocess the image from a local file\n",
        "def preprocess_image_from_file(image_path):\n",
        "    transform = transforms.Compose([\n",
        "        transforms.Grayscale(num_output_channels=1),  # Convert to grayscale\n",
        "        transforms.Resize((28, 28)),                 # Resize to 28x28 pixels\n",
        "        transforms.ToTensor(),                      # Convert image to tensor\n",
        "        transforms.Normalize((0.5,), (0.5,))        # Normalize the tensor\n",
        "    ])\n",
        "\n",
        "    # Open the image file\n",
        "    try:\n",
        "        image = Image.open(image_path).convert('RGB')\n",
        "    except IOError as e:\n",
        "        print(f\"Error opening image: {e}\")\n",
        "        return None\n",
        "\n",
        "    # Apply the transform to the image\n",
        "    image_tensor = transform(image)\n",
        "\n",
        "    # Add a batch dimension (needed for model input)\n",
        "    image_tensor = image_tensor.unsqueeze(0)  # Shape: [1, 1, 28, 28]\n",
        "\n",
        "    return image_tensor\n",
        "\n",
        "# Preprocess the image\n",
        "image_tensor = preprocess_image_from_file(image_path)\n",
        "if image_tensor is None:\n",
        "    print(\"Failed to preprocess image.\")\n",
        "else:\n",
        "    # Load model (assuming you have the model and device setup as in your previous code)\n",
        "    model, _, _, device = network()  # Make sure your model is loaded\n",
        "    # model.load_state_dict(torch.load('path_to_your_model.pth'))  # Uncomment if needed\n",
        "\n",
        "    # Predict the class and probabilities\n",
        "    with torch.no_grad():\n",
        "        model.eval()  # Set the model to evaluation mode\n",
        "        image_tensor = image_tensor.to(device)\n",
        "        image_tensor = image_tensor.view(image_tensor.shape[0], -1)\n",
        "        proba = torch.exp(model(image_tensor))\n",
        "        predicted_class = torch.argmax(proba, dim=1).item()\n",
        "\n",
        "    # Plot the image and probabilities\n",
        "    desc = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
        "            'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle Boot']\n",
        "\n",
        "    fig, (ax1, ax2) = plt.subplots(figsize=(13, 6), nrows=1, ncols=2)\n",
        "\n",
        "    # Display the image\n",
        "    ax1.imshow(image_tensor.squeeze().cpu().numpy().reshape(28, 28), cmap='gray')\n",
        "    ax1.axis('off')\n",
        "    ax1.set_title(desc[predicted_class])\n",
        "\n",
        "    # Display the probabilities\n",
        "    ax2.bar(range(10), proba.squeeze().cpu().numpy())\n",
        "    ax2.set_xticks(range(10))\n",
        "    ax2.set_xticklabels(desc, size='small')\n",
        "    ax2.set_title('Predicted Probabilities')\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "KSOOMfsQ2V9F",
        "outputId": "1a829072-c10b-4ffc-878f-d9d0c10b45cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-960d7595-31be-48ef-b45d-7bf81b9fdce8\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-960d7595-31be-48ef-b45d-7bf81b9fdce8\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving jcrew.png to jcrew.png\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1300x600 with 2 Axes>"
            ],
            "image/png": 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8sV660riuC46rZs2aJRqNRuzt7WXHjh162ycmJoqdnZ1oNBqZOnWq3jjkwoUL6vipf//+eumK22YqIlPjntu3b8u+ffvU4PugoCCDOl+1apWcOnXKYJ9xcXFqMPeaNWsM1vfs2VMAiKenp176P//8U1q3bq0GUpanPlAJ1Prss8/UZb179xYA0qtXL5PpzO17jPUDWVlZ8sorrwgA8fb2lkuXLhkcz9Szs7ltvyimguDu3r0rv/76q3Tv3l29Z6SlpRmkN/eZ9lGf/4szzq+MGARHREREREREREREpe5RguCuXbsmQP4sJMWl/AH94T/aK1JSUtSXuydOnNBbV5yXhyNGjDB57OK+PLxx44ZYWFiIt7e3wbqiguCSkpLUX68bC+a4d++e+ovvadOm6a1T/gheMICloIMHD6rrH/6V/aMqThBcZGSk0bT79u1TX5g8HAgjkv+LeuVX8CtXrtRbZ24QXIcOHQSAbNq0qVjnd+bMGdFoNOLo6CjJyclGtxk9erQAkKFDh+otV+rFysrKZNonwdRLwby8PFm8eLH6clm5Bp5kEJy5bSAnJ0dcXV0FgMTExBikW716tQCQhg0b6i1fsGCBAJDnnnvOIPhW2W/z5s2NvuxU2py/v7/RwLvypiRBcLdv31brdPPmzSJSvPtjTEyM+vLX2AtBEZFu3boJ8L/ZMUVEZsyYIUD+zFPFsWbNGgEg3bt3L9b2ldnLL79sdlk8Sj9TFDc3N9FoNAYzjlb2IDgRkdmzZ6uzQyn/3N3dpXv37jJr1iy5cuVKifepBJAaG+8ox+jYsaPRtAEBAQJAvvzyS73lSpBvy5YtjaZTghnNGduFhoYaHYNUxiA4EfPqvCKMY8urJ13eyuym8+bNM5qfo0ePikajkapVq5YoaKw413VSUpLk5eXJu+++K0D+7H2//vqrwfa9evUSAPLuu+8aPVZycrLY2dmJVqvVK5/itpmKSBn3mPpnbW0t48ePNzleMWXnzp1G77m///672i4L/uBCcf36dfUHGeWlDzx06JAA+bMgFmwX27ZtU8vo9u3bRtOa2/c83A+kpaVJu3btBIC0aNHCILhLYerZ2dy2X5SCz9PG/mm1Whk+fLjRmdXNfZ4pjef/pzUIzgJERERERERERERE5YizszNsbGxw584d/PTTTyVOP3ToUINlLi4uqFu3LgDgwoULJd7nwIEDS5zmYVu3bkVeXh66detW4rTbtm1DXl4evLy80LVrV4P1tra2GDFiBACYLLMWLVogJCTEYHnLli3RokULAEBMTEyJ8/aoTJXtf/7zHwBAREQE/P39DdbXqlULffr0AWD6nEvKw8MDALB+/XpkZ2cXuf2GDRsgIujatStq165tdJuXXnoJALB7926j61944QWTaZ+kZcuWoXXr1mjdujWCg4NRo0YNDBkyBLm5uWjevDk+/fTTJ54nc9uAVqtF3759AQArV640SKcs69+/v97y9evXAwCGDBmCKlWqGKTTarX4xz/+AcB0ffbv3x9arbbwE6tg7O3t1f++e/euwXpT17BSnv369YODg4PRbYxdH8p1uHPnTqSmphaZP2X7Q4cO4eLFi0VuX5mlpaUB0K+z4nrUfiYzMxOrV6/G0KFD0bFjR4SFhan3lLt370JEcOLEiRLnq6IbM2YMDh48iFdeeQV2dnYAgOTkZGzatAlvv/026tWrh/feew85OTkGaePi4jB+/Hh0794dzz//vFqe//73vwEAx48fN3lcY2MhAOo44OGxkFKnb731lsnzKExubi42b96MUaNGITIyEm3atFHze/78+SLzW5k8Sp0D5XccW149yfK+cuUKDh8+DCsrKwwaNMjo/po3bw4PDw+kpaXh6NGjBusf5brOycnBwIEDER0djXr16uHAgQMIDAzU2yY7OxtbtmwBAAwbNszofmrXro3g4GDk5uYiNjbW6DaVtc24u7sjNDRU/de8eXPUqFEDmZmZ+Oabb9Sxy8P++OMP/POf/0SvXr0QERGh1tv48eMBGNbb9u3bISIICAhAcHCwwf6cnZ3RvXv3Uj+/R7F8+XIAQLt27eDm5qYuf+GFF+Dq6orMzEysWbOm0H2UtO8p6Pr16wgPD8fu3bvRsWNH7N69GzVr1ix2/kur7RfG2dlZr/0EBwfDxcUFubm5WLNmDb755huDNOY+z5TG8//TyvApjoiIiIiIiIiIiKgMWVhY4J133sG0adMQGRkJf39/REREoFWrVggPD4eTk5PJtDVr1kT16tWNrnNxccG5c+eQnp5e4jz5+vqWOM3DNm/eDABmBcGdPXsWAODn52dymyZNmgAAEhMTja4vLG2jRo0QFxdnMu3jZKpslXNu3LixybRFnXNJjR49GitWrMB3332HmJgYdOjQAaGhoQgLC1OPVVB8fDwAIDY2Fq1btza6z8zMTAD5L06NKY22VRqSk5ORnJwMIP8arF69OsLCwtCzZ08MHToUlpaWTzxPj9IG+vfvj5kzZ2LTpk1IT09Xg4Ju3LiBnTt3QqPRoF+/fnpplPr88ssvsXTpUqPHu379OoDyX5+lqWDgW9WqVQ3WmzpnpTzXrVuHn3/+2eg2f//9NwD98uzevTt8fHxw+vRpuLu7IyIiAmFhYQgNDUXLli2h0+n09tGyZUuEh4cjNjYW3t7eaNOmDcLDw9GqVSu0bt0aNjY2JTrfikypH3P6uUfpZxISEhAZGYmkpKRCj/HXX3+VOF+VQUhICNatW4ecnBzEx8fj2LFj2LVrF2JiYnD37l1ER0fj/v37mDt3LoD8QJfBgwcbDeItqLDy9PLyMrrcxcUFgH4buXPnDlJSUgCYrn8fHx9UqVLFaCDRtWvXEBkZWWSQ29NU/yWtc0V5HseWZ0+qvJV+zcLCAi+++KLJ/ChtvWDfVhrXde/evfHrr7+iadOmiImJwbPPPmuwzfnz55GRkQGNRmMQ7F/QuXPnDPJYUGVtM4MHD8bkyZMNlm/ZsgX9+/fH4MGDAUAvyPGrr77Cu+++W+gPZB6uN6VPbdq0qck0zZo1K7I9PCkZGRlYu3YtAOC1117TW6fVatGvXz9ER0dj+fLlJgPMgJL1PQWlpaWhVatWuHjxIvr3749ly5YZ/UFKYUqr7RemU6dORgPd9u/fj759+2L8+PHIyMjAlClT1HXmPs+UxvP/04pBcERERERERERERPREjRo1yuiL0gkTJqBTp04AgClTpsDd3R1z5szByZMncfLkScyePRtarRadOnXCF198AW9vb4N9KDNAGGNhkf9hDBEpcZ4L229xZGRkYOfOnXB2dsZzzz1X4vRKEIqxl10KV1dXvW0fprx8KGydqbSPk6myLY1zLqlGjRrh4MGDmDJlCrZt24bVq1dj9erVAIAGDRpg8uTJ6gxjwP8CeP744w/88ccfhe47IyPD6PJHbVulZdKkSUZfCpalR2kD/v7+CAgIQHx8PH744Qf1hd6aNWuQk5OD559/HnXq1NFLo9TnyZMni8zb/fv3jS4vL/VZmgq2bWP3EVPnrJTn2bNn1Rd5phQsTxsbG+zbtw9Tp07F2rVr8dNPP6kzXDg6OmLkyJH46KOP1MBMjUaDrVu3Yvr06VixYgX27NmDPXv2qHkbOHAgpk+fbjSAr7JRZm4xZ0Y8c6+3vLw89OjRA0lJSWjWrBmmTJmCwMBA1KxZU62jNm3aYP/+/Xjw4EGJ81WZVKlSBYGBgQgMDMSQIUNw/fp19OzZE/v378fChQsxfvx4uLm5YebMmVi5ciWsra0xY8YMdOjQAXXq1IGtrS00Gg2WLVuG119/vdDyNHVdGhsLFaxPU2MFrVaLGjVqqIHABQ0aNAjHjx+Hp6cnPv30U7Rq1QouLi6wsrICkB9QsXLlyqey/otb54ryOo6tKB53eSv9WkZGBg4cOFBkfgr2baVxXSuzKnp6epqcIUvJo4iUOI8FPS1tRvGPf/wDU6ZMwdixY/Hhhx+if//+qFKlCg4ePIjRo0cDAEaMGIEBAwbAy8sLDg4O0Gq1uHjxIurXr28QIKwEe5maCbeodU/axo0bcefOHdjb2+Pll182WD9gwABER0fjyJEjOH36tMnArJL0PQVlZGTg3r17APIDB0saAAeUXts3R1hYGL766iu89NJL+PzzzzFy5Ej1h3vmjq/K4lm4suDnUImIiIiIiIiIiOiJOnXqFA4cOGDwr+CLVY1GgyFDhuDkyZNISUnB+vXrMXLkSDzzzDPYunUrIiIi1D90VwQ7d+7E/fv30aVLF/UlQEkoL0mUmVqMuXbtmt62DzP24vrhdeXpZcyjnLNGowFg+kWL8pLFmKZNm2Ljxo34+++/sW/fPkyfPh3BwcG4cOEC+vXrhw0bNqjbKrOLTZ8+HSJS5L/K4FHKtqQetd0rs0AUnGXD1KdQgf/V5y+//FJkXe7du9e8k6qA9u3bBwCwtrYudEaThynluXr16iLL89KlS3ppnZ2dMXfuXKSmpiI+Ph7z589H165dcefOHUybNg3jxo0zONb06dNx5coVnD17FsuWLcOrr76KnJwczJs3Ty94tTJTZqQ8ffp0sT4lW5C511tcXBwSEhJgY2ODHTt2oGvXrqhVq5be7JFP0wxgJeHi4oL58+cDyP+c6JEjRwBAnWVm5syZGDNmDHx9fWFnZ6fef0u7PAvWp6mxQm5urtHjpqSkYPv27QDyZ1J69dVXUadOHTUA7nHktyIzVef0eJR2eSv9mre3d7HGfQU/KVoa1/WGDRvg7u6OH374Ab169TIaMKfk0dLSslh5LG8/gChLYWFhAPLva8q45NtvvwUAREVFYe7cuQgODkb16tWh1WoBmK43pR4KC04qT4FLyqdQldmTNRqN3r+Cs5gZmwntUbm4uGDDhg2wtLTEuHHj8MUXX5R4H2Xd9pX2k5mZqc4aCZg/viqN5/+nFYPgiIiIiIiIiIiI6Inau3dvkS+KCnJxcUGPHj0wZ84cJCQkwMXFBVeuXMHWrVufbMZNUF5eFWbTpk0ATH8Ktah9NGzYEEB+YIMpp06d0tv2YWfOnDGZVllnKm1ZUPLy22+/mdzG1DkrsxCYepmvzKRRGCsrK4SFhWH8+PGIi4vDG2+8AQBYsGCBuo3yCRolH0+D0ijb4nqUNgAAffv2hVarxe7du3H16lUkJCTg6NGjsLGxQVRUlMH2T2N9FiUnJwcLFy4EAHTo0KFEn8UtjfLUaDTw9/fHsGHDsGXLFqxYsQIAsHjxYuTm5hpN4+3tjUGDBmHNmjXqZ1i3bt1q1mevKprOnTvD0dERubm5Bp/9K4q5/YzyCVRfX1+jMxPdvn1b/fTYw4rTf1Z2DRo0UP9b+dSeUqZt2rQxmubQoUOlmodq1aqpM82YGiucPXvW6KdQlbw+88wzRmcGysnJwa+//lqKua34jNV5Wars12FplrfSr126dKnEn6Utjeu6fv36iI2NhYeHB3744Qf07NnT4Jy8vb1hZWWF7Oxsk/deMi4vL0/9byW4zdx68/HxAQC9YKiHnThxwpxslrrLly9j9+7dAAAnJye4uLgY/efo6Agg/wclxvqDR9WlSxds3LgRVlZWeOedd/D555+XKH1Zt31j7Qcw/3nmUZ7/K/t9vSgMgiMiIiIiIiIiIqIKo0aNGuofef/8888yzk0+GxsbAKY/d5mXl4etW7fC1tYWL774oln76NixIywsLHD+/Hn8+OOPBuszMjLUmS4iIyON7uPw4cOIi4szWB4XF6cu79ixo9G0ZUE5j59//tno5ymvXbuGNWvW6G2r8PLyApB/zg9LS0tT05VEaGgoAP12FxUVBY1Gg40bN5r1+cGKSCnbEydOGH2ZXDBI8FE9ShsA8j8f9MILLyAvLw+rV69WZ4Hr1q2b0U9j9uzZEwAwd+5cZGVlldp5VFQigrFjxyIhIQEWFhaYMGFCidIr5bl8+XLcunWrVPKkXIdZWVnFmjUnJCRE/aRWeekzHid7e3u89957AIDPPvtMncXPlAcPHiA6OhqA+f2Mra0tgPzAWGMzRM6ePdvky/Ki+r6KLjU1tchZQP/73/+q/6185l0pU2WGl4ISExON1s+jUj5HP2fOHKPr//WvfxldruQ1LS3N6KflVqxYgRs3bpRSLss/c+u8LFXk6/BJl7enpyeaN2+O7OxsfPXVVyVKW1rXdb169bB3717UrVsXmzdvRlRUlN54zMbGRr1Hz5w5s0R5fNrt378fQH4AUb169QAUXm+ZmZkm75kdOnSARqPBiRMncPToUYP1N2/eVH8kVda+/fZb5OXlwc3NDSkpKSb/nT17FjqdDtevX0dMTMxjyUvnzp2xefNmWFtb4/3338eMGTOKnbas277SfoD8gFWFuc8zj/L8X5Hv66WBQXBERERERERERERUrpw5cwZvvPEGDhw4oPeLaiB/RjXlF/dBQUFlkT0Dyh+54+LijH4K8pdffkFqairat2+v/kHa1D6UWfIe5uHhgddeew0A8Oabb+oFs92+fRt9+vTB9evX4eTkhDfffNPoMXQ6HQYOHKj3y/hz585hwIABAPJfOvj7+xfnlJ+I1q1b4/nnn4eIoHfv3jh79qy67urVq+jZsycyMzPh4+OjBtsounTpAgBYsmSJ3guJ1NRU9O3bF7dv3zZ6zKFDh+K7775DWlqa3vLk5GR1VqWC7c7f3x+DBw9GZmYmIiIisGPHDoN9Xrp0CTNnzsTXX39dwhIon9q2bQtbW1tcv34dH3zwgXqN5uXlYcGCBepMXaXhUdqAQrluVq5cidWrV+ste9iQIUPg5+eHU6dOoXPnzkhISNBbLyI4evQo3n777VKfiak8ycrKwo4dO9CuXTu13UdHRyM4OLhE++natSteeOEFpKSkICIiwmgQbkJCAiZNmqT3InjWrFmYOXMmkpOT9bZNT0/HtGnTAOTfE52cnAAA3333HSZNmmQw60dWVhamTp2KnJwc2NrawtfXt0T5r6jee+89REZGIjs7Gx06dMC0adMMPo2anp6ONWvWICAgAPPmzQNgfj/z3HPPQafT4erVq5g4caI6Q19eXh7mzZuH6dOnw9ra2mhelb7v9OnTlTJQatWqVWjSpAnmzZtnEECRm5uLDRs2qH1wUFCQ+rnh8PBwAMCHH36oly4+Ph5du3ZVP8NXmsaNGwedTodffvkFH3zwgRpUIyJYsmQJli5dqgaUFuTn54eaNWsiJycHI0eORGZmprpu/fr1GDVqlMn6r4zMrfOyVNQ4tjwri/L+4osvoNVqMXHiRHzyyScGn7S8d+8eNm7ciMGDB+stL83rum7duoiNjYWnpyd+/PFHvPzyy3rB+5988gns7e2xePFijB49Gjdv3tRLn52dje3bt+OVV14xOavq02bTpk3q5zEjIyPh7OwM4H/1Nn/+fL3P6d64cQNRUVEGYxWFp6enOjZ99dVX9WbZTElJQY8ePcpFgJKIqJ837d+/PywsTIcPOTk5oXPnzgAezydRFR06dMCWLVtgY2ODDz/8EJ988kmx05ZV24+NjcVbb70FIH/GyMDAQHWduc8zj/L8r9zX//vf/xr9bHKlJ0RERERERERERESlDIAAkD179pQ47fHjx9X0dnZ2EhAQIMHBwfLss8+qy19//XW9NHv27BEA4uHhYXK/4eHhAkCWL1+ut3zSpEkCQAYMGKC3PCkpST1eYdLS0qRmzZoCQBwdHaVly5YSHh4uvXr1EhGRd955x+hxCzpw4IBoNBoBIO7u7tK6dWsJDw+XGTNm6B0nNDRUzZO3t7cEBgaKtbW1AJCqVavK3r17DfY9YMAAASCjRo0ST09PsbCwkCZNmkiTJk3EwsJCAIinp6dcvXq10PM0R2HtQFmXlJRkMv3Vq1elYcOGAkA0Go00btxYmjZtKlWqVBEAUqtWLfntt98M0mVnZ0tQUJCarn79+tK0aVPR6XRSu3ZtmTZtmgCQ8PBwvXQBAQECQCwsLKRBgwYSEhIiPj4+ajl5eHhIcnKyXprMzEzp1auXej41a9aU4OBgCQwMFGdnZ3X5hAkT9NIp9TJp0qTiFudjoVwXJcnHrFmz1PNydHSUoKAgcXJyEgsLC1m6dKnJujV1zoVdv+a2AcW9e/fEwcFBzZOLi4vk5OSY3P6PP/5Q24GSp5CQEPH39xd7e3t1+c6dO/XSeXh4mH3PKwtKvbu7u0toaKiEhoZKSEiINGzYUCwtLdXzdHZ2lnXr1hmkL+798datW9K2bVt1W1dXV2nRooU0a9ZMHB0d1eWLFy9W04wePVpd7ubmJsHBwdKkSROxtbUVAGJtbS07duxQt589e7a6vZOTkwQGBkrTpk2latWq6vX8zTfflF7hVQDZ2dkycuRI9d6l0WikQYMG0qJFC4M6HjRokJrO3H7m448/1qsD5Z4AQN544w2T/W9eXp56fdvb20uLFi0kPDzc4N5cUX355ZdquSjtOSgoSBo3bqy2TwDSoEED+f3339V0J0+eFDs7OwEgVlZW4u/vLz4+Puo1O336dKN9mEjRfaupMY+IyMKFC9WxiKOjowQHB4urq6sAkDFjxpi8zxW871erVk0CAwPFzc1NAEiHDh2kX79+Jb73V1Tm1nl5HseWZ2VR3iIiq1evFhsbGwEglpaW0qRJEwkJCREvLy/RarVqXgp6HNf15cuXpX79+gJAOnbsKBkZGeq6n3/+WWrUqCEARKvViq+vr7Rs2VIaNmwoOp1O3eeDBw/UNMVtMxWRsXFPaGioNG/eXC0nANKoUSP5888/1XTp6eni6+ur9qXe3t7SrFkz0el0YmVlJUuWLDFZZqmpqXpp/fz81LQ1atSQiRMnCgBp167dkywKPXv37lXzn5iYWOT2GzduFACi0+kkNTVVXW5u31PYtbhr1y71Ops8ebLeusKuT3PaflGUZxhnZ2e99hMcHCwuLi5696AzZ84YpDf3ecbccVlSUpJYWVmpzz6tWrWS8PBwGT16dLHPuSLjTHBERERERERERERUrnh7e2PJkiXo3bs3ateujT/++APHjx9HXl4eOnXqhO+//x5Lliwp62yqHBwcsGvXLnTr1g06nQ5HjhxBbGysOlPU5s2bodVq1dnJjGnVqhW+//57tG7dGnfu3MGBAwcQGxuLxMREvePs3r0bc+bMQcuWLXHt2jX89ttvqFWrFoYPH46TJ0+qsxUY88wzzyAuLg5vvvkm/v77b5w9exa1atXCqFGjEBcXh1q1apVeoZSSWrVq4ciRI/jkk08QEBCApKQkJCYmokGDBnj//fcRHx8PPz8/g3Q6nQ47d+7EW2+9hdq1a+Py5ctITU3F4MGDcezYMdSuXdvo8b788ku8/fbbCAwMRHp6Oo4dO4arV68iICAAkyZNwokTJwzSWllZYe3atYiJiUFUVBSsra0RHx+P06dPw8HBAa+++ipWr16tfqKwMhg7dixWrlyJwMBAZGRk4Pz58/D398eOHTsMZj55VOa2AYWtrS169Oih/n+fPn0KnW2lTp06iIuLw6JFixAREYF79+7h119/xcWLF1GvXj0MHz4cO3bsKPRaq0iSk5Nx4MABHDhwACdOnMCtW7fg5+eHwYMHY+3atUhOTsYrr7xi9v4dHR2xa9curF27FpGRkRARHD9+HGfPnoWzszMGDRqETZs2oW/fvmqaYcOGYdq0aWjbti20Wi1OnTqFc+fOwc3NDUOHDkV8fLzep6V79OiBmTNnolOnTrC3t0dCQgLOnDkDR0dH9OvXD4cPH1ZnAnpa6HQ6zJkzB6dPn8Y777yDZs2a4datWzh27BhSUlLQpEkTvP3224iPj8eyZcvUdOb2M1OnTsWSJUvQtGlTpKWl4dy5c6hfvz6WLFmCxYsXm8ynRqNBTEwM+vTpg6pVq+LYsWOIjY1FbGzsYymXJ2348OGIjY3FhAkT0Lp1a+Tk5CA+Ph7nzp2DnZ0d2rdvj3nz5uHUqVPw9PRU0zVp0gQHDx5Et27dYGNjg7Nnz+LBgwd46623cPz4cbi6uj6W/A4dOhS7du3CCy+8gJycHJw5cwZubm5YsmQJZs+ebTLd4MGDsXHjRjz33HPIzs5GYmIiatasiejoaGzduvWxzFxXXplb52WpqHFseVZW5d27d28kJCRg3Lhx8PHxQVJSEo4ePYo7d+4gLCwMn332GXbv3q2X5nFc1+7u7oiNjYWXlxe2bduGbt26qbMxtmvXTp1ttVmzZrh69SqOHDmCGzduICgoCBMnTsSxY8eMzvBYmRUc9xw4cADx8fEAgDZt2uBf//oXjh49qlcXdnZ22L9/P4YNGwZXV1ckJSXh2rVreOmllxAXF4eIiAiTx6pZsyYOHTqEd999F3Xr1sX58+eRkpKCPn364NixY+qMtlWrVn28J12I5cuXAwBatmwJHx+fIrePjIyEk5MTHjx4gFWrVj3WvEVEROA///kPbG1tMXnyZEyaNKlY6R5n279x44Ze+zl69CiysrIQEhKCTz75BKdPnzY687C5zzPmjsvq1q2Lbdu24cUXX0R2djYOHTqE2NhYnDhxosTnXBFpRIr4UDYRERERERERERERmSUhIQGNGjVCWFgY9u3bVyZ5GDhwIL799ltMmjRJ/cwPERERERERlY0RI0Zg/vz5GDt2LGbNmlXW2SGqNDgTHBEREREREREREdFjsnnzZgBAt27dyjgnREREREREVNbu3LmD77//HkD+LHREVHoYBEdERERERERERET0mHzwwQcQEYwbN66ss0JERERERERPQHp6OiZPnoyrV6/qLb948SK6deuG1NRU1KtXD5GRkWWUQ6LK6en6yDMRERERERERERERERERERER0WOSk5ODKVOmYOrUqahXrx6cnJxw69YtXLhwASICR0dHrFmzBjqdrqyzSlSpcCY4IiIiIiIiIiIiIiIiIiIiIqJSYGdnh+nTp6NNmzbIzMzEiRMncOXKFfj4+GD06NGIj49HSEhIWWeTqNLRiIiUdSaIiIiIiIiIiIiIiIiIiIiIiIiIzMGZ4IiIiIiIiIiIiIiIiIiIiIiIiKjCYhAcERERERERERERERERERERERERVVgMgiMiIiIiIiIiIiIiIiIiIiIiIqIKi0FwREREREREREREREREREREREREVGExCI6IiIiIiIiIiIiIiIiIiIiIiIgqLAbBERERERERERERERERERERERERUYXFIDgiIiIiIiIiIiIiIiIiIiIiIiKqsBgER0RERERERERERERERERERERERBUWg+CIiIiIiIiIiIiIiIiIiIiIiIiowmIQHBEREREREREREREREREREREREVVYDIIjIiIiIiIiIiIiIiIiIiIiIiKiCotBcERERERERERERERERERERERERFRhMQiOiIiIiIiIiIiIiIiIiIiIiIiIKiwGwREREREREREREREREREREREREVGFxSA4IiIiIiIiIiIiIiIiIiIiIiIiqrAYBEdEREREREREREREREREREREREQVFoPgiIiIiIiIiIiIiIiIiIiIiIiIqMJiEBwRERERERERERERERERERERERFVWAyCIyIiIiIiIiIiIiIiIiIiIiIiogrr/wFW6zE807epvwAAAABJRU5ErkJggg==\n"
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        }
      ]
    }
  ]
}