upload

.gitignore

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+
+flagged/image/tmp_kzg5jrp.jpg
+flagged/image/tmpawmdga4z.jpg
+flagged/log.csv
+flagged/image/tmpv22yik0n.jpg
+flagged/image/tmptuiort3g.jpg
+flagged/image/tmpsyptwjk0.jpg
+flagged/image/tmpo70zn1zc.jpg
+flagged/image/tmpm7kl_i0r.jpg
+flagged/image/tmpfjoni2co.jpg
+flagged/image/tmpct6wib32.jpg
+*.jpg
+Checkpoint/baseline_V0.pth.tar
+Data/sample_submission.csv
+Data/solution.csv
+Data/train.csv
+Data/test.zip
+Data/train.zip

Code/Datadownload.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Token is valid.\n",
+      "Your token has been saved in your configured git credential helpers (manager-core).\n",
+      "Your token has been saved to C:\\Users\\Oussama\\.cache\\huggingface\\token\n",
+      "Login successful\n"
+     ]
+    }
+   ],
+   "source": [
+    "from huggingface_hub import login\n",
+    "login()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e5eb52f9282f43cfa4f06b1d9c6dc08b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading readme:   0%|          | 0.00/1.24k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading and preparing dataset None/None to C:/Users/Oussama/.cache/huggingface/datasets/competitions___parquet/competitions--aiornot-759454878caed5d9/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "37ae63ab36cc448e90aa292609f4e5d4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e208b22a102847b4862d336b32cce3e1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading data:   0%|          | 0.00/354M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c31d5357bf9c44e99b19820ea10ad70a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading data:   0%|          | 0.00/356M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b7de3daec8a54b28a07cd580734f4bf4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading data:   0%|          | 0.00/415M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "67b76cdca655426d9174aa7655345ecb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading data:   0%|          | 0.00/418M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "373dd6818e5c4500b9d48de516bcb427",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading data:   0%|          | 0.00/416M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9fc356325be34db1844de564ee0395fc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading data:   0%|          | 0.00/416M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8d8d0259ea1840d38f316bfc3809fa44",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Extracting data files:   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1780ace548794a58b318e723ae297b50",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating train split: 0 examples [00:00, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fbd137787e1340bfaef5b62f6fbc7556",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating test split: 0 examples [00:00, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset parquet downloaded and prepared to C:/Users/Oussama/.cache/huggingface/datasets/competitions___parquet/competitions--aiornot-759454878caed5d9/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec. Subsequent calls will reuse this data.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2087104741e1465d9fc4b033d5cd8bdf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from datasets import load_dataset\n",
+    "\n",
+    "# If the dataset is gated/private, make sure you have run huggingface-cli login\n",
+    "dataset = load_dataset(\"competitions/aiornot\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

Code/Testing NB.ipynb

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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU",
+    "gpuClass": "standard"
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "L6gytYO-DHMK",
+        "outputId": "b0c87fe1-77a4-45c7-8ea4-b8211cc0c4a7"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
+          ]
+        }
+      ],
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "%pip install efficientnet-pytorch"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "OoBBN22XDRNG",
+        "outputId": "c63a35aa-a077-44c7-93e5-bc9ba9732770"
+      },
+      "execution_count": 33,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
+            "Requirement already satisfied: efficientnet-pytorch in /usr/local/lib/python3.9/dist-packages (0.7.1)\n",
+            "Requirement already satisfied: torch in /usr/local/lib/python3.9/dist-packages (from efficientnet-pytorch) (2.0.0+cu118)\n",
+            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.9/dist-packages (from torch->efficientnet-pytorch) (4.5.0)\n",
+            "Requirement already satisfied: sympy in /usr/local/lib/python3.9/dist-packages (from torch->efficientnet-pytorch) (1.11.1)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from torch->efficientnet-pytorch) (3.11.0)\n",
+            "Requirement already satisfied: networkx in /usr/local/lib/python3.9/dist-packages (from torch->efficientnet-pytorch) (3.1)\n",
+            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.9/dist-packages (from torch->efficientnet-pytorch) (2.0.0)\n",
+            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.9/dist-packages (from torch->efficientnet-pytorch) (3.1.2)\n",
+            "Requirement already satisfied: lit in /usr/local/lib/python3.9/dist-packages (from triton==2.0.0->torch->efficientnet-pytorch) (16.0.1)\n",
+            "Requirement already satisfied: cmake in /usr/local/lib/python3.9/dist-packages (from triton==2.0.0->torch->efficientnet-pytorch) (3.25.2)\n",
+            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.9/dist-packages (from jinja2->torch->efficientnet-pytorch) (2.1.2)\n",
+            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.9/dist-packages (from sympy->torch->efficientnet-pytorch) (1.3.0)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import os\n",
+        "from PIL import Image\n",
+        "import torch\n",
+        "from torch import nn, optim\n",
+        "import torch.nn.functional as F\n",
+        "from torch.utils.data import DataLoader, Dataset\n",
+        "import albumentations as A\n",
+        "from albumentations.pytorch import ToTensorV2 \n",
+        "from tqdm import tqdm\n",
+        "from torchvision import models\n",
+        "from efficientnet_pytorch import EfficientNet\n",
+        "from sklearn import metrics"
+      ],
+      "metadata": {
+        "id": "phJgllqcDSuH"
+      },
+      "execution_count": 34,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
+      ],
+      "metadata": {
+        "id": "DyUTFa31DTdp"
+      },
+      "execution_count": 35,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class Dataset(Dataset):\n",
+        "    def __init__(self, root_images, root_file, transform = None):\n",
+        "        self.root_images = root_images\n",
+        "        self.root_file = root_file\n",
+        "        self.transform = transform\n",
+        "        self.file = pd.read_csv(root_file)\n",
+        "\n",
+        "\n",
+        "    def __len__(self):\n",
+        "        return self.file.shape[0]\n",
+        "    \n",
+        "    def __getitem__(self,index):\n",
+        "        img_path = os.path.join(self.root_images, self.file['id'][index])\n",
+        "        image = np.array(Image.open(img_path).convert('RGB'))\n",
+        "    \n",
+        "        if self.transform is not None:\n",
+        "            augmentations = self.transform(image = image)\n",
+        "            image = augmentations['image'] \n",
+        "        \n",
+        "        return image"
+      ],
+      "metadata": {
+        "id": "kTk-mXXUDUUA"
+      },
+      "execution_count": 36,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "learning_rate = 0.0001\n",
+        "batch_size = 32\n",
+        "epochs = 10\n",
+        "height = 224 \n",
+        "width = 224\n",
+        "IMG = '/content/drive/MyDrive/Colab Notebooks/AI images or Not/test'\n",
+        "FILE = '/content/sample_submission.csv'"
+      ],
+      "metadata": {
+        "id": "HXEpa4PlDU85"
+      },
+      "execution_count": 37,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def get_loader(image, file, batch_size, test_transform):\n",
+        "    \n",
+        "    test_ds = Dataset(image , file, test_transform)\n",
+        "    test_loader = DataLoader(test_ds, batch_size= batch_size, shuffle= False)\n",
+        "\n",
+        "\n",
+        "\n",
+        "    return test_loader "
+      ],
+      "metadata": {
+        "id": "i-VOTQp2DVbK"
+      },
+      "execution_count": 38,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "normalize = A.Normalize(\n",
+        "    mean = [0.485 , 0.456 , 0.406],\n",
+        "    std = [0.229 , 0.224, 0.255],\n",
+        "    max_pixel_value= 255.0\n",
+        ")\n",
+        "\n",
+        "\n",
+        "test_transform = A.Compose(\n",
+        "    [A.Resize(width=width , height= height),\n",
+        "    normalize,\n",
+        "    ToTensorV2()\n",
+        "    ]\n",
+        ")\n"
+      ],
+      "metadata": {
+        "id": "RD4GnrT6DVpr"
+      },
+      "execution_count": 39,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class Net(nn.Module):\n",
+        "    def __init__(self):\n",
+        "        super().__init__()\n",
+        "        self.model = EfficientNet.from_pretrained('efficientnet-b4')\n",
+        "        self.fct = nn.Linear(1000,1)\n",
+        "    \n",
+        "    def forward(self,img):\n",
+        "        x = self.model(img)\n",
+        "        # print(x.shape)\n",
+        "        x = self.fct(x)\n",
+        "        return x"
+      ],
+      "metadata": {
+        "id": "HYH0pBe9DV3M"
+      },
+      "execution_count": 40,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def load_checkpoint(checkpoint, model, optimizer):\n",
+        "    print('====> Loading...')\n",
+        "    model.load_state_dict(checkpoint['state_dict'])\n",
+        "    optimizer.load_state_dict(checkpoint['optimizer'])"
+      ],
+      "metadata": {
+        "id": "1Ype_u3qDV-n"
+      },
+      "execution_count": 41,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "test = pd.read_csv(FILE)\n",
+        "test"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "id": "Jf_Is1qDGz-W",
+        "outputId": "cf79a4c0-2bca-473c-886e-726d7956015d"
+      },
+      "execution_count": 42,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "             id  label\n",
+              "0         0.jpg      0\n",
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+              "43440  9998.jpg      0\n",
+              "43441  9999.jpg      0\n",
+              "\n",
+              "[43442 rows x 2 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-e57e96ec-2c2a-4dd2-b93e-600b15eda5bc\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>id</th>\n",
+              "      <th>label</th>\n",
+              "    </tr>\n",
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+              "    <tr>\n",
+              "      <th>0</th>\n",
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+              "      <td>1.jpg</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>10.jpg</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>100.jpg</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1000.jpg</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
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+              "      <td>9999.jpg</td>\n",
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+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>43442 rows × 2 columns</p>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e57e96ec-2c2a-4dd2-b93e-600b15eda5bc')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-e57e96ec-2c2a-4dd2-b93e-600b15eda5bc button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-e57e96ec-2c2a-4dd2-b93e-600b15eda5bc');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 42
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model = Net().to(device)\n",
+        "optimizer = optim.Adam(model.parameters(), lr= learning_rate)\n",
+        "\n",
+        "checkpoint_file = '/content/drive/MyDrive/Colab Notebooks/AI images or Not/baseline_V0.pth.tar'\n",
+        "test_loader = get_loader(IMG, FILE, batch_size, test_transform)\n",
+        "checkpoint = torch.load(checkpoint_file, map_location=torch.device('cpu'))\n",
+        "load_checkpoint(checkpoint, model, optimizer)\n",
+        "\n",
+        "model.eval()\n",
+        "k = 0\n",
+        "for x in tqdm(test_loader):\n",
+        "  x = x.to(device).to(torch.float32)\n",
+        "  p = torch.sigmoid(model(x)).cpu().detach().numpy()\n",
+        "\n",
+        "  for i in range(len(p)):\n",
+        "    test['label'][k] = (p[i] > 0.75).astype('float')\n",
+        "    k += 1"
+      ],
+      "metadata": {
+        "id": "qWB6WzrlDWD7",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "52e74e4b-96e7-40e7-d1b3-a22c7b70098d"
+      },
+      "execution_count": 43,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Loaded pretrained weights for efficientnet-b4\n",
+            "====> Loading...\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "  0%|          | 0/1358 [00:00<?, ?it/s]<ipython-input-43-383dee41b09a>:16: SettingWithCopyWarning: \n",
+            "A value is trying to be set on a copy of a slice from a DataFrame\n",
+            "\n",
+            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+            "  test['label'][k] = (p[i] > 0.75).astype('float')\n",
+            "100%|██████████| 1358/1358 [4:56:02<00:00, 13.08s/it]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "test"
+      ],
+      "metadata": {
+        "id": "-zS8tYPBDWG7",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "outputId": "4a4c0b81-ff75-4ed7-a5df-6f98644f03e2"
+      },
+      "execution_count": 44,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "             id  label\n",
+              "0         0.jpg      0\n",
+              "1         1.jpg      0\n",
+              "2        10.jpg      0\n",
+              "3       100.jpg      1\n",
+              "4      1000.jpg      0\n",
+              "...         ...    ...\n",
+              "43437  9995.jpg      1\n",
+              "43438  9996.jpg      0\n",
+              "43439  9997.jpg      0\n",
+              "43440  9998.jpg      0\n",
+              "43441  9999.jpg      1\n",
+              "\n",
+              "[43442 rows x 2 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-00389cce-5634-451c-81fb-649bced26029\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>id</th>\n",
+              "      <th>label</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0.jpg</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1.jpg</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>10.jpg</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>100.jpg</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1000.jpg</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
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+              "    <tr>\n",
+              "      <th>43437</th>\n",
+              "      <td>9995.jpg</td>\n",
+              "      <td>1</td>\n",
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+              "    <tr>\n",
+              "      <th>43438</th>\n",
+              "      <td>9996.jpg</td>\n",
+              "      <td>0</td>\n",
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+              "    <tr>\n",
+              "      <th>43439</th>\n",
+              "      <td>9997.jpg</td>\n",
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+              "      <th>43440</th>\n",
+              "      <td>9998.jpg</td>\n",
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+              "    <tr>\n",
+              "      <th>43441</th>\n",
+              "      <td>9999.jpg</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>43442 rows × 2 columns</p>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-00389cce-5634-451c-81fb-649bced26029')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-00389cce-5634-451c-81fb-649bced26029 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-00389cce-5634-451c-81fb-649bced26029');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 44
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "test.to_csv('sub.csv', index=False)"
+      ],
+      "metadata": {
+        "id": "nX_vnorKDWKK"
+      },
+      "execution_count": 45,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "JmJa1KolDWM5"
+      },
+      "execution_count": 45,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def predict(image):\n",
+        "    image = np.array(image)\n",
+        "    transform = A.Compose(\n",
+        "        [A.Resize(width=width, height=height),\n",
+        "         normalize,\n",
+        "         ToTensorV2()\n",
+        "         ]\n",
+        "    )\n",
+        "    image = transform(image=image)[\"image\"].unsqueeze(0).to(device).to(torch.float32)\n",
+        "    with torch.no_grad():\n",
+        "        model.eval()\n",
+        "        output = torch.sigmoid(model(image))\n",
+        "        label = (output > 0.75).item()\n",
+        "    return \"AI Image\" if label else \"Not AI Image\""
+      ],
+      "metadata": {
+        "id": "TKs8s0TyDWP0"
+      },
+      "execution_count": 46,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "%pip install gradio"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "k7bGi6MqqO-r",
+        "outputId": "120d9571-3381-418a-9056-ff8b84199ca7"
+      },
+      "execution_count": 47,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
+            "Collecting gradio\n",
+            "  Downloading gradio-3.27.0-py3-none-any.whl (17.3 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m17.3/17.3 MB\u001b[0m \u001b[31m60.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: matplotlib in /usr/local/lib/python3.9/dist-packages (from gradio) (3.7.1)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.9/dist-packages (from gradio) (1.22.4)\n",
+            "Requirement already satisfied: markdown-it-py[linkify]>=2.0.0 in /usr/local/lib/python3.9/dist-packages (from gradio) (2.2.0)\n",
+            "Collecting aiohttp\n",
+            "  Downloading aiohttp-3.8.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.0 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m54.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting orjson\n",
+            "  Downloading orjson-3.8.10-cp39-cp39-manylinux_2_28_x86_64.whl (140 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.5/140.5 kB\u001b[0m \u001b[31m16.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: pillow in /usr/local/lib/python3.9/dist-packages (from gradio) (8.4.0)\n",
+            "Collecting ffmpy\n",
+            "  Downloading ffmpy-0.3.0.tar.gz (4.8 kB)\n",
+            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "Requirement already satisfied: markupsafe in /usr/local/lib/python3.9/dist-packages (from gradio) (2.1.2)\n",
+            "Collecting huggingface-hub>=0.13.0\n",
+            "  Downloading huggingface_hub-0.13.4-py3-none-any.whl (200 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m200.1/200.1 kB\u001b[0m \u001b[31m20.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from gradio) (2.27.1)\n",
+            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.9/dist-packages (from gradio) (4.5.0)\n",
+            "Requirement already satisfied: altair>=4.2.0 in /usr/local/lib/python3.9/dist-packages (from gradio) (4.2.2)\n",
+            "Collecting fastapi\n",
+            "  Downloading fastapi-0.95.1-py3-none-any.whl (56 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.0/57.0 kB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting httpx\n",
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+            "\u001b[?25hCollecting websockets>=10.0\n",
+            "  Downloading websockets-11.0.2-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (129 kB)\n",
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+            "\u001b[?25hCollecting pydub\n",
+            "  Downloading pydub-0.25.1-py2.py3-none-any.whl (32 kB)\n",
+            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.9/dist-packages (from gradio) (6.0)\n",
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+            "\u001b[?25hRequirement already satisfied: pydantic in /usr/local/lib/python3.9/dist-packages (from gradio) (1.10.7)\n",
+            "Collecting semantic-version\n",
+            "  Downloading semantic_version-2.10.0-py2.py3-none-any.whl (15 kB)\n",
+            "Requirement already satisfied: pandas in /usr/local/lib/python3.9/dist-packages (from gradio) (1.5.3)\n",
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+            "\u001b[?25hCollecting aiofiles\n",
+            "  Downloading aiofiles-23.1.0-py3-none-any.whl (14 kB)\n",
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+            "Collecting linkify-it-py<3,>=1\n",
+            "  Downloading linkify_it_py-2.0.0-py3-none-any.whl (19 kB)\n",
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+            "Collecting async-timeout<5.0,>=4.0.0a3\n",
+            "  Downloading async_timeout-4.0.2-py3-none-any.whl (5.8 kB)\n",
+            "Collecting multidict<7.0,>=4.5\n",
+            "  Downloading multidict-6.0.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (114 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.2/114.2 kB\u001b[0m \u001b[31m15.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting frozenlist>=1.1.1\n",
+            "  Downloading frozenlist-1.3.3-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (158 kB)\n",
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+            "\u001b[?25hCollecting yarl<2.0,>=1.0\n",
+            "  Downloading yarl-1.8.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (264 kB)\n",
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+            "\u001b[?25hCollecting aiosignal>=1.1.2\n",
+            "  Downloading aiosignal-1.3.1-py3-none-any.whl (7.6 kB)\n",
+            "Requirement already satisfied: charset-normalizer<4.0,>=2.0 in /usr/local/lib/python3.9/dist-packages (from aiohttp->gradio) (2.0.12)\n",
+            "Collecting starlette<0.27.0,>=0.26.1\n",
+            "  Downloading starlette-0.26.1-py3-none-any.whl (66 kB)\n",
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+            "\u001b[?25hCollecting httpcore<0.18.0,>=0.15.0\n",
+            "  Downloading httpcore-0.17.0-py3-none-any.whl (70 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m70.6/70.6 kB\u001b[0m \u001b[31m10.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: certifi in /usr/local/lib/python3.9/dist-packages (from httpx->gradio) (2022.12.7)\n",
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+            "Collecting h11>=0.8\n",
+            "  Downloading h11-0.14.0-py3-none-any.whl (58 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m8.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+            "Collecting uc-micro-py\n",
+            "  Downloading uc_micro_py-1.0.1-py3-none-any.whl (6.2 kB)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/dist-packages (from python-dateutil>=2.8.1->pandas->gradio) (1.16.0)\n",
+            "Building wheels for collected packages: ffmpy\n",
+            "  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for ffmpy: filename=ffmpy-0.3.0-py3-none-any.whl size=4707 sha256=030fcfbd0063a8e91f56986ba5a3eaeb8d3d94a5b7f0a2c726f9367cfd7d2fbf\n",
+            "  Stored in directory: /root/.cache/pip/wheels/91/e2/96/f676aa08bfd789328c6576cd0f1fde4a3d686703bb0c247697\n",
+            "Successfully built ffmpy\n",
+            "Installing collected packages: pydub, ffmpy, websockets, uc-micro-py, semantic-version, python-multipart, orjson, multidict, h11, frozenlist, async-timeout, aiofiles, yarl, uvicorn, starlette, mdit-py-plugins, linkify-it-py, huggingface-hub, httpcore, aiosignal, httpx, fastapi, aiohttp, gradio-client, gradio\n",
+            "Successfully installed aiofiles-23.1.0 aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 fastapi-0.95.1 ffmpy-0.3.0 frozenlist-1.3.3 gradio-3.27.0 gradio-client-0.1.3 h11-0.14.0 httpcore-0.17.0 httpx-0.24.0 huggingface-hub-0.13.4 linkify-it-py-2.0.0 mdit-py-plugins-0.3.3 multidict-6.0.4 orjson-3.8.10 pydub-0.25.1 python-multipart-0.0.6 semantic-version-2.10.0 starlette-0.26.1 uc-micro-py-1.0.1 uvicorn-0.21.1 websockets-11.0.2 yarl-1.8.2\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import gradio as gr"
+      ],
+      "metadata": {
+        "id": "Q5a9SQbcqLH7"
+      },
+      "execution_count": 48,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "\n",
+        "inputs = gr.inputs.Image()\n",
+        "outputs = gr.outputs.Textbox()\n",
+        "iface = gr.Interface(fn=predict, inputs=inputs, outputs=outputs, capture_session=True)\n",
+        "iface.launch()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 775
+        },
+        "id": "sEsxRg9IqLue",
+        "outputId": "1ea4931d-4001-4c37-a0f9-97017b2e55a6"
+      },
+      "execution_count": 49,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.9/dist-packages/gradio/inputs.py:257: UserWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.9/dist-packages/gradio/deprecation.py:40: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
+            "  warnings.warn(value)\n",
+            "/usr/local/lib/python3.9/dist-packages/gradio/outputs.py:22: UserWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.9/dist-packages/gradio/deprecation.py:40: UserWarning: `capture_session` parameter is deprecated, and it has no effect\n",
+            "  warnings.warn(value)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
+            "Note: opening Chrome Inspector may crash demo inside Colab notebooks.\n",
+            "\n",
+            "To create a public link, set `share=True` in `launch()`.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "(async (port, path, width, height, cache, element) => {\n",
+              "                        if (!google.colab.kernel.accessAllowed && !cache) {\n",
+              "                            return;\n",
+              "                        }\n",
+              "                        element.appendChild(document.createTextNode(''));\n",
+              "                        const url = await google.colab.kernel.proxyPort(port, {cache});\n",
+              "\n",
+              "                        const external_link = document.createElement('div');\n",
+              "                        external_link.innerHTML = `\n",
+              "                            <div style=\"font-family: monospace; margin-bottom: 0.5rem\">\n",
+              "                                Running on <a href=${new URL(path, url).toString()} target=\"_blank\">\n",
+              "                                    https://localhost:${port}${path}\n",
+              "                                </a>\n",
+              "                            </div>\n",
+              "                        `;\n",
+              "                        element.appendChild(external_link);\n",
+              "\n",
+              "                        const iframe = document.createElement('iframe');\n",
+              "                        iframe.src = new URL(path, url).toString();\n",
+              "                        iframe.height = height;\n",
+              "                        iframe.allow = \"autoplay; camera; microphone; clipboard-read; clipboard-write;\"\n",
+              "                        iframe.width = width;\n",
+              "                        iframe.style.border = 0;\n",
+              "                        element.appendChild(iframe);\n",
+              "                    })(7860, \"/\", \"100%\", 500, false, window.element)"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": []
+          },
+          "metadata": {},
+          "execution_count": 49
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import gradio as gr\n",
+        "import torch\n",
+        "import numpy as np\n",
+        "from PIL import Image\n",
+        "\n",
+        "# define the predict function\n",
+        "def predict(image):\n",
+        "    # preprocess the image\n",
+        "    image = np.array(image)\n",
+        "    image = test_transform(image=image)['image']\n",
+        "    image = image.unsqueeze(0).to(device)\n",
+        "\n",
+        "    # get the model prediction\n",
+        "    with torch.no_grad():\n",
+        "        output = model(image)\n",
+        "        pred = torch.sigmoid(output).cpu().numpy().squeeze()\n",
+        "    \n",
+        "    # return the prediction as a string\n",
+        "    return f\"This image is {'AI generated' if pred > 0.75 else 'NOT AI generated'}\"\n",
+        "\n",
+        "# define the input interface with examples\n",
+        "inputs = gr.inputs.Image(shape=(224, 224))\n",
+        "outputs = gr.outputs.Textbox()\n",
+        "examples = [\n",
+        "    ['/content/drive/MyDrive/Colab Notebooks/AI images or Not/train/3.jpg'],\n",
+        "    ['/content/drive/MyDrive/Colab Notebooks/AI images or Not/train/10.jpg'],\n",
+        "    ['/content/drive/MyDrive/Colab Notebooks/AI images or Not/train/14.jpg'],\n",
+        "    ['/content/drive/MyDrive/Colab Notebooks/AI images or Not/train/4515.jpg']\n",
+        "    ['/content/drive/MyDrive/Colab Notebooks/AI images or Not/train/4518.jpg'],\n",
+        "]\n",
+        "iface = gr.Interface(fn=predict, inputs=inputs, outputs=outputs, examples=examples)\n",
+        "\n",
+        "# launch the gradio app\n",
+        "iface.launch()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 428
+        },
+        "id": "nMuNn5FCvEuS",
+        "outputId": "ad4760a5-9458-483a-b9bc-c655f0bf6429"
+      },
+      "execution_count": 55,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<>:28: SyntaxWarning: list indices must be integers or slices, not str; perhaps you missed a comma?\n",
+            "<>:28: SyntaxWarning: list indices must be integers or slices, not str; perhaps you missed a comma?\n",
+            "/usr/local/lib/python3.9/dist-packages/gradio/inputs.py:257: UserWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.9/dist-packages/gradio/deprecation.py:40: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
+            "  warnings.warn(value)\n",
+            "/usr/local/lib/python3.9/dist-packages/gradio/outputs.py:22: UserWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n",
+            "  warnings.warn(\n",
+            "<ipython-input-55-ad9875932060>:28: SyntaxWarning: list indices must be integers or slices, not str; perhaps you missed a comma?\n",
+            "  ['/content/drive/MyDrive/Colab Notebooks/AI images or Not/train/4515.jpg']\n"
+          ]
+        },
+        {
+          "output_type": "error",
+          "ename": "TypeError",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-55-ad9875932060>\u001b[0m in \u001b[0;36m<cell line: 25>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     26\u001b[0m     \u001b[0;34m[\u001b[0m\u001b[0;34m'/content/drive/MyDrive/Colab Notebooks/AI images or Not/train/10.jpg'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m     \u001b[0;34m[\u001b[0m\u001b[0;34m'/content/drive/MyDrive/Colab Notebooks/AI images or Not/train/14.jpg'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m     \u001b[0;34m[\u001b[0m\u001b[0;34m'/content/drive/MyDrive/Colab Notebooks/AI images or Not/train/4515.jpg'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m     \u001b[0;34m[\u001b[0m\u001b[0;34m'/content/drive/MyDrive/Colab Notebooks/AI images or Not/train/4518.jpg'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m ]\n",
+            "\u001b[0;31mTypeError\u001b[0m: list indices must be integers or slices, not str"
+          ]
+        }
+      ]
+    }
+  ]
+}

Code/app.py

@@ -0,0 +1,163 @@
+import os
+
+import albumentations as A
+import gradio as gr
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import torch
+import torch.nn.functional as F
+from albumentations.pytorch import ToTensorV2
+from efficientnet_pytorch import EfficientNet
+from PIL import Image
+from sklearn import metrics
+from torch import nn, optim
+from torch.utils.data import DataLoader, Dataset
+from torchvision import models
+from tqdm import tqdm
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+class Dataset(Dataset):
+    def __init__(self, root_images, root_file, transform=None):
+        self.root_images = root_images
+        self.root_file = root_file
+        self.transform = transform
+        self.file = pd.read_csv(root_file)
+
+    def __len__(self):
+        return self.file.shape[0]
+
+    def __getitem__(self, index):
+        img_path = os.path.join(self.root_images, self.file["id"][index])
+        image = np.array(Image.open(img_path).convert("RGB"))
+
+        if self.transform is not None:
+            augmentations = self.transform(image=image)
+            image = augmentations["image"]
+
+        return image
+
+
+learning_rate = 0.0001
+batch_size = 32
+epochs = 10
+height = 224
+width = 224
+IMG = "AI images or Not/test"
+FILE = "Data/sample_submission.csv"
+
+
+def get_loader(image, file, batch_size, test_transform):
+
+    test_ds = Dataset(image, file, test_transform)
+    test_loader = DataLoader(test_ds, batch_size=batch_size, shuffle=False)
+
+    return test_loader
+
+
+normalize = A.Normalize(
+    mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.255], max_pixel_value=255.0
+)
+
+
+test_transform = A.Compose(
+    [A.Resize(width=width, height=height), normalize, ToTensorV2()]
+)
+
+
+class Net(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.model = EfficientNet.from_pretrained("efficientnet-b4")
+        self.fct = nn.Linear(1000, 1)
+
+    def forward(self, img):
+        x = self.model(img)
+        # print(x.shape)
+        x = self.fct(x)
+        return x
+
+
+def load_checkpoint(checkpoint, model, optimizer):
+    print("====> Loading...")
+    model.load_state_dict(checkpoint["state_dict"])
+    optimizer.load_state_dict(checkpoint["optimizer"])
+
+
+# test = pd.read_csv(FILE)
+# test
+
+model = Net().to(device)
+optimizer = optim.Adam(model.parameters(), lr=learning_rate)
+
+checkpoint_file = "Checkpoint/baseline_V0.pth.tar"
+test_loader = get_loader(IMG, FILE, batch_size, test_transform)
+checkpoint = torch.load(checkpoint_file, map_location=torch.device("cpu"))
+load_checkpoint(checkpoint, model, optimizer)
+
+model.eval()
+
+
+# define the predict function
+def predict(image):
+    # preprocess the image
+    image = np.array(image)
+    image = test_transform(image=image)["image"]
+    image = image.unsqueeze(0).to(device)
+
+    # get the model prediction
+    with torch.no_grad():
+        output = model(image)
+        pred = torch.sigmoid(output).cpu().numpy().squeeze()
+
+    # check if prediction is AI generated, not AI generated, or uncertain
+    if pred >= 0.6:
+        prediction = "AI generated"
+        confidence = pred
+    elif pred <= 0.4:
+        prediction = "NOT AI generated"
+        confidence = 1 - pred
+    else:
+        prediction = "uncertain"
+        confidence = abs(0.5 - pred) * 2
+
+    # return the prediction and confidence as a string
+    return f"This image is {prediction} with {confidence:.2%} confidence."
+
+
+# define the input interface with examples
+inputs = gr.inputs.Image(shape=(224, 224))
+outputs = gr.outputs.Textbox()
+examples = [
+    ["Data/train/3.jpg"],
+    ["Data/train/10.jpg"],
+    ["Data/train/14.jpg"],
+    ["Data/train/4515.jpg"],
+    ["Data/train/4518.jpg"],
+    ["Data/train/6122.jpg"],
+    ["Data/train/6123.jpg"],
+    ["Data/train/6124.jpg"],
+    ["Data/train/6125.jpg"],
+    ["Data/train/7461.jpg"],
+    ["Data/train/7462.jpg"],
+    ["Data/train/7463.jpg"],
+    ["Data/train/7464.jpg"],
+    ["Data/train/7465.jpg"],
+    ["Data/train/8546.jpg"],
+    ["Data/train/8543.jpg"],
+    ["Data/train/9120.jpg"],
+    ["Data/train/10120.jpg"],
+]
+iface = gr.Interface(
+    fn=predict,
+    inputs=inputs,
+    outputs=outputs,
+    title="AI image detector 🔎",
+    description="Check if an image is AI generated or real.",
+    examples=examples,
+)
+
+# launch the gradio app
+iface.launch()

Code/test.ipynb

@@ -0,0 +1,256 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Token is valid.\n",
+      "Your token has been saved in your configured git credential helpers (manager-core).\n",
+      "Your token has been saved to C:\\Users\\Oussama\\.cache\\huggingface\\token\n",
+      "Login successful\n"
+     ]
+    }
+   ],
+   "source": [
+    "from huggingface_hub import login\n",
+    "login()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e5eb52f9282f43cfa4f06b1d9c6dc08b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading readme:   0%|          | 0.00/1.24k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading and preparing dataset None/None to C:/Users/Oussama/.cache/huggingface/datasets/competitions___parquet/competitions--aiornot-759454878caed5d9/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "37ae63ab36cc448e90aa292609f4e5d4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e208b22a102847b4862d336b32cce3e1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading data:   0%|          | 0.00/354M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c31d5357bf9c44e99b19820ea10ad70a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading data:   0%|          | 0.00/356M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b7de3daec8a54b28a07cd580734f4bf4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading data:   0%|          | 0.00/415M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "67b76cdca655426d9174aa7655345ecb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading data:   0%|          | 0.00/418M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "373dd6818e5c4500b9d48de516bcb427",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading data:   0%|          | 0.00/416M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9fc356325be34db1844de564ee0395fc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading data:   0%|          | 0.00/416M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8d8d0259ea1840d38f316bfc3809fa44",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Extracting data files:   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1780ace548794a58b318e723ae297b50",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating train split: 0 examples [00:00, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fbd137787e1340bfaef5b62f6fbc7556",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating test split: 0 examples [00:00, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset parquet downloaded and prepared to C:/Users/Oussama/.cache/huggingface/datasets/competitions___parquet/competitions--aiornot-759454878caed5d9/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec. Subsequent calls will reuse this data.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2087104741e1465d9fc4b033d5cd8bdf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from datasets import load_dataset\n",
+    "\n",
+    "# If the dataset is gated/private, make sure you have run huggingface-cli login\n",
+    "dataset = load_dataset(\"competitions/aiornot\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}