diff --git "a/deepfake vision transformer  trainer/deepfake vit.ipynb" "b/deepfake vision transformer  trainer/deepfake vit.ipynb"
new file mode 100644--- /dev/null
+++ "b/deepfake vision transformer  trainer/deepfake vit.ipynb"
@@ -0,0 +1,6776 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JmYRMwEOYkbU"
+      },
+      "source": [
+        "# `Deepfake Classification - ViT`"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "c6rbUun0tdC5"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install evaluate datasets accelerate\n",
+        "!pip install git+https://github.com/huggingface/transformers.git"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "dhLosa2Utm5M"
+      },
+      "outputs": [],
+      "source": [
+        "import warnings\n",
+        "warnings.filterwarnings(\"ignore\")\n",
+        "\n",
+        "import gc\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import itertools\n",
+        "from collections import Counter\n",
+        "import matplotlib.pyplot as plt\n",
+        "from sklearn.metrics import accuracy_score, roc_auc_score, confusion_matrix, classification_report, f1_score\n",
+        "from imblearn.over_sampling import RandomOverSampler\n",
+        "import evaluate\n",
+        "from datasets import Dataset, Image, ClassLabel\n",
+        "from transformers import (\n",
+        "    TrainingArguments,\n",
+        "    Trainer,\n",
+        "    ViTImageProcessor,\n",
+        "    ViTForImageClassification,\n",
+        "    DefaultDataCollator\n",
+        ")\n",
+        "import torch\n",
+        "from torch.utils.data import DataLoader\n",
+        "from torchvision.transforms import (\n",
+        "    CenterCrop,\n",
+        "    Compose,\n",
+        "    Normalize,\n",
+        "    RandomRotation,\n",
+        "    RandomResizedCrop,\n",
+        "    RandomHorizontalFlip,\n",
+        "    RandomAdjustSharpness,\n",
+        "    Resize,\n",
+        "    ToTensor\n",
+        ")\n",
+        "from PIL import Image as PILImage\n",
+        "from PIL import ImageFile\n",
+        "\n",
+        "# Enable loading truncated images\n",
+        "ImageFile.LOAD_TRUNCATED_IMAGES = True"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
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+        "outputId": "48cbce0b-f4c5-41e4-b097-44c20abfb66b"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "55d2432a06454b4daa15616b663afff5",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "README.md:   0%|          | 0.00/394 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "35ddd24c9ac04aa7bea40b3e5c55b6eb",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "train-00000-of-00005.parquet:   0%|          | 0.00/366M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "89a5069c87d34e389cb314b5c1df757d",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "train-00001-of-00005.parquet:   0%|          | 0.00/356M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "a47c3da03fc1422a9dbc579aedc43364",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "train-00002-of-00005.parquet:   0%|          | 0.00/347M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "6f706db30a0b431b8bd3504d85007227",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "train-00003-of-00005.parquet:   0%|          | 0.00/348M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "64e0352e159b471e8ed52f0285f9d7f4",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "train-00004-of-00005.parquet:   0%|          | 0.00/377M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "7f8b0799b58a42a080bbf2e57bede065",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating train split:   0%|          | 0/190335 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from datasets import load_dataset\n",
+        "dataset = load_dataset(\"Hemg/deepfake-and-real-images\", split=\"train\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "o8rgwG0nuc00"
+      },
+      "outputs": [],
+      "source": [
+        "file_names = []\n",
+        "labels = []\n",
+        "\n",
+        "for example in dataset:\n",
+        "    file_path = example['image'].filename  # Get the file path\n",
+        "    label = example['label']  # Get the label\n",
+        "    file_names.append(file_path)  # Add the file path to the list\n",
+        "    labels.append(label)  # Add the label to the list"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Yz8qs87tuhjs",
+        "outputId": "9c7240a0-36d8-44ca-c4f5-eead879f7f35"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "190335 190335\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Print the total number of file names and labels\n",
+        "print(len(file_names), len(labels))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7CW5l8Td_V-4"
+      },
+      "outputs": [],
+      "source": [
+        "# Create a pandas dataframe from the collected file names and labels\n",
+        "df = pd.DataFrame.from_dict({\"image\": file_names, \"label\": labels})"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9SZz49oNBSHf",
+        "outputId": "213e0f5a-1571-41dc-9c61-5ff4c2033fe8"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "(190335, 2)\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(df.shape)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "ZubCrfrhBZGo",
+        "outputId": "df97e9dc-9e03-4a8f-cc8b-07567d8c9546"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df"
+            },
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+              "\n",
+              "  <div id=\"df-fe9604c3-9544-44a2-9d40-58934551b21b\" 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",
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+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>image</th>\n",
+              "      <th>label</th>\n",
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+              "</table>\n",
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+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fe9604c3-9544-44a2-9d40-58934551b21b')\"\n",
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+              "\n",
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+              "      background-color: #E8F0FE;\n",
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+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
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+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
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+              "      width: 32px;\n",
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+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\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",
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+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-fe9604c3-9544-44a2-9d40-58934551b21b 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-fe9604c3-9544-44a2-9d40-58934551b21b');\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",
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+              "\n",
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+              "    </g>\n",
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+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
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+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
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+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-bc8353d8-3c3e-4507-97d9-c3d9f5eaaa2f button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "  image  label\n",
+              "0            0\n",
+              "1            0\n",
+              "2            0\n",
+              "3            0\n",
+              "4            0"
+            ]
+          },
+          "execution_count": 10,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "oFwJ-2_B_br5",
+        "outputId": "95eb8b30-9319-4dde-8922-189e4e5d4125"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([0, 1])"
+            ]
+          },
+          "execution_count": 11,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df['label'].unique()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ZzX_P-onunr7",
+        "outputId": "edb22f83-62a9-47f3-eacf-47149ea31cfe"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "243"
+            ]
+          },
+          "execution_count": 12,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "y = df[['label']]\n",
+        "df = df.drop(['label'], axis=1)\n",
+        "ros = RandomOverSampler(random_state=83)\n",
+        "df, y_resampled = ros.fit_resample(df, y)\n",
+        "del y\n",
+        "df['label'] = y_resampled\n",
+        "del y_resampled\n",
+        "gc.collect()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "WaJ_C30L_N_L"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "# Create a DataFrame from the collected file names and labels\n",
+        "df = pd.DataFrame.from_dict({\"image\": file_names, \"label\": labels})"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 273
+        },
+        "id": "ha4Bpgoz7dfu",
+        "outputId": "57532457-75d6-4a61-9de7-696198e1f324"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/jpeg": 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",
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",
+            "text/plain": [
+              "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=256x256>"
+            ]
+          },
+          "execution_count": 14,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "dataset[10][\"image\"]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "-MfUvn2A-tBc",
+        "outputId": "10490f1d-a2cf-4d3d-9690-253956ba7878"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[0, 0, 0, 0, 0]\n"
+          ]
+        }
+      ],
+      "source": [
+        "labels_subset = labels[:5]\n",
+        "\n",
+        "# Printing the subset of labels to inspect the content.\n",
+        "print(labels_subset)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9u1W0MBhBpMA"
+      },
+      "outputs": [],
+      "source": [
+        "# Define the list of unique labels\n",
+        "labels_list = ['Real', 'Fake']\n",
+        "\n",
+        "# Initialize dictionaries to map labels to IDs and vice versa\n",
+        "label2id, id2label = dict(), dict()\n",
+        "for i, label in enumerate(labels_list):\n",
+        "    label2id[label] = i\n",
+        "    id2label[i] = label\n",
+        "\n",
+        "# Create ClassLabels object\n",
+        "ClassLabels = ClassLabel(num_classes=len(labels_list), names=labels_list)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4CfU5GJkByam",
+        "outputId": "f4ffb66b-3ee7-4279-9ad8-5db38a093576"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Mapping of IDs to Labels: {0: 'Real', 1: 'Fake'} \n",
+            "\n",
+            "Mapping of Labels to IDs: {'Real': 0, 'Fake': 1}\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Print the resulting dictionaries for reference\n",
+        "print(\"Mapping of IDs to Labels:\", id2label, '\\n')\n",
+        "print(\"Mapping of Labels to IDs:\", label2id)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 81,
+          "referenced_widgets": [
+            "0a71e58cc6234eaf856a7d31dedc58ed",
+            "32d938c327494e46b797f5fe877f0d6d",
+            "0cc5cc95109345e7aa3c4b793c5b9a8b",
+            "b4ab1343613840928e44c26728938f30",
+            "03137c84db8a40ad8e57370ea901abb8",
+            "ca16b50cc04b4187a81021dcf985e145",
+            "260be0ca0dcc436790d05a24082ee931",
+            "fc669d9ad8214514a987847cb4a7fd8d",
+            "15be2c9a14854fce93487ff3238779de",
+            "249f59a47f10440b8af61ab4e21dd6d3",
+            "1e5667457a6d4305ba57bb846c74d246",
+            "e9527623d1d742cb993585c550da2eb0",
+            "86e2e5853ad84e4b977af704d50229ba",
+            "45557b845fb24cfa90d8aa07dc6bc3b8",
+            "68032ef6222d460eb1b034936a7a18e3",
+            "5e471f12ea3f474e8ca29bee24f70298",
+            "2096845cd2374270b516c77bfa8c93b8",
+            "81486b99a11d465181cbb32d7a6e03c6",
+            "8088e5e1e13c4494a1dc04d808fe6a17",
+            "d98d725d1ecb4777ad812c2e268b2ec2",
+            "6c92462ad0834cbabbb97ed1cd7a142f",
+            "8e4eb3a590094a77898a6a6b1b63f3bf"
+          ]
+        },
+        "id": "M9XI2VNYB35G",
+        "outputId": "b130fe43-4a12-43d2-f344-68f5ce05affc"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "0a71e58cc6234eaf856a7d31dedc58ed",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Map:   0%|          | 0/190335 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "e9527623d1d742cb993585c550da2eb0",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Casting the dataset:   0%|          | 0/190335 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "\n",
+        "# Mapping labels to IDs\n",
+        "def map_label2id(example):\n",
+        "    example['label'] = ClassLabels.str2int(example['label'])\n",
+        "    return example\n",
+        "\n",
+        "dataset = dataset.map(map_label2id, batched=True)\n",
+        "\n",
+        "# Casting label column to ClassLabel Object\n",
+        "dataset = dataset.cast_column('label', ClassLabels)\n",
+        "\n",
+        "# Splitting the dataset into training and testing sets using an 60-40 split ratio.\n",
+        "dataset = dataset.train_test_split(test_size=0.4, shuffle=True, stratify_by_column=\"label\")\n",
+        "\n",
+        "# Extracting the training data from the split dataset.\n",
+        "train_data = dataset['train']\n",
+        "\n",
+        "# Extracting the testing data from the split dataset.\n",
+        "test_data = dataset['test']"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 49,
+          "referenced_widgets": [
+            "bd41861ce39d42b88553b51aee5757cb",
+            "cd8bb4e745034726ad445431027549be",
+            "b65260b9d6e34eeb9736040ca44a0b1a",
+            "beb4833cb7b24d9c96bada9b927567cf",
+            "f7e3a2729f964681b72909605eb76117",
+            "2d7308a707ad41c2b78114ab7c6c0259",
+            "058a610c43f4452e9b7b07475412c487",
+            "5148cd40ba8c4be5a8dfd133c5acb411",
+            "4925b7e03a9f4a4681684f8f24069dc0",
+            "65137c746548419d882ca2e97afd4f0a",
+            "a062dfa32e3144aab132974f2118580d"
+          ]
+        },
+        "id": "FvEEn_iDB8uX",
+        "outputId": "8d5b1c6e-6329-4e25-91a3-13e506dd3f2f"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "bd41861ce39d42b88553b51aee5757cb",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "preprocessor_config.json:   0%|          | 0.00/160 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Define the pre-trained ViT model string\n",
+        "model_str = \"google/vit-base-patch16-224-in21k\"\n",
+        "\n",
+        "# Create a processor for ViT model input\n",
+        "processor = ViTImageProcessor.from_pretrained(model_str)\n",
+        "\n",
+        "# Retrieve the image mean and standard deviation used for normalization\n",
+        "image_mean, image_std = processor.image_mean, processor.image_std\n",
+        "size = processor.size[\"height\"]\n",
+        "\n",
+        "# Define transformations for training and validation data\n",
+        "_train_transforms = Compose(\n",
+        "    [\n",
+        "        Resize((size, size)),\n",
+        "        RandomRotation(90),\n",
+        "        RandomAdjustSharpness(2),\n",
+        "        ToTensor(),\n",
+        "        Normalize(mean=image_mean, std=image_std)\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "_val_transforms = Compose(\n",
+        "    [\n",
+        "        Resize((size, size)),\n",
+        "        ToTensor(),\n",
+        "        Normalize(mean=image_mean, std=image_std)\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "# Define functions to apply transformations\n",
+        "def train_transforms(examples):\n",
+        "    examples['pixel_values'] = [_train_transforms(image.convert(\"RGB\")) for image in examples['image']]\n",
+        "    return examples\n",
+        "\n",
+        "def val_transforms(examples):\n",
+        "    examples['pixel_values'] = [_val_transforms(image.convert(\"RGB\")) for image in examples['image']]\n",
+        "    return examples\n",
+        "\n",
+        "# Set transforms for training and test data\n",
+        "train_data.set_transform(train_transforms)\n",
+        "test_data.set_transform(val_transforms)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9nEcswLACNVt"
+      },
+      "outputs": [],
+      "source": [
+        "def collate_fn(examples):\n",
+        "    pixel_values = torch.stack([example[\"pixel_values\"] for example in examples])\n",
+        "    labels = torch.tensor([example['label'] for example in examples])\n",
+        "    return {\"pixel_values\": pixel_values, \"labels\": labels}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 154,
+          "referenced_widgets": [
+            "62888618a9304d21a926cb33e33a3270",
+            "79eb4384a0cd4ef39315919785232e65",
+            "81b76521ef4e4823bfb0dd19546fdb96",
+            "75b4d9432412413fafdcd3dc5e9619c4",
+            "5fc3bc87b3244462be508db322efd8d6",
+            "5dec4a4466ca49d597ec98a260a934b2",
+            "d9e4f6e4f9b34fad927c0dcc986d0137",
+            "00b0d3b2c9ab401cb7db290139470204",
+            "0f0284e85a5c4f03900ec6ca9c80c66c",
+            "b7d39c492bb24846aad7c8ef0c2c9810",
+            "e0e9ec9d4a0a44399b90b61abfd2a088",
+            "d9a6f4fa75af411da6772a98d6b4be20",
+            "947653644d65447986c38b1563f23df7",
+            "25b79499f590496daea769facabbdf9f",
+            "d56ecf1f4bc943bfb0ea248c1c34d92f",
+            "db4a82085d8f418faf4ece3abc3289cf",
+            "d0009cc736af4eb49f9aba6dfb308e1f",
+            "01e2233b1d3441ef834c7932d657e752",
+            "7610e162bec347d08e9bb1d48c56fa3e",
+            "4164c8bd0e0b4719a863ca70ab660d1e",
+            "7ad6111f1330464885385e9b97c3881c",
+            "0644c34cc62b4e9fa91ebeb12c198151"
+          ]
+        },
+        "id": "goTy8XdcCS7t",
+        "outputId": "f1dbb381-d6af-415b-e6af-a06d8bcbb31c"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "62888618a9304d21a926cb33e33a3270",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "config.json:   0%|          | 0.00/502 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "d9a6f4fa75af411da6772a98d6b4be20",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "model.safetensors:   0%|          | 0.00/346M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Some weights of ViTForImageClassification were not initialized from the model checkpoint at google/vit-base-patch16-224-in21k and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
+            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "85.800194\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Create a ViTForImageClassification model\n",
+        "model = ViTForImageClassification.from_pretrained(model_str, num_labels=len(labels_list))\n",
+        "model.config.id2label = id2label\n",
+        "model.config.label2id = label2id\n",
+        "\n",
+        "# Calculate and print the number of trainable parameters in millions for the model.\n",
+        "print(model.num_parameters(only_trainable=True) / 1e6)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 49,
+          "referenced_widgets": [
+            "62386db6987e40f680b5db5b23b4fc4f",
+            "248aa551c1304ab996413e8b1e0076ab",
+            "3091664c65b541d8857b5947ec4b065f",
+            "d9b286de3c104cf18a5ba9070c03b709",
+            "4156fe5de51b43ee86d758b9bb51c922",
+            "ee1b1c9c318e4ae2860c2c001383f799",
+            "fe1a9299c5c44c568e1ea726201ca42f",
+            "e95350f418514972b8e71c087e75d49c",
+            "44cb9ebb93e349fc823d5de470905fe9",
+            "fd43c8103ef2465db26116b6b75147b8",
+            "20bcd91d09f94b33b2a16d865b5a11c3"
+          ]
+        },
+        "id": "bRleo0I-CqPv",
+        "outputId": "9916b534-83aa-40d5-f214-d712069b71b5"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "62386db6987e40f680b5db5b23b4fc4f",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading builder script:   0%|          | 0.00/4.20k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Load the accuracy metric from a module named 'evaluate'\n",
+        "accuracy = evaluate.load(\"accuracy\")\n",
+        "\n",
+        "# Define a function 'compute_metrics' to calculate evaluation metrics\n",
+        "def compute_metrics(eval_pred):\n",
+        "    # Extract model predictions from the evaluation prediction object\n",
+        "    predictions = eval_pred.predictions\n",
+        "\n",
+        "    # Extract true labels from the evaluation prediction object\n",
+        "    label_ids = eval_pred.label_ids\n",
+        "\n",
+        "    # Calculate accuracy using the loaded accuracy metric\n",
+        "    # Convert model predictions to class labels by selecting the class with the highest probability (argmax)\n",
+        "    predicted_labels = predictions.argmax(axis=1)\n",
+        "\n",
+        "    # Calculate accuracy score by comparing predicted labels to true labels\n",
+        "    acc_score = accuracy.compute(predictions=predicted_labels, references=label_ids)['accuracy']\n",
+        "\n",
+        "    # Return the computed accuracy as a dictionary with the key \"accuracy\"\n",
+        "    return {\n",
+        "        \"accuracy\": acc_score\n",
+        "    }"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "xtOJErdaCcy-"
+      },
+      "outputs": [],
+      "source": [
+        "# Define training arguments\n",
+        "args = TrainingArguments(\n",
+        "    output_dir=\"Deep-Fake-Detector-Model\",\n",
+        "    logging_dir='./logs',\n",
+        "    evaluation_strategy=\"epoch\",\n",
+        "    learning_rate=1e-6,\n",
+        "    per_device_train_batch_size=32,\n",
+        "    per_device_eval_batch_size=8,\n",
+        "    num_train_epochs=2,\n",
+        "    weight_decay=0.02,\n",
+        "    warmup_steps=50,\n",
+        "    remove_unused_columns=False,\n",
+        "    save_strategy='epoch',\n",
+        "    load_best_model_at_end=True,\n",
+        "    save_total_limit=1,\n",
+        "    report_to=\"none\"\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qX_SWsL3C2DN"
+      },
+      "outputs": [],
+      "source": [
+        "# Create a Trainer instance\n",
+        "trainer = Trainer(\n",
+        "    model,\n",
+        "    args,\n",
+        "    train_dataset=train_data,\n",
+        "    eval_dataset=test_data,\n",
+        "    data_collator=collate_fn,\n",
+        "    compute_metrics=compute_metrics,\n",
+        "    tokenizer=processor,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 143
+        },
+        "id": "0CnRoQf5C4D-",
+        "outputId": "8de19ba5-f097-4b4b-89f9-465211656ff7"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "    <div>\n",
+              "      \n",
+              "      <progress value='9517' max='9517' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+              "      [9517/9517 06:03]\n",
+              "    </div>\n",
+              "    "
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": [
+              "{'eval_loss': 0.698646068572998,\n",
+              " 'eval_model_preparation_time': 0.0053,\n",
+              " 'eval_accuracy': 0.501471090445793,\n",
+              " 'eval_runtime': 363.9299,\n",
+              " 'eval_samples_per_second': 209.2,\n",
+              " 'eval_steps_per_second': 26.151}"
+            ]
+          },
+          "execution_count": 25,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "trainer.evaluate()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 193
+        },
+        "id": "iZUij-VwELXU",
+        "outputId": "c0d5e7f8-bc0b-4bc8-e43b-427b91b1c1cb"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "    <div>\n",
+              "      \n",
+              "      <progress value='7138' max='7138' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+              "      [7138/7138 50:50, Epoch 2/2]\n",
+              "    </div>\n",
+              "    <table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              " <tr style=\"text-align: left;\">\n",
+              "      <th>Epoch</th>\n",
+              "      <th>Training Loss</th>\n",
+              "      <th>Validation Loss</th>\n",
+              "      <th>Model Preparation Time</th>\n",
+              "      <th>Accuracy</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <td>1</td>\n",
+              "      <td>0.227800</td>\n",
+              "      <td>0.514502</td>\n",
+              "      <td>0.005300</td>\n",
+              "      <td>0.691610</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>2</td>\n",
+              "      <td>0.186500</td>\n",
+              "      <td>0.495183</td>\n",
+              "      <td>0.005300</td>\n",
+              "      <td>0.699871</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table><p>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "    <div>\n",
+              "      \n",
+              "      <progress value='19034' max='9517' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+              "      [9517/9517 31:37]\n",
+              "    </div>\n",
+              "    "
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": [
+              "TrainOutput(global_step=7138, training_loss=0.2889665345512341, metrics={'train_runtime': 3051.3234, 'train_samples_per_second': 74.853, 'train_steps_per_second': 2.339, 'total_flos': 1.7699325411952042e+19, 'train_loss': 0.2889665345512341, 'epoch': 2.0})"
+            ]
+          },
+          "execution_count": 26,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "trainer.train()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 161
+        },
+        "id": "b8caa0kpH9uf",
+        "outputId": "20beebb1-07ff-4a3f-c7f3-c9de1f4ab58e"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "    <div>\n",
+              "      \n",
+              "      <progress value='9517' max='9517' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+              "      [9517/9517 05:59]\n",
+              "    </div>\n",
+              "    "
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": [
+              "{'eval_loss': 0.49518314003944397,\n",
+              " 'eval_model_preparation_time': 0.0053,\n",
+              " 'eval_accuracy': 0.6998712795859932,\n",
+              " 'eval_runtime': 359.7866,\n",
+              " 'eval_samples_per_second': 211.609,\n",
+              " 'eval_steps_per_second': 26.452,\n",
+              " 'epoch': 2.0}"
+            ]
+          },
+          "execution_count": 27,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "trainer.evaluate()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 55
+        },
+        "collapsed": true,
+        "id": "GQo5AFJ1N1GC",
+        "outputId": "27aa4dc0-7d59-49bd-8a2a-238b69102944"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'test_loss': 0.49518314003944397, 'test_model_preparation_time': 0.0053, 'test_accuracy': 0.6998712795859932, 'test_runtime': 381.0474, 'test_samples_per_second': 199.802, 'test_steps_per_second': 24.976}\n"
+          ]
+        }
+      ],
+      "source": [
+        "outputs = trainer.predict(test_data)\n",
+        "print(outputs.metrics)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 855
+        },
+        "id": "vZE0GZXnIVtZ",
+        "outputId": "885bb00a-b9da-4995-dbad-776580260022"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Accuracy: 0.6999\n",
+            "F1 Score: 0.6739\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x600 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Classification report:\n",
+            "\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "        Real     0.6276    0.9823    0.7659     38054\n",
+            "        Fake     0.9594    0.4176    0.5819     38080\n",
+            "\n",
+            "    accuracy                         0.6999     76134\n",
+            "   macro avg     0.7935    0.7000    0.6739     76134\n",
+            "weighted avg     0.7936    0.6999    0.6739     76134\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Extract the true labels from the model outputs\n",
+        "y_true = outputs.label_ids\n",
+        "\n",
+        "# Predict the labels by selecting the class with the highest probability\n",
+        "y_pred = outputs.predictions.argmax(1)\n",
+        "\n",
+        "# Define a function to plot a confusion matrix\n",
+        "def plot_confusion_matrix(cm, classes, title='Confusion Matrix', cmap=plt.cm.Blues, figsize=(10, 8)):\n",
+        "    \"\"\"\n",
+        "    This function plots a confusion matrix.\n",
+        "\n",
+        "    Parameters:\n",
+        "        cm (array-like): Confusion matrix as returned by sklearn.metrics.confusion_matrix.\n",
+        "        classes (list): List of class names, e.g., ['Class 0', 'Class 1'].\n",
+        "        title (str): Title for the plot.\n",
+        "        cmap (matplotlib colormap): Colormap for the plot.\n",
+        "    \"\"\"\n",
+        "    # Create a figure with a specified size\n",
+        "    plt.figure(figsize=figsize)\n",
+        "\n",
+        "    # Display the confusion matrix as an image with a colormap\n",
+        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
+        "    plt.title(title)\n",
+        "    plt.colorbar()\n",
+        "\n",
+        "    # Define tick marks and labels for the classes on the axes\n",
+        "    tick_marks = np.arange(len(classes))\n",
+        "    plt.xticks(tick_marks, classes, rotation=90)\n",
+        "    plt.yticks(tick_marks, classes)\n",
+        "\n",
+        "    fmt = '.0f'\n",
+        "    # Add text annotations to the plot indicating the values in the cells\n",
+        "    thresh = cm.max() / 2.0\n",
+        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
+        "        plt.text(j, i, format(cm[i, j], fmt), horizontalalignment=\"center\", color=\"white\" if cm[i, j] > thresh else \"black\")\n",
+        "\n",
+        "    # Label the axes\n",
+        "    plt.ylabel('True label')\n",
+        "    plt.xlabel('Predicted label')\n",
+        "\n",
+        "    # Ensure the plot layout is tight\n",
+        "    plt.tight_layout()\n",
+        "    # Display the plot\n",
+        "    plt.show()\n",
+        "\n",
+        "# Calculate accuracy and F1 score\n",
+        "accuracy = accuracy_score(y_true, y_pred)\n",
+        "f1 = f1_score(y_true, y_pred, average='macro')\n",
+        "\n",
+        "# Display accuracy and F1 score\n",
+        "print(f\"Accuracy: {accuracy:.4f}\")\n",
+        "print(f\"F1 Score: {f1:.4f}\")\n",
+        "\n",
+        "# Get the confusion matrix if there are a small number of labels\n",
+        "if len(labels_list) <= 150:\n",
+        "    # Compute the confusion matrix\n",
+        "    cm = confusion_matrix(y_true, y_pred)\n",
+        "\n",
+        "    # Plot the confusion matrix using the defined function\n",
+        "    plot_confusion_matrix(cm, labels_list, figsize=(8, 6))\n",
+        "\n",
+        "# Finally, display classification report\n",
+        "print()\n",
+        "print(\"Classification report:\")\n",
+        "print()\n",
+        "print(classification_report(y_true, y_pred, target_names=labels_list, digits=4))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Qj1F9FLgIedG"
+      },
+      "outputs": [],
+      "source": [
+        "trainer.save_model()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "F-jYKwyWIiue"
+      },
+      "outputs": [],
+      "source": [
+        "# Import the 'pipeline' function from the 'transformers' library.\n",
+        "from transformers import pipeline\n",
+        "pipe = pipeline('image-classification', model=model_name, device=0)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Iy0WsizHIm_m"
+      },
+      "outputs": [],
+      "source": [
+        "image = test_data[1][\"image\"]\n",
+        "image"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "TG7rBUMnIpXl"
+      },
+      "outputs": [],
+      "source": [
+        "pipe(image)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "CtTHa2_DIq0x"
+      },
+      "outputs": [],
+      "source": [
+        "id2label[test_data[1][\"label\"]]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17,
+          "referenced_widgets": [
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+            "4c2c410c77e041038474102d2c02d4c1",
+            "6073b832608a48b6b76f956aa33d7d77",
+            "fb2cb086a4e94df19225694ca07f2db3"
+          ]
+        },
+        "id": "BYY0rKBJIsgI",
+        "outputId": "943606ca-604f-490f-dcc7-78bf5a2d11d8"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "2e54075312cd4688b6e457c6df438ad9",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from huggingface_hub import notebook_login\n",
+        "notebook_login()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7AQz322sS3Zc"
+      },
+      "outputs": [],
+      "source": [
+        "# Import the HfApi class from the huggingface_hub library.\n",
+        "from huggingface_hub import HfApi\n",
+        "\n",
+        "api = HfApi()\n",
+        "repo_id = f\"prithivMLmods/{model_name}\"\n",
+        "\n",
+        "try:\n",
+        "    # Attempt to create a new repository on the Hugging Face Model Hub using the specified repo_id.\n",
+        "    api.create_repo(repo_id)\n",
+        "\n",
+        "    # If the repository creation is successful, print a message indicating that the repository was created.\n",
+        "    print(f\"Repo {repo_id} created\")\n",
+        "except:\n",
+        "    # If an exception is raised, print a message indicating that the repository already exists.\n",
+        "    print(f\"Repo {repo_id} already exists\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "PRhqtwwqRmiO"
+      },
+      "outputs": [],
+      "source": [
+        "api.upload_folder(\n",
+        "    folder_path=model_name,  # The path to the folder to be uploaded\n",
+        "    path_in_repo=\".\",  # The path where the folder will be stored in the repository\n",
+        "    repo_id=repo_id,  # The ID of the repository where the folder will be uploaded\n",
+        "    repo_type=\"model\",  # The type of the repository (in this case, a model repository)\n",
+        "    revision=\"main\" # Revision name\n",
+        ")"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
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+      "gpuType": "A100",
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