{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JmYRMwEOYkbU"
      },
      "source": [
        "# `Fire Detect  - 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": 2,
      "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,
      "metadata": {
        "id": "20RJuU8_uY2k"
      },
      "outputs": [],
      "source": [
        "from datasets import load_dataset\n",
        "dataset = load_dataset(\"--your--dataset-goes--here\", split=\"train\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "o8rgwG0nuc00"
      },
      "outputs": [],
      "source": [
        "from pathlib import Path\n",
        "\n",
        "file_names = []\n",
        "labels = []\n",
        "\n",
        "for example in dataset:\n",
        "    file_path = str(example['image'])  # Convert the image object to a string or path\n",
        "    label = example['label']  # Get the label\n",
        "\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": {
        "id": "Yz8qs87tuhjs"
      },
      "outputs": [],
      "source": [
        "# Print the total number of file names and labels\n",
        "print(len(file_names), len(labels))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "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": {
        "id": "9SZz49oNBSHf"
      },
      "outputs": [],
      "source": [
        "print(df.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZubCrfrhBZGo"
      },
      "outputs": [],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oFwJ-2_B_br5"
      },
      "outputs": [],
      "source": [
        "df['label'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZzX_P-onunr7"
      },
      "outputs": [],
      "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": 11,
      "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": {
        "id": "ha4Bpgoz7dfu"
      },
      "outputs": [],
      "source": [
        "dataset[10][\"image\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-MfUvn2A-tBc"
      },
      "outputs": [],
      "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": 14,
      "metadata": {
        "id": "9u1W0MBhBpMA"
      },
      "outputs": [],
      "source": [
        "# Define the new list of unique labels\n",
        "labels_list = ['Fire Needed Action', 'Normal Conditions', 'Smoky Environment']\n",
        "\n",
        "# Initialize dictionaries to map labels to IDs and vice versa\n",
        "label2id, id2label = {}, {}\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": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4CfU5GJkByam",
        "outputId": "6d206be1-ad03-41c3-a6d4-7127f490f037"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mapping of IDs to Labels: {0: 'Fire Needed Action', 1: 'Normal Conditions', 2: 'Smoky Environment'} \n",
            "\n",
            "Mapping of Labels to IDs: {'Fire Needed Action': 0, 'Normal Conditions': 1, 'Smoky Environment': 2}\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": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
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        },
        "id": "M9XI2VNYB35G",
        "outputId": "b39d946d-841f-493b-f023-11f88fba4a7c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/6060 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "482c35ac85834319987d7b901227d688"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Casting the dataset:   0%|          | 0/6060 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e821343bf5d54850b0d53fc572cbf7e0"
            }
          },
          "metadata": {}
        }
      ],
      "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": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
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          ]
        },
        "id": "FvEEn_iDB8uX",
        "outputId": "d6cd3d21-1707-425f-e268-f693ea880ef6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "preprocessor_config.json:   0%|          | 0.00/160 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "57f0f1a79fb543f580b15560b5ca088a"
            }
          },
          "metadata": {}
        }
      ],
      "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": 18,
      "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": 19,
      "metadata": {
        "colab": {
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      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/502 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/346M [00:00<?, ?B/s]"
            ],
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "85.800963\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": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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        },
        "id": "bRleo0I-CqPv",
        "outputId": "84be13e1-71ec-46cc-d9e2-3b6a29c59c56"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/4.20k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fc63188eae5c41d5933f40e540903742"
            }
          },
          "metadata": {}
        }
      ],
      "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": 21,
      "metadata": {
        "id": "xtOJErdaCcy-"
      },
      "outputs": [],
      "source": [
        "# Define training arguments\n",
        "args = TrainingArguments(\n",
        "    output_dir=\"Fire-Normal-Smoke\",\n",
        "    logging_dir='./logs',\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    learning_rate=5e-6,\n",
        "    per_device_train_batch_size=64,\n",
        "    per_device_eval_batch_size=16,\n",
        "    num_train_epochs=6,\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": 22,
      "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": {
        "id": "0CnRoQf5C4D-"
      },
      "outputs": [],
      "source": [
        "trainer.evaluate()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iZUij-VwELXU"
      },
      "outputs": [],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b8caa0kpH9uf"
      },
      "outputs": [],
      "source": [
        "trainer.evaluate()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "GQo5AFJ1N1GC"
      },
      "outputs": [],
      "source": [
        "outputs = trainer.predict(test_data)\n",
        "print(outputs.metrics)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vZE0GZXnIVtZ"
      },
      "outputs": [],
      "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": 28,
      "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": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
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        },
        "id": "BYY0rKBJIsgI",
        "outputId": "cae1d0df-4220-4b61-fa17-cfabf0f9e9f1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
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              "model_id": "d35991d81d964d1c91f7980206bcde8a"
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          "metadata": {}
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7AQz322sS3Zc",
        "outputId": "c8c477a9-29bb-40ad-f2e8-69e227233e65"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Repo Fire-Normal-Smoke created\n"
          ]
        }
      ],
      "source": [
        "# Import the HfApi class from the huggingface_hub library.\n",
        "from huggingface_hub import HfApi\n",
        "\n",
        "api = HfApi()\n",
        "repo_id = f\"Fire-Normal-Smoke\"\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",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ],
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        "id": "SLq1frDtxm2r",
        "outputId": "454b10e3-4b03-43fd-d16e-9c6ef2a72b98",
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      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
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    {
      "cell_type": "code",
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      "metadata": {
        "id": "PRhqtwwqRmiO"
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      "outputs": [],
      "source": [
        "api.upload_folder(\n",
        "    folder_path=\"Fire-Normal-Smoke\",  # 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",
        ")"
      ]
    }
  ],
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