Untitled1.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jlq7oGlpguCe"
      },
      "source": [
        "# AI Art Style Detector Project - Topics Used\n",
        "\n",
        "## Machine Learning and Deep Learning Topics:\n",
        "\n",
        "### 1. Image Preprocessing:\n",
        "- **Image Loading**: Loading images from file input using Keras's `image.load_img()`.\n",
        "- **Resizing**: Resizing the input image to a fixed size (`224x224`) before feeding it into the model.\n",
        "- **Normalization**: Scaling pixel values to the range `[0, 1]` for efficient model input.\n",
        "\n",
        "### 2. Model Loading and Inference:\n",
        "- **Loading Pre-trained Models**: Using `tensorflow.keras.models.load_model()` to load a trained deep learning model (like a CNN for image classification).\n",
        "- **Prediction**: Using the model to make predictions by feeding the preprocessed image data into the model and getting class probabilities.\n",
        "\n",
        "### 3. Transfer Learning:\n",
        "- **Pre-trained Models**: The model is likely built on a pre-trained CNN model (such as VGG16, ResNet, etc.) through **transfer learning**, where the lower layers are frozen, and only the higher layers are fine-tuned for the specific art style classification task.\n",
        "   \n",
        "### 4. Classification:\n",
        "- **Categorical Output**: The model predicts which art style category (e.g., Impressionism, Surrealism) an artwork belongs to.\n",
        "- **Softmax Activation**: The output layer of the model typically uses **softmax** activation to produce probabilities for each art style class.\n",
        "\n",
        "---\n",
        "\n",
        "## Web Application Development Topics (Using Streamlit):\n",
        "\n",
        "### 1. Streamlit Layout:\n",
        "- **Column Layouts**: Using `st.columns()` to create responsive, side-by-side layouts for displaying images and results.\n",
        "- **Expander**: Using `st.expander()` to allow users to reveal additional information about the model and its functionality.\n",
        "\n",
        "### 2. File Uploading:\n",
        "- **Image Upload**: Using `st.file_uploader()` to allow users to upload images directly from their local device into the web app.\n",
        "- **Image Display**: Using `st.image()` to display the uploaded image on the web app.\n",
        "\n",
        "### 3. Interactive Widgets:\n",
        "- **Dropdown/Selectbox**: Using `st.selectbox()` to allow users to interactively select art styles and get more information about them.\n",
        "- **Buttons and Inputs**: You could add buttons and input fields to extend functionality, like adding manual entry for predicting specific images.\n",
        "\n",
        "### 4. Visualization:\n",
        "- **Plotly Charts**: Using **Plotly** to visualize art style distributions (like bar charts), making the app more interactive and engaging.\n",
        "- **Matplotlib/Seaborn** (Optional): Visualizing the results or image transformations (though Plotly is integrated here).\n",
        "\n",
        "### 5. Styling the UI:\n",
        "- **Custom CSS**: Using custom CSS injected into the Streamlit app with `st.markdown()` to enhance the look and feel of the app (e.g., custom colors, fonts, and element styling).\n",
        "   \n",
        "### 6. Streamlit Features:\n",
        "- **Markdown Rendering**: Using `st.markdown()` to render HTML and CSS for custom styling or display content.\n",
        "- **File Handling**: Streamlit handles file uploading, downloading, and processing in a straightforward way using `st.file_uploader()`.\n",
        "\n",
        "---\n",
        "\n",
        "## Deep Learning Topics in Model Development (for Art Style Classification):\n",
        "\n",
        "### 1. Convolutional Neural Networks (CNNs):\n",
        "- **Convolutional Layers**: CNNs are well-suited for image classification tasks due to their ability to automatically learn spatial hierarchies of features.\n",
        "- **Pooling Layers**: Max-pooling layers to reduce the spatial dimensions of the image while retaining important features.\n",
        "- **Fully Connected Layers**: Dense layers to perform the final classification.\n",
        "\n",
        "### 2. Transfer Learning:\n",
        "- Using pre-trained networks like **VGG16**, **ResNet**, or **Inception** as feature extractors, and fine-tuning the final layers for specific art styles.\n",
        "   \n",
        "### 3. Activation Functions:\n",
        "- **ReLU (Rectified Linear Unit)**: For non-linear transformations in hidden layers.\n",
        "- **Softmax**: For multi-class classification, used in the final output layer to output probabilities for each class.\n",
        "\n",
        "### 4. Model Training (Optional):\n",
        "- **Data Augmentation**: Techniques to artificially expand the dataset (e.g., rotations, flips, etc.).\n",
        "- **Loss Function**: Typically **categorical cross-entropy** for multi-class classification tasks.\n",
        "- **Optimizer**: Such as **Adam**, to adjust weights during training.\n",
        "\n",
        "### 5. Evaluation Metrics:\n",
        "- **Accuracy**: How often the model predicts the correct class.\n",
        "- **Confusion Matrix**: (Optional) To evaluate the model’s performance across different art styles.\n",
        "\n",
        "---\n",
        "\n",
        "## Other Relevant Topics:\n",
        "\n",
        "### 1. Data Handling and Preprocessing:\n",
        "- **Numpy**: Used for image array manipulation and preparing input data.\n",
        "- **Pandas**: For organizing and visualizing art style statistics (e.g., counts, distributions).\n",
        "\n",
        "### 2. Model Evaluation and Fine-tuning (Optional):\n",
        "- **Hyperparameter Tuning**: Tweaking the learning rate, batch size, etc., to improve model performance.\n",
        "- **Cross-validation**: Ensuring the model performs well on unseen data.\n",
        "\n",
        "---\n",
        "\n",
        "## In Summary:\n",
        "The main topics used in this project are:\n",
        "\n",
        "- **Machine Learning**: CNNs, transfer learning, model prediction, image preprocessing, and classification.\n",
        "- **Deep Learning**: Using pre-trained models, fine-tuning, and evaluating the model’s performance.\n",
        "- **Streamlit Web Development**: Interactive web app development, custom UI with CSS, file handling, and visualizations.\n",
        "- **Data Science**: Data manipulation, model deployment, and visualization using Pandas and Plotly.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "atG_3xNvU720"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The syntax of the command is incorrect.\n"
          ]
        }
      ],
      "source": [
        "!mkdir -p ~/.kaggle\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s_XA6A_YU7zn",
        "outputId": "9e66b83c-065f-4b5b-c274-44a57986ebac"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "'cp' is not recognized as an internal or external command,\n",
            "operable program or batch file.\n"
          ]
        }
      ],
      "source": [
        "!cp kaggle.json ~/.kaggle/\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FW1jquyKU7wu",
        "outputId": "381ed4f7-26cd-4372-8510-5930a1aa320f"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "'chmod' is not recognized as an internal or external command,\n",
            "operable program or batch file.\n"
          ]
        }
      ],
      "source": [
        "!chmod 600 ~/.kaggle/kaggle.json\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8hv1Lom6Uec_",
        "outputId": "3a93e47f-896f-4478-84a2-e2d4e29a5e46"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "'chmod' is not recognized as an internal or external command,\n",
            "operable program or batch file.\n"
          ]
        }
      ],
      "source": [
        "!chmod 600 kaggle.json\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tm2JYiyWVCGC",
        "outputId": "0d222bc9-c378-4822-8999-e57859643897"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.6.17)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.17.0)\n",
            "Requirement already satisfied: certifi>=2023.7.22 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2024.12.14)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.32.3)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.67.1)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle) (8.0.4)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.2.3)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle) (6.2.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle) (0.5.1)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.4.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.10)\n"
          ]
        }
      ],
      "source": [
        "!pip install kaggle\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OWi7m5uXSobo",
        "outputId": "1542c1cb-adab-4b3b-db59-612707a19593"
      },
      "outputs": [],
      "source": [
        "#!/bin/bash\n",
        "!kaggle datasets download steubk/wikiart"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ado9rLlWD67",
        "outputId": "32eea455-9996-4e3f-b87c-ee0f40ee5485"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "ERROR:root:Internal Python error in the inspect module.\n",
            "Below is the traceback from this internal error.\n",
            "\n",
            "\n",
            "KeyboardInterrupt\n",
            "\n"
          ]
        }
      ],
      "source": [
        "import zipfile\n",
        "\n",
        "with zipfile.ZipFile(\"/content/wikiart.zip\", \"r\") as zip_ref:\n",
        "    zip_ref.extractall(\"wikiart_data\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QKleAJNUWFED"
      },
      "outputs": [],
      "source": [
        "!ls wikiart_data\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WPI-bMGJWG-w"
      },
      "source": [
        "# **1. Data Preprocessing**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qAUPwMBYWN8p"
      },
      "source": [
        "**Import Libraries**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KwB9KW7vWLFy"
      },
      "outputs": [],
      "source": [
        "import os # For operating system\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt # for plotting\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.applications import VGG16\n",
        "from tensorflow.keras import layers, models\n",
        "from sklearn.model_selection import train_test_split\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jAfmnJEIb02C"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.applications import MobileNetV2\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "from tensorflow.keras.optimizers import AdamW\n",
        "from tensorflow.keras.mixed_precision import set_global_policy"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XlwZJ5DSXgGF"
      },
      "source": [
        "**(B) Load ans Explore the Data**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2wIkoq7AXRpC",
        "outputId": "317f840d-2a2a-4021-e8c4-24a6485e6b2c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['Contemporary_Realism', 'Northern_Renaissance', 'Action_painting', 'wclasses.csv', 'Cubism', 'Color_Field_Painting', 'Realism', 'Rococo', 'Fauvism', 'Romanticism', 'High_Renaissance', 'New_Realism', 'Naive_Art_Primitivism', 'Synthetic_Cubism', 'Art_Nouveau_Modern', 'Baroque', 'Minimalism', 'Impressionism', 'Symbolism', 'Mannerism_Late_Renaissance', 'Abstract_Expressionism', 'Early_Renaissance', 'Analytical_Cubism', 'Post_Impressionism', 'Ukiyo_e', 'classes.csv', 'Pointillism', 'Pop_Art', 'Expressionism']\n"
          ]
        }
      ],
      "source": [
        "# set dataset directory path\n",
        "dataset_dir = '/content/wikiart_data'\n",
        "# check the classes available in the dataset\n",
        "classes = os.listdir(dataset_dir)\n",
        "print(classes)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "upLuzm4hcWtO",
        "outputId": "b71042b1-8292-4ba5-eff1-30183c52574d"
      },
      "outputs": [
        {
          "ename": "OSError",
          "evalue": "[Errno 28] No space left on device",
          "output_type": "error",
          "traceback": [
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            "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-27-d9e77e74453b>\u001b[0m in \u001b[0;36m<cell line: 13>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;31m# Extract the zip file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mzipfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mZipFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/content/wikiart.zip\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"r\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mzip_ref\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m     \u001b[0mzip_ref\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextractall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/content/wikiart_data\"\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     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;31m# Create directories if they don't exist\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/zipfile.py\u001b[0m in \u001b[0;36mextractall\u001b[0;34m(self, path, members, pwd)\u001b[0m\n\u001b[1;32m   1658\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1659\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mzipinfo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmembers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1660\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_extract_member\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzipinfo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpwd\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   1661\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1662\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/zipfile.py\u001b[0m in \u001b[0;36m_extract_member\u001b[0;34m(self, member, targetpath, pwd)\u001b[0m\n\u001b[1;32m   1713\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmember\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpwd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpwd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msource\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1714\u001b[0m              \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtargetpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"wb\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1715\u001b[0;31m             \u001b[0mshutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopyfileobj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\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   1716\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1717\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mtargetpath\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/shutil.py\u001b[0m in \u001b[0;36mcopyfileobj\u001b[0;34m(fsrc, fdst, length)\u001b[0m\n\u001b[1;32m    196\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mbuf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    197\u001b[0m             \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 198\u001b[0;31m         \u001b[0mfdst_write\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuf\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    199\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    200\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_samefile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdst\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;31mOSError\u001b[0m: [Errno 28] No space left on device"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import shutil\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "import zipfile\n",
        "\n",
        "# Define paths\n",
        "dataset_dir = \"/content/wikiart_data\"  # All images in this folder\n",
        "train_dir = \"/content/train\"      # Folder for training images\n",
        "val_dir = \"/content/val\"          # Folder for validation images\n",
        "\n",
        "# Extract the zip file\n",
        "with zipfile.ZipFile(\"/content/wikiart.zip\", \"r\") as zip_ref:\n",
        "    zip_ref.extractall(\"/content/wikiart_data\")\n",
        "\n",
        "# Create directories if they don't exist\n",
        "os.makedirs(train_dir, exist_ok=True)\n",
        "os.makedirs(val_dir, exist_ok=True)\n",
        "\n",
        "# Create subdirectories for classes\n",
        "classes = [d for d in os.listdir(dataset_dir) if os.path.isdir(os.path.join(dataset_dir, d))]\n",
        "for cls in classes:\n",
        "    os.makedirs(os.path.join(train_dir, cls), exist_ok=True)\n",
        "    os.makedirs(os.path.join(val_dir, cls), exist_ok=True)\n",
        "\n",
        "# Split dataset\n",
        "for cls in classes:\n",
        "    cls_dir = os.path.join(dataset_dir, cls)\n",
        "    images = os.listdir(cls_dir)\n",
        "    # Check if the images list is empty before using train_test_split\n",
        "    if not images:\n",
        "        print(f\"Warning: No images found in {cls_dir}. Skipping this directory.\")\n",
        "        continue  # Skip to the next class\n",
        "    # added to handle if there is only one image in the directory\n",
        "    if len(images) == 1:\n",
        "        print(f\"Warning: Only one image found in {cls_dir}. Skipping this directory.\")\n",
        "        continue\n",
        "    train_images, val_images = train_test_split(images, test_size=0.2, random_state=42)  # 80% train, 20% val\n",
        "\n",
        "    # Move files to respective folders\n",
        "    for img in train_images:\n",
        "      try:\n",
        "        shutil.move(os.path.join(cls_dir, img), os.path.join(train_dir, cls, img))\n",
        "      except shutil.Error as e:\n",
        "        print(f\"Error moving file {img} from {cls_dir} to {train_dir}/{cls}: {e}\")\n",
        "    for img in val_images:\n",
        "      try:\n",
        "        shutil.move(os.path.join(cls_dir, img), os.path.join(val_dir, cls, img))\n",
        "      except shutil.Error as e:\n",
        "        print(f\"Error moving file {img} from {cls_dir} to {val_dir}/{cls}: {e}\")\n",
        "\n",
        "print(\"Dataset split completed.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KRDb2vLAX1m-"
      },
      "source": [
        "**(c) Image Resizing and Normalization**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CPa6EY8bXxMN",
        "outputId": "6a2ac532-d5ec-4e80-e8e3-902ac557fdcc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 65166 images belonging to 27 classes.\n",
            "Found 16278 images belonging to 27 classes.\n"
          ]
        }
      ],
      "source": [
        "# Set parameters\n",
        "image_size = (128, 128)  # Smaller image size for memory efficiency\n",
        "batch_size = 16  # Reduced batch size\n",
        "num_classes = 10  # Adjust based on your dataset\n",
        "\n",
        "# Data augmentation and rescaling\n",
        "train_datagen = ImageDataGenerator(\n",
        "    rescale=1.0 / 255,\n",
        "    rotation_range=20,\n",
        "    width_shift_range=0.2,\n",
        "    height_shift_range=0.2,\n",
        "    shear_range=0.2,\n",
        "    zoom_range=0.2,\n",
        "    horizontal_flip=True\n",
        ")\n",
        "\n",
        "val_datagen = ImageDataGenerator(rescale=1.0 / 255)\n",
        "\n",
        "# Data generators\n",
        "train_gen = train_datagen.flow_from_directory(\n",
        "    train_dir,\n",
        "    target_size=image_size,\n",
        "    batch_size=batch_size,\n",
        "    class_mode='categorical'\n",
        ")\n",
        "\n",
        "val_gen = val_datagen.flow_from_directory(\n",
        "    val_dir,\n",
        "    target_size=image_size,\n",
        "    batch_size=batch_size,\n",
        "    class_mode='categorical'\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i9U4kDsnZ4rW"
      },
      "source": [
        "# **2. Model Architecture**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_Lr-JPXQcBJo"
      },
      "outputs": [],
      "source": [
        "# Load pre-trained MobileNetV2 with frozen layers\n",
        "base_model = MobileNetV2(weights='imagenet', include_top=False, input_shape=(128, 128, 3))\n",
        "base_model.trainable = False  # Freeze base layers to reduce computation\n",
        "\n",
        "# Build the model\n",
        "model = tf.keras.Sequential([\n",
        "    base_model,\n",
        "    tf.keras.layers.GlobalAveragePooling2D(),\n",
        "    tf.keras.layers.Dense(256, activation='relu'),\n",
        "    tf.keras.layers.Dropout(0.5),\n",
        "    tf.keras.layers.Dense(num_classes, activation='softmax', dtype='float32')  # Ensure outputs are float32\n",
        "])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3iO9Hv-na53V"
      },
      "source": [
        "**(b) compile the model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M-IwFmTBZ9P5"
      },
      "outputs": [],
      "source": [
        "# Compile the model\n",
        "optimizer = AdamW(learning_rate=0.001)\n",
        "model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7dw_MJpYbHze"
      },
      "source": [
        "**(c) Train the model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ro14UX1HbFx7"
      },
      "outputs": [],
      "source": [
        "# Callbacks\n",
        "checkpoint = ModelCheckpoint('best_model.h5', save_best_only=True, monitor='val_loss')\n",
        "early_stopping = EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)\n",
        "\n",
        "# Train the model\n",
        "history = model.fit(\n",
        "    train_gen,\n",
        "    validation_data=val_gen,\n",
        "    epochs=20,\n",
        "    callbacks=[checkpoint, early_stopping]\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B23w_jvmbpVd"
      },
      "source": [
        "# **4. Evaluate the model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RorPfsd_bmB2"
      },
      "outputs": [],
      "source": [
        "# Plot training and validation accuracy\n",
        "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.title('Model Accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "# Plot training and validation loss\n",
        "plt.plot(history.history['loss'], label='Training Loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
        "plt.title('Model Loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fB4FmTpIcEbc"
      },
      "source": [
        "# 5. Model testing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uJyJa4rfcD0Z"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.preprocessing import image\n",
        "\n",
        "# Load a test image\n",
        "img_path = '/path_to_test_image/test_image.jpg'\n",
        "img = image.load_img(img_path, target_size=(img_size, img_size))\n",
        "img_array = image.img_to_array(img) / 255.0  # Normalize\n",
        "img_array = np.expand_dims(img_array, axis=0)\n",
        "\n",
        "# Predict the style\n",
        "prediction = model.predict(img_array)\n",
        "predicted_class = classes[np.argmax(prediction)]\n",
        "print(f\"Predicted Art Style: {predicted_class}\")\n"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.9.19"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}