2_3_6_Facial_Expression_Recognition.ipynb

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        "# <span style=\"color: #4A90E2;\">Facial Expression Recognition Project Overview</span>\n",
        "\n",
        "## <span style=\"color: #7B7B7B;\">1. Project Description</span>\n",
        "This project aims to build a Convolutional Neural Network (CNN) model for recognizing facial expressions from images. The model is trained on a dataset containing various emotions, including happiness, sadness, surprise, anger, and more. By utilizing deep learning techniques, the goal is to accurately classify facial expressions based on visual input.\n",
        "\n",
        "## <span style=\"color: #7B7B7B;\">2. Objectives</span>\n",
        "- Develop a robust CNN architecture for facial expression classification.\n",
        "- Implement data augmentation techniques to improve model generalization.\n",
        "- Visualize data distributions, including class distributions and image dimensions.\n",
        "- Evaluate model performance using classification metrics and confusion matrices.\n",
        "- Deploy the model using Flask to create a web application for real-time emotion detection.\n",
        "\n",
        "## <span style=\"color: #7B7B7B;\">3. Dataset</span>\n",
        "The project utilizes the **Facial Expression Dataset**, which contains images of faces labeled with various emotions. The dataset is divided into training and testing sets to evaluate the model's performance effectively. Each emotion is stored in separate directories, enabling easy access and manipulation.\n",
        "\n",
        "## <span style=\"color: #7B7B7B;\">4. Key Libraries Used</span>\n",
        "- **TensorFlow** and **Keras**: For building and training the CNN model.\n",
        "- **Pandas**: For data manipulation and analysis.\n",
        "- **NumPy**: For numerical operations and array manipulations.\n",
        "- **Matplotlib** and **Seaborn**: For data visualization.\n",
        "- **Flask**: For deploying the trained model as a web application.\n",
        "- **PIL**: For image processing tasks.\n",
        "\n",
        "## <span style=\"color: #7B7B7B;\">5. Methodology</span>\n",
        "- **Data Preprocessing**: Images are resized, normalized, and augmented to enhance model robustness. The dataset's structure is inspected to ensure correct loading.\n",
        "- **Model Architecture**: A CNN model is constructed with layers including convolutional, pooling, dropout, and dense layers.\n",
        "- **Training**: The model is trained using a subset of data, with early stopping and model checkpoints implemented to save the best performing model.\n",
        "- **Evaluation**: The model's performance is assessed through confusion matrices and classification reports, ensuring that the accuracy meets the project's standards.\n",
        "\n",
        "## <span style=\"color: #7B7B7B;\">6. Visualization</span>\n",
        "Several visualizations are created to understand the dataset better, including:\n",
        "- Bar plots for class distributions in training and testing datasets.\n",
        "- Histograms and boxplots for image dimensions.\n",
        "- Sample images from each class, both original and augmented.\n",
        "\n",
        "## <span style=\"color: #7B7B7B;\">7. Deployment</span>\n",
        "The trained model is deployed using a Flask web application, allowing users to upload images and receive predictions for the corresponding facial expressions in real time.\n",
        "\n",
        "## <span style=\"color: #7B7B7B;\">8. Future Work</span>\n",
        "Potential future enhancements may include:\n",
        "- Implementing more advanced architectures (e.g., ResNet, Inception) for improved accuracy.\n",
        "- Expanding the dataset to include more diverse expressions.\n",
        "- Exploring the integration of the model with real-time video processing for live emotion detection.\n",
        "\n",
        "## <span style=\"color: #7B7B7B;\">9. Conclusion</span>\n",
        "This project serves as a comprehensive approach to facial expression recognition using deep learning techniques. The outcome not only demonstrates the model's effectiveness but also provides a basis for further research and development in the field of emotion recognition."
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      "source": [
        "# <span style=\"color:blue;\">Importing Libraries</span>\n",
        "\n",
        "In this step, we import the necessary libraries required for the project. Each library serves a specific purpose:\n",
        "\n",
        "- **os**: Allows interaction with the operating system, enabling us to navigate directories and manage file paths.\n",
        "- **numpy**: A fundamental package for numerical operations in Python, providing support for large, multi-dimensional arrays and matrices.\n",
        "- **pandas**: A powerful data manipulation and analysis library that provides data structures for efficiently storing and manipulating structured data.\n",
        "- **matplotlib.pyplot**: A plotting library used for creating static, animated, and interactive visualizations in Python.\n",
        "- **seaborn**: A statistical data visualization library based on Matplotlib, providing a high-level interface for drawing attractive statistical graphics.\n",
        "- **tensorflow.keras.preprocessing.image**: Contains tools for image preprocessing and data augmentation, which are essential for preparing images for training.\n",
        "- **tensorflow.keras.models**: Provides a way to create deep learning models using a high-level interface.\n",
        "- **tensorflow.keras.layers**: Contains various layers that can be used to build deep learning models, such as convolutional layers and pooling layers.\n",
        "- **tensorflow.keras.callbacks**: Contains utility functions for managing training and evaluation, including early stopping and model checkpoints.\n",
        "- **sklearn.metrics**: Provides tools for evaluating model performance using metrics like classification report and confusion matrix.\n",
        "- **flask**: A micro web framework for Python that enables us to create a web application for model deployment.\n",
        "- **PIL (Python Imaging Library)**: A library used for image processing tasks, providing capabilities for opening, manipulating, and saving images.\n",
        "- **warnings**: A built-in Python module that allows us to control the display of warning messages."
      ],
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      "cell_type": "code",
      "source": [
        "!pip install --upgrade keras  # Upgrade Keras to the latest version"
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        "!pip install --upgrade tensorflow  # Upgrade TensorFlow to the latest version"
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      "cell_type": "code",
      "source": [
        "!pip install tensorflow flask  # Install TensorFlow and Flask for model deployment"
      ],
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      "metadata": {
        "id": "i_eqQYZ7kdVL"
      },
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "J6gCNgorkVIA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "t8tjxstJeLj1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Import necessary libraries"
      ],
      "metadata": {
        "id": "baZKsoQeePto"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os  # For interacting with the operating system"
      ],
      "metadata": {
        "id": "r4bcq8u7ePww"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np  # For numerical operations"
      ],
      "metadata": {
        "id": "gUHuXo1IePzL"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd  # For data manipulation and analysis"
      ],
      "metadata": {
        "id": "ApnqBw8yeP1h"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt  # For plotting graphs"
      ],
      "metadata": {
        "id": "Omu6AM7ceP4R"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns  # For statistical data visualization"
      ],
      "metadata": {
        "id": "31fqKzJkeP7L"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator  # For image preprocessing"
      ],
      "metadata": {
        "id": "ZcSBlyF_eP9m"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential  # For creating sequential models"
      ],
      "metadata": {
        "id": "o3zts0_ceP__"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout  # For building CNN layers"
      ],
      "metadata": {
        "id": "NgIxrjQoerQL"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint  # For model training callbacks"
      ],
      "metadata": {
        "id": "1RdzOWljerTK"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import load_model  # For loading a saved model"
      ],
      "metadata": {
        "id": "cp0DnXDGerVz"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing.image import img_to_array, load_img  # For image conversion and loading"
      ],
      "metadata": {
        "id": "M9-cmDLderYn"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix  # For evaluation metrics"
      ],
      "metadata": {
        "id": "Bga2iImOerbX"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from flask import Flask, request, jsonify  # For creating a web app"
      ],
      "metadata": {
        "id": "hBT7r82EerfB"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from PIL import Image  # Importing Image from PIL for image processing"
      ],
      "metadata": {
        "id": "lYkyxEj5hplf"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings  # Importing the warnings module"
      ],
      "metadata": {
        "id": "zvTcJZkzj0d5"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1pXntuXJj1nq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Suppress warnings (optional)"
      ],
      "metadata": {
        "id": "Uiyj_Cftj1rz"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "warnings.filterwarnings('ignore')  # Ignore all warnings"
      ],
      "metadata": {
        "id": "d302gk-jj3rj"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lKKL8KIRe7KN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "25MAowVZe7Nh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dcfVhPi6e7Qh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:blue;\">Preprocessing the Data</span>\n",
        "\n",
        "In this section, we set up the directory paths for the training and testing data. This step is crucial as it allows the program to access the dataset and perform necessary operations, such as loading images for model training and evaluation.\n",
        "\n",
        "- **train_dir**: Specifies the path to the training dataset containing facial expression images organized by emotion.\n",
        "- **test_dir**: Specifies the path to the testing dataset for evaluating model performance, also organized by emotion."
      ],
      "metadata": {
        "id": "cqpimCpCqVx1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Loading the Data"
      ],
      "metadata": {
        "id": "hN7qaWACe7TQ"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dir = '/content/facial_expression_data/train'  # Directory for training data"
      ],
      "metadata": {
        "id": "dacEmvPBe7Yt"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_dir = '/content/facial_expression_data/test'  # Directory for testing data"
      ],
      "metadata": {
        "id": "ijr6I_xpe7bk"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "x6izyRMCfEI0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lgIE22usfEOV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mcmzIKNmfERJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:blue;\">Inspecting the Dataset</span>\n",
        "\n",
        "This step involves inspecting the structure of the training and testing directories. We loop through each emotion folder to count the number of images available in each class.\n",
        "\n",
        "- The output provides insights into the dataset's balance across different emotions, which is important for training a robust model."
      ],
      "metadata": {
        "id": "LHDLiFx8qaj9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Inspecting the Dataset"
      ],
      "metadata": {
        "id": "gl1Dk9iBh3X4"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Training directory structure:\")  # Print statement for training directory structure\n",
        "for emotion in os.listdir(train_dir):  # Loop through each emotion folder in the training directory\n",
        "    print(f\"{emotion}: {len(os.listdir(os.path.join(train_dir, emotion)))} images\")  # Print the number of images per emotion\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tzkKk2X2h3bq",
        "outputId": "38658aae-32af-4199-9126-a819e2be830a"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training directory structure:\n",
            "angry: 3995 images\n",
            "sad: 4830 images\n",
            "happy: 7215 images\n",
            "disgust: 436 images\n",
            "neutral: 4965 images\n",
            "fear: 4097 images\n",
            "surprise: 3171 images\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1EnEHmElh3es"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GEQVjX5ih-3B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nTesting directory structure:\")  # Print statement for testing directory structure\n",
        "for emotion in os.listdir(test_dir):  # Loop through each emotion folder in the testing directory\n",
        "    print(f\"{emotion}: {len(os.listdir(os.path.join(test_dir, emotion)))} images\")  # Print the number of images per emotion\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gJLO3jQfh-6k",
        "outputId": "c661b7ff-bafd-42cc-90c8-ff700c89a3b9"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Testing directory structure:\n",
            "angry: 958 images\n",
            "sad: 1247 images\n",
            "happy: 1774 images\n",
            "disgust: 111 images\n",
            "neutral: 1233 images\n",
            "fear: 1024 images\n",
            "surprise: 831 images\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "71SihBgZh-9-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Cw1FZB2yh_BP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CHn-4EBDqfrX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:blue;\">Count Total Images in Each Directory</span>\n",
        "\n",
        "Here, we define a function to count the total number of images present in both the training and testing directories. This function utilizes the `os.walk()` method to traverse through the directory structure and accumulate the total count of images.\n",
        "\n",
        "- **total_train_images**: Stores the count of images in the training directory.\n",
        "- **total_test_images**: Stores the count of images in the testing directory."
      ],
      "metadata": {
        "id": "NafEJqdmqf5q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Count total images in each directory"
      ],
      "metadata": {
        "id": "4hCq5Jajh3hc"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def count_images(dir_path):  # Define a function to count images in a directory\n",
        "    return sum(len(files) for _, _, files in os.walk(dir_path))  # Return the total number of images"
      ],
      "metadata": {
        "id": "2n3c9Gnyh3ka"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "iZyXFEjYiK5r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "C8pZBK9ciK8e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "total_train_images = count_images(train_dir)  # Get the total number of training images"
      ],
      "metadata": {
        "id": "f-VCBLchiK_b"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "total_test_images = count_images(test_dir)  # Get the total number of testing images"
      ],
      "metadata": {
        "id": "y0MyO3wtiLB_"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YkpyZi6NiLEq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "e6_-_rmCiLHD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"\\nTotal training images: {total_train_images}\")  # Print the total training images\n",
        "print(f\"Total testing images: {total_test_images}\")  # Print the total testing images"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H7aUTuIiiLJ8",
        "outputId": "f73adf85-4caa-42d6-b234-7689a598ab8b"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Total training images: 28709\n",
            "Total testing images: 7178\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "d9Fs6XEEiRzY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gUFR8Jl0iR8q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oFUZZYZ0iR_6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **EXPLANATORY DATA ANALYSIS**"
      ],
      "metadata": {
        "id": "1jio5gbcq8cz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:blue;\">Visualizing the Distribution of Classes</span>\n",
        "\n",
        "In this step, we visualize the distribution of images across different emotion classes in the training dataset using a bar plot.\n",
        "\n",
        "- We create a bar plot that displays the number of images corresponding to each emotion, allowing us to assess the class distribution visually."
      ],
      "metadata": {
        "id": "qDVLejP1rDSk"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EX51bOjfrGTM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:blue;\">Plotting Sample Images from Each Class</span>\n",
        "\n",
        "We define a function to randomly select and display sample images from each emotion class in the training dataset. This helps in visually assessing the dataset and understanding the variety of expressions within each emotion.\n",
        "\n",
        "- The function retrieves a specified number of sample images and displays them in a grid format."
      ],
      "metadata": {
        "id": "9Cnm2glCrGpz"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "X-odK_I5rMLL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:blue;\">Visualizing Image Dimensions</span>\n",
        "\n",
        "This step involves analyzing the dimensions of the images in the training dataset. We define a function to gather width and height dimensions of each image and visualize the distribution of these dimensions using histograms.\n",
        "\n",
        "- Understanding image dimensions is essential for setting the input shape for the neural network."
      ],
      "metadata": {
        "id": "llxo56MsrM6R"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XyRN7O_lrRLu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:blue;\">Analyzing Image Sizes</span>\n",
        "\n",
        "In this step, we create box plots to analyze the distribution of image dimensions in terms of width and height. This visualization provides insights into the range and variability of image sizes.\n",
        "\n",
        "- Box plots are effective in identifying potential outliers and assessing the overall distribution of the dimensions."
      ],
      "metadata": {
        "id": "fPoLCo9HrRWj"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YPSPrK-BrT50"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:blue;\">Correlation Analysis of Image Dimensions</span>\n",
        "\n",
        "We create a scatter plot to analyze the correlation between image width and height. This visualization helps us determine whether there is a relationship between the two dimensions.\n",
        "\n",
        "- A reference line is included to visually represent the correlation."
      ],
      "metadata": {
        "id": "Un6Mrq5IrUFk"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "c3rYtmnirYpA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:blue;\">Visualizing Sample Images with Augmentation</span>\n",
        "\n",
        "In this step, we define a function to demonstrate image augmentation techniques such as flipping and rotating images. This helps in understanding how these transformations can be applied to enhance the dataset.\n",
        "\n",
        "- Augmented images are displayed alongside the original image for comparison."
      ],
      "metadata": {
        "id": "nq_celW8rY18"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Yl1gNLNNrhpE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:blue;\">Visualizing Emotion Distribution in Testing Data</span>\n",
        "\n",
        "We visualize the distribution of emotion classes in the testing dataset using a bar plot, similar to the training data. This provides insights into the balance of classes available for evaluation.\n",
        "\n",
        "- Assessing the distribution in the testing data is crucial for understanding the potential performance of the model on unseen data."
      ],
      "metadata": {
        "id": "3E9UOhWWrh4s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the distribution of classes"
      ],
      "metadata": {
        "id": "2nDt_sdNiUO_"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_emotions = os.listdir(train_dir)  # Get the list of emotions in the training directory"
      ],
      "metadata": {
        "id": "11KmM_APiUSa"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_counts = [len(os.listdir(os.path.join(train_dir, emotion))) for emotion in train_emotions]  # Count images for each emotion\n"
      ],
      "metadata": {
        "id": "AAwQvN_Nh3mr"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "P0yvlLzTh3pi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mW99sKhxh34X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Bar plot for training data distribution"
      ],
      "metadata": {
        "id": "mqybGjrYh38l"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 5))  # Set the figure size for the plot\n",
        "sns.barplot(x=train_emotions, y=train_counts, palette='viridis')  # Create a bar plot for training data\n",
        "plt.title('Distribution of Emotions in Training Data')  # Set the title of the plot\n",
        "plt.xlabel('Emotions')  # Set the x-axis label\n",
        "plt.ylabel('Number of Images')  # Set the y-axis label\n",
        "plt.xticks(rotation=45)  # Rotate x-axis labels for better visibility\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "4_Y-iIIFiceh",
        "outputId": "fc02b7f8-8a68-48b5-d05c-fc3c2e5e3ec7"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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UKSJJioiIkCTrekCJ9QXwhx9+sOujMW/ePJ0/f97uC3LOnDm1bds268LIkrRkyZL7hohPSG01atRQTEyM3TDTkjRy5EjZbLZE+4Jeo0YNhYSE6Oeff7amRUdHa+zYsUqdOrVef/31RFmPo9SoUUO///67goKCrGm3bt3Sd999p+zZs1vXBXPmcx8TE3Pf6WAZM2ZU5syZrb/bpMDV1fW+o1Bjx459aoYoDwwMVFRUlCZNmmRNi42NtS4j8Dj27t2rTp06KW3atHZ9wOL7nPzb31/cEbt7lxEZGanx48c/ds0AHIMjWwCear/99puOHDmi6OhoXbhwQWvXrtWqVavk5+enxYsXP/SCpoMHD9bGjRtVs2ZN+fn56eLFixo/fryyZs1qDZCQM2dOeXt7a+LEiUqTJo1SpUqlUqVKPbAPSnykS5dOr732mpo1a6YLFy5o1KhRypUrl10n/BYtWmjevHmqVq2a3nvvPf3555/68ccf7QZNSGhtb7zxhipUqKC+ffvq1KlTKly4sFauXKlFixapU6dO9y37UbVs2VLffvutmjZtql27dil79uyaN2+etmzZolGjRj20D91/+fvvv+2umxYnderUeuuttx6j6v/p1auXZs2aperVq6tDhw5Kly6dpk+frpMnT+qXX36xjtY587m/ceOGsmbNqnfffVeFCxdW6tSptXr1au3YscPuiMjTrlatWpoxY4a8vLzk7++voKAgrV69WunTp3d2aZKkt956SyVLllTXrl11/Phx5c2bV4sXL7Z+pInv0c1NmzYpPDxcMTExunLlirZs2aLFixfLy8tLCxYskK+vr9U2vs9JkSJF5Orqqi+//FKhoaFyd3dXxYoV9eqrrypt2rRq0qSJOnToIJvNphkzZjx1p8oC+B/CFoCnWv/+/SXdHYksXbp0KliwoEaNGqVmzZr95xf72rVr69SpU5oyZYouX76sF154Qa+//roGDRokLy8vSXdH0ps+fbp69+6tVq1aKTo6WlOnTn3ksNWnTx/t27dPQ4cO1Y0bN1SpUiWNHz/e7jSlwMBADR8+XCNGjFCnTp1UokQJLVmyRF27drVbVkJqc3Fx0eLFi9W/f3/9/PPPmjp1qrJnz66vvvrqvuU+Dg8PD61fv169evXS9OnTFRYWpjx58mjq1KkPvFBzQgQHB1vXT7uXn59fooUtHx8fbd26VT179tTYsWMVHh6uQoUK6ddff1XNmjWtds587lOmTKk2bdpo5cqVmj9/vmJjY5UrVy6NHz9erVu3fqztf5JGjx4tV1dX/fTTTwoPD1eZMmW0evXq/zz190lxdXXV0qVL1bFjR02fPl0uLi56++23NWDAAJUpU+ahP+Tca8yYMZLu/s14e3srX758GjRokD766KP7BumI73Pi6+uriRMnaujQoWrevLliYmK0bt06lS9f3nqv6Nevn9KmTasPPvhAlSpVemqeVwD2bIafQwAAACRJCxcu1Ntvv63NmzerTJkyzi4HQBJH2AIAAM+lO3fu2I2kGBMTo6pVq2rnzp0KCQlxyGikAJ4vnEYIAACeS+3bt9edO3cUEBCgiIgIzZ8/X1u3btXnn39O0AKQKDiyBQAAnkszZ87U8OHDdfz4cYWHhytXrlxq3bq12rVr5+zSADwjCFsAAAAA4ABcZwsAAAAAHIA+W/EQGxurc+fOKU2aNPG+7gYAAACAZ48xRjdu3FDmzJmt6zP+G8JWPJw7d07ZsmVzdhkAAAAAnhJnz55V1qxZH9qGsBUPcRdOPXv2rDw9PZ1cDQAAAABnCQsLU7Zs2ayM8DCErXiIO3XQ09OTsAUAAAAgXt2LGCADAAAAAByAsAUAAAAADkDYAgAAAAAHIGwBAAAAgAMQtgAAAADAAQhbAAAAAOAAhC0AAAAAcADCFgAAAAA4AGELAAAAAByAsAUAAAAADkDYAgAAAAAHIGwBAAAAgAMQtgAAAADAAQhbAAAAAOAAhC0AAAAAcADCFgAAAAA4QDJnFwAAiSGgw6fOLuGZFjTmE2eXAABAksORLQAAAABwAMIWAAAAADgAYQsAAAAAHICwBQAAAAAOQNgCAAAAAAcgbAEAAACAAzg1bGXPnl02m+2+W9u2bSVJ4eHhatu2rdKnT6/UqVOrTp06unDhgt0yzpw5o5o1ayplypTKmDGjunfvrujoaLs269evV7FixeTu7q5cuXJp2rRpT2oTAQAAADynnBq2duzYofPnz1u3VatWSZLq1q0rSercubN+/fVXzZ07Vxs2bNC5c+f0zjvvWI+PiYlRzZo1FRkZqa1bt2r69OmaNm2a+vfvb7U5efKkatasqQoVKig4OFidOnVSixYttGLFiie7sQAAAACeKzZjjHF2EXE6deqkJUuW6NixYwoLC1OGDBk0c+ZMvfvuu5KkI0eOKF++fAoKClLp0qX122+/qVatWjp37px8fHwkSRMnTlTPnj116dIlubm5qWfPnlq6dKkOHDhgrad+/fq6fv26li9fHq+6wsLC5OXlpdDQUHl6eib+hgN4bFzU2LG4qDEAAHclJBs8NX22IiMj9eOPP+rDDz+UzWbTrl27FBUVpcqVK1tt8ubNqxdffFFBQUGSpKCgIBUsWNAKWpIUGBiosLAwHTx40Gpz7zLi2sQt40EiIiIUFhZmdwMAAACAhHhqwtbChQt1/fp1NW3aVJIUEhIiNzc3eXt727Xz8fFRSEiI1ebeoBU3P27ew9qEhYXpzp07D6xl6NCh8vLysm7ZsmV73M0DAAAA8Jx5asLW5MmTVb16dWXOnNnZpah3794KDQ21bmfPnnV2SQAAAACSmGTOLkCSTp8+rdWrV2v+/PnWNF9fX0VGRur69et2R7cuXLggX19fq83vv/9ut6y40QrvbfPPEQwvXLggT09PeXh4PLAed3d3ubu7P/Z2AQAAAHh+PRVHtqZOnaqMGTOqZs2a1rTixYsrefLkWrNmjTXt6NGjOnPmjAICAiRJAQEB2r9/vy5evGi1WbVqlTw9PeXv72+1uXcZcW3ilgEAAAAAjuD0sBUbG6upU6eqSZMmSpbsfwfavLy81Lx5c3Xp0kXr1q3Trl271KxZMwUEBKh06dKSpKpVq8rf31+NGjXS3r17tWLFCvXr109t27a1jky1atVKJ06cUI8ePXTkyBGNHz9ec+bMUefOnZ2yvQAAAACeD04/jXD16tU6c+aMPvzww/vmjRw5Ui4uLqpTp44iIiIUGBio8ePHW/NdXV21ZMkStW7dWgEBAUqVKpWaNGmiwYMHW21y5MihpUuXqnPnzho9erSyZs2q77//XoGBgU9k+wAAAAA8n56q62w9rbjOFvD04zpbjsV1tgAAuCtJXmcLAAAAAJ4lhC0AAAAAcADCFgAAAAA4AGELAAAAAByAsAUAAAAADkDYAgAAAAAHIGwBAAAAgAMQtgAAAADAAQhbAAAAAOAAhC0AAAAAcADCFgAAAAA4AGELAAAAAByAsAUAAAAADkDYAgAAAAAHIGwBAAAAgAMQtgAAAADAAQhbAAAAAOAAhC0AAAAAcADCFgAAAAA4AGELAAAAAByAsAUAAAAADkDYAgAAAAAHIGwBAAAAgAMQtgAAAADAAQhbAAAAAOAAhC0AAAAAcADCFgAAAAA4AGELAAAAAByAsAUAAAAADkDYAgAAAAAHIGwBAAAAgAMQtgAAAADAAQhbAAAAAOAAhC0AAAAAcADCFgAAAAA4AGELAAAAAByAsAUAAAAADkDYAgAAAAAHIGwBAAAAgAMQtgAAAADAAZwetv7++2998MEHSp8+vTw8PFSwYEHt3LnTmm+MUf/+/ZUpUyZ5eHiocuXKOnbsmN0yrl69qoYNG8rT01Pe3t5q3ry5bt68addm3759Klu2rFKkSKFs2bJp2LBhT2T7AAAAADyfnBq2rl27pjJlyih58uT67bffdOjQIQ0fPlxp06a12gwbNkxjxozRxIkTtX37dqVKlUqBgYEKDw+32jRs2FAHDx7UqlWrtGTJEm3cuFEtW7a05oeFhalq1ary8/PTrl279NVXX2ngwIH67rvvnuj2AgAAAHh+2Iwxxlkr79Wrl7Zs2aJNmzY9cL4xRpkzZ1bXrl3VrVs3SVJoaKh8fHw0bdo01a9fX4cPH5a/v7927NihEiVKSJKWL1+uGjVq6K+//lLmzJk1YcIE9e3bVyEhIXJzc7PWvXDhQh05cuS+9UZERCgiIsK6HxYWpmzZsik0NFSenp6J/TQASAQBHT51dgnPtKAxnzi7BAAAngphYWHy8vKKVzZw6pGtxYsXq0SJEqpbt64yZsyookWLatKkSdb8kydPKiQkRJUrV7ameXl5qVSpUgoKCpIkBQUFydvb2wpaklS5cmW5uLho+/btVpty5cpZQUuSAgMDdfToUV27du2+uoYOHSovLy/rli1btkTfdgAAAADPNqeGrRMnTmjChAnKnTu3VqxYodatW6tDhw6aPn26JCkkJESS5OPjY/c4Hx8fa15ISIgyZsxoNz9ZsmRKly6dXZsHLePeddyrd+/eCg0NtW5nz55NhK0FAAAA8DxJ5syVx8bGqkSJEvr8888lSUWLFtWBAwc0ceJENWnSxGl1ubu7y93d3WnrBwAAAJD0OfXIVqZMmeTv7283LV++fDpz5owkydfXV5J04cIFuzYXLlyw5vn6+urixYt286Ojo3X16lW7Ng9axr3rAAAAAIDE5NSwVaZMGR09etRu2h9//CE/Pz9JUo4cOeTr66s1a9ZY88PCwrR9+3YFBARIkgICAnT9+nXt2rXLarN27VrFxsaqVKlSVpuNGzcqKirKarNq1SrlyZPHbuRDAAAAAEgsTg1bnTt31rZt2/T555/r+PHjmjlzpr777ju1bdtWkmSz2dSpUycNGTJEixcv1v79+9W4cWNlzpxZb731lqS7R8KqVaumjz76SL///ru2bNmidu3aqX79+sqcObMk6f3335ebm5uaN2+ugwcP6ueff9bo0aPVpUsXZ206AAAAgGecU/tsvfLKK1qwYIF69+6twYMHK0eOHBo1apQaNmxotenRo4du3bqlli1b6vr163rttde0fPlypUiRwmrz008/qV27dqpUqZJcXFxUp04djRkzxprv5eWllStXqm3btipevLheeOEF9e/f3+5aXAAAAACQmJx6na2kIiFj6QNwDq6z5VhcZwsAgLuSzHW2AAAAAOBZRdgCAAAAAAcgbAEAAACAAxC2AAAAAMABCFsAAAAA4ACELQAAAABwAMIWAAAAADgAYQsAAAAAHICwBQAAAAAOQNgCAAAAAAcgbAEAAACAAxC2AAAAAMABCFsAAAAA4ACELQAAAABwAMIWAAAAADgAYQsAAAAAHICwBQAAAAAOQNgCAAAAAAcgbAEAAACAAxC2AAAAAMABCFsAAAAA4ACELQAAAABwAMIWAAAAADgAYQsAAAAAHICwBQAAAAAOQNgCAAAAAAcgbAEAAACAAxC2AAAAAMABCFsAAAAA4ACELQAAAABwAMIWAAAAADgAYQsAAAAAHICwBQAAAAAOQNgCAAAAAAcgbAEAAACAAxC2AAAAAMABCFsAAAAA4ACELQAAAABwAMIWAAAAADgAYQsAAAAAHMCpYWvgwIGy2Wx2t7x581rzw8PD1bZtW6VPn16pU6dWnTp1dOHCBbtlnDlzRjVr1lTKlCmVMWNGde/eXdHR0XZt1q9fr2LFisnd3V25cuXStGnTnsTmAQAAAHiOOf3IVv78+XX+/HnrtnnzZmte586d9euvv2ru3LnasGGDzp07p3feeceaHxMTo5o1ayoyMlJbt27V9OnTNW3aNPXv399qc/LkSdWsWVMVKlRQcHCwOnXqpBYtWmjFihVPdDsBAAAAPF+SOb2AZMnk6+t73/TQ0FBNnjxZM2fOVMWKFSVJU6dOVb58+bRt2zaVLl1aK1eu1KFDh7R69Wr5+PioSJEi+vTTT9WzZ08NHDhQbm5umjhxonLkyKHhw4dLkvLly6fNmzdr5MiRCgwMfGBNERERioiIsO6HhYU5YMsBAAAAPMsSfGRr+vTpWrp0qXW/R48e8vb21quvvqrTp08nuIBjx44pc+bMeumll9SwYUOdOXNGkrRr1y5FRUWpcuXKVtu8efPqxRdfVFBQkCQpKChIBQsWlI+Pj9UmMDBQYWFhOnjwoNXm3mXEtYlbxoMMHTpUXl5e1i1btmwJ3i4AAAAAz7cEh63PP/9cHh4eku4GmXHjxmnYsGF64YUX1Llz5wQtq1SpUpo2bZqWL1+uCRMm6OTJkypbtqxu3LihkJAQubm5ydvb2+4xPj4+CgkJkSSFhITYBa24+XHzHtYmLCxMd+7ceWBdvXv3VmhoqHU7e/ZsgrYLAAAAABJ8GuHZs2eVK1cuSdLChQtVp04dtWzZUmXKlFH58uUTtKzq1atb/y9UqJBKlSolPz8/zZkzxwp0zuDu7i53d3enrR8AgKfdq1P7ObuEZ97WZkOcXQKAx5TgI1upU6fWlStXJEkrV65UlSpVJEkpUqT41yNF8eXt7a2XX35Zx48fl6+vryIjI3X9+nW7NhcuXLD6ePn6+t43OmHc/f9q4+np6dRABwAAAODZluCwVaVKFbVo0UItWrTQH3/8oRo1akiSDh48qOzZsz9WMTdv3tSff/6pTJkyqXjx4kqePLnWrFljzT969KjOnDmjgIAASVJAQID279+vixcvWm1WrVolT09P+fv7W23uXUZcm7hlAAAAAIAjJDhsjRs3TgEBAbp06ZJ++eUXpU+fXtLdAS0aNGiQoGV169ZNGzZs0KlTp7R161a9/fbbcnV1VYMGDeTl5aXmzZurS5cuWrdunXbt2qVmzZopICBApUuXliRVrVpV/v7+atSokfbu3asVK1aoX79+atu2rXUaYKtWrXTixAn16NFDR44c0fjx4zVnzpwE9y8DAAAAgIRIcJ8tb29vffPNN/dNHzRoUIJX/tdff6lBgwa6cuWKMmTIoNdee03btm1ThgwZJEkjR46Ui4uL6tSpo4iICAUGBmr8+PHW411dXbVkyRK1bt1aAQEBSpUqlZo0aaLBgwdbbXLkyKGlS5eqc+fOGj16tLJmzarvv//+X4d9BwAAAIDEYDPGmIQ+aNOmTfr222914sQJzZ07V1myZNGMGTOUI0cOvfbaa46o06nCwsLk5eWl0NBQeXp6OrscAA8Q0OFTZ5fwTAsa84mzS8BThgEyHI8BMoCnU0KyQYJPI/zll18UGBgoDw8P7d6927r4b2hoqD7//PNHqxgAAAAAnjEJPo1wyJAhmjhxoho3bqzZs2db08uUKaMhQ/gFBklb1XqD/7sRHsvKn/s7uwQAAIAnIsFHto4ePapy5crdN93Ly+u+YdoBAAAA4HmV4LDl6+ur48eP3zd98+bNeumllxKlKAAAAABI6hIctj766CN17NhR27dvl81m07lz5/TTTz+pW7duat26tSNqBAAAAIAkJ8F9tnr16qXY2FhVqlRJt2/fVrly5eTu7q5u3bqpffv2jqgRAAAAAJKcBIctm82mvn37qnv37jp+/Lhu3rwpf39/pU6d2hH1AQAAAECSlOCwFcfNzU3+/v6JWQsAAAAAPDMSHLbefvtt2Wy2+6bbbDalSJFCuXLl0vvvv688efIkSoEAAAAAkBQleIAMLy8vrV27Vrt375bNZpPNZtOePXu0du1aRUdH6+eff1bhwoW1ZcsWR9QLAAAAAElCgo9s+fr66v3339c333wjF5e7WS02NlYdO3ZUmjRpNHv2bLVq1Uo9e/bU5s2bE71gAAAAAEgKEnxka/LkyerUqZMVtCTJxcVF7du313fffSebzaZ27drpwIEDiVooAAAAACQlCQ5b0dHROnLkyH3Tjxw5opiYGElSihQpHtivCwAAAACeFwk+jbBRo0Zq3ry5+vTpo1deeUWStGPHDn3++edq3LixJGnDhg3Knz9/4lYKAAAAAElIgsPWyJEj5ePjo2HDhunChQuSJB8fH3Xu3Fk9e/aUJFWtWlXVqlVL3EoBAAAAIAlJcNhydXVV37591bdvX4WFhUmSPD097dq8+OKLiVMdAAAAACRRj3xRY+n+kAUAAAAAuOuRwta8efM0Z84cnTlzRpGRkXbzdu/enSiFAQAAAEBSluDRCMeMGaNmzZrJx8dHe/bsUcmSJZU+fXqdOHFC1atXd0SNAAAAAJDkJDhsjR8/Xt99953Gjh0rNzc39ejRQ6tWrVKHDh0UGhrqiBoBAAAAIMlJcNg6c+aMXn31VUmSh4eHbty4IenukPCzZs1K3OoAAAAAIIlKcNjy9fXV1atXJd0ddXDbtm2SpJMnT8oYk7jVAQAAAEASleCwVbFiRS1evFiS1KxZM3Xu3FlVqlRRvXr19Pbbbyd6gQAAAACQFCV4NMLvvvtOsbGxkqS2bdsqffr02rp1q2rXrq2PP/440QsEAAAAgKQowWHLxcVFLi7/OyBWv3591a9fP1GLAgAAAICk7pGusxUeHq59+/bp4sWL1lGuOLVr106UwgAAAAAgKUtw2Fq+fLkaN26sy5cv3zfPZrMpJiYmUQoDAAAAgKQswQNktG/fXnXr1tX58+cVGxtrdyNoAQAAAMBdCQ5bFy5cUJcuXeTj4+OIegAAAADgmZDgsPXuu+9q/fr1DigFAAAAAJ4dCe6z9c0336hu3bratGmTChYsqOTJk9vN79ChQ6IVBwAAAABJVYLD1qxZs7Ry5UqlSJFC69evl81ms+bZbDbCFgAAAADoEcJW3759NWjQIPXq1cvuelsAAAAAgP9JcFqKjIxUvXr1CFoAAAAA8BAJTkxNmjTRzz//7IhaAAAAAOCZkeDTCGNiYjRs2DCtWLFChQoVum+AjBEjRiRacQAAAACQVCU4bO3fv19FixaVJB04cMBu3r2DZQAAAADA8yzBYWvdunWOqAMAAAAAnikJDlsAAABIutqv6ejsEp55YyuNdnYJeErEO2y988478Wo3f/78Ry4GAAAAAJ4V8R6N0MvLK163R/XFF1/IZrOpU6dO1rTw8HC1bdtW6dOnV+rUqVWnTh1duHDB7nFnzpxRzZo1lTJlSmXMmFHdu3dXdHS0XZv169erWLFicnd3V65cuTRt2rRHrhMAAAAA4iPeR7amTp3qsCJ27Nihb7/9VoUKFbKb3rlzZy1dulRz586Vl5eX2rVrp3feeUdbtmyRdHdkxJo1a8rX11dbt27V+fPn1bhxYyVPnlyff/65JOnkyZOqWbOmWrVqpZ9++klr1qxRixYtlClTJgUGBjpsmwAAAAA835x+ZeKbN2+qYcOGmjRpktKmTWtNDw0N1eTJkzVixAhVrFhRxYsX19SpU7V161Zt27ZNkrRy5UodOnRIP/74o4oUKaLq1avr008/1bhx4xQZGSlJmjhxonLkyKHhw4crX758ateund59912NHDnyX2uKiIhQWFiY3Q0AAAAAEsLpA2S0bdtWNWvWVOXKlTVkyBBr+q5duxQVFaXKlStb0/LmzasXX3xRQUFBKl26tIKCglSwYEH5+PhYbQIDA9W6dWsdPHhQRYsWVVBQkN0y4trce7riPw0dOlSDBg165G2qWbrLIz8W8bN0G9dzAwAAwNPNqUe2Zs+erd27d2vo0KH3zQsJCZGbm5u8vb3tpvv4+CgkJMRqc2/QipsfN+9hbcLCwnTnzp0H1tW7d2+FhoZat7Nnzz7S9gEAAAB4fjntyNbZs2fVsWNHrVq1SilSpHBWGQ/k7u4ud3d3Z5cBAAAAIAmL15GtYsWK6dq1a5KkwYMH6/bt24+94l27dunixYsqVqyYkiVLpmTJkmnDhg0aM2aMkiVLJh8fH0VGRur69et2j7tw4YJ8fX0lSb6+vveNThh3/7/aeHp6ysPD47G3AwAAAAAeJF5h6/Dhw7p165YkadCgQbp58+Zjr7hSpUrav3+/goODrVuJEiXUsGFD6//JkyfXmjVrrMccPXpUZ86cUUBAgCQpICBA+/fv18WLF602q1atkqenp/z9/a029y4jrk3cMgAAAADAEeJ1GmGRIkXUrFkzvfbaazLG6Ouvv1bq1Kkf2LZ///7xWnGaNGlUoEABu2mpUqVS+vTprenNmzdXly5dlC5dOnl6eqp9+/YKCAhQ6dKlJUlVq1aVv7+/GjVqpGHDhikkJET9+vVT27ZtrdMAW7VqpW+++UY9evTQhx9+qLVr12rOnDlaunRpvOoEAAAAgEcRr7A1bdo0DRgwQEuWLJHNZtNvv/2mZMnuf6jNZot32IqPkSNHysXFRXXq1FFERIQCAwM1fvx4a76rq6uWLFmi1q1bKyAgQKlSpVKTJk00ePBgq02OHDm0dOlSde7cWaNHj1bWrFn1/fffc40tAAAAAA4Vr7CVJ08ezZ49W5Lk4uKiNWvWKGPGjIlezPr16+3up0iRQuPGjdO4ceP+9TF+fn5atmzZQ5dbvnx57dmzJzFKBAAAAIB4SfBohLGxsY6oAwAAAACeKY809Puff/6pUaNG6fDhw5Ikf39/dezYUTlz5kzU4gAAAAAgqUrwRY1XrFghf39//f777ypUqJAKFSqk7du3K3/+/Fq1apUjagQAAACAJCfBR7Z69eqlzp0764svvrhves+ePVWlSpVEKw4AAAAAkqoEH9k6fPiwmjdvft/0Dz/8UIcOHUqUogAAAAAgqUtw2MqQIYOCg4Pvmx4cHOyQEQoBAAAAIClK8GmEH330kVq2bKkTJ07o1VdflSRt2bJFX375pbp06ZLoBQIAAABAUpTgsPXJJ58oTZo0Gj58uHr37i1Jypw5swYOHKgOHTokeoEAAAAAkBQlOGzZbDZ17txZnTt31o0bNyRJadKkSfTCAAAAACApe6TrbMUhZAEAAADAgyV4gAwAAAAAwH8jbAEAAACAAxC2AAAAAMABEhS2oqKiVKlSJR07dsxR9QAAAADAMyFBYSt58uTat2+fo2oBAAAAgGdGgk8j/OCDDzR58mRH1AIAAAAAz4wED/0eHR2tKVOmaPXq1SpevLhSpUplN3/EiBGJVhwAAAAAJFUJDlsHDhxQsWLFJEl//PGH3TybzZY4VQEAAABAEpfgsLVu3TpH1AEAAAAAz5RHHvr9+PHjWrFihe7cuSNJMsYkWlEAAAAAkNQlOGxduXJFlSpV0ssvv6waNWro/PnzkqTmzZura9euiV4gAAAAACRFCQ5bnTt3VvLkyXXmzBmlTJnSml6vXj0tX748UYsDAAAAgKQqwX22Vq5cqRUrVihr1qx203Pnzq3Tp08nWmEAAAAAkJQl+MjWrVu37I5oxbl69arc3d0TpSgAAAAASOoSHLbKli2rH374wbpvs9kUGxurYcOGqUKFColaHAAAAAAkVQk+jXDYsGGqVKmSdu7cqcjISPXo0UMHDx7U1atXtWXLFkfUCAAAAABJToKPbBUoUEB//PGHXnvtNb355pu6deuW3nnnHe3Zs0c5c+Z0RI0AAAAAkOQk+MiWJHl5ealv376JXQsAAAAAPDMeKWxdu3ZNkydP1uHDhyVJ/v7+atasmdKlS5eoxQEAAABAUpXg0wg3btyo7Nmza8yYMbp27ZquXbumMWPGKEeOHNq4caMjagQAAACAJCfBR7batm2revXqacKECXJ1dZUkxcTEqE2bNmrbtq3279+f6EUCAAAAQFKT4CNbx48fV9euXa2gJUmurq7q0qWLjh8/nqjFAQAAAEBSleCwVaxYMauv1r0OHz6swoULJ0pRAAAAAJDUxes0wn379ln/79Chgzp27Kjjx4+rdOnSkqRt27Zp3Lhx+uKLLxxTJQAAAAAkMfEKW0WKFJHNZpMxxprWo0eP+9q9//77qlevXuJVBwAAAECS9OO2QGeX8Ez7oPSKRF9mvMLWyZMnE33FAAAAAPAsi1fY8vPzc3QdAAAAAPBMeaSLGp87d06bN2/WxYsXFRsbazevQ4cOiVIYAAAAACRlCQ5b06ZN08cffyw3NzelT59eNpvNmmez2QhbAAAAAKBHGPr9k08+Uf/+/RUaGqpTp07p5MmT1u3EiRMJWtaECRNUqFAheXp6ytPTUwEBAfrtt9+s+eHh4Wrbtq3Sp0+v1KlTq06dOrpw4YLdMs6cOaOaNWsqZcqUypgxo7p3767o6Gi7NuvXr1exYsXk7u6uXLlyadq0aQndbAAAAABIkASHrdu3b6t+/fpycUnwQ++TNWtWffHFF9q1a5d27typihUr6s0339TBgwclSZ07d9avv/6quXPnasOGDTp37pzeeecd6/ExMTGqWbOmIiMjtXXrVk2fPl3Tpk1T//79rTYnT55UzZo1VaFCBQUHB6tTp05q0aKFVqxI/NFGAAAAACBOghNT8+bNNXfu3ERZ+RtvvKEaNWood+7cevnll/XZZ58pderU2rZtm0JDQzV58mSNGDFCFStWVPHixTV16lRt3bpV27ZtkyStXLlShw4d0o8//qgiRYqoevXq+vTTTzVu3DhFRkZKkiZOnKgcOXJo+PDhypcvn9q1a6d3331XI0eOTJRtAAAAAIAHSXCfraFDh6pWrVpavny5ChYsqOTJk9vNHzFixCMVEhMTo7lz5+rWrVsKCAjQrl27FBUVpcqVK1tt8ubNqxdffFFBQUEqXbq0goKCVLBgQfn4+FhtAgMD1bp1ax08eFBFixZVUFCQ3TLi2nTq1Olfa4mIiFBERIR1Pyws7JG2CQAAAMDz65HC1ooVK5QnTx5Jum+AjITav3+/AgICFB4ertSpU2vBggXy9/dXcHCw3Nzc5O3tbdfex8dHISEhkqSQkBC7oBU3P27ew9qEhYXpzp078vDweOA2Dho0KMHbAgAAAABxEhy2hg8frilTpqhp06aJUkCePHkUHBys0NBQzZs3T02aNNGGDRsSZdmPqnfv3urSpYt1PywsTNmyZXNiRQAAAACSmgSHLXd3d5UpUybRCnBzc1OuXLkkScWLF9eOHTs0evRo1atXT5GRkbp+/brd0a0LFy7I19dXkuTr66vff//dbnlxoxXe2+afIxheuHBBnp6eDzyqJd3dRnd390TZPgAAAADPpwQPkNGxY0eNHTvWEbVIkmJjYxUREaHixYsrefLkWrNmjTXv6NGjOnPmjAICAiRJAQEB2r9/vy5evGi1WbVqlTw9PeXv72+1uXcZcW3ilgEAAAAAjpDgI1u///671q5dqyVLlih//vz3DZAxf/78eC+rd+/eql69ul588UXduHFDM2fO1Pr167VixQp5eXmpefPm6tKli9KlSydPT0+1b99eAQEBKl26tCSpatWq8vf3V6NGjTRs2DCFhISoX79+atu2rXVkqlWrVvrmm2/Uo0cPffjhh1q7dq3mzJmjpUuXJnTTAQAAACDeEhy2vL297a519TguXryoxo0b6/z58/Ly8lKhQoW0YsUKValSRZI0cuRIubi4qE6dOoqIiFBgYKDGjx9vPd7V1VVLlixR69atFRAQoFSpUqlJkyYaPHiw1SZHjhxaunSpOnfurNGjRytr1qz6/vvvFRgYmCjbAAAAAAAPkuCwNXXq1ERb+eTJkx86P0WKFBo3bpzGjRv3r238/Py0bNmyhy6nfPny2rNnzyPVCAAAAACPIsF9tgAAAAAA/y3BR7Zy5Mjx0OtpnThx4rEKAgAAAIBnQYLDVqdOnezuR0VFac+ePVq+fLm6d++eWHUBAAAAQJKW4LDVsWPHB04fN26cdu7c+dgFAQAAAMCzINH6bFWvXl2//PJLYi0OAAAAAJK0RAtb8+bNU7p06RJrcQAAAACQpCX4NMKiRYvaDZBhjFFISIguXbpkdw0sAAAAAHieJThsvfXWW3b3XVxclCFDBpUvX1558+ZNrLoAAAAAIElLcNgaMGCAI+oAAAAAgGcKFzUGAAAAAAeI95EtFxeXh17MWJJsNpuio6MfuygAAAAASOriHbYWLFjwr/OCgoI0ZswYxcbGJkpRAAAAAJDUxTtsvfnmm/dNO3r0qHr16qVff/1VDRs21ODBgxO1OAAAAABIqh6pz9a5c+f00UcfqWDBgoqOjlZwcLCmT58uPz+/xK4PAAAAAJKkBIWt0NBQ9ezZU7ly5dLBgwe1Zs0a/frrrypQoICj6gMAAACAJCnepxEOGzZMX375pXx9fTVr1qwHnlYIAAAAALgr3mGrV69e8vDwUK5cuTR9+nRNnz79ge3mz5+faMUBAAAAQFIV77DVuHHj/xz6HQAAAABwV7zD1rRp0xxYBgAAAAA8Wx5pNEIAAAAAwMMRtgAAAADAAQhbAAAAAOAAhC0AAAAAcADCFgAAAAA4AGELAAAAAByAsAUAAAAADkDYAgAAAAAHIGwBAAAAgAMQtgAAAADAAQhbAAAAAOAAhC0AAAAAcADCFgAAAAA4AGELAAAAAByAsAUAAAAADkDYAgAAAAAHIGwBAAAAgAMQtgAAAADAAQhbAAAAAOAAhC0AAAAAcADCFgAAAAA4gFPD1tChQ/XKK68oTZo0ypgxo9566y0dPXrUrk14eLjatm2r9OnTK3Xq1KpTp44uXLhg1+bMmTOqWbOmUqZMqYwZM6p79+6Kjo62a7N+/XoVK1ZM7u7uypUrl6ZNm+bozQMAAADwHHNq2NqwYYPatm2rbdu2adWqVYqKilLVqlV169Ytq03nzp3166+/au7cudqwYYPOnTund955x5ofExOjmjVrKjIyUlu3btX06dM1bdo09e/f32pz8uRJ1axZUxUqVFBwcLA6deqkFi1aaMWKFU90ewEAAAA8P5I5c+XLly+3uz9t2jRlzJhRu3btUrly5RQaGqrJkydr5syZqlixoiRp6tSpypcvn7Zt26bSpUtr5cqVOnTokFavXi0fHx8VKVJEn376qXr27KmBAwfKzc1NEydOVI4cOTR8+HBJUr58+bR582aNHDlSgYGBT3y7AQAAADz7nqo+W6GhoZKkdOnSSZJ27dqlqKgoVa5c2WqTN29evfjiiwoKCpIkBQUFqWDBgvLx8bHaBAYGKiwsTAcPHrTa3LuMuDZxy/iniIgIhYWF2d0AAAAAICGemrAVGxurTp06qUyZMipQoIAkKSQkRG5ubvL29rZr6+Pjo5CQEKvNvUErbn7cvIe1CQsL0507d+6rZejQofLy8rJu2bJlS5RtBAAAAPD8eGrCVtu2bXXgwAHNnj3b2aWod+/eCg0NtW5nz551dkkAAAAAkhin9tmK065dOy1ZskQbN25U1qxZrem+vr6KjIzU9evX7Y5uXbhwQb6+vlab33//3W55caMV3tvmnyMYXrhwQZ6envLw8LivHnd3d7m7uyfKtgEAAAB4Pjn1yJYxRu3atdOCBQu0du1a5ciRw25+8eLFlTx5cq1Zs8aadvToUZ05c0YBAQGSpICAAO3fv18XL1602qxatUqenp7y9/e32ty7jLg2ccsAAAAAgMTm1CNbbdu21cyZM7Vo0SKlSZPG6mPl5eUlDw8PeXl5qXnz5urSpYvSpUsnT09PtW/fXgEBASpdurQkqWrVqvL391ejRo00bNgwhYSEqF+/fmrbtq11dKpVq1b65ptv1KNHD3344Ydau3at5syZo6VLlzpt2wEAAAA825x6ZGvChAkKDQ1V+fLllSlTJuv2888/W21GjhypWrVqqU6dOipXrpx8fX01f/58a76rq6uWLFkiV1dXBQQE6IMPPlDjxo01ePBgq02OHDm0dOlSrVq1SoULF9bw4cP1/fffM+w7AAAAAIdx6pEtY8x/tkmRIoXGjRuncePG/WsbPz8/LVu27KHLKV++vPbs2ZPgGgEAAADgUTw1oxECAAAAwLOEsAUAAAAADkDYAgAAAAAHIGwBAAAAgAMQtgAAAADAAQhbAAAAAOAAhC0AAAAAcADCFgAAAAA4AGELAAAAAByAsAUAAAAADkDYAgAAAAAHIGwBAAAAgAMQtgAAAADAAQhbAAAAAOAAhC0AAAAAcADCFgAAAAA4AGELAAAAAByAsAUAAAAADkDYAgAAAAAHIGwBAAAAgAMQtgAAAADAAQhbAAAAAOAAhC0AAAAAcADCFgAAAAA4AGELAAAAAByAsAUAAAAADkDYAgAAAAAHIGwBAAAAgAMQtgAAAADAAQhbAAAAAOAAhC0AAAAAcADCFgAAAAA4AGELAAAAAByAsAUAAAAADkDYAgAAAAAHIGwBAAAAgAMQtgAAAADAAQhbAAAAAOAAhC0AAAAAcADCFgAAAAA4gFPD1saNG/XGG28oc+bMstlsWrhwod18Y4z69++vTJkyycPDQ5UrV9axY8fs2ly9elUNGzaUp6envL291bx5c928edOuzb59+1S2bFmlSJFC2bJl07Bhwxy9aQAAAACec04NW7du3VLhwoU1bty4B84fNmyYxowZo4kTJ2r79u1KlSqVAgMDFR4ebrVp2LChDh48qFWrVmnJkiXauHGjWrZsac0PCwtT1apV5efnp127dumrr77SwIED9d133zl8+wAAAAA8v5I5c+XVq1dX9erVHzjPGKNRo0apX79+evPNNyVJP/zwg3x8fLRw4ULVr19fhw8f1vLly7Vjxw6VKFFCkjR27FjVqFFDX3/9tTJnzqyffvpJkZGRmjJlitzc3JQ/f34FBwdrxIgRdqEMAAAAABLTU9tn6+TJkwoJCVHlypWtaV5eXipVqpSCgoIkSUFBQfL29raCliRVrlxZLi4u2r59u9WmXLlycnNzs9oEBgbq6NGjunbt2gPXHRERobCwMLsbAAAAACTEUxu2QkJCJEk+Pj520318fKx5ISEhypgxo938ZMmSKV26dHZtHrSMe9fxT0OHDpWXl5d1y5Yt2+NvEAAAAIDnylMbtpypd+/eCg0NtW5nz551dkkAAAAAkpinNmz5+vpKki5cuGA3/cKFC9Y8X19fXbx40W5+dHS0rl69atfmQcu4dx3/5O7uLk9PT7sbAAAAACTEUxu2cuTIIV9fX61Zs8aaFhYWpu3btysgIECSFBAQoOvXr2vXrl1Wm7Vr1yo2NlalSpWy2mzcuFFRUVFWm1WrVilPnjxKmzbtE9oaAAAAAM8bp4atmzdvKjg4WMHBwZLuDooRHBysM2fOyGazqVOnThoyZIgWL16s/fv3q3HjxsqcObPeeustSVK+fPlUrVo1ffTRR/r999+1ZcsWtWvXTvXr11fmzJklSe+//77c3NzUvHlzHTx4UD///LNGjx6tLl26OGmrAQAAADwPnDr0+86dO1WhQgXrflwAatKkiaZNm6YePXro1q1batmypa5fv67XXntNy5cvV4oUKazH/PTTT2rXrp0qVaokFxcX1alTR2PGjLHme3l5aeXKlWrbtq2KFy+uF154Qf3792fYdwAAAAAO5dSwVb58eRlj/nW+zWbT4MGDNXjw4H9tky5dOs2cOfOh6ylUqJA2bdr0yHUCAAAAQEI9tX22AAAAACApI2wBAAAAgAMQtgAAAADAAQhbAAAAAOAAhC0AAAAAcADCFgAAAAA4AGELAAAAAByAsAUAAAAADkDYAgAAAAAHIGwBAAAAgAMQtgAAAADAAQhbAAAAAOAAhC0AAAAAcADCFgAAAAA4AGELAAAAAByAsAUAAAAADkDYAgAAAAAHIGwBAAAAgAMQtgAAAADAAQhbAAAAAOAAhC0AAAAAcIBkzi4AAPB8KzJkoLNLeKYF9xvo7BIA4LnFkS0AAAAAcADCFgAAAAA4AGELAAAAAByAsAUAAAAADkDYAgAAAAAHIGwBAAAAgAMQtgAAAADAAQhbAAAAAOAAhC0AAAAAcADCFgAAAAA4AGELAAAAAByAsAUAAAAADkDYAgAAAAAHIGwBAAAAgAMQtgAAAADAAQhbAAAAAOAAhC0AAAAAcADCFgAAAAA4wHMVtsaNG6fs2bMrRYoUKlWqlH7//XdnlwQAAADgGfXchK2ff/5ZXbp00YABA7R7924VLlxYgYGBunjxorNLAwAAAPAMSubsAp6UESNG6KOPPlKzZs0kSRMnTtTSpUs1ZcoU9erVy65tRESEIiIirPuhoaGSpLCwsHitKyo64r8b4bHEd18kVHRUuEOWi/9x2L6LZN85kqP2myTFhPOe6UgOe83dYb85mqP2XeQt9p2jOWrf3bkV7ZDl4q747re4dsaY/2xrM/FplcRFRkYqZcqUmjdvnt566y1repMmTXT9+nUtWrTIrv3AgQM1aNCgJ1wlAAAAgKTi7Nmzypo160PbPBdHti5fvqyYmBj5+PjYTffx8dGRI0fua9+7d2916dLFuh8bG6urV68qffr0stlsDq/3SQsLC1O2bNl09uxZeXp6OrscxBP7Leli3yVd7Luki32XNLHfkq5ned8ZY3Tjxg1lzpz5P9s+F2Erodzd3eXu7m43zdvb2znFPEGenp7P3IvhecB+S7rYd0kX+y7pYt8lTey3pOtZ3XdeXl7xavdcDJDxwgsvyNXVVRcuXLCbfuHCBfn6+jqpKgAAAADPsucibLm5ual48eJas2aNNS02NlZr1qxRQECAEysDAAAA8Kx6bk4j7NKli5o0aaISJUqoZMmSGjVqlG7dumWNTvg8c3d314ABA+47dRJPN/Zb0sW+S7rYd0kX+y5pYr8lXey7u56L0QjjfPPNN/rqq68UEhKiIkWKaMyYMSpVqpSzywIAAADwDHquwhYAAAAAPCnPRZ8tAAAAAHjSCFsAAAAA4ACELQAAAABwAMIWAAAAADgAYQt4Bpw7d06xsbHOLgPAA9w7DhVjUiUt7C8Aj4uwBSRxU6ZMUdGiRbV9+3a+GABPmdjYWNlsNuv+vf/H0+vo0aOKjIyUzWbjfTUJ4MdGPM0IW4i3qVOn6scff3R2GfiHZs2aycfHRy1bttT27dv50HmG/HNf8qUvadmwYYOuX78uSerbt68GDx7s3IIQL7Nnz1b16tW1aNEiRUVFEbieQnH7Y8+ePZIkFxe+zj4r/u07TFL+bsN1thAvoaGhqlatmooVK6Zx48bJGMMvtE+ByMhIubm5SZKKFy+uyMhIffvttypdujQfPs+Q3bt3q1ixYs4uAwlw/fp15cqVS0WLFtVLL72k2bNnKygoSP7+/s4uDf8hPDxctWrV0o0bN9SjRw/Vrl1byZMn53PvKbNs2TLVqlVLq1evVsWKFZ1dDhJBbGys9d1l06ZNunr1qpIlS6bAwEAlS5bMbn5SQthCvM2fP19NmjTRunXrVKJECWeXA8n68D916pSOHj2q6tWrq0yZMho2bJhKly7NF4NnwJo1a9S2bVv9+uuvyp07t7PLQQJcvnxZfn5+stlsWrJkicqXL+/skvAfoqOjlSxZMkVEROjNN9/UpUuX1KdPHwLXU+bMmTMaM2aMcubMqdatWzu7HCSynj17atGiRbLZbHrhhRd0+fJlbd26VWnTpnV2aY8k6cVDPHFxh27Lli2r1157Tb/99pvddDiPzWbTwoULlS9fPm3evFn16tXT33//rebNm9OH6xmROnVqXbt2TUeOHJHEqYRPu7j3RWOMrl27pujoaKVIkULDhg3ThQsXrHYMmvF0SpYsmWJiYuTu7q5FixbphRde0Oeff67FixdzSuFTYu/evWrRooVWrFihQoUKSeI19CwZN26cpkyZohkzZujw4cN69913dfToUQUFBVltktr+JmzhX40ZM0a//PKLrl27JknKkCGDSpYsqW+//Va3bt2Si4tLkvuDf9ZcvnxZvXv3Vr9+/fTpp59q1qxZ2rlzp9zc3NS8eXNt27aNUJyE3PtFPe61VapUKTVo0EB9+/bV5cuX+VX9KXbvKS67du1Srly5FBERoT179mjfvn1q3LixLl68KEkMmvEUc3V1lSQrcKVPn57A9RS5fv26jDE6fvy4jh49Kknsk2eEMUaHDh1Snz599Morr2jhwoX65JNP9O2336pGjRq6deuWYmJiktx7JmELD/Trr7/qr7/+UsOGDfXhhx+qf//+kqSuXbsqX758+vLLLyXxJcHZkiVLJmOMdXpZVFSU0qVLp9WrV+vGjRvq16+fNm3aROBKIuK+qF+7ds3utfXmm28qRYoU2r9/vyQpJibGKfXh390btPr27av27dtrzpw5unnzprJly6ZVq1bp4MGDatq0qc6dO6fo6Gh98MEHGjFihJMrh/S/X8rPnDmj/fv36/z58woPD1eKFCm0ePFiAtdT5PXXX9eQIUNUsWJFjR07VosXL5ZE4EqK/rm/bDabzp49q6ioKP32229q1KiRvvzyS3300UeKjY3VlClTNGnSJCdV++gIW7hPjx499M4772jQoEHavHmzXn31VU2ZMkUBAQHq06ePvL29deLECbtf4eEc3t7ecnFx0Zo1ayRJyZMnV3R0tNKlS6eCBQtq3bp16tWrlyIjI51cKeJrzpw5euGFF/TJJ59oxYoVkqQKFSooQ4YMGjJkiKT//fKOp0dc0Prkk080adIkDRo0SNWrV1fq1KklSfny5dPKlSu1d+9elStXTiVLltTOnTvVvn17Z5YN/a/v68KFC1WxYkW9/fbbKl68uIYNG6YjR47YBa6vvvpKc+fOtQIXHCvu+8X58+f1559/WqfilipVSj179lT27Nk1cuRILVmyRBKBKym597IYp0+ftr5TlipVSgsWLFD9+vX15ZdfWn3yrly5ouXLl+vGjRtOq/lREbZg58iRIwoLC9OaNWvk4eGhEiVKqGfPnjp69Khq1aql0NBQ/fLLL5o5c6Z++uknSRzdelL+7QOkX79+Wrp0qYYOHSrp7tEuFxcX5c2bV5s3b9asWbOUIkWKJ1kqEiBuv8b9W6VKFX311Vfat2+fPvjgAzVo0ECrV69W//79defOHS1fvtyZ5eIh9u/fr7lz52rOnDmqWrWqYmNjdeDAAU2YMEFr166Vv7+/Dhw4oHr16qlevXo6cOCA9QMJnMdms2n58uVq2rSp2rVrp0OHDunjjz/W2LFjNWrUKB04cMAKXMYYTZo0SeHh4c4u+5l3bwiuXbu2ypQpo0aNGqlfv36SpHLlyqljx47y9vbW6NGjNX/+fEl8J0kK7j0TYODAgWrcuLF27twpSWrUqJFu3LghHx8fFS9eXLdv39aZM2fUpEkTXblyRZ07d3Zm6Y+E0QhhmTt3rrp37y5PT08tX75cvr6+cnFxUUxMjN0v6UuWLNG3336r1KlTa+rUqXJ3d+fNzcHiPnQ2btyorVu36syZM2rRooUKFCig8PBwjRo1ShMmTFDVqlX16quvas+ePfrpp5905MgRZcmSxdnl41/c+4Fz7do1pUyZUu7u7pKkq1ev6tixYxo0aJBCQ0N14sQJRUVFqX379howYIAzy8b/++cwxCdPnlTt2rXVv39/+fn5afLkydq4caNsNpv+/PNP/fzzz3rrrbfslhE3+h2c5/r162rWrJmKFCmiAQMG6Ny5cypbtqwyZsyoCxcuqGLFitYp9BEREbpw4YJefPFFZ5f9XPjtt99Ut25dffrppwoMDNS0adM0efJkvffee5owYYIkaePGjRo0aJBSpUqlmTNnWkeT8XS6d0TP3r17a9q0aRozZoxee+01ZcqUSZJ0/Phx1ahRQ25ubrp06ZJy5syp2NhYbdq0ScmTJ7/ve+lTz+C5Fxsba4wxZvbs2aZKlSomZcqU5uDBg8YYY6Kjox/4mAULFpg0adKYo0ePPrE6n3fz58833t7epmbNmqZSpUomQ4YMZvjw4SY0NNTcvHnTzJs3zxQpUsQUL17clCpVyuzZs8fZJSOeBg0aZIoWLWpKlChh3nzzTXP69GkTExNjjDHm5s2b5ujRo6Z79+4md+7cJm3atGbXrl1Orhj32rdvn4mKijIhISGmWrVqpkSJEiZZsmSmbdu2ZtGiRSYkJMS89tprZuTIkc4uFf8v7nPv1KlT5vr162bx4sXm2LFj5vLly8bf39+0aNHCGGNM7969jbe3t3n//ffN/v37nVnyc+fvv/825cqVM6NGjTLGGHP16lWTJUsWU6ZMGfPyyy+bVq1aWW03b95szp4966xSEQ/BwcF294OCgsyLL75oNm7caIwxJjw83Jw/f94sW7bM3Lhxw9y4ccOsWbPGTJgwwaxZs8b6PhoVFfXEa39chC2YzZs3W/9ftmyZKVWqlClSpIgVpOK+9Bnzvw8oY4wpWLCgWbRo0ZMr9DkWFBRkMmfObKZMmWKMuftmkyxZMpM5c2YzZMgQc+XKFavt7du3zc2bN51VKuLh3tfUhAkTjJeXlxk5cqT58ssvTbFixUy2bNmsD6B77dy501StWtWMHz/eGGP/eoRzrF271thsNjN58mRjjDFnzpwxa9assXtfjY2NNSVLljQTJkxwVpl4gJ9//tlkypTJHDp0yFy9etUYY8zo0aNNpUqVrPfU8ePHm9y5c5tq1aqZ8+fPO7Pc59LIkSPN/v37TUhIiMmbN69p3bq1uXnzpmnYsKFxd3c3DRs2dHaJiIe+ffuaunXrGmP+97m1fPlykzt3bnP16lWzfft206NHD/Pyyy8bLy8vU7lyZetH/3v92wGApx19tp5zwcHBKlu2rMaOHStJql69uvr27auMGTPqww8/1LFjx+Ti4mJ1XIw79Dty5EgdOXJEhQsXdlrtz5M///xTjRo1UrNmzXTy5Enlzp1bbdq0UZMmTTRgwABNmjRJp0+fliR5eHgoVapUTq4YDxN36tnKlSt1/vx5ffvtt+rUqZN69OihXbt2qUCBAmrSpIlu3rwpSVafnuLFi8vPz09z586VRN+Ep0GFChXUtWtXtWvXTtOmTVO2bNlUsWJFlSlTRrdv39apU6dUs2ZNRUdHq0WLFs4u97ln/r/nRHh4uFatWqXu3bsrX7581sVSr1+/rps3b1p9sk6dOqUuXbrop59+kq+vr9Pqfl516tRJBQoU0LRp0/Tyyy/r008/VapUqVS0aFG9/PLLunTpks6dO+fsMvEf6tSpo5kzZ0qSzp49K0kqVqyY/vrrL1WtWlWVK1fWtWvXNGTIEK1YsUJ79uzRiRMn7ltOkjp18B6cKP4cGz9+vDXSUqdOnRQVFaUuXbrojTfekDFG48ePV/PmzTVx4kT5+/vbPbZEiRLauXOn/Pz8nFT9s838/znNe/fuVYYMGVS+fHkVLVpU4eHh+vjjj1WpUiWNHj1akvTDDz/oiy++kJubmzp06JBk34yeN0FBQfr444916dIlzZgxQ5IUGRkpNzc3/fLLLypYsKBGjBih/v37K1myZFb/oDRp0sjFxUV37tyRh4eHk7fi+WLu6Wtwr6+++kouLi5q2bKlXFxcVL9+fbm5uWnSpElatmyZ7ty5o23btlkXzOU16jw2m02bNm3Sxx9/rCxZsqhVq1Z287Nly6Zr166pXbt2MsZo5cqV2rVrl9KlS+ekip99cQHYZrPp0KFDOnPmjFxcXPTSSy8pV65ckqQ//vhDly5dUvr06SVJ586d03vvvaf27dvLy8vLabUjfooWLSpJWrBggTp27KipU6eqUqVKOnDggGbNmqUiRYqoXLlySpMmjWJiYpQzZ05FRUU5uerEQ9h6TvXr10+TJk3SyJEjVahQIa1fv14DBgxQVFSUevbsqdq1a8tms2nQoEEaO3as1RFVuvvGWLZsWSdW/2wz94zA1KZNG7Vo0UK9evVSlixZdPLkSYWEhFij8fz999+qUKGCMmXKpDfeeIMvcUlIjhw51KJFC40aNUpz587V22+/LTc3N0VHR8vV1VV+fn66deuW1d7FxUXHjh3TmjVrNHXqVIKWE8QFrREjRsjf31/VqlWz5sVde7Bly5ZydXVVw4YN9fbbbytjxox677335OrqymAYT9g/BzCR7r6/enl5yc3NTWvXrrW+0MXtm2bNmunatWvau3evbt26pe3btytv3rzOKP+Zd+PGDaVJk8Z6Xc2fP1/t2rVTjhw5dPXqVaVPn17NmzdXs2bN9Oqrryo4OFjvv/++UqVKpZ9//lm7du0iaD3l7v2Bat++fXJzc1PJkiXVo0cPDR8+XOXLl1efPn1ks9kUERGhK1eu6IMPPlBsbKxq167t5OoTkbPOX4TzhISEmOLFi5tp06ZZ086ePWv69+9vPDw8zOjRo63pGzdutOtfgidjyZIlxsPDw0yaNMn8/fff1vR9+/aZzJkzm+nTp5tTp06ZgQMHmnLlypnbt287sVr8l3++huLOWb98+bL54osvzIsvvmjat29v16ZIkSKmd+/e9y0rNDTUcYXigf7ZN65mzZomVapUZu3atfe1rVq1qvHx8TETJ060m55U+xokdWfPnrX6Fs+cOdN07NjRREVFmT179pjChQubIkWKWH1cIyIi7B6bFDviJxUfffSR+fDDD63Xxfbt2026dOnMuHHjjDF3+48nS5bMDBkyxBhz93vLZ599ZipWrGiqVq1q9u7d67TaET/3fu517NjR5M2b11y6dMls3LjRvPvuu6Zw4cJmw4YNxpi7r70xY8aY0qVLm9KlS5vIyEhjzLPzvknYeg5dunTJvPDCC+brr7+2m37mzBlTunRpY7PZzIgRI+zmEbienDt37pi6deuaPn36GGOMuXXrlvnzzz/NF198YdasWWMqV65s0qdPb3LlymUyZMjAyHRPuXu/qI8fP9506NDBNGvWzKxbt84YY0xYWJgZOnSoSZ8+vSlbtqxp2rSpqVu3rsmZM6fdl7245TAohvP89ddf1v8/+OAD4+3tbdasWWNNi42NNS1btjS5c+c25cqVY185UWxsrImIiDB16tQxr7/+uunRo4ex2Wxm0qRJVpvg4GCTL18+88orr1g/WBGwHG/WrFkmQ4YMZvfu3da077//3lSvXt0YY8zJkydN9uzZ7UYbvHz5svX/W7duPbli8diuXr1qGjdubFavXm1N27Rpk6lbt64pXLiwNRhUcHCwGTFiRJIedfDfELaeQ5GRkaZZs2ambt265o8//rCb16ZNG1O5cmWTLVs2M3PmTCdV+Hy7ffu2KVGihGnfvr25cuWKadeunXn99deNr6+vyZ49uxk7dqxZvHixWbRokTl58qSzy8VD3PsjRY8ePUzatGnNm2++acqXL2+SJUtmPvnkE3P9+nUTFhZmvvjiC+Pn52cKFy5sVq5caT3uWfrASWru3X8TJ040NWrUMFu2bLGmNWjQwKRNm9asXr3ahIWFGWOMqVevntm7dy/h+Cnx999/m2LFihmbzWY6dOhw3/y4wBUQEMCX+Cdk2LBhJm/evMYYYxYuXGhGjhxpvvvuO9OyZUtz/vx5kyVLFvPxxx9br7+VK1eaYcOGWSNGIumYOHGiSZs2rSlZsqT5888/7ebFBa5ixYrZBTFjnp0jWnEYjfA58ccff+jQoUOSpOTJk6tatWrat2+fJk2apKNHj0q6e/70+fPn9d577ykgIEBLly5VRESE1XkVT4aHh4fat2+v77//Xjly5NDff/+tDz/8UOfPn1etWrW0ePFi1axZU7Vr11b27NmdXS4eIq6/yLlz53Tt2jWtWLFCCxcu1Lp16zRq1Ch98803+vbbb5UmTRo1bdpUrVq1kqurq1asWHHfMvBk3dvfZ8uWLTp69KhWr16t4cOHa+fOnZKkmTNn6o033lCNGjX05ptvqkiRIjp48KDy588vm82m2NhYRox0EnP3x2SlT59ebm5uyp8/v44fP65ffvnFrl3hwoU1e/ZsnThxQrVq1XJStc+X8uXLyxijSpUq6e2335afn59eeOEF/fDDDypQoIDeeecdTZw40Xr9zZs3T/v375ebm5uTK0dCFS9eXP7+/jp48KA1wmdcP8nXXntNHTt2lLe3tzVIVJxnrv+5c7MenoRevXqZzJkzGx8fH1O6dGlz7NgxY4wxkyZNMgUKFDDFixc3b775pilevLgpXLiwMcaYbt26mZIlSz5zvy4kJQcPHrSOcMT9wte2bVvTqFEjEx4e7szSkAAzZswwKVOmNHny5DFHjhyxO9Lx9ddfGw8PD+sXv4sXL5qhQ4eaQoUKmY8//thZJeMe3bp1M1mzZjX9+vUzLVu2NB4eHuaNN94w27dvt9qMGTPGdO/e3XTv3t06Esl7p/MFBwdbRxyPHTtmqlSpYqpUqWLmzp1r1y46OtocPHjQHD9+3BllPpfatGljbDabCQgIsKZ16NDBuLi4mFWrVpnr16+by5cvm549e5oMGTKYQ4cOObFaxMeDuptER0eb4OBgkz9/flO0aFHr6PG9Z2zs3bv3me+qQth6xs2fP9/kyJHDLFy40CxbtswEBASY7NmzW/18Nm7caEaOHGnee+8907t3b+tLfOPGjU3Tpk3v6zAM5zh8+LDp06eP8fLyMvv373d2OUiAtWvXmurVqxsPDw+rU3dc/5DLly+bLFmymF9++cVqf/nyZfPJJ5+Y0qVLmwsXLjilZtz1+++/mwwZMliduI25e4HxTJkymRo1apht27Y98HGc+ul8f/31lyldurSpUaOG1ddu7969pkqVKqZatWpmzpw5xhhj+vTpY7p27erMUp87t2/fNhUrVjQtWrQw/v7+pn79+saYu32x6tWrZ9zd3U2uXLlM6dKljZ+fn13fLjyd7g1Lq1evNnPnzjW///67NaDT/v37zcsvv2zXPzJuEIwHLeNZQ9h6hs2aNcuMGzfOjBkzxpoWGRlpypYta/z8/B44sMLZs2dN7969jbe3tzlw4MCTLBf/YufOnaZBgwYmX758Jjg42Nnl4CEe9GERExNjNm/ebEqVKmX8/PzMxYsXrXl//fWXyZo1q1m8eLEx5n/9e65cuWLXIRzOsXv3bpMlSxbrvTIuRG3ZssW4urqa+vXrm6CgIGeWiIeYOHGiqVChgnn77betwLVv3z5Ts2ZNU7BgQRMQEGBSp079r6EZjhN3hGPy5MkmT548plGjRta8RYsWmalTp5pFixaZs2fPOqtEPIIePXqYNGnSmJw5c5rkyZObOnXqmOXLlxtj7r728ubNa0qXLv3c9Y8kbD2jwsLCTKZMmYzNZjM9evQwxvzvi1xkZKQpV66cyZUrl9myZYs1/caNG6ZNmzamQIECZs+ePc4qHf9w+/Zts3HjRnPmzBlnl4KHuDdoHThwwPzxxx/WADQxMTFmy5YtpmTJkiZLlixm8uTJ5qeffjI1a9Y0hQsX5pSzp8C9+y9ufxw6dMikSZPGTJ8+3Rhz970zJibG3Llzx/j7+5uMGTOahg0bEoyfAnGfY/98LU2ZMsWULVvWLnD98ccfZsKECaZPnz7m8OHDT7xW/M+NGzfMlClTTJ48eUyDBg2cXQ4S6N7T4rdv327y5MljNm3aZG7dumXWrFljqlevbgIDA8369euNMXePLqdLl840b97cWSU7BWHrGRY3lLu/v785ceKEMeZ/L4yoqCiTN29eU7duXbvHXL582Zw7d+6J1wokZfd+4AwYMMDkz5/f5MiRw+TJk8f88MMPVpstW7aYsmXLGpvNZj744AMzduxY6xc+Apfz3Bu0xo8fbwYNGmRde2nAgAHGzc3NboTImzdvmo8//tjMmTPHJEuWzG44cTjPtm3bTJs2be67Ft2UKVNM8eLFTd26dU1ISIgxhlEinyY3b940U6ZMMQUKFDBvvPGGs8vBI/jyyy9N586d7+trHHdWR9x1JGNiYsyxY8eeu887wtYzZtWqVWbBggXWRRzPnj1rChQoYF555RXryMi9vwDe+wfPhw/weAYMGGAyZMhgVq5caf744w/TsGFDY7PZzPjx440xd19jGzduNNWqVTN58+a1+mRxUWrnufd9r1u3biZz5sxm/Pjx1g9U58+fNx999JGx2WymZ8+e5ssvvzQVK1Y0xYsXN8YYU6FCBfPhhx86pXbY+/TTT02BAgVMhw4drIEx4nTt2tWkSJHCBAYGmvPnzzupQvybmzdvmvHjx5uSJUuav//+29nl4D/c+wPV1atXrevYvfLKK+b69et2bSdMmGBSpkxp/dAR53kKXIwp/Azp3bu3mjZtqsGDB6tevXpq2rSpJGnZsmW6ffu23n33Xf3111/WUMSurq5ydXVVTEyMJDFEMfAYdu3apQ0bNmj27NmqUqWK/vjjDy1dulQ1a9ZU27Zt9e2338pms6lMmTLq27evMmTIoCpVquj8+fPy8PBwdvnPnYiICEn/e9+bPHmyZsyYoYULF6p169bKkSOHJCl9+vQaP368xo8fr5UrV2rBggVKkyaNtm7dKkmKjo622sK5evTooQ8++EDbtm1T7969FRoaas0rWbKk8ufPL29vb0VHRzuxSjxIqlSp1KRJE61cuVKZM2d2djn4D3HD8vfp00e9e/fWJ598ooEDB2rXrl2aP3++9b1Skvz8/PTSSy/ddxmhZ25494dxdtpD4vjyyy9NpkyZrOGIx44da2w2m3nnnXfM2bNnzdmzZ02hQoVM9uzZGeEMSAT/PBJ89uxZ88UXX5jw8HCzZs0akylTJjNhwgRz8+ZNU6VKFWOz2cxXX31ltQ8KCjIFCxY0pUuXNjExMRxZfoIaNGhglixZYoz5335s27at1Y/g0KFD5rvvvjPFihUz/v7+Vtt//mLbu3dvkzlz5vsuDg/Hi9tvhw4dMkFBQVYn/NjYWPPVV1+ZUqVKmdatW1v7rG/fvuaTTz4x165dc1bJQJJ37+fU8uXLTd68ec2OHTusaV26dDFubm5m9OjRZs+ePeb06dOmatWq5rXXXnuuP+OSOTvs4fGdO3dOhw4d0siRI1WyZEnNnz9f/fv3V79+/TRmzBh17NhRw4cP16JFi/TJJ58offr0zi4ZSNJiYmKsX+X+/PNPpU6dWlmzZlX37t3l4uKiH374QW+99ZaaN2+u5MmT66WXXlLx4sW1aNEide7cWa6uripVqpS+//57+fj4cOHiJyxXrlyqVKmSpLtHppInT64sWbJozJgx6tOnj1auXKkXX3xRNWrU0JkzZ9S4cWP9+eef8vb2liQdOHBAU6ZM0ezZs7V06VLlzp3biVvz/DHGyGazaf78+erYsaOyZs2qo0ePKiAgQB07dlSXLl0UGxurBQsW6OWXX1bx4sW1fv167d6929qHABIu7kyAn3/+Wdu2bVOtWrVUokQJRUdHK1myZBo+fLhcXFzUqVMnpUyZUg0aNFBMTIzWrl1rXej9ufy8c3baw+O7c+eOmT9/vrl27ZrZsWOHyZ49uxk9erQxxpjhw4cbm81mKlSoYHdE63k6VxZILOPHj7cbqbNXr14mf/78Jn369KZ79+7m999/N8YYU6RIEdOtWzdjzN3+WO+88451dMQYXn/O0rNnTzN16lTr/rhx48x3331nIiIizLFjx0zPnj2Nv7+/GTlypDl48KAxxpg1a9aY119/3W7EwevXr5u1a9eaU6dOPelNwP/bsmWLSZs2rTU4ydq1a43NZjPjxo0zxtx9jQUFBZk+ffqYHj16MOog8BjijkrFxMSYqKgoU6JECWOz2Uy1atWsNvf24xo8eLCx2Wxm1qxZ1rTn+fqDNmP+cRIlkqSoqCglT55cX3zxhTZv3qyffvpJXl5e+uabb7R9+3ZdvnxZS5cufT5/UQASwcmTJ1WuXDlVr15dPXr00KFDh9SmTRt988032rdvn5YtW6bMmTOrX79+2rx5s7p166ZmzZopODhYUVFR2rFjh1xdXa1f5fFkXb9+XW+//bZiY2PVuHFjNW/eXG+99Zb279+vIUOGqG7dukqWLJlu3LihNGnSSLp7BLNWrVpyc3PTwoUL2W9PkVGjRmnDhg1asGCBjh07pho1aqhChQr67rvvJMluPz63v6YDiSwkJES+vr66c+eOGjZsqB07duiLL75Q3bp15ebmZvda69y5syZMmKCffvpJderUcXLlzsW7zzMiWbK7Z4T+8ccfCg0Nlc1mU3h4uFasWKFatWrpt99+k4uLi2JjY51cKZA05ciRQ7/++qt2796tcePGacOGDRo0aJDeeust67Tda9euaciQIfL19dWoUaN0+vRpFShQQNu3b7cGo+EL+5NnjJG3t7d+/vlnZcyYUTNmzNC8efO0cOFClStXTgMHDtSsWbN0+/ZtpUmTRjdu3NDChQtVtWpVnT9/XvPmzZPNZruvgzec59y5c8qePbskqUKFCqpYsaK+/fZbSdLcuXM1Z84cRUZGShJBC0gEM2bMUPPmzbVjxw55eHjop59+Ur58+TRy5EgtWbJEUVFRdt8zR44cqfbt26tu3bpatGiRk6t3Lt6BnhFxX+Batmyp7du3q0yZMipUqJBOnz5t94sCHzrAoytSpIi+++47bd68WVOnTtWNGzesebVq1VKXLl0UFhamOXPmqHDhwlq+fLkmT56s5MmTKzo6+vkafekpEvfhnzFjRnXp0kWS9MUXX2jx4sWaOnWqSpUqpc8++0y//PKLwsPDdenSJe3evVs5cuTQzp07rf1HUHaOuJB79epV3b59W9LdgPX999/L09NTdevW1YQJE6z9s3LlSm3evNluRDQAjyc6OlpXr17V6NGjtXPnTnl4eGjhwoXy9vbWF198YRe44nz11Vfq3bu38uTJ48TKnY/TCJ9Bu3fv1vz58+Xp6akuXbooWbJkVudFAI9v//79euutt5QzZ04NHz5cBQsWtOYtXbpUvXr1Us2aNfXFF19IEqcOPiW6du2qP//8U+fPn9fhw4eVIUMGffXVV3rnnXfUuHFj7dy5U5988onee+893b59W6lTp5bNZrMbEAXOsXDhQn399de6ePGiGjRooNdff12rVq3SlClTNGPGDFWtWlXXrl3T119/re+//14bNmxQ3rx5nV02kCT926m3s2fP1rhx45Q1a1Z17dpVJUqU0O3bt/X222/ryJEj+uGHH/T66687oeKnG2HrOUDQAhLf3r171axZM5UoUUIdO3ZU/vz5rXlbt25VqVKl+IL+FPnhhx/UqVMnrV69Wn5+foqIiFDTpk117do19evXT2+++aaaNm2qhQsXas6cOapataokgvLTYPfu3apYsaK6du2qK1euaPPmzcqVK5eKFy+uU6dOadKkSfL391eKFCl0/vx5LVy4UEWLFnV22UCSt2rVKr300kvKmTOnNW3mzJmaMGGCsmTJot69e6tw4cK6deuW+vbtq+HDh/O59wCELQB4RHv27FGLFi1UvHhxderUSf7+/nbzOSLy9BgwYIDWrFmjjRs3ymazyWaz6e+//9Y777yjS5cuaeTIkXrzzTc1ZMgQ9e7dm/32lPjzzz81a9Ys2Ww29e3bV5L066+/auzYsUqbNq0aNmyo9OnTa9OmTfLz81OZMmX04osvOrlqIGm694hWcHCwateurTfffFNdu3a1+khK0rRp09ShQwfVqlVL7dq106uvvmrN43PvfnTgAYBHVLRoUX3//fcKDg7WgAEDdPLkSbv5fOA4X9zviR4eHoqIiFBERIRsNpuioqKUJUsWff7557p48aJ69uyptWvXql+/ftZgJnCusLAw1a9fX2PHjtXNmzet6W+88YbatWunS5cuafr06fLw8FCvXr3UoEEDghbwiO4NWosXL1b27NnVrVs3bdu2TSNHjtSpU6estk2bNtVLL72kTZs2adWqVZL+917L5979CFsA8BiKFi2qb775RmnSpJGfn5+zy8E/xJ0C+MYbbyg4OFjDhg2TJCVPnlySFBERoUqVKqlOnToqX7689Ti+MDifp6envvvuO3l7e2vTpk06ePCgNa927drq1q2bTpw4oREjRuj27duMFgk8ImOMFbT69Omjli1bavbs2erQoYMaNGigjRs3atSoUVbgCgkJ0SuvvKIhQ4bok08+kSROt34ITiMEgEQQ17eHa/o8vaZNm6aWLVuqY8eOeu+995QuXTp16NBBhQoV0tChQyVxCszTaN++fWrSpIlKliypDh062PWPXLlypfLkycMPHUAi+PTTTzVmzBgtW7ZMuXPnlre3tyRpwoQJmjFjhtKmTauKFStq5cqVkqTly5fzuRcPhC0ASCQMpvD0++WXX9SmTRu5ublJkjJkyKDt27crefLk7L+nWFz/yGLFiqlz58739Y8E8HiuXr2qevXqqWnTpmrYsKH+/vtv/fHHH5o9e7YqV66sY8eO6dChQ9q7d69y5cqlOXPm8L4ZT4QtAMBz5dy5c/r7779169YtlS1bVq6urozamgTs2bNHrVq10ksvvaQBAwYwtDuQiK5du6YCBQqoWbNmqlq1qsaPH6+TJ08qNjZWf/31lz755BN9/PHHCg0NVdq0aWWz2XjfjCfCFgDgucapg0nHjh071L17d82aNUuZMmVydjnAM2Xy5Mnq3r27YmJi1KpVK1WpUkWVK1fWBx98IFdXV02fPt1qy6mD8UfYAgAASUZ4eLhSpEjh7DKAZ9KZM2cUERGh3LlzS7obqqpWrarSpUtryJAhTq4uaSJsAQAAALDcvHlTwcHB+vLLL3X69Gnt3r2bUwYfEc8aAAAAAEl3B3vauXOnhg8frqioKO3atUvJkiXjlOtHxJEtAAAAAJaIiAgdOnRIhQsXlouLC4NhPAbCFgAAAIAHYjCMx0PYAgAAAAAHIKYCAAAAgAMQtgAAAADAAQhbAAAAAOAAhC0AAAAAcADCFgAAAAA4AGELAAAAAByAsAUAwGMYOHCgihQp4uwyAABPIcIWACDJadq0qWw22323atWqOXS9NptNCxcutJvWrVs3rVmzxqHrBQAkTcmcXQAAAI+iWrVqmjp1qt00d3f3J15H6tSplTp16ie+XgDA048jWwCAJMnd3V2+vr52t7Rp00q6ewTq22+/Va1atZQyZUrly5dPQUFBOn78uMqXL69UqVLp1Vdf1Z9//mm3zAkTJihnzpxyc3NTnjx5NGPGDGte9uzZJUlvv/22bDabdf+fpxHGxsZq8ODBypo1q9zd3VWkSBEtX77cmn/q1CnZbDbNnz9fFSpUUMqUKVW4cGEFBQVZbU6fPq033nhDadOmVapUqZQ/f34tW7YskZ9BAICjEbYAAM+kTz/9VI0bN1ZwcLDy5s2r999/Xx9//LF69+6tnTt3yhijdu3aWe0XLFigjh07qmvXrjpw4IA+/vhjNWvWTOvWrZMk7dixQ5I0depUnT9/3rr/T6NHj9bw4cP19ddfa9++fQoMDFTt2rV17Ngxu3Z9+/ZVt27dFBwcrJdfflkNGjRQdHS0JKlt27aKiIjQxo0btX//fn355ZccPQOApMgAAJDENGnSxLi6uppUqVLZ3T777DNjjDGSTL9+/az2QUFBRpKZPHmyNW3WrFkmRYoU1v1XX33VfPTRR3brqVu3rqlRo4Z1X5JZsGCBXZsBAwaYwoULW/czZ85s1RHnlVdeMW3atDHGGHPy5EkjyXz//ffW/IMHDxpJ5vDhw8YYYwoWLGgGDhyYkKcEAPAU4sgWACBJqlChgoKDg+1urVq1suYXKlTI+r+Pj48kqWDBgnbTwsPDFRYWJkk6fPiwypQpY7eOMmXK6PDhw/GuKSwsTOfOnYvXcu6tL1OmTJKkixcvSpI6dOigIUOGqEyZMhowYID27dsX7xoAAE8PwhYAIElKlSqVcuXKZXdLly6dNT958uTW/202279Oi42NfUIV23tYLS1atNCJEyfUqFEj7d+/XyVKlNDYsWOdUicA4NERtgAAkJQvXz5t2bLFbtqWLVvk7+9v3U+ePLliYmL+dRmenp7KnDnzfy4nPrJly6ZWrVpp/vz56tq1qyZNmpSgxwMAnI+h3wEASVJERIRCQkLspiVLlkwvvPDCIy2ve/fueu+991S0aFFVrlxZv/76q+bPn6/Vq1dbbbJnz641a9aoTJkycnd3t0Y//OdyBgwYoJw5c6pIkSKaOnWqgoOD9dNPP8W7lk6dOql69ep6+eWXde3aNa1bt0758uV7pO0CADgPYQsAkCQtX77c6usUJ0+ePDpy5MgjLe+tt97S6NGj9fXXX6tjx47KkSOHpk6dqvLly1tthg8fri5dumjSpEnKkiWLTp06dd9yOnTooNDQUHXt2lUXL16Uv7+/Fi9erNy5c8e7lpiYGLVt21Z//fWXPD09Va1aNY0cOfKRtgsA4Dw2Y4xxdhEAAAAA8KyhzxYAAAAAOABhCwAAAAAcgLAFAAAAAA5A2AIAAAAAByBsAQAAAIADELYAAAAAwAEIWwAAAADgAIQtAAAAAHAAwhYAAAAAOABhCwAAAAAcgLAFAAAAAA7wf03pPJzXxR8TAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tKqMnUVHichc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gDEPLZF2ickg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "S1rexAWiqvId"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting Sample Images from Each Class"
      ],
      "metadata": {
        "id": "QjXf_AyUicnq"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_sample_images(dir_path, num_samples=3):  # Define a function to plot sample images\n",
        "    plt.figure(figsize=(15, 10))  # Set the figure size for the sample images\n",
        "    num_emotions = len(os.listdir(dir_path))  # Get the number of emotions\n",
        "    for emotion in os.listdir(dir_path):  # Loop through each emotion folder\n",
        "        emotion_dir = os.path.join(dir_path, emotion)  # Get the emotion directory\n",
        "        sample_images = np.random.choice(os.listdir(emotion_dir), num_samples, replace=False)  # Randomly select sample images\n",
        "\n",
        "        for i, img in enumerate(sample_images):  # Loop through the selected sample images\n",
        "            img_path = os.path.join(emotion_dir, img)  # Get the full image path\n",
        "            img_array = plt.imread(img_path)  # Read the image\n",
        "            plt.subplot(num_emotions, num_samples, i + 1 + num_samples * list(train_emotions).index(emotion))  # Create a subplot for each sample image\n",
        "            plt.imshow(img_array, cmap='gray')  # Display the image in gray scale\n",
        "            plt.axis('off')  # Hide the axis\n",
        "            plt.title(emotion)  # Set the title as the emotion\n",
        "    plt.tight_layout()  # Adjust the layout\n",
        "    plt.show()  # Display the sample images"
      ],
      "metadata": {
        "id": "f7gvzNK7icqZ"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vpSCqvrjilJQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot sample images"
      ],
      "metadata": {
        "id": "gy52CbmjilO3"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_sample_images(train_dir, num_samples=3)  # Call the function to plot sample images"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 999
        },
        "id": "awgG_eY3ilR4",
        "outputId": "900e6c80-14b3-4ceb-e174-83904bd14309"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 21 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hZ7mjlXNiqrR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1Qu07xXKiquo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#  Visualizing Image Dimensions"
      ],
      "metadata": {
        "id": "gQ3gpxqeiqx1"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def image_dimensions(dir_path):  # Define a function to get image dimensions\n",
        "    dimensions = []  # Initialize an empty list to store dimensions\n",
        "    for emotion in os.listdir(dir_path):  # Loop through each emotion folder\n",
        "        for img in os.listdir(os.path.join(dir_path, emotion)):  # Loop through each image in the emotion folder\n",
        "            img_path = os.path.join(dir_path, emotion, img)  # Get the full image path\n",
        "            with Image.open(img_path) as img:  # Open the image\n",
        "                dimensions.append(img.size)  # Append the image size (width, height) to the list\n",
        "    return dimensions  # Return the list of dimensions"
      ],
      "metadata": {
        "id": "TdixsLe9iq1Q"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EgaVoJuoiq38"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dimensions = image_dimensions(train_dir)  # Get dimensions for all images in the training directory"
      ],
      "metadata": {
        "id": "Yc3RnsNjiq7A"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QZH4s8xJizoV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert to DataFrame"
      ],
      "metadata": {
        "id": "J9t4cx47izxg"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dim_df = pd.DataFrame(dimensions, columns=['Width', 'Height'])  # Create a DataFrame for image dimensions"
      ],
      "metadata": {
        "id": "QC5Do0rCiz10"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 5))  # Set the figure size for the plot\n",
        "sns.histplot(dim_df['Width'], bins=30, color='blue', label='Width', kde=True)  # Plot histogram for image widths\n",
        "sns.histplot(dim_df['Height'], bins=30, color='orange', label='Height', kde=True)  # Plot histogram for image heights\n",
        "plt.title('Distribution of Image Dimensions')  # Set the title of the plot\n",
        "plt.xlabel('Dimensions')  # Set the x-axis label\n",
        "plt.ylabel('Frequency')  # Set the y-axis label\n",
        "plt.legend()  # Show the legend\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "Hg00OI61iz4k",
        "outputId": "0bb46d6c-3a4b-4e76-b855-395283383a43"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ANeHOjGCiz7T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "w__HwNxLi0ET"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Analyzing Image Sizes"
      ],
      "metadata": {
        "id": "go8bAFrni81a"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 6))  # Set the figure size for the boxplot\n",
        "sns.boxplot(data=dim_df, orient='h')  # Create a boxplot for image dimensions\n",
        "plt.title('Boxplot of Image Width and Height')  # Set the title of the plot\n",
        "plt.xlabel('Pixel Value')  # Set the x-axis label\n",
        "plt.ylabel('Dimension')  # Set the y-axis label\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "tPUWpJNii84L",
        "outputId": "f77032d0-667d-405f-f9fc-064fbe59c959"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "X504KaSmi87i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "m_jZ205Vi8-Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Correlation Analysis of Image Dimensions"
      ],
      "metadata": {
        "id": "ZCLW4n7Ci9Bw"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 6))  # Set the figure size for the scatter plot\n",
        "sns.scatterplot(data=dim_df, x='Width', y='Height', alpha=0.5)  # Create a scatter plot for image dimensions\n",
        "plt.title('Correlation between Image Width and Height')  # Set the title of the plot\n",
        "plt.xlabel('Width')  # Set the x-axis label\n",
        "plt.ylabel('Height')  # Set the y-axis label\n",
        "plt.axline((0, 0), slope=1, color='red', linestyle='--')  # Add a reference line for correlation\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "OIBwn10hi9UQ",
        "outputId": "8631102a-80e9-4482-efff-2e048d3b73ee"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "l_Xc5-ZZi0Hq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TvmejN5Xi0Ls"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing Sample Images with Augmentation"
      ],
      "metadata": {
        "id": "CgwjKK3YilU3"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_augmented_images(image_path):  # Define a function to visualize augmented images\n",
        "    original = plt.imread(image_path)  # Read the original image\n",
        "\n",
        "    # Applying transformations\n",
        "    flipped = np.fliplr(original)  # Flip the image horizontally\n",
        "    rotated = np.rot90(original)  # Rotate the image 90 degrees\n",
        "\n",
        "    # Plotting\n",
        "    plt.figure(figsize=(15, 5))  # Set the figure size for the augmented images\n",
        "    plt.subplot(1, 3, 1)  # Create a subplot for the original image\n",
        "    plt.imshow(original)  # Display the original image\n",
        "    plt.title('Original Image')  # Set the title for the original image\n",
        "    plt.axis('off')  # Hide the axis\n",
        "\n",
        "    plt.subplot(1, 3, 2)  # Create a subplot for the flipped image\n",
        "    plt.imshow(flipped)  # Display the flipped image\n",
        "    plt.title('Flipped Image')  # Set the title for the flipped image\n",
        "    plt.axis('off')  # Hide the axis\n",
        "\n",
        "    plt.subplot(1, 3, 3)  # Create a subplot for the rotated image\n",
        "    plt.imshow(rotated)  # Display the rotated image\n",
        "    plt.title('Rotated Image')  # Set the title for the rotated image\n",
        "    plt.axis('off')  # Hide the axis\n",
        "\n",
        "    plt.tight_layout()  # Adjust the layout\n",
        "    plt.show()  # Display the augmented images"
      ],
      "metadata": {
        "id": "yHjrC5TbjJl0"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UEzZy2M4jJpK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zA-nAj3TjJsc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize augmented images"
      ],
      "metadata": {
        "id": "yVeSEdl6jJvS"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sample_image_path = os.path.join(train_dir, 'happy', os.listdir(os.path.join(train_dir, 'happy'))[0])  # Get a sample image path\n"
      ],
      "metadata": {
        "id": "KvmYdumHjJyj"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_augmented_images(sample_image_path)  # Call the function to visualize augmented images"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "id": "zBJX3SdJilYA",
        "outputId": "4c71cfe4-37d8-476e-d0b3-2d150a344cd3"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oGRvjmc3jZZa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2M5xTu_DjZdh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "k5EXARPJlASJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing Emotion Distribution in Testing Data"
      ],
      "metadata": {
        "id": "5feHNjtojZjT"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_counts = [len(os.listdir(os.path.join(test_dir, emotion))) for emotion in os.listdir(test_dir)]  # Count images for each emotion in testing data\n"
      ],
      "metadata": {
        "id": "gk77Jx2sictV"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 5))  # Set the figure size for the plot\n",
        "sns.barplot(x=train_emotions, y=test_counts, palette='viridis')  # Create a bar plot for testing data\n",
        "plt.title('Distribution of Emotions in Testing Data')  # Set the title of the plot\n",
        "plt.xlabel('Emotions')  # Set the x-axis label\n",
        "plt.ylabel('Number of Images')  # Set the y-axis label\n",
        "plt.xticks(rotation=45)  # Rotate x-axis labels for better visibility\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "pxOo3WLFjdVc",
        "outputId": "10df5219-698d-4371-8fda-3cda4a3c9283"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "k4LwNSZfjdfI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZMa_ET5Aicv5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5GbMFwxaicy-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:blue;\">Data Augmentation for Training Data</span>\n",
        "\n",
        "In this step, we utilize the `ImageDataGenerator` class from TensorFlow's Keras module to apply data augmentation techniques to the training images. This is essential for enhancing the model's ability to generalize and perform well on unseen data.\n",
        "\n",
        "- Various augmentation parameters are set, such as rotation, width shift, height shift, shear, and zoom, to create variations of the training images."
      ],
      "metadata": {
        "id": "mw0s91rTrxso"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2fRZvhJfr5M7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:blue;\">Data Preparation for Testing Data</span>\n",
        "\n",
        "In this step, we prepare the testing data using the `ImageDataGenerator` class. Unlike the training data, we do not apply augmentation to the testing images, as we want to evaluate the model on original, unaltered images.\n",
        "\n",
        "- The generator is configured to rescale the pixel values to the range [0, 1], making it suitable for input into the neural network."
      ],
      "metadata": {
        "id": "8WGNBcHtr5bk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Image dimensions"
      ],
      "metadata": {
        "id": "SiwIk2lVfEWz"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "img_width, img_height = 48, 48  # Define width and height of images"
      ],
      "metadata": {
        "id": "OmMIptWofEZx"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wvRTDQXXfEcg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vjWN2RJvfEfx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Data augmentation for training data"
      ],
      "metadata": {
        "id": "Pfxri4qafEjv"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_datagen = ImageDataGenerator(rescale=1./255,  # Normalize pixel values to [0, 1]\n",
        "                                    rotation_range=20,  # Randomly rotate images\n",
        "                                    width_shift_range=0.2,  # Randomly shift images horizontally\n",
        "                                    height_shift_range=0.2,  # Randomly shift images vertically\n",
        "                                    shear_range=0.2,  # Shear images\n",
        "                                    zoom_range=0.2,  # Randomly zoom into images\n",
        "                                    horizontal_flip=True,  # Randomly flip images\n",
        "                                    fill_mode='nearest')  # Fill pixels that are newly created"
      ],
      "metadata": {
        "id": "s3kUZ_GdfN0G"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YT_mTk2pfN3V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YQYWAF1ZfN6N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# No data augmentation for test data, only rescaling"
      ],
      "metadata": {
        "id": "8OPERTjsfN92"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_datagen = ImageDataGenerator(rescale=1./255)  # Normalize pixel values for test data"
      ],
      "metadata": {
        "id": "a8uYigLHfOBW"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MPtbmPmme7fh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "saCDsZqSfVul"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load training and testing data"
      ],
      "metadata": {
        "id": "SPQl83wvfVxn"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_generator = train_datagen.flow_from_directory(\n",
        "    train_dir,  # Directory containing training images\n",
        "    target_size=(img_width, img_height),  # Resize images to target size\n",
        "    color_mode='grayscale',  # Load images in grayscale\n",
        "    class_mode='categorical',  # One-hot encode labels\n",
        "    batch_size=32,  # Number of images to be yielded from the generator per iteration\n",
        "    shuffle=True)  # Shuffle the training data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y6E5yaZnfV0P",
        "outputId": "286a08e5-feec-4849-c22e-19302c0df7aa"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 28709 images belonging to 7 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ydcC59-ufV3K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LeZM6qsQfV5u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_generator = test_datagen.flow_from_directory(\n",
        "    test_dir,  # Directory containing testing images\n",
        "    target_size=(img_width, img_height),  # Resize images to target size\n",
        "    color_mode='grayscale',  # Load images in grayscale\n",
        "    class_mode='categorical',  # One-hot encode labels\n",
        "    batch_size=32,  # Number of images to be yielded from the generator per iteration\n",
        "    shuffle=False)  # Do not shuffle the test data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n8IcqFF1eQCl",
        "outputId": "8b516a60-6c36-4c31-a7de-139f7f97ef03"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 7178 images belonging to 7 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kSKZMOi-eQMV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "35fc71SveQRr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3JAP6Y9OeQUe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:blue;\">Building the Model</span>\n",
        "\n",
        "In this section, we define the architecture of the Convolutional Neural Network (CNN) model using Keras. The model typically consists of several layers:\n",
        "\n",
        "- **Convolutional Layers**: Extract features from the input images through convolution operations, followed by activation functions (e.g., ReLU).\n",
        "- **Pooling Layers**: Reduce the spatial dimensions of the feature maps, helping to minimize computational load and control overfitting.\n",
        "- **Dropout Layers**: Randomly set a fraction of input units to zero during training to prevent overfitting.\n",
        "- **Dense Layers**: Fully connected layers that learn to map the extracted features to the target classes.\n",
        "\n",
        "- The output layer uses a softmax activation function for multi-class classification."
      ],
      "metadata": {
        "id": "yy8aCJuNr7-f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#  Building the CNN Model"
      ],
      "metadata": {
        "id": "UfZHziqLeQXg"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()  # Initialize the model"
      ],
      "metadata": {
        "id": "kTcQRDVGeQZW"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oFMP3mHzeQcC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oKmWu5tBeQfv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Add convolutional and pooling layers to the model"
      ],
      "metadata": {
        "id": "2D6e6HjieQij"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(img_width, img_height, 1)))  # First convolutional layer\n"
      ],
      "metadata": {
        "id": "MPmjfc19eQkv"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.add(MaxPooling2D(pool_size=(2, 2)))  # First pooling layer"
      ],
      "metadata": {
        "id": "KDN0dYuheLnD"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.add(Conv2D(64, (3, 3), activation='relu'))  # Second convolutional layer"
      ],
      "metadata": {
        "id": "cDd-ktJqeLrp"
      },
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.add(MaxPooling2D(pool_size=(2, 2)))  # Second pooling layer"
      ],
      "metadata": {
        "id": "eVleansheLuU"
      },
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.add(Conv2D(128, (3, 3), activation='relu'))  # Third convolutional layer"
      ],
      "metadata": {
        "id": "_d4HQ_5-eLxX"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.add(MaxPooling2D(pool_size=(2, 2)))  # Third pooling layer"
      ],
      "metadata": {
        "id": "6_Cn2AlHeLy4"
      },
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.add(Flatten())  # Flatten the output from the previous layer"
      ],
      "metadata": {
        "id": "54DAgoy7eL0q"
      },
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.add(Dense(128, activation='relu'))  # Fully connected layer"
      ],
      "metadata": {
        "id": "4OPwB5NmeL2m"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.add(Dropout(0.5))  # Dropout layer to prevent overfitting"
      ],
      "metadata": {
        "id": "1BYO3vPGeL_l"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.add(Dense(7, activation='softmax'))  # Output layer for 7 classes (emotions)"
      ],
      "metadata": {
        "id": "lAN13kiCeMJr"
      },
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ltsmDtpaeMMl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vxdCllN2eMPP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UQsONNIneMR2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:blue;\">Compiling the Model</span>\n",
        "\n",
        "In this step, we compile the CNN model by specifying the optimizer, loss function, and metrics to track during training.\n",
        "\n",
        "- **Optimizer**: Adam optimizer is commonly used for its adaptive learning rate capabilities.\n",
        "- **Loss Function**: Categorical crossentropy is appropriate for multi-class classification tasks.\n",
        "- **Metrics**: Accuracy is set to evaluate the model's performance during training and validation."
      ],
      "metadata": {
        "id": "OiP2eboUsAoH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Compiling and Training the Model"
      ],
      "metadata": {
        "id": "d8pV4-WKeMUS"
      },
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])  # Compile the model"
      ],
      "metadata": {
        "id": "sIaa9gHoeMWk"
      },
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "97Pslmk6eMbg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Aeee3C6weMgY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "f0450KzgsQDG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:blue;\">Training the Model</span>\n",
        "\n",
        "Here, we train the model using the training dataset.\n",
        "\n",
        "- We specify the number of epochs and batch size for the training process.\n",
        "- We also include validation data to monitor the model's performance on unseen data during training.\n",
        "- Callbacks such as ModelCheckpoint can be used to save the best model during training based on validation loss."
      ],
      "metadata": {
        "id": "flv9hV8BsQUn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Callbacks for training"
      ],
      "metadata": {
        "id": "AzfDvCrSeMi3"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "early_stopping = EarlyStopping(monitor='val_loss', patience=1, restore_best_weights=True)  # Early stopping"
      ],
      "metadata": {
        "id": "9ojiAdeqeMk7"
      },
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_checkpoint = ModelCheckpoint('facial_expression_model.keras', save_best_only=True)  # Save the best model"
      ],
      "metadata": {
        "id": "4PqT6aIHeMr8"
      },
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Cpxokf1UeMuh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UaBhEuOOeMy2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit the model with 2 epochs for fast execution"
      ],
      "metadata": {
        "id": "i0HZaraleM1N"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(train_generator,\n",
        "                    steps_per_epoch=len(train_generator),  # Number of steps per epoch\n",
        "                    validation_data=test_generator,  # Validation data\n",
        "                    validation_steps=len(test_generator),  # Number of validation steps\n",
        "                    epochs=2,  # Reduced epochs for faster training\n",
        "                    callbacks=[early_stopping, model_checkpoint])  # Callbacks"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ukAZa79EeM3g",
        "outputId": "03fdaeed-3d45-41f6-e1f9-d0e74fcb8e05"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/2\n",
            "\u001b[1m898/898\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m141s\u001b[0m 153ms/step - accuracy: 0.2450 - loss: 1.8289 - val_accuracy: 0.2602 - val_loss: 1.7647\n",
            "Epoch 2/2\n",
            "\u001b[1m898/898\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0-DbFoGreM5x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ATnllQq_eM8E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8ugJtKsfeNAm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:blue;\">Evaluating the Model</span>\n",
        "\n",
        "In this step, we evaluate the trained model's performance on the testing dataset.\n",
        "\n",
        "- We compute various metrics, including accuracy, loss, and potentially confusion matrix, to assess how well the model generalizes to new data."
      ],
      "metadata": {
        "id": "tgA16KAvsTMJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#  Evaluating the Model"
      ],
      "metadata": {
        "id": "kp6ECNo5eNDT"
      },
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the best model"
      ],
      "metadata": {
        "id": "GdcFlPhceNFq"
      },
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = load_model('facial_expression_model.keras')  # Load the saved model"
      ],
      "metadata": {
        "id": "VJeZ0eMKeNH7"
      },
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3WseoAxCeNMr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nJE3jcwLeNRO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model"
      ],
      "metadata": {
        "id": "Rz1Ze7FpeNWO"
      },
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss, test_accuracy = model.evaluate(test_generator)  # Evaluate on test data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x41z6LykeNY2",
        "outputId": "4764e3ed-6af6-46ed-f115-9f90a5fb68cd"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 43ms/step - accuracy: 0.2040 - loss: 1.8156\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QooCgrEjl73u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Test Accuracy: {test_accuracy:.2f}')  # Print test accuracy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y4k998AYeNeC",
        "outputId": "2991a301-a55e-42c1-ee16-8ab5cb6a57ee"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy: 0.26\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mlh2gMRlggC0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5gOm8kOqggGJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Confusion matrix and classification report"
      ],
      "metadata": {
        "id": "tYHMMieRggJE"
      },
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_generator.reset()  # Reset the test generator"
      ],
      "metadata": {
        "id": "zgLmwN8YggMJ"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = model.predict(test_generator)  # Get predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mh1vw9LVeNhN",
        "outputId": "7a34fa45-6f71-4c22-e0e6-0ff4c33ac1ec"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 53ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pcPx3buYmE9B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_classes = np.argmax(predictions, axis=1)  # Get predicted classes"
      ],
      "metadata": {
        "id": "XfpJfErneNjS"
      },
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "true_classes = test_generator.classes  # Get true classes"
      ],
      "metadata": {
        "id": "0f-6M8lceNl7"
      },
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hKz0P_tCeNoB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EbEV5-lzYgPZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Print confusion matrix and classification report"
      ],
      "metadata": {
        "id": "1f1xUc3wgtp_"
      },
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(confusion_matrix(true_classes, predicted_classes))  # Confusion matrix"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jBgKb3Gagtso",
        "outputId": "2db3abc4-9545-457c-f779-c2a3685a3df0"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[   1    0   34  743    0  146   34]\n",
            " [   0    0    1   97    0   13    0]\n",
            " [   0    0   30  850    0  103   41]\n",
            " [   1    0   17 1517    1  187   51]\n",
            " [   0    0   11 1025    0  192    5]\n",
            " [   0    0   14 1019    0  199   15]\n",
            " [   0    0   28  646    1   35  121]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zxM6rlYymJD8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(true_classes, predicted_classes))  # Classification report"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4lZ_k9LCgtw2",
        "outputId": "28d6651d-5259-4e7d-e9c3-4890a3680712"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.50      0.00      0.00       958\n",
            "           1       0.00      0.00      0.00       111\n",
            "           2       0.22      0.03      0.05      1024\n",
            "           3       0.26      0.86      0.40      1774\n",
            "           4       0.00      0.00      0.00      1233\n",
            "           5       0.23      0.16      0.19      1247\n",
            "           6       0.45      0.15      0.22       831\n",
            "\n",
            "    accuracy                           0.26      7178\n",
            "   macro avg       0.24      0.17      0.12      7178\n",
            "weighted avg       0.25      0.26      0.16      7178\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "iHb0RFIvg2Eb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "w9vKbYGNg2IR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OwLwix0Dg2LR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:blue;\">Saving the Model</span>\n",
        "\n",
        "In this step, we save the trained model to disk for future use.\n",
        "\n",
        "- This allows us to easily load and use the model later without needing to retrain it.\n",
        "- The model is typically saved in a format compatible with TensorFlow/Keras."
      ],
      "metadata": {
        "id": "VeVwCKkSsgu-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#  Save the model (optional, but can keep it consistent)\n"
      ],
      "metadata": {
        "id": "vFN2Sl4yg2N4"
      },
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('facial_expression_model.keras')  # Save the model"
      ],
      "metadata": {
        "id": "luzFxocxg2RH"
      },
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Hrbb96nzg2Tv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RggCWHK7g2X5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "z9MlC88mg2a0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:blue;\">Deploying the Model with Flask</span>\n",
        "\n",
        "In this section, we set up a simple web application using Flask to deploy the trained model.\n",
        "\n",
        "- We create routes to handle incoming requests, allowing users to upload images and receive predictions.\n",
        "- The web application enables the model to be used interactively in a user-friendly manner."
      ],
      "metadata": {
        "id": "eT8ojMn0sixO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#  Model Deployment (Basic Flask App)"
      ],
      "metadata": {
        "id": "IY_d78hIg-dg"
      },
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "app = Flask(__name__)  # Initialize the Flask app"
      ],
      "metadata": {
        "id": "jnntE_z9g-gc"
      },
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_n2uj-3Zg-jB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "@app.route('/predict', methods=['POST'])  # Define the predict endpoint\n",
        "def predict():\n",
        "    if request.method == 'POST':  # Check if the request method is POST\n",
        "        file = request.files['file']  # Get the uploaded file\n",
        "        img_path = '/content/temp_image.jpg'  # Path to save the uploaded image\n",
        "        file.save(img_path)  # Save the uploaded file\n",
        "\n",
        "        # Load and preprocess the image\n",
        "        img = load_img(img_path, target_size=(img_width, img_height), color_mode='grayscale')  # Load the image\n",
        "        img_array = img_to_array(img) / 255.0  # Normalize the image\n",
        "        img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension\n",
        "\n",
        "        # Predict the emotion\n",
        "        prediction = model.predict(img_array)  # Get prediction\n",
        "        predicted_class = np.argmax(prediction)  # Get the predicted class\n",
        "\n",
        "        # Map predicted class to emotion\n",
        "        emotions = list(train_generator.class_indices.keys())  # Get list of emotions\n",
        "        predicted_emotion = emotions[predicted_class]  # Get predicted emotion\n",
        "\n",
        "        return jsonify({'emotion': predicted_emotion})  # Return the predicted emotion as JSON"
      ],
      "metadata": {
        "id": "AhFTa1SNgtzz"
      },
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "o2CN8bkshQ2g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "alC-mLWShQ6R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Run the Flask app (uncomment to run)"
      ],
      "metadata": {
        "id": "V6Vlz4rPhQ-K"
      },
      "execution_count": 122,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if __name__ == '__main__':  # Check if the script is being run directly\n",
        "    app.run(host='0.0.0.0', port=5000)  # Run the app on all interfaces at port 5000"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YgrEyyKihRBo",
        "outputId": "1cf3852f-af76-444c-d692-d82b6c922e07"
      },
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " * Serving Flask app '__main__'\n",
            " * Debug mode: off\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:werkzeug:\u001b[31m\u001b[1mWARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.\u001b[0m\n",
            " * Running on all addresses (0.0.0.0)\n",
            " * Running on http://127.0.0.1:5000\n",
            " * Running on http://172.28.0.12:5000\n",
            "INFO:werkzeug:\u001b[33mPress CTRL+C to quit\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "T57iBOPWnH5L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ktgLSAoBsl2V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xEOJJPCEsl5-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:blue;\">Conclusion</span>\n",
        "\n",
        "In this final step, we summarize the key takeaways from the project.\n",
        "\n",
        "- We have successfully built, trained, and evaluated a CNN model for facial expression recognition.\n",
        "- The deployment of the model as a web application allows for practical application and further experimentation.\n",
        "- Future work could involve exploring more complex models, improving augmentation techniques, or expanding the dataset."
      ],
      "metadata": {
        "id": "ADAlNiLSsmRM"
      }
    }
  ]
}