DermaAI.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rAkW4ss9i2V3"
      },
      "source": [
        "# **Derma AI: Skin Disease Image Classifier for Accurate and Accessible Diagnosis**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oKGbjZ2ui2SV"
      },
      "source": [
        "# **Business Understanding**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_J7wBw_2jRou"
      },
      "source": [
        "## **_Overview_**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gVgSsrm5jRlM"
      },
      "source": [
        "A skin disease is a medical condition that affects the skin's appearance, texture, and function. Skin diseases can range from mild conditions, such as acne or a rash, to more serious conditions, such as psoriasis, eczema, or skin cancer. Skin diseases can affect people of all ages, races, and genders. They can be caused by a variety of factors, including genetics, exposure to irritants or allergens, infections, hormonal imbalances, and autoimmune disorders.Symptoms of skin diseases can vary widely depending on the specific condition, but may include redness, swelling, itching, dryness, scaling, blistering, or changes in skin color or texture.<br>\n",
        "Proper diagnosis and treatment of skin diseases is crucial for maintaining healthy skin and preventing complications. Early diagnosis is particularly important as it can prevent permanent scarring, disfigurement, and even life-threatening conditions such as skin cancer. Additionally, early diagnosis can help prevent the spread of infectious skin diseases such as bacterial or viral infections. The use of skin disease classification tools can aid in the early and accurate diagnosis of skin diseases, leading to improved outcomes and better overall health for patients.<br>\n",
        "This project aims to develop a skin disease image classifier that can identify skin diseases in images uploaded by users.  The classifier will be integrated into a website where users can upload photos of their skin diseases and receive an accurate diagnosis. The machine learning algorithm used in the  project will utilise a dataset of images scraped from the [DermNet NZ Image Library](https://dermnetnz.org/image-library) and [ISIC 2019 Challenge](https://challenge.isic-archive.com/data/#2019) websites."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l0wfvMNFjRic"
      },
      "source": [
        "## **_Business Problem_**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XS7uVtIslr6u"
      },
      "source": [
        "Skin diseases are common and can have significant impacts on a person's health and quality of life. However, patients may struggle to access dermatological expertise due to various reasons, such as lack of dermatologists in some areas, long wait times for appointments, and high costs associated with consultations, while dermatologists face the challenge of accurately identifying and diagnosing skin diseases quickly. To address these challenges, we propose building an AI-powered skin disease classifier that improves diagnosis accuracy and accessibility for both dermatologists and patients.<br>\n",
        "By leveraging Neural Network techniques and the vast collection of clinical images of skin diseases available on  [DermNet NZ Image Library](https://dermnetnz.org/image-library) and [ISIC 2019 Challenge](https://challenge.isic-archive.com/data/#2019), we can build a skin disease classifier that accurately identifies and classifies skin diseases in a matter of seconds. This can aid dermatologists in their clinical decision-making process and reduce the time it takes to diagnose skin diseases, leading to better patient outcomes. Furthermore, patients can use the classifier to self-diagnose their skin diseases and seek medical attention if needed, thus increasing accessibility and affordability of dermatological care. Ultimately, our goal is to improve the diagnosis and management of skin diseases, leading to better patient outcomes and reduced healthcare costs."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d_ra4Zr0jRf1"
      },
      "source": [
        "## **_Objectives_**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zV2Q3d9CjRdF"
      },
      "source": [
        "### **_General Objective_**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QnFseRl7jRad"
      },
      "source": [
        "To develop a skin disease image classifier using neural networks to accurately diagnose and classify skin diseases.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rpOvxWKKjRN-"
      },
      "source": [
        "### **_Specific Objectives_**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KWR_lmjvjq9H"
      },
      "source": [
        "\n",
        "**_i._** To develop an advanced skin disease image classifier capable of accurately identifying the most common skin conditions.\n",
        "\n",
        "**_ii._** To integrate the image classifier into a user-friendly platform that provides patients with convenient and affordable access to dermatological care.\n",
        "\n",
        "**_iii._** To enhance the speed and accuracy of skin disease diagnosis for both dermatologists and patients, resulting in better treatment outcomes."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NXhHzcXvjq5g"
      },
      "source": [
        "To achieve the objectives of this study, the project will seek to answer the following questions:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SbRvKEYejq2q"
      },
      "source": [
        "**_i._** What are the most common skin diseases and how can they be accurately diagnosed through image classification?\n",
        "\n",
        "**_ii._** How can the developed image classifier be integrated into a user-friendly website to provide accessible dermatological care?\n",
        "\n",
        "**_iii._** How can the developed image classifier aid dermatologists in their clinical decision-making process and reduce the time it takes to diagnose skin diseases?\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W4XGZYREj0OH"
      },
      "source": [
        "## **_Success Criteria_**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BxEOFSS8j2jW"
      },
      "source": [
        "The model will be considered a success if it achieves a high accuracy in classifying skin diseases. Accuracy is a measure of how many predictions are correct out of the total number of predictions made by the classifier. A high accuracy score indicates that the classifier is good at avoiding misclassifications, which is crucial in the case of skin disease classification as misdiagnosis can lead to unnecessary treatments or interventions.\n",
        "\n",
        "In addition to accuracy, the F1 score will be considered as a secondary metric. A high F1 score indicates that the classifier has a good balance between precision and recall, which is important in the case of skin disease classification as it ensures that the classifier is both accurate and comprehensive in its predictions."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VRvzA01Qj5J1"
      },
      "source": [
        "# **Data Understanding**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YYHILl27j721"
      },
      "source": [
        "The data that will be used in this project will be scraped from [DermNet NZ Image Library](https://dermnetnz.org/image-library) and [ISIC 2019 Challenge](https://challenge.isic-archive.com/data/#2019) websites."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UUmQFbg_i2MN"
      },
      "source": [
        "# **Data Scrapping**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Y_eJE7xN4ZBB",
        "outputId": "d5de8d68-7846-4928-eacb-19caecee5c26"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: selenium in c:\\users\\admin\\anaconda3\\lib\\site-packages (4.5.0)\n",
            "Requirement already satisfied: certifi>=2021.10.8 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from selenium) (2021.10.8)\n",
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            "Requirement already satisfied: PySocks!=1.5.7,<2.0,>=1.5.6 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from urllib3[socks]~=1.26->selenium) (1.7.1)\n",
            "Requirement already satisfied: pycparser in c:\\users\\admin\\anaconda3\\lib\\site-packages (from cffi>=1.14->trio~=0.17->selenium) (2.21)\n",
            "Requirement already satisfied: h11<1,>=0.9.0 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from wsproto>=0.14->trio-websocket~=0.9->selenium) (0.9.0)\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n",
            "[notice] A new release of pip is available: 23.0.1 -> 23.1.2\n",
            "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: webdriver_manager in c:\\users\\admin\\anaconda3\\lib\\site-packages (3.8.5)\n",
            "Requirement already satisfied: python-dotenv in c:\\users\\admin\\anaconda3\\lib\\site-packages (from webdriver_manager) (0.21.0)\n",
            "Requirement already satisfied: tqdm in c:\\users\\admin\\anaconda3\\lib\\site-packages (from webdriver_manager) (4.64.0)\n",
            "Requirement already satisfied: requests in c:\\users\\admin\\anaconda3\\lib\\site-packages (from webdriver_manager) (2.27.1)\n",
            "Requirement already satisfied: packaging in c:\\users\\admin\\anaconda3\\lib\\site-packages (from webdriver_manager) (21.3)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from packaging->webdriver_manager) (3.0.4)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from requests->webdriver_manager) (2.0.4)\n",
            "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from requests->webdriver_manager) (2.10)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from requests->webdriver_manager) (1.26.9)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from requests->webdriver_manager) (2021.10.8)\n",
            "Requirement already satisfied: colorama in c:\\users\\admin\\anaconda3\\lib\\site-packages (from tqdm->webdriver_manager) (0.4.6)\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n",
            "[notice] A new release of pip is available: 23.0.1 -> 23.1.2\n",
            "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
          ]
        }
      ],
      "source": [
        "# install required libraries\n",
        "!pip install selenium\n",
        "!pip install webdriver_manager"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b67kW0cihn1F"
      },
      "source": [
        "**import required libraries**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EOh6sB_Nlwba"
      },
      "outputs": [],
      "source": [
        "# import required libraries\n",
        "import selenium\n",
        "from selenium import webdriver\n",
        "from selenium.webdriver.chrome.service import Service\n",
        "from webdriver_manager.chrome import ChromeDriverManager\n",
        "from selenium.webdriver.support.ui import WebDriverWait\n",
        "import pandas as pd\n",
        "import re\n",
        "import time\n",
        "from IPython.core.display import HTML\n",
        "import webbrowser\n",
        "import requests as rq\n",
        "import os\n",
        "import pathlib\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ViYdImV24npi"
      },
      "source": [
        "**Scraping Dermnet website**\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vWwDtCwA4pm6",
        "outputId": "06f6be6f-033f-43f4-b6c1-dcc27e35de39"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[WDM] - Downloading: 100%|██████████| 6.80M/6.80M [00:02<00:00, 3.24MB/s]\n"
          ]
        }
      ],
      "source": [
        "# Creating an instance of the Chrome web browser\n",
        "driver = webdriver.Chrome(service=Service(ChromeDriverManager().install()))\n",
        "\n",
        "# URL to the dermnet website\n",
        "url = \"https://dermnetnz.org/image-library\"\n",
        "\n",
        "# Navigating to the specified url in chrome\n",
        "driver.get(url)\n",
        "\n",
        "# maximizing the browser window so that it takes up the entire screen\n",
        "driver.maximize_window()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ux14Jruj5Clq",
        "outputId": "b0fea53c-a612-423c-dc2b-713c55c39966"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The first 10 skin condition names: \n",
            " ['Acne affecting the back images', 'Acne affecting the face images', 'Acne and other follicular disorder images', 'Acquired dermal macular hyperpigmentation images', 'Acral lentiginous melanoma images', 'Actinic keratosis affecting the face images', 'Actinic keratosis affecting the hand images', 'Actinic keratosis affecting the legs and feet images', 'Actinic keratosis affecting the scalp images', 'Actinic keratosis dermoscopy images']\n",
            "\n",
            "The first 10 links to skin condition image links:\n",
            "['https://dermnetnz.org/topics/acne-affecting-the-back-images/?stage=Live', 'https://dermnetnz.org/topics/acne-face-images/?stage=Live', 'https://dermnetnz.org/image-catalogue/acne-and-other-follicular-disorder-images/?stage=Live', 'https://dermnetnz.org/topics/acquired-dermal-macular-hyperpigmentation-images/?stage=Live', 'https://dermnetnz.org/topics/acral-lentiginous-melanoma-images/?stage=Live', 'https://dermnetnz.org/topics/actinic-keratosis-face-images/?stage=Live', 'https://dermnetnz.org/topics/actinic-keratosis-affecting-the-hand-images/?stage=Live', 'https://dermnetnz.org/topics/actinic-keratosis-leg-and-foot-images/?stage=Live', 'https://dermnetnz.org/topics/actinic-keratosis-scalp-images/?stage=Live', 'https://dermnetnz.org/topics/actinic-keratosis-dermoscopy-images/?stage=Live']\n"
          ]
        }
      ],
      "source": [
        "# Finding all the skin_disorders listed on the main page\n",
        "skin_disorder_tag_main_page = driver.find_elements(\"xpath\", '//a[@class=\"imageList__group__item\"]')\n",
        "\n",
        "# For loop to extract the skin disorder names and the link to the skin_disorders\n",
        "disorder_names = []\n",
        "link_list= []\n",
        "for tag in skin_disorder_tag_main_page:\n",
        "    disorder_names.append(tag.text)\n",
        "    link_list.append(tag.get_attribute(\"href\"))\n",
        "\n",
        "# Previewing the lists created:\n",
        "print(f'The first 10 skin condition names: \\n {disorder_names[:10]}\\n')\n",
        "print(f'The first 10 links to skin condition image links:\\n{link_list[:10]}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6txoDJVO5HIO",
        "outputId": "fd3f953c-f118-4637-824c-7a7d1e8da7c5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The are 294 skin conditions listed in the DermNet website.\n"
          ]
        }
      ],
      "source": [
        "# The number of skin_disorders listed in the website\n",
        "print(f'The are {len(disorder_names)} skin conditions listed in the DermNet website.')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lt_tf1ZW5JFX",
        "outputId": "532571d5-d279-4acf-d51f-67d55e72ceba"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>skin_disorder_name</th>\n",
              "      <th>link</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Acne affecting the back images</td>\n",
              "      <td>https://dermnetnz.org/topics/acne-affecting-th...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Acne affecting the face images</td>\n",
              "      <td>https://dermnetnz.org/topics/acne-face-images/...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Acne and other follicular disorder images</td>\n",
              "      <td>https://dermnetnz.org/image-catalogue/acne-and...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Acquired dermal macular hyperpigmentation images</td>\n",
              "      <td>https://dermnetnz.org/topics/acquired-dermal-m...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Acral lentiginous melanoma images</td>\n",
              "      <td>https://dermnetnz.org/topics/acral-lentiginous...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                 skin_disorder_name  \\\n",
              "0                    Acne affecting the back images   \n",
              "1                    Acne affecting the face images   \n",
              "2         Acne and other follicular disorder images   \n",
              "3  Acquired dermal macular hyperpigmentation images   \n",
              "4                 Acral lentiginous melanoma images   \n",
              "\n",
              "                                                link  \n",
              "0  https://dermnetnz.org/topics/acne-affecting-th...  \n",
              "1  https://dermnetnz.org/topics/acne-face-images/...  \n",
              "2  https://dermnetnz.org/image-catalogue/acne-and...  \n",
              "3  https://dermnetnz.org/topics/acquired-dermal-m...  \n",
              "4  https://dermnetnz.org/topics/acral-lentiginous...  "
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Creating a dataframe with two columns, the skin_disorder names and the links to the images of the skin disorders\n",
        "name_link_df = pd.DataFrame({'skin_disorder_name': disorder_names, 'link': link_list})\n",
        "\n",
        "# Saving the dataframe as a csv file\n",
        "name_link_df.to_csv('Data/name_link.csv', index=False)\n",
        "\n",
        "# Previewing the first five rows of the dataframe\n",
        "name_link_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lDXixgoY5LZb"
      },
      "outputs": [],
      "source": [
        "# initialize the webdriver\n",
        "driver = webdriver.Chrome(service=Service(ChromeDriverManager().install()))\n",
        "\n",
        "# create an empty list to store the dataframes for each link/disease pair\n",
        "dfs = []\n",
        "# loop through each link/disease pair\n",
        "for link, disease_name in zip(link_list, disorder_names):\n",
        "    # create an empty list to store the image URLs\n",
        "    image_disease = []\n",
        "\n",
        "    try:\n",
        "        # navigate to the link\n",
        "        driver.get(link)\n",
        "\n",
        "        # maximize the window to ensure all elements are visible\n",
        "        driver.maximize_window()\n",
        "\n",
        "        # find all the elements on the page with the class \"imageLinkBlock__item__image\"\n",
        "        skin_image_disorder = driver.find_elements(\"xpath\", '//div[@class=\"imageLinkBlock__item__image\"]')\n",
        "\n",
        "        # loop through each element and find all the images within it\n",
        "        for image in skin_image_disorder:\n",
        "            list_ = image.find_elements(\"tag name\", \"img\")\n",
        "            for lists in list_:\n",
        "                # add each image URL to the image_disease list\n",
        "                image_disease.append(lists.get_attribute(\"src\"))\n",
        "\n",
        "        # create a list of dictionaries, where each dictionary represents a row in the DataFrame\n",
        "        # each dictionary contains an image URL and the disease name\n",
        "        rows = [{'skin_disorder_name': disease_name, 'images': url} for url in image_disease]\n",
        "\n",
        "        # create the DataFrame using the list of dictionaries\n",
        "        df = pd.DataFrame(rows, columns=['skin_disorder_name', 'images'])\n",
        "\n",
        "        # add the dataframe to the list of dataframes\n",
        "        dfs.append(df)\n",
        "\n",
        "    except Exception as e:\n",
        "        # if an error occurs, print the error message and move to the next link/disease pair\n",
        "        print(f\"Error occurred for {disease_name}: {str(e)}\")\n",
        "        continue\n",
        "\n",
        "# concatenate all the dataframes into a single dataframe\n",
        "result_df = pd.concat(dfs)\n",
        "\n",
        "# Turning all the skin_disorder_names to lower case:\n",
        "result_df['skin_disorder_name'] = result_df['skin_disorder_name'].map(lambda x: x.lower())\n",
        "\n",
        "# save the result dataframe to a CSV file named \"data.csv\"\n",
        "result_df.to_csv('Data/data1-294.csv', index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eIHxpf9D5NzQ",
        "outputId": "f255df2b-8438-4eb2-d7ae-541d736ab71d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>skin_disorder_name</th>\n",
              "      <th>images</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>acne affecting the back images</td>\n",
              "      <td>https://dermnetnz.org/assets/Uploads/acne/acne...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>acne affecting the back images</td>\n",
              "      <td>https://dermnetnz.org/assets/Uploads/acne/acne...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>acne affecting the back images</td>\n",
              "      <td>https://dermnetnz.org/assets/Uploads/acne/acne...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               skin_disorder_name  \\\n",
              "0  acne affecting the back images   \n",
              "1  acne affecting the back images   \n",
              "2  acne affecting the back images   \n",
              "\n",
              "                                              images  \n",
              "0  https://dermnetnz.org/assets/Uploads/acne/acne...  \n",
              "1  https://dermnetnz.org/assets/Uploads/acne/acne...  \n",
              "2  https://dermnetnz.org/assets/Uploads/acne/acne...  "
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# DataFrame with all the 294 images:\n",
        "image_df = pd.read_csv('Data/data1-294.csv')\n",
        "\n",
        "# Previewing the first 3 rows of the dataframe\n",
        "image_df.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VU71YUp35Pim"
      },
      "outputs": [],
      "source": [
        "# Function takes in the image url and returns an html <img> tag that displays the image\n",
        "def to_img_tag(path):\n",
        "    return '<img src=\"'+ path + '\" width=\"50\" >'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ec_MlLrf5RiN"
      },
      "outputs": [],
      "source": [
        "# Save the HTML table to a file\n",
        "with open('Data/image_table.html', 'w') as f:\n",
        "    f.write(image_df.to_html(escape=False,formatters=dict(images=to_img_tag)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_ph-tlaH5TvZ",
        "outputId": "8c4beb71-bd38-4abb-e91e-78aa39330e42"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Open the HTML file in the default web browser\n",
        "webbrowser.open('Data/image_table.html')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Aju2bB1j5WGQ",
        "outputId": "df759dd5-f0cd-45ee-f0b8-b971b839f5ac"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Successfully downloaded: acne affecting the back images0.jpg\n",
            "Successfully downloaded: acne affecting the back images1.jpg\n",
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          ]
        }
      ],
      "source": [
        "# Downloading and Saving the images into a folder:\n",
        "def save_image(folder: str, name: str, url: str, index:int):\n",
        "    # Get the data from the url\n",
        "    image_source = rq.get(url)\n",
        "\n",
        "    # If there's a suffix, we will grab that\n",
        "    suffix = pathlib.Path(url).suffix\n",
        "\n",
        "    # Check if the suffix is one of the following\n",
        "    if suffix not in ['.jpg', '.jpeg', '.png', '.gif']:\n",
        "        # Default to .png\n",
        "        output = name + str(index) + '.png'\n",
        "\n",
        "    else:\n",
        "        output = name + str(index) + suffix\n",
        "\n",
        "    # Check first if folder exists, else create a new one\n",
        "    if not os.path.exists(folder):\n",
        "        os.makedirs(folder)\n",
        "\n",
        "    # Create our output in the specified folder (wb = write bytes)\n",
        "    with open(f'{folder}{output}', 'wb') as file:\n",
        "        file.write(image_source.content)\n",
        "        print(f'Successfully downloaded: {output}')\n",
        "\n",
        "\n",
        "if __name__ == '__main__':\n",
        "    # Load the dataframe with image urls and disease names\n",
        "    df = pd.read_csv('Data/data1-294.csv')\n",
        "\n",
        "    # Loop through the dataframe\n",
        "    for index, row in df.iterrows():\n",
        "        # Get the image url and disease name\n",
        "        image_url = row['images']\n",
        "        disease_name = row['skin_disorder_name']\n",
        "\n",
        "        # Save the image\n",
        "        save_image('Images/', disease_name, image_url, index)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2VAcUVt4i2FJ"
      },
      "source": [
        "# **Data Cleaning**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8OtVuOedlw4V"
      },
      "outputs": [],
      "source": [
        "# import required libraries\n",
        "import shutil\n",
        "import os\n",
        "from PIL import Image\n",
        "import imagehash\n",
        "import random"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VSPpBXCvGAuD",
        "outputId": "39e58e22-8108-48dc-8377-a5ae2cd37e56"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(13992, 2)\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>skin_disorder_name</th>\n",
              "      <th>images</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>acne affecting the back images</td>\n",
              "      <td>https://dermnetnz.org/assets/Uploads/acne/acne...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>acne affecting the back images</td>\n",
              "      <td>https://dermnetnz.org/assets/Uploads/acne/acne...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>acne affecting the back images</td>\n",
              "      <td>https://dermnetnz.org/assets/Uploads/acne/acne...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>acne affecting the back images</td>\n",
              "      <td>https://dermnetnz.org/assets/Uploads/acne/acne...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>acne affecting the back images</td>\n",
              "      <td>https://dermnetnz.org/assets/Uploads/acne/acne...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               skin_disorder_name  \\\n",
              "0  acne affecting the back images   \n",
              "1  acne affecting the back images   \n",
              "2  acne affecting the back images   \n",
              "3  acne affecting the back images   \n",
              "4  acne affecting the back images   \n",
              "\n",
              "                                              images  \n",
              "0  https://dermnetnz.org/assets/Uploads/acne/acne...  \n",
              "1  https://dermnetnz.org/assets/Uploads/acne/acne...  \n",
              "2  https://dermnetnz.org/assets/Uploads/acne/acne...  \n",
              "3  https://dermnetnz.org/assets/Uploads/acne/acne...  \n",
              "4  https://dermnetnz.org/assets/Uploads/acne/acne...  "
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# load and preview dataset\n",
        "image_df = pd.read_csv('Data/data1-294.csv')\n",
        "print(image_df.shape)\n",
        "image_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x-8lDMoEGj2Y"
      },
      "source": [
        "### **<u>Acne</u>**\n",
        "\n",
        "**Meaning**<br>\n",
        "Acne is a common skin condition that occurs when hair follicles become clogged with oil and dead skin cells. This leads to the formation of pimples, blackheads, whiteheads, and sometimes deeper cysts. Acne usually appears on the face, neck, chest, back, and shoulders, and can affect people of all ages, although it is most common during puberty.<br>\n",
        "\n",
        "**Causes**<br>\n",
        "The causes of acne are multifactorial and can include hormonal imbalances, genetics, stress, certain medications, and an overproduction of sebum, the oily substance that lubricates the skin. Certain factors such as diet and hygiene practices have also been implicated in the development of acne, although the evidence for these is less clear.<br>\n",
        "\n",
        "**Symptoms**<br>\n",
        "The symptoms of acne can vary depending on the severity of the condition. Mild acne may only present with a few blackheads or whiteheads, while moderate acne can involve a combination of pimples, blackheads, and whiteheads. Severe acne may include deep, painful cysts that can lead to scarring. Acne can also have a significant impact on a person's self-esteem and mental health, particularly if it is severe or persistent.\n",
        "\n",
        "**Treatment**<br>\n",
        "Treatment options for acne depend on the severity of the condition. Mild acne can often be managed with over-the-counter topical treatments that contain benzoyl peroxide or salicylic acid. These products work by reducing the amount of oil on the skin and unclogging pores. More severe acne may require prescription medications, such as topical retinoids or oral antibiotics, which can help to reduce inflammation and kill the bacteria that cause acne. In cases of severe, persistent acne, isotretinoin, a powerful oral medication, may be prescribed. Additionally, lifestyle modifications such as maintaining good hygiene practices, avoiding certain foods, and managing stress can also be helpful in managing acne.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dztlobl3GtvM"
      },
      "source": [
        "#### **Cleaning Acne images**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "spwOYV7XGyfy"
      },
      "source": [
        "**i. Moving acne images in the Images folder to their own folder**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6aRc4ko4GntM",
        "outputId": "4bc97bcb-9765-409e-d08d-1d9440f4122c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['acne affecting the back images',\n",
              " 'acne affecting the face images',\n",
              " 'acne and other follicular disorder images',\n",
              " 'facial acne images']"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Labels representing acne in DermNet's scrapped data\n",
        "acne_labels = list(image_df[image_df['skin_disorder_name'].str.contains('acne')]['skin_disorder_name'].unique())\n",
        "\n",
        "# removing acne labels whose images will not be used because there are not clear\n",
        "acne_labels.remove('infantile acne images')\n",
        "acne_labels.remove('steroid acne images')\n",
        "\n",
        "acne_labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AsotmgReG-Hf",
        "outputId": "83e2c639-3ce3-45d8-f5b6-ca0455382edd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "There are 702 acne images\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "['acne affecting the back images0.jpg',\n",
              " 'acne affecting the back images1.jpg',\n",
              " 'acne affecting the back images10.jpg',\n",
              " 'acne affecting the back images11.jpg',\n",
              " 'acne affecting the back images12.jpg']"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Getting the acne images file names\n",
        "original_acne_img = [image_name for image_name in os.listdir('Images/') \\\n",
        "                     if ('acne affecting the back images' in image_name) |\\\n",
        "                        ('acne affecting the face images' in image_name) |\\\n",
        "                        ('acne and other follicular disorder images' in image_name) |\\\n",
        "                        ('facial acne images' in image_name)\n",
        "                        ]\n",
        "\n",
        "# Confirming the number of acne images before any cleaning\n",
        "print('There are', len(original_acne_img),'acne images')\n",
        "original_acne_img[:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HrfY_IiyG-01",
        "outputId": "9edd0f38-b575-447d-e9b3-7c7f1a7e817d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "There are 702 acne images.\n"
          ]
        }
      ],
      "source": [
        "# Creating a new folder with just acne images to make cleaning easier\n",
        "folder_name = 'cleaned_images/acne_images/'\n",
        "\n",
        "# Checking if the folder exists and deleting it if it exists\n",
        "if os.path.exists(folder_name):\n",
        "    # deleting the folder and its contents\n",
        "    shutil.rmtree(folder_name)\n",
        "\n",
        "# create the new folder\n",
        "os.mkdir(folder_name)\n",
        "\n",
        "# Moving the images into that folder\n",
        "for img in original_acne_img:\n",
        "    origin = os.path.join('Images/', img)\n",
        "    destination = os.path.join(folder_name, img)\n",
        "    shutil.copy(origin, destination)\n",
        "\n",
        "# Confirming that the number of acne images after moving them to a separate folder is still 702\n",
        "acne_img = [image_name for image_name in os.listdir('cleaned_images/acne_images/')]\n",
        "print('There are', len(acne_img),'acne images.')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2MalKKLEHOLH"
      },
      "source": [
        "**ii. Combining the images into one folder**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c-wanBgnHYt-",
        "outputId": "5f863b49-1cf0-4356-8c7a-0bd1194ad427"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['07Acne081101.jpg',\n",
              " '07Acne0811011 - Copy.jpg',\n",
              " '07Acne0811011.jpg',\n",
              " '07AcnePittedScars.jpg',\n",
              " '07AcnePittedScars1 - Copy.jpg']"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# extra acne images\n",
        "extra_acne = [image_name for image_name in os.listdir('extra_images/extra_acne_images')]\n",
        "extra_acne[:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "olb3Jw3hHfVG",
        "outputId": "4df17227-65b2-4f7b-adc4-dcdf7cd2ea89"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "There are a total of 1427 acne images.\n"
          ]
        }
      ],
      "source": [
        "# moving the extra images into the acne folder\n",
        "for img in extra_acne:\n",
        "    origin = os.path.join('extra_images/extra_acne_images/', img)\n",
        "    destination = os.path.join('cleaned_images/acne_images/', img)\n",
        "    shutil.copy(origin, destination)\n",
        "\n",
        "# Confirming that the total acne images is 1427 before any cleaning\n",
        "acne_img = [image_name for image_name in os.listdir('cleaned_images/acne_images/')]\n",
        "print('There are a total of', len(acne_img),'acne images.')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j02nPOyKHkVr"
      },
      "source": [
        "**iii. Removing duplicate images from the folder**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pqqQG7mLHY48"
      },
      "outputs": [],
      "source": [
        "# Function for removing duplicated images.\n",
        "def drop_duplicated_images(folder):\n",
        "\n",
        "    # Define a threshold for image similarity\n",
        "    threshold = 8\n",
        "\n",
        "    # Define a dictionary to store the hash values and file paths of the images\n",
        "    image_hashes = {}\n",
        "    duplicated_images = []\n",
        "\n",
        "    # Loop through all the image files in a directory\n",
        "    for filename in os.listdir(folder):\n",
        "        # Load the image file\n",
        "        image = Image.open(os.path.join(folder, filename))\n",
        "\n",
        "         # Compute the hash value of the image using the average hash algorithm\n",
        "        hash_value = imagehash.average_hash(image)\n",
        "\n",
        "        # Check if the hash value is already in the dictionary\n",
        "        if hash_value in image_hashes:\n",
        "            # If a similar hash value already exists, delete the duplicate image\n",
        "            duplicated_images.append(filename)\n",
        "            os.remove(os.path.join(folder, filename))\n",
        "        else:\n",
        "             # Otherwise, add the hash value and file path to the dictionary\n",
        "            image_hashes[hash_value] = os.path.join(folder, filename)\n",
        "\n",
        "    return duplicated_images"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6R1sHPAfHq-9",
        "outputId": "bf0d7913-a830-4d13-da1c-4ac980d9a3ab"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "There are 1109 acne images after removing duplicated images\n"
          ]
        }
      ],
      "source": [
        "# Dropping duplicates\n",
        "duplicated_images = drop_duplicated_images('cleaned_images/acne_images/')\n",
        "\n",
        "# number of acne images after removing duplicated images (1109)\n",
        "acne_img = [image_name for image_name in os.listdir('cleaned_images/acne_images/')]\n",
        "print('There are', len(acne_img),'acne images after removing duplicated images')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aTyu7k64IHGJ"
      },
      "source": [
        "Acne and other follicular disorder images has a collection of different images. Only images that have acne as a specific label will be included, the others will be dropped from the dataset. </br>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cw1lKEEeIIEI",
        "outputId": "2f04dc3b-1722-4de3-8bba-bc3d74ef8732"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of acne images left: 1000\n"
          ]
        }
      ],
      "source": [
        "# dropping those images from the acne_images folder\n",
        "indexes_to_drop = [295, 296, 297, 298, 300, 303, 304, 307, 308, 309, 310, 311, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 325, 326, 328, 329, 330, 333, 337, 338, 339, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 354, 355, 359, 361, 362, 363, 364, 366, 367, 368, 371, 372, 373, 374, 375, 376, 378, 380, 381, 382, 384, 385, 387, 388, 389, 390, 392, 393, 395, 396, 397, 398, 402, 403, 405, 408, 409, 411, 413, 415, 416, 417, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 431, 432, 433, 434, 436, 437, 438, 441, 443, 444, 445, 446, 447]\n",
        "\n",
        "for filename in os.listdir('cleaned_images/acne_images/'):\n",
        "    for index in indexes_to_drop:\n",
        "        if f\"images{index}\" in filename.lower():\n",
        "            os.remove(os.path.join('cleaned_images/acne_images/', filename))\n",
        "\n",
        "print(\"Number of acne images left:\", len(os.listdir('cleaned_images/acne_images/')))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pv6DcygXIYJB"
      },
      "source": [
        "### **<u>Atopic dermatitis(Eczema) </u>**\n",
        "\n",
        "**Meaning**<br>\n",
        "Atopic dermatitis, also known as eczema, is a chronic inflammatory skin condition that is characterized by dry, itchy, and inflamed patches of skin. It is a common condition that can affect people of all ages, but it is most common in infants and children. <br>\n",
        "\n",
        "**Causes**<br>\n",
        "The exact causes of atopic dermatitis are not fully understood, but it is believed to be a combination of genetic and environmental factors. People with atopic dermatitis often have a genetic predisposition to the condition, and environmental triggers such as allergens, irritants, and stress can exacerbate the symptoms.\n",
        "\n",
        "**Symptoms**<br>\n",
        "The symptoms of atopic dermatitis can vary depending on the severity of the condition. Mild cases may only present with dry, itchy skin, while more severe cases can lead to red, inflamed, and weeping skin lesions. In some cases, the skin may become thickened and scaly. Atopic dermatitis can also cause significant discomfort and interfere with a person's quality of life.\n",
        "\n",
        "**Treatment**<br>\n",
        "Treatment options for eczema include using gentle soaps and moisturizers, avoiding harsh chemicals and irritants, and taking short, lukewarm baths or showers. Prescription creams or ointments containing corticosteroids or immunosuppressants may be used for more severe cases of eczema. Antihistamines can also be helpful in reducing itching. <br>\n",
        "Preventing flare-ups of eczema can be done by avoiding triggers such as certain foods, allergens, and irritants. Regular use of moisturizers can also help to keep the skin hydrated and reduce the risk of flare-ups.<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ltzWXG32IcxZ"
      },
      "source": [
        "#### **Cleaning Eczema images**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oVNaUo6zIc7W"
      },
      "source": [
        "**i. Moving eczema images in the Image folder to their own folder**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TjNSJUIZIXgP",
        "outputId": "9801b191-b0cb-443d-b280-24d16b813f3d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "8"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Labels representing eczema in Dermnet's scraped data.\n",
        "eczema_labels = image_df[(image_df['skin_disorder_name'].str.contains('eczema')) | \\\n",
        "                         (image_df['skin_disorder_name'].str.contains('atopic dermatitis images')) |\\\n",
        "                         (image_df['skin_disorder_name'].str.contains('hand dermatitis images')) |\\\n",
        "                         (image_df['skin_disorder_name'] == 'dermatitis images') |\\\n",
        "                         (image_df['skin_disorder_name'].str.contains('nummular dermatitis images'))] \\\n",
        "                         ['skin_disorder_name'].unique()\n",
        "len(eczema_labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IHe0qJ5HIkhe",
        "outputId": "c0d4bd27-43b4-454c-d96e-d0b5c3b33837"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "There are 631 eczema images.\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "['atopic dermatitis images1058.jpg',\n",
              " 'atopic dermatitis images1059.jpg',\n",
              " 'atopic dermatitis images1060.jpg',\n",
              " 'atopic dermatitis images1061.jpg',\n",
              " 'atopic dermatitis images1062.jpg']"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Getting the eczema images file names\n",
        "eczema_img = [image_name for image_name in os.listdir('Images/') if ('eczema' in image_name) |\n",
        "                                                                    ('atopic dermatitis images' in image_name) |\n",
        "                                                                    ('hand dermatitis images' in image_name) |\n",
        "                                                                    (image_name.startswith('dermatitis images'))|\n",
        "                                                                    ('nummular dermatitis images' in image_name)\n",
        "                                                                     ]\n",
        "\n",
        "# Confirming the number of eczema images before any cleaning\n",
        "print('There are', len(eczema_img),'eczema images.')\n",
        "eczema_img[:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "32MmSiaxIk5C"
      },
      "outputs": [],
      "source": [
        "# Creating a new folder with just eczema images to make cleaning easier\n",
        "folder_name = 'cleaned_images/eczema_images/'\n",
        "\n",
        "# Note📝: For reproducibility of the code, this step is important.\n",
        "         # If the folder is not dropped before an error will occur if you rerun this cell\n",
        "\n",
        "# Checking if the folder exists and deleting it if it exists\n",
        "if os.path.exists(folder_name):\n",
        "    # deleting the folder and its contents\n",
        "    shutil.rmtree(folder_name)\n",
        "\n",
        "# create the new folder\n",
        "os.mkdir(folder_name)\n",
        "\n",
        "# Moving the images into that folder\n",
        "for img in eczema_img:\n",
        "    origin = os.path.join('Images/', img)\n",
        "    destination = os.path.join(folder_name, img)\n",
        "    shutil.copy(origin, destination)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RPTERinlIrwF",
        "outputId": "6c0cefc9-cfd5-4ee5-c2a6-6719be451493"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "There are 631 eczema images.\n"
          ]
        }
      ],
      "source": [
        "# Confirming that the number of eczema images after moving them to a separate folder is still 631\n",
        "eczema_img = [image_name for image_name in os.listdir('cleaned_images/eczema_images/')]\n",
        "print('There are', len(eczema_img),'eczema images.')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TpAb7Gp-IvQP"
      },
      "source": [
        "**ii. Combining the images into one folder**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JoNDN3AiI3bw",
        "outputId": "3738701d-cce1-4ebe-b3a7-25efeb38f4b1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['eczema234.jpg',\n",
              " 'eczema289.jpg',\n",
              " 'eczema301.jpg',\n",
              " 'eczema403.jpg',\n",
              " 'eczema500.jpg']"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Extra eczema images\n",
        "extra_eczema = [image_name for image_name in os.listdir('extra_images/extra_eczema')]\n",
        "\n",
        "# The folder has a mixture of images. We will filter out the eczema images only\n",
        "extra_eczema_images = [image_name for image_name in extra_eczema\\\n",
        "                        if ('dermatitis' in image_name) |\\\n",
        "                        ('eczema' in image_name)]\n",
        "extra_eczema_images[:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dUgGj8DPI3k5",
        "outputId": "29b10e9f-9e64-4a19-eccb-5e7e57a97b95"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "There are a total of 1367 eczema images.\n"
          ]
        }
      ],
      "source": [
        "# This was done by moving the extra images into the eczema folder\n",
        "for img in extra_eczema_images:\n",
        "    origin = os.path.join('extra_images/extra_eczema_images_clean/', img)\n",
        "    destination = os.path.join('cleaned_images/eczema_images/', img)\n",
        "    shutil.copy(origin, destination)\n",
        "\n",
        "# Confirming that the total acne images is 1367 before any cleaning\n",
        "eczema_img = [image_name for image_name in os.listdir('cleaned_images/eczema_images/')]\n",
        "print('There are a total of', len(eczema_img),'eczema images.')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D8702_HjJAzd"
      },
      "source": [
        "**iii. Removing duplicate images from the folder**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ybEjSvqlI9iC",
        "outputId": "93dfe3f5-3200-4a1b-e467-73cdaadc3620"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "There are 1000 eczema images after removing duplicated images.\n"
          ]
        }
      ],
      "source": [
        "# Using a function created earlier to drop duplicates\n",
        "duplicated_images = drop_duplicated_images('cleaned_images/eczema_images/')\n",
        "\n",
        "# Confirming the number of images after dropping duplicates\n",
        "eczema_img = [image_name for image_name in os.listdir('cleaned_images/eczema_images/')]\n",
        "print('There are', len(eczema_img),'eczema images after removing duplicated images.')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YdXK-rUcJRgA"
      },
      "source": [
        "### **<u>Actinic keratosis</u>**\n",
        "**Meaning** <br>\n",
        "Actinic keratosis(AK) is a skin condition that is caused by long-term exposure to UV rays, resulting in the formation of rough, scaly patches on the skin. It is considered a precancerous condition because it has the potential to develop into squamous cell carcinoma, a type of skin cancer\n",
        "\n",
        "**Causes** <br>\n",
        "The primary cause of actinic keratosis is long-term exposure to UV rays from the sun or other sources such as tanning beds. People with fair skin, light-colored hair, and light-colored eyes are at a higher risk of developing AK. Other risk factors include a history of frequent sunburns, a weakened immune system, and exposure to chemicals such as coal tar or arsenic.\n",
        "\n",
        "**Symptoms** <br>\n",
        "The most common symptom of actinic keratosis is the formation of rough, scaly patches or lesions on the skin. These patches can be pink, red, or brown in color and may feel like sandpaper. They are usually found on areas of the skin that are frequently exposed to the sun, such as the face, scalp, ears, neck, hands, and arms. In some cases, the patches may itch or burn, and they may become inflamed or bleed if they are scratched or rubbed.\n",
        "\n",
        "**Treatment** <br>\n",
        "The treatment of actinic keratosis depends on the severity of the condition. Mild cases may be treated with topical creams or gels that contain medications such as imiquimod, fluorouracil, or diclofenac. These medications work by stimulating the immune system or causing the abnormal cells to die off. In more severe cases, cryotherapy (freezing the lesions with liquid nitrogen) or curettage (scraping off the lesions with a special tool) may be necessary. In rare cases where the lesions have developed into skin cancer, surgical removal may be required. It is also important to take steps to prevent further damage to the skin, such as wearing protective clothing and sunscreen, avoiding tanning beds, and staying out of the sun during peak hours.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JErwun_SJVZg"
      },
      "source": [
        "**i. Moving actinic keratosis images in the Image folder to their own folder**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OEZY-JqeJEUM",
        "outputId": "e764ee73-1ad3-4f78-8510-0a4fec944bb7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['actinic keratosis affecting the face images'\n",
            " 'actinic keratosis affecting the hand images'\n",
            " 'actinic keratosis affecting the legs and feet images'\n",
            " 'actinic keratosis affecting the scalp images'\n",
            " 'actinic keratosis dermoscopy images'\n",
            " 'actinic keratosis on the nose images'\n",
            " 'actinic keratosis treated with imiquimod images'\n",
            " 'granular parakeratosis images' 'keratosis pilaris images'\n",
            " 'seborrhoeic keratosis dermoscopy images' 'seborrhoeic keratosis images'\n",
            " 'solar keratosis affecting the face images'\n",
            " 'solar keratosis affecting the hand images'\n",
            " 'solar keratosis affecting the legs and feet images'\n",
            " 'solar keratosis affecting the scalp images'\n",
            " 'solar keratosis on the nose images'\n",
            " 'solar keratosis treated with imiquimod images']\n"
          ]
        }
      ],
      "source": [
        "# image labels with the name keratosis in DermNet's scrapped data\n",
        "print(image_df[image_df['skin_disorder_name'].str.contains('keratosis')]['skin_disorder_name'].unique())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KAT0y5FvJdD7"
      },
      "source": [
        "Actinic keratosis is also known as solar keratosis or senile keratosis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZabTj26uJZ_e"
      },
      "outputs": [],
      "source": [
        "# extra keratosis images and dataframe\n",
        "df = pd.read_csv('Data/ISIC_2019_Training_GroundTruth.csv')\n",
        "\n",
        "# filter df to get rows where AK = 1.0\n",
        "df1 = df.copy()\n",
        "df1 = df1[df1['AK'] == 1.0]\n",
        "df1['skin_disorder_name'] = df1['images']\n",
        "\n",
        "# drop the unwanted columns from df\n",
        "df1 = df1.drop(['MEL', 'NV', 'BCC', 'AK', 'BKL', 'DF', 'VASC', 'SCC', 'UNK'], axis=1)\n",
        "\n",
        "# Loop through each file in the folder and add the skin disorder name to the list\n",
        "img_names = []\n",
        "image_paths = []\n",
        "\n",
        "for file in os.listdir('extra_images/extra_actinic_keratosis_images'):\n",
        "    if file.endswith(\".jpg\"):\n",
        "        skin_disorder_name = file.split(\".\")[0]\n",
        "        img_names.append(skin_disorder_name)\n",
        "        image_paths.append(file)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tKrSKmkhJgFq",
        "outputId": "eb1282a6-0704-4f7b-bdd8-db23f28dd1e4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "There are 1391 actinic keratosis images\n"
          ]
        }
      ],
      "source": [
        "# Getting the keratosis images file names\n",
        "keratosis_img = [image_name for image_name in os.listdir('Images/') if ('actinic keratosis' in image_name) | ('solar keratosis' in image_name)]\n",
        "AK_img = [image_name for image_name in os.listdir('extra_images/extra_AK_and_BKL_images') if any(x in image_name for x in df1['images'].tolist())]\n",
        "AK_img2 = [image_name for image_name in os.listdir('extra_images/extra_actinic_keratosis_images')]\n",
        "\n",
        "# Checking if the folder exists and deleting it if it exists\n",
        "if os.path.exists('cleaned_images/keratosis_images/'):\n",
        "    # deleting the folder and its contents\n",
        "    shutil.rmtree('cleaned_images/keratosis_images/')\n",
        "\n",
        "# Creating a new folder with just keratosis images to make cleaning easier\n",
        "os.mkdir('cleaned_images/keratosis_images/')\n",
        "for img in keratosis_img:\n",
        "    origin = os.path.join('Images/', img)\n",
        "    destination = os.path.join('cleaned_images/keratosis_images/', img)\n",
        "    shutil.copy(origin, destination)\n",
        "\n",
        "for img in AK_img:\n",
        "    origin = os.path.join('extra_images/extra_AK_and_BKL_images/', img)\n",
        "    destination = os.path.join('cleaned_images/keratosis_images/', img)\n",
        "    shutil.copy(origin, destination)\n",
        "\n",
        "for img in AK_img2:\n",
        "    origin = os.path.join('extra_images/extra_actinic_keratosis_images/', img)\n",
        "    destination = os.path.join('cleaned_images/keratosis_images/', img)\n",
        "    shutil.copy(origin, destination)\n",
        "\n",
        "# Confirming that the number of keratosis images after moving them to a separate folder is still 1391\n",
        "keratosis_img = [image_name for image_name in os.listdir('cleaned_images/keratosis_images/')]\n",
        "print('There are', len(keratosis_img),'actinic keratosis images')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LkSLYx9jJmbG"
      },
      "source": [
        "**ii. Removing duplicate images from the folder**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q0kaUPLUJjLa",
        "outputId": "88ae1747-2831-45d2-a216-f41bafaffc8a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of actinic keratosis images after removing duplicated images: 1000\n"
          ]
        }
      ],
      "source": [
        "# call function to drop duplicates from image folder\n",
        "duplicated_images = drop_duplicated_images('cleaned_images/keratosis_images/')\n",
        "\n",
        "# number of images after removing duplicates\n",
        "keratosis_img = [image_name for image_name in os.listdir('cleaned_images/keratosis_images/')]\n",
        "print('Number of actinic keratosis images after removing duplicated images:', len(keratosis_img))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jThWFF52Jsxv"
      },
      "source": [
        "### **<u>Benign Keratosis-like Lesions**</u>\n",
        "**Meaning** <br>\n",
        "Benign Keratosis-like Lesions (BKL) are a group of benign skin lesions that resemble actinic keratosis (AK) but are not classified as AK because they do not have the same degree of dysplasia. BKL lesions can appear as small, scaly, or waxy bumps on the skin, ranging in color from light tan to dark brown. They typically occur on areas of the skin that have been exposed to the sun, such as the face, neck, scalp, and hands. Examples of BKL lesions include seborrheic keratosis, solar lentigo, and lichen planus-like keratosis.\n",
        "\n",
        "**Causes** <br>\n",
        "The exact cause of BKL is not known, but it is believed to be related to long-term sun exposure. Other factors that may contribute to the development of BKL include a weakened immune system, age, and a history of other skin conditions.\n",
        "\n",
        "**Symptoms**<br>\n",
        "BKL lesions typically appear as small, scaly, or waxy bumps on the skin. They may be light tan to dark brown in color and may have a rough, textured surface. They can be single or multiple and can occur on any part of the body, but are most commonly found on the face, neck, scalp, and hands.\n",
        "\n",
        "**Treatment**<br>\n",
        "BKL lesions are usually benign and do not require treatment unless they are causing symptoms or affecting the patient's appearance. Treatment options may include cryotherapy (freezing the lesion with liquid nitrogen), curettage (scraping the lesion off the skin), or topical medications such as 5-fluorouracil or imiquimod. In some cases, BKL lesions may be biopsied to confirm the diagnosis or rule out other skin conditions. It is important to protect the skin from sun exposure and to seek medical attention for any suspicious skin lesions."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LbRG2jZNJ2Jg"
      },
      "source": [
        "**i. Moving Benign Keratosis-like Lesions images to their own folder**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "643RrbfyJym-",
        "outputId": "695e1db7-8926-4d0e-ea2a-eaea9561c4d6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "There are 1003 BKL images\n"
          ]
        }
      ],
      "source": [
        "# filter df to get rows where BKL = 1.0\n",
        "BKL_df = df.copy()\n",
        "BKL_df = BKL_df[BKL_df['BKL'] == 1.0]\n",
        "BKL_df = BKL_df[~BKL_df[\"images\"].str.contains(\"downsampled\")]\n",
        "BKL_df['skin_disorder_name'] = BKL_df['images']\n",
        "\n",
        "# drop the unwanted columns and rows from df\n",
        "BKL_df = BKL_df.drop(['MEL', 'NV', 'BCC', 'AK', 'BKL', 'DF', 'VASC', 'SCC', 'UNK'], axis=1)\n",
        "BKL_df = BKL_df[:1003]\n",
        "\n",
        "# Getting the BKL images file names\n",
        "BKL_img = [image_name for image_name in os.listdir('extra_images/extra_AK_and_BKL_images') if any(x in image_name for x in BKL_df['images'].tolist())]\n",
        "\n",
        "# Checking if the folder exists and deleting it if it exists\n",
        "if os.path.exists('cleaned_images/BKL_images/'):\n",
        "    # deleting the folder and its contents\n",
        "    shutil.rmtree('cleaned_images/BKL_images/')\n",
        "\n",
        "# Creating a new folder with just BKL images to make cleaning easier\n",
        "os.mkdir('cleaned_images/BKL_images/')\n",
        "for img in BKL_img:\n",
        "    origin = os.path.join('extra_images/extra_AK_and_BKL_images/', img)\n",
        "    destination = os.path.join('cleaned_images/BKL_images/', img)\n",
        "    shutil.copy(origin, destination)\n",
        "\n",
        "# Number of BKL images after moving them to a separate folder\n",
        "BKL_img = [image_name for image_name in os.listdir('cleaned_images/BKL_images/')]\n",
        "print('There are', len(BKL_img),'BKL images')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wfEoU8a1J8h_"
      },
      "source": [
        "**ii. Removing duplicated images from the folder**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z6a2MmbZJ5Zi",
        "outputId": "a5d60c10-3ba9-450f-d9b1-0fac4e573555"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of BKL images after removing duplicated images: 1000\n"
          ]
        }
      ],
      "source": [
        "# use the function to drop duplicates from image folder\n",
        "duplicated_images2 = drop_duplicated_images('cleaned_images/BKL_images/')\n",
        "\n",
        "# number of images after removing duplicates\n",
        "BKL_img = [image_name for image_name in os.listdir('cleaned_images/BKL_images/')]\n",
        "print('Number of BKL images after removing duplicated images:', len(BKL_img))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RDDgvlrtKA3E"
      },
      "source": [
        "### **<u>Melanoma**</u>\n",
        "**Definition**<br>\n",
        "Melanoma is a disease in which malignant (cancer) cells form in melanocytes (cells that color the skin). There are different types of cancer that start in the skin. Melanoma can occur anywhere on the skin. Unusual moles, exposure to sunlight, and health history can affect the risk of melanoma.\n",
        "\n",
        "The most common type of melanoma is superficial spreading melanoma. It tends to spread across the surface of the skin, has uneven borders, and varies in color from brown to black, pink, or red.\n",
        "\n",
        "Nodular melanoma is another type that grows down into deeper layers of the skin and may appear as a raised bump or growth.\n",
        "\n",
        "Lentigo maligna melanoma tends to appear on parts of the body that get more sun, especially the face, and it often affects older people. It looks like a large, uneven dark patch on the surface of the skin.\n",
        "\n",
        "Metastatic melanoma occurs when the cancer spreads, or metastasizes, to other parts of the body, possibly including the lymph nodes, organs, or bones.\n",
        "\n",
        "Other rare types of melanoma also exist, and while it most commonly affects the skin, some affect internal tissues, as well as the eyes."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7368ufpxKYw2"
      },
      "source": [
        "**i. Moving Melanoma images to their own folder**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9hqGuIQrKUCe",
        "outputId": "6738ce10-5c99-4108-db77-200fcc68567a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['acral lentiginous melanoma images' 'amelanotic melanoma images'\n",
            " 'hypomelanotic malignant melanoma images'\n",
            " 'lentigo maligna melanoma images' 'melanoma in situ images'\n",
            " 'melanoma of nail unit images' 'metastatic melanoma images'\n",
            " 'nodular melanoma images' 'superficial spreading melanoma images']\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "## Labels representing melanoma in DermNet's scrapped data\n",
        "melanoma_labels = image_df[image_df['skin_disorder_name'].str.contains('melanoma')]['skin_disorder_name'].unique()\n",
        "print(melanoma_labels)\n",
        "\n",
        "#number of labels representing melanoma\n",
        "len(melanoma_labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TTz4N8AGKbny",
        "outputId": "493dca9c-f4e1-4f77-c4e3-feb793f353f9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "There are 5074 melanoma images\n"
          ]
        }
      ],
      "source": [
        "# Getting the melanoma images file names\n",
        "melanoma_img = [image_name for image_name in os.listdir('Images/') if 'melanoma' in image_name]\n",
        "\n",
        "# Checking if melanoma folder exists and deleting it if it exists\n",
        "if os.path.exists('cleaned_images/melanoma/'):\n",
        "    # deleting the folder and its contents\n",
        "    shutil.rmtree('cleaned_images/melanoma/')\n",
        "\n",
        "# Creating a new folder with just melanoma images to make cleaning easier\n",
        "os.mkdir('cleaned_images/melanoma/')\n",
        "for img in melanoma_img:\n",
        "    origin = os.path.join('Images/', img)\n",
        "    destination = os.path.join('cleaned_images/melanoma/', img)\n",
        "    shutil.copy(origin, destination)\n",
        "\n",
        "for filename in os.listdir('extra_images/extra_melanoma_images/'):\n",
        "    src_path = os.path.join('extra_images/extra_melanoma_images/', filename)\n",
        "    dst_path = os.path.join('cleaned_images/melanoma/', filename)\n",
        "    shutil.copy(src_path, dst_path)\n",
        "\n",
        "# Number of melanoma images after moving them to a separate folder\n",
        "melanoma_img = [image_name for image_name in os.listdir('cleaned_images/melanoma/')]\n",
        "print('There are', len(melanoma_img),'melanoma images')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "94w2jRelKhlz"
      },
      "source": [
        "**ii. Removing duplicated images from the folder**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lZGJRz4YKeat",
        "outputId": "dbdbe76a-9782-4179-c45b-867d58d75260"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "There are 4631 melanoma images after removing duplicate images\n"
          ]
        }
      ],
      "source": [
        "# Dropping duplicates\n",
        "duplicated_images = drop_duplicated_images('cleaned_images/melanoma/')\n",
        "\n",
        "melanoma_img = [image_name for image_name in os.listdir('cleaned_images/melanoma/')]\n",
        "print('There are', len(melanoma_img),'melanoma images after removing duplicate images')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IOovvmx6Klex"
      },
      "outputs": [],
      "source": [
        "# Function to randomly select 1000 images\n",
        "def reduce_images(folder_path):\n",
        "    # Get the list of image file name\n",
        "    file_names = os.listdir(folder_path)\n",
        "\n",
        "    # Shuffle the file names\n",
        "    random.shuffle(file_names)\n",
        "\n",
        "    # Select the first 1000 file names\n",
        "    selected_file_names = file_names[:1000]\n",
        "\n",
        "    # Create a new folder to store the selected images\n",
        "    selected_folder_path = f'{folder_path}_images'\n",
        "    os.mkdir(selected_folder_path)\n",
        "\n",
        "    # Copy the selected images to the new folder\n",
        "    for file_name in selected_file_names:\n",
        "        file_path = os.path.join(folder_path, file_name)\n",
        "        selected_file_path = os.path.join(selected_folder_path, file_name)\n",
        "        shutil.copy(file_path, selected_file_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tRFBlCjfKn0_",
        "outputId": "857d1de7-94a7-4b94-d2f9-3038abdaf5a5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of melanoma images: 1000\n"
          ]
        }
      ],
      "source": [
        "# select 1000 images\n",
        "reduce_images('cleaned_images/melanoma')\n",
        "print('Number of melanoma images:', len([image_name for image_name in os.listdir('cleaned_images/melanoma_images/')]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kZcd4mTIKrRS"
      },
      "source": [
        "### **<u>Psoriasis </u>**\n",
        "\n",
        "**Meaning**<br>\n",
        "Psoriasis is a chronic autoimmune skin disorder characterized by the rapid buildup of skin cells that form thick, silvery scales and itchy, dry, and red patches on the skin. It is a non-contagious condition that can affect any part of the body, including the scalp, nails, and joints. Psoriasis occurs when the immune system mistakenly attacks healthy skin cells, causing the skin cells to grow too quickly and accumulate on the skin's surface. The condition is typically lifelong and can vary in severity from mild to severe. While there is no cure for psoriasis, there are treatments available that can help manage symptoms and improve quality of life. <br>\n",
        "\n",
        "**Causes**<br>\n",
        "The exact cause of psoriasis is not fully understood, but it is believed to be a combination of genetic, environmental, and immune system factors. Some of the known factors that can trigger or exacerbate psoriasis include:<br>\n",
        "\n",
        "<ol>\n",
        "  <li>Genetics: Psoriasis tends to run in families, suggesting a genetic component to the condition.</li>\n",
        "  <li>Immune system dysfunction: Psoriasis is believed to be an autoimmune disorder, in which the immune system mistakenly attacks healthy skin cells, causing inflammation and other symptoms.</li>\n",
        "  <li>Environmental factors: Certain environmental factors can trigger or worsen psoriasis, such as infections, injuries to the skin, stress, smoking, and alcohol consumption.</li>\n",
        "  <li>Medications: Certain medications, such as lithium, beta-blockers, and antimalarials, can trigger or worsen psoriasis.</li>\n",
        "  <li>Hormonal changes: Changes in hormone levels, such as those that occur during puberty, pregnancy, and menopause, can trigger or worsen psoriasis.</li>\n",
        "</ol>\n",
        "\n",
        "**Symptoms**<br>\n",
        "Psoriasis symptoms can vary depending on the type and severity of the condition, but some common symptoms include:<br>\n",
        "<ol>\n",
        "  <li>Red, inflamed patches of skin: These patches may be covered with thick, silvery scales that may flake off or bleed if scratched.</li>\n",
        "  <li>Dry, cracked skin: The affected skin may be dry and itchy, and may crack and bleed in severe cases.</li>\n",
        "  <li>Thickened, pitted, or ridged nails: Psoriasis can affect the nails, causing them to become thickened, discolored, pitted, or ridged.</li>\n",
        "  <li>Joint pain and stiffness: In some cases, psoriasis can also cause joint pain and stiffness, a condition called psoriatic arthritis.</li>\n",
        "  <li>Itching and burning: Psoriasis patches may be itchy and burning, which can cause discomfort and distress.</li>\n",
        "  <li>Soreness or discomfort: Psoriasis patches can be painful and tender to the touch.</li>\n",
        "</ol>\n",
        "\n",
        "\n",
        "**Treatment**<br>\n",
        "Treatment options for eczema include using gentle soaps and moisturizers, avoiding harsh chemicals and irritants, and taking short, lukewarm baths or showers. Prescription creams or ointments containing corticosteroids or immunosuppressants may be used for more severe cases of eczema. Antihistamines can also be helpful in reducing itching. <br>\n",
        "Preventing flare-ups of eczema can be done by avoiding triggers such as certain foods, allergens, and irritants. Regular use of moisturizers can also help to keep the skin hydrated and reduce the risk of flare-ups.<br>\n",
        "<ol>\n",
        "  <li>Topical medications: These are creams, ointments, gels, or foams that are applied directly to the affected skin to reduce inflammation and itching. Topical medications may include corticosteroids, vitamin D analogues, retinoids, and tar preparations.</li>\n",
        "  <li>Phototherapy: This involves exposing the skin to ultraviolet light to slow down the growth of affected skin cells and reduce inflammation. Phototherapy can be done in a doctor's office or at home using a special light box.</li>\n",
        "  <li>Systemic medications: These are medications that are taken orally or by injection to suppress the immune system and reduce inflammation. Systemic medications may include methotrexate, cyclosporine, and biologics.</li>\n",
        "  <li>Lifestyle changes: Making changes to your diet, reducing stress, and avoiding triggers such as smoking and alcohol consumption may help to reduce the frequency and severity of psoriasis flare-ups.</li>\n",
        "  <li>Moisturizers: Applying moisturizers regularly can help to soothe dry, itchy skin and reduce the risk of flare-ups.</li>\n",
        "</ol>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n-fO64drKumP"
      },
      "source": [
        "**i. Moving psoriasis images in the Images folder to their own folder**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "204YTBrcK5nL",
        "outputId": "001cae60-dbbb-47a1-c6ad-9e73f315c10c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['chronic plaque psoriasis images', 'facial psoriasis images', 'flexural psoriasis images', 'generalised pustular psoriasis images', 'genital psoriasis images', 'guttate psoriasis images', 'nail psoriasis images', 'palmoplantar psoriasis images', 'psoriasis affecting the face images', 'psoriasis of the scalp images', 'pustular psoriasis of the hand and feet images']\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "11"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Labels representing acne in DermNet's scrapped data\n",
        "psoriasis_labels = list(image_df[image_df['skin_disorder_name'].str.contains('psoriasis')]['skin_disorder_name'].unique())\n",
        "print(psoriasis_labels)\n",
        "\n",
        "# Count of labels representing psoriasis\n",
        "len(psoriasis_labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gEfoQl3IK9my",
        "outputId": "6fd0b8f7-0345-4ada-b937-a5ac313b936d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "There are 1057 psoriasis images\n"
          ]
        }
      ],
      "source": [
        "# Getting the psoriasis images file names\n",
        "psoriasis_img = [image_name for image_name in os.listdir('Images/') if 'psoriasis' in image_name]\n",
        "\n",
        "# Checking if psoriasis folder exists and deleting it if it exists\n",
        "if os.path.exists('cleaned_images/psoriasis/'):\n",
        "    # deleting the folder and its contents\n",
        "    shutil.rmtree('cleaned_images/psoriasis/')\n",
        "\n",
        "# Creating a new folder with just psoriasis images to make cleaning easier\n",
        "os.mkdir('cleaned_images/psoriasis/')\n",
        "for img in psoriasis_img:\n",
        "    origin = os.path.join('Images/', img)\n",
        "    destination = os.path.join('cleaned_images/psoriasis/', img)\n",
        "    shutil.copy(origin, destination)\n",
        "\n",
        "for filename in os.listdir('extra_images/extra_psoriasis_images/'):\n",
        "    src_path = os.path.join('extra_images/extra_psoriasis_images/', filename)\n",
        "    dst_path = os.path.join('cleaned_images/psoriasis/', filename)\n",
        "    shutil.copy(src_path, dst_path)\n",
        "\n",
        "# Number of psoriasis images after moving them to a separate folder\n",
        "psoriasis_img = [image_name for image_name in os.listdir('cleaned_images/psoriasis/')]\n",
        "print('There are', len(psoriasis_img),'psoriasis images')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G-pjNGFELFMt"
      },
      "source": [
        "**ii. Removing duplicated images from the folder**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G2KU0VFLLErF",
        "outputId": "9b4f2078-07dc-477d-e607-862eadac3271"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "There are 1006 psoriasis images after removing duplicate images\n"
          ]
        }
      ],
      "source": [
        "# Dropping duplicates\n",
        "duplicated_images = drop_duplicated_images('cleaned_images/psoriasis/')\n",
        "\n",
        "psoriasis_img = [image_name for image_name in os.listdir('cleaned_images/psoriasis/')]\n",
        "print('There are', len(psoriasis_img),'psoriasis images after removing duplicate images')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6JlbCxAeLI2A",
        "outputId": "a06db33e-e7f7-4bce-f012-18a9dd9f0fd7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of psoriasis images: 1000\n"
          ]
        }
      ],
      "source": [
        "# select 1000 images\n",
        "reduce_images('cleaned_images/psoriasis')\n",
        "print('Number of psoriasis images:', len([image_name for image_name in os.listdir('cleaned_images/psoriasis_images/')]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iLUIjMOZLSJw"
      },
      "source": [
        "### **<u>Basal cell carcinoma</u>**\n",
        "**Meaning**<br>\n",
        "Basal cell carcinoma (BCC) is a malignant tumor that also arises from the basal cells in the skin. It is the most common type of skin cancer. Malignant tumors are more dangerous than benign tumors because they can grow quickly and invade and destroy surrounding tissues, leading to significant damage to the body's normal functions.\n",
        "\n",
        "**Causes**<br>\n",
        "Basal cell carcinoma are primarily caused by exposure to ultraviolet (UV) radiation from the sun or tanning beds. Prolonged exposure to UV radiation damages the DNA in the skin cells, leading to mutations that can cause the cells to grow and divide uncontrollably, eventually forming a tumor. Other factors that may increase the risk of developing these skin cancers include having fair skin, a history of sunburns or intense sun exposure, a weakened immune system, a family history of skin cancer, and certain genetic conditions.\n",
        "\n",
        "**Symptoms**<br>\n",
        "Basal cell carcinoma can have similar symptoms, but there are some differences. The condition typically present as raised, pearly, or translucent bumps or lesions on the skin that may be pink, red, or white in color. These lesions can sometimes ulcerate or bleed, and may develop a crust or scab. This sometimes appear as a flat, scaly, or pigmented patch on the skin.\n",
        "\n",
        "**Treatment**<br>\n",
        "Basal cell carcinoma treatment plan depends on the size, location, and extent of the tumor. Surgical removal of the tumor is the primary treatment, and there are different techniques available, such as excision, curettage and electrodesiccation, Mohs surgery, or radiation therapy. For small tumors, excision may be sufficient, while Mohs surgery is recommended for larger or more advanced tumors to ensure complete removal. Radiation therapy may be used as an alternative to surgery for some cases. Systemic chemotherapy or immunotherapy is rarely used for advanced basal cell carcinoma that has spread to other parts of the body."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mp1lNhzDLS3a"
      },
      "source": [
        "**i. Moving BCC images in the Images folder to their own folder**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zdnQJIcKLLDu",
        "outputId": "b149c424-6530-4506-a1d2-4a04695bf928"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "There are 1073 BCC images\n"
          ]
        }
      ],
      "source": [
        "# filter df to get rows where BKL = 1.0\n",
        "BCC_df = df.copy()\n",
        "BCC_df = BCC_df[BCC_df['BCC'] == 1.0]\n",
        "BCC_df = BCC_df[~BCC_df[\"images\"].str.contains(\"downsampled\")]\n",
        "BCC_df['skin_disorder_name'] = BCC_df['images']\n",
        "\n",
        "# drop the unwanted columns and rows from df\n",
        "BCC_df = BCC_df.drop(['MEL', 'NV', 'BCC', 'AK', 'BKL', 'DF', 'VASC', 'SCC', 'UNK'], axis=1)\n",
        "BCC_df = BCC_df[:1073]\n",
        "\n",
        "# Getting the BCC images file names\n",
        "BCC_img = [image_name for image_name in os.listdir('extra_images/extra_Bcc_images') if any(x in image_name for x in BCC_df['images'].tolist())]\n",
        "\n",
        "# Checking if the folder exists and deleting it if it exists\n",
        "if os.path.exists('cleaned_images/Bcc_images'):\n",
        "    # deleting the folder and its contents\n",
        "    shutil.rmtree('cleaned_images/Bcc_images')\n",
        "\n",
        "# Creating a new folder with just BCC images to make cleaning easier\n",
        "os.mkdir('cleaned_images/Bcc_images')\n",
        "for img in BCC_img:\n",
        "    origin = os.path.join('extra_images/extra_Bcc_images/', img)\n",
        "    destination = os.path.join('cleaned_images/Bcc_images/', img)\n",
        "    shutil.copy(origin, destination)\n",
        "\n",
        "# Number of BCC images after moving them to a separate folder\n",
        "BCC_img = [image_name for image_name in os.listdir('cleaned_images/Bcc_images')]\n",
        "print('There are', len(BCC_img),'BCC images')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JdGpd9J5LgzP"
      },
      "source": [
        "**ii. Removing duplicated images from the folder**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a7ECj4RvLhf6",
        "outputId": "f23c226a-f8f1-48ca-d957-734e379d3567"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of BCC images after removing duplicated images: 1000\n"
          ]
        }
      ],
      "source": [
        "# use the function to drop duplicates from image folder\n",
        "duplicated_images2 = drop_duplicated_images('cleaned_images/Bcc_images/')\n",
        "\n",
        "# number of images after removing duplicates\n",
        "BCC_img = [image_name for image_name in os.listdir('cleaned_images/Bcc_images')]\n",
        "print('Number of BCC images after removing duplicated images:', len(BCC_img))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F-CHvYr9Ljx4"
      },
      "source": [
        "### **<u>Tinea </u>**\n",
        "\n",
        "**Meaning**<br>\n",
        "Tinea is popularly known as Ringworm (scalp and body). It is a type of fungal infection of the skin, hair, or nails. It is caused by a group of fungi called dermatophytes, which can thrive on the skin's keratin, a tough protein that forms the outer layer of the skin, hair, and nails. It can affect various parts of the body, including the feet (athlete's foot), groin (jock itch), scalp (tinea capitis), beard area (tinea barbae), and body (tinea corporis).<br>\n",
        "Tinea infections are highly contagious and can spread through contact with infected skin or objects. Treatment for tinea typically involves antifungal medications, which can be applied topically or taken orally. It is also important to practice good hygiene and avoid sharing personal items, such as towels and clothing, to prevent the spread of infection. <br>\n",
        "\n",
        "**Causes**<br>\n",
        "Tinea is caused by a group of fungi called dermatophytes. These fungi can thrive on the skin, hair, or nails and cause infections in various parts of the body. The specific type of dermatophyte that causes tinea may vary depending on the affected area. <br>\n",
        "Some of the common causes of tinea include:<br>\n",
        "\n",
        "<ol>\n",
        "  <li>Direct contact with an infected person or animal - Tinea can spread from person to person or from animal to person through direct contact with infected skin or hair.</li>\n",
        "  <li>Sharing personal items - Sharing personal items such as towels, clothing, or hairbrushes can also spread tinea.</li>\n",
        "  <li>Warm and humid environment - Dermatophytes thrive in warm and humid environments, making certain areas of the body more susceptible to tinea infections, such as the feet and groin.</li>\n",
        "  <li>Weakened immune system - People with weakened immune systems, such as those with HIV or undergoing chemotherapy, may be more prone to tinea infections.</li>\n",
        "  <li>Skin injury or irritation - Skin that is injured or irritated, such as from scratching or wearing tight-fitting clothing, may be more susceptible to tinea infections.</li>\n",
        "</ol>\n",
        "Preventing the spread of tinea involves good hygiene practices, such as keeping the skin clean and dry, avoiding sharing personal items, and wearing protective clothing in public areas such as locker rooms and swimming pools.<br>\n",
        "\n",
        "**Symptoms**<br>\n",
        "The symptoms of tinea can vary depending on the affected area of the body. Some common symptoms of tinea infections include:<br>\n",
        "<ol>\n",
        "  <li>Itching - Tinea infections can cause intense itching, which can be worse at night.</li>\n",
        "  <li>Scaling or flaking - Tinea infections can cause the skin to become scaly or flaky.</li>\n",
        "  <li>Blisters - Some types of tinea infections, such as tinea pedis (athlete's foot), can cause small fluid-filled blisters.</li>\n",
        "  <li>Hair loss - Tinea infections of the scalp can cause hair to become brittle and break off, resulting in hair loss.</li>\n",
        "  <li>Thickened or discolored nails - Tinea infections of the nails can cause the nails to become thickened, discolored, and brittle.</li>\n",
        "</ol>\n",
        "\n",
        "**Treatment**<br>\n",
        "The treatment for tinea infections typically involves antifungal medications, which can be applied topically or taken orally. The specific treatment will depend on the location and severity of the infection. Some common treatment options include:<br>\n",
        "<ol>\n",
        "  <li>Topical antifungal medications - These medications are applied directly to the skin or nails and include creams, ointments, sprays, and powders. Topical antifungal medications are often effective for mild to moderate tinea infections.</li>\n",
        "  <li>Oral antifungal medications - These medications are taken by mouth and may be prescribed for more severe or widespread tinea infections. Oral antifungal medications include terbinafine, fluconazole, and itraconazole.</li>\n",
        "  <li>Medicated shampoo - A medicated shampoo may be recommended for tinea infections of the scalp. These shampoos contain antifungal medication and are used to help control the infection and reduce symptoms.</li>\n",
        "  <li>Removal of infected nails - In severe cases of tinea infections of the nails, the infected nail may need to be removed to allow for the application of antifungal medication to the underlying nail bed.</li>\n",
        "</ol>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YQ5J2fqxLobN"
      },
      "source": [
        "**i. Moving tinea images in the Images folder to their own folder**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Su7pjeSJLwiy",
        "outputId": "95aa0e9d-caf6-40e1-f20d-0beafdde87c5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['tinea corporis images', 'tinea pedis images'], dtype=object)"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "## Labels representing tinea in DermNet's scrapped data\n",
        "tinea_labels = image_df[image_df['skin_disorder_name'].str.contains('tinea')]['skin_disorder_name'].unique()\n",
        "tinea_labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AUAj4PYvLymo",
        "outputId": "6eeb2a3d-e4c2-45e1-a3ff-0c78dcec8778"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "There are 1678 tinea images\n"
          ]
        }
      ],
      "source": [
        "# Getting the tinea images file names\n",
        "tinea_img = [image_name for image_name in os.listdir('Images/') if 'tinea' in image_name]\n",
        "\n",
        "# Checking if tinea folder exists and deleting it if it exists\n",
        "if os.path.exists('cleaned_images/tinea/'):\n",
        "    # deleting the folder and its contents\n",
        "    shutil.rmtree('cleaned_images/tinea/')\n",
        "\n",
        "# Creating a new folder with just tinea images to make cleaning easier\n",
        "os.mkdir('cleaned_images/tinea/')\n",
        "for img in tinea_img:\n",
        "    origin = os.path.join('Images/', img)\n",
        "    destination = os.path.join('cleaned_images/tinea/', img)\n",
        "    shutil.copy(origin, destination)\n",
        "\n",
        "for filename in os.listdir('extra_images/extra_tinea_images/'):\n",
        "    src_path = os.path.join('extra_images/extra_tinea_images/', filename)\n",
        "    dst_path = os.path.join('cleaned_images/tinea/', filename)\n",
        "    shutil.copy(src_path, dst_path)\n",
        "\n",
        "# Number of tinea images after moving them to a separate folder\n",
        "tinea_img = [image_name for image_name in os.listdir('cleaned_images/tinea/')]\n",
        "print('There are', len(tinea_img),'tinea images')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v_hez_sUL1Zn"
      },
      "source": [
        "**ii. Removing duplicated images from the folder**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xE_yEKhbL0vP",
        "outputId": "5aba14d2-6c19-464a-a6b2-9140143e5b52"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "There are 1661 tinea images after removing duplicate images\n"
          ]
        }
      ],
      "source": [
        "# Dropping duplicates\n",
        "duplicated_images = drop_duplicated_images('cleaned_images/tinea/')\n",
        "\n",
        "tinea_img = [image_name for image_name in os.listdir('cleaned_images/tinea/')]\n",
        "print('There are', len(tinea_img),'tinea images after removing duplicate images')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wC_mlKgZL017",
        "outputId": "774b17f5-e5e6-4a43-96cf-9d07516179a5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of tinea images: 1000\n"
          ]
        }
      ],
      "source": [
        "# select 1000 images\n",
        "reduce_images('cleaned_images/tinea')\n",
        "print('Number of tinea images:', len([image_name for image_name in os.listdir('cleaned_images/tinea_images/')]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pK6bYsKVi19w"
      },
      "source": [
        "# **Modelling**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4OjTnMOhu4et"
      },
      "source": [
        "*   **_import necessary libraries_**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "07hKBP_QtkxJ"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import random\n",
        "import shutil\n",
        "import time\n",
        "import zipfile\n",
        "import PIL\n",
        "from PIL import Image\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "from joblib import dump,load\n",
        "from sklearn.model_selection import train_test_split\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, BatchNormalization\n",
        "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "from keras import regularizers\n",
        "from keras.models import Model\n",
        "from tensorflow.keras.layers import GlobalAveragePooling2D\n",
        "from tensorflow.keras.optimizers import Adamax\n",
        "from keras.applications import EfficientNetB2\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.applications.vgg16 import VGG16\n",
        "from sklearn.metrics import f1_score, recall_score, confusion_matrix, classification_report"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qKFYMVkRtu8T",
        "outputId": "5f99682a-8b73-4676-8626-9eaf87361a84"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xQyyBejEqaIk"
      },
      "source": [
        "**_Unzipping the data_**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GHEUOwBCsXYW",
        "outputId": "eb5a61fa-db9b-4b59-fa59-4f704568fcae"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting patool\n",
            "  Downloading patool-1.12-py2.py3-none-any.whl (77 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.5/77.5 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: patool\n",
            "Successfully installed patool-1.12\n"
          ]
        }
      ],
      "source": [
        "pip install patool"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c68IG2UFsa2b",
        "outputId": "2c83deed-cd99-4910-e8a8-4e6f173c04fd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "patool: Extracting /content/drive/MyDrive/cleaned_images.rar ...\n",
            "patool: running /usr/bin/unrar x -- /content/drive/MyDrive/cleaned_images.rar\n",
            "patool:     with cwd='/content/drive/MyDrive'\n",
            "patool: ... /content/drive/MyDrive/cleaned_images.rar extracted to `/content/drive/MyDrive'.\n"
          ]
        }
      ],
      "source": [
        "import patoolib\n",
        "\n",
        "# Checking if the folder exists and deleting it if it exists\n",
        "if os.path.exists('/content/drive/MyDrive/cleaned_images.rar'):\n",
        "     #deleting the folder and its contents\n",
        "  shutil.rmtree('/content/drive/MyDrive/cleaned_images')\n",
        "\n",
        "# Extract the images from the zip file\n",
        "  patoolib.extract_archive(\"/content/drive/MyDrive/cleaned_images.rar\",\n",
        "                         outdir=\"/content/drive/MyDrive\")\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Tzi8CB8tkxW"
      },
      "source": [
        "**Checking for class imbalance**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fYAuKE0_tkxX",
        "outputId": "b897bf17-2e5c-456d-ee4c-81f399edb9aa"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'acne_images': 1000,\n",
              " 'Bcc_images': 1000,\n",
              " 'BKL_images': 1000,\n",
              " 'eczema_images': 1000,\n",
              " 'keratosis_images': 1000,\n",
              " 'melanoma_images': 1000,\n",
              " 'psoriasis_images': 1000,\n",
              " 'tinea_images': 1000}"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Define the root directory of your dataset\n",
        "root_dir = \"/content/drive/MyDrive/cleaned_images\"\n",
        "\n",
        "subdirs = [\"acne_images\", \"Bcc_images\", \"BKL_images\", \"eczema_images\",\n",
        "           \"keratosis_images\", \"melanoma_images\", \"psoriasis_images\", \"tinea_images\"]\n",
        "\n",
        "{disorder :len(os.listdir(f'{root_dir}/{disorder}')) for disorder in subdirs}\n",
        "\n",
        "# Observation:\n",
        "  # There is no class imbalance. All classes have 1000 images each."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WL6Uf3X5B0OW"
      },
      "source": [
        "**Finding the average resolution for images dataset**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tbndb1lzB087",
        "outputId": "ca98e784-d661-4b87-bae8-3369f4bee880"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The average height is:500 and the average width is:579\n"
          ]
        }
      ],
      "source": [
        "widths = []\n",
        "heights = []\n",
        "\n",
        "for disorder in subdirs:\n",
        "  for img in os.listdir(f'{root_dir}/{disorder}'):\n",
        "    img_path = os.path.join(f'{root_dir}/{disorder}', img) # Making image file path\n",
        "    im = Image.open(img_path)\n",
        "    widths.append(im.size[0])\n",
        "    heights.append(im.size[1])\n",
        "\n",
        "avg_height = round(sum(heights)/len(heights))\n",
        "avg_width = round(sum(widths)/len(widths))\n",
        "\n",
        "print(f'The average height is:{avg_height} and the average width is:{avg_width}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ST7VgHm1tkxa"
      },
      "source": [
        "* **_Train-Validation-Test Split_**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fRXSNgXQtkxb"
      },
      "outputs": [],
      "source": [
        "# Define the root directory of your dataset\n",
        "root_dir = \"/content/drive/MyDrive/cleaned_images\"\n",
        "\n",
        "subdirs = [\"acne_images\", \"Bcc_images\", \"BKL_images\", \"eczema_images\",\n",
        "           \"keratosis_images\", \"melanoma_images\", \"psoriasis_images\", \"tinea_images\"]\n",
        "\n",
        "# Checking if the folder exists and deleting it if it exists\n",
        "for folder_name in ['train', 'validation', 'test']:\n",
        "  if os.path.exists(f'{root_dir}/{folder_name}'):\n",
        "    # deleting the folder and its contents\n",
        "    shutil.rmtree(f'{root_dir}/{folder_name}')\n",
        "\n",
        "# create subdirectories for train, validation and test sets\n",
        "train_dir = os.path.join(root_dir, 'train')\n",
        "validation_dir = os.path.join(root_dir, 'validation')\n",
        "test_dir = os.path.join(root_dir, 'test')\n",
        "\n",
        "os.makedirs(train_dir, exist_ok=True)\n",
        "os.makedirs(validation_dir, exist_ok=True)\n",
        "os.makedirs(test_dir, exist_ok=True)\n",
        "\n",
        "# set the ratio for train, validation and test sets\n",
        "train_ratio = 0.8\n",
        "val_ratio = 0.1\n",
        "test_ratio = 0.1\n",
        "\n",
        "for subdir in subdirs:\n",
        "    subdir_path = os.path.join(root_dir, subdir)\n",
        "    train_subdir = os.path.join(train_dir, subdir)\n",
        "    validation_subdir = os.path.join(validation_dir, subdir)\n",
        "    test_subdir = os.path.join(test_dir, subdir)\n",
        "    os.makedirs(train_subdir, exist_ok=True)\n",
        "    os.makedirs(validation_subdir, exist_ok=True)\n",
        "    os.makedirs(test_subdir, exist_ok=True)\n",
        "\n",
        "    image_names = [name for name in os.listdir(subdir_path)]\n",
        "    image_names = random.sample(image_names, len(image_names))\n",
        "\n",
        "    for i, image_name in enumerate(image_names):\n",
        "            # Defining the source path:\n",
        "            source_path = os.path.join(subdir_path, image_name)\n",
        "\n",
        "            if i < len(image_names) * train_ratio:\n",
        "                  target_path = os.path.join(train_subdir, image_name)\n",
        "                  shutil.copyfile(source_path, target_path)\n",
        "            elif i < len(image_names) * (train_ratio + val_ratio):\n",
        "                  target_path = os.path.join(validation_subdir, image_name)\n",
        "                  shutil.copyfile(source_path, target_path)\n",
        "            else:\n",
        "                  target_path = os.path.join(test_subdir, image_name)\n",
        "                  shutil.copyfile(source_path, target_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rnr_pLf7tkxe",
        "outputId": "015e3360-276e-4195-b4ab-154d0eec791c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'acne_images': 800,\n",
              " 'Bcc_images': 800,\n",
              " 'BKL_images': 800,\n",
              " 'eczema_images': 800,\n",
              " 'keratosis_images': 800,\n",
              " 'melanoma_images': 800,\n",
              " 'psoriasis_images': 800,\n",
              " 'tinea_images': 800}"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Checking the distribution of the image after the train-validation-test split\n",
        "{disorder :len(os.listdir(f'{root_dir}/train/{disorder}')) for disorder in subdirs}\n",
        "\n",
        "# Observation:\n",
        "  # There is no class imbalance as a result of the train-validation-test split. Each class has 800 images in the training set."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BHhvZEBMtkxh",
        "outputId": "b5d77341-9b7d-476d-89ed-b8387ecd0cb4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "6400"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# No. of images in train set\n",
        "train_images = [images for disease in subdirs \\\n",
        "                     for images in os.listdir(f'{root_dir}/train/{disease}')]\n",
        "len(train_images)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EnaXFd-wtkxi",
        "outputId": "2f9a3588-09b4-48de-bc55-a27e2cc95e7d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "800"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# No of images in validation set\n",
        "validation_images = [images for disease in subdirs \\\n",
        "                     for images in os.listdir(f'{root_dir}/validation/{disease}')]\n",
        "len(validation_images)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cAGV2I57tkxj",
        "outputId": "1ac5b3d0-8219-4a29-c907-4b4cd907ef6c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "800"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# No of images in test set\n",
        "test_images = [images for disease in subdirs \\\n",
        "                     for images in os.listdir(f'{root_dir}/test/{disease}')]\n",
        "len(test_images)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dHI4wPTMtkxk"
      },
      "outputs": [],
      "source": [
        "# Define the batch size and number of epochs for training\n",
        "batch_size = 32\n",
        "epochs = 40\n",
        "# Define the input shape of the images (300 was choosen so that we can be able to use EfficientNetB2 model later on)\n",
        "input_shape = (300, 300, 3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a2zowcqaYf0e"
      },
      "outputs": [],
      "source": [
        "# Function to plot loss and accuracy metrics vs number of epochs with train and validation sets\n",
        "\n",
        "def visualize_training_results(results):\n",
        "    history = results.history\n",
        "\n",
        "    start_epoch = 0\n",
        "    epoch_count = len(history['accuracy']) + start_epoch\n",
        "    epochs = []\n",
        "    for i in range(start_epoch, epoch_count):\n",
        "        epochs.append(i+1)\n",
        "\n",
        "    # Obtaining the epoch with the lowest validation loss\n",
        "    index_loss = np.argmin(history['val_loss'])\n",
        "    val_lowest = history['val_loss'][index_loss]\n",
        "\n",
        "    # Obtaining the epoch with the highest validation accuracy\n",
        "    index_acc = np.argmax(history['val_accuracy'])\n",
        "    acc_highest = history['val_accuracy'][index_acc]\n",
        "\n",
        "    # Best epoch labels:\n",
        "    epoch_loss_label = 'best epoch= '+ str(index_loss+1 +start_epoch)\n",
        "    epoch_acc_label =  'best epoch= '+ str(index_acc + 1+ start_epoch)\n",
        "\n",
        "    # setting the style to be used\n",
        "    plt.style.use('fivethirtyeight')\n",
        "\n",
        "    # create two axis objects side by side\n",
        "    fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(20,8))\n",
        "\n",
        "    # plot loss on the first axis\n",
        "    ax1.plot(history['val_loss'], color='r', label='Training loss')\n",
        "    ax1.plot(history['loss'], color='g', label='Validation loss')\n",
        "    ax1.scatter(index_loss+1 +start_epoch,val_lowest, c= 'blue', s=100, label=epoch_loss_label)\n",
        "    ax1.legend()\n",
        "    ax1.set_title('Training and Validation Loss')\n",
        "    ax1.set_xlabel('Epochs')\n",
        "    ax1.set_ylabel('Loss')\n",
        "\n",
        "      # plot recall and f1 score metrics on the second axis\n",
        "    ax2.plot(history['val_accuracy'], color='r', label= 'Training Accuracy')\n",
        "    ax2.plot(history['accuracy'], color='g',label= 'Validation Accuracy')\n",
        "    ax2.scatter(index_acc+1 +start_epoch, acc_highest, c='blue', s=100,  label=epoch_acc_label)\n",
        "    ax2.legend()\n",
        "    ax2.set_title('Traing and Validation  Accuracy score')\n",
        "    ax2.set_xlabel('Epochs')\n",
        "    ax2.set_ylabel('Accuracy')\n",
        "\n",
        "    plt.tight_layout()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6jkezX2xXJiz"
      },
      "source": [
        "**Model 1(Baseline Model):**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fhif01zFtkxk"
      },
      "outputs": [],
      "source": [
        "# Create an instance of ImageDataGenerator for data augmentation\n",
        "train_datagen = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "val_datagen = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "test_datagen = ImageDataGenerator(rescale=1./255)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sf25DysHtkxl",
        "outputId": "0920322a-43f0-4bdd-df5f-ec3a469f6064"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 6400 images belonging to 8 classes.\n",
            "Found 800 images belonging to 8 classes.\n",
            "Found 800 images belonging to 8 classes.\n"
          ]
        }
      ],
      "source": [
        "# Create generators for loading the train, validation, and test sets\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "    train_dir,\n",
        "    target_size=input_shape[:2],\n",
        "    batch_size=batch_size,\n",
        "    class_mode='categorical')\n",
        "\n",
        "val_generator = val_datagen.flow_from_directory(\n",
        "    validation_dir,\n",
        "    target_size=input_shape[:2],\n",
        "    batch_size=batch_size,\n",
        "    class_mode='categorical')\n",
        "\n",
        "test_generator = test_datagen.flow_from_directory(\n",
        "    test_dir,\n",
        "    target_size=input_shape[:2],\n",
        "    batch_size=batch_size,\n",
        "    class_mode='categorical')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LbQS6Qibtkxm"
      },
      "outputs": [],
      "source": [
        "# Define CNN model\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(64, 3, input_shape=input_shape, activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(64, 3, activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Conv2D(128, 3, activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(128, 3, activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Conv2D(256, 3, activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(256, 3, activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(512, activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(8, activation='softmax'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EoWhsp_Gtkxn",
        "outputId": "ab34b25b-37fb-4d4b-cb63-b9f81fa5eb65"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 454, 454, 64)      1792      \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 454, 454, 64)     256       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 452, 452, 64)      36928     \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 452, 452, 64)     256       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 226, 226, 64)     0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 226, 226, 64)      0         \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 224, 224, 128)     73856     \n",
            "                                                                 \n",
            " batch_normalization_2 (Batc  (None, 224, 224, 128)    512       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 222, 222, 128)     147584    \n",
            "                                                                 \n",
            " batch_normalization_3 (Batc  (None, 222, 222, 128)    512       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 111, 111, 128)    0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 111, 111, 128)     0         \n",
            "                                                                 \n",
            " conv2d_4 (Conv2D)           (None, 109, 109, 256)     295168    \n",
            "                                                                 \n",
            " batch_normalization_4 (Batc  (None, 109, 109, 256)    1024      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " conv2d_5 (Conv2D)           (None, 107, 107, 256)     590080    \n",
            "                                                                 \n",
            " batch_normalization_5 (Batc  (None, 107, 107, 256)    1024      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 53, 53, 256)      0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 53, 53, 256)       0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 719104)            0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 512)               368181760 \n",
            "                                                                 \n",
            " batch_normalization_6 (Batc  (None, 512)              2048      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 512)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 8)                 4104      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 369,336,904\n",
            "Trainable params: 369,334,088\n",
            "Non-trainable params: 2,816\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JymRIcKYtkxo"
      },
      "outputs": [],
      "source": [
        "early_stopping = [EarlyStopping(monitor='val_loss', patience=10),\n",
        "                  ModelCheckpoint(filepath='/content/drive/MyDrive/skin_disorder_classifier1.h5',\n",
        "                                  monitor='val_accuracy', save_best_only=True)\n",
        "                  ]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UL8N6mUtvit7"
      },
      "outputs": [],
      "source": [
        "# Train the model using the train and validation generators\n",
        "history = model.fit(train_generator,\n",
        "          steps_per_epoch=train_generator.samples // train_generator.batch_size,\n",
        "          epochs=epochs,\n",
        "          verbose=0,\n",
        "          validation_data=val_generator,\n",
        "          validation_steps=val_generator.samples // val_generator.batch_size,\n",
        "          callbacks = early_stopping\n",
        "          )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cwIKNmuntkxq",
        "outputId": "5ce97dee-29de-40fa-b4ba-0c981a6f174c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "25/25 [==============================] - 10s 409ms/step - loss: 1.3787 - accuracy: 0.6513\n",
            "Validation accuracy: 0.6512500047683716\n"
          ]
        }
      ],
      "source": [
        "#Test accuracy\n",
        "test_loss, test_acc = model.evaluate(test_generator, verbrose=0)\n",
        "print((f'Test Accuracy: {test_acc:.2f}'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "id": "mkdKg-N4tkxr",
        "outputId": "1f816b22-825f-459d-ee4b-549cf0e04e2a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1500x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "visualize_training_results(history)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HtC9gOOHXY_C"
      },
      "source": [
        "**Model 2(Data Augmentation):**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iKwIeJ6TXflh"
      },
      "outputs": [],
      "source": [
        "# Create an instance of ImageDataGenerator for data augmentation\n",
        "train_datagen = ImageDataGenerator(\n",
        "    rescale=1./255,\n",
        "    rotation_range=20,\n",
        "    width_shift_range=0.2,\n",
        "    height_shift_range=0.2,\n",
        "    shear_range=0.2,\n",
        "    zoom_range=0.2,\n",
        "    horizontal_flip=True,\n",
        "    vertical_flip=True,\n",
        "    brightness_range=[0.2,1.2],\n",
        "    fill_mode='nearest'\n",
        "    )\n",
        "\n",
        "val_datagen = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "test_datagen = ImageDataGenerator(rescale=1./255)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GEBqXXvqXkCm",
        "outputId": "3ee492a7-0732-429e-9811-1601e41ddccf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 6400 images belonging to 8 classes.\n",
            "Found 800 images belonging to 8 classes.\n",
            "Found 800 images belonging to 8 classes.\n"
          ]
        }
      ],
      "source": [
        "# Create generators for loading the train, validation, and test sets\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "    train_dir,\n",
        "    target_size=input_shape[:2],\n",
        "    batch_size=batch_size,\n",
        "    class_mode='categorical')\n",
        "\n",
        "val_generator = val_datagen.flow_from_directory(\n",
        "    validation_dir,\n",
        "    target_size=input_shape[:2],\n",
        "    batch_size=batch_size,\n",
        "    class_mode='categorical')\n",
        "\n",
        "test_generator = test_datagen.flow_from_directory(\n",
        "    test_dir,\n",
        "    target_size=input_shape[:2],\n",
        "    batch_size=batch_size,\n",
        "    class_mode='categorical')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sgo2ricJEJZa",
        "outputId": "367db1aa-abbf-4710-e3ef-06a2f6672747"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/40\n",
            "200/200 [==============================] - 150s 631ms/step - loss: 2.1219 - accuracy: 0.3086 - val_loss: 2.3447 - val_accuracy: 0.1850\n",
            "Epoch 2/40\n",
            "200/200 [==============================] - 114s 569ms/step - loss: 1.5693 - accuracy: 0.4466 - val_loss: 1.7570 - val_accuracy: 0.3125\n",
            "Epoch 3/40\n",
            "200/200 [==============================] - 128s 638ms/step - loss: 1.3168 - accuracy: 0.5223 - val_loss: 1.8337 - val_accuracy: 0.3237\n",
            "Epoch 4/40\n",
            "200/200 [==============================] - 118s 589ms/step - loss: 1.1318 - accuracy: 0.5800 - val_loss: 1.7469 - val_accuracy: 0.3812\n",
            "Epoch 5/40\n",
            "200/200 [==============================] - 120s 597ms/step - loss: 1.0029 - accuracy: 0.6233 - val_loss: 1.2640 - val_accuracy: 0.4975\n",
            "Epoch 6/40\n",
            "200/200 [==============================] - 129s 643ms/step - loss: 0.9256 - accuracy: 0.6509 - val_loss: 1.0318 - val_accuracy: 0.6150\n",
            "Epoch 7/40\n",
            "200/200 [==============================] - 118s 590ms/step - loss: 0.8491 - accuracy: 0.6789 - val_loss: 1.7821 - val_accuracy: 0.4487\n",
            "Epoch 8/40\n",
            "200/200 [==============================] - 122s 610ms/step - loss: 0.7982 - accuracy: 0.6997 - val_loss: 1.4157 - val_accuracy: 0.5362\n",
            "Epoch 9/40\n",
            "200/200 [==============================] - 119s 591ms/step - loss: 0.6891 - accuracy: 0.7461 - val_loss: 1.3421 - val_accuracy: 0.5050\n",
            "Epoch 10/40\n",
            "200/200 [==============================] - 138s 690ms/step - loss: 0.6081 - accuracy: 0.7798 - val_loss: 1.0389 - val_accuracy: 0.6225\n",
            "Epoch 11/40\n",
            "200/200 [==============================] - 120s 598ms/step - loss: 0.5378 - accuracy: 0.8052 - val_loss: 1.4999 - val_accuracy: 0.4938\n",
            "Epoch 12/40\n",
            "200/200 [==============================] - 127s 634ms/step - loss: 0.4788 - accuracy: 0.8261 - val_loss: 0.9044 - val_accuracy: 0.6687\n",
            "Epoch 13/40\n",
            "200/200 [==============================] - 122s 606ms/step - loss: 0.3689 - accuracy: 0.8697 - val_loss: 1.2791 - val_accuracy: 0.6000\n",
            "Epoch 14/40\n",
            "200/200 [==============================] - 122s 610ms/step - loss: 0.3139 - accuracy: 0.8886 - val_loss: 1.3291 - val_accuracy: 0.5825\n",
            "Epoch 15/40\n",
            "200/200 [==============================] - 130s 649ms/step - loss: 0.2586 - accuracy: 0.9092 - val_loss: 1.5281 - val_accuracy: 0.5638\n",
            "Epoch 16/40\n",
            "200/200 [==============================] - 113s 561ms/step - loss: 0.1904 - accuracy: 0.9362 - val_loss: 1.4166 - val_accuracy: 0.5875\n",
            "Epoch 17/40\n",
            "200/200 [==============================] - 118s 586ms/step - loss: 0.1534 - accuracy: 0.9478 - val_loss: 1.3689 - val_accuracy: 0.6162\n",
            "Epoch 18/40\n",
            "200/200 [==============================] - 115s 576ms/step - loss: 0.1407 - accuracy: 0.9533 - val_loss: 2.2255 - val_accuracy: 0.4613\n",
            "Epoch 19/40\n",
            "200/200 [==============================] - 115s 571ms/step - loss: 0.1256 - accuracy: 0.9567 - val_loss: 1.2890 - val_accuracy: 0.6475\n",
            "Epoch 20/40\n",
            "200/200 [==============================] - 108s 541ms/step - loss: 0.1076 - accuracy: 0.9650 - val_loss: 2.5038 - val_accuracy: 0.5138\n",
            "Epoch 21/40\n",
            "200/200 [==============================] - 116s 578ms/step - loss: 0.0934 - accuracy: 0.9695 - val_loss: 1.3180 - val_accuracy: 0.6725\n",
            "Epoch 22/40\n",
            "200/200 [==============================] - 110s 546ms/step - loss: 0.0879 - accuracy: 0.9716 - val_loss: 1.6237 - val_accuracy: 0.5987\n"
          ]
        }
      ],
      "source": [
        "# Define CNN model\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(32, 3, input_shape=input_shape, activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(32, 3, activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Conv2D(64, 3, activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(64, 3, activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Conv2D(128, 3, activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(128, 3, activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(256, activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(8, activation='softmax'))\n",
        "\n",
        "# compile the model\n",
        "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# early_stopping to prevent overfitting\n",
        "early_stopping = [EarlyStopping(monitor='val_loss', patience=10),\n",
        "                  ModelCheckpoint(filepath='/content/drive/MyDrive/skin_disorder_classifier2.h5',\n",
        "                                  monitor='val_accuracy', save_best_only=True)\n",
        "                  ]\n",
        "# Train the model using the train and validation generators\n",
        "history = model.fit(train_generator,\n",
        "          steps_per_epoch=train_generator.samples // train_generator.batch_size,\n",
        "          epochs=epochs,\n",
        "          validation_data=val_generator,\n",
        "          validation_steps=val_generator.samples // val_generator.batch_size,\n",
        "          callbacks = early_stopping\n",
        "          )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ac4SGoAwUvmW",
        "outputId": "92c31d2d-9043-4788-a5f3-64841740f7e2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "25/25 [==============================] - 11s 427ms/step - loss: 1.6772 - accuracy: 0.6025\n",
            "Test accuracy: 0.6025000214576721\n"
          ]
        }
      ],
      "source": [
        "#test accuracy\n",
        "test_loss, test_acc = model.evaluate(test_generator)\n",
        "print('Test accuracy:', test_acc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "_txDF3MJU_FO",
        "outputId": "afbcc899-930e-4814-d66e-c4fbd0187286"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2000x800 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# visualize loss and accuracy\n",
        "visualize_training_results(history)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tlAVNBp9tkxr"
      },
      "source": [
        "**Model 3(EfficientNetB2):**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TvKoM4svH5xY"
      },
      "outputs": [],
      "source": [
        "# TypeError: Unable to serialize [2.0896919 2.1128857 2.1081853] to JSON. Unrecognized type <class 'tensorflow.python.framework.ops.EagerTensor'>.\n",
        "# Due to the error above, we need to install tensor flow version 2.9.2 for the EfficientNet model to be able to run"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qFXZhdGNH63l",
        "outputId": "1b720de9-4461-4d78-fd79-dba8da18c708"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.9.2\n"
          ]
        }
      ],
      "source": [
        "# pip uninstall tensorflow\n",
        "# pip install tensorflow==2.9.2\n",
        "print(tf. __version__)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DIyA_qngW59_"
      },
      "outputs": [],
      "source": [
        "# Create an instance of ImageDataGenerator for data augmentation\n",
        "train_datagen = ImageDataGenerator(\n",
        "    rescale=1./255,\n",
        "    rotation_range=20,\n",
        "    width_shift_range=0.2,\n",
        "    height_shift_range=0.2,\n",
        "    zoom_range=0.2,\n",
        "    horizontal_flip=True,\n",
        "    brightness_range = [0.2, 1.2]\n",
        "    )\n",
        "\n",
        "val_datagen = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "test_datagen = ImageDataGenerator(rescale=1./255)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f_lGT2k-W7On",
        "outputId": "78c52868-1ac7-4587-b8fb-845b57855475"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 6400 images belonging to 8 classes.\n",
            "Found 800 images belonging to 8 classes.\n",
            "Found 800 images belonging to 8 classes.\n"
          ]
        }
      ],
      "source": [
        "# Create generators for loading the train, validation, and test sets\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "    train_dir,\n",
        "    target_size=input_shape[:2],\n",
        "    batch_size=batch_size,\n",
        "    class_mode='categorical')\n",
        "\n",
        "val_generator = val_datagen.flow_from_directory(\n",
        "    validation_dir,\n",
        "    target_size=input_shape[:2],\n",
        "    batch_size=batch_size,\n",
        "    class_mode='categorical')\n",
        "\n",
        "test_generator = test_datagen.flow_from_directory(\n",
        "    test_dir,\n",
        "    target_size=input_shape[:2],\n",
        "    batch_size=batch_size,\n",
        "    class_mode='categorical')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PrLRGXGto0K_"
      },
      "source": [
        "**_Define function to print text in RGB foreground and background colors_**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gKtZ_0-csWx2"
      },
      "source": [
        "* The function is used to print text in different colors\n",
        "* It takes three arguments: the text message, foreground color, and background color\n",
        "* The foreground and background colors are specified as tuples of three numbers representing the RGB values\n",
        "* The function uses escape sequences to set the text color and background color before printing the message\n",
        "* The function then resets the print color to default after printing the message"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qPXq0efoAo8X"
      },
      "outputs": [],
      "source": [
        "def print_in_color(txt_msg,fore_tupple,back_tupple,):\n",
        "    #prints the text_msg in the foreground color specified by fore_tupple with the background specified by back_tupple\n",
        "    #text_msg is the text, fore_tupple is foregroud color tupple (r,g,b), back_tupple is background tupple (r,g,b)\n",
        "    rf,gf,bf=fore_tupple\n",
        "    rb,gb,bb=back_tupple\n",
        "    msg='{0}' + txt_msg\n",
        "    mat='\\33[38;2;' + str(rf) +';' + str(gf) + ';' + str(bf) + ';48;2;' + str(rb) + ';' +str(gb) + ';' + str(bb) +'m'\n",
        "    print(msg .format(mat), flush=True)\n",
        "    print('\\33[0m', flush=True) # returns default print color to back to black\n",
        "    return"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xrLoWWF3o5xG"
      },
      "source": [
        "**_Create a subclass of callbacks to control learning rate and print training results for each epoch_**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "08uo_UkErq_F"
      },
      "source": [
        "The custom callback function used in the model combines early stopping, reduce learning rate on plateau, and model checkpoint, and is designed to improve training efficiency and prevent overfitting. In addition,the function provides a summary of model's performance at each epoch, allows for early stopping, and always returns the best-performing model. The function also takes several customizable parameters to suit individual needs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DKVW3wZFAWjr"
      },
      "outputs": [],
      "source": [
        "class LRA(keras.callbacks.Callback):\n",
        "    def __init__(self,model, base_model, patience,stop_patience, threshold, factor, dwell, batches, initial_epoch,epochs, ask_epoch):\n",
        "        super(LRA, self).__init__()\n",
        "        self.model=model\n",
        "        self.base_model=base_model\n",
        "        self.patience=patience # specifies how many epochs without improvement before learning rate is adjusted\n",
        "        self.stop_patience=stop_patience # specifies how many times to adjust lr without improvement to stop training\n",
        "        self.threshold=threshold # specifies training accuracy threshold when lr will be adjusted based on validation loss\n",
        "        self.factor=factor # factor by which to reduce the learning rate\n",
        "        self.dwell=dwell\n",
        "        self.batches=batches # number of training batch to runn per epoch\n",
        "        self.initial_epoch=initial_epoch\n",
        "        self.epochs=epochs\n",
        "        self.ask_epoch=ask_epoch\n",
        "        self.ask_epoch_initial=ask_epoch # save this value to restore if restarting training\n",
        "        # callback variables\n",
        "        self.count=0 # how many times lr has been reduced without improvement\n",
        "        self.stop_count=0\n",
        "        self.best_epoch=1   # epoch with the lowest loss\n",
        "        self.initial_lr=float(tf.keras.backend.get_value(model.optimizer.lr)) # get the initiallearning rate and save it\n",
        "        self.highest_tracc=0.0 # set highest training accuracy to 0 initially\n",
        "        self.lowest_vloss=np.inf # set lowest validation loss to infinity initially\n",
        "        self.best_weights=self.model.get_weights() # set best weights to model's initial weights\n",
        "        self.initial_weights=self.model.get_weights()   # save initial weights if they have to get restored\n",
        "\n",
        "    def on_train_begin(self, logs=None):\n",
        "        if self.base_model != None:\n",
        "            status=base_model.trainable\n",
        "            if status:\n",
        "                msg=' initializing callback starting train with base_model trainable'\n",
        "            else:\n",
        "                msg='initializing callback starting training with base_model not trainable'\n",
        "        else:\n",
        "            msg='initialing callback and starting training'\n",
        "        print_in_color (msg, (244, 252, 3), (55,65,80))\n",
        "        msg='{0:^8s}{1:^10s}{2:^9s}{3:^9s}{4:^9s}{5:^9s}{6:^9s}{7:^10s}{8:10s}{9:^8s}'.format('Epoch', 'Loss', 'Accuracy',\n",
        "                                                                                              'V_loss','V_acc', 'LR', 'Next LR', 'Monitor','% Improv', 'Duration')\n",
        "        print_in_color(msg, (244,252,3), (55,65,80))\n",
        "        self.start_time= time.time()\n",
        "\n",
        "    def on_train_end(self, logs=None):\n",
        "        stop_time=time.time()\n",
        "        tr_duration= stop_time- self.start_time\n",
        "        hours = tr_duration // 3600\n",
        "        minutes = (tr_duration - (hours * 3600)) // 60\n",
        "        seconds = tr_duration - ((hours * 3600) + (minutes * 60))\n",
        "\n",
        "        self.model.set_weights(self.best_weights) # set the weights of the model to the best weights\n",
        "        msg=f'Training is completed - model is set with weights from epoch {self.best_epoch} '\n",
        "        print_in_color(msg, (0,255,0), (55,65,80))\n",
        "        msg = f'training elapsed time was {str(hours)} hours, {minutes:4.1f} minutes, {seconds:4.2f} seconds)'\n",
        "        print_in_color(msg, (0,255,0), (55,65,80))\n",
        "\n",
        "    def on_train_batch_end(self, batch, logs=None):\n",
        "        acc=logs.get('accuracy')* 100  # get training accuracy\n",
        "        loss=logs.get('loss')\n",
        "        msg='{0:20s}processing batch {1:4s} of {2:5s} accuracy= {3:8.3f}  loss: {4:8.5f}'.format(' ', str(batch), str(self.batches), acc, loss)\n",
        "        print(msg, '\\r', end='') # prints over on the same line to show running batch count\n",
        "\n",
        "    def on_epoch_begin(self,epoch, logs=None):\n",
        "        self.now= time.time()\n",
        "\n",
        "    def on_epoch_end(self, epoch, logs=None):  # method runs on the end of each epoch\n",
        "        later=time.time()\n",
        "        duration=later-self.now\n",
        "        lr=float(tf.keras.backend.get_value(self.model.optimizer.lr)) # get the current learning rate\n",
        "        current_lr=lr\n",
        "        v_loss=logs.get('val_loss')  # get the validation loss for this epoch\n",
        "        acc=logs.get('accuracy')  # get training accuracy\n",
        "        v_acc=logs.get('val_accuracy')\n",
        "        loss=logs.get('loss')\n",
        "        if acc < self.threshold: # if training accuracy is below threshold adjust lr based on training accuracy\n",
        "            monitor='accuracy'\n",
        "            if epoch ==0:\n",
        "                pimprov=0.0\n",
        "            else:\n",
        "                pimprov= (acc-self.highest_tracc )*100/self.highest_tracc\n",
        "            if acc>self.highest_tracc: # training accuracy improved in the epoch\n",
        "                self.highest_tracc=acc # set new highest training accuracy\n",
        "                self.best_weights=self.model.get_weights() # traing accuracy improved so save the weights\n",
        "                self.stop_count=0 # set stop counter to 0\n",
        "                if v_loss<self.lowest_vloss:\n",
        "                    self.lowest_vloss=v_loss\n",
        "                color= (0,255,0)\n",
        "                self.best_epoch=epoch + 1  # set the value of best epoch for this epoch\n",
        "            else:\n",
        "                # training accuracy did not improve check if this has happened for patience number of epochs\n",
        "                # if so adjust learning rate\n",
        "                if self.count>=self.patience -1: # lr should be adjusted\n",
        "                    color=(245, 170, 66)\n",
        "                    lr= lr* self.factor # adjust the learning by factor\n",
        "                    tf.keras.backend.set_value(self.model.optimizer.lr, lr) # set the learning rate in the optimizer\n",
        "                    self.count=0 # reset the count to 0\n",
        "                    self.stop_count=self.stop_count + 1 # count the number of consecutive lr adjustments\n",
        "                    self.count=0 # reset counter\n",
        "                    if self.dwell:\n",
        "                        self.model.set_weights(self.best_weights) # return to better point in N space\n",
        "                    else:\n",
        "                        if v_loss<self.lowest_vloss:\n",
        "                            self.lowest_vloss=v_loss\n",
        "                else:\n",
        "                    self.count=self.count +1 # increment patience counter\n",
        "        else: # training accuracy is above threshold so adjust learning rate based on validation loss\n",
        "            monitor='val_loss'\n",
        "            if epoch ==0:\n",
        "                pimprov=0.0\n",
        "            else:\n",
        "                pimprov= (self.lowest_vloss- v_loss )*100/self.lowest_vloss\n",
        "            if v_loss< self.lowest_vloss: # check if the validation loss improved\n",
        "                self.lowest_vloss=v_loss # replace lowest validation loss with new validation loss\n",
        "                self.best_weights=self.model.get_weights() # validation loss improved so save the weights\n",
        "                self.count=0 # reset count since validation loss improved\n",
        "                self.stop_count=0\n",
        "                color=(0,255,0)\n",
        "                self.best_epoch=epoch + 1 # set the value of the best epoch to this epoch\n",
        "            else: # validation loss did not improve\n",
        "                if self.count>=self.patience-1: # need to adjust lr\n",
        "                    color=(245, 170, 66)\n",
        "                    lr=lr * self.factor # adjust the learning rate\n",
        "                    self.stop_count=self.stop_count + 1 # increment stop counter because lr was adjusted\n",
        "                    self.count=0 # reset counter\n",
        "                    tf.keras.backend.set_value(self.model.optimizer.lr, lr) # set the learning rate in the optimizer\n",
        "                    if self.dwell:\n",
        "                        self.model.set_weights(self.best_weights) # return to better point in N space\n",
        "                else:\n",
        "                    self.count =self.count +1 # increment the patience counter\n",
        "                if acc>self.highest_tracc:\n",
        "                    self.highest_tracc= acc\n",
        "        msg=f'{str(epoch+1):^3s}/{str(self.epochs):4s} {loss:^9.3f}{acc*100:^9.3f}{v_loss:^9.5f}{v_acc*100:^9.3f}{current_lr:^9.5f}{lr:^9.5f}{monitor:^11s}{pimprov:^10.2f}{duration:^8.2f}'\n",
        "        print_in_color (msg,color, (55,65,80))\n",
        "        if self.stop_count> self.stop_patience - 1: # check if learning rate has been adjusted stop_count times with no improvement\n",
        "            msg=f' training has been halted at epoch {epoch + 1} after {self.stop_patience} adjustments of learning rate with no improvement'\n",
        "            print_in_color(msg, (0,255,255), (55,65,80))\n",
        "            self.model.stop_training = True # stop training\n",
        "        else:\n",
        "            if self.ask_epoch !=None:\n",
        "                if epoch + 1 >= self.ask_epoch:\n",
        "                    if base_model.trainable:\n",
        "                        msg='enter H to halt  or an integer for number of epochs to run then ask again'\n",
        "                    else:\n",
        "                        msg='enter H to halt ,F to fine tune model, or an integer for number of epochs to run then ask again'\n",
        "                    print_in_color(msg, (0,255,255), (55,65,80))\n",
        "                    ans=input('')\n",
        "                    if ans=='H' or ans=='h':\n",
        "                        msg=f'training has been halted at epoch {epoch + 1} due to user input'\n",
        "                        print_in_color(msg, (0,255,255), (55,65,80))\n",
        "                        self.model.stop_training = True # stop training\n",
        "                    elif ans == 'F' or ans=='f':\n",
        "                        if base_model.trainable:\n",
        "                            msg='base_model is already set as trainable'\n",
        "                        else:\n",
        "                            msg='setting base_model as trainable for fine tuning of model'\n",
        "                            self.base_model.trainable=True\n",
        "                        print_in_color(msg, (0, 255,255), (55,65,80))\n",
        "                        msg='{0:^8s}{1:^10s}{2:^9s}{3:^9s}{4:^9s}{5:^9s}{6:^9s}{7:^10s}{8:^8s}'.format('Epoch', 'Loss', 'Accuracy',\n",
        "                                                                                              'V_loss','V_acc', 'LR', 'Next LR', 'Monitor','% Improv', 'Duration')\n",
        "                        print_in_color(msg, (244,252,3), (55,65,80))\n",
        "                        self.count=0\n",
        "                        self.stop_count=0\n",
        "                        self.ask_epoch = epoch + 1 + self.ask_epoch_initial\n",
        "\n",
        "                    else:\n",
        "                        ans=int(ans)\n",
        "                        self.ask_epoch +=ans\n",
        "                        msg=f' training will continue until epoch ' + str(self.ask_epoch)\n",
        "                        print_in_color(msg, (0, 255,255), (55,65,80))\n",
        "                        msg='{0:^8s}{1:^10s}{2:^9s}{3:^9s}{4:^9s}{5:^9s}{6:^9s}{7:^10s}{8:10s}{9:^8s}'.format('Epoch', 'Loss', 'Accuracy',\n",
        "                                                                                              'V_loss','V_acc', 'LR', 'Next LR', 'Monitor','% Improv', 'Duration')\n",
        "                        print_in_color(msg, (244,252,3), (55,65,80))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4ohzPsmRtkxu"
      },
      "outputs": [],
      "source": [
        "# Create an instance of the EfficientNetB2 model\n",
        "base_model = EfficientNetB2(include_top=False, weights=\"imagenet\", input_shape=input_shape, pooling = 'max')\n",
        "\n",
        "# Added a normalzation\n",
        "x = base_model.output\n",
        "x = BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001 )(x)\n",
        "\n",
        "# Added a fully connected layer with 256 hidden units, regularizers and ReLU activation\n",
        "x = Dense(256, kernel_regularizer = regularizers.l2(l = 0.016),activity_regularizer=regularizers.l1(0.006),\n",
        "                bias_regularizer=regularizers.l1(0.006) ,activation='relu')(x)\n",
        "\n",
        "# Added a dropout layer to prevent overfitting\n",
        "x = Dropout(0.45, seed=123)(x)\n",
        "\n",
        "# The final softmax layer with 8 output units\n",
        "output = Dense(8, activation='softmax')(x)\n",
        "\n",
        "# created the model with the base model and the fully connected layers\n",
        "model_EfficientNetB2 = Model(inputs=base_model.input, outputs=output)\n",
        "\n",
        "# compile the model with the Adam optimizer\n",
        "model_EfficientNetB2.compile(optimizer=Adamax(learning_rate=.001), loss='categorical_crossentropy', metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RtfZt4NgDP1W"
      },
      "outputs": [],
      "source": [
        "epochs =40\n",
        "patience= 1 # number of epochs to wait to adjust learning_rate if monitored value does not improve\n",
        "stop_patience =3 # number of epochs to wait before stopping training if monitored value does not improve\n",
        "threshold=.9 # if train accuracy is < threshhold adjust monitor accuracy, else monitor validation loss\n",
        "factor=.5 # factor to reduce learning_rate by\n",
        "dwell=True # if True and monitored metric does not improve on current epoch set  modelweights back to weights of previous epoch\n",
        "freeze=False # if true free weights of  the base model\n",
        "ask_epoch=20 # number of epochs to run before asking if you want to halt training\n",
        "batches= train_generator.samples // train_generator.batch_size\n",
        "callbacks=[LRA(model= model_EfficientNetB2, base_model= base_model, patience=patience, stop_patience=stop_patience, threshold=threshold,\n",
        "                   factor=factor, dwell=dwell, batches=batches,initial_epoch=0,epochs=epochs, ask_epoch=ask_epoch )]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PWv4pobkKcuH",
        "outputId": "3a36ac2e-a1ab-4125-ec24-4a6ffa9b769b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[38;2;244;252;3;48;2;55;65;80m initializing callback starting train with base_model trainable\n",
            "\u001b[0m\n",
            "\u001b[38;2;244;252;3;48;2;55;65;80m Epoch     Loss   Accuracy  V_loss    V_acc     LR     Next LR  Monitor  % Improv  Duration\n",
            "\u001b[0m\n",
            "\u001b[38;2;0;255;0;48;2;55;65;80m 1 /40     7.839   47.891   7.57303  14.125   0.00100  0.00100  accuracy     0.00    306.79 \n",
            "\u001b[0m\n",
            "\u001b[38;2;0;255;0;48;2;55;65;80m 2 /40     5.221   66.953   5.72205  10.000   0.00100  0.00100  accuracy    39.80    289.82 \n",
            "\u001b[0m\n",
            "\u001b[38;2;0;255;0;48;2;55;65;80m 3 /40     3.757   73.609   4.58298  16.625   0.00100  0.00100  accuracy     9.94    288.07 \n",
            "\u001b[0m\n",
            "\u001b[38;2;0;255;0;48;2;55;65;80m 4 /40     2.701   78.203   3.81709  14.000   0.00100  0.00100  accuracy     6.24    291.09 \n",
            "\u001b[0m\n",
            "\u001b[38;2;0;255;0;48;2;55;65;80m 5 /40     1.980   79.844   3.20592  13.625   0.00100  0.00100  accuracy     2.10    294.23 \n",
            "\u001b[0m\n",
            "\u001b[38;2;0;255;0;48;2;55;65;80m 6 /40     1.455   83.547   2.90506  19.375   0.00100  0.00100  accuracy     4.64    294.39 \n",
            "\u001b[0m\n",
            "\u001b[38;2;0;255;0;48;2;55;65;80m 7 /40     1.122   85.094   2.78819  14.625   0.00100  0.00100  accuracy     1.85    292.15 \n",
            "\u001b[0m\n",
            "\u001b[38;2;0;255;0;48;2;55;65;80m 8 /40     0.918   86.266   2.58439  15.250   0.00100  0.00100  accuracy     1.38    289.27 \n",
            "\u001b[0m\n",
            "\u001b[38;2;0;255;0;48;2;55;65;80m 9 /40     0.751   88.484   2.61003  16.625   0.00100  0.00100  accuracy     2.57    287.55 \n",
            "\u001b[0m\n",
            "\u001b[38;2;0;255;0;48;2;55;65;80m10 /40     0.676   88.969   2.38315  16.000   0.00100  0.00100  accuracy     0.55    287.26 \n",
            "\u001b[0m\n",
            "\u001b[38;2;245;170;66;48;2;55;65;80m11 /40     0.588   90.906   2.53483  19.500   0.00100  0.00050  val_loss    -6.36    288.02 \n",
            "\u001b[0m\n",
            "\u001b[38;2;0;255;0;48;2;55;65;80m12 /40     0.546   92.500   1.78358  49.000   0.00050  0.00050  val_loss    25.16    287.68 \n",
            "\u001b[0m\n",
            "\u001b[38;2;245;170;66;48;2;55;65;80m13 /40     0.480   93.969   2.58807  12.875   0.00050  0.00025  val_loss    -45.11   292.45 \n",
            "\u001b[0m\n",
            "\u001b[38;2;0;255;0;48;2;55;65;80m14 /40     0.477   94.156   1.32609  63.125   0.00025  0.00025  val_loss    25.65    289.46 \n",
            "\u001b[0m\n",
            "\u001b[38;2;245;170;66;48;2;55;65;80m15 /40     0.438   95.187   2.42649  27.875   0.00025  0.00013  val_loss    -82.98   292.52 \n",
            "\u001b[0m\n",
            "\u001b[38;2;0;255;0;48;2;55;65;80m16 /40     0.428   95.609   0.76593  83.375   0.00013  0.00013  val_loss    42.24    287.83 \n",
            "\u001b[0m\n",
            "\u001b[38;2;245;170;66;48;2;55;65;80m17 /40     0.423   95.703   0.87560  79.125   0.00013  0.00006  val_loss    -14.32   289.18 \n",
            "\u001b[0m\n",
            "\u001b[38;2;0;255;0;48;2;55;65;80m18 /40     0.418   96.078   0.71247  84.625   0.00006  0.00006  val_loss     6.98    287.64 \n",
            "\u001b[0m\n",
            "\u001b[38;2;245;170;66;48;2;55;65;80m19 /40     0.406   96.109   0.90248  79.500   0.00006  0.00003  val_loss    -26.67   288.15 \n",
            "\u001b[0m\n",
            "\u001b[38;2;0;255;0;48;2;55;65;80m20 /40     0.403   96.531   0.59648  88.250   0.00003  0.00003  val_loss    16.28    285.60 \n",
            "\u001b[0m\n",
            "\u001b[38;2;0;255;255;48;2;55;65;80menter H to halt  or an integer for number of epochs to run then ask again\n",
            "\u001b[0m\n",
            "\u001b[38;2;0;255;255;48;2;55;65;80m training will continue until epoch 22\n",
            "\u001b[0m\n",
            "\u001b[38;2;244;252;3;48;2;55;65;80m Epoch     Loss   Accuracy  V_loss    V_acc     LR     Next LR  Monitor  % Improv  Duration\n",
            "\u001b[0m\n",
            "\u001b[38;2;245;170;66;48;2;55;65;80m21 /40     0.394   96.594   0.61094  88.875   0.00003  0.00002  val_loss    -2.42    286.88 \n",
            "\u001b[0m\n",
            "\u001b[38;2;245;170;66;48;2;55;65;80m22 /40     0.401   96.438   0.61018  88.625   0.00002  0.00001  val_loss    -2.30    288.43 \n",
            "\u001b[0m\n",
            "\u001b[38;2;0;255;255;48;2;55;65;80menter H to halt  or an integer for number of epochs to run then ask again\n",
            "\u001b[0m\n",
            "H\n",
            "\u001b[38;2;0;255;255;48;2;55;65;80mtraining has been halted at epoch 22 due to user input\n",
            "\u001b[0m\n",
            "\u001b[38;2;0;255;0;48;2;55;65;80mTraining is completed - model is set with weights from epoch 20 \n",
            "\u001b[0m\n",
            "\u001b[38;2;0;255;0;48;2;55;65;80mtraining elapsed time was 1.0 hours, 51.0 minutes, 26.05 seconds)\n",
            "\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "history_EfficientNetB2 = model_EfficientNetB2.fit(train_generator,\n",
        "                                 epochs=epochs,\n",
        "                                 verbose=0,\n",
        "                                 callbacks=callbacks,\n",
        "                                validation_data=val_generator,\n",
        "                                 shuffle=False,\n",
        "                                 initial_epoch=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_QWIC8AgZ5m0"
      },
      "outputs": [],
      "source": [
        "# Saving model to drive\n",
        "model_EfficientNetB2.save(\"/content/drive/MyDrive/skin_disorder_classifier_EfficientNetB2.h5\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oV62D6YJH7Yv",
        "outputId": "e59d3b8e-9569-453f-d4a4-2948ece1073c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test Accuracy: 0.88\n"
          ]
        }
      ],
      "source": [
        "# Evaluate the model\n",
        "test_loss, test_acc = model_EfficientNetB2.evaluate(test_generator, verbose=0)\n",
        "print((f'Test Accuracy: {test_acc:.2f}'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "5wxlrGTzfVA6",
        "outputId": "e06bc0db-258a-421d-f11e-f0b54104dd1d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2000x800 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Visualizing the model's performance\n",
        "visualize_training_results(history_EfficientNetB2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ZcI-Z4rtkxz"
      },
      "source": [
        "**Model 4(VGG16)**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "egeLbtat4b-q"
      },
      "outputs": [],
      "source": [
        "# Create an instance of ImageDataGenerator for data augmentation\n",
        "train_datagen = ImageDataGenerator(\n",
        "    rescale=1./255,\n",
        "    rotation_range=20,\n",
        "    width_shift_range=0.2,\n",
        "    height_shift_range=0.2,\n",
        "    zoom_range=0.2,\n",
        "    horizontal_flip=True,\n",
        "    vertical_flip=True,\n",
        "    brightness_range=[0.2,1.2]\n",
        "    )\n",
        "\n",
        "val_datagen = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "test_datagen = ImageDataGenerator(rescale=1./255)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s8UtcF274dJE",
        "outputId": "fef73f36-bab0-4472-e239-853e1226f0f3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 6400 images belonging to 8 classes.\n",
            "Found 800 images belonging to 8 classes.\n",
            "Found 800 images belonging to 8 classes.\n"
          ]
        }
      ],
      "source": [
        "# Create generators for loading the train, validation, and test sets\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "    train_dir,\n",
        "    target_size=input_shape[:2],\n",
        "    batch_size=batch_size,\n",
        "    class_mode='categorical')\n",
        "\n",
        "val_generator = val_datagen.flow_from_directory(\n",
        "    validation_dir,\n",
        "    target_size=input_shape[:2],\n",
        "    batch_size=batch_size,\n",
        "    class_mode='categorical')\n",
        "\n",
        "test_generator = test_datagen.flow_from_directory(\n",
        "    test_dir,\n",
        "    target_size=input_shape[:2],\n",
        "    batch_size=batch_size,\n",
        "    class_mode='categorical')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AdIbT7Udtkxz",
        "outputId": "995edd8c-e5b8-4143-a27e-b98a54fd8f65"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "58889256/58889256 [==============================] - 3s 0us/step\n"
          ]
        }
      ],
      "source": [
        "# Load the pre-trained VGG16 model (excluding the top layers)\n",
        "vgg16_model = VGG16(weights='imagenet', include_top=False, input_shape=input_shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "53VXBsfYtkxz"
      },
      "outputs": [],
      "source": [
        "# Freeze the weights of the pre-trained layers\n",
        "for layer in vgg16_model.layers:\n",
        "    layer.trainable = False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yKZVuu9atkx0"
      },
      "outputs": [],
      "source": [
        "# Add a new classifier on top of the pre-trained layers\n",
        "model = Sequential()\n",
        "model.add(vgg16_model)\n",
        "model.add(Flatten())\n",
        "model.add(Dense(512, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(8, activation='softmax'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4bEUe947tkx0"
      },
      "outputs": [],
      "source": [
        "# Compile the model with categorical crossentropy loss and Adam optimizer\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics=['accuracy'])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0jgndeVUtkx0",
        "outputId": "00836766-8914-4b9f-ed65-07fc1bb79815",
        "scrolled": false
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "200/200 [==============================] - 243s 1s/step - loss: 1.9324 - accuracy: 0.2095 - val_loss: 1.6353 - val_accuracy: 0.3075\n",
            "Epoch 2/20\n",
            "200/200 [==============================] - 233s 1s/step - loss: 1.7840 - accuracy: 0.2317 - val_loss: 1.5845 - val_accuracy: 0.3375\n",
            "Epoch 3/20\n",
            "200/200 [==============================] - 230s 1s/step - loss: 1.7656 - accuracy: 0.2222 - val_loss: 1.5319 - val_accuracy: 0.3438\n",
            "Epoch 4/20\n",
            "200/200 [==============================] - 230s 1s/step - loss: 1.7476 - accuracy: 0.2366 - val_loss: 1.5089 - val_accuracy: 0.3288\n",
            "Epoch 5/20\n",
            "200/200 [==============================] - 229s 1s/step - loss: 1.7260 - accuracy: 0.2495 - val_loss: 1.5235 - val_accuracy: 0.3388\n",
            "Epoch 6/20\n",
            "200/200 [==============================] - 229s 1s/step - loss: 1.7412 - accuracy: 0.2336 - val_loss: 1.4956 - val_accuracy: 0.2937\n",
            "Epoch 7/20\n",
            "200/200 [==============================] - 232s 1s/step - loss: 1.7309 - accuracy: 0.2334 - val_loss: 1.4627 - val_accuracy: 0.3288\n",
            "Epoch 8/20\n",
            "200/200 [==============================] - 235s 1s/step - loss: 1.7324 - accuracy: 0.2353 - val_loss: 1.4642 - val_accuracy: 0.3262\n",
            "Epoch 9/20\n",
            "200/200 [==============================] - 231s 1s/step - loss: 1.7147 - accuracy: 0.2488 - val_loss: 1.4914 - val_accuracy: 0.3175\n",
            "Epoch 10/20\n",
            "200/200 [==============================] - 227s 1s/step - loss: 1.7223 - accuracy: 0.2281 - val_loss: 1.4447 - val_accuracy: 0.3963\n",
            "Epoch 11/20\n",
            "200/200 [==============================] - 229s 1s/step - loss: 1.6882 - accuracy: 0.2522 - val_loss: 1.4127 - val_accuracy: 0.4087\n",
            "Epoch 12/20\n",
            "200/200 [==============================] - 229s 1s/step - loss: 1.6815 - accuracy: 0.2627 - val_loss: 1.4752 - val_accuracy: 0.3388\n",
            "Epoch 13/20\n",
            "200/200 [==============================] - 228s 1s/step - loss: 1.6910 - accuracy: 0.2595 - val_loss: 1.4462 - val_accuracy: 0.3400\n",
            "Epoch 14/20\n",
            "200/200 [==============================] - 231s 1s/step - loss: 1.6969 - accuracy: 0.2567 - val_loss: 1.4540 - val_accuracy: 0.3388\n",
            "Epoch 15/20\n",
            "200/200 [==============================] - 229s 1s/step - loss: 1.6794 - accuracy: 0.2534 - val_loss: 1.4378 - val_accuracy: 0.3400\n",
            "Epoch 16/20\n",
            "200/200 [==============================] - 228s 1s/step - loss: 1.6650 - accuracy: 0.2708 - val_loss: 1.3914 - val_accuracy: 0.4400\n",
            "Epoch 17/20\n",
            "200/200 [==============================] - 240s 1s/step - loss: 1.6920 - accuracy: 0.2655 - val_loss: 1.4231 - val_accuracy: 0.3787\n",
            "Epoch 18/20\n",
            "200/200 [==============================] - 228s 1s/step - loss: 1.6863 - accuracy: 0.2659 - val_loss: 1.4155 - val_accuracy: 0.3550\n",
            "Epoch 19/20\n",
            "200/200 [==============================] - 230s 1s/step - loss: 1.6762 - accuracy: 0.2678 - val_loss: 1.4033 - val_accuracy: 0.3613\n",
            "Epoch 20/20\n",
            "200/200 [==============================] - 229s 1s/step - loss: 1.6647 - accuracy: 0.2733 - val_loss: 1.4166 - val_accuracy: 0.3338\n"
          ]
        }
      ],
      "source": [
        "# Train the model using the train and validation generators\n",
        "history = model.fit(train_generator,\n",
        "          steps_per_epoch=len(train_generator),\n",
        "          epochs=20,\n",
        "          validation_data=val_generator,\n",
        "          validation_steps=len(val_generator))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m3PhMEWxtkx0",
        "outputId": "85b5ec83-654c-446c-b448-03db793e1aab"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "67/67 [==============================] - 849s 13s/step - loss: 1.1702 - accuracy: 0.5155\n",
            "Test accuracy: 0.5154929757118225\n"
          ]
        }
      ],
      "source": [
        "# Evaluate the model on the test set\n",
        "test_loss, test_acc = model.evaluate(test_generator, steps=len(test_generator))\n",
        "print(\"Test accuracy:\", test_acc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NaFB6dDVtkx1",
        "outputId": "34ad0156-2aa7-4920-e150-3e0733b7462a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['/content/drive/MyDrive/skin_disease_classifier_VGG16']"
            ]
          },
          "execution_count": 63,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Saving model to drive\n",
        "model_EfficientNetB2.save(\"/content/drive/MyDrive/skin_disease_classifier_VGG16.h5\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "id": "pQhKroy4ZcnN",
        "outputId": "b82da814-03aa-473c-b9c3-8dd7da5451b1"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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8UhkU7927N126dKFYsWJoNBqePHnC+fPn8fX1zbAbBkIIkZayW256584dBg4cSHh4OGq1mnfeeYeuXbuSJ08erly5wty5c5k8eTJ16tRJVYxg3T5ZsWIFx48fp0GDBrz77ru88cYbhIeH4+3tzeLFiwkLC2P48OGcPHnSaKHB2NhY+vXrpwzyNm/enPfffx83NzcePHjA33//zd69e3nx4kWqz3nAgAEcOnQInU6Hp6cnn3/+ucnt9DcHABo0aECZMmWU1+Li4ihdujSdO3emdu3alChRAltbW+7du4efnx+rVq0iKCiIwYMH4+/vb1HulBzW6q+4uDiKFClC586dqVu3LqVLl8be3p6HDx9y4sQJ/v77b16+fMkHH3yAn58fFStWVN77/vvv061bN6ZMmcKOHTuSXCA3OSZPnqwM1BUvXpwxY8ZQq1YtoqKi2LdvH/PmzePVq1d89NFHuLi40L59e7P7Ss31aG1arVZ5QqFNmzbkz58fgDfeeINatWpx5swZPDw8Eh0YP3bsGN27dycmJgYbGxt69epF586dKVWqFDExMfz777/s37/f5O9pZGQk3bp14/z580B8nvnuu+9SpUoVHBwcCAgI4MSJE1aZpJUcb7/9Ng8fPuT999+nU6dO5M+fn7t375IvXz5lG/1kn06dOilPUep0Ou7fv8+2bdvw8vLi7t27DBo0iEOHDpn87pfSPhs6dCheXl6Ehobi7e1tdsA7MjJSueY7duxo8XfaGTNmKIPibdu2pW/fvri5ueHo6Mjz58+5fPkyu3fvNppwA/Hf+YYMGaJ8LyhZsiQffPABtWrVwsnJiWfPnnH69GmzJaOs+bu2YMECLl++TP369Xnvvfd44403CAsLM3ja+Y8//lBu6FasWJH33nuP8uXLKz/7FStWsHfvXpYuXYqTkxOTJ0+2qC9zOlVwcHDSBWWFEMlStWpV7t+/j5ubGxcvXjR4LeFdwFy5crFjxw6zM2wBbt68Sbly5cy+vmrVKj755BMAvL29TQ7Q6mePjh8/3ujO9eszo+fNm8egQYMMttFqtfTo0QM/Pz80Gg3//PMPBQoUMNhm+vTpykwJU6u/6/tE/99bt241mtV6+PBhOnfuDMT30+uPj50/f54WLVqg0+moUaMG27dvJ3fu3AbbeHt7M3ToUOX/TZ1zcjx69AgXFxdy5cpl8vWQkBA6derE5cuXadiwITt37jTaJuHPOk+ePOzYsYMqVaoYbPP8+XPq16/Ps2fPqFq1qlEZjejoaKpXr87jx49xcnLC19eXypUrG2xz9epV2rdvrzyi2rhx4xR/EU34c1yyZIkysJtQTEwMb775Js+fP6d48eJcvHhRmbUbHh5OWFgYRYoUMbl/nU7HyJEjWbNmDblz5+bKlSvkzZvXYJtDhw4ps2m2bt1K06ZNzcb4+rVmrf7S6XTcvn2bsmXLmu2radOm8fPPP6NSqTh16pTR7+ndu3eVciqmfq8sOedhw4YpSfZPP/3ERx99ZPC6Vqvl/fffZ9OmTWa3scb1mFzu7u7K7BRTfw/MiYmJoUaNGjx8+BBHR0e2bNlC3bp1DbYJCgqiQ4cOXL9+HRsbGw4cOEC1atWU1z/88EM8PT1xc3PD39/f7Gz+4OBg8ubNqwxyR0ZG4ubmRkxMDCNHjmTq1Klm4wwKClK+jAkhRFaQE3PTQYMGKZ/vf//9N7169TJ4PSIigi5dunD69GmlzZLPrIRS2ycJcwZ97HPnzjV6KmrGjBlKfrx69WqjUhqLFi1i3LhxQPzAtalZjKNGjWLlypXK/5vKO5Lj5cuXVKxYkfDwcCpUqMCJEydMbjd//nxlIsScOXMM8vTbt29TunRpszecL1++TPv27Xn58iVffPGFyRn++pzDXP6b2LVmrf66e/euciPdlIcPH9K2bVsePXpE3759Td7I0f8emvodteScr169SuPGjdFqtZQrV45du3YZ/W6cOXOGzp07ExERQZEiRTh//jz29vYG52ON69HaDhw4QPfu3QFYvny5QSnHv/76i/HjxwNw/Phxg5sPelFRUdSuXZsHDx7g4ODAmjVraNWqlcljBQUF4ejoaDDL+bvvvuP3338HYMiQIfz2228mJ6JotVoCAgIoVqyY0pbw75qpmegJJXbNJvweZGNjg6enJ23btjW7r6T+Nh04cICePXui1Wr5/fffjSbFpKbPdDodNWvW5M6dOzRp0sSgJFdC69atU25mrFu3jnbt2pmN15QqVarw4MEDunTpYvC7aiq+1/P3xYsX88UXXwDQrl07li9fbnZm++tPxqTF71qfPn3466+/TD4Re+3aNZo0aUJMTAyjR4/mhx9+MLnd999/z5w5c7CxseHEiROUL1/ebJ8IQ1JjXIgMMHr06ES/eACJfpABDB48WKkJae7DJrnc3d1NDt7Z2NgwatQoIH7gKjn1fRMzb948k6UemjRposzaMfXI1/Lly5WZvLNnzzYaFAfo1q2bMrieGsWKFTM7KA6QN29eJcn39/dPsp7dhAkTjAYhAQoUKMDgwYMBuHTpklGtzB07dvD48WMAxowZYzTIC/DWW2+ZnaVjqQEDBihfThI+9prQ7t27lXqP/fr1M/hAzp07t9lBcQCVSsXUqVNRq9WEh4dz4MABq8StZ63+UqlUiQ6KQ3yyWqBAAXQ6nVFpH2sKCAhg69atQPys/tcHvCH+d/S3335TZogk9VRHSq/HtLZ9+3YePnwIxH8RfX1QHCB//vzMnj0biP/isXjxYoPXnzx5AkD16tUTLXHj4uJi8EX8xYsXxMTEACS5KJgMigshsqvskps+fvwYHx8fANq3b280KA7xNwH0nyepZc0+cXV15ddffzU54DFixAhl8NVUrvz3338DkC9fPn7++WeT+58+fXqqnzIEcHJyUm7qX79+nTNnzpjcTp9POjg4KIOaemXKlEn0KazKlSvz9ttvA6RJrmWt/ipVqlSis6WLFy+uXK8+Pj7JWuQ+pf7++2+lFOBvv/1mNFAHUKtWLaXuc0BAQKILqKbmerQ2famLPHny0KFDB4PXevXqpZRoNFeKwtPTUylJ+NVXX5kd4IX4XC/hAGlISIhyvVSqVIlff/3V7NOZNjY2BoPiaaV///6JDopD0n+bWrRoQceOHQFM3lhKTZ+pVCrle8WRI0fMlnzRl1EpVqwYrVu3TjReU/TlXSzN37VarfIZULhwYRYtWpRouZfXy0VZ+3ctT548Zn/XIH62eExMDJUqVTI7KA7xTyoXLVpUqbsukk8GxoXIAMmp6ZaQTqcjMDCQf//9V6nldeXKFeWD99KlS6mKR79wiSk1a9ZU/juxOmZJqVSpksHsTnPHMXUM/SBqxYoVE/3SZmldsuQIDw/n7t27XL16Ven3hAlwYjM7Ei4KY4r+nHU6nVHN54QDx4nNOB40aJBVyjuULl1aqeG4b98+nj59arRNwkWWEtaJNCUmJoaHDx9y7do1pd8eP36sJCapvWZfl1b9pdVqefz4MTdu3FDO49q1a1b73UvMoUOHiI2NBUi0tEnevHnp0aMHEF9z0NzCPqm5HtNawhqyiZ1ro0aNlFJLr9ed1d+YOXr0KLdv3072sfPnz4+dnR0Qf43r+1wIIXKS7JKbHjp0SFlIOrG8sGrVqiZvFKdGavuka9euZkvZ5cmTR5n99/o5BwQEKAvDd+3aFWdnZ5P7cHJyUvKF1EqYB5qaUHH16lUuXLgAxN/keP0pwdcFBwdz+/Ztg3xb/55//vlHuYFtDWnZX6Ghody5c8fgPPQTbkJDQ9M0v9LnRaVLl6ZZs2Zmt0s4c//1XCqhlF6P1hYWFqbcVOrevbvBrFuAggULKoOq69atM7lOkP5mmb29vcWlNw8dOqSsjzV8+PA0LxuTHJb+vQZ49uwZN2/eNPjbpL/xc/nyZaPtU9NnEP+dS61Wo9PplAHwhO7evas8oTpw4MAU1cTW5/5eXl5EREQk+32XLl1SBv0HDx6c5N+n11n7d61Dhw7kyZPH7Ov6J9S7dOlidlAc4uu06ycXmXuSR5gmNcaFSGdOTk4GNfYSo19U7+jRo4SFhZndLrmrcJtj6pEzvYS1ypK7crUpiS3qAf9/dOz1Y0RGRiqrnid83MiUhF+UUuP58+fMmzePLVu2cPPmzURndyTW9wUKFDB5B1kv4ez5189bX++4aNGiFC1a1Ow+ChaMX/neGon2gAED8Pf3JzY2lvXr1/Pxxx8rrwUHBysJUp06dXjjjTeM3h8TE8OyZcvw9PTkwoULRguDJJTaa/Z11uwvnU7HunXrWLlyJadPn+bVq1dmt7X2eSR09epV5b9NzaBOqE6dOsqikFeuXDH5mGZqrse0pj/XokWLJrqIF8Sf6/Xr17l//z5hYWHKF9oBAwbg4eFBUFAQDRs2pGPHjrRq1YoGDRqYvF717O3t6dWrFx4eHnh7e3P69Gm6d+9OkyZNaNCgQZILmgohRFaXnXLThJ+dSc2Ar1mzplVucFurTxI7ZzCfKydcOLpWrVqJ7iOp15OrWbNmlChRggcPHrBp0yamTp1qMGBobtHNhC5fvsz8+fPZs2eP0cJ+CWm1WoKDg61WZ9za/XXv3j3mzp2Lj4+PyYVYE3r+/DmlS5dOVpyWiIqK4ubNm0DSOaOrqyslS5bk3r17RouOJ5TS69HavL29lUFPcwPC/fr1w9fXl0ePHnHgwAGj2c36mzRVqlRJdADSFH1dcYCGDRta9N60ktybeseOHeOvv/7iwIEDidbL1z8RnFBq+gzic/p27dqxc+dOPDw8+Prrrw0GdVevXo1OpzOYXW6pgQMHMmPGDI4fP0716tXp1q0bTZs2pUGDBokudpyan2la/K7pnyoy5d69e8qi2OYW+zVF/yStSB6ZMS5EOkvOHUmdTseoUaOUD/nEkmwg0YG75Ejs0aGEH2D6GTjWPkbC47x+lz9hSYekHme0xuOh586do27dusyaNYt///03yUceE+v75J4zGPetPnlJzjkVLlw4yW2So3v37krMr8/+8fLyIioqCjD9BefFixe0bduWcePGcerUqUQHxSH116yp40Pq+ysyMpK+ffvy4Ycfcvjw4STjtPZ5JJQwgU3qvBImf+YS39Rcj2nNkp+fuXNt1qwZv/32G7lz5yYyMhIvLy+lLEuFChX4+OOPzZaD+vnnn5VSTA8ePOCPP/6gf//+lClThiZNmvDzzz8rSakQQmQ32Sk3TVgrPLGbwZD6vNHafZLcz2lzOSMkfU7WyhlVKpUyI//Zs2fs3r1beU2r1bJ+/XogfkZny5Ytjd6/YsUKmjdvzurVqxMdFNezZr5lzf7avXs3DRo0YNGiRUkOikPa5Y0Jr3tLcqnEBktTej1am74khJubG40aNTK5TceOHZXBW1MlJPQ5XGKDpeYkHDROyfvTQnImbUyfPp0OHTrg5eWV5CKypq7L1PSZnn7G9MOHD5WFeAGDUh+NGzdO8c2icePG8c4776BSqXj69CmLFy9m6NChVKxYkbp16/L9999z7949o/el5meaFr9riX0Gp/T7hyUz6IUMjAuR7hJ7/EVv5cqVygISVatWZf78+Zw4cYL79+/z/PlzgoODCQ4OVu6ap2W9upwkOjqad955h6CgIDQaDSNHjmT79u1cu3aNwMBApd/PnTunvCet+94aZVKSK0+ePMrg4IULFwxmXenLqNjZ2Zms1zl+/HilX9zd3fHw8ODChQs8fvyYFy9eKH2nnw2cVv2W2v6aOXOm8uWucePGLFu2jLNnz/Lw4UOCgoKU89DPLkiv3730vA4yUmrPc9iwYVy4cIGff/6Zjh07KrMKnzx5wpo1a2jfvj2jRo0yugHn7OzMqlWrOHDgAJ9++im1a9fG1tYWnU7HpUuXmDZtGrVq1VKemhBCiOxEctOUyYx9kp75QsJSNQnL7R08eJBHjx4B8QvKvV4i4fr164wdO5bY2FgKFSrE5MmTOXDgALdv3+bp06dKv82dO1d5T2bMG58/f877779PREQETk5OfPXVV+zevZt///2XJ0+eKOeRsLZwdrsG0trdu3c5evQoAPfv3ydfvny4uLgY/VO0aFFCQ0OB+Fr++v/OrpIqO+Ln56fMLC5dujS//vorR44c4e7duzx79ky5NvUL0KaVtm3bKqWkEpZT8fPzU24k6dcSSAlbW1tmz57NiRMnGD9+PI0aNVLK/9y4cYM5c+ZQp04dli9fnoqzMM9av2uJ/TwT3nj67LPPOHr0aLL+8fLyskpsOYWUUhEiE1qxYgUAZcuWZdeuXWbv2Ce8Y5ldJbyDmtQd09TO6Dx48KBSJ+/XX381W+s4qbvu1qCfCWCq1vfrrPmo1IABA5RZPmvXruXHH3/kzp07HDt2DIivgfb6LIXQ0FDlw7dv376JLgCZVtesNfpLp9MpX3AbNmzI1q1bzQ4WpMfvXsJHxZ8+fZroY4wJZ1slfF9WoY85OT+/pM61QIECDB8+nOHDh6PT6bhy5Qo7duxg0aJFPHnyhJUrV1K5cmWTi5nWqFFDefQ+PDwcf39/1q1bx4YNGwgNDeXdd9/l7NmzmWbGkBBCpJeskpsmzFGeP3+e6GdnavPGzNInCc85qc9Ra+aM5cuXp169epw4cQIfHx+Cg4NxcXExmLFr6inDNWvWEBsbi1qtZvv27WbLLaZ1zgip6y9vb2/lydZVq1bRokULk9ulx++EJecE/8+lMnvOuHbtWotvJrx69YrNmzcbfI8rUKAADx8+TNbTCa9LuHBjYGCgxSX2En6XSOxc9HXMrUE/EOzi4sKePXvMzmxO7NpMTZ/pqdVqBg4cyMyZM9mxYwdBQUHkz59fGSTPkycPXbt2TfH+9d544w0mTJjAhAkTiIqK4uTJk2zevJmVK1cSFRXFZ599Rs2aNZX1zl7/mVoivX/XEj75pFarqVSpUor2IxInM8aFyIT0C8J07NjRbJKt0+kM6mNlVw4ODkrdy6TO9+zZs6k6VsIZ0okttpPa4ySH/kPv8ePHPH782Ox2z549M/mIWEq1aNFCubO/fv16o1WtTX3BuXXrlrIoUmL9dv369TSrQ2iN/nrx4oWSvHTv3t3soPjLly+5ceOG2WNYa/bAW2+9pfz3qVOnEt329OnTyn9nxYRJf66PHz/m4cOHiW6rP1c3NzezC2bpqVQqKleuzLhx49i1a5eyYNPmzZuTjCl37ty0adOGhQsXMnHiRCD+sURfX98k3yuEENlNVslN33zzTeW/Ez7hZ0pq87nM0icJP/fPnDmT6LZJvW4pfV4YFRWFl5cX4eHhykKJ1atXN5mT6PPtKlWqJLoGUVrl29bqL/155MuXz+ygOCR9HtbIG+3t7SlXrhxgmBOa8uTJEyUXzuw5o760Y8WKFfn777+T/Kd48eIG79PTr1N18eJFi2eTJ1yrQD973RJOTk7Kfyc2EJ3YdwtL6f82NW3aNNFyH4ldm6nps4TefvttVCoV0dHReHp6EhwczPbt2wHo3bt3kiV7LGVvb0+TJk2YOXMm8+bNA+JLt2zZskXZJjU/0/T+XStVqpRyg9ff3z9F+xBJk4FxITKh2NhYIPHaUNu3bycgICC9QspQzZs3B+DatWuJfsl5PQmyVMJHlcz1vVarTbPHsRJKmGCbqpWnt2bNGqs+lmljY0Pfvn0BePToEX5+fqxbtw6AQoUK0bZtW6P36K9XSPya1S8OmRas0V/JPY8VK1YYbPs6/SN8QJK11hPTtGlTbG3jH+wytZq7XsIZ+2XLljW58GZml7D+aGLneuzYMa5du2b0nuQoXbq0UsPQ1CJDidH/DUrJe4UQIjvIKrlpkyZNlBvbieWFFy9eTPXCm5mlT4oWLaoslLh161azkxDCw8OTdWPYEj169FBuOnt6erJ161Zl5qu5RTf1+XZi/RYQEMDOnTutGquetfpLfx5RUVFGJdr0IiIiDMrMmKLPG1OTM8L/86Jbt25x5MgRs9vpn3RI+J7MyN/fn9u3bwPxT6T26tUryX/0s4/9/f2Vp4Ah/olXiO/jv//+26I4mjZtSu7cuQFYtGhRot8BTElYPzuxGy3671vWkJy/TefPn0904k1q+iyhUqVKKdfZ6tWrWb9+PZGRkUDqyqgkh7n8vUqVKkp5z9WrVxusaZYc6fm7plarlZ/F0aNHk7zhK1JGBsaFyITKli0LgI+Pj8myHbdv307zmmCZiX5RDYivrWXqUTNvb29lhkpK6fsd4gdQTfnxxx/TZTaUu7s7RYoUAeC3334zmM2ud+3aNWbOnGn1Yyf8IvPNN98oSWnv3r2VgdqEypYtq/x8PDw8TA4879y5k0WLFlk9Vj1r9FfBggWV0j0bNmxQFhtN6MyZM0ybNi3RWPLnz4+dnR2A0ncpUaRIEbp06QLAoUOHTN5Y0Ol0fP755wQFBQEwfPjwFB8vI7m7uyuzfH7//XeTSV9wcDBjxowB4mdXvf/++wavb9q0KdEvAHfu3OHWrVsABjcP7ty5w+HDhxONL+GCQVnxxoMQQqRWVslNixcvTrt27QDw9fVl06ZNRtu8evVK+TxJjczUJ++++y4AQUFBfPXVVya3+eabb5L16L8lXFxc6NSpExB/83rOnDkAaDQa+vTpY/I9+n67efOmyUWxIyIieP/999N0gXNr9Jf+PCIiIkzW842Li2P06NGJPskI/1+c7+nTp0ku4JqY9957T7kpNHbsWJOzk8+dO8dvv/0GxOeZ3bp1S/Hx0lrCiS7JjVO/nU6nM7gx1q9fPyXPnDFjhkFe97qgoCCDay9v3rzK9XLlyhXGjh1r9kaIVqs1+nm/9dZbSjmMhQsXKoPCCfn5+Vn1e5L+2jx27JiS+yb07NkzkyUFE0pNn71OvwjnpUuXlO9ilStXpmbNmomfSCJevHjBjh07Ep0gZi5/t7Gx4dNPPwXiZ3UPHz480fhff5o1vX/XPvvsM2Xto2HDhhnc9HmdTqdj586dqb7xm9NIjXEhMqEBAwbw7bff8vjxY9q2bcunn35KpUqViIyM5ODBgyxYsIDo6GiqV6+e4Y+spocaNWowdOhQZSHEli1bMnr0aKpUqUJYWBhbt27l77//pnbt2sojTSl5LLF169YUKlSIp0+fMmXKFO7du0fnzp0pUKAAt27dYvny5fj5+dGgQQOl5nZasbOzY8aMGQwdOpSwsDDat2/Pp59+StOmTVGpVBw5ckT5sC1btqzJpCelKlasSK1atThz5gxXrlxR2s3N/MmfPz/t2rXD19eXPXv20KNHD959911KlizJ06dP2bJlC2vWrKF06dKEhISkuqanKdboL/1s+UWLFnH58mU6dOjAyJEjKVeuHKGhoezatYu///6b3LlzU6RIEf7991+Tsdja2lKrVi2OHTvGqlWrqFatGlWrVkWj0QDxj9wmt87ctGnT8PPzIygoiM8//5wTJ07Qp08fChQowJ07d/jrr7+Ux+rq1avHBx98kJLuSxOrV69Ochs7Ozv69OmDRqNhzpw59OnTh/DwcNzd3RkxYgStW7fG3t6es2fPMnv2bGWhnlGjRil1AvW+//57xowZQ8eOHWncuDHly5fHycmJFy9ecObMGRYuXKiU/NF/wYH4xZy6dOlChQoVcHd3p2bNmhQvXhwbGxseP37M9u3blRtlJUqUoH379tbqIiGEyDKyUm46bdo0Dh48SEREBB988AFHjx6lS5cu5M2blytXrjB37lyuXr2q5DoplZn65L333mP16tVcuHCBVatW8fDhQ9577z1KlCjBw4cPWbJkCXv37qVmzZpWL1EyYMAAZWBYPzGhbdu2BrVxE+rfvz8LFy5Eq9XSt29fRo8eTYMGDXBwcODcuXPMnz+fmzdvpmm+bY3+6tGjB5MnTyYqKoqRI0dy8eJFWrZsSZ48ebh69SoLFy7k3LlzSZ5H/fr1gfhB1bFjxzJ8+HCDvks4cScxb731FmPGjGHWrFlcu3aNpk2bMmbMGGrWrElUVBT79u1j3rx5REREoFKpmDNnjjLbP7PR1wmH+BIU5cuXT9b76tevT9GiRXn8+DFr165l/PjxqFQq7O3tWbhwId26dSMyMpLevXvTu3dvunTpgpubG7Gxsdy6dYv9+/ezZcsWjh49ajCQ+vXXX7N//34uXbrEihUrOH36NO+++y5Vq1bF3t6ewMBATp48ycaNG+nTpw8TJkxQ3mtra8uwYcOYOXMm//zzD507d2b06NGUKlWKoKAgduzYwfLly6ldu7bVrvcBAwbg4+Oj5NRjxoxRyoecOHGCefPmERgYqKwRYEpq+yyhTp06UbBgQZ49e6aUrUztbPHQ0FAGDhxIiRIl6NKlC3Xq1KFkyZJoNBqePn3K3r17Wbp0KQDOzs7KE9F67733nvLd1dfXlwYNGvD+++9Tu3ZtnJyceP78OWfPnsXLy4sqVaqwYMEC5b3p/bv21ltvMW3aNL788ktu375NkyZNGDx4MK1atcLV1ZXo6GgePXrEyZMn2bJlC/fu3WPt2rVUqVIlxcfMaWRgXIhM6KOPPmL//v3s27ePf//9l1GjRhm87ujoyJ9//omvr2+Gf/lILz///DOPHz/G19eX69ev88knnxi8XqpUKRYvXqzceU5YziK5cufOzZ9//smgQYOIjIxk6dKlygeqXpMmTfjll19o2LBhyk8mmbp168bkyZP57rvvCA0NZfLkyQav58qVi6VLl/L7779bdWAc4hOqhF8WK1eubDQQmdCvv/7K5cuXefDgAQcOHODAgQMGr5coUYLVq1ebnT1kDdbor4kTJ3Ls2DEuXrzI2bNnjWYl58uXjxUrVjBt2jSzA+MQf2e/f//+BAUFGe1j/PjxBglzYooWLcqWLVvo168fDx8+ZO3atSYfDW/YsCEeHh5JrlKfnkaOHJnkNnny5FGuCX0971GjRhEeHs7MmTNNzvD/4IMP+OGHH0zuLzQ0FE9PT7OPLavVar777jtldltC169f5/r162ZjLVGiBB4eHsrjtEIIkZNkpdy0bNmyrF69msGDBxMeHs7ixYtZvHixwTbjx49Hq9Vy5syZFOWMkLn6xNbWFk9PT7p27cqNGzfYv38/+/fvN9imVatWfPLJJ/Ts2dOqx27dujWurq4Gi9iZm0wBUKtWLSZMmMD06dMJCQkxytcAPvnkE9566600Gxi3Rn8VL16cX3/9ldGjRxMZGcns2bOZPXu2wTY9e/Zk6NChic4WbdasGXXr1uXkyZOsX7+e9evXG7xuyeKd3377LREREfz555/cv3+fzz//3GgbBwcH5syZk6lv9G/fvl2pa23JTFuVSkXnzp1ZtGgRd+7cwd/fn0aNGgHQuHFjNm7cyLvvvsvz589Zt25dssuXODo6snXrVt555x38/Py4fPmyyb415/PPP+fIkSP4+/tz6tQpg4VBAapWrcqKFSsSrblviW7dujFo0CBWr17N48ePGT9+vMHrarWaadOmERwcbHZgHFLXZwlpNBoGDBjA3LlzgfiJMa8PVKfUgwcPDAatX+fi4sLy5cuVNbT0bGxsWLlyJaNGjWLDhg3cvXuXb7/91uQ+TA0wp/fv2vDhw8mdOzdffvklL1++5M8//+TPP/80ua2NjQ25cuVK9TFzEimlIkQmpNFoWLduHTNmzKBmzZrkypULR0dHypYty7vvvoufnx/du3fP6DDTlZ2dHWvXrmXevHk0bNiQPHnykCtXLipWrMjnn3+On5+fwSxc/SIVlmrdujX79++nb9++FC1aFI1GQ8GCBWncuDFz5sxhy5Yt6fpBM2rUKHx8fOjSpQuFChXC3t4eNzc3Bg8ezP79+9Msqe3du7dSDgTiZ/ckpkSJEhw8eJDRo0dTvnx57O3tyZMnD1WqVGH8+PEcPnzYYEGstJLa/sqbNy++vr588803VKpUCQcHB5ycnKhYsSKjRo3i8OHDNG7cOMk42rdvj7e3N506dVKuo5SqUqUKJ06cYPLkyTRo0IB8+fKh0WhwdXWlXbt2LFq0iB07dhiskp5V9enTh1OnTvHJJ59QqVIlnJ2dlZ9hv3798PX15ZdffjG5MOrWrVuZMWMG3bt3p3LlyhQuXBhbW1ucnZ2pXLkyw4cP58iRI8qjk3qNGjVi+/btfP755zRr1oxy5cqRJ08ebG1tKViwIM2aNWPatGkcP36cqlWrpldXCCFEppLVctOWLVty9OhRhg0bhpubG3Z2dhQuXJj27duzceNGJkyYoJStSGnOmNn6pGjRohw8eJCJEydSqVIlHB0dyZs3L3Xr1uXXX39lw4YNqcpHzFGr1QYTH/Lnz59kvjV+/HjWrVtHq1atcHFxwc7OjuLFi9OlSxe8vLyYMmWK1eN8nTX6a/DgwezcuRN3d3cKFiyIRqOhSJEitGnThqVLl7JkyRKzi7nr2djYsGnTJr744guqVKmCk5NTihfkVKlU/PTTT+zevZv+/ftTsmRJJZd96623+Pjjjzlx4gT9+vVL0f7TS0rKqOgl/J17fd2h5s2bc+7cOSZNmkSjRo0oUKAAtra2ODk5UalSJYYNG8aWLVtMznzOly8f3t7erF27ll69euHm5oaDgwP29vaUKFGCjh07MmfOHKPJWxA/sO7l5cWPP/5I1apVyZUrF87OzlStWpUff/yR3bt3U7hwYYvOMynz5s3jr7/+omHDhkb59K5duxgxYkSy9pOaPkso4TXn7u5O/vz5U3V+JUuWZN++fXz99de0adOGChUq4OLigq2tLfny5aN+/fp88803nD592qDWeEKOjo4sXryYnTt3MmjQIMqUKUOuXLmU3+PWrVszbdo0k3+PMuJ3bdCgQVy4cIGJEyfSqFEjChYsiK2tLbly5aJ06dJ06NCBadOmceHCBZo1a2a14+YEquDgYOut2iaEEBnI39+fjh07ArB58+ZEV4gXQgghhBA5V7du3ZQSeT4+PhkdjhBCZFvr1q1T1kLatGkTrVq1yuCIhPg/mTEuhMg2Nm7cCMQ/HqmvoyaEEEIIIURCjx8/5ujRowDUqVMng6MRQojsbeXKlQC4ubnJ5DWR6cjAuBAiS3jx4kWiNfYSLrDRoUOHbFFWQgghhBBCWC6xtVdevXrFxx9/rCzInNnLSgghRFZ25swZDh06BMA777yTZHkhIdKbLL4phMgS/vnnH/r27Uu3bt1o0aIFZcqUQa1Wc//+fXbs2MG6deuIi4vDwcGB7777LqPDFUIIIYQQGeSzzz4jJCSEHj16UKNGDfLly8fLly85e/YsS5Ys4ebNmwAMHDhQ1o8QQggru3fvHtHR0Vy7do2JEycC8Ws5vffeexkcmRDGZGBcCJFlhIWFsWrVKlatWmXydWdnZ5YsWWK1Fb2FEEKInObMmTNMnz6d48ePExsbS6VKlRg5ciQ9evRI0f6Cg4Np2LAhjx8/pnXr1krZs4QSe8prwIABLFiwIEXHFjnbuXPnOHfunNnXO3bsyMyZM9MvICGEyCHc3d25f/++QdvUqVPlqW6RKcnAuBAiS6hWrRoLFixg7969XLx4kWfPnhESEoKTkxNly5aldevWDB8+nEKFCmV0qEIIIUSWdPDgQXr16oWDgwM9e/bEycmJLVu2MGzYMB48eMCoUaMs3ue4ceMIDQ1Ncjs3NzcGDhxo1C6zeUVKTJo0iW3btnHw4EEePnzI8+fP0el0FCxYkLp169KvXz86dOiQ0WEKIUS25uzsTMWKFRk9ejRdu3bN6HCEMEkVHBysy+gghBBCCCGEEBknNjaWunXr8ujRI3bv3k21atUACAkJoXXr1ty7d49Tp05RsmTJZO/T29uboUOH8ssvvzBu3LhEZ4w3btyY7du3W+18hBBCCCGESIpUvRdCCCGEECKHO3jwILdv36Z3797KoDjE1wQdO3Ys0dHReHh4JHt/z5494/PPP6dfv360a9cuLUIWQgghhBAiVaSUihBCCCGEEDnc4cOHAWjVqpXRa61btwbgyJEjyd7fZ599hlqtZsaMGYSEhCS5fUhICMuWLeP58+fky5eP+vXrU7ly5WQfTwghhBBCCEvJwHgmFhkZyaNHjyhWrBgODg4ZHU6WIH2WMtJvlpM+SxnpN8tJn6WM9JvlpM9ytps3bwJQrlw5o9dcXV1xcnLi1q1bydqXp6cnW7duZfXq1bi4uCRrYPzSpUuMGTPGoK1NmzYsWLAg2euHREZGJms7a4iJieHp06cUKlQIjUaTbsfNyqTPUkb6zXLSZykj/WY56bOUkX6znPRZ8ln6PUYGxjO5uLi4jA4hy5E+SxnpN8tJn6WM9JvlpM9SRvrNctJnOZd+gcw8efKYfN3Z2TlZi2g+fvyY8ePH07t3b9zd3ZN17E8++YSuXbtSvnx5NBoNV69e5ZdffmH37t3069eP3bt3o1ark9zPo0eP0v0aDggISNfjZQfSZykj/WY56bOUkX6znPRZyki/WU76LHFqtZqyZcta9B4ZGBdCCCGEEEJYxejRo9FoNMyYMSPZ75kyZYrB/9erVw9PT0+6dOnCkSNH2L59O127dk1yP8WKFbM43pSKjo4mMDAQV1dX7Ozs0u24WZn0WcpIv1lO+ixlpN8sJ32WMtJvlpM+SzsyMC6EEEIIIUQOp58pbm5WeFhYGC4uLonuY82aNezevZvly5dToECBVMVjY2PD0KFDOXLkCMePH0/WwHhGlACys7OT0kMWkj5LGek3y0mfpYz0m+Wkz1JG+s1y0mfWZ5PRAQghhBBCCCEylr62uL7WeEKBgYG8fPkyyUdTL1y4AMDQoUNxcXFR/qlevToAe/fuxcXFhSZNmiQrJv3gekRERLLPQwghhBBCiOSSGeNCCCGEEELkcI0bN2bWrFns27ePXr16Gby2d+9eZZvE1KtXj/DwcKP28PBwNm3aRPHixWnVqhUlSpRIVkynTp0CoGTJksnaXgghhBBCCEvIwLgQQgghhBA5XPPmzSldujQbNmzgww8/pFq1agCEhIQwa9Ys7Ozs6N+/v7J9QEAAoaGhuLq6kjdvXgB69uxJz549jfZ99+5dNm3axJtvvsncuXMNXrt8+TIVKlRAo9EYtB8/fpw5c+ag0Wjo3r27lc9WCCGEEEIIGRgXQgghhBAix7O1teX333+nV69euLu707NnT5ycnNiyZQv3799n8uTJlCpVStn+xx9/xMPDg3nz5jFo0KAUH/ePP/5g165dNGjQgOLFi6PRaPjnn3/Yt28fKpWKmTNnUqZMGWucohBCCCGEEAZkYDwNaLVawsPDiYyMTPV+7OzsCAkJISwszErRZW/SZymTXfrNwcGB3LlzY2MjyycIIYQQlmrWrBk+Pj5Mnz4dLy8vYmJiqFSpEj/++KPJmeDW0KlTJ0JCQrh06RIHDhwgOjoaV1dXevXqxYgRI6hdu7ZVjyd5esaRPksZ6TfLJafP5HuDEDnL06cq5s+3Y88eW2JjVdja6mjTJpaPP46mUCFdRocnMpAMjFuZVqvl+fPnODk5UbBgQVQqVar2FR0djZ2dnXxgJ5P0Wcpkh37T6XRERkby/PlzChQokGXPQwghhMhItWvXZsOGDUlut2DBAhYsWJCsfZYqVYrg4GCTr3Xp0oUuXbpYEmKKSZ6esaTPUkb6zXJJ9Zl8bxAi53j1Cj78MBcnT6oJDFSh1f7/s//yZTVr19pRt24cCxdG4OCQgYGKDCOfAFYWHh6Ok5MTjo6OqUq2hRCWUalUODo64uTkZHLhLyGEEELkbJKnCyFAvjcIkVO8egUdO+Zm505bHj+2MRgUB9BqVTx+bMPOnbZ07JibVD5MJrIoGRi3ssjISBzkNpMQGcbBwSHVj0cLIYQQIvuRPF0IkZB8bxAie/vww1xcvqwmJibxm+ExMSouXVIzfHiudIpMZCYyMJ4GZAaKEBlHfv+EEEIIYY7kCUIIPfl7IET6U925g92uXdjfuZOmx3n6VMXJk0kPiuvFxMRv/+yZ/F3IaWRgXAghhBBCCCGEEEIIkWY0q1fjXL8++YYMoUrfvuT+7bc0O9b8+XYEBlo2yB0YqGLePLs0ikhkVjIwngVEx0UTGhWa0WEIIYQQQgghhBBCCGGZ0FAcv/4aVVQUACqdjty//ILNlStpcrg9e2yNaoonRatVsWePbZrEIzIvGRjPpKJio3gS8YQ7L+9wNegqd0LuoNVpMzosIYQQQgghhBBCCCGSzfboUVQhIQZtKq0Wu7Vr0+R4sbEpK4mS0veJrEsGxjOhwPBArjy/wuPwx7yKewVAnC6Ol9EvMzgyIYQQQgghhBBCCCGST336tMl2zaZNoNNZ/Xi2tinbZ0rfJ7IuGRjPhHJrcptsD44KTt9AsggXFxfc3d1TtY9Dhw7h4uLC9OnTrRRV6lWtWpWqVatmdBhCCCGEEEKkSHbN04UQQlhGffasyXabBw9Qnzxp9eO1aROLjY1lg9w2NjratIm1eiwic5PiOZlQbk1uNDYaYrQxBu3BkcGUcC6BjSrz3c9wcXGxaPvg4OA0iUMIIYQQQgjxf5Knpy0PDw9GjBgBwL59+6hVq1YGRySEEJmMTmd2xjiAZuNG4urVs+ohP/44mrVr7Xj8OPmlUVxddYwcGW3VOETmJwPjmZBKpSKvfV6evXpm0K4vp5LHPk8GRWbe+PHjjdoWLFhAaGioydes6cSJEzg6OqZqH7Vr1+bEiRMUKFDASlEJIYQQQgiR8SRPT1srV65EpVKh0+lYtWqVDIwLIcRrbO7cwebFC7Ova7y9iZw2DdRqqx2zUCEddevGsXOnipiYpAfHNZr47QsWlFIqOY0MjGdSLg4uRgPjEF9OJTMOjE+YMMGobc2aNYSGhpp8zZoqVKiQ6n3kypXLKvsRQgghhBAiM5E8Pe3cvHmTo0eP0rFjR27cuMGGDRuYOnVqqm8GCCFEdqI+cybR120CAlAfPUpc06ZWPe7ChRF07JibS5fUiQ6OazQ6qlSJY+HCCKseX2QNMjCeznK3bZu87YA8MRHoMLxbpUJFLo0jYP2VcsN377b6Pl939+5dqlevzoABAxgzZgyTJk3i6NGjvHjxgvPnz1OqVCm2bt3K5s2bOXPmDAEBAWg0GipXrsxHH31Et27djPbp4uJC48aN2b59u9I2cuRIPDw8OHfuHD4+Pvz999/cvXuXwoULM3jwYL788ktsbP5fkubQoUN06dKF8ePHG3xB0Nf49vf3Z/LkyXh7exMUFET58uUZP368yXju3r3LDz/8wP79+4mJiaF69ep88803HDx4kBkzZrB161aapuIPfnh4OHPmzMHLy4t79+7h6OhIvXr1GDt2LA0aNDDYNjIykkWLFrF27Vru379PbGwsBQoUoHbt2nz++efK+Wm1WlavXs3q1au5desWkZGR5M+fn6pVq/LJJ5+kKl4hhBBCiKwguXk6ADodWp0OG5UKVNbPy18neXrmzNNXrVoFQP/+/bl+/TpTp07F29ub/v37m9z+zp07/Pbbb+zfv5+AgADy5MlDxYoVGThwIIMGDTLY9siRI8ybN4+TJ08SEhJCwYIFqVWrFiNHjqRhw4YAjBgxAg8PD+Xnk9D06dONzilhX7Zq1YqffvqJ06dPExoaqpTQWblyJTt27ODSpUs8efIER0dHatWqxZgxY2jWrJnJ83o91kKFClGzZk0l1ilTpjBz5kyWLl1Kjx49jN6/cuVKRo0axXfffcfYsWOT3f9CiKwhsTIqehovL6sPjDs4wI4d4Xz4YS5OnlQTGKhCq/3/Z7aNjQ5X1/iZ4gsXRuDgYNXDiyxCBsbTma0Fiwpkvnnh1nP79m3atm1LpUqVGDhwIEFBQdjZ2QEwadIkNBoNDRo0oEiRIjx79oydO3cydOhQZsyYwYcffpjs43z33XccOXKE9u3b06pVK7Zv385PP/1ETEwM3377bbL2ERsbS8+ePQkODqZLly68evWKTZs28c4777Bx40ZatWqlbPvo0SPat29PQEAAbdq0oVq1aty4cYMePXqYTSQtERkZSdeuXTl9+jTVq1dnxIgRPHnyBC8vL/bu3cvff/9N9+7dle1HjBiBl5cXlStXZuDAgdjb2/Pw4UMOHTrE2bNnlS8UkyZN4vfff6dMmTL06dMHJycnHj16xLFjx/Dz85OBcSGEEEJke5bk6dmZ5OnJExcXh4eHBy4uLnTo0IGaNWsybdo0Vq5caXJg3N/fn379+hEWFkbr1q3p1asXwcHBXLhwgT///NNgYHzBggV8/fXXODo64u7uTtGiRXny5AnHjh3D29tbGRhPqRMnTjBr1iyaNm3KO++8w4MHD5TXxo0bR5UqVWjRogUFCxbk0aNH7Nixg+7du7Ny5UqjxVQTxtq5c2dKlCihfI/QxzpkyBBmzZrFihUrTA6Mr1ixAltbW6ObA0KI7MHcwpsJaby9ifz5Z7C17jCloyOsWBHBs2cq5s2zY88eW2JjVdjaxi+0OXJktJRPyeFkYFxkiGPHjvHll1/y9ddfG722fv16SpcubdD28uVL2rVrx9SpU3n77bfJlStXso5z/vx5jhw5QpEiRQD48ssvqVWrFgsXLmT8+PFKkp+Yx48fU7NmTbZt26Zs36dPH7p168a8efMMEu4ffviBgIAAvv32Wz7//HOlXT8LIrXmzJnD6dOn6du3L3/99Req/2Yoffjhh7Rt25ZPP/2U1q1b4+zsTEhICJs3b6ZGjRrs3bsXdYJ6XXFxcYSFhRnEV6RIEQ4dOoSTk5PBMV8kUgtMCCGEEEJkL5KnJ8+uXbsICAhg2LBh2NvbU7JkSRo2bMjRo0e5desWZcuWVbaNiorivffe4+XLl6xfv542bdoY7Ovhw4fKf1+8eJFvvvmGIkWK4OPjg5ubG9HR0djZ2aFSqQgICLA41tft37+fP/74g8GDBxu9duzYMaOfcUBAAC1btuS7774zGBh/PdaEs9Z1Op0Sa8mSJWndujV79uzh7t27BttdvXqVkydP4u7ujqura6rPTQiRycTGoj53LsnNbJ4/x/bgQWIT/N22poIFdXz/fRTffx+VJvsXWZdN0psIYX2urq588cUXJl97PREDcHJyYuDAgYSGhnImifpUCY0bN05JtgEKFChAp06dCAsL48aNG8nez7Rp0wyS8+bNm+Pm5mYQS1RUFN7e3hQqVIhPPvnE4P2DBw/mjTfeSPbxzPHw8ECj0fD9998rg+KA8thrSEiI8qiqfhEgBwcHg8dRAdRqNS4uLgZtGo3GYPBcL1++fKmOWwghhBBCZA2SpyfPypUrAQxmh/fv319ZhDOhHTt28OjRI/r27Ws0KA5QvHhx5b+XLVuGVqvlm2++MSqPolKpKFq0qMWxvq569eomB8XB9M+4SJEidOnShZs3b3Lv3r0UxTps2DB0Op3Sb3orVqwAYOjQoSk9HSFEJmZz9SqqV6+Sta1m48Y0jkYIYzIwLjJElSpVzM4Cefr0KV9//TX16tWjaNGiuLi44OLiwsSJEwEsmiVRo0YNozZ94hkSEpKsfeTNm9dkgli8eHGDfdy4cYOoqChq1qyJvb29wbYqlYp69eolO25TQkNDuXPnDmXLljVInvX05U4uXrwIQJ48eWjXrh3Hjh2jWbNm/Prrrxw/fpyYmBij9/bs2ZP79+/TuHFjpkyZgp+fH6+S+eElhBBCCCGyD8nTkxYYGMiuXbsoW7Ys9evXV9q7d++Oo6MjHh4exMXFKe2n/6uv2yoZMyEt2TalatWqZfa1O3fuMHr0aGrUqIGrq6vyM164cCFg+DO2JNb27dtTrFgx1qxZo/RNdHQ0np6elChRwuQNAyFE1pecMip6mm3bIEpmdIv0JaVU0lls3brJ31inI1YbR5TW+A+DxsYWO7W9iTdlDYUKFTLZ/uLFC1q2bMmDBw9o0KABzZs3J2/evKjVai5evMiOHTuIsuAPpbOzs1GbflZ0wmQ1MXnymK72rlar0Wq1yv/rS5MULFjQ5PaFCxdO1vHM0e/fXN/pHz1MWCJl2bJlzJo1i/Xr1zN58mQg/nwGDhzId999pzzqOn36dIoXL46npyczZ85k5syZODg40L17d6ZOnUqBAgVSFbsQQgghRGZnaZ6enotvpifJ05Pm4eFBbGws/fr1M4qnU6dObNy4kT179tC+fXsgfoILkKzZ3qGhoahUKoPZ9NZm7md869YtWrVqRVhYGE2bNqVDhw44OztjY2PD4cOHOXLkiMHP2JJY1Wo1b7/9NjNmzGD37t106NCBbdu2ERQUxAcffGD0hKsQInuwNbPw5vN27Siwa5dBmyokBNt9+4jt2DE9QhMCkIHxdGfJivJarZao6Cj+Df2XWF2swWu2NrZUKVjFoJxGVmIu7pUrV/LgwQO++eYbxo0bZ/Dab7/9xo4dO9IjvBTRJ/fPnj0z+fqTJ0+ssv+nT58muv+EXzJy5crFxIkTmThxInfu3OHQoUMsXbqUP//8k8jISGbPng2Ara0tH3/8MWPGjCEwMJAjR46wevVq1q5dy5MnT9i0aVOqYhdCCCGEyOwszdP1dZ+z24Ce5OlJ05dKmT59OtOnTze5zcqVK5WB8bx58wLxNdGTkjdvXqU+d7FixRLdVn/tmbqRoB+MN8Xcz3j+/PkEBwfz119/GQ36f/bZZxw5ciTFsQIMGTKEmTNnsnz5cjp06MCKFSuwsbExW9ZFCJH1qU2U2IotWZKnffsaDYwDaLy8ZGBcpKvslcVlQypU5LXPa9Qeq43lZczLDIgobd2+fRuATp06Gb3m7++f3uFY5I033sDe3p5z584ZzZbR6XScPHkyVfvPkycPpUuX5tatWzx69Mjo9cOHDwNQtWpVk+8vXbo0b7/9Ntu3b8fJyYmdO3ea3K5o0aL07t2bjRs3UrZsWQ4cOCBlVYQQQgghcjjJ0+MdPXqUf//9lzJlyvD222+b/KdgwYL4+voqE1pq164NwL59+5LcvyXb6tcMMvXd4MKFC8k9JYW5n7FOp+P48eNG21sSK8SXuGnXrh27d+/m+PHj+Pn50bp1a9zc3CyOVQiRBUREYHPlilFzbM2avKxalTgTN9Q0O3aAjD+IdCQD41mAqYFxgODI4PQNJB3ok6Jjx44ZtK9fv55dJu4mZib29vZ069aNJ0+esGDBAoPXPDw8uH79eqqPMWDAAGJiYvjxxx/R6XRK+6VLl1izZg158uRRVop/9uwZV0x8CAUHBxMVFaXUV4yKijKZ6IaHhxMeHo5Go8l2M6GEEEIIIYRlJE+Pp1888vPPP2fu3Lkm/xkyZAgxMTGsXbsWgI4dO1K8eHHWrVvH3r17jfaZcGB72LBhqNVqpk6darDQJcQPUCecda6vFb5mzRqD7by9vY1mdyeHuZ/xb7/9ZvJ7hSWxJnxPbGws77zzDjqdjiFDhlgcpxAia1BfuIDKxBMtMTVqgI0NkV27Gr2mevkS20z+mSKylyxRSsXT0xN/f3/OnTvHlStXiI6OZt68eQwaNMii/Tx8+JBffvmFPXv2EBgYSIECBWjVqhVff/01JUqUSKPoU89J44StytaonEpwVDAldCWybDkVU/r168fs2bP58ssvOXToEG5ubly6dAk/Pz+6dOnC1q1bMzrERH333XccOHCAH374gSNHjlCtWjVu3LiBr68vbdq0Yc+ePakaZP7000/ZtWsXnp6eXL9+nebNm/P06VO8vLyIjY3lr7/+Uh4VffToEc2aNaNKlSpUrlyZYsWKERQUxI4dO4iJiWHUqFEAvHr1io4dO1KuXDlq1KiBm5sb4eHh+Pj4EBgYyKhRo4wWKRJCCCGEEDmL5Onx5Um8vb3JnTs33bt3N7vdwIEDmTVrFitXrlRy6aVLl9K7d2969+5NmzZtqFKlCmFhYVy8eJGIiAgOHToEQOXKlZk+fTrjx4+nYcOGdOrUiWLFivHs2TP8/f1p164dP/30ExA/s7tMmTKsWbOGhw8fUq1aNa5fv87Bgwdp166dxTcshg0bxurVqxkyZAjdu3cnf/78nDp1ivPnz9O+fXt8fX0Ntn89Vnd3d9zc3AgMDOTo0aMGseq1adMGNzc37t+/j6urKx2lZIIQ2ZapMioAMTVrAhDZrRu5//zT6HW7TZuI7dYtTWMTQi9LTAOdMmUKy5YtUz48U+L27du0aNGCZcuWUaFCBT766CNq1aqFh4cHLVu2VB4by4xUKhV5HXJGOZXixYuzfft2mjdvzoEDB1i2bBnR0dF4eXnRoUOHjA4vSSVKlGDXrl10796d48ePs2DBAp4+fcqmTZsoU6YMYHqhoeRycHBgy5YtjBs3jrCwMObPn8+2bdto3Lgx27ZtM0jQS5YsyVdffUWePHnw8/Nj3rx57Nq1i+rVq7NhwwY++OADAHLnzs0PP/yAm5sb/v7+zJ8/H29vb9zc3Fi8eDGTJk1KVZ8IIYQQQoisT/J02LRpExEREXTt2hUnJyez25UvX54GDRpw/fp15cnMevXq4efnx+DBg7ly5Qp//PEH3t7eaDQaRo4cafD+4cOHs2XLFpo0acKePXtYsGAB+/fvp0qVKvTo0UPZztHRkc2bN+Pu7s6ZM2dYsmQJkZGR7Nixg5r/DTxZonr16mzatInq1auzbds2Vq1aRd68efHx8aFGjRom35Mw1t27dzN37lyTserZ2Ngo9csHDhyIrW2WmKsnhEgBUwPjOhsbYv4r/xpbowZxpUsbbWO7axf8t2iyEGlNFRwcrEt6s4x14MABypYtS8mSJfntt9/48ccfLZ4x3q9fP3x9ffnpp5/46KOPlPbNmzfzzjvv0Lp1azZu3JjqWJ8+fWp2lW9LJVzU52XMS24G3zTapqBjQdzySE02vcy8EFKHDh04ceIE9+7dSzSRzgiZud9Swpq/h+ZERkZy//593NzccHBwSNNjZSfSb5aTPksZ6TfLSZ+J7C6t8vTskDulh8zcZ5Knp69+/fqxa9cuTp8+TdmyZa2+f0v6LD2+N2QVkgdYTvoscU41a6J+bRJqXKVKPNu3T+m3vD//jMOsWUbvjVi0iJg+fdIr1ExPrrW0kyU+WVu0aEHJkiVT/P7IyEj27t1L4cKF+fDDDw1e6969O1WrVmXv3r3cuXMnlZGmHSc7J9QqtVF7cFSwQa1pkfECAgKM2jw9PTl27BgtWrTIdMm2EEIIIYQQOYHk6Rnvn3/+YdeuXbRs2TJNBsWFEJmD6sULo0FxgLj/Fu3Vi+nZ0+T7NZs2pUlcQrwuRzy3FBQURGxsLG5ubibrcZcqVYqLFy9y8OBBSpt4jCMzsFHZ4OLgwvNXzw3aY7WxhMeE42QnSVxm0bBhQ6pVq0bFihVRq9VcvHiRw4cP4+zszOTJkzM6PCGEEEIIIXIkydMzzvr167lx44ayIOn48eMzOCIhRFoyV1887r9Fg/W0lSsTV6EC6tcWQbbdsweCg8HFJY0iFCJejhgYd3FxQa1Wc//+fXQ6ndHg+N27dwG4edO4VIkpkZGRZl/TarVotdqUB5uAfia4TqdDq9WS1y6v0cA4wIvIF+SyzWWVY2Z1r/dZRhg2bBg+Pj6cPXuWiIgIChYsSO/evfniiy+oUKFChsWVmMzQb9ak1WoT/T21hujoaIN/i+SRfrOc9FnKSL9ZTvos+eQRViGypnfffZedO3ca5Ol9+vRh3LhxVKhQIaPDy9aWLVuGv78/bm5uzJ07l/r162d0SEKINGRuYDz2tYFxVCpievZE/dpCvaqYGDTbtxNjQQllIVIiRwyM58qVi0aNGnHo0CEWL16sLDoIsGXLFi5evAhASEhIsvb36NEj4uLiTL5mZ2dn9S+UMTEx8fvGDrVKTZzO8NjBkcEUspO6aAnp+ywjfPnll3z55ZcmX8vsgw0Z2W/WFBkZSWhoaLocKzAwMF2Ok91Iv1lO+ixlpN8sJ32WOLVaLY//C5FFffvtt3z77bcZHUaOtH379owOQQiRjtSnTxu16Rwc0FaqBK+Np8X07InDawPjEF9ORQbGRVrLEQPjANOmTaNjx46MGzcOHx8fKleuzK1bt9ixYweVK1fm8uXLyV7MpFixYmZfCwkJwc7Oziox63Q6YmJi0Gg0yiz3vPZ5CYoMMtguVhdLjCqG3JrcVjluVmaqz0TSslu/OTg44OrqmqbHiI6OJjAwEFdXV6v9zucE0m+Wkz5LGek3y0mfCSGEEEKIVNPpTM4Yj6tWDTQao4FxbYUKxFWpgvrSJYN22wMHUD1/jq5AgTQNV+RsOWZgvGrVquzbt4/p06dz6NAhDh06RNmyZZk9ezYhISF8++23FCxYMFn7Suzx2bCwMKutFq4vaaFSqZR95nPIZzQwDhASHYKzvbNVjpuVmeozkbTs1m82Njbp9pi7nZ2dPFKfAtJvlpM+SxnpN8tJnwkhhBBCiJRSPXyIzZMnRu2v1xdPKKZnT6OBcVVcHJotW4geNszqMQqhl/VHwCxQoUIFli5dyr///suTJ084duwYQ4YM4cqVKwDUrFkzgyNMmpOdE2qV2qg9ODJYqRMthBBCCCGEEEIIIUR6M1VGBRIfGI/u2dNku2bTJqvEJIQ5OWpg3JSwsDB8fHzInz8/LVu2zOhwkmSjsiGvfV6j9hhtDOEx4RkQkRBCCCGEEEIIIYQQoD571mR7XO3aZt+jK13aeGFOQH34MKqAAKvFJsTrst3A+PPnz7l+/TrPnz83aH/16hWxsbEGbVFRUYwaNYoXL17w5ZdfZpnHhl0cXEy2B0cFp2scQgghhBBCCCGEEELo2ZpaeDNvXrRJLF4eY2LWuEqnQ7N5s7VCE8JIlqgxvmLFCvz9/QGUsicrV67k8OHDADRs2JAhQ4YAsHDhQmbMmMH48eOZMGGCso9z587x9ttv07JlS4oXL05YWBi+vr48ePCAoUOH8uGHH6bzWaWcs50zapWaOJ3hggXBkcEUdyqeLRZPFEIIIYQQQgghhBBZiFaL+tw5o+bYWrUgibGqmO7dcZw40ahd4+VF9EcfWStCIQxkiYFxf39/PDw8DNqOHTvGsWPHlP/XD4ybU6JECZo0aYK/vz9PnjzB0dGR6tWrM3XqVLp165YmcacVfTmV1xfh1JdTcbJzyqDIhBBCCCGEEEIIIUROZHPjBqqwMKP2xMqo6OlKlCC2YUNs/5sYq2d7/Diq+/fRublZLU4h9LLEwPiCBQtYsGBBsradMGGCwUxxPTc3N5YtW2blyDKOi4OL0cA4xJdTkYFxIYQQQgghhBBCCJGezC68WbNmst4f06OH0cA4gGbzZqJHjUpVbEKYku1qjOcU+nIqrwuJDEGn02VAREIIIYQQQgghhBAip0rJwpsJxXTrhs7GeKhSs3FjquISwhwZGM+ibFQ25LHPY9QerY0mIiYiAyJKe6tXr8bFxYXVq1cbtFetWpWqVaumej/WNH36dFxcXDh06FCaHcMSmS0eIYQQQgiRfUieLoQQAkzPGNcWL46uSJFkvV/n6kpckyZG7bbnzmFz61aq4xPidTIwnoXls89nsj04Kjh9AwHef/99XFxc2LBhQ6LbhYaGUrRoUUqWLMmrV6/SKTrrO3ToEC4uLkyfPj2jQxFCCCGEEMIsydOzjvDwcNzc3HBxceGLL77I6HCEEMIyUVGoL140ak5uGRW96F69TLZrNm1KUVhCJEYGxrMwZ3vT5VSCI4PTvZzK22+/DcCqVasS3W7jxo28evWK3r174+joaJVjb9myhS1btlhlX9YyfPhwTpw4Qe1kPi4khBBCCCFEWpA83VBmztO9vLwICwtDpVKxfv16IiMjMzokIYRINvXly6hiYozak1tGRS+2Sxd0tsZLIsrAuEgLMjCehSVaTiU2fcupNGvWjFKlSnHw4EHu379vdjt9Qq5P0K2hTJkylClTxmr7s4YCBQpQoUIFcuXKldGhCCGEEEKIHEzydEOZOU9ftWoVtra2fPTRR4SEhLB169aMDkkIIZLN3MKbsbVqWbQfXf78xLZsabz/K1ew+eefFMUmhDnGt2BEmmq7tm2yt9WhQ6fToVKpUKEyuU2sLpao2Cijdo2NBju1nUWx7e6/26LtE1KpVAwaNIhp06axevVqvvrqK6Ntrl69yunTp6lcuTI1a9YkJCSEpUuXsmvXLm7dusXz588pUKAAzZs356uvvkp2Eq2vW3jxtUd2Xrx4waRJk9i2bRsvX77kzTffZOzYsWb3s3LlSnbs2MGlS5d48uQJjo6O1KpVizFjxtCsWTNlu+nTpzNjxgwAZsyYofw3wPnz5ylVqpSyzdatW2natKnBcXbu3Mm8efO4cOEC0dHRlCtXjgEDBvDRRx9hm+Cu6N27d6levToDBgxg3LhxfPvttxw+fJiYmBjq1q3LlClTLKrZaI4+nvPnzxMTE2M2HoCDBw/y+++/c+nSJYKCgsibNy/ly5enX79+vPPOO8p2586dY9asWZw5c4YnT57g7OxMyZIlcXd3l8dChRBCCJEpWTtPtybJ07N/nn7jxg2OHTtG+/bt+fjjj/nzzz9ZuXIlvcyUFIiOjmbRokVs2LCBGzduoNPpKFGiBK1bt+bLL7/ExcVF2fbp06fMnj0bX19fHjx4gIODA+XKlaN79+6MGjUKiC9B06VLF8aPH8+ECRMMjpXwfBcsWKC068/x0KFDTJ06lR07dhAQEMDvv//OoEGDOHfuHKtXr+bw4cM8fPiQ6OhoypYtS58+ffjkk0/QaDRG55VUrDdv3qROnTq0adOG9evXG70/LCyMN998k6JFi3LixAmLfgZCiNQxNzAeV6OGxfuK6dkTzW7jzz7Npk1Eff21xfsTwhwZGE9nJwNOZnQIaWbgwIH89NNPrFmzhvHjx6NSGX5J0C+io5+Fcv36daZNm0bTpk3p3LkzuXLl4vr162zYsIFdu3bh5+dHyZIlUxRLREQE7u7uXLlyhXr16tG4cWMePnzIu+++S6tWrUy+Z9y4cVSpUoUWLVpQsGBBHj16xI4dO+jevTsrV67E3d0dgCZNmnDv3j08PDxo3LgxTRIsDJE3b95E4/rjjz+YOHEi+fLlo3fv3uTKlYudO3cyceJE/P39WbVqlVG/3bt3jzZt2vDmm28yePBgbt++zY4dO+jSpQsnTpygcOHCKeqj1+Pp0aMHzs7O+Pj4mIzH19eX/v37kzdvXjp16kSRIkV49uwZly5dwtPTUxkYv3DhAu3bt0etVtOpUyfc3NwICQnhn3/+YdmyZTIwLoQQQohMSfJ0ydMzMk9fuXIlAAMGDMDNzY0mTZpw6NAh7t69S9GiRQ22ffXqFT169ODYsWOUK1eOgQMHYm9vz82bN1m2bBn9+/dXBsZv3LhBly5dCAgIoGHDhri7uxMREcHVq1f59ddflYHxlIqOjqZr166Eh4fTsWNH1Gq1ct7Lly/Hx8eHRo0a0bZtW169esXhw4f58ccfOXPmjHLOesmJtVy5cjRt2pS9e/fy4MEDSpQoYbCPDRs2EB4ezsCBA1N1XkIIy6nPnjVqi6tQAZL4+2tKTKdO6OzsUEVHG7RrNm0iasIEUKX9TWmRM8jAuLCaEiVK0KpVK/bs2cPBgwdp3ry58lpsbCzr1q3D3t6efv36AVChQgWuXbtGvnyGi4gePHiQ7t27M3PmTH7//fcUxfL7779z5coVhg4dypw5c5T2fv36mZ11cezYMUqXLm3QFhAQQMuWLfnuu++UhFs/s8TDw4MmTZoYzagw5/bt2/zwww8UKlSI/fv3K0nct99+S/fu3dm+fTuenp7079/f4H1Hjhzhhx9+YMyYMUrblClTmDlzJqtXr+azzz5L1vETi2fv3r0ULlwYOzs7vvvuO5PxrFq1Cp1Ox9atW41mwAQFBSn/7enpSVRUFKtXr1b6zNR2QgghhBAifUienriMztNjY2NZu3YtefPmpUOHDkB8fxw6dIhVq1Yxbtw4g+2nTp3KsWPH6NevH/Pnz0et/v+6UyEhIQb/P3z4cAICApgzZw5Dhw412M/Dhw+TFV9iAgMDqVKlCr6+vka16ceOHcvMmTMN4tHpdIwaNYpVq1Zx7NgxGjRoYHGsw4YN4+DBg6xatcroCYgVK1ZgZ2dH3759U31uQggLhIZic/26UbOlC28q8uYltm1bNNu3GzSr//0Xm4sX0VarlrL9CvEaqTEurMrc4j4+Pj48efKETp06KQl23rx5jZJtiK+D+Oabb3LgwIEUx+Hp6YmdnR1fv/aITevWrQ2+CCT0erINUKRIEbp06cLNmze5d+9eiuMBWL9+PbGxsXzyyScGMxvs7e354YcfAFizZo3R+0qVKsXo0aMN2vT9fObMmXSPx9RiTPnz50/xdkIIIYQQIu1Jnm5eRufp+p9B9+7dcXBwAKBbt27kypULDw8PtFqtsm1sbCzLly8nT548zJgxw2DQGeJ/dk5OTgCcPn2as2fP0qhRI6OBZoDixYsnO8bE/PjjjyZzfzc3N6P4VCoV77//PoDBdWRJrJ07d6Zw4cKsXr3aoG8uXbrE2bNn6dixIwULFkztaQkhLKA+dw6VTmfUbunCmwnF9Oxpsl0W4RTWJAPjwqo6depEwYIF2bZtGyEhIUq7ucV8Dh06xMCBA6lYsSIFCxbExcUFFxcXrly5QkBAQIpiCAsL4+7du5QtWxZXV1ej1xs2bGjyfXfu3GH06NHUqFEDV1dXJZaFCxcCpDgevQsXLgAYPNKpV69ePRwcHIzqL0J87T4bG8NfVX1imLCP0zoe/QyeNm3aMG7cOLZu3crz58+N3tujRw9sbGwYPHgwI0eOZMOGDTx69CjFcQohhBBCiNSTPN28jM7T9SVFEs5Id3Z2xt3dnUePHrF//36l/fr164SFhVGrVi2DOuKmnP6v3q+5EjXW4ODgQOXKlU2+Fh0dzR9//EGrVq1wc3MjX758uLi40KJFC8Dw52ZJrBqNhkGDBnH//n327duntC9fvhyAIUOGpPR0hBAppDZzMzDOwoU3E4rp0AGdiYWS7TZtAhOD8EKkhJRSSWd1i9RN9raWLOoTFRdFrDbWqN3R1hEbVfrd/9BoNPTr14958+axYcMG3nvvPQIDA9mzZw8lSpRQkiCAzZs3M2zYMJycnGjVqhUlS5bE0dERlUrFmjVruH//fopiCAsLAzA7S8BUrb9bt27RqlUrwsLCaNq0KR06dMDZ2RkbGxsOHz7MkSNHiIoyXuQ0JXEVKlTI6DWVSkWhQoV4/Pix0WvOzs5GbfrFf+Li4tItnu7du7N69WrmzZvHkiVLWLRoESqViqZNmzJlyhSq/fcoU506ddi2bRuzZs1iw4YNSs3KWrVq8cMPPxgskCSEEEIIkVmkVZ6eWUiennRcGZGnP378mD179lC6dGmjGwP9+/dn/fr1eHh40LFjRwBCQ0MBjOqOm2LJtilVsGBBo9rrekOGDMHHx4fy5cvTo0cPChUqhK2tLSEhIfz5558GPzdLYx06dCizZ89mxYoVtGnThsjISNavX0+pUqVo0aIFMTExqT85IUSy2ZpYeFOn0RBXpUrKd5o7NzHt22Pn5WXQbHPvHurTp4mrUyfl+xbiPzIwns4sWVFeq9USHR2NnZ2d0UyE1wVHBnM75LZRu2suV4o5F7M4ztR4++23mTdvHitXruS9997D09OT2NhYBg0aZHAeP/30Ew4ODhw4cIBy5coZ7GNTKh6N0Seoz549M/n6kydPjNrmz59PcHAwf/31l1JbUe+zzz7jyJEjKY7n9biePn1qtFiRTqfj6dOnJpPrtJIwntcXrTEXj7u7O+7u7oSFhXH8+HG2bt3KypUr6d27NydOnFBmrTRq1IhGjRrx6tUrTp06hY+PD3///Tf9+vXD39/f5OOwQgghhBAZKa3y9MxE8vTE48qIPH3NmjXExcVx584dszPAd+3axfPnzylUqJCyiKipgfrXWbKt/udvakBfP2htirlB8TNnzuDj40Pr1q1Zt26dQUmVkydP8ueff6Y4Vogvr9OqVSt27tzJ06dP2b9/P8HBwXzyySdmYxJCpB2TC29WqQL/lYdKqZiePY0GxiG+nIoMjAtryDpZnEiUs52zyZnhL6JeoEvnR0zefPNN6taty7lz57h06RKrV69GpVIxaNAgg+1u375NhQoVjJLtgIAA7ty5k+LjOzs7U6pUKW7dukVgYKDR6/7+/kZtt2/H31To1KmTQbtOp+P48eNG2+sTO0tmbOtnVB8+fNjotVOnThEZGWm0qGVaSk08zs7OtGnThjlz5jBw4ECePHmiPP6YkKOjI02bNmXq1KmMHTuWV69eGTwKKoQQQggh0o/k6aZlVJ6u0+mUUjYDBw7k7bffNvqnXr16REdH4+npCcAbb7xBnjx5OHPmDMHBwYnuv/Z/tX0TlhsxRz8ob6oEor7UjCX0P7d27doZ1Rk39XO2JFa9d955h5iYGDw8PFixYgVqtdroWhZCpD1VYCA2Dx4YtaemjIpebNu26EzcmNRs3gwJ1hgQIqVkYDybUNuoyWOXx6g9Oi6aV7Gv0j0efY3CL774gmvXrtGiRQuj2Rdubm7cvn3bYGZIZGQkY8eOTfWjb3379iU6Oppp06YZtO/btw8/Pz+j7d3c3ID4Fe8T+u2337hy5YrR9vrFiCxZyb1Pnz7Y2toyb948g5kQ0dHRfP/990B8QpxeLI3nyJEjJr9gPH36FIhfnAjgxIkTREZGJrmdEEIIIYRIf5KnG8uoPP3w4cPcvn2bRo0aMX/+fObOnWvyH/h/LXhbW1veeecdQkNDGT9+vFF+HhISwsuXL4H4Uoa1atXi6NGjSv3thBIOgr/xxhs4Ozuzc+dOXrx4obQ/efKEmTNnWnxu5n5uV69eZdasWUbbWxKrXseOHSlatCjz58/nyJEjtGvXLk3LxmRrOh2qoCCp2yxSJC3qiyscHIh57cYogM2jR6hf+/siREpIKZVsJJ9DPoKjgo3ag6OCyaUxXrAgLfXo0YMJEyYoidDri/kADB8+nC+//JJmzZrRtWtX4uLi2L9/PzqdjipVqnDp0qUUH3/06NFs376d5cuX888//9CoUSMePnyIl5cX7du3x9fX12D7YcOGsXr1aoYMGUL37t3Jnz8/p06d4vz58ya3r1ChAkWLFmXTpk3Y29tTrFgxVCoVw4cPVx4DfF2ZMmX44YcfmDhxIo0bN6ZHjx7kypULHx8fbty4QadOnYweD01LCeNp2rQpXbp0wdnZGV9fX5PxjB8/noCAABo0aEDJkiVRqVQcO3aM06dPU7duXaUm4uzZszl8+DANGzakVKlSODg4cP78efz8/ChdujSdO3dOt3MUQgghhBCGJE83llF5un7RzcRmOb/xxhvUrVuXkydPcurUKerUqcPXX3/NyZMn8fT05NSpU7Rp0wZ7e3vu3LnD3r172blzpzILftGiRXTu3JlPP/2UtWvXUq9ePSIjI/nnn3+4cOGCMrPbzs6O4cOH8+uvv9KsWTM6derEy5cv8fHxoXHjxsp2yVW7dm1q166Nl5cXAQEB1K1blwcPHrBz507atWuHt7e30XuSG6uera0tgwcP5pdffgHi644LC+l0aDZswH7GDPL++y/58ufn5ezZ0LVrRkcmshC1iafHAeL+exIktWJ69sTuv6dmEtJs2kRco0ZWOYbIuWTGeDZirpxKcGRwupdTcXZ2pnv37kD8rA13d3ejbT744AN+++038uXLx4oVK9i2bRuNGzdm9+7dZpPW5MqdOzfbt2/nnXfe4ebNmyxYsIDr16+zZMkSupr4kK9evTqbNm2ievXqbNu2jVWrVpE3b158fHyoUaOG0fZqtZqVK1dSp04dNm7cyLRp05g6dWqSjzN+8sknrFmzhrfeeot169axcOFCNBoNU6ZMYcWKFeleDy9hPBs3bmTRokVm4xk7dixNmzbl8uXLLFu2jJUrVxIVFcWPP/6Il5eX8ojke++9R6dOnbh16xYeHh4sWbKEwMBAPv/8c/bu3UuePMZPNgghhBBCiPQhebpp6Z2nh4SEsHXrVnLnzk23bt0S3bZ///7A/wfSHRwc2Lx5M5MnTyZXrlysWLGCJUuWcP36dYYNG2bwBEC5cuXw8/Pjo48+4vHjxyxYsIB169YRHh7OF198YXCcb775hq+++gqdTsfSpUs5fvw448aNY9KkSRafn1qtxtPTk8GDB3Pnzh0WLlzIP//8w+TJk/nxxx9NvseSWPX0M/mLFStG27ZtLY4zJ7O5coXcnTuT64MPUP/7LwCaoCDyjhiBykSpIyHMMTVjXOfkhPaNN6yy/9iWLdGaWINB4+0NsbFWOYbIuVTBwcHyrIwVPX361ORq5imRkkV9bgffNjlrvGL+iuk+azwjZNWFkDJadus3a/4emhMZGcn9+/dxc3PDIZULiuQk0m+Wkz5LGek3y0mfiewuo/P0nE76LGWk3xLn7e3N0KFDGTduHN988w1gWZ+lx/eGTCckBIfp07FbtAiVmVr8r6ZPJ3rEiHQOLOuR3AnQ6XAuUwab124+xjZpQvi2bSbfkpJ+cxw1Crv/bg4m9HLzZuJatLA06ixHrrW0I5+s2YyLg4vJdlOD5UIIIYQQQgghRFak0+n4448/sLW1lTIqyaHTofHwwLluXez//NPsoDiAxscnHQMTWZnN7dtGg+JgpfriCUT36mWy3W7jRqseR+Q8MjCezeSxy5NpyqkIIYQQQgghhBDWdPnyZWbNmkX//v05efIkb7/9NiVKlMjosDI1mwsXyN2xI7lGjMAmwaK65qiPHIGQkHSITGR15hbejLVSfXG9uCZN0BYsaNRuu3UrREdb9VgiZ5GB8WxGbaPG2c7ZqD0qLorI2MgMiEgIIYQQQgghhLCOc+fOMWnSJPz9/enXrx+TJ0/O6JAyr+BgHMaNw6lFC2z/W3A3OVSxsdju35+GgYnswuzCm1aeMY6tLTH/rY+RkE1wsFyrIlVkYDwbyueQz2T7i6gX6RyJEEIIIYQQQghhPYMGDSI4OJh79+7x119/4eTklNEhZT5aLZqVK3GuUwf7RYtQabVmN9Wp1SbbNTt3plV0IhsxNWNcW6gQujR4iiOmRw+T7ZpNm6x+LJFzyMB4NpTHLg82Jn60Uk5FCCGEEEIIIYTIvmzOnSN3u3bkGjUKm2fPEt02pnVrXh4/jrZkSaPXbHfvhkTqkAtBTAzqCxeMmuNq1QKVyuqHi2vYEG3Rokbtmh07IFIqJIiUkYHxbEhto8bZXsqpCCGEEEIIIYQQOYEqKAiHzz7DqWVLbE+dSnRbrZsb4atWEbFhA9ry5Ynp0MFoG5ugINQnT6ZVuCIbsLl6FdWrV0btVi+johzQxmQ5FVVYWPyNHCFSQAbGsykXexeT7cFRwekahxBCCCGEEEIIIdJIXByaZctwql0b+6VLUSXylLjO3p7IceMIO36c2M6dlVm9sSYGxgFsfXzSJGSRPajPnjXZHmflhTcTiunVy2S7lFMRKSUD49lUXvu8qDB+dCU4Mjj9gxFCCCGEEFnCmTNn6NOnDyVLlqRYsWK0adMGLy+vFO8vODiYt956CxcXF3qZ+TILsHfvXjp16kSJEiVwc3Ojc+fO+Pn5pfi4QgiRE6hPnyZ3mzbkGjMGmxeJrykW0749L/39ifrmG8iVy+C12MaN0ebObfQeja+vVeMV2Yttei28mXDftWubLP2j8fWF8PA0O67IvmRgPJtS26jJY5/HqD0yLpJXscaPugghhBBCiJzt4MGDtG/fnmPHjtGjRw+GDRtGYGAgw4YNY+7cuSna57hx4wgNDU10G09PT3r16sX169cZMGAA/fv3559//qF79+54e3un6LhCCJGdqZ49w3H0aHK3aYOtmVm7etpSpQj38CDC0xNt2bKmN7K3J7pFC6Nm9dWrqO7cSX3AIltSmxgYjytdGl3+/Gl3UJWK6J49jZsjItDIEw4iBWRgPBszW05FZo0LIYQQQogEYmNj+fTTT7GxsWH79u3MmTOHqVOncvjwYcqXL8/kyZO5d++eRfv09vZm/fr1/PDDD2a3CQ4O5ssvv6RAgQL4+fnxyy+/8Msvv+Dn50f+/PkZO3YsYWFhqTw7IYTIJuLisFu8GKc6dbBbsSLxsikODkROmEDYsWPEduyY5K6j2rY12S6zxoVJ4eHY/POPUXNallHRizExMA5STkWkjAyMZ2NSTkUIIYQQQiTHwYMHuX37Nr1796ZatWpKe968eRk7dizR0dF4eHgke3/Pnj3j888/p1+/frRr187sdps3byYkJIThw4dTvHhxpb148eJ88MEHPH/+nG3btqXspIQQIhtRnziBU8uWOH7xBTbBwYluG9OpE2HHjhE1fjw4OiZr/1GtW6NTGY8fSJ1xYYr6wgVUcXFG7XE1a6b5sbVVqxJXvrxRu+3u3RASkubHF9mLDIxnY1JORQghhBBCJMfhw4cBaNWqldFrrVu3BuDIkSPJ3t9nn32GWq1mxowZ6XpcIYTIblRPnuA4YgRO7dqhvnAh0W3jypYlfP16ItasQVe6tEXH0RUqRHiVKkbttocPQxIlsUTOY6qMCqTPjHFUKpOzxlXR0Wh27Ej744tsxTajAxBpy8XehZAo4ztmwZHBODol785xch06dIguXbowfvx4JkyYYNV9i6S5u7tz5MgRgpOYPSCEEEII8bqbN28CUK5cOaPXXF1dcXJy4tatW8nal6enJ1u3bmX16tW4uLgQksjsrcSOq2/Tb5OUyMjIRF/XarVotdpk7Sspuv/KF+h0OqvtMy0dPnyYrl278uWXX/LVV19lSAxZrc+sqUuXLhw5coSgoCCL35uT+y2lLOkzrVab5N+ODBMbi+OyZTj9/DM2SQxM6xwdeTlmDBEffQT29pCCc4qOjiamaVOcLl40aFfFxKDbtYuozp0t3md2Fx0dbfDvnMT+1CmjNp1aTXiFCklef9botxh3dxx+/tmoXb1+PWE9eqR4v5lVTr7WLOXg4GDR9jIwns3lsc+DChU6DGuPBUcFU9SpaAZFlfb0yacMEmeM27dvs3btWs6fP8/58+d5/Pgxbm5uXHwtyXqdVqtl9erVrF69mitXrhATE0OxYsWoX78+M2bMwNnZOZ3OQAghhMhZ9Atk5slj/LQhgLOzc5KLaAI8fvyY8ePH07t3b9zd3VN1XP3nfnKOC/Do0SPiTDzWrWdnZ2f1L5QxMTFW3V9a0ccZFxeX4V+qO3fujL+/PwEBARkaR3rSD86mahDIStfanTt3WL9+PRcvXuTChQsEBARQokQJTpkY5EpIq9Wydu1a1q5dyz///ENMTAxFixalbt26TJ06FScnJ6vEZ03J6bPIyMhk/41JT05nzlDyl1/I9e+/SW4b1KoVDz77jOgiReDJk1Qd17FpU0rMn2/UHuvlxf3q1VO17+wsMDAwo0NId3lPnjRqe1W2LPeDgiCZNwFT1W+5cpGrfHmj3xGNnx+PLl4kzsUl5fvOxHLitWYJtVpNWXOLDJshA+NZzNOnKubPt2PPHltiY1XY2upo0yaWjz+OplAh44U3bG1scbZ3JjTK8MM+MjaSyNhIHGwtu5MiRHIcPXqUGTNmoFarqVixYrL+eEdFRTFkyBB8fX2pXLkyAwcOxN7engcPHrB7926++eYbGRgXQgghMrnRo0ej0WiSLKGSFooVK5bo6yEhIdjZ2VnlWDqdjpiYGDQaDar/avI+fapiwQJ79uzREBcHajW0aRPDiBFRJvP09KTRaID4L4zW6gNL6fvMxia+mmdGxZERUnPOpq611Dh16hS//vorarWaChUq8OTJE1QqVaKxRUVFMWzYMCVPHzBggJKn79u3j1evXpE/f/5Ux2YtlvSZg4MDrq6u6RRZ0mwCA3GaNAnHjRuT3Da2fHnCpkwhpkULrHEG0dHRBOp0xBYvju3Dhwav5T92jLhixeL/sAlFdHQ0gYGBuLq65qi/aaqgIBxeu0YAqFcPNze3JN9vrX7T9u4NP/1k0GYTF0e58+d5NXhwivebGeXUay09yMB4FvHqFXz4YS5OnlQTGKhCq/3/B/zly2rWrrWjbt04Fi6M4PWnBlzsXYwGxiG+nEoRpyJpHbrIgRo3bszu3bupUqUKjo6OyUo2f/jhB3x9ffnhhx8YM2aMwWvy2KgQQgiRtvQzts3NnAwLC8MlidlXa9asYffu3SxfvpwCBQpYfNzXB9bCwsIMtklKUo/OhoWFKQOUqaXPTVQqFVFRNmbz9CtX1Hh62pvN09OLfnBQpVJZrQ8s9Xo+l1FxZKSUnHPCa80afdakSROTeXpi+540aVKSeXpm+nla0mc2NjYWP3afJrRa7BYswOGnn1D997fPHF3u3ESOG0f0xx9jY2eHVaNXqYhu1w7bpUsNmm2ePyf35cvE1atnzaNlG3Z2dpnjOkontleumH6hbl2L+iG1/abr29doYBwg19at6N5/P8X7zcxy2rWWHjLPp5cw69Ur6NgxNzt32vL4sY1Bsg2g1ap4/NiGnTtt6dgxt1E5p7z2eVFhfKc8OCo4zWL29/fH3d2dEiVKULJkSd5++22zdSmfPn3KhAkTqFmzJoULF6Zs2bK8/fbbXDHxx/bmzZt8/PHHVKtWjcKFC1O6dGkaN27MV199pdSSK1KkiLJIk4uLi/LPiBEjkhW7JfFUrVqVqlWrEhwczJgxY6hQoQKurq40bdqUDRs2mNx/eHg406ZNo27duri6ulK6dGn69u3LsWPHTG6v0+lYtWoVHTt2pGTJkhQtWpRatWoxZswY7t+/b7R9TEwM06dPp2rVqhQuXJjatWuzePHiZJ27tZQuXZq6devimMwV0B89esSiRYto2LChUbIN8QlrZkq2hRBCiOwmsXregYGBvHz5MslHUy/8tyjc0KFDDXKw6v89fr93715cXFxo0qRJso6bWP3xzCK1eXpGkDxd8nTJ0zMfh/HjcfzmmyQHxaN79iTsxAmix4yBNJo1GtW2rcl2W1/fNDmeyHrMLrxZq1a6xqEtW5bYGjWM2tWHDqFKZVkhkXPIjPEsYMSI3Fy+rCYmJvHHwGJiVFy6pGb48FysWBGhtNva2OJs50xotOEMoFexr9KknMqpU6f47bffaN26NcOHD+fq1ats27YNf39/9uzZQ+kEq2Pfvn2bzp078/DhQ1q1aoW7uztPnz5l69at7Nu3D29vb+rUqQPE16xs1aoVERERtGvXjp49exIeHs6tW7f4+++/mTJlCjY2Nnz++eesW7eO+/fvM378eOVYVatWTTJ2S+LRi4mJoXv37oSHh9OvXz8iIiLw8vLi/fff5/nz53z44YfKtpGRkXTt2pXTp09TvXp1RowYwZMnT/Dy8mLv3r38/fffdO/eXdleq9UybNgwvL29KVasGL1798bZ2Zl79+7h5eVFmzZtjB5Veu+99zhz5gxt2rRBrVbj5eXFF198gUajYejQoZb8KNONt7c3sbGxdO/enbCwMHbu3MmDBw8oVKgQrVu3TvLRaCGEEEKkTuPGjZk1axb79u2jV69eBq/t3btX2SYx9erVIzw83Kg9PDycTZs2Ubx4cVq1akWJEiUMjrthwwb27dtH3bp1U3TcjJTaPD29SZ4uebqlJE9Pe6qHD7H7++9Et4l7801ezZhBXPPmaR5PdKNG6HLnRvXa33PNzp1Effttmh9fZH7qM2eM2nSOjmjfeivdY4np1Qvbc+cM2lRaLRpvb6I/+CDd4xFZjwyMZ3JPn6o4edI2yWRbLyZGxcmTap49U1Gw4P9rGbo4uBgNjEP8rPEittYtp7J3715+++03hg0bprQtXbqUzz77jPHjx+Pp6am0f/TRRwQEBLBx40Zat26ttI8bN46WLVsyevRojh49CsCWLVsICQlh+vTpRrNKXrx4ga2tLVqtlnHjxnHs2DHu37/PhAkTLIrdknj0AgICKFu2LLt27VJqPY0dO5ZmzZrx3Xff0aVLFyVhnDNnDqdPn6Zv37789ddfymOtH374IW3btuXTTz+ldevWSi3txYsX4+3tTfPmzVm7dq3BzI5Xr16ZXEH90aNHHD16VHns+KOPPqJhw4b88ccfBgl3cHAwCxYsAOJnu8TFxaFWqxOtwzdixIgkH6NOiXP/fZCFhIRQt25dg8WY7Ozs+P777xk5cqTVjyuEEEKIeM2bN6d06dJs2LCBDz/8kGrVqgHxn82zZs3Czs6O/v37K9sHBAQQGhqKq6srefPmBaBnz5707NnTaN93795l06ZNvPnmm8ydO9fgtR49evD999+zcOFCBg8eTPHixQF4+PAhixYtokCBAnTu3DmtTjtVrJWnpyfJ0yVPt5Tk6WnPdt8+VGZKR+qcnYkcP57oDz+E/9YKSHMODsS2bIlm2zaDZvWVK6ju3UNXsmT6xCEyJ53O5MB4XLVq6XeNJhDTvTuOJm7YaDZtkoFxkSwyMJ7JLVyYi8BAyxZZCQxUMW+eHd9/H6W06cup6DBMwoMjgymS27oD4+XLlzea8TB06FD++OMPdu3axbNnzyhYsCDnz5/n+PHjDB482CC51e9jyJAhzJs3jytXrlCpUiXlNVOP/eXLly/Vcac0HoDvvvvOYAGE4sWL89FHHzF16lQ2btzIqFGjAPDw8ECj0fD9998bJLbVq1dnwIABLF++nO3btytfPP/++2/UajWzZs0yOm9HR0eTffHdd98Z1OJ84403qF+/PkeOHCEsLExJ5kNCQixeGGvgwIFpknA/e/YMgBkzZtCyZUs2b95M8eLFOXr0KGPGjOGbb76hQoUKtDXzWJ8QQgghUsfW1pbff/+dXr164e7uTs+ePXFycmLLli3cv3+fyZMnU6pUKWX7H3/8EQ8PD+bNm8egQYNSfFwXFxd++eUXPvzwQ5o3b06PHj0A8PLyIigoiKVLl2baxbetlaenJ8nTJU+3lOTpac/Wz89ke3TfvkROmoSuSPqvCxbTvr3RwDiAxtdXBhtzONWDB9g8fWrUnt5lVPR0bm7E1q+P7fHjBu22/v6oHj5E998NdyHMkYHxTG7fPnujWoVJ0WpV7Nlja5Bwp2c5lfr16xvVmbOxsaF+/frcvHmTS5cu0aJFC06dOgXE1wqcPn260X5u3Lih/LtSpUp06NCBSZMm8cUXX+Dn50fr1q1p0qSJwSOfqWFpPHq2trbUM7EIScOGDQG4ePEiEL+o1J07d6hYsaIyGyqhpk2bsnz5ci5evEj//v15+fIl165do2zZshbV1qxhosaW/nghISFKwl2qVCmCg4OB+EdBo6OjsbOzy5AagfoFcgoVKsSKFSvIlSsXAO3bt+f333+nT58+/PHHH5JwCyGEEGmoWbNm+Pj4MH36dLy8vIiJiaFSpUr8+OOPJmeCW0u/fv0oUKAAv/76K2vWrEGlUlG9enXGjRtHixYt0uy4qWWtPD09SZ4eT/L05JM8PY1ptdgeOGDUHPfmm7xauDD94/lPbPv2JtttfXxkYDyHMzVbHDJuYBwgpkcPo4FxAI2XF9GffJIBEYmsRAbGM7m4uJS9LzbWOElPr3IqhQsXTrQ9JCQEiH+sEsDX1xffRBby0NeqLFWqFLt37+ann35i9+7deHl5AVChQgW+/vprg5p/KWFpPHoFChQwmaS+fr5h/y2kUqhQIZP71a8Ir98uNDT+Z1W0aNFknwNgMAtFT61WAxCX0gsqjeljbt68uZJs67Vu3Rp7e3vOnj2bEaEJIYQQOUrt2rXNLkyY0IIFC5RSD0lJOMhnTps2bWjTpk2y9pdZWDNPTy+Sp8eTPD35JE9PWzZXrmDz36z8hGLToZZ4YnSFCxNbuza2ry2yaHvoELx8CU5OGRSZyGivXxN6cbVrp3Mk/xfTvTsOEyag0hlWSJCBcZEcMjCeyf2XJ1nM1ta4bmF6lVN5Ymb1X327vg6lfkbEzz//zPDhw5O170qVKrFixQpiYmI4d+4cu3fv5q+//mLYsGEUKVLE5IyQ5EpJPADPnz9Hq9UaJd3mzvepiceOEm6v306fhD5+/DjZsVgiM9UufOONN4D/91VCNjY2ODk5KV9EhBBCCCEyA2vm6elF8vR4kqcnn+TpacvUbHGA2EzwtExshw5Gg6Cq6Ghs9+8ntkuXDIpKZDRTM8a1Li5oy5TJgGji6YoUIa5xY2wPHzZotz19GtWdO+is9PSSyJ5kYDyTa9UqiqtXbS16TNPGRkebNrFG7bY2tjjZOREWbZi4vIp9RVRsFPa29qmOF+D48eNGCahWq+XEiROoVCqqVKkCoKwaf/LkSYsSXACNRkPdunWpW7cuZcuW5aOPPsLX11dJuBPOvFAn81tLSuOJjY3lxIkTNGjQwKDd398fgKpVqwLxCXTp0qW5desWjx49MlrB/fB/f8T12zs5OfHmm29y48YNbt68adFjmsmRmWoXNm3alJkzZ3Lt2jWj1549e8bz588pX7681Y8rhBBCCJFS1szT04vk6fEkT08+ydPTlqmBcZ1aTWzjxukfzGti2rfHYepUo3aNj48MjOdUcXGo/1uQ16C5Vi1I5MZdeojp1ctoYBzAbtMmosaOzYCIRFaR/kXKhEWGD4/A1dWyWSWurjpGjow2+ZqLg4vJ9uCoYAsjM+/ff/9l+fLlBm3Lly/n33//pV27dhQsWBCIf1S3Tp06bNiwgU2bNhntR6vVKkkoxK+Irn9sMSH9zA57+/8P7OuTwgcPHiQ7bkvjSWjSpElER/+/zx8+fMiff/6Jvb09vXr1UtoHDBhATEwMP/74I7oEj/lcunSJNWvWkCdPHtzd3ZX2999/n7i4OD7//HNevXplcMzIyEjlsdKU0D/WHBwcTFBQEAEBAQQFBSltpv5JuOiWNTVp0oSKFSvi5+fH/v37lXadTsekSZMAUv0IrhBCCCGENVk7T08PkqdLnm4pydPTUFQUtkePGjXH1akDJsrupDdt1apoS5QwarfdtQv+qz0vchabGzdQvXxp1J6R9cX1Yrp2RWfiZqvGxGeGEAnJjPFMrlAhHXXrxrJzp4aYmKTvwGk0OurWjaNgQdNJuou9Cw94YFRO5UXkC1xzu1ol5tatWzN+/Hh27drFW2+9xdWrV/Hx8aFAgQJGMx8WL15Mly5dePfdd1mwYAHVq1fHwcGBBw8ecPLkSZ49e0ZgYCAAa9euZdmyZTRq1IgyZcrg7OzMP//8w+7du8mXLx+DBg1S9tusWTO2bNnCkCFDaNu2Lfb29lSpUoWOHTsmGrsl8egVKVKEiIgIGjduTIcOHYiIiMDLy4ugoCBmzJhhMOPk008/ZdeuXXh6enL9+nWaN2/O06dP8fLyIjY2lr/++kt5RBPgvffe48iRI3h5eVG7dm06duyIs7MzDx48YO/evcydO5fOnTun+GeVVp4/f87EiROV/4+JiSEoKIgRI0YobVOmTKFAgQJA/MyhefPm0bVrV/r06UOXLl0oVqwYx44d4/Tp01SvXp3PPvss3c9DCCGEEMIca+fp6UHydMnTJU/PPNQnT6KKiDBqz+j64gqVipj27bH/+2+DZpunT1GfORM/gC9yFHUmrC+upytQgNgWLdDs3WvQrr50CZvr19FWqJBBkYnMTgbGs4A//wzH3d2ZS5fUiSbdGo2OKlXiWLjQ+MNVLz3KqdSpU4cvvviCKVOm8Ndff6FWq3F3d2fSpElGK9OXLl2aQ4cO8ccff7Bjxw5Wr16NWq3G1dWVRo0a0bVrV2Xb3r17ExUVxfHjxzl9+jTR0dEUK1aMd999l9GjR+Pm5qasmj5kyBDu37/Pxo0bmT17NrGxsQwYMCDJhNuSePQ0Gg2bN2/mhx9+wNPTk5CQEN544w1+/vlnevfubbCtg4MDW7ZsYfbs2Xh5eTF//nwcHR1p3LgxY8eOpWHDhgbbq1QqlixZQsuWLVm5ciVr165Fp9NRtGhRevToYXJl+8zg5cuXeHh4GLSFh4cbtH311VdKwg3x183evXuZPn06fn5+hIWFUaJECcaOHcvYsWPJnTt3usUvhBBCCJEc1szT04Pk6ZKnS56eeWTm+uJ6sSYGxgFsfXxkYDwHMlVfHDLHjHGAmJ49jQbGIX7WeNRXX2VARCIrUAUHB2fclIVs6OnTp2ZXM7eUVqslOjoaOzs7oqJs+PDDXJw8qSYwUGVQy9DGRoera/wMlIULI3BwSHy/zyKecT/svlF7MadiVps1nlES9pmpFeitTV9n8OLFi2l+rLSU3v2W1qz5e2hOZGQk9+/fx83NDYekfumEQvrNctJnKSP9ZjnpM5HdZYU8PTuTPD1lsluenh4s6bP0+N5gTu62bbE9edKgTZc7N6G3b4OdXbrHYzIPiIwkT9myRjPb46pU4aWZ8kU5SU7LnXK3bInt2bMGbdoSJQi7dMmi/aRZvwUHk6dCBVTRhiXL4ipU4OXx4xleBz01ctq1lp5kxngW4egIK1ZE8OyZinnz7Nizx5bYWBW2tvEL+IwcGZ3sxzJdHFx4EGZcTiU4MjjLD4wLIYQQQgiRnqyZpwshcoiQEJNlKWIbN86QQXGzHByIbd4czc6dBs3qS5dQ3b+Pzs0tgwIT6S4yErWJAfC4mjUzIBgzXFyIbd3a+Hq9fh2by5fR/rfAtBAJycB4FlOwoI7vv4/i+++jUrwPc+VUImIjrFZORQghhBBCiJzEGnm6ECJnsD18GJWJBSwzTX3xBGI6djQaaATQ7NpF9HvvZUBEIiOoL11CFRNj1B6bCeqLJxTTs6fp63XTJqJkYFyYIM9i5VAu9i4m24OjgtM1DiGEEEIIIYQQIifJCvXF9WLbtTPZbuvjk86RiIyU2euL68V07IjO0dGoXbNpE+jk6S1hLEsMjHt6ejJmzBhatGhB4cKFcXFxYfXq1RbvJzg4mKlTp9KoUSNKlChB2bJladmyJQsXLiQyMjINIs+88trnNdkeHBmcvoFkcRcvXszydQuFEEIIIYTIbiRPF5mZrZ+fUZu2cGG0lSplQDSJ0xUpQqyJchm2Bw9CeHgGRCQygqnSPzqVirjMttCwk5PJmznqO3dQv1YfXQjIIgPjU6ZMYdmyZdy/fx9X15TVwA4ODqZFixb88ssv5M2bl3feeYdevXoRHBzMl19+Sd++fZWV0nMCjVqDk52TUXtEbARRcfL4pxBCCCGEEEIIYW2qhw9RX79u1B7bokWmXRwwtkMHozZVVJTZme8i+zE1qKytUAHy5MmAaBIX3bOnyXbNpk3pHInICrLEwPjcuXO5cOECN2/e5N13303RPpYvX86dO3cYMWIEO3fuZMqUKfzyyy8cP36cmjVrcvDgQfz9/a0ceeaWzz6fyXaZNS6EEEIIIYQQQlifqdnikDnri+vFtG9vsl3j65vOkYgMERJi8mZOplp4M4HYdu3QORlPBNV4eUEOmhArkidLDIy3aNGCkiVLpmofd+7cAaDda49U2NnZ0apVKwCePXuWqmNkNWbLqUidcSGEEEIIIYQQwurM1hfPxAPj2urV0RYrZtRu6+srA405gPrcOZPtcZls4U2FoyMxnToZNds8fIj6xIkMCEhkZlliYNwa3nrrLQB27dpl0B4dHc3+/ftxdHSkXr16VjmWLosU9NeoNThpTJRTiYkgOi46AyISIvWyyu+fEEIIIdKf5AlCCL0M+Xug05mcMR73xhvoSpRI/3iSS6UyOWvcJjDQ7KCpyD5szS28mVkHxoGYHj1Mtms2bkznSERmZ5vRAaSXt99+m/Xr17NgwQLOnTtHnTp1iIqKYvfu3YSHh7NkyRKKFi2arH0ltlCnjY0NEREROJpYBddS+g9qnU6XZvXP89rn5WXMS6P2F69eUChXoTQ5ZlpKjz7LjrJTv7169QobG5s0X1A3Ojra4N8ieaTfLCd9ljLSb5aTPks+BweHjA5BpICDgwORkZFWydOFEFlfZGRkuv89t7l6FZvAQKP22BYt0jWOlIht3x77pUuN2m19fIirVSsDIhLpxeTCmxoNcZUrZ0A0yRPbqhW6vHlRhYQYtGu8vYn86SdQqzMoMpHZ5JiBcUdHR7Zs2cLYsWPx8PBQ6omr1WqGDx9O/fr/Y+++46qs/jiAf5672SAqKkPEPXIrbtyoZWbm3paZWmmWlWY2tOH4mXumuSktV5qguXDi3hsXoKKibO5+fn+QJN2LzHsvFz7v18sXds55nvO9J2R873m+JzDH97p//z4MBkOW/XK5HB4eHlAoFBAK4PAMnU6X73tkxVFwNNv+TP0MbjLzpVbsgSXXrCiz53UTRRFarRbPnj2DTqfD06dPrTJvrJkfbCl7XLfc45rlDdct97hmLyeVShEQEGDrMCgPnJycEBcXByA9SV4QP6cTkf0RRRFqtRrJycnw9PS06tz2WEblOX1QEEQHBwhpaZna5aGh0EycaKOoyBrMHbxpeOUVQKm0QTQ5pFRC99prUKxbl6lZ8ugRpIcOwWAH/+bIOopNYvzJkyfo168fnjx5go0bNyIwMBBpaWn466+/MGnSJISFhWH//v1wzcGJuuXM1NZ6kdFoRFpaWr53rBqNRmi1WigUCkgklqt68+TxE7N1xR3LOkIpLcRf6Myw1poVNUVl3WQyGUqXLm2V16DVahEbGwsvLy8oFAqLz1dUcN1yj2uWN1y33OOaUVEnkUjg6emJlJSUfJ8tZDQaM3ab2vPPTtbENcsbrlvu5WTNVCoVPD09rb6m5sqoiBIJ9C1aWDWOPHFwgL5VK5MDN6Xnz0O4fx9iNnkSsk/CgweQxMSYtNvDUwK6Hj1MEuNA+iGcTIzTc8UmMT5x4kQcP34chw4dQq1atQAArq6uGDp0KNRqNSZMmIClS5fik08+yfZeOXncytHR/E7s3FCr1YiKioKXl5dFH/G68/AOxh8cb9L+favvMar+KIvNawnWWrOihuuWdwqFgmuWB1y33OOa5Q3XLfe4ZlSUSSQSuLi4wMXFJV/3UavVSExM5M9OucA1yxuuW+4V2jXT6SA7dMik2VC/PuDubv148kDXubNJYhwA5GFh0A4daoOIyNKkWdUXt4PEuL5VKxg9PSH552mx5+Rbt0I9YwYgl9soMipMis1bzrt374aHh0dGUvxFLVu2BACcP3/e2mEVCq9Xeh0CTB8l3Xpjqw2iISIiIiIiIipapCdPQkhJMWm3h/riz+k7djTbLgsNtXIkZC3myqgAhfvgzQwyGXTdupk0S549M/v0BhVPxSYxrtPpkJSUZPZAqee1BpWFuT6SBXk5eaGpd1OT9ogHEbiffN8GEREREREREREVHfZcX/w5sVw5GOrUMWmXHTgApKbaICKyNLMHb7q4wFi5sg2iyT1d9+5m2+V//GHlSKiwKnKJ8bi4OFy/fj0j2f1cYGAg9Ho9pk+fnqldrVZjxowZAP7dOV4cdats+i4aAGy7sc3KkRAREREREREVLWbrizs4wNC4sQ2iyTtdp04mbYJazR24RZEoQmamlIqhbl3ATs48MDRrBmOZMibt8h07gPh46wdEhY5dfCavXr0aI0eOxMiRI7FlyxYAwJo1azLaVq9enTF26dKlaNy4MZYuXZrpHpMnT4aTkxNmzpyJ9u3bY+LEiRg3bhwaNWqEQ4cOoXHjxujTp481X1ahwnIqRERERERERBaQmAjpiRMmzfpmzQA7e3JdbyYxDgAyM7XHyb5Jbt2CkJBg0m4P9cUzSKVmy6kIiYlw6tcP0GhsEBQVJnaRGD969ChCQkIQEhKCc+fOAQCOHTuW0Xb06NFs71GnTh0cOHAAffv2xcOHD7Fs2TKEhITA1dUVX3zxBbZu3QqFQmHpl1JolXUuiyblmpi0H7t/DLfjb9sgIiIiIiIiIiL7JztyBILBYNJuT/XFnzPUqWN+B25YGCCKNoiILMVcGRUA0NtTYhyArkcPs+2yI0fgMHIkYDRaOSIqTGS2DiAnFi1ahEWLFuVo7IQJEzBhwgSzfZUqVcrxfYqjbpW74ej9zG8yiBDRaHUjBFcIRt/qfdGxQkcopMX3DQQiIiIiIiKi3MiyvrgdJsYhkUDfsSMULzy5DwCSBw8gOXcOxrp1bRMXFTipmTIqgJ0cvPkCQ6NG0LdoAdmhQyZ9ik2bIHp7Qz1lig0io8LALnaMk3V0rdTVbLveqMeOyB0YsH0Aqv9cHZ/v/xznH523cnRERERERERE9sdc/W1jyZIw1qxpg2jyz1ydcQCQh4ZaORKyJHOJcWPp0hC9vW0QTT4IAlKXLYPRx8dst3LePCiWLLFyUFRYMDFOGbxdvM2WU3lRXFocFp9djFbrW6HF2hZYcHoBHqc+tlKERERERERERPZDePgQ0itXTNr1QUF2c4Dhf+lbt4aoUpm0y5gYLzp0OkjPm26INNSvDwim59MVdmLZskjZuBGiq6vZftXnn0P2559WjooKA/v8KkwWM6Gp+TI05lx8chFfhH+B6j9XR99tffHnzT+hNWgtGB0RERERERGR/TC3Wxz4JzFurxwdoW/VyqRZdvYshAcPbBAQFTTJ5csQ1GqTdnsro/IiY/XqSFm3DqKZ8wUFUYTj8OGQRkTYIDKyJSbGKZMg3yDseGsHmnk3y/E1eqMeO2/txMDtA1FtWTV8uv9TnH10FiIP3iAiIiIiIqJiTLZvn9l2u6wv/gJ9FuVUZLt2WTkSsgRZVvXF7ezgzf8ytGyJtCzOHhTUajj27QvJzZtWjopsiYlxMtHcpzn+6vkXzg49i88CP4Ofq1+Or32qfoqlZ5ei9frWaL62OeadmofYlFgLRktERERERERUCImi2R3jhoAAiH45/z27MNJ17Gi2nXXGi4YsD96088Q4AOh69EBaFodtSp4+hVOPHhAePbJyVGQrTIxTlvzd/DGh6QScHXoWf/b4E/1q9IOT3CnH11+Ou4wvD36JGj/XQO+tvbH1xlZo9BoLRkxERERERERUOEiuX4fETGkRe98tDgCijw8Mr7xi0i7bvx9IS7N+QFSgpKdOmbQZKlSA6OFhg2gKnvb996EZPtxsn+TuXTj27g2kpFg5KrIFJsYpWxJBgpa+LbGw40JcG34NCzsuREufljm+3iAaEHY7DIN3DEbVZVUxft94nIk9w1IrREREREREVGTJ9u83227X9cVfoDNTTkVIS4MsPNwG0VCBSUmB5OpVk2Z7ri9uQhCg/vFH6F591Wy37MwZOA4bBuj1Vg6MrI2JccoVZ4Uz+tXohz/f+hPnhp7DhCYT4O/mn+Pr4zXxWHZuGdqEtEHTNU0x9+RcPEx5aLmAiYiIiIiIiGzAXGJcFAQYzBxcaY+yrDMeFmblSKggSc+dg2A0mrQXhTIqmUilSP35Z+gbNzbbLQ8Lg+qTTwBu6izSmBinPCvvVh6fNfkMZ4acwV89/8KAmgPgLHfO8fVXn17F5EOTUePnGui1pRc2X98Mtd701GMiIiIiIiIiu6LXQ3bokEmzoW7dIlOOwlCvHoylS5u0y8PCmEy0Y+bKqABFMDEOAA4OSA0JgaFiRbPdypUroZw1y8pBkTUxMU75JggCmnk3w/wO83Ht3WtYErwEQb5BECDk6HqjaMSuO7sw9K+hqLqsKj7e+zFOPjjJUitERERERERkl6SnT0NISjJpLwr1xTNIJNCbOYRTEhMDyYULNgiICoL0zBmTNlEqhaF2bRtEY3mipydSf/8dxpIlzfarpkyBPCTEylGRtTAxTgXKSe6E3tV7Y2uPrTg37By+aPoFAtwDcnx9giYBy88vR/vf2iNwdSBmHZ+FqMQoC0ZMREREREREVLCyrC9elBLjMF9nHADkoaFWjoQKiszMjnFj9eqAo6MNorEOY4UKSN2wAWIWr9Hhgw8g27fPylGRNTAxThbj5+qH8YHjcWrwKYT2CsWgWoPgonDJ8fXXn13Ht0e+Re0VtfH6H69j/eX1SNKavuNOREREREREVJiYrS+uUsEQGGj9YCxI37o1RKXSpF3GxLhdEp48geTuXZP2InXwZhYM9esjdcUKiBLTVKmg18Nx0CA+CVEEMTFOFicIApqUa4K57efi2vBrWNZpGdr4tclxqRURIsKjwjFq1yhUXVoV74a+i31398FgNFg4ciIiIiIiIqJcSk6G9MQJk2Z906aASmWDgCzI2Rn6li1NmmWnT0OIjbVBQJQf5sqoAIC+GCTGgfQDZdX/+5/ZPiEpCU69ekGIjrZyVGRJTIyTVTnKHdGzWk9sfnMzLrx9AZObTUYlj0o5vj5Vn4oNVzeg++buqLW8Fr46+BWuxF2xYMREREREREREOSc7ehSCTmfSXtTKqDynz6KciiwszMqRUH5lefBmvXpWjsR2tEOHQv3xx2b7JA8ewKlnTyA+3rpBkcUwMU424+Pig3GNx+HEoBPY1WsXhtQaAleFa46vf5DyAHNOzUHTNU0RtD4Ii84swuO0xxaMmIiIiIiIiOjlsqwvHhRk3UCsRGfmAE4AkDMxbnfMHrzp4JBeY7wY0UyaBG3v3mb7pFeuwGnAAECjsXJUZAlMjJPNCYKAxuUaY3b72bj27jUs77wcHf07QipIc3yPc4/OYcKBCai7ui4+OvERtkVug1qvtmDURERERERERKbMJcaNHh4w1q5t/WCsQPTzg6FmTZN22b59gJq/l9sNUTS7Y9xQpw4gk9kgIBsSBKTNm5flm1myQ4fgMHo0YDRaOTAqaEyMU6HiIHNAj6o9sOGNDbj8zmV81+o7vFLqlRxfbxANOPToEN7d/S6qLK2CsX+PxbH7xyCKogWjJiIiIiIiIgKER48gvXTJpF0fFASYOdSvqNB17mzSJqSmQnbwoA2iobwQ7t2D5MkTk3ZD/fo2iKYQUCiQsno1DDVqmO/+/Xcov/3WykFRQSu6X5XJ7nk5eWF0/dE42P8gDvU/hA8afAAvR68cX5+oTcTKiyvRaUMn1FtZDz8e+xF3Eu5YLmAiIiIiIiIq1mQHDphtL6r1xZ/TBwebbWedcfuR1cGbhmJy8KZZbm5I2bgRRm9vs92q2bOhWLbMykFRQWJinOxCrVK1MKXlFFx65xJ+f+N3vFX1LaikOT/N+07CHfx47EfU/aUuOm/ojFUXViFeHW+5gImIiIiIiKjYybK+eBFPjBsaNICxZEmTdnloKMAnuO2CLKuDN4vrjvF/iN7eSNmwAaKr+TPxVJ99BtmOHVaOigoKE+NkV2QSGdr7t8fPnX/G9XevY16HeWju3TxX9zh6/yjG7BmDqsuqYuiOoQi7HQadwfTEcCIiIiIiIqIcE0WzO8aN5ctD9Pe3fjzWJJFAb+YQTkl0NCRmSstQ4WOuvrjRwwPGov65mwPGmjWRsmYNRLncpE8wGuH4zjuQnjhhg8gov5gYJ7vlqnTFwJoDsaPnDpwbeg5fNP0CAW4BOb5eY9Bg843N6L21N2osr4EJBybg3KNzrEdOREREREREuSaJjIQkOtqkvajvFn9O16mT2XZ5aKiVI6FcMxggPXfOtLl+fUAQbBBQ4WMICkLawoVm+4S0NDj26QNJZKSVo6L8YmKcioTybuUxPnA8Dvc5jBXNVmBwjcFwV7rn+PrHqY+x6MwiBK0PQvO1zbH07FIka5MtFzAREREREREVKcW1jMpz+jZtICoUJu2sM174Sa5dg5CSYtJe3Muo/JeuZ0+kff212T5JXBwc33oLwuPH1g0qKwkJkG3ZAoeRI+HUsSMcxoyBEBtr66gKHSbGqUgRBAGveLyCaa2m4drwa1j92mp0CegCmUSW43tcjruMT/d/iho/18Ck8Em4m3DXghETERERERFRUZBlYrxVK+sGYisuLtC3aGHSLD15svAkC8ks6enTZtuL9cGbWdCOGQPN22+b7ZPevg3HPn0AM28yWIPk1i0oFi6E0+uvw7ViRTgNGQJFSAhkx49DsWoVnDp1AuLjbRJbYcXEOBVZSpkSr1d6HetfX49rw69heuvpqO+V83c7E7WJmH96PuqtrIdB2wfhaMxRllkhIiIiIiIiUwYDZOHhps21a0P09LRBQLahN1NORRBF7hov5LJMjHPHuClBgHr6dOg6dzbbLTt1Co5vvw3o9ZaPRa+H9NAhqL78Es6NGsGlfn04TJwIWXg4BDPzS2/fhuN77wFGo+VjsxNMjFOx4OngiXfrvou9ffciYlAExjUaB29n7xxdaxSN2HZzGzpv7Iw2IW3w25XfoDVoLRwxERERERER2Qvp2bMQEhNN2vVt2tggGtvRBQebbZczMV6oycwdvOnjA7F0aRtEYwekUqQuXw59w4Zmu+WhoVB99hlgic2V8fGQ//47HN55B64VK8L5tdegnDcP0hs3cnS5PDQUinnzCj4uO5Xz+hJERUTVElUxuflkTGo2CQejD+LXy79i281tSNFl/6jL2UdnMSJsBL469BXeqfMOhr4yFJ4O9vnuvyiKuPTkEv669ReOxxxHUmoS6kbVRWPvxmhQpgHKu5aHwEM2iIiIiIiIslXc64s/J5YvD0ONGpBevpypXbZvH6DRAEqljSKjLKnVkFy6ZNLMMirZcHRE6q+/wqlDB0hv3zbpVi5fDtHHB5qPPsrfPKII6Y0b8Nq4ER7Hj0N+4gQEgyFft1R9+y0MDRvC0Lx5/mIrApgYp2JLIkgQ5BuEIN8gzGw7E3/e/BMrL6zEsfvHsr32YcpDTD0yFTMjZqJX9V54r+57qFGyhhWizh+dQYcj94/gr8i/sPPWTtxLvJepP+JJBJacXwIAKKEqgfpe9VG/TH00KNMA9b3qo5RjKVuETUREREREVKiZS4yLSiX0TZpYPxgb0wUHmyTGheRkyA4fhr5tWxtFRVmRXrhgtuyGnmVUsiWWLInUP/6AU4cOkMTFmfSrvvkGRm9v6Hr1yt2NtVpIjx6FPDQUsrAwSG/dKqCI0wkGAxzffhvJ4eHF/qkAJsaJADjJndCneh/0qd4Hpx+exuKzi7Hp+ibojS+vCaU2qLH64mqsvrgarf1aY2S9kejg3wESofBUKUrSJmHPnT3469ZfCLsdhgRNQo6ue6p+ir/v/o2/7/6d0ebr4osGZRpkJMrrlK4DZ4WzpUInIiIiAI8ePULpYv5LCxFRoZaaCmlEhEmzITAQcHCwQUC2pe/UCfjpJ5N22c6dTIwXQqwvnj/GgACk/vYbnLp2hZCWZtLvMHo0jF5eMAQFvfQ+QlwcZLt3QxYaCvnevWZLM+WUKJHAEBgIXXAwxDJl4DBqFIT/1BWXPHwIx3feQcrmzYBUmue57B0T40T/Ub9MfSzttBRft/gay88tx4oLK/BM/Szb6/bf24/99/ajontFvFfvPfSt3tdmSeP7yfexM3In/rr1Fw5GHyywmuhRSVGISorClhtbAKTvuq9Wolr6rnKvBqhfpj5qeNaAXCovkPmIiIgIqFWrFoKDgzF48GC0a9eOpc6IiAoZ2bFjELSmv3MVtzIqzxkaNoTR09NkB608NBTq6dMBfh8rVKRm6ouLggBD3brWD8ZOGRo2ROry5XAcMMAkAS3odHAaOBDJO3fCWLPmvx2iCMmVK5CHhUEWGgrp8eMQ8lGTXHR1ha5dO+g7dYK+fftMh/5KYmKgmjLF5BpZeDiUP/4IzRdf5Hlee8fEOFEWyjmXw5fNv8THjT/GxqsbsejMIlx9ejXb6yLjIzF+33hMPTIVg2sNxvA6w+Hr6mvRWEVRxOW4y/gr8i/8desvnIk9Y9H5njOKRlyOu4zLcZex9tJaAIBKqkLt0rVR36t+xu7yCm4V+Es8ERFRHul0Omzfvh07duxAuXLlMGDAAAwYMAA+Pj62Do2IiPBP/WwzimtiHFIp9B06QPHrr5maJVFRkFy5AmONwl+GtDgxt2PcWLUq4OJig2jsl75LF6inT4fDJ5+Y9AmJiXDq2RMpO3ZAcutW+q7wsDBI7t0zc6ecMwQEQN+pE3SdOsHQtCkgN79JUfPRR5BGREC+a5dJn3LmTBiaNIG+Xbt8xWKvmBgnyoaj3BGDXxmMQbUGYf+9/Vh0ZhF23TH9YvJfCZoEzD01FwtOL0DXSl0xst5INC7buMASxHqjHkdi/q0Xfjfxbp7vpZAo4ChzRLw2Pt9xqQ1qHH9wHMcfHM9oc1e6m9Qr93Lyyvdc2TEYDVAb1NDoNVl+LOlYEtVKVINUUnwfHSIiosLtzJkzWLVqFX799VfExMRg+vTpmDlzJtq2bYtBgwahc+fOkBbjR2CJiGzNbH1xNzcY6tSxfjCFhK5zZ5PEOJC+a1zDxHjhER8P6c2bJs0so5I32nfegRAdDdXs2SZ9kvv34VKvXr7uL0qlMDRpAl2nTtB36gRj5co5u1AiQdrixZC2agVJdHSmLkEU4TB8eHq98WK46YKJcaIcEgQBbcq3QZvybXDj6Q0sObsE6y+vR6o+9aXXGUQDttzYgi03tqC+V32MrDcS3Sp3g0KqyHUMSdok7L27Fzsid2DX7V2I18Tn8dWkJ6uDA4LRJaALmns1x9OHTwF34FL8JZyOPY1TD0/hbOzZbF9fTsRr4rH33l7svbc3o83HxSdjV7m/mz+0Bm22SewXP6r12Y/Jrkb8c+Wcy6Fn1Z7oVb0Xapasmf0FREREVuTv74+vvvoKkyZNQlhYGFatWoU9e/Zg9+7d+Pvvv1GqVCn069cPAwcOREBAgK3DJSIqVoQnTyC9cMGkXd+qVbGu26tv0waiXA5Bp8vULgsLg2bcOBtFRf8lPXvWbDsT43mnmTwZkpgYKDZuLJD7Gd3doWnbFvcbNIBLjx5QlimTp/uIJUogdeVKOHXubPLvUvL0KRyHDUPKjh1Z7jovqpgYJ8qDyiUqY2bbmZjUbBJWXVyFpWeXIiY5JtvrTseexvDQ4Zh8cDLeqfMOhrwyBJ4Oni+95kHyA4TeCsVft/7CgagD+aoXXt61PLpU7IIuAV3Q1LspZJL0LwFqtRrPhGfwdfVFldJV0L1KdwDpu9KvPb2GUw9P4UzsGZx6eAqXnlyCQTTkOYbnopOiEZ0UjW03t+X7Xvl1P/k+5pyagzmn5qBmyZroXa03elTtAW8Xb1uHRkRElEEqlaJLly7o0qULHjx4gLVr12LdunW4e/cuZs+ejTlz5qB58+YYMmQIunbtCoUi92/CExFR7sjCw822F9syKs+5ukLfvDnk/9lNLz1+HMKTJxBLlrRNXJSJLKuDNxs0sHIkRYhEgrT58yF58ACyQ4fydAtD1arQBwenl0hp3BhqvR7PoqLg7O6er9AMDRtCPWUKHD7/3KRPdvw4VF99BfX33+drDnvDxDhRPrir3DGm4RiMqjcK2yO3Y9GZRZlKiGTlQcoDTDkyBTMiZqB39d54r957qO5ZHUB6vfArcVfw162/sDNyJ07Fmh6EkRv1veqjS8Uu6BzQGTU8a+SqlItMIkPNkjVRs2RNDKo1CACQqkvFhccX/k2Wx57Crfhb+YqxMLn05BImH5qMrw59hZa+LdG7Wm90rdQVrkpXW4dGRESUoWzZshg/fjzGjx+PAwcOYPXq1di2bRsOHz6Mw4cPw93dHX369MHw4cNRoUIFW4dLRFRkmSujAjAxDgD6Tp1MEuOCKEK2axd0/frZJijKxOzBmwoFDDX5JHW+KJVIWbsWzp07Q3rlSrbDRZkMhubN/y2R8t+f3fQ5exo+J7QjRkB67BgUW7aY9CkXLoS+SRPoX3+9wOYr7JgYJyoAcqkc3at0R/cq3XHywUksPrsYW25sybaUh9qgxqqLq7Dq4iq08WuDqiWqIvR2KO4k3MlzLAqpAq18WqFLxS7oFNAJ5ZzL5fle5jjKHRFYLhCB5QIz2p6mPc1Ikj8vw/I49XGBzmttIkSER4UjPCocH+/9GF0qdkGvar3Qrnw7yKXF69EiIiIqvFJTU3Hv3j1ERUXBYDBAFEUAwLNnz7Bo0SIsW7YMw4YNw3fffQeZjD/6ExEVKFE0e/Cm0ccHRpa2gq5TJ7M7U+VhYUyMFxLmDt40vPIKwKfO8s/dHSkbN8K5QwdIHjww6TZ6ekLfoQN0nTunv5Hm5maduAQBaXPnQnrhAqSRkSbdju+/j+RatYrN1zD+dExUwBqWbYify/6Mb1p8g+Xnl+OXC7/gmfpZttftu7cP++6ZP808O25KNwRXSK8X3rZ8W6vvbi7hUALt/NuhnX/6KcaiKCI6KTojSX469jTOxp5Fsi7ZqnEVFLVBjU3XN2HT9U3wdPDEm1XeRPeA7igp8vE/IiKyjVOnTmH16tXYvHkzkpOTIYoiSpUqhf79+2Pw4MF49OgRVqxYgU2bNmHZsmVwcXHBpEmTsr3v6dOn8cMPPyAiIgJ6vR41atTA6NGj0b179xzFtXv3boSEhODChQuIjY2FTqeDj48PAgMDMXbsWFSqVMnkGveXPBbct29fLFq0KEdzExFZm+TOHUiiokza9a1bA7l4UreoEv39YahWDdKrVzO1y/buBbRaJl9tTLh/32zClvXFC47o44Pkv/+Gw+efQ3ruHER3d+jat4c+OBiGhg1tdw6BqytSV62Cc/v2ENTqTF1CYiIcBw9G8u7dgEplm/isiIlxIgvxdvHG5OaT8UnjT7Dh6gYsOrMI155eK7D7+7n6oUtAF3Su2BnNyjUrVLuYBUGAr6svfF190a1yNwCAwWjA9WfX0xPlD0/jVGx6vfKcHJApESRQSVVQypRQSVVQydL/KKXKfH3UG/XYeWsndkTuQJo+LUevLS4tDsvOLcOyc8vg6+iL3jV6o1+tfghwLx7vphIRke3Ex8cjJCQEa9aswdWrVyGKIgRBQMuWLTF06FC89tprGbvC/f390bhxY7z77rsIDg7Gb7/9lm1iPDw8HD169IBKpcKbb74JZ2dnbNu2DUOHDkV0dDQ++OCDbGPctWsXTpw4gYYNG6J9+/aQy+W4du0aQkJCsHHjRmzYsAFBQUEm1/n6+qKfmd2Dr7zySg5Xh4jI+lhGJXv64GCTxLiQlATpkSMwcJ1sytxucYCJ8YImensjdc0aW4dhwlirFtJmzICjmZ/vpBcuQPX551DPnm39wKyMiXEiC3OUO2LIK0MwuNZg7Lu3D4vOLMLuO7vzdK+6petmHJ5Zs2TNXNULtzWpRIrqntVR3bM6BtQcAABQ69W4GncVKboUOMgcMhLf//0ok8gs9lp7VO2BJG0Stt/cjg1XN+BA1AEYRWOOro1KjcLMkzMx8+RMNCrTCL2r90b3Kt2zPVDVWkRRRExyDG4+u4nrT6/jxrMbuP70Ou4k3IGTwglDag3B27XfhlRio3epiYgoR57XEN+xYwe0Wi1EUYSnpyf69euHIUOGIOAlj7rWr18ftWvXxtmzZ186h16vx5gxYyCRSLBjxw7Url0bAPDpp5+iXbt2mDJlCrp16wY/P7+X3mfKlCmYMWOG2dfQrVs3fP3119hnpuyAn58fJkyY8NJ7ExEVNlkmxs28AVhc6Tp1gnLOHJN2+c6dTIzbWJaJcR68WWzoBg6E9uhRKNavN+lTrlwJQ9Om0PXubYPIrIeJcSIrEQQBbcu3RdvybXHt6TUsObMEIVdCXrpTWS6Ro5VvK3QO6IzOAZ3h7eJtxYgtTyVToa5XXVuHAReFC/rW6Iu+NfriQfID/HH9D/x25TdceHwhx/c48fAETjw8gc8PfI72/u3Ru1pvdAroBAeZgwUjT5emT0Pks8iMxPfNZzdx/Vn6xxRdSpbXfbr/U2y+sRmLOy5GebfyFo+TiIjy5o033sj4e/PmzTF06FB07doVihw+gq5SqWA0vvxN3/DwcNy+fRv9+/fPSIoDgJubG8aNG4dRo0YhJCQEn332WbZzmRMUFAR3d3fculV0DuwmomLOYIA0PNy0uVYtiKVK2SCgwsnQuDGMHh6QPMtcXlQeGgr1jz+y5IwNmUuMi66uMJope0ZFV9rMmZCePQvp5csmfQ4ffQRD7dowVq9ug8isg4lxIhuoWqIqZrWbhS+bf4lVF1Zh2blliEmOAQC4KlzT64VX7IJ25dtZvV54cVfWuSzer/8+3q//Pi4/uYyNVzdi47WNiE6KztH1eqMeobdCEXorFC4KF7xe6XX0rt4bLXxaQCJI8hyXKIp4lPooU+L7xtMbuP7sOqISoyBCzNN9j8YcRYt1LfBj0I/oV6OfXT2FQERUXLi7u2fsDq9cuXKur9+xY0e2Yw4dOgQAaNu2rUlfu3bpZ4gcPnw413M/d/z4ccTHx6Np06Zm+xMSErBy5UrExcXBw8MDgYGBqFmzZp7nIyKyNMmFCybJXoBlVExIpdB36ADFhg2ZmiV370Jy7RqM1arZKLBizmiEzNzBm3XrApK8/95KdsjRMb3eeJs2EJIznwsnpKbCccgQJO/ZAzg72yhAy2JinMiGPFQeGNtoLN5v8D7OPToHiSBBrZK1ClW98OKsRska+KrFV/iy+Zc4EnMEG65uwJbrW5CoTczR9UnaJKy7vA7rLq+Dt7M33qr6FnpV74WaJbP+RV9r0OJW/C3ceHYjI/H9vBRKTufNrSRtEkbvHo2/bv2FOe3moKQjDxUlIipMrl27luPd4XkVGRkJAKhYsaJJn5eXF5ydnXO123vv3r2IiIiAVqtFZGQkwsLC4Onpie+//97s+IsXL2Ls2LGZ2tq3b49FixahVA53Xqr/c3iUJWm12kwfKXtcs7zhuuWetdbMcbf58pipzZpBa8WvRwXFkusmtmtnkhgHAPz5J9T+/gU+n7XY879PaWQkhETT3y81depY/PupPa+brVh8zXx9YZg5E+7vvWfSJb12DYoPP0TiggV28YRHVk8vZoWJcaJCQCaRoUEZ1vEqrCSCBC18WqCFTwtMbz0dYbfDEHIpBH/f/Rt6MfvDQwEgJjkGc07NwZxTc1CzZE30rtYb9cvU/zcJ/k8i/E7CHRhEg4VfkXk7Infg+IPjmNd+HjoFdLJJDEREZMrSSXEASPznl2NXV/NPqrm4uGSMyYm9e/di/vz5Gf8dEBCAFStWoG7duiZj33//fbz++uuoVKkS5HI5rly5ghkzZmD37t3o3bs3du/eDak0+/Mw7t+/D4PBut9DY2NjrTpfUcA1yxuuW+5Zes2qmEmMG2Uy3PbxgTEqyqJzW5Il1k1apQrqSKWQ/Pdr9I4diHqhXJi9ssd/nyX+/hvmtkM98PFBvJU+f+1x3WzNomvWoAH8evZE6Y0bTbocNm3CoypV8LhHD8vNXwCkUulLz94xh4lxIqJcUMlU6Fa5G4J9g3Eh8gJOq09j081NiHgQkeN7XHpyCZMPTbZglFkr51wOBqMBsanmv6E+Tn2MPtv6YHCtwfiu1XdwVhTNx6WIiOzJhQsXsHjxYgQFBaFXr15ZjtuwYQMOHDiA0aNHo0aNGlaM0NTUqVMxdepUJCcn49q1a5g+fTqCg4Mxf/589OzZ02Tsixo3bozffvsNXbt2xeHDh7Fjxw68/vrr2c5Zrly5An0NL6PVahEbGwsvLy+rvHFRFHDN8obrlntWWbO0NLicO2fSrG/UCN5VqlhmTguz9LrpmjaF8p+yXc85nz8PPycniCVKFPh81mDP/z5d7t0z2+7WoQNcLPz91J7XzVastWbizJnQ3bgBuZlD2/1mzYJzmzbQ16ljsfltgYlxIqI8cle4Y3DFwRjRYATuJNzBhqsbsOHqBtx8dtOmcSmlSlT0qIjKHpVR2aMyqpSogioeVVDRoyJcFC5I0iZh4oGJWHNpTZb3WHVxFQ5EHcCS4CUILBdoxeiJiOi/1q5di5CQkJcmxYH0kifr16+Hu7s7vvvuu1zN8XyneFa7wpOSkuDu7p6rewKAs7MzGjRogHXr1qF169YYO3Ys2rRpg5IlX162SyKRYPDgwTh8+DAiIiJylBjP7aOzBUGhUNhkXnvGNcsbrlvuWXLNpMeOQdBoTNqNbdva/f8nS62bsXNn4D+JccFohPPBg9D17l3g81mTPf77VJw/b9Jm9PKCokIFq5XLsMd1szWLr5lKhbRVqyANCoIkPj5Tl6DVwuPdd5F04ACQh58JCytW1CciKgD+bv74NPBTnBh0Anv77MWIuiNQ0sGytbpLO5ZGM+9mGFJrCL5r9R02dNuAs0PP4v7o+zgy4AhWvboKk5pNQq9qvVDXqy5cFC4AABeFC+Z1mIf1Xde/NMY7CXfQeWNnfHv4W2gNrP9GRGQrBw8ehJOTE4KCgl46LigoCE5OTjhw4ECu53heW/x5rfEXxcbGIjk5OdePpr5IJpOhZcuWSElJwZkzZ3J0jaenJwAgNTU1z/MSEVmCbP9+s+08eDNr+s6dzbbLQkOtHAlBq4XUTGLcUL++XdSQJssSy5dH2uLFZvskd+/CcdQoQBStHJXlcMc4EVEBEgQB9cvUR/0y9TG15VTsu7cPG65uwI7IHUjTp+X6fjKJDAFuAahcojKqeFRBJY9KqFKiCip7VIa7yj1fsXap2AWNyjbCh39/iJ23dpodYxSNmHViFnbf2Y2lnZaiumf1fM1JRES5FxMTA19f3xyN9fPzw/3793M9R/PmzTFr1izs3bsXPf5TP3LPnj0ZY/Lj4cOHAAC5PGeHjJ88eRJA+msiIipMzCXGRVdXGOrVs34wdsIYEABDlSqQXr+eqV2+Zw/StFqAJTWsRnL5stknHgwNeO4ZpdN36gT12LFQzZ5t0if/6y8o5s+H9oMPrB+YBTAxTkRkIXKpHB0rdETHCh2RpE3C9pvbseHqBhyIOgCjaMw01l3pjqolqmZKfFcpUQXlXctDLs1ZAiEvSjmWwvqu67H28lpM2D8Bybpks+MuPL6A1utbY3LzyRhZbyQkAh84IiKyFq1Wm+Nkslwuz9MO66CgIPj7++P333/HiBEjULt2bQBAQkICZs2aBYVCgT59+mSMf/jwIRITE+Hl5QU3N7eM9jNnzqCemcTQnj17sH37dri5uaFRo0YZ7ZcuXUKVKlVMXl9ERATmzJkDuVyON4rAwWxEVHQIT59Caq6+eIsWgIwplpfRBwebJMaFxERIjx6FIZunoqjgyE6fNttuqF/fypFQYaaZNAmy48chO3LEpE/19dcwNGwIQ9OmNoisYPGrNhGRFbgoXNC3Rl/0rdEXD5If4PiD40jWJqOCewVU8agCTwdPCDZ6bE0QBAysORAtfVpiZNhIHL1/1Ow4jUGDL8K/QOitUCzsuBC+rjnbvUhERPlTpkwZ3LhxA2q1+qV1JdVqNW7cuIHSpUvneg6ZTIa5c+eiR48eePXVV/Hmm2/C2dkZ27ZtQ1RUFKZMmYLy5ctnjP/mm28QEhKCBQsWoH///hntbdq0QY0aNVCzZk2UK1cOqampuHjxIo4ePQq5XI758+fDyckpY/z8+fOxa9cuNGnSBN7e3pDL5bh69Sr27t0LQRAwc+ZMVKhQIdevhwqOcO8e5Lt2QRYWBsnt2zBWrAjN+PEwNGxo69CIbEJ68CAEM2UEWEYle7pOnaCcN8+kXR4aysS4FUlPnTLbziceKBOZDKkrVsC5ZUtIHj/O1CUYDHAcNgzJ4eEQS5WyUYAFwy62/P32228YO3YsWrdujdKlS8Pd3R3r1q3L1T1effVVuLu7v/TPr7/+aqFXQET0r7LOZdGtcjf0r9kfzbyboaRjSZslxV/k7+aP7W9tx9fNv4ZckvXOxIPRB9F8bXP8duU3iEWothgRUWHVrFkzqNVqLFiw4KXjFi5ciLS0NDRr1ixP87Rq1QqhoaEIDAzE5s2bsWLFCpQuXRorVqzABzl8XHby5Mnw8vLC4cOHsWjRIqxZswaPHz/GkCFDcOjQIXTt2jXT+C5duiAwMBAXL17EunXrsHTpUly7dg09evTA7t27MWzYsDy9FsoHgwHS48ehnDIFzs2awbV2bTh88gnku3dDevMm5GFhcAoOhnL6dMBgsHW0RFbH+uJ5ZwgMhNHMoX2y0NAiVbO4sJOaOevDEBAA0cPDBtFQYSaWKYPUn3+GKDFNH0sePIDD8OF2/7OAXewYnzp1KqKiouDp6QkvLy9ERUXl+h79+vVDixYtTNr1ej1mzZoFiUSS7YFGRERFnVQixdhGY9G2fFu8F/YeLsddNjsuUZuIEWEj8Netv/BT259QwqGElSMlIio+RowYgZCQEPzwww/Q6/UYPXo0nJ2dM/pTUlKwYMECTJs2DRKJBO+9916e52rQoAF+//33bMctWrQIixYtMmkfN24cxo0bl+P5unbtapIsJxtITIRs3z7IQ0Mh270bkidPXjpcMBig+v57yPbtQ+qSJRBZB56KEXOJcWO5cjBWrmz9YOyNTAZ9hw5QbNyYqVl6+zYkN27AWKWKjQIrRpKTIbl61aSZ9cUpK4agIGgmTIDqu+9M+uT790M5fTo0EybYILKCYReJ8Xnz5iEgIAB+fn746aef8M033+T6Hi8+4vmirVu3QhRFdOjQAWXLls1vqERERULt0rWxt+9eTD0yFQtOL4AI8zs4tt7Yioj7EZjfYT7a+7e3cpRERMVD7dq1MXHiRHz33XeYNm0aZs+ejerVq8PNzQ0JCQm4cuUKNBoNRFHEpEmTULduXVuHTHZAuHMnPREeGgrZ4cMQdLpc30N29ChcWrRA2uzZ0L35pgWiJCpchDt3IL1926Rd37o1UAieALUH+k6dTBLjQPqucS0T4xYnPXcOgtFo0s764vQymo8/hjQiAvK//zbpU06fDkNgIPRt29ogsvyzi1IqrVu3tthp9GvXrgUADBw40CL3JyKyVyqZClNbTcW2Htvg4+KT5biHKQ/x1pa38PHej5GiS7FihERExccnn3yC+fPnw8vLC2q1GmfOnMH+/ftx5swZqNVqlClTBosWLcLHH39s61CpsNLrIT16FKqvvoJzkyZwrVsXDp9/Dvn+/XlKij8nJCbCcdgwOIwaBSQlFWDARIWPLDzcbDvLqOScrl07iFKpSbs8NNQG0RQ/Uh68SXkhkSBtyRIYvb1NugRRhMPw4RBiYmwQWP7ZxY5xS4mJicGePXtQpkwZBAcH2zocIqJCqaVvSxwecBif7/8cIVdCshy3/Pxy7L+3H0uCl6BhWR7IRURU0Pr3749evXohIiICly9fRlJSElxcXFCzZk0EBgZCJivWP9qTOfHxkO/dm74rfPduSJ49y9ftRInE7E5DAFCsXw/p0aNI+/lnPpJPRVaW9cVZljXn3N1haNoUskOHMjVLIyIgPHtmuTrXej2ExEQgMRFCQkL6n8RECCkpEB0cILq5Aa6uEN3cILq6QnR1BRQKy8RiTf+8biEhAUhIgGzfPpMholQKQ+3aNgiO7Ino6YnUX36BU5cuEPT6TH2SuDg4DhuGlO3bAXnW55UVRsX6p+d169bBaDSib9++ufpFQq1WWzCqf2m12kwfKXtcs7zhuuVecVszJZT4KegntPdtj/Hh4/FU/dTsuMj4SARvCMaH9T/EuPrjIJdm/qZY3NatIORkzQxGA2JTYxGdFI2Y5BhEJ//zMSkasamxcFe6o6V3S3Sv3B2+Lr7WCt2m+LmWe1yznFOpVDabWy6Xo0WLFmbPziECAElkJGQ7d0IeFgbp0aMmv7zmhujkBH3r1tB16gR9x46Q3LsHh+HDIb1zx+x46e3bcAoOhmbiRGjGjAHM7AolsltGI2QHDpg0G2rUgOjlZYOA7JcuONgkMS4YDJD9/Td0PXuaXmA0AklJGQleITHR9O+JicBL+oSU3D/ZKqpUmRLlz/+Of/5b6uSE0gYDVH5+kHl6Zh7r7g64uOTv6+Dz1/2y15WQkJ7sz6JPSE3NfpqaNQEHh7zHScWGoXFjqL/9Fg4TJ5r0ySIioPrmG6inTrVBZHlXbBPjoihi3bp1AHJfRuX+/fswWPHU1djYWKvNVVRwzfKG65Z7xW3NastrY13zdZh6fioOPz5sdoxBNOCnUz8h9GYovq37Lfyd/U3GFLd1y69UfSqO3jyKh2kP//2j/vfvj9SPYBBf/n3pYMxBfH/8e9T1qItO3p3Qrmw7uCvcrfMCbIifa7nHNXs5qVSKgIAAW4dB9C+dDtJjx9LrhYeFQXrzZr5uZ/Txga5zZ+iDg6Fv0QJ44Y0gg5cXksPD4TB+PBS//Wb2ekGvh+rbbyHbsyf9YE6frMuxFXqiCOmRI5CePQtjjRrQt2rFZH8xJrl4EZK4OJN27hbPPX3nzsCXX5q0K6dNg/z3302SvEhKgiCaP/PIkgS1GoJaDWTxs5ESgLPZnn+Jzs6ZkuqZEuxOTkBaWubXa4PXrWcZFcoF7ciRkB09Cvmff5r0KefPh75pU+hffdUGkeVNsU2Mh4eH4+7du2jevHmuf7kpV66chaLKTKvVIjY2Fl5eXlAUhUd4rIBrljdct9wrzmvmC1/8Xul3rL2yFpOPTEaaPs3suCsJVzDw0EBMajIJw2oNg0SQFOt1y4pRNOJx6mNEJ0eb7PiOSUr/e7wmvsDmO/vsLM4+O4uZl2eirW9bvFn5TXQs3xGOcscCm6Mw4Oda7nHN7IdGo8GzZ8+ge0ldaF/f4vF0SHElPHsG2e7dkIWFQf733+k7BvNIFAQYGjWCvlMn6IKDYaxR4+WHCLq6Im3JEug7dIDDuHHpyRszZIcPw7lFC6TNmQN9t255js8mDAbIt26F8n//g/TSpYxmbd++SFu4kIcsFlNZllFhffFcM1aqBEOlSiZv5Elv3sz3m3uFjZCcDCE5Gbh/39ahZIn1xSlXBAGp8+fD+eJFs4cRO44ciaTwcIj+/taPLQ+KbWJ8zZo1AIBBgwbl+lprPz6rUChs+siuPeKa5Q3XLfeK85oNrz8c7QLaYUToCJx4eMLsGLVBjUmHJ2FP1B4s6LgAJRQlABSvdTOKRtyOv417ifcQlRSFqKQoRCdGp39MikZMUgx0xrwfepZXeqMeu+7uwq67u+Asd8ZrlV5Dz6o9EeQXBJmk6Px4YM3PNaNoxB/X/sDm65uhM+pQzrkcvF284e3sDR9XH/g4+6CcSzk4yAr3o6rF6d+nPdHr9ViwYAFCQkJw48YNiC/ZQSYIAuLM7GokO6fXQ75uHRS//ppeizeLWt85Ibq4QN+2bXqJlA4dIJYsmet76N56C/pGjeD47ruQRUSYHSOJj4fT4MHQDhwI9ddf5zleq9FqIf/1Vyhnz4b01i2TbkVICHSvvgr9a6/ZIDiyNXOJcVEmg75ZM+sHUwTog4OLXBLcHolKZfoOfqLccHND6qpVcO7QAYJGk6lLSEyE0+DBSA4Ly/TUWWFVdH7zzYX4+Hhs374dbm5u6GZvuxeIiAqRAPcA7Oy1E7NPzsaPx36E3mi+jum+e/vQdE1TTGs5DQ2URfdALp1Bh2tPr+Hco3M4//g8zj86jwuPLyBZl2zr0F4qWZeMX6/8il+v/IrSjqXRvUp39KrWC/W96kPgrrgc2Xd3HyYfmowLjy9kO7akQ0mThLm3izd8XNI/lnEqU6TenKD80+l0ePPNN3H48OGXJsSfy8kYsjOiCIcxY6D4pxRkXhj8/dN3hXfuDEPTpgVyqJxYvjxSduyAcuZMKKdPz/pgzjVr4HnkCOK++goojE8zpKZCsXo1lPPmQRIT89Khytmz0x8R5/fH4kWthuzoUZNmQ6NG6XWkKdd0nTpBuWCBrcMo1kRXV6TOmwexVClbh0J2yFi7NtKmT4fjmDEmfdJz56CaOBHqWbNsEFnuWPy3rvj4eNy/fx8VK1aEUqm09HQ58ttvv0GtVmPgwIHcEUVElE8yiQyfNP4E7cu3x4iwEbj29JrZcQmaBLz393voWK4jRkpHomrpqijjVAYSQWLliAuGWq/G5SeXce7RuYxE+KUnl6AxaLK/2AoECCjjVAaeDp64Encl2xrkzz1KfYQlZ5dgydklqOheEW9VfQu9qvVCRY+KFo7YPl18fBFfHfoKe+7uyfE1T9Ke4EnaE5x7dM5sv1SQoqxzWXg7e2dKmL+YSPd08OSbFsXIihUrcOjQITRu3BiLFy/GqFGjEBERgbi4ODx9+hQnTpzA3LlzcfbsWcyePRu9evWydchUwOQbN+Y6KS5KJDA0aZK+Kzw4GMYqVSyTzJXJoPn8c+hbt4bj8OGQREWZHxYZiWrDhiHl889hHDcOkBSC7//x8VAuXw7FwoVma0ebIzt5EtLDh2HgAbjFivT4cQhppuUDWV887wzNmsFQowakly8X2D1FhcKklvfzgzJN6nw/73d2BlJTc3+wpZnPB1sRlUrzB4Saeb0vHg5qrF4dkMttHT7ZMd2gQdAeOWL23BHlihUwNG1q/kDdQiTfifFz585h+/btaNq0Kdq2bZvRnpaWhvfffx+bN28GALi5uWH27NkW36EdFxeHuLg4eHp6wtPT0+yY52VUBgwYYNFYiIiKk7pedbG/3358c+gbLD67OMtxu+7vwq5tuwAADjIH+Lv5w9/NHxXcKiDAPQAV3CqggnsF+Lr4Qi4tHD+oJWmTcPHxxYwk+LnH53At7lqOk82W4CBzgK+LL3xcfODrmv7x+R9fV1+Ucy4HhTR9N+CjlEfYdH0TNl7diFOxp3I8R2R8JKZFTMO0iGmo71UfPav1RI8qPVDaqbSlXpbdiEmKwXdHv0PI5RCIKNjduQbRgOik9JrzeGB+jEqqSk+Wm9l57u/mjwD3ALt904lMbdq0CYIgYMGCBahQoUJGuyAI8PT0RKdOndCpUye8//77GDVqFHx9fdG0aVMbRkwFSYiKgsMnn+RorOjmBl2HDukHZ7ZvD9HDw8LR/cvQpAmSDh2Cw8cfQ/H772bHSPR6uEydCv3Bg0hdtAiilc5u+i/h8WMoFi+GctmyLGukv4xyzhykMjFerMgOHDDbzvri+SCVInXNGjh8+CGkx48DRmPOErpubpkT3i/0WbVsg1YLzaNHiL1+HeWcnKBUq80eoPnif+O/fTodRKnU/Os2twbPX/d/+uyhXAUVUYKAtFmzID1/HtIrV0y6HcaOhaF2bRirVrVBcDmT78T42rVrsXz5cmzcuDFT+/fff49NmzZl/Hd8fDyGDx+OypUro0aNGrmaY/Xq1Tj6z2NLl/95N3HNmjU4dOgQAKBp06YZtcKXLl2KadOm4bPPPsOECRNM7nX27FlcvHgRderUQZ06dXIVBxERvZyDzAE/tv4RnQI6YfSu0YhJfvnjyGn6NFyJu4IrcabfRKWCFD4uPpmS5c8Tfv5u/nCSO1nkNTxTP8P5R+czJcEjn0UWePIzO6UdS8PXxRdlncrCVXRFjXI14O/hDx9XH/i6+KKEqkSOdwyXdiqN9+q9h/fqvYdb8bew8epGbLy2ETef5byu4+nY0zgdexpfhH+B1r6t0bNaT7xW6TW4KIrX48MJmgTMOTkHC08vhNqgtlkcaoMakfGRiIyPNNvvqnBFndJ1UN+rPuqXqY96XvXg6+LLXeZ26urVq/D19UWlSpUytRuNRkhe2HX7448/YvPmzZg7dy4T40WF0QjHkSNfmrw1VK4MfXAwdJ06wRAYaNvdf25uSFu2DPr27eEwfjyEpCSzw2QHDsC5eXOkzZtn1XrdQnQ0lPPmQbF6db52e8p374bk4kUYa9UqwOioMDNbX9zZGYYGRbdEoDUYK1ZEyo4dgMGQ/hSJPf2colBALFkSmrQ06H19IcttcloUAZ0u/Wu2Pb1uov9yckLqypVwbtsWQkpKpi4hJQWOgwcjec8ewMkyv7/nV74T40eOHIFKpUKbNm0y2rRaLVatWgW5XI5169ahcePG+OGHH7BkyRIsXrwYc+fOzdUcR48eRUhISKa2Y8eO4dixYxn/ndNDNPNz6CYREeVMa7/WODzgMMbvG4+N1zZmf4EZBtGAu4l3cTfxLvZhn0m/l6MXKrhXyEiaV3CrkLHr3EPlkaME4MOUh+llUF5IhEclmX8EvCAppcpMu7tf/LuvS/pub5Us/YdrtVqNqKgo+Pr6Fkj5rwD3AHzW5DN8Gvgpzj46iw1XN2DTtU2ITY3N0fVG0Yi99/Zi7729GLd3HDoHdEbPaj3Rrny7jB3qRZHWoMUvF37B9IjpiEvL/nF7V4UrUnQpNnuqIFGbiIPRB3Ew+mBGW0mHkqjnVQ/1vOqlJ8y96nP3v53QaDQo9UL9z+dfCxITE+Hu7p7R7uzsjCpVquDUqZw/GUKFm2LBAsj+2Qz0X+pPP4Wud28YKxayUleCAF2fPtA3aQLHd96B7ORJs8Mkz57BacAAaIYOhfq77wBHR4uFJImMhHL2bMh//RWCLmcHXhtq1YLm448huXIFqunTTfqVc+cibenSgg6VCqP4eEjPnDFp1jdvzjIUBUUqtXUE1icIBXLWA1FhYKxaFWlz5sDxnXdM+qRXr8Lho4+QtmRJoXwTSIiPj8/XFrjKlSvD1dU10w/ghw4dQteuXfH6669j1apVANJLq1SuXBleXl78YT2HCjoZUhxwzfKG65Z7XLOc23RtE8btHYd4TbzV5nRVuGZKlj//+zNN+m7w54nwnCaD86qMUxnUKV0HtUvXRk3PmvBz9YOvqy9KOpTM8c5da3yu6Y16HIw6iA1XN2B75HYkac3v8HsZD5UHulfujp7VeiKwXKDNy3gU1LqJoohtN7fhm8Pf4Fb8rWzHl1CVwGdNPsPQV4ZCKkjxMOUhYpJiEJMck1EeJSYp/e8xyTF4nPo4z7EVBB8Xn4xEeS2PWvDUeqJGQA1+XStkXnnlFSiVSpz8J8H47rvv4vfff0doaCgaN26caWzdunXx4MEDxMZa9utbcWeNr82SixfTd19ptSZ92j59kLY467JlhYZOB+W0aVDOmpXlwZwAYKhSBanLlsFYwE/0Si5cgPKnnyDfsuWl879I37gxNB9/DH3HjoAgQHj6FC61akFITc00TpRKkXT6NMTy5Qs05v/iz5y5V9BrJvvzTzgNHGjSnvbDD9COHJnv+xcW/FzLPa5Z3nDdcs9e1kz18cdQLl9uti919mzohgyxbkA5kO8d4/Hx8Sj/nx8Gjh8/DkEQ0K5du4w2BwcH+Pv7IzLS/CO/RERUNL1Z9U008W6CiQcm4s+bf1plB22iNjFjB7i1+Ln6oU7pOul/SqUnw72cvKw2f37IJDK0Kd8Gbcq3wSz9LITeCsWGqxvw952/oTPmbGfdM/UzrLiwAisurICviy96VuuJntV6orpndQtHbznH7h/Dl+Ff4sTDE9mOVUlVGFV/FMY0HAM3pVtG+/M64FlR69W4n3w/c8L8hUR6TFIMErW5r3+bU8+T9X/e/DOjraJbxYzyK/XL1EftUrXhKLfcTk7Knr+/P86d+/frWYMGDbBx40YsXbo0U2J8165duHv3Lvz9/W0QJRUotRqO775rNilu9PVF2rRpNggqD+RyaCZNgr5tWzgMHw5pjPkSa9Lr1+Hcvj3UX30F7ahR+T6YU3r8OJT/+x/kYWE5vkbXti0048bB0Lx5ph1tYokS0A4aBOV/3ogQDAYoFyyA2sxucipasqwv/sJT80REBKi//x7SU6cgO3s2U7vRz6/A3/wuKPlOjDs4OODJkyeZ2p7XAw8MDMzUrlAoMtVBJCKi4qGcczmsfHUl7sffx5FrR5CiSkF0ajTuxN/B7YTbuBV/C0/VT20dZo4IEFDJo1JGErx26dqoXao2PFTWO+DMkhxkDuhepTu6V+mOp2lPsfXGVmy4tgFHY47m+B5RSVGYdWIWZp2YhVola+HViq8iuEIw6nrVtflO8py48fQGvjn8DbZHbs92rAABfWv0xRdNv3hpAjwrKpkKAe4BCHAPyHJMgiYhPVmexc7z+8n3oTFocj13ViITIhGZEJlRBkkqSFHNs1pG+ZV6ZeqhhmeNIl06p7Bp27YtDh8+jDNnzqBevXro0aMHpk6dik2bNuHu3bto0qQJYmNjsWXLFgiCgDfffNPWIVM+qb77DtJ/zlZ6kSgISF28GHBzM3NV4WVo1gxxe/ZA9v77KPH332bHCDodHCZNgmzvXqQtXAixTJncTSKKkO3fD+X//pdl+RlzdK+9lp4Qr18/yzGa0aOh+PlnCHp9pnbFmjXQfPYZRE/P3MVKdsVcfXFjmTKF+jA5IiKbUCqRunIlXIKCICQkAAB0nTql/+zyQvm/wiTfifEqVarg9OnTuHLlCqpXr464uDgcPHgQnp6eqPqfbxQPHjxAyZIl8zslERHZqRKqEqhToo7ZR8ASNAm4nXA7U7L8dsJt3Em4g5ikGKsffgmk76SuWqLqvzvBS9dBrZK14KxwtnostlDCoQSG1h6KobWH4l7iPfxx7Q9svLoRl+NMkzVZufjkIi4+uYhpEdNQ2rE02vu3R3CFYLTxawNXpasFo8+9x6mPMe3YNPxy4ZccPdnQrnw7fN3ia7xS6hWLxuWmdIOb0g01Spo/vFwURTxJe4LopGjcS7yHc4/O4XTsaZyJPYMETUK+5zeIBlx6cgmXnlzCmkvpZ7UopUrUKlnr353lXvVR2aMypJJiWCPUCl5//XWcPXsWDx8+BACULFkS8+fPx4gRI3Dy5EmcOnUKopj+NbJFixb49NNPbRku5ZP04EEo5s8326f98MP0Hc12SHR3x63vv4f01VfhOmmSyQFdz8n37oW0eXOkLVgAfadO2d/YaITsr7+gnDULstOncxaLVApdjx7QfPQRjNWzf7JJ9PWFrkcPKH77LVO7kJYGxdKl0EyYkKN5yf4IUVGQ3jQ9rFwfFFQoa+USEdma6O+P1EWL4DhoENRffgntBx/k+0kwS8p3YvyNN97AqVOn0LNnT3Tr1g379u2DVqs12akSFRWFhw8fonXr1vmdkoiIiiA3pRvqlq6LuqXrmvSp9WrcTbyL2/G3cTvhnz///P1uwt0cl/t4GaVUiZola2ZKglf3rJ5xCGZx5+fqh48afYSPGn2Ei48vYuPVjfj92u+ISTb/WLw5j1IfYf3l9Vh/eT1kEhmalmuKjhU6IrhCMCp7VM5x3fWClqJLwcLTCzHn5Bwk65KzHV+rZC1MaTkFbcoXjkeoBUFAKcdSKOVYCvW86qFb5W4A0hPmt+JvZSTJz8SewblH55CqT83mjtnTGDQ4FXsKp2L/PTfGWe6Mul51EVg2EE3KNUGjso3grnLP91wEVKxYMePcnue6deuGevXqZewad3BwQPPmzdGlSxeb/VuiAhAfD8eRIyGIpm8GG2rVgnriRBsEVYAEAeq+fSFp1QoO77wDmZkDDQFAEhcHpz59oBk+HOpvvwUcHEwH6fWQ//EHlD/9BOnVqzmaXlQooB0wAJoPP4SYy5JDmjFjTBLjANIT4x9+CDg55ep+ZB+yLKPCvAYRUZb0Xbog6dQpi5/DURDynRh/9913sXPnThw5cgQLFy4EkH4g52effZZp3ObNmwEALVu2zO+URERUzKhkKlQtURVVS5g+smowGhCTHPNvsvw/yXNziU5nuTNeKf0KapeqnZEEr+JRBXKp3Bovx+7VKlULtUrVwlctvsKRmCPYeHUjttzYkqvdyXqjHgejD+Jg9EF8efBL+Lv5I7hCMIIrBKO5d3MoZUoLvoJ0BqMB6y6vww9Hf8CDlAfZjvdx8cEXTb9A7+q97aIkjCAIqOhRERU9KqJntZ4A0tf92tNr6cnyh2dw5tEZXHx8sUDeXErWJeNQ9CEcik4vYSBAQPWS1dGkbBM08W6CwLKB8HP1Y9K2APn5+WHs2LG2DoMKkMOnn0ISHW3SLiqVSF26FFBa/mujNRgrVkTKrl1Q/vADlD/9ZPaNAABQLlsG2aFD6Qdz1qqV3qhWQxESAuXs2ZDcvZuj+UQnJ2iHDYNm1CiIZcvmLeYaNaALDjapWy559gyK1auL1CGM9C9zZVSAf3aMExFRluwhKQ4UQGJcoVDgzz//xM6dO3Hjxg34+vri1VdfNXlEXiqV4r333kO3bt3yOyUREVEGqUQKP1c/+Ln6Icg38y8pz0tM3I6/jXuJ9yCXylGzZE0EuAfYRWKzsJMIErTwaYEWPi0wvfV07L6zGxuvbUTordBc17y+k3AHS84uwZKzS+Akd0KQbxCCKwSjQ4UOKOdcrkDjFkURu+/sxleHvsKVuCvZjndVuGJco3EYUW8EHGRmdi3aEZlEhpola6JmyZoYWHMgAECj1+DSk0uIiI7A4TuHcSPlBm7E34BRNOZrLhEiLj+5jMtPLmPFhRUA0s8bCCwbmJ4oLxeIWiVrQSbJ94+jRV7t2rXh7OyMffv2QVlEEqNkSr55MxQbNpjtU0+eDGMN8+WU7JZcDs3kydC3aQPHESMguX/f7DDplStwbtcO6smTAaMRygULIPmnrFB2jO7u0I4YAe2IERBLlMh3yJqxY80e6KlcsADad94B5HyDvUgxGs0mxg1Vq0IsV7A/mxARkW0UyG8iEokEr7766kvHjB49uiCmIiIiyrEXS0w0LtfY1uEUaUqZEq9Veg2vVXoNCZoE/HnzT2y7sQ3hUeFQG9S5uleKLgV/3foLf936CwDwSqlXEFwhGB39O6JBmQb5qmN9NvYsJh+ajPCo8GzHyiVyvF37bYwPHA9Ph6J7sJpSpkT9MvVRw70G2ru1h6+vL/QSPc4/Pp+xs/x07GncTrid77nuJ9/H5hubsflG+pOEznJnNCzbEE3KNUGTck3QsEzDYlPDPzceP34MT09PJsWLMOH+fag++shsnz4oqEjvRja0bInkw4fh8OGHkP/5p9kxgkYDhy++yPE9jV5e0Lz/PrRDhgAuLgUUKWBo2hT6wEDIIiIytUuioyH/4w/o+vQpsLlsymiEYuVKSI8fB0QRRl9fGH19Ifr6wujjA6OPD+DoaOsoLU5y+TIkT56YtHO3OBFR0cEtOkRERFSg3JRuGFBzAAbUHIBUXSoORh/Ertu7EHY7DNFJpiUCsnPh8QVceHwBM4/PhKeDJ9qVb4dOFTqhbfm2Oa5hfTfhLqYemYqN1zbmaHz3yt0xuflkVHCvkOt4iwJnhTOaeTdDM+9mGW3P1M9wJvZMpprl95PN7/DMqWRdMvbf24/99/YDAKSCFLVK1cpIlAeWCyzwJwbskY+PD9Tq3L3BRHbEaITD6NGQxMebdIlubkhduLBQH1pVEEQPD6SuXg35mjVw+PxzCKl5OwvB6OcHzZgx0PbvD6gsc0aIZswYyPr1M2lXzp0LXe/e9n8goyjC4cMPoVi79qXDjJ6emZPlzz/6+UH08YHo6Wn3a5FlGRXWFyciKjLynRh/8uQJLl26BG9vb1SqVClT3y+//IKff/4ZDx48QIMGDfD999+jcuXK+Z2SiIiI7ISj3DGjdvhMcSYux13Grtu7sOv2LkQ8iMh1uY64tDhsuLoBG65ugFSQIrBcYPpu8godUa1ENZP61fGaeCw4sQBLzi6B1qDN9v5NvZtiSospaFi2Ya7iKg48VB5oW74t2pZvm9H2MOUhTj88jdOxp3H64WmceHgCSdqkPM9hEA049+gczj06hyVnlwBIP/j1eaK8SbkmqOZZrdiVQurSpQvmz5+PO3fuwD+XBwZS4adYuhTyffvM9qX9738Qvb2tHJGNCAJ0gwbB0LQpHN95B9Jz53J8qaFqVWg++gi6Hj0sXs5E36kTDNWqmRz4Kb18GbJdu6APDrbo/JYm27Ej26Q4kH5AqiQuDjh71my/6OiYsbs8U/L8n7+L5coV+tIz5g7eFKVS6Js3t0E0RERkCflOjC9evBizZs3CggULMiXGV65ciY8//hjiPwep/P3337hw4QKOHDmCEgVQ342IiIjsiyAIGfWtP2r0EZ6pn2HPnT3YdWcXdt/ZjWfqZ7m6n0E04EjMERyJOYKvDn0FXxffjCR53RJ1se7WOqz8eyXiNfHZ3quKRxV83eJrdA7ozMMhc6GMUxl0qdgFXSp2AZB+oOmlJ5cQ8SACx2KO4dj9Y4hJjsnXHPcS7+Fe4j1suJpee9lN6YbAsoEILJf+p0GZBnZf+z07H3/8MbZv347Bgwdj3bp18PHxsXVIVEAkV69C9fXXZvu0b70F3VtvWTegQsBYuTKSd++GaupUKOfOfelYfd260IwbB/1rr1lvV71EAs0HH8DRTKlQ5ezZ9p0YT02Fw4QJBXIrITUV0uvXIb1+3Wy/KJFALFv2353mZnafF2QZnFzTaiE7fNik2dCgAeDmZoOAiIjIEvKdGD948CCkUim6du2aqX3WrFkAgA8++ACBgYFYsGABjh07hoULF2LSpEn5nZaIiIjsnIfKA29VewtvVXsLBqMBJx+ezCi5cvHJxVzfLyopCj+f/xk/n/85x9eUciyFCU0mYFCtQTwEsgBIJVLULl0btUvXxvA6wwEAUYlRiLgfgWP30xPll55cgggxz3MkaBKw684u7LqzC0B6Lfg6pevg48Yfo3NA5wJ5HYXNokWL0K5dO6xYsQINGzZEUFAQqlWrBseX1Pj97LPPrBgh5YlWC8d334VgpkyO0dsbaTNn2iCoQkKhgPrbb6Fr2xaO771nctimvlkzaD75BPo2bWxSrkPXsyeM338PSUzmN/5kR49CGhEBQ2Cg1WMqCMqffoIkKsoqcwlGI4SYGJM1fJGhShVoBw6EbsAAiB4eVonrOemJE2ZL+rC+OBFR0ZLv3wCjoqLg5eUFZ+d/D0q6cOECoqKi0KRJE3z77bcAgIYNG6JWrVoICwtjYpyIiIgykUqkGTuAv2z+JWKSYrD7zm6E3g5F+L1wpOrzVm82K44yR3zQ4AO83+B9uChsuCOtGPB19YWvqy/eqpa+8zVBk4CTD07i6P2jiLgfgZMPTyJNn5bn++uMOpx8eBJ6o76gQi50fvzxRwiCAFEUYTAYsGvXLuzevdvsWFEUIQgCE+N2QPnjj5CeP2+2L3XhQsDd3boBFUKG1q2RfPgwFIsXQxYeDmOlStAOGABD06a2DUyhgGbUKLMHgipnz0ZqSIgNgsofye3b2e7Qtzbp9etw+PJLqL77DroePaAZPhzGunWtMjfrixMRFQ/5Tow/ffoUtWrVytR27NgxAOn1EJ/z8vJCQEAA7ty5k98piYiIqIjzdvHGkFeGYMgrQ6DWq3Eo+hDCbodh1+1duJt4N8/3lQgSDKw5EBOaTkAZpzIFGDHllJvSDe3826GdfzsAgM6gw4XHFzIS5cfuH8Oj1Ee5vm9gWfvcoZkTffr0YYmfIkZ69CiUs2eb7dOMGgUDd6VmED09ofniC2jMJKFtSTt4MJQzZpgcmirfuROSq1dhrFbNNoHlkerzzyFoNCbt+sBApK5dC0l0NIR79yCJjoYkKirjoxAdnV5r3IIEtRqKdeugWLcO+gYNoH3nHei6d7fYAatAFvXFnZxgaNTIYnMSEZH15TsxLpFIkJycnKnt+PHjEAQBTZo0ydTu6urKxDgRERHlikqmQnv/9mjv3x7TW0/H9WfXM0quHLt/LMc7hTsFdMLXzb9GNU/7SlYUdXKpHPXL1Ef9MvUxuv5oiKKI2wm3M0qvRNyPwLWn1156j4ruFVHaqbSVIra+RYsW2ToEKkiJiXAcMQKC0fTwYUP16lBPnmyDoCjXnJ2hfecdqMyUvFHOnYu0hQttEFTeyHbuhDwszKRdlEiQNmMGxFKlYChVCqhXz/wNUlLSE+XR0RCeJ81fSKIL9+9DMBgKJtZTpyA7dQrGSZOgHTgQ2qFDIZYvXyD3zpCQAOmpUybN+ubNAYWiYOciIiKbyndi3M/PD7du3cKzZ8/g4eEBnU6HvXv3wsHBAfX+840zLi4Onp6e+Z2SiIiIiilBEFC1RFVULVEVHzT4AAmaBOy7uw9ht8Ow+85uPEl7YnJNPa96+LbFt2jp29IGEVNuCYKAAPcABLgHoF+NfgCAp2lPMw70jHgQgdOxp6E1aDOuCSxXdHeLU9HjMGECJPfumbSLcjlSly616C5YKljaESOgnD/fpE68fONGqL/4AqK3t40iy4W0NDhkUXpJ+/bbMNaunf09nJxgrFoVxqpVzfcbDBAePDDdaf7if/9ns112JHFxUM2eDeXcudB37Ajt8OHpNecL4BBW2eHDZhP5rC9ORFT05Dsx3rZtW1y7dg1vv/02hg8fjq1bt+Lp06fo2rUrZLJ/b5+QkIA7d+6gQYMG+Z2SiIiICEB6WY43qryBN6q8AaNoxJnYMwi7HYaImAgYtAYMqD0AvWr2gkTI/y/KZDslHEqgc0DnjMM11Xo1zj46i2Mxx3DswTG09Wtr4wiJcka2bRsU69aZ7VNPmgTjK69YOSLKD7FUKWgHDIDy58yHPgs6HZQLF0L93Xc2iiznlLNnm32jxliyJNQFVb5GKoXo4wODjw/M7hsXRQjx8em7zf/5IwsNhTyLOt8vEoxGyENDIQ8NhaFiRWiHDYOmR498hcv64kRExUe+E+Njx47Fpk2bsG/fPuzfvx+iKEKlUpkc+BMaGgpRFNHU1gelEBERUZEkESRoUKYBGpRpALVajaioKPj6+jIpXgSpZCo0KdcETco1yX5wERAVFZXra3x9fS0QCeWH8PAhHMaONdunb9YM2vfft25AVCA0778PxYoVJqVxFKtWQT1+fKE+RFW4cyfLWvfqr7+2XuyCANHDA6KHR8YOde1770Fy/ToUP/8Mxa+/QkhMzPY20shIOHzxBVRTp0Lo2BGSDz4AGjbMdTjm6osbS5WCsUaNXN+LiIgKt3wnxkuXLo29e/di7ty5uHnzJnx9ffHee++h6n8eozp69Chq1aqF4ODg/E5JRERERFRs1KlTJ1fjBUFAnIUPw6NcEkU4fPABJE+fmna5uCB18WJAKrVBYJRfor8/dN27Q/HHH5naheRkKH/+GZpPPrFRZNlzyOrAzUaNoOvXzwYRZWasUgXq6dOhnjwZig0boPj5Z0gvX872OiEtDaW2bgW2boU+MDD9sM5u3XJUH1yIiYH0mum5FvrWrQEegkxEVOTkOzEOAOXKlcOPP/740jGzs3gnmoiIiIiIsiaKokXHk+UpVqyAfPdus31p06dD9POzckRUkDQffmiSGAcAxeLF0IweDTg42CCql5OFhUEeGmrSLgoC0mbMKJBa3QXG2RnaYcOgHToU0qNHoVi2DPI//4Sgz/7wbVlEBGQRETBOnAjt4MHQDhkC0ccn6/FmdosDrC9ORFRUFaLvdkRERERE9F/Pnj3L8k9MTAwOHDiAgQMHQqVSYeHChXj27JmtQ6YXSG7cgGrSJLN9um7doOvTx8oRUUEz1qkDXbt2Ju2SJ0+yrClvU2o1VFkduDlsGIx161o3npwSBBiaNUPaL78g6eJFqCdMgLFs2RxdKnn8GKqZM+FSuzYcBwyA9MABwMybiFnWF2dinIioSCqQHePPPXr0CPv27cONGzeQlJQEFxcXVKlSBW3atEGpUqUKcioiIiIiomLP0dERtWvXxty5c1GzZk188MEH8Pf357k+hYVOB4cRIyCkpZl0GcuUQdpPP7E8QxGhGTMG8j17TNqV8+ZBO2QIICvQX73zRTlnDqR37pi0G0uUgCaLN3EKG7FMGWg++wyaceMg++svKJcuhezw4WyvE4xGyLdvh3z7dhgqV4b27beh7dsXcHMDRNHsjnFDpUoQeW4DEVGRVCDfnTUaDb788kusWrUKOp3OpF8ul2Po0KH45ptvoFQqC2JKIiIiIiJ6wfDhwzFt2jT89NNPTIwXEsoZMyA7fdpsX9qCBRBLlLByRGQphpYtoa9f3+T/t+TuXci3boWuRw8bRZaZcOcOlD/9ZLZP/fXXED08rBxRPsnl0HfrBn23bpBcvpxetigkBJKUlGwvld64AYfPP4dqyhRoe/WCoVUrSGJjTcbpW7e2QOBERFQY5LuUitFoRN++ffHzzz9Dq9WiZMmSaNq0Kbp3746mTZuiZMmS0Gq1WLp0Kfr378+ah0REREREFiCRSODv748TJ07YOhQCID1xAsr//c9sn2b4cOjNlN4gOyYI0IwZY7ZLOXu22bIdtuAwcSIEtdqkXd+gAXQDBtggooJjrFED6pkz8eTsWdwdPx76ypVzdJ2QkgLlL7/AcehQs/0so0JEVHTlOzG+du1a7Nu3Dy4uLpg7dy4uX76MHTt2YPny5dixYweuXLmCefPmwc3NDXv37sW6wlhjjYiIiIioCIiKioLaTNKLrCw5Ob2EisFg0mWoUgXqb76xQVBkafrXXoOhYkWTdumFC5Dt22eDiDKT7d4N+V9/mbSLggD1zJmF68DNfBBdXPC4Vy/EhYcjeetW6F5/HaJUmrd7SSTQt2xZwBESEVFhke/vfL/99hsEQcDq1asxcOBAyP5TO00qlWLAgAFYuXIlRFFESEhIfqckIiIiIqL/mDdvHp48eYJKlSrZOpRiTzVpEqS3bpm0izIZUpcuBRwdbRAVWZxUCs2HH5rtUs6ebd1Y/kujyfrAzSFDYKhXz8oBWYEgwBAUhNTVq5F0/jzU48fDWLp0rm5hqFcPcHe3THxERGRz+a4xfunSJZQvXx5B2TxeFBQUBH9/f1y6dCm/UxIRERERFRvTpk3Lsk8URTx+/BgnTpzAxYsXIQgChgwZYr3gyIRs504oV64026f5/HMY69a1ajxkXbrevWH8/nuTWtWy8HBIz5yxWQJaOW+e2TdrjB4e0Hz5pQ0isi7R2xuaL76AZvx4yP/8E4qff4bs6NFsr2N9cSKioi3fifG0tDQEBATkaKyHhwcePHiQ3ymJiIiIiIqNH3/8EYIgZNn//AwfQRAwatQovP3229YKjf5DePwYDlnsGNYHBkIzdqx1AyLrU6mgGTkSDl9/bdKlnD0bqatWWT0k4d69LOvdq7/6qngdAqtQQNejB3Q9ekBy4QIUy5dDsWEDhNRUs8P1r71m5QCJiMia8p0Y9/Lywo0bN5CWlgYHB4csx6WmpuLGjRsonctHl4iIiIiIirM+ffpkmRgXBAGOjo6oWLEiOnXqBH9/f+sGR/8SRTh8+CEkjx+bdjk7I23xYkCW71+/yA5ohw6FatYsCImJmdpl27ZBcvMmjFYud+QwcSKEtDSTdn39+tANHGjVWAoT4yuvQD17NtRffw1FSAgUy5dDevNmRr/m7beLZokZIiLKkO+fzFq2bImQkBBMnDgRP/30U5bjJk6ciJSUFHTr1i2/UxIRERERFRuLFi2ydQiUA/I1ayDfudNsX9r338NYoYKVIyKbcXODZtgwqP5TV1wQRSjnzUPanDlWC0W2Zw/k27ebtGccuJnHQymLFHd3aEeOhHbECEgjIiC5cQPGypVhaNrU1pEREZGF5fvwzTFjxkAul2PVqlVo0aIF1q5di9OnTyM6OhqnT5/G2rVr0bx5c6xevRoKhQIfZvFoIb2ETgeYOdGeiIiIiIhsT3LrFhwmTDDbp+vSpVjvyi2utO+9B1GhMGmXh4RAePjQOkFoNFB9+qnZLt2gQTDUr2+dOOyFRAJD06bpa8OkOBFRsZDvHeNVqlTB4sWLMWrUKFy6dMls4lsURahUKixatAhVqlTJ75TFiqDVwu3ddyHx8EDa/PmAJN/vZRARERERUUHR6+Hw3nsQUlJMuoylSqXvDn5JjXgqmsQyZaDr2xeK/9QUF7RaKBYvhsZMDfKCplywANLISJN2o4cH1JMnW3x+IiKiwq5Asqzdu3dHeHg4+vfvj9KlS0MUxYw/pUuXxsCBAxEeHo433nijIKYrPtRqVPz0U6h27oRi/XqoPv4Y+OdwJSIiIiIqHkJDQ1GnTh3MnTv3pePmzJmDOnXq4O+//7ZSZAQAyp9+guz4cbN9afPnQyxVysoRUWGh+eADiGbeFFGuWAEkJFh0biEqCsqZM83H9eWXED09LTo/ERGRPSiw7ceVK1fG/PnzcfXqVdy9exeXLl3C3bt3cfXqVcydOxeVK1dGUFAQ6tatW1BTFm0pKXAfNAjuhw9nNCl/+QWqCROYHCciIiIqRjZu3IioqCh06tTppeOCg4Nx7949bNy40UqRkezsWSinTTPbpxk6FPrgYCtHRIWJsVIl6Lt2NWkXEhOhWLnSonM7TJoEITXVpN1Qpw60gwdbdG4iIiJ7YZG6HK6urihXrhxcXV0ztUdHR+PevXuWmLJoEUU4Dh4MZXi4SZdy8WKovv6ayXEiIiKiYuLs2bPw8PDItiRhtWrVUKJECZw6dcpKkRVvErUabu+/D0GvN+kzBARAPWWKDaKiwkYzdqzZduWiRYBGY5E5Zfv2Qb51q9m+tP/9jwduEhER/YMFqwsjQYB29GiISqXZbuWcOVD+8IOVgyIiIiIiW3jw4AH8/PxyNNbX1xexsbEWjogAwGfuXMhu3jRpF6VSpC1dCjg72yAqKmwM9etD37KlSbvk4UPIf/ut4CfUarM8cFM7cCAMDRsW/JxERER2ionxQkrfpg3ily2DUWb+fFTV9OlQ/u9/Vo6KiIiIiKxNJpNBrVbnaKxGo4HIJwstTrF3L0pnUbJG88knTD5SJpqPPjLbrpwzBzAYCnQuxcKFkN64YdIuurlB/dVXBToXERGRvWNivBDTduyIW99/DzGLR91UU6ZAMX++laMiIiIiImsqX748bty4ke1O8NjYWFy/fj3Hu8spb4SnT+GaRXkMfYMG0HzyiXUDokJP36YNDLVrm7RLIyMh2769wOYRYmKgmjHDbJ/6yy8hlixZYHMREREVBUyMF3LxbdogYcECiBLz/6scJk2CYtkyK0dFRERERNbSrl07GAwGTJgw4aXjJk6cCFEU0a5dOytFVgyJIhzGjoX00SPTLkdHpC1ZAsjlNgiMCjVBgGbMGLNdyjlzCuz8KNWkSRBSUkzaDbVrQzt0aIHMQUREVJQwMW4HNG+8gbQFCyAKgtl+h/HjIV+92spREREREZE1jBw5Ei4uLtiyZQtef/11HDhwAKmpqQCA1NRU7N+/H926dcOmTZvg7OyM0aNH2zjiokv+22+Qb9tmtk89dSqMlSpZOSKyF7pu3WAsX96kXXb6NKQHD+b7/tIDB6DYvNlsX9rMmTxwk4iIyAwmxu2Erm9fpP30U5b9DmPGWObwFiIiIiKyKS8vL6xYsQIODg44ePAgunfvDh8fH5QqVQo+Pj548803ER4eDkdHR/zyyy8oU6aMrUMusvQtW0LfqpVJu65jR+7IpZeTyaD54AOzXco5c/J3b60WDlkduNm/PwyNG+fv/kREREWU+ZMdX2LatGl5niwtLS3P1xKgGzIEaRoNHD77zKRPEEU4jBwJUamE/o03rB8cEREREVlM+/btsXfvXkydOhW7d++GRqOBXq8HAKhUKgQHB2PixImoUqWKjSMt2kRvb6Rs2QLJ/PlwmjoVEq0WRk9PpM2bB2TxdCfRc9r+/aH88UdInjzJ1C7fsweS8+dhNFOHPCcUixdDeu2aSbvo5gb111/n6Z5ERETFQa4T4z/++COEPP7QJ4pinq+ldNoRI9J3BHz5pUmfYDTC8Z13kCqXQ//qqzaIjoiIiIgspWrVqlizZg00Gg0iIyORlJQEFxcXVKpUCQqFwtbhFR8SCVLffRd3K1dG1e++g/bzzyF6edk6KrIHDg7QjhgB1XffmXQp585F2s8/5/qWwv37UGWxeU39xRcQS5XK9T2JiIiKi1wnxps1a8bkto1pP/gAglpt9gcqQa+H45AhSF2/HvoOHWwQHRERERFZklKpRI0aNWwdRrGnrlQJT0NDoXJ2tnUoZEe077wD5ezZJodkyjdtgnrSJIj+/rm6n+rLL80fuFmrFrTDhuUnVCIioiIv14nxHTt2WCIOyiXN+PGARgPVzJkmfYJOB8cBA5CyYQMMQUE2iI6IiIiIqBiQ5frXKSrmRA8PaIcMgXLBgkztgtEI5fz5UJv5/S4r0vBwKP74w2xf2syZ/PwkIiLKBg/ftGOaL76A5v33zfYJGg2c+vSB9PBhK0dFRERERAXp119/RYkSJfDDDz+8dNwPP/yAEiVK4I8sEmVEVDhoRo2CKJebtCvWroXw+HHObqLTZX3gZt++MDRpkp8QiYiIigUmxu2ZIEA9ZQo0w4eb705Lg1Pv3pCeOGHlwIiIiIiooGzbtg0AMHDgwJeO69+/P0RRxJYtW6wQFRHllejtDV3PnibtgloNxZIlObqHYskSSK9eNb23qyvU33yT7xiJiIiKAybG7Z0gQD1tGrSDB5vvTk6GU48ekJw9a924iIiIiKhAXLp0CaVKlYKPj89Lx/n5+aF06dK4ePFinuc6ffo0evbsCT8/P5QrVw7t27fH5s2bc3z97t27MWzYMDRq1Ah+fn4oW7YsGjVqhPfffx83b97M8ro9e/agS5cu8PHxga+vL1577TUcOHAgz6+DqLDTjBljtl3x889AcvJLrxUePIDqxx/N9qknToRYunS+4yMiIioOmBgvCiQSpP30E7S9e5vtFhIT4dS9OyT5+CWJiIiIiGwjNjY226T4c97e3oiNjc3TPOHh4QgODsaxY8fQvXt3DB06FLGxsRg6dCjmzZuXo3vs2rULJ06cQK1atdC/f38MHz4cAQEBCAkJQfPmzc0mu3/77Tf06NED169fR9++fdGnTx9cvXoVb7zxBrZu3Zqn10JU2BmrVoWuc2eTdkl8PBSrVr30WtXkyRDMJM8NNWtC+847BRYjERFRUcfTOIoKiQRpCxYAOh0UmzaZdj97Bqc33kDKjh0wVq1qgwCJiIiIKC9UKhUSEhJyNDYxMRGyPBy4p9frMWbMGEgkEuzYsQO1a9cGAHz66ado164dpkyZgm7dusHPz++l95kyZQpmzJhh0n7gwAF069YNX3/9Nfbt25fRHh8fj08//RSenp44cOAAvL29AQBjx45Fq1atMG7cOLRt2xYuLi65fk1EhZ1m7FjId+40aVcuXAjt8OGAQmHSJz10CIqNG83eL23GDB64SURElAt2sWP8t99+w9ixY9G6dWuULl0a7u7uWLduXZ7ulZSUhO+//x5NmzZF2bJl4efnh1atWuHHLB5FsysyGdKWLIHu1VfNdkuePIFTt26QREZaOTAiIiIiyqtKlSrh1q1buHPnzkvH3b59G5GRkQgICMj1HOHh4bh9+zbeeuutjKQ4ALi5uWHcuHHQarUICQnJ9j4qlcpse1BQENzd3XHr1q1M7Vu2bEFCQgLefffdjKQ4kL7zffjw4YiLi8P27dtz/XqI7IEhMBD6pk1N2iUxMZD//rvpBS87cLNXLxiaNSvoEImIiIo0u0iMT506FStXrkRUVBS8vLzyfJ+oqCi0atUKM2bMQJkyZfDuu++iX79+KFu2bMahRnZPLkfqihXQdexotlvy8CGcXn8dQja/WBERERFR4RAcHAxRFDFmzBhoNBqzY7RaLcaOHQtBENDZTHmG7Bw6dAgA0LZtW5O+du3aAQAOHz6c6/s+d/z4ccTHx6NGjRpWnZeosMuq1rhy7lzAaMzUpli2DNLLl03Giq6uUE+ZYpH4iIiIijK7eM5q3rx5CAgIgJ+fH3766Sd8k4dTtvV6PQYNGoSHDx9i69ataNWqlUl/kaFUInX1ajj26QP5/v0m3ZKYGDh364bkHTsg5rBeJRERERHZxrvvvovly5fj4MGDCAoKwqhRo9C4cWO4ubkhISEBx48fx8KFC3Ht2jWUKVMGI0aMyPUckf88UVixYkWTPi8vLzg7O5vs9n6ZvXv3IiIiAlqtFpGRkQgLC4Onpye+//77HM/7vC0yh087qtXqHMeXX1qtNtNHyh7XLAutWkFRrRrkV69mapZevQrxzz+hbdMGAKCPjobyhx/M3iL5k0+Q5uYGWPHfQGHGz7W84brlHtcsb7huucc1y7msnl7Mil0kxlu3bp3ve2zduhVnzpzB+PHjTZLiAPJUi7FQU6mQum4dnHr2hOzIEZNuyd27cOrWDSk7dkAsU8YGARIRERFRTri5ueHXX39Fr169cO3aNYwdO9ZkjCiKKF26NEJCQuDu7p7rORITEwEArq6uZvtdXFwyxuTE3r17MX/+/Iz/DggIwIoVK1C3bt0cz/u8rnhO571//z4MBkOOYywIeT3otDjjmplK6dsXAV99ZdIu+9//EPvPUxbKyZMhSUoyGZNasSKudegAREVZPE57w8+1vOG65R7XLG+4brnHNXs5qVSa65KCRSwbnLVN/xxI+cYbbyA6Ohq7du1CQkICKlSogPbt28PZ2TnH97LWbpR8vyMklUKzejXce/eG4tQp0+7ISDh27YqnmzZBLFUqP6EWGnwXLW+4brnHNcsbrlvucc3yhuuWe1yznMvtTpSCULduXRw+fBizZ8/Gn3/+iagXkmB+fn7o1q0bPvzwQ5QsWdLqsZkzdepUTJ06FcnJybh27RqmT5+O4OBgzJ8/Hz179rTInOXKlbPIfc3RarWIjY2Fl5cXFGYOSCRTXLOXGDYMhqVLIY2JydTsfP48fO/exbOnT1Hyr7/MXqqeORO+FSpYI0q7wc+1vOG65R7XLG+4brnHNbOcYpMYP3fuHADgyJEjmDRpUqb6jCVLlsQvv/yCli1b5uhe1t6Nkt93hGJmzECV0aPhdOWKSZ/sxg04v/kmri1cCEMedhcVVnwXLW+4brnHNcsbrlvucc3yhuuWe1yzl8vLTpSCUqpUKXz33Xf47rvvkJycjKSkJLi4uGTa4HHy5EmsXbsWs2fPztW9n+/Yzmp3dlJSUp52ojs7O6NBgwZYt24dWrdujbFjx6JNmzYZCfwX5y1RooTJnC+OyY4t3rBQKBQ2mdeecc3MUKmgff99OEyYYNLlvnAhXLLYDa7t2RPSNm0gtXR8doqfa3nDdcs9rlnecN1yj2tW8IpNYvzx48cAgM8//xwffPABhg8fDpVKhd9//x1ffvkl+vfvj+PHj6NMDsqKWGs3SkG+I5SyaRMUPXpAbuawFscbN1Dr44/xbONGiG5u+ZrH1vguWt5w3XKPa5Y3XLfc45rlDdct97hm9sXZ2TkjIf7kyROEhIRg/fr1uHbtGgDkOjH+Yj3v/5Y7iY2NRXJyMurXr5/neGUyGVq2bImLFy/izJkz6NChQ8a8Z86cQWRkpEliJHq7hAAAhEdJREFU/GX1x4mKGu2gQVBOnw7Js2eZ2pV795odL7q4QP3tt9YIjYiIqMgqNolx4z8negcHB+Prr7/OaB8xYgTu37+POXPmYM2aNRg/fny297L2uzMF8o5Q2bJI27YNktdeg/Q/B7sAgPz8eZQYMAApmzYB/9RztGd8Fy1vuG65xzXLG65b7nHN8obrlntcM/tgNBoRFhaGtWvXYvfu3dDr9RBFEQDQoEGDXN+vefPmmDVrFvbu3YsePXpk6tuzZ0/GmPx4+PAhAEAul2ea9/fff8fevXvRqFEji8xLZBecnKAdPhyq6dNzNFz92WcQy5a1cFBERERFm8TWAVjL80cwO3fubNL3vO3MmTNWjcnaxJIlkbJlCwxZ7LqRnTgBp169gJQUK0dGRERERDlx/fp1fPXVV6hRowb69++PnTt3QqfToWTJkhg9ejSOHj2Kv//+O9f3DQoKgr+/P37//XecP38+oz0hIQGzZs2CQqFAnz59MtofPnyI69evIyEhIdN9svp5es+ePdi+fTvc3NwyJcC7d+8OV1dXLF26FDEv1FeOiYnBsmXL4Onpiddeey3Xr4fIHmlHjIDo4JDtOEP16tCOGGGFiIiIiIq2YrNjvHLlyoiLi4ObmVIhz9usdaimLYllyiBl2zY4d+kCyd27Jv2yo0fh1K8fUn79FcjBD2VEREREZFnJycnYtGkT1q5di5MnTwIARFGEXC7PSIpfuXIFUmneKw3LZDLMnTsXPXr0wKuvvoo333wTzs7O2LZtG6KiojBlyhSUL18+Y/w333yDkJAQLFiwAP37989ob9OmDWrUqIGaNWuiXLlySE1NxcWLF3H06FHI5XLMnz8fTk5OGePd3d0xY8YMjBgxAkFBQejevTsAYPPmzXj69Cl++eUXuBSBpxmJckL09IR2wAAoly176bi06dOBF568ICIiorwpNonxli1b4tixYxl1F1/0vM3Pz8/aYdmE6O2N5G3b4Pzqq5BER5v0yw4cgOPgwUhdswZQKm0QIREREREdPnwYa9euxZ9//onU1NSMUimvvPIK+vXrh7feeguVK1eGVCrNV1L8uVatWiE0NBQ//PADNm/eDJ1Ohxo1auCbb77Bm2++maN7TJ48GQcPHsThw4fx5MkTSCQS+Pj4YMiQIRg5ciSqVq1qck3v3r3h6emJ//3vf1i/fj0EQUCdOnUwfvx4tG7dOt+vi8ieaN5/H4oVKyAYDGb7tT16wNCypZWjIiIiKpqKXGI8Li4OcXFx8PT0hKenZ0Z7//79MXfuXCxduhT9+/fPOEAzKSkJ//vf/wAgY4dKcSCWL4+UrVvh9OqrkPxT7/FF8l274DhsGFJXruRuBCIiIiIrep4gvn37dkYyvFSpUujZsyf69euHmjVrWmzuBg0a4Pfff8923KJFi7Bo0SKT9nHjxmHcuHG5nrd9+/Zo3759rq8jKmrE8uWh69EDig0bTPucnaGeMsUGURERERVNdpEYX716NY4ePQoAuHz5MgBgzZo1OHToEACgadOmGDRoEABg6dKlmDZtGj777DNMmDAh4x7+/v749ttv8dlnn6FFixZ47bXXoFQqERYWhnv37mHo0KEICgqy8iuzLWPFiv8mx588MemX79gBh3ffRdqyZYDMLj5ViIiIiOze1KlTIQgCFAoFOnXqhD59+qBDhw4FsiuciAo/zYcfmk2Mqz/7DOI/G7yIiIgo/+wi23n06FGEhIRkajt27BiOHTuW8d/PE+MvM2LECPj5+WHu3LnYtGkT9Ho9qlWrhk8++SRH1xdFxqpVkbJlC5y6doXk2TOTfsXmzYCTE9LmzQMEwQYREhERERVPMpkMKpUKDg4OTIoTFSPGWrWgHjsWqtmzM9o0QUE8cJOIiKiA2UViPKtHNc2ZMGFCpp3i/9W5c2d07ty5oEIrEoy1aiFl82Y4v/46hMREk37F2rUQ3dygnjqVyXEiIiIiCxs/fjx+/fVXREVFYcOGDdiwYQPKlSuHPn36oG/fvqhYsaKtQyQiC9NMngxD/foQ9u/H49KloRo5EiqFwtZhERERFSkSWwdAhYOxbl2kbNoE0dnZbL9ywQIo/6nFTkRERESWM3HiRJw7dw6bN29G9+7doVQqERMTg1mzZqFRo0bo2LEjVq5ciYSEBFuHSkSWIpFA//rrSPr+ezzu0QNQKm0dERERUZHDxDhlMDRsiJQNGyA6OJjtV02dCsWyZVaOioiIiKj4EQQBrVu3xvLly3HlyhVMnz4dtWvXhiiKOHHiBMaNG4eqVasCAAwGA4xGo40jJiIiIiKyL0yMUyaGZs2QunYtRLncbL/D+PGQmzkIhoiIiIgsw93dHcOHD8f+/ftx6NAhjBgxAiVKlIBGowEAxMXFoWrVqvjiiy8yDqonIiIiIqKXY2KcTOjbtUPqsmUQs6gn7jByJGQ7d1o5KiIiIiKqWbMmfvzxR1y5cgUrV65Ehw4dIJFI8OTJEyxatAgtWrRAu3btbB0mEREREVGhx8Q4maV/4w2kvXAK+osEgwGOQ4ZAevCgdYMiIiIiIgCAXC5Ht27dsGHDBly4cAGTJk1CQEAARFHEmTNnbB0eEREREVGhx8Q4ZUk3eDDSvv3WbJ+g0cCpXz9I+YsXERERkU2VLVsWH3/8MU6ePInt27ejb9++tg6JiIiIiKjQY2KcXkr74YdQjxtntk9ISoJjjx6QXLtm5aiIiIiIyJzmzZtjwYIFtg6DiIiIiKjQY2KcsqX58kto3n7bbJ/k6VM4de8O4e5dK0dFRERERERERERElDdMjFP2BAHqGTOgfests92S+/fTk+OxsVYOjIiIiIiIiIiIiCj3mBinnJFIkLZoEXQdO5rtlt66Bac33wTi460bFxEREREREREREVEuMTFOOSeXI3XlSuibNjXbLb10CU69egEpKVYOjIiIiIiIiIiIiCjnmBin3HF0RMqvv8JQp47Zbtnx43AcOBDQaKwcGBEREREREREREVHOMDFOuefmhpQ//oChcmWz3fK9e+EwYgRgMFg5MCIiIiIiIiIiIqLsMTFOeSKWLImUzZth9PEx26/YsgUOY8cComjdwIiIiIiIiIiIiIiywcQ45Zno44OULVtgLFnSbL9izRqoJk9mcpyIiIiIiIiIiIgKFSbGKV+MlSoh5Y8/ILq6mu1XzpsH5axZVo6KiIiIiIiIiIiIKGtMjFO+GevUQcpvv0F0cDDbr5oyBYrly60cFREREREREREREZF5TIxTgTA0bYrU1ashymRm+1WffAL5xo1WjoqIiIiIiIiIiIjIFBPjVGD0HTogbelSiIJg0ieIIhxGjoQsLMwGkRERERERERERERH9i4lxKlC6N99E2k8/me0T9Ho4Dh4M6aFDVo6KiIiIiIiIiIiI6F9MjFOB0w0ZgrSvvzbbJ6jVcOrbF5KzZ60aExEREREREREREdFzTIyTRWjHjoV67FizfUJSEpx69IDk+nXrBkVEREREREREREQEJsbJgjRffQXN0KFm+yRxcXDq3h3CvXtWjoqIiIiIiIiIiIiKOybGyXIEAeqZM6F9802z3ZKYmPTk+KNHVg6MiIiIiIiIiIiIijMmxsmypFKkLV4MXYcO5rsjI+H05ptAfLx14yIiIiIiIiIiIqICJYoiLj25hNUXV9s6lGzJbB0AFQMKBVJXrYJTjx6QHT1q0i29eBFOffogZdMmwNHRBgESERERERERERFRXtxJuIPwqHAcuHcA4dHheJz6GADQxq8NfF19bRxd1pgYJ+twdERKSAicu3aF9MIFk27ZsWNwHDQIqevXAwqFDQIkIiIiIiIiIiKi7DxKeZSeCI86gPCocNxNvGt23IGoAxhQc4CVo8s5JsbJetzdkbJpE5w6d4b05k2Tbvnff8Ph3XeRtnw5IJXaIEAiIiIiIiIiIiJ6UYImAUdijqTvCI8Kx+W4yzm6LjwqnIlxoufEUqWQsnkznDt3hiQ62qRfsWUL4OqKtDlzAEGwfoBERERERERERFSgopOi8f3R73Hi/gk4CU5oXaE12vi3QeNyjeEgc7B1ePQfar0aEQ8iEH4vfVf4mdgzMIiGXN/nwL0DEEURQiHN8TExTlYn+voiZfNmOHXqBElcnEm/YvVqiB4eUH/zjQ2iIyIiIiIiIiKignI05igGbh+IJ2lPMtrOPjuL2adnQyFVoGGZhmjp0xItfVuiUZlGUMqUNoy2eNIb9TgbezajPErE/QioDep83/dx2mPcTbwLfzf//AdpAUyMk00YK1dGyh9/wPn11yEkJpr0K+fMgahQQDN+PGuOExERERERERHZobWX1uKjPR9BZ9SZ7dcatDgScwRHYo5gWsQ0qKQqNC7XOCNRXt+rPhRS5oUKmiiKuPr0Kg7cO4ADUQdwOPowErWm+bm8qFaiGlr5tUKQbxCaezeHu8q9QO5rCUyMk80Y69ZFSkgInHr0gKA2fRdKNWMGFOvWQTN6NLSDBwPOzjaIkoiIiIiIiIiIcsNgNGDyoclYcHpBrq5TG9QIjwpHeFQ4cBRwlDmiSbkmaOnbEi18WqCeVz3IJExn5sXdhLsZh2WGR4XjUeqjArmvr4svgvyCEOQbhJa+LVHGqUyB3Nca+JlENmVo3hypq1fDsV8/CHq9Sb/k/n04fPEFlDNmQDt8OLQjRkAsWdIGkRIRERERERERUXYSNAl4Z+c72H1nd77vlapPxd57e7H33l4AgLPcGU29m2bsKK9dqjakEmm+5ykKjKIRT9Oe4lHqIzxOfYxHqY/wKPURrj+9jgNRB3An4U6BzOPp4IlWPq0ykuH+bv6FtoZ4dpgYJ5vTd+yItCVL4PDOOxBE0ewYSXw8VDNmQDl/PrQDB0Lz/vsQ/fysHCkREREREREREWXlVvwt9N3WF9eeXstyjFSQ5ukgRwBI1iVj953dGUl3V4Urmvk0S0+U+7RErVK1IBEkebp3YaQ36tOT24nXERkViXh9fKak9+OUxxmJ8CdpT/K8ri/jLHdGc5/maOWbXh6lRskaRWaNmRinQkHXoweEhAQ4jBv30nFCWhqUS5dCsXw5dD16QDNmDIw1a1opSiIiIiIiIiIiMudA1AEM3j4Y8Zr4LMd8WO9D9PbqjWeqZ4h4FIGD0QcRcT8Cafq0PM2ZqE1E6K1QhN4KBQB4qDzQ3Ls5WvqmJ8qre1YvdLuZtQYtHqc+zpzgTn1s9uPTtKcQYX4TqaUopAo0Lts4IxFe36s+5FK5VWOwFibGqdDQDhsGY/nyUH35JaSXL790rGAwQLFhAxQbNkAXHAzN2LEwNG1qpUgLmMEA6alTkEZEABIJdG+8AdHb29ZRERERERERERHlyPJzy/Hp/k+z3LGslCoxr8M8vO7/OqKiotCwTEO08G+Bjxt/DI1eg1Oxp3Aw6iAORh/EiQcnoDFo8hTHM/UzbI/cju2R2wEAJR1KooVPC7T0aYkWvi1QyqEUtEYttAYt9EY9tIYX/v7fdqMWOoMOOqMOWsO/f3/+3y+7Tm/4t11n0EFtUONJ6hM8Sn300jcObEGAgLpedRHkm14aJbBcIBzljrYOyyqYGKdCRd+uHZLbtoVs924of/oJsqNHs71GHhYGeVgY9E2awDhqFFC1qhUizafERMj27YM8NBSy3bshefIko0v17bfQjB4NzUcfAS4uNgySiIiIiIiIiChrOoMOEw5MwM/nf85yjJejF9Z1XYeGZRtCrVab9CtlSjTzboZm3s3wGT6DWq/G8QfHcTD6IA5FHcLJhyehM+ryFN+TtCfYcmMLttzYkqfri6qqJaqilW8rtPJthZY+LeGucrd1SDbBxDgVPoIAfceO0HfsCGlEBJSzZ0O+c2e2l8mOHYPHsWOoUbEidB99BPTuDcgLz6Mewp07kO/cCVlYGGSHD0PQmf+iLmg0UM2aBcXatVBPmgRd//6AlAdJEBEREREREVHh8Uz9DIN3DEZ4VHiWY+qUroP1XdfD2yXnT8arZKqMpC2aAqm61PRE+T87yk/HnobeqC+Il1Bs+Lj4ZJRGaeXbCmWdy9o6pEKBiXEq1AyBgUgNCYHkyhUo58yB/PffIehf/sXPMTISeP99GH/8EZoPPoB24EDA0QaPgOj1kB4/nr4rPCwM0mtZHzxhjuTRIzh++CEMy5Yh7fvvYWjZ0kKBEhERERERERHl3LWn19B3W1/cir+V5Zg3Kr+BhR0X5rssh6PcEa39WqO1X2sAQLI2GcfuH8tIlJ99dBZG0ZivOeyRs9wZpRxLobRj6Uwfn/8p7Vg6o81V6WrrcAslJsbJLhirV0fa4sVQT5wI5YIFUKxZAyE19aXXSKKj4fDZZ1BOnw7tiBHQDh8O0cPDsoHGx0O+Zw9kz0ukxMfn+5bSCxfg3LUrdK+9BvWUKTBWqJD/OImIiIiIiIiI8uDvO39j2F/DkKhNzHLMhCYT8GngpxY5+NJZ4Yz2/u3R3r89ACBBk4CjMUdxMPogDkYdxIXHF6x+YGVBcVO6mSS6SyhKQKqWoqp3VZRzK5fRV1zqgFsSE+NkV0Q/P6inTYPm00+hWLoUiqVLIXn27KXXSOLioPr+eyjnzIF2yBBoRo0q0MMtJTduQBYaCnloKKTHjkEwmD9oIr/k27dDFhYG7XvvQf3JJ4Cbm0XmISIiIiIiIrK084/OY/7p+Tj18BQ8VB6o6FERldwrobJHZVT0qIgA9wA4yZ1sHSa9QBRFLDqzCJMOTspyh7aDzAGLgxejW+VuVovLTemGTgGd0CmgE4D0Ei+How+nJ8qjD+Lyk8sWj0GAAIVUAYVUAblEnv5HKs/03yUcSpgkvTPt8nYoBaVMaXJvtVqNqKgo+Pr6QqVSWfy1FCdMjJNdEj09oZkwAZoPPoBizRoo58+HJCbmpdcIKSnpu82XLoWuVy9oxoyBsUqV3E+u00F65AjkYWHpJVIiI/P4KtIZfX2h69QJhmbNoFi5ErIDB7IcK+h0UM6bB3lICDQTJ0I7aBAg4z9jIiIiIiIisg/nH53H9Ijp2B65PVP7yYcnTcZ6O3tnJMwrefz7x8/VDzIJfxe2Jo1eg4/3fYy1l9ZmOcbb2RvrXl+HuqXrWi8wMzxUHnit0mt4rdJrAIC4tDgcij6EG89uQBTFjGR1RtJaKodCooBcKv+3/fnf/2l/McGd6TqpAgqJAlIJz4azR/wqQvbN2RnakSOhffttyH//HYqffoLsxo2XXiLodFCsWwf5+vXQv/oqNGPHwtCw4cuviYuDbPduyMLCIN+zB0Ji1o8LZUeUSGBo3Bj64GDogoNhrF4d+OfRIt0bb0AWGgrVl19CevNmlveQPHkCh3HjoPj5Z6i/+w76Nm3yHA8RERERERGRpWWVEH+ZmOQYxCTHmBzuKJfI4e/m/2+y3L1SegLdoxK8HL0sUr6jOHuc+hiDtg/C0ftHsxzTqEwjrO26Fl5OXlaMLGc8HTytuoOd7AcT41Q0KBTQ9euHpDfeQNL69fALCYHi1KmXXiKIIuTbt0P+//buOyqKqw0D+LOVpaOoKAoidmOLXVGsibGiqLHFlth77CbGEo0lGo0aezT23rEroiig0Rijhih2QWND6Szb5vuDDxLCLrCwLAs8v3M4yNw7M+9cZ5fLu3fuPXYMmqZNkfTll9C0apWcpBYEiO/e/WfhzF9/hUiX/YUcBAcHqFu3hqZtW2g++giCs7OBoETQtGuHuNatIf/5ZygWLYIoOtrgcSWhobDt2hXqtm2hnDcPuooVsx0jERERERERkallJyGeGbVOjfvv7+P++/QD4+zl9ijvlJwkT/lesUhFeDp5cgHCbLjz5g56H+2N8Nhwg3V6Vu2J5a2XQyHlNB+UvzAxTgWLWIyo5s1h37cvbG/cgNWPP0J29mymu0kvX4b08mVoa9SApn59yM6dg/jZsxyFoi1XDppPPkmeJqVxY0Auz/rOcjlUI0dC3asXrBYsgHzTpgznLpedPg2pvz9UgwcjaerU3F9klIiIiIiIiCgDuZEQz4pYVSxuvr6Jm69vpitzsXFJHVmeMj2Lp5MnyjqWhbXU2qxx5gfHHx7H0FNDEa+O11suggizm87G2LpjOUqf8iUmxqlgEomg9fJCgpcXxLdvw2r5csgOHsx01Lfk9m1Ibt/O1ikFiQTaRo2g/uQTaD75BLoKFVKnSMkuoWhRKBcvhuqLL6D45psMk/wijQZWa9dCtmcPkqZNg+rzzwGZLEfnJyIiIiIiIjKGsQlxG6kNytiXwePox1Dr1Lka26uEV3iV8ArBz4PTlZWyLQUPRw+UdSyLco7l4OHoAQ9HD5RzLIfiNsULVeJXEAQsu7YMc4PnQoCgt46dzA4b2m1AO892Zo6OyHSYGKcCT1ejBhJ//hnKGTNg9dNPkG/fDpFSaZpjOzlB89FHyfOFt2kDODmZ5LjpzlOlChL27YP07FkoZsyA5N49g3XF79/DeupUyDduTJ5//KOPciUmIiIiIiIiohTZSYgPqTUEY+qOQTGbYtDoNAiPCceD9w/wIOoBHr5/iPvv7+Nh1ENExEbkcvTA3/F/4+/4v/XOo20rs0VZx7LwcPh/stypHFytXSGPl6OEtgQUKDhTiCg1Sow9NxZ77+41WMfdwR27O+9GtWLVzBgZkekxMU6FhuDhAeWSJUiaMgXydetgtWFDthbR1FaqlDxFStu20DZsCEjN9zLSfPQR4lq0gHzzZljNnw/x+/cG60rCwmDbowfUrVsnzz9etarZ4iQiIiIiIqLCIacJ8RRSsRTlnMqhnFM5fIS0A7wS1Al4FPUoNWn+4P0/ifOopChTXo5e8ep4hL4NRejb0HRloosilLYvnTrCPGWUecr3Iooi+Wa0+cv4l+h7tC9+e2V4zbYmpZtga4etaf7viPKrfJEY37NnD0JCQnDz5k2EhoZCpVJh1apV6Nu3b5aPcenSJXTq1MlgubHHo/xLKFECSd98g6Rx45ITzKtXQ/zypeH6Uim0Xl7JU6S0bQudp6cZo9VDJoNqyBCoevSA4vvvIV+/HiKNxnB1f39IL1yAatAgJE2fbnjhTyIiIiIiIqIsMlVCPEv7ymxQvXh1VC9ePV1ZZGJkcsL8/QM8jHqY5t9J2iSjzpMdAgRExEYgIjYClyMupyt3kDvonZ7Fw9EDbg5ukIotIzV389VN9PHrgxdxLwzW6V+9P5a0XAK5xIg11IgsmGW8+jIxb948hIeHw9nZGS4uLggPN7wSbma8vLzQtGnTdNtr1KiRkxApP3JwgGrsWKiGDYNszx5YLV8OycOHAACdszM0H30Edbt20LRoATg65m2s+jg5QTl/PlSffw7FjBmQnTplsKpIq4XVzz9DvncvlFOmQDV0qHGLgRIRERERERHBvAnxrHC2doaztTMaujZMs10n6BARG5E6sjxlepYH7x/gWcwzg3Nnm1qMKga339zG7Tfp1zOTiWUo71QeFYtWRKUilVK/VyhSAQ5WDmaJDwAOhx3GiDMjkKhJ1FsuFokx33s+htUelm9GvxNlRb5IjK9cuRKenp5wd3fHsmXLMGfOnGwfq2nTppg+fboJo6N8z8oK6v79of7sM4j/+gsQiaCrXBmQSPI6sizRVaiAhN27Ibl4EdbTp0MSmv7RrhSimBhYz5gB+aZNUM6dC0379jleIJSIiIiIiIgKPktLiGdGLBLD3cEd7g7uaFm2ZZoypUaJJ9FP8OD9AzyJeYKn0U/xOOpx6r9zexHQFGqdGnff3cXdd3fTlZWyLYVKRSuhUtFKqFikYup3VztXkyWndYIOC68sxPdXvzdYx0HugM0dNqNV2VYmOSeRJckXifEWLVrkdQhUGIjF0H3wQV5HkW3a5s0Rd+kSZNu2QTFvHsRv3xqsK3n0CLZ9+0Lj7Y3E+fOBChXMGCkRERERERHlF/ktIZ4VCqkCVZyroIpzlXRlWp0WL+Je4En0EzyOfoyn0U9T//0k+gneKd+ZJcaUxUAvhl9Ms91OZocKRSqkS5p7OnrCSmqV5ePHq+Mx4vQIHH1w1GCd8k7lsbvzblQsWjHb10FkyfJFYtyUHj16hNWrV0OpVMLV1RXe3t5wdXU16hhKpTKXoktLpVKl+U6ZY5sByl69ENe+PWyXL4fNhg0QZdAW0sBA2Hl7Q9SzJ6QDBkDl4mLGSPM33mvZw3YzHtsse9huxmObZZ1CocjrEIiIiHJdQUyIZ4VELIGbgxvcHNzQzK1ZuvLopGg8iX6C+2/v449nfyBaHI3wuHA8jn6M8JhwaAVtrsYXp47Dzdc3cfP1zTTbxSIxPBw90owur1S0EioXrYwiiiJp6kbERqD30d56p3dJ0cK9BTa33wwnhVMuXAWRZSh0ifF9+/Zh3759qT9LpVIMHToUc+fOhSSLU2e8ePECWm3uvtH926tXr8x2roKCbQZg4EBYtWmDMitWoEhAgMFqIp0Odrt2ocbRo3gxeDDCe/fON9PIWALea9nDdjMe2yx72G7GY5tlTCKRwDOvF+ImIiLKRcYmxG1lthhSawhG1xmdrxPiWeVo5YhaJWqhskNl1JLVgpubW+qH5hqdBhGxEXgS/ST1K2Wk+eOox4hRxeRaXDpBh0dRj/Ao6hFOPz6dpqyYdbHU+cvLOpTF2ptr8TrhtcFjDa09FPO951vMwqBEuaXQ3OHFihXD7Nmz0bZtW7i7uyMhIQG//vor5syZg9WrV0MkEuG7777L0rGMHWGeXSqVCq9evYKLiwvkXCgxS9hm/+HmBlXjxngXHAz7WbMgu23402BJfDzcli+Hy+XLiPnxR2gr8lGpjPBeyx62m/HYZtnDdjMe24yIiKhwuxd9D7NCZ+HE4xNZql/YEuJZIRVL4eHoAQ9Hj3RlgiAgKikqNUn+MCp5UdD77+/j/rv7iFPH5VpcbxPf4u3ztwh5HpJhPalYisUtFmNQzUG5FguRJSk0ifGqVauiatWqqT/b2tqiQ4cOqFevHry8vLBu3TqMHz8exYsXz/RY5n58Vi6X85FdI7HN/qNVKyQ0bw7Zzp3J849nMBpQ/ttvcG7TBsoZM6AaOZKjxzORrXtNq4Xkxg1AKoW2Vi1ALM6d4CwYX6PGY5tlD9vNeGwzIiKiwuX2m9tYELyACfFcJhKJUERRBEUURfChy4dpygRBwN/xf+P+u/sIex+GsHfJX/ff38eLuBdmia+Iogi2dtiqd/oYooKq0CTGDXFxcUH79u2xdetWXL9+He3atcvrkIhyh0QCdb9+UHfpAqsff4TVTz9BlJSkt6ooKQnW33wDmZ8fEletgo6jx01DECA7eBCKOXMgfvYMAKBp2BAJW7ZAKFkyj4MjIiICbty4gQULFuDq1avQaDSoVq0aRo0aha5du2a6ryAIOHfuHE6ePIkrV64gIiICarUanp6e8PX1xahRo/R+6OLk5GTwmL1798aaNWtycklERJSBjX9sxJQLU7I0LzYT4rlHJBLB1c4VrnauaO7ePE1ZrCoWD94/SE2Up3x/8P4B1Dq1Sc5fpWgV7Oq8C+WcypnkeET5RaFPjAOAs7MzACAhISGPIyEyA3t7JH3zDVQDBkAxezbkBw8arCr99VfYNWsG5ddfc/R4DkmCg6H45htIf/stzXbp1auwbdcO8UeOQHB3z6PoiIiIgMDAQHTr1g0KhQK+vr6ws7PD0aNHMWjQIERERGDMmDEZ7p+UlIQePXrAysoKTZs2RevWraFUKnH+/HnMnTsXx48fx7Fjx2BjY5NuXzc3N/Tp0yfd9ho1apjs+oiIKK2D9w5iYsDETOsxIZ637OX2+NDlw3SjzDU6DZ5GP0XY+7DUkeb3393HvXf3EJUUleXjty3XFhs+2QAHKwcTR05k+ZgYB3D9+nUAgDuTUlSICO7uSNy0CaovvoBi9GhIHz/WW0+kVCaPHj92LHn0eIUKZo40fxM/eADF7NmQHTO8cI3k8WPYtW+P+MOH2b5ERJQnNBoNxo0bB7FYjOPHj6NmzZoAgClTpqB169aYO3cufHx8MuwvSyQSzJgxA4MHD04zClytVqNfv344deoUfv75Z4wdOzbdvu7u7pg+fbrJr4uIiPQLeR6C4WeGZ1iHCXHLJhVLUb5IeZQvUh7tPP+Z/UAQBEQmRqZJmKdMzfIs5hkECKn7j6kzBjOazIBEzEFwVDgVuMR4ZGQkIiMj4ezsnDoSHABu3ryJ2rVrp6u/Zs0aXLp0CeXLl0edOnXMGCmRZdB6eSHS3x/4+muU2L0bIkHQW0969SrsmjZNnnt8xAiOHs+EKDISVosWQb5pE0QaTab1xRERsG3fHvGHDkH3wQdmiJCIiOgfgYGBePz4Mfr27ZuaFAcAR0dHTJgwASNHjsSuXbswdepUg8eQyWSYNGmS3u0TJkzAqVOnEBQUpDcxTmSp7ry5g1///hUVilRAszLNIBKJ8jokohx78P4B+vj1gUqr0lvOhHj+JhKJUMymGIrZFEOT0k3SlCVqEvHw/UNEKiNR3qk8ytiXyaMoiSxDvkiMb926FSEhySvnhoaGAgC2bduGy5cvAwAaN26M/v37AwDWr1+PRYsWYerUqWlGnfTr1w8ymQwffvghXF1dkZCQgGvXruHWrVtwdHTE+vXrIWGijworGxuET5gAWc+ecJwwAZJHj/RWEymVsJ4xI3n0+E8/cXSzPkol5OvWQfHDDxDFxBi1q/j1a9h26ICEAwegrVs3lwIkIiJKL6Vf3apVq3RlrVu3BgAEBQVl+/gymQwADPa3o6OjsXnzZkRGRqJIkSJo2LAhPuAHxZSHXsa/xNcXv8aBsAOp29qUbYN1n6yDs7VzBntSfhaTFIPbb26jnFM5uNq55nU4ueJtwlt0P9wd75Xv9ZYPrj4Y05pMY0K8gLKWWqN68ep5HQaRxcgXifGQkBDs2rUrzbYrV67gypUrqT+nJMYN+eKLL+Dv74/g4GC8e/cOYrEYbm5uGDFiBEaPHo3SpUvnSuxE+Ym6USPEXb4MxbffQr5uneHR41euJI8e/+YbqIYP5+hxANDpIDtwIHlhzYiITKsLEglE2vQL3IijomDr44P4PXug9fLKjUiJiIjSefjwIQCgfPny6cpcXFxgZ2eHRwY+OM+K7du3A9CfeAeAO3fuYPz48Wm2tWnTBmvWrEHx4sWzdA6lUpnt+IylUqnSfKfM5Zc20+q02Bq6FfN/nY9YVWyasnNPz6HZ9mZY/9F61CtZzyzx5Jd2syTZaTOVVoWfbv6Elb+vRKImEVKxFBPqTsCXdb4sUE8JJGoS0dOvJ55EP9Fb3qdcH8xsMBNysdys76n5FV+f2cN2Mx7bLOv0LfSeEVFUVJT+zBflOaVSifDwcLi5uRn9H1tYsc2yR1+7SYKCYD1qFCRPnmS4r6ZRo+S5x/X8IV2Q/bvNbK9fT15Y8/ffM91PcHCAcvJkqHv2hE2/fpBevaq/nrU1ErZtg6ZNG1OHnqf4GjUe2yx72G7GY5sVbl27dkVAQABu3LgBT0/PdOVVq1ZFfHw8nj17ZvSxz549i549e6JixYoIDAyElZVVmvIZM2agc+fOqFChAmQyGf766y8sXrwYZ8+eRZ06dXD27NksPdn56NEjaPV86EyUVfei72HBnQX4M+rPDOtJRBKMrTIWvcv1LlBJ08Lq9vvbmHdrHh7Fpf/wr0+5PhhfdXyB+H/WClpMvzEdAS8D9Ja3LNkSC+sshFgkNnNkRESmIZFI9PZjM5IvRowTkflpvbwQFxQExZw5sFq/3mC9dKPHxYWnI2X15AkcZ8yA4vTpTOsKUilUgwcjacoUCEWLAgDiDxyAbd++kF68mK6+KDERNr17I2HjRmg6dzZ57EREROZw48YNfP7553BwcMDmzZvTJcUBYN68eWl+btCgAfbs2YNOnTohKCgIx48fR+cs/C50dTXftAcqlQqvXr2Ci4sL5HK52c6bn1lym8Wp4rD4+mJsuL0BOkGXaX2toMWyv5bhnvIelrVYBkcrx1yLzZLbzVJltc3i1fFY+OtC/Hz759TFCP9r5+OdsLa1xjyvefk+OT4reJbBpHhdl7pY9/E6REdG814zAl+f2cN2Mx7bLPcwMU5EhtnaQvn991B37gzr0aMNjh4XJSbC+quvIPPzSx49buQndPmN6M0b2H/3HUps26Z3OpT/UnfuDOWsWelH1dvZIX7PHtgMHAjZqVPpz6NWw2bQICSuWgV1r16mCp+IiCgdBwcHAECMgfUxYmNj4eTkZNQxf//9d3Tt2hUikQgHDx5E1apVs7yvWCzGgAEDEBQUhKtXr2YpMZ4XTzrI5XI+YWEkS2ozQRBw7OExTLswDc/jnhu9/4nHJxD6LhRbOmxBrRK1ciHCf1hSu+UXGbXZuSfnMN5/PCJiM58CceOdjYAYWNxycb4dTb3+5nqsu7VOb5mHowd2++yGvdge0ZHRvNeygW2WPWw347HNTC9/vqsTkVlpmzZFXFAQkoYOzbCeNCQEdl5ekK9ZA+gyH22T7yQmwmrpUtjXqQObzZszTYpr6tVD3KlTSNi61fBUMwoFErZtg8rXV2+xSKuFzfDhkG/alNPoSRAK5n1JRGQCKXOLp8w1/m+vXr1CXFycUY+m/v777+jSpQsEQcDBgwdRp04do2Nydk5e4DAhIcHofYky8yzmGXod7YV+x/plmhQv51jOYNmT6Cf4eM/H2Hx7MwQD6/OQ5YhMjMTQU0PR/XD3LCXFU2y8tRET/Cdk6YkCS3Pi4QlMuzhNb1kRRRHs77IfxW2ytpYDEVFBw8Q4EWXN/0ePx/n5QVe2rMFqosREWE+fDtsOHSDOwSJdFkWng2z3btjXrw/Ft99CFBubcfWyZZHwyy+IP3sW2kaNMj++TIbEDRug6tfPYBXrCRMgX7nS2MgJANRqKL7+Gg4eHrCvWBGKqVMhevcur6MiIrIoXv9f8Pn8+fPpyvz9/dPUyUxKUlyn02H//v2oVy97ixRev34dAODu7p6t/Yn0UWvVWH59ORptbYTTjzOeDs/TyROHuh7C9QHXMa3RNIigfyqNJG0SxvuPx7DTwxCvjs+NsCmHBEHA3rt70WBrA+y9uzdbx9h8ZzPGnhubr5LjN17ewBcnv9Abs5XECjs77USFIhXyIDIiIsvAxDgRGUXbrBlig4KQNGRIhvVSR4+vXZuvR+lKAgNh17IlbIYPhzgi41ElgqMjEufNQ+yvv0LdtStgzDyEEgkSly9H0vDhBqtYf/MNrObPTx75TFkjCLAePx5Wq1ZBFB0NcWQkrNatg13dusmj8LlIGxERAKB58+bw8PDA/v37cevWrdTt0dHRWLp0KeRyOXr9a1qvly9fIiwsDNHR0WmOc/PmTXTp0gVarRb79u1DgwYNMjzvn3/+CbVanW771atXsXz5cshkMnTp0iVnF0f0f1dfXEXznc0x6/IsJGgMP4kgl8gxpeEUBH8WjJZlW0IilmBao2k42PUgnK2dDe639+5etN7VGncj7+ZG+JRNz2KeocfhHhh6aigiEyMN1rOSWGGW1yxs6bAFMrFMb53tf27HqDOjoNVZfh/ySfQT9DzaE4maRL3laz5eg8alG5s5KiIiy8I5xonIeHZ2UC5eDHWnTrAZPRriZ8/0VhMlJsJ62jTIjh5Nnnu8nOHHUC2N+N49KGbOhCwrC2vKZFANHYqkSZMgFCmSg5OKoVywAIKdHRRLluitovj+e4ji4qD87jvjEu+FlHzzZsh37Ei3Xfz+ffIo/M2bkfj991kb2U9EVIBJpVKsWLEC3bp1Q4cOHeDr6ws7OzscPXoU4eHhmDt3Lsr+64mxOXPmYNeuXVi1ahX69u0LAHj//j26dOmC6OhotGnTBgEBAQgISLvQm6OjI0aOHJn6808//YQzZ86gUaNGKF26NGQyGe7evYvz589DJBJhyZIlKJeP+g9kmd4r32P25dnYcmdLpnWblWmGH1r9gEpFK6Ura1m2JS71vYTPT3yOKy+u6N3/7ru7aLWrFX5s8yM+rfJpjmOn7NPqtFj7+1rMDZ6b6Uh+r9JeWN5meeroablEjgHHB0ClVaWru+uvXdAKWqz+eDWkYstMqUQpo/Dp4U/xJuGN3vI5TefAt7L+qRyJiAoTy3wXJ6J8QevtjdjgYChmz4bVzz8brCcNDoadlxeUs2ZBNWQIILbch1VEr1/DauFCyLdsydLCmspOnaD+9lvTJf1FIiTNmAHBzg7Ws2frrWK1ejVE8fFIXLoUkEhMc94CSHLtGhRTpmRc59Yt2H3yCVSffgrlnDkQSpUyU3RERJbH29sbp06dwoIFC3Do0CGo1WpUq1YNc+bMga+BtTD+LSYmBlFRUQCAc+fO4dy5c+nquLm5pUmMt2/fHtHR0bhz5w4uXLgAlUoFFxcXdOvWDSNGjEDdunVNdn1U+AiCgD1392BG4Ay8TXybYd1i1sUwz3seelbpCVEGgw9c7Vzh180P3wZ/i5W/6Z/mLkGTgKGnhiLkeQgWNF8AhZQLpZnbg9gHGHZ4GG68vpFhPQe5A+Y2m4t+1fulWViznWc7bO+4Hf2O9UOSNindfnvv7oVWp8W6T9ZZXHI8SZOEvn59EfY+TG/5oBqDMLbuWDNHRURkmURRUVF8Jt9CKZVKhIeHw83NjavOZhHbLHtM0W6SwMAMR4+n0Hh5IWHVKggeHtk6T65JSIDV6tWw+vFHiOLiMq2uql8fD4cPR9EOHXLtXpP//DOsJ00yHEOPHkhcvRqQ6X/U0xKZ6zUqev0adi1aQPziRZb3EezsoJw8GaoRIwC5PNdiMxbf17KH7WY8thlR1vH1Yjxzt9n9d/cx4fwEXIq4lGndAdUHYHbT2SiiMO7Jv+MPj2PE6RGIUcUYrFOrRC1s6bAFHo4eRh07Be814yRpkrAwZCFW3lgJjaDJsG7H8h2xuOVilLIzPDDC/4k/+vj10ZscB4AuFbtgwycbIJNYRn9cEAQMOz3M4DzqH3t8jJ2dd+pN5vNeMx7bLHvYbsZjm+Ueyx22SUT5Ssro8aQvvsiwnjQoCPZeXpBv2GAZc4/rdJDt3An7evWgmDcv06S4tlw5xG/ZgvdHjyK+Zs1cDU01eDASVq+GYGCEvXzfPtgMGAAk6e+oF1oaDWwGDTIqKQ4Aorg4WM+aBbsmTSDVM8qRiIiILJ9So8T8kPnw2uGVaVK8mnM1nPr0FJa3WW50UhwAOpTvgMC+gahdorbBOn+8/gPeO7xx7MExo49Pxrny4gqa7WiGZb8tyzAp7mLjgq0dt2J7p+0ZJsUBoLVHa+zx2QOFRH8i6vD9w/j85Od6p1zJC9+FfGcwKV6zeE1sar/J4ka4ExHlJSbGich07Oyg/OEHxB05Ap2bm8Fqovh4WE+eDNvOnSF68sR88QkCoFYDsbEQRUZCev487Jo3h83IkZkmUXVFiiBxwQLEXb0KjY+P2eb3Vvfpg4RffoFgYFS47MQJ2PTqBcRnPG9iYaKYNQvSoCC9ZaoePaCtUCHD/SUPHsC2e3fY9O5t3vuTiIiIciTgaQCabG+C769+n2Gi0lpqjTlN5+Bin4to5JqzdUY8HD1w6tNT+KKm4cEhMaoYfHbsM3wd+DXU2vSLzVLOxCTFYHLAZLTb287g9CEpBlQfgKv9r6Jzhc5ZPn4L9xbY02UPbKQ2esv9Hvhh4PGBeZ4c33pnK5b8qn+dojL2ZbDHZw/s5HZmjoqIyLLxo0IiMjlt8+bJc4/PmgWrTZsM1pNevgx7Ly8oZ8yA9oMPIEpKApKS0n1HUhJESiWgUqX9/t86GdVVqQClEiLBuNmjBLkcqmHDoJw4EXByymHLZI/GxwcJ1taw6d8/+Vr+QxYQANvu3RG/ezfg6JgHEVoO2cGDsFq1Sm+ZumVLJK5dC2i1kK9bB8WiRRk+ISA7eRLS8+eRNGYMkiZMAGz0/zFEREREeetV/Ct8Hfg19t/bn2ndtuXa4vsW36OsY9lM62aVQqrAD61+QCPXRhjvP97gQo+rbqzC9b+vY1P7TShtX9pk5y/MTj06hYnnJ+J53PMM63k6eeLH1j/C2807W+dp7tYce7vsRc8jPfX+/554dAL9jvXD1g5bYSW1ytY5cuL80/P40v9LvWUOcgfs9dmb6eh4IqLCiIlxIsod9vZQLl0KtY9P8tzj4eF6q4ni42E9fbqZg8saVbduUH7zjUXMh675+GPE79sH29699SZzpSEhsPXxQcKBAxCcnfMgwrwnDg2F9Zgxest0bm5I3LgxebFSiQSqMWOg7tEDilmzIN+zx+AxRUlJUCxZAvnu3UicN8+sTwsQERFRxnSCDr/c+gVzguZkOM83kLxo5qIWi9CxfMcMF9fMiR5VeqBWiVoYcHwA/or8S2+dq39fhfdOb2z4ZANalW2VK3EUBm8S3mDahWk4EHYgw3oSkQRj6o7B1EZTYS21ztE5m5Zpiv1d9qPH4R6IU6fvj59+fBqfHfsM2zpuM+uCq7ff3MaA4wOgFbTpyqRiKbZ13IZqxaqZLR4iovyEU6kQUa5KGT2eNGhQXoeSZZrGjRF37hwSN260iKR4Cm2zZog/fBiCgVHh0ps3YduxI0QvX5o5MgsQHQ2bfv0g0jOljGBlhfht2yAULZp2e8mSSFy3DnGnTkGbyXzx4ogI2A4cCNvOnSH+S/8fukRERGQ+t17fwsd7PsbEgIkZJsXFIjFGfjgSV/tfRacKnXItKZ6iUtFKONfrHHpW7WmwTmRiJLod6ob5IfOh1aVPZpJhgiBgZ+hONNjaINOkeM1iNXG+93nMbjo7x0nxFI1LN8bBrgdhL7fXW372yVn0OdoHiZpEk5wvM89jn6PnkZ6IVcXqLV/RZgWauzc3SyxERPkRE+NElPvs7aFctgxxhw9DV6ZMXkdjkNbTE/HbtiH+xAlo69XL63D00tarh7hjx6ArVkxvueSvv2Dbrh1Ez56ZObI8pNPBZvhwSB4+1Fuc+MMP0NWubXB3baNGiAsIQOKyZdAVyXjhLemlS7Br2hSKadOAqKgcBE1ERETZEaeKw9eBX6Plrpa4/vJ6hnXruNRBQO8AzG8+32AiMzfYymyx9uO1WNFmBawk+qfVECDg+6vfw/eQL94kvDFbbPnZk+gn8D3ki5FnRuK98r3BetZSa4ytMhYnfE+gVolaJo+jgWsDHOp6CA5yB73l55+dR68jvZCgTjD5uf8tJikGnx75FC/i9K+VNLXhVPSp1idXYyAiyu+YGCcis9G2aJE8enzgwLwOJQ1d0aJIXLQIcVeuQNOpk8VPlaGrUQPxJ09C5+qqt1zy+DHs2reH+MEDM0eWN6yWLoXs5Em9ZUmffw71Z59lfhCJBKpBgxB34waSBg+GIDb861Gk1cJq7VrY16sH2datgE6X3dCJiIjICMceHEPDrQ2x6sYqvdNGpHCQO2BJyyU42/NsriRGs0IkEqF/9f442/MsyjmWM1jvYvhFNNvRDMHPg80YXf6i0Wnw042f0GRbEwQ8C8iwrrebNwJ6BKBf+X6QinNv5th6perhSLcjcLTS/yTnxfCL+PTIpwbnm88ptVaNQScG4c+3f+ot7121N6Y1mpYr5yYiKkg4xzgRmZeDA5Q//giNjw+sx441OPd4RgSpFFAoIMjlab7DygqClVWG31P//f/9dJ6e0DRvDtjlrxXadRUrIu7ECdh26QLJkyfpysUREbBt3x7xhw5B98EH5g/QTKT+/rD67ju9ZZp69aBcsMCo4wlFikC5ZAlUAwbAesoUSENCDNYVv30Lm7Fjodm8GcrFi6GtW9eocxEREVHWPIt5hqkXpuLkI/0fhP9bt0rd8F3z71DStqQZIstczRI1caHPBYw+Oxp+D/z01nkZ/xKd9nfCTK+ZGFN3DMQijl9LcfvNbYw9Nxa/v/o9w3qOVo74zvs79K3WF0lJSQiPMf5vDGN96PIhjnQ7gq4Hu+odwX454jK6H+6OvT57TfrEgiAImBgwEf5P/fWWe7t5Y3mb5bk+bRARUUHAxDgR5QlNy5aIvX4d0qAgiKKj0ySr03xPSWYrFIBcnpzclkjyOnyLIHh4IP7kyeTk+L176crFr1/DtkMHJBw8CG2dOnkQYe4SPXkC6y++gEgQ0pXpihdHwpYtyfdLNuhq1ED8iROQ7d8PxcyZEP/9t8G60hs3YNe6NVR9+0I5axaEEiWydU6TiY2F5P59iO/dgzgsDOKnTwF7e2i8vKD+5BPAQf9jv0RERJZGEARsvLURMy/NRIIm42kpyjmWw5KWS9Dao7WZoss6RytHbO2wFWtvrsU3l76BRqdJV0craDHr8iyEvAjB2o/XwknhZP5ALYhSo8Tiq4ux/Lfletvr37pU7IJFLRbBxdbFTNH9o3aJ2jja7Sh8DvjgnfJduvKQ5yHocbgH9vrshYOVafpgS68txdY7W/WWVXWuiq0dtkIukZvkXEREBR0T40SUd6ysoGnVKq+jyNeEUqUQf/w4bH19Ibl1K125OCoKtj4+iN+9G1ovrzyIMJckJsK2Xz+I9czzLUgkSNi0CULp0jk7h0gEdY8eULdrB6slS2C1ahVEarXB6vIdOyDz84Ny2jSohgwBZLKcnT8jggDRmzcQ37sHSVhYahJccv8+xM+f649vyxYIcjk0rVpB3aVLcpLcySn3YiQiIsqB57HPMfrs6EynzpCJZRhXbxwmNphosgUWc4NIJMKID0egXsl6GHRiECJiI/TWO/XoFLx3emNLhy340OVDM0eZNwRBQIwqBm8S3uB1wmuEx4Rj8a+L8eB9xtMClrIthSWtlqBD+Q5milS/GsVrwK+7H3wO+OBt4tt05VdeXEG3Q92wv+t+g1OvZNW+u/swN3iu3jIXGxfs8dlT6D9UISIyBhPjRET5nFCsGOKOHoXtp59C+uuv6cpFsbGw7d4dCdu3Q9Pa8kZRGU0QYP3ll5Dcvq23WDlnDrTNmpnufHZ2SJo9G+p+/aCYPh2yM2cMVhXFxMD6q68g37oViYsWQdu8ec7OrdVC8vQpHIODYfP+PawePUoeBR4WpvdDgcyIVCrITp2C7NQpCDJZcpK8c2eoO3RgkpyIiCyCIAjYd28fJgdMRnRSdIZ1vUp7YVnrZahUtJKZosu5+qXq42Kfixh+ejjOPjmrt86zmGdou7ct5nvPR99Kfc0coWnoBB2ilFF4nfAarxNepya99X1/k/AGSdoko47/Rc0vMNNrZo4TzabyQbEP4NfdD50PdNa7mOq1l9fge9AXB7oeyHbi+nLEZYw6O0pvma3MFnu67IG7g3u2jk1EVFgxMU5EVBA4OSH+4EHY9ukDaWBgumJRYiJsevVCwsaN0HTunAcBmo5840bId+/WW6by9YVqlP4/GHJKV748EvbuhfTUKSimT4fk8WODdSV378LOxwdqHx8kzpsHwc0t44MrlRA/eJBmChTJvXsQP3wIkVKJYia+FgAQqdWQnT4N2enTEMaNg6ZFC6h9fKDp0AFC0aK5cEYiIqKMRSZGYsL5CThy/0iG9ZytnTGv2Tz0qtorX86j7GztjD0+e7D02lLMD5kPnZB+IW+VVoVJAZNwOfwyxpcfb/4g9dDqtIhMjEyT2NaX7H6b8BZvEt9kOgVKdlQsUhHL2yxHk9JNTH7snKrqXBXHuh9D5/2d8SrhVbry3179hi4Hu+CQ7yEUURQx6tj33t1DX7++UGlV6crEIjE2td+E2iVqZzd0IqJCi4lxIqKCws4O8Xv3wmbgQMhOnUpXLFKrYTNoEBJXr4a6Z888CDDnJL/+CsX06XrLtFWrInHFCiCX/0DWfPIJ4lq0gNWqVbD64QeIEgzPeSo7cgTSM2eQ9OWXSBo7FlAqk6c+CQtLMwWK+OlTiHTp/yg2F5FGA9m5c5CdOwfhyy+h8fZOTpJ37AjB2TnP4iIiosLj1KNTGHtuLF4nvM6wXr8P+mFO0zkoap2/P8QVi8SY1GASGpRqgMEnBxu87sMPDiPgaQCKWheFRCKBGGKIRclfIpEo9d9ikRgi/OdnkShN/f/W++/+YpE4TX2doEOk8p9EeGRipN4kvjlIxVKMrzcekxpMgkKqyJMYsqJy0crJyfEDnfF3fPo1am6+vgmfAz447Hs4y/fw6/jX6HG4h8EnKBa3WIy25drmKG4iosKKiXEiooJEoUDCtm2wHjYM8oMH0xWLtFpYDx8OUXw8VJ9/ngcBZp/o1SvYDBigd55vwcEBCdu3A3Z25glGoUDSxIlQ9ewJxcyZets6hSgxEYr582G1bBlEiYnmie9fBGtro84r0mggO38esvPnIUyYAE2zZtD4+EDdsSOE4sVzMVIiIiqMYpJi8HXg19j257YM67naueKnj35Cq7IFa30abzdvBPYNxBcnvkDQ8yC9daLV0YhWZzytTEFW16UuVny0Ah8U+yCvQ8mSikUr4niP4+i0vxOex6Vf++XWm1vodKATjvgeQTGbjJ8LjFfHo9fRXngW80xv+di6Y/FFrS9MEjcRUWEkzusAiIjIxGQyJG7YANVnn+ktFgkCrCdMgNWyZYAgmDm4bFKrYTNwIMR/px95AwAJa9dCV768mYMChDJlkLhpE+L8/KCtVi3DurmdFNeVLAmNtzeShgxB4uLFiDtyBDF//YWYFy8Qe+UKlNOnZxrjf4m0WsguXID1l1/CvnJl2HbqBPnGjRC9Sv94MBERmUe8Oh5Cfvn9nYnLEZfRdEfTTJPin1b5FMGfBRe4pHiKkrYlcaTbEUyoPyGvQ7EoJW1L4jvv73Cm55l8kxRP4enkieM9jqOMfRm95X++/dPgfOQptDotBp8cjBuvbugt71qxK2Y3nW2KcImICi2OGCciKogkEiSuWAHB1hZW69bpraKYMwfSgAAkrFgBwcPDvPEZSTFzJqQhIXrLlJMmQdO+vZkjSkvbrBniAgMh37gRivnzIYrOnVFdglgMnYcHdJUqQVe5MrQp3ytWBBwNLz6lq1IFSVWqIGnqVIjDwiA7cgSyw4ch+fPPLJ9bpNNBeukSpJcuQTFpErRNmkDt4wN1p04QSpUyxeUREVEG/nz7J0acHoFbb26hpG1JfPbBZxhaayhK2JbI69CMptQoMTd4LlbfWA0BhpP8RRVFsaz1MvhU9DFjdHlDKpZiptdMNHRtiGGnhiEqKSqvQ8o1VhIrFLcpjhI2JVK/p/w75cvF1gUVnCpAIpbkdbjZ5uHogePdj6PTgU56R3yHRoai0/5OONLtCFxsXdKUCYKA6YHTcfLRSb3HbuTaCGvaroFYxLGOREQ5IYqKiioYww0KIKVSifDwcLi5uUGhsNx51CwJ2yx72G7GyzdtJgiw+u47KJYsMVzFxgbKGTOgGjYMkOTuHx/ZaTfZ/v2wGTxYb5m6dWsk7N2b63EbQ/T2LRTffgvZtm0QZXNEn6BQQFexIrSVKkHl6YlXRYvCsVEjyKpVA6ysTBar+MGD5CT5kSOQ3LqVvVhFImgbNfonSV66tMniy4l88xq1IGwzoqwz9+sl9G0oOuzvgPfK92m2W0ms0Ltqb4yqMwoVi1bM9ThyIqXN3ineYVzAONx9dzfD+m3LtcWKNivSJQwLg2cxzzDo+CD89uq3vA4ly2xltumS3f/+/u9/O8gdcnXRVEv7ffYs5hk6H+iMJ9FP9JZXLFIRR7sdRSm7fwYarL6xGl8FfqW3fnmn8jjT8wycrU27DoyltVt+wDbLHrab8dhmuYeJcQvGG994bLPsYbsZL7+1mfzHH2E9e3aGdTT16yPxp5+gq1w51+Iwtt3Ef/4Ju48+0rvApc7dHXEXL0IoUiQ3Qs0xye+/QzF5MqTXrxusoytSBLrKlaGrVAnaihVTR4EL7u6AOHkEkLnuNfGjR5AePQrZ4cOQ3ryZ7eNoGjaEunNnqH18IJTR//iwOeS316glYJsRZZ05Xy+Poh6h3d52eJWQ8TRW7TzbYWzdsWjk2ihXk47ZFRsfi7kX52LTg03Q6DQG69nJ7LCgxQJ8Vu0zi7wOc1FpVdhyZwsuPbuEqLgoWCmsIBKJoBN0yV/Q/fNvQQcBAgRBSLMtzRd0EIR/1UH6Omn2/399J4VTulHdqclu6+IoYZv8b1uZbV43WSpL/H0WERuBzgc641HUI73l5Z3Kw6+7H1ztXHH0wVEMODZA79MUztbOONvzLDydPE0eoyW2m6Vjm2UP2814bLPcw6lUiIgKAdX48YCtLawnTzZYR3rtGuyaNUPSlClIGjcOkMnMF6A+UVGw+ewzvUlxQaFA/LZtFpsUBwDthx8i/swZyA4cgGz/fkCng658+eQk+P+nQBGKFQMs5I9+nacnVOPHQzV+PERPnkB29ChkR45A+ptxo9WkV69CevUqrL/+Gpp69aDu0gWqAQMAe/tcipyIqOB6HvscPgd8Mk2KA8DJRydx8tFJ1C9ZH6PrjkbH8h0tZhqKsHdhGHpyKG6+uZlhvSalm2D1x6vh4ehhlrgsmVwix5BaQ9Cvcj8mQwqAMvZlcKz7MXQ+0BkP3j9IV/4w6iE67OuA2U1nY9ipYXqT4gqJArs67cqVpDgRUWHFCamIiAoJ1ZAhiN+/H7oMproQqVRQzJsHu1atIP7jDzNG9x86HWyGDYPk8WO9xYlLl0JXq5aZg8oGsRjqHj2QsGcPEvbtg3LhQqg+/xzapk0hFC9uMUnx/xI8PKAaOxbx/v6IuXULifPmQVO/vtHHkV6/DusZM2DfsCHE2ZyqhYiosHqT8AZdDnZBeGy4Uftde3kNA44PQL0t9fDzHz8jQZ3+A2Zz0Qk6rP19Lbx3eGeYFLeSWGGe9zwc636MSXEqsFztXHGs+zFUKlJJb/nj6McYcHwAlFplujIRRFj3yTo0cG2Q22ESERUqTIwTERUimjZtEBsSgqTPP8+wnuT2bdi1agWruXMBZfrOeW6zWrIEstOn9ZYlDR4MdZ8+Zo6o8BLc3aEaPRrxZ88i5s4dJM6fD02jRkYdQ/ziBew6doTk119zKUoiooIlShkF30O+uP/+fraP8Tj6MSYFTEL1jdUxP2Q+3ia8NWGEmQuPCYfPAR9MuzhNb6IvRa0StXChzwWMrjOaCwlSgVfStiSOdT+GKkWrGLXfXO+5hWIRWiIic2PPg4iosHFwgHLpUsT5+UFbrpzBaiKtFooffoCdt7dZE5rSs2dhtWCB3jJNgwZQzp9vtlgoLaFMGahGjkT8qVOICQ1F4qJF0DRuDCELI99FMTGw7doVkgsXcj1OIqL8LE4Vh0+PfIrbb27rLa9fsj4u9LmAgdUHwkqS+YLM75Tv8P3V71F9Y3V86f+l3mkcTEkQBOwM3Qmv7V64FHHJYD2JSIIpDafgXM9zqOpcNVdjIrIkJWxLwK+7H6oVq5al+kNqDcGoD0flclRERIUTE+NERIWUtlkzxAUFIWn0aAhiw78OJGFhsG3bFopp04D4+FyNSfTkCWwGD4ZISD+voq54cSRs3gzI5bkaA2WN4OoK1bBhiD95ErF//YXExYuhado0w3tJFB8P208/hfT4cTNGSkSUfyg1Snx27DP8+rf+D6Q/KPYB9nXZh9olauPHNj/izhd3MLnhZBRRZL7mhlKrxC+3f0H9LfXR168vrr64aurw8SbhDfr69cXIMyMRo4oxWK9ikYo40/MMvmr8FWSSPF7ThCgPFLcpDr9ufqherHqG9T7x/AQLmy8s1AvREhHlJibGiYgKMxsbKOfNQ/yZM9BWMfxIp0gQYLV2LeybNIHk4sXciSUhAbaffQZRdHS6IkEiQcLmzRBcXXPn3JQjQsmSyXPYHzuG2Lt3kbh0KTQffqi3rkilgk3//pDt3WvmKImILJtaq8bnJz7HhWcX9JaXdyqPQ10PwUnhlLqtuE1xfN34a9z54g4Wt1ycpfm5BQg4/vA42u5ti4/3fAy/B37Q6rQ5jt/vgR8ab2uME49OZFhvSI0huNjnIuqWrJvjcxLlZ87WzvDr7odaJfSvm/Ohy4fY2G6jxSyiS0RUEDExTkRE0Narh7iLF6GcPBmCVGqwnvjpU9j5+MB67FhATwI72wQB1uPHQ3Lnjt5i5dy50Hp5me58lGuEEiWg+vxzxJ86BXWnTnrriLRaWA8bBvmmTWaOjojIMukEHUadHWUwqVzGvgwO+x5GCdsSesttZbYYUmsIfhvwG7Z02IK6LllLOv/696/od6wfGmxtgE23NiFRk2h07NFJ0Rh+ejj6HeuHt4mG5zEvbVcaqxuuxlyvubCR2Rh9HqKCqIiiCI74HsGHLmkHFHg4emB3592wldnmUWRERIUDE+NERJTMygpJX3+NuIAAaGrXzrCqfOtW2DdqBOnJkyY5tXzDBsgNjCBWdesG1YgRJjkPmZGVFRJ++QWqXr30FosEAdYTJkC+fLmZAyMisiyCIGDS+UnYe1f/78ESNiVwxPcI3BzcMj2WRCyBT0UfnOt1Did6nEA7z3ZZiuFh1ENMOD8B1TdWx8IrC7O8UOfFZxfhtd0Lu//anWG93lV7I6BHAOoXq5+l4xIVJk4KJ5zscRJzms5Bh/IdMLrOaFzscxEuti55HRoRUYHHxDgREaWhq1ED8efOIXH2bAhWhhf1Ev/9N2x794b1kCEQRUZm+3ySK1eg+OorvWXaatWQuGIFwHkV8yepFImrVyNpyBCDVaxnzYLV3LmAnnnliYgKOkEQMPvybGy6rf8JGicrJxzsehDli5Q36rgikQhNSjfBrs678Gv/XzGg+oAsLdQZmRiJhVcWovrG6ph4fiIeRT3SWy9BnYCpF6bC56APImIjDB6vmHUxbO+4HWvaroGDlYNR10BUmCikCoyrNw47Ou3APO95cLRyzOuQiIgKBSbGiYgoPakUqvHjEXf5MjSNGmVYVb5vH+waNIDswAGjk5uily9hM3AgRBpNujLBwQEJ27cDtnyENF8Ti6H8/nsoJ040WEXxww9QTJkC6HRmDCyXCALE9+9D/NdfQKLxUxIQUeGy9NpSLP9N/5MztjJb7O+yH9WLZ7w4X2YqFa2E5W2W4/bntzGpwSQ4WTlluo9Sq8TGWxtRd3Nd9DvWD7+++Gcx0N9e/gbvnd5Yd3NdhsfoUL4DQvqFoGOFjjmKn4iIiCi3GJ5IloiICj1dxYqIP3EC8p9/hmLOHIji4/XWE0dGwuaLL6Devx+JS5dCKFUq84Or1bAZNAjily/1FiesXw+dp2dOwidLIRIh6ZtvINjbw3r2bL1VrDZsgCguDokrVwIZzHNvySS//w7Fl19CevMmAEAQiSC4u0NbqRJ0FSpAV6kStBUrQlepEoTixfkkBFEht/7meswNnqu3zEpihV2dd6FeqXomO18J2xKY0WQGvqz/JXb8uQOrbqzC05inGe4jQIDfAz/4PfBDI9dGqFmiJjb+sRFawfBinQ5yByxqsQi9qvaCiO9zREREZMHy51+eRERkPmIxVEOHQt22LazHj4csIMBgVdnJk5AGBSHxu++g/uyzDBN/ihkzIA0J0VumnDIFmk8+yXHoZFlU48cD9vZQTJoEkZ6nC+S7dkEUF4eEn38GMpjGx+IkJEAxfz7kq1dD9K9R7yJBgOjpU4ifPgXOnk2zi+DomJwwr1gxTeJcV64cIJOZ+wqIyMx2hu7ElAtT9JZJxVJs6bAF3m7euXJuW5kthtYeis9rfo5jD45hxW8rcOPVjUz3u/LiCq68uJJhHW83b6z6aFWW5kMnIiIiymtMjBMRUZYIZcsi4eBByHbsgPXXX0MUHa23nigmBjZjxkB98CASf/wRQtmy6eooDhyA1Tr9j2CrP/oISdOmmTR2shyqL76AYGcH65EjIdKmH3Eo8/ODTZ8+SNi2DbCxyYMIjSO5eBHW48ZB8uSJUfuJoqMhvXYNuHYtzXZBKoWuXLl/Eub/H2GurVgRcHIyXeBElGeO3D+C0WdH6y0TQYR1bdfhE8/c/3BYKpaiS6Uu8Knog+DnwVjx2wqcfnw6W8dSSBSY02wOhtQaArGIs3USERFR/sDEOBERZZ1IBPVnn0HTujWsJ06E7MQJg1VlAQGQNmkC5cyZUA0ZAoiT/1C2DguDw6RJevfRlS2LxPXrU+tSwaTu2ROCrS1sPv8cIpUqXbnM3x+23bohfvduwNFCF5+KioL1jBmQb99u0sOKNBpI7t+H5P79dK8vXfHiehPmgpsbIJGYNI58RxA4NQ3lC/5P/DH45GDoBP1rKixvsxzdKncza0wikQheZbzgVcYL997dw0+//YQ9d/dApU3//qxPHZc6WNt2LSoVrZTLkRIRERGZFjMPRERkNKFUKSTs2IGETZugK1bMYD1RfDysp06FbYcOEN+/D1FUFMpPmQKRnkUJBYUC8du2QShSJDdDJwuh6dgR8Xv3QjAwKlwaEgK7zp0hevvWzJFlTnrkCOwbNjR5Ujwz4jdvIA0OhtXmzbD++mvY9ugBh9q14eDqCrsmTWA9cCCsvvsOsoMHIfr7b7PGZnbx8ZCeOQPFtGmwa9QIskOH8joiokwFPw/GZ8c+g1qn1ls+z3se+lfvb+ao0qpctDJWfrQStz6/hYn1J2a4UKdULMVXjb/CmZ5nmBQnIiKifIkjxomIKHtEIqh9faFp3hyKadMg37fPYFVpSAjsmjaFonx5yJ4/11sncfly6GrWzK1oyQJpW7RA/KFDsO3RA6KYmHTlkj/+gG2HDog/dAiCq2seRJiW6O+/YT15MmTHjmVYT5DLoRo4EFCpIAkLg/j+fYjfvMm9uJKSIAkNhSQ0NM12bfny0Hp5QdO0KTReXhBKl861GHKdVgvJH39AGhAA6fnzkPz6K0Tqf5KL0vPnofb1zcMAiTJ289VN9DrSC4ma9B8MA8DUhlMxuo7+6VXyQknbkvjG6xt8Wf9LbP9zO1b/vhrPYp6lllcpWgVrP1mL2iVq512QRERERDnExDgREeWI4OyMxA0boPb1hfXEiRC/eKG3nigpCbL/JO5SJA0ZAnXPnrkZJlkobcOGiDt2DLa+vhDrGR0uuXcPdu3aIe7IEQgeHuYPEAAEAbJt22A9Y4beBP6/aRo2ROKKFdBVrpxmu+j9++QEeVgYJP//Ln7wAOJHj/TOtW4KkocPIXn4EPKtWwEA2nLl0ibK3Sx7cTzRs2fJifCAAEgvXoT4/XuDdaUBAZxOhSzW3ci78D3kixiV/vePER+OwLRGlrm2hp3cDsM/HI7BtQbj3JNz+OP1H6jiXAWflPsEVtJ8tEgyERERkR5MjBMRkUlo2rVDbJMmsJ45E/ItW7K+X8OGUH73XS5GRpZOV7Mm4k+cgG3XrhDreaJA/PQp7D75BPGHDkFXtapZYxM/egTrceMgvXQpw3qCnV3yfPqDB+udI18oUgTaBg2gbdAAaSZRUKkgfvIkbcL8/n1IwsIyTcIbS/L4MSSPH6dOAaMrWxaafyXK4eJi0vMZLSYG0kuXIL1wIXlU+MOHWd5V/Pw5xPfvQ1eJ0zmQZXkS/QRdDnbBO+U7veX9PuiH+d7zIbLwD3WkYik+8fzELIuCEhEREZkLE+NERGQ6jo5IXL4cKl9f2IwdC/HTpxlW17m4IGHzZkAuN098ZLF0lSoh7uRJ2Pr4QPL4cbpy8cuXydOqHDwIXe3auR+QRgP56tVQzJ8PkVKZYVX1Rx8hcenS7I3Alsuhq1QJukqVoPn3dkGA6PXr5CT5fxPm4eEQCYLx5/oP8dOnkD99CvnOnQAA2zJlIKlVC7I2bSBq2RJC2bK5OwJbo4Hkxg1Iz5+H9MIFSK5dy9HoeWlAAFRMjJMFeRH3Aj4HfPAy/qXe8q4Vu+LH1j9afFKciIiIqKBiYpyIiExO27w5YoODoZg7F/J16/Qm8QSpFAmbN0MoVSoPIiRLJLi7I/7kSdj6+qabLxsAxO/ewa5TJ8Tv2QNtkya5Fof41i1Yjx0L6c2bGdbTFS0K5cKFUPfoYfoEskgEwcUFWhcXaJs2TVuWkADxw4dppmSR/P+7KCEh26eURESgWEQEcPw4AEBXpkzyiHIvL2ibNYPOwyNn1ykIED9+nDpPuPTSJZOMitcVLQpNixYcLU4W5W3CW3Q92BVPY/R/QPyxx8dY98k6SMQSM0dGRERERCmYGCciotxha5ucNOzaFdZjxkASFpamWPndd9A2bpxHwZGlEkqWRPyxY7Dp3h3SGzfSlYtiY2HbrRsStm2Dpk0b055cqYTV99/DavnyTEcuqz79FMr58yEUK2baGLLCxga6GjWgq1Ej7XadDuKnTyEJDob08mVIg4IgfvZM/zGyQBwRAfmePZDv2ZN8eFfX1GlXtE2bQufpmWmiXPT+PSSBgZAGBEAWEJDpUyRZIcjl0DZqBE3LllC3bJm8aK+e6WuI8kp0UjS6He6Ge+/u6S33Ku2FLR23QC7h01JEREREeYmJcSIiylXahg0RFxgIqzVrINmzByqxGJqRI4HPPsvr0MhCCUWLIv7IEdj26gVpUFC6clFiImx690bCzz9D4+NjknNKgoJgPW4cJA8eZFhPV6YMEpcuhebjj01yXpMSi6ErVw66cuWg7tsXACB6+hTSoKDkr8uXc5SYFr94AfnevZDv3QsA0JUsCU3TpqkLeuoqVADUakiuXUtdNFPy++8Q6XQ5vjRttWrQtGgBTatW0DRuDNja5viYRLkhXh2Pnkd64o/Xf+gtr+NSB7t9dsNaam3myIiIiIjov/JFYnzPnj0ICQnBzZs3ERoaCpVKhVWrVqHv///oyw6VSoVWrVrhzp07qFixIq5du2bCiImIKA2FAklffgnliBEIDw+Hm5sbFHkdE1k2e3vE798Pm4EDITt9Ol2xSK2GzaBBSFy5MjUJnC0xMVDMng2rTZsyrCaIRFANHgzlzJmAvX32z2dmQtmyUJctC3WfPgAAUUREapJcEhQEyaNH2T62+OVLyPfvB/bvBwDoSpSAKD4eovj4HMetK1EiORHesiU0LVpwyiXKF5I0Sejn1w9XXlzRW17NuRr2d9kPe3n+eQ8hIiIiKsjyRWJ83rx5CA8Ph7OzM1xcXBAeHp7jYy5atAiP9SzuRURERBbC2hoJ27bBetgwyA8dSlcs0ulgM2oUEmNjoRo+3OjDS0+ehPXEiRC/eJFhPW2lSkhcsQLaRo2MPoelEcqUgbpnT6h79gQAiF68+Gc0+eXLkD58mO1ji1+/zn5cCgU0TZokJ8JbtoTugw9yd+FPIhPT6DQYfGowzj87r7fc08kTB30Poqh1UTNHRkRERESG5IvE+MqVK+Hp6Ql3d3csW7YMc+bMydHxfvvtN/z4449YuHAhJk+ebKIoiYiIyOTkciT+/DNgZwf5tm16q1hPmwZRbCySJk3KUjJV9OYNFFOnQn7wYIb1BKkUSePHJx9XUTCfcRBcXaHu0QPqHj2gVCrx8sYNlH32DNZXr0IaFJRubQBT0tasmTpPuLZRowLbxlTw6QQdRp8dDb8HfnrLS9uVxmHfwyhpW9LMkRERERFRRvJFYrxFixYmO5ZSqcSIESPQqFEjDB48mIlxIiIiSyeRIHHFCgj29rBavVpvFcV330EUEwPlt98aTo4LAmS7d0Px1VcQv3+f4Sk1deogccUK6KpXz2n0+Yq6eHEk1akDUa9eAADRq1eQBgdD8v/FPCV372b72DpX19QR4ZrmzSEUL26qsInyjCAImHphKnb/tVtveTHrYjjsexjuDu5mjoyIiIiIMpMvEuOm9O233yIiIgJ79uyBKJuP6CqVShNHpZ9KpUrznTLHNssetpvx2GbZw3YzHtvsH8pvvoGtjQ3slizRW261ciW00dGIXbAAKq0WwD/tJn72DA5Tp8IqICDDcwjW1oibNg0JgwcDEglgxO/8N29EWL/eBufPW0GrTd69VaskDB2agOLFhSwfJ6/ovdccHYF27ZK/kDzaXn7lCuTBwZCFhECWQaJcZ2MDdZMmSGreHKrmzaGtWDHthxZm6k/lBgVHt9P/Lfx1ITb8sUFvmaOVIw75HkLFohXNHBURERERZUWhSowHBQVh7dq1+O6771CuXLlsH+fFixfQ/v8PbnN49eqV2c5VULDNsoftZjy2Wfaw3YzHNvu/nj3hotXCbdkyvcU2W7ci8dUrRMyeDUilePXiBUrs3YvSa9ZAkpiY4aFjGjTAk+nToSpTBshk3vF/UypFmDmzHO7csUNkpBQ6nTi17K+/JNi9W44aNeLw7bePYWVl+QnyTO+12rWTv0aOhDQqCnY3bsD+xg3Y3L8PQSJBXI0aiGnQAPE1a0KQyf7ZLyIiN8M2G4lEAk9Pz7wOgyzAlodb8NPdn/SW2cpssc9nH2oUr2HmqIiIiIgoqwpNYjw+Ph6jRo1CgwYNMGzYsBwdy9XV1URRZUylUuHVq1dwcXGBXC43yznzO7ZZ9rDdjMc2yx62m/HYZnpMnYqYMmVgP2kSREL6RLPz6dOw0WrxuH9/lF+2DFa//57h4XSOjoidPRvKXr3gYuTTZImJQJcuRREaKoVanX5fnU6MN2/kuHSpCEaNsseRI+8sdirtbN1rbm5AjRrAgAFI+P8mCYAi//8iKqh+ufOLwaS4XCLHzk470cC1gZmjIiIiIiJjFJrE+DfffIOXL19i//79EIvFme+QAXM/PiuXy/nIrpHYZtnDdjMe2yx72G7GY5ulJXzxBRKLFIH10KEQaTTpyq3PnUO1c+cyPY7axweJ338PuLggO607dKiNwaR4mvOoRQgNlWLs2KLYujUhw7p5jfcaUcb2/LUH0y9P11smEUnwS/tf0Ny9uZmjIiIiIiJj5SxDnE9cunQJmzZtwtdff40KFSrkdThERERkAmpfXyTs2AEhG0lcXcmSiN++HQlbtkBwccnW+d+8EeHaNUmmSfEUanVy/bdvs7fGCRHlvb8i/8LIMyP1lokgwpq2a9ChfAczR0VERERE2VEoEuO3b98GkDxq3MnJKc0XANy/fx9OTk5wd+dq8URERPmJpm1bxO/bB8HOLsv7JA0ciNgrV6Dp2DFH5169Wo5Xr4xLcr96JcKqVZwShyi/qlK0Cr5q/JXesqWtluLTKp+aOSIiIiIiyq5CMZVKtWrV0K9fP71l27Ztg4ODA3x8fGBtbW3myIiIiCintM2aIf7IEdh06wZxVJThep6eSFy+HNpmzUxy3nPnpNDpjEuM63QinDsnxaxZSSaJgYjMSyQSYWKDibASWWFG0IzU7d82/RaDag7Kw8iIiIiIyFgFLjEeGRmJyMhIODs7w9nZGQDQokULtGjRQm/9bdu2wcXFBStXrjRjlERERGRK2rp1EX/8OGy7doX49es0ZYJEAtXo0VBOmwaY8ENwjSZ7U6Jkdz8ishyDawyGOk6NubfmYkL9CRhbb2xeh0RERERERsoXifGtW7ciJCQEABAaGgogOaF9+fJlAEDjxo3Rv39/AMD69euxaNEiTJ06FdOn618Uh4iIiAoe3QcfIP7UKdj07w/JnTsAAHXNmlCuWAFd7domP59UKph1PyKyLB3LdESzis1Qt3TdvA6FiIiIiLIhXyTGQ0JCsGvXrjTbrly5gitXrqT+nJIYJyIiosJL5+mJuIsXofn1V7yMjESx1q2hyKWp0tq00eDPPyVGTaciFgto00aTK/EQkflVL1YdIhGfAiEiIiLKj/JFYnzNmjVYs2ZNlupOnz7dqJHiURnMRUpERET5kEQCzYcfIjE8HMjFhNXIkSrs3i3H339n/RwuLgJGjVLlWkxERERERESUNeK8DoCIiIgoPypeXED9+lrIZFmbGkUmS65frBinUiEiIiIiIsprTIwTERERZdP69QmoXj3z5LhMJqB6dS3Wr08wU2RERERERESUESbGiYiIiLJJoQBOnIhHu3YalCqlg1icNkEuFgsoVUqHdu00OHkyHgpFHgVKREREREREaeSLOcaJiIiILJW1NbB1awLevhVh1So5zp2TQqMRQSpNXmhz1CgVp08hIiIiIiKyMBwxTkRERGQCxYoJmDUrCZcuxSMkJA6XLsVj1qwkJsUpX7lx4wZ69OgBd3d3uLq6ok2bNjh06FCW9hUEAWfPnsWECRPQpEkTuLu7o1SpUvDy8sIPP/wApVJpcF9/f3+0b98eZcqUgZubGzp27IiLFy+a6rKIiIiIiNLhiHEiIiIiIkJgYCC6desGhUIBX19f2NnZ4ejRoxg0aBAiIiIwZsyYDPdPSkpCjx49YGVlhaZNm6J169ZQKpU4f/485s6di+PHj+PYsWOwsbFJs9+ePXswbNgwFCtWDL179wYAHDp0CF26dMHmzZvh4+OTa9dMRERERIUXE+NERERERIWcRqPBuHHjIBaLcfz4cdSsWRMAMGXKFLRu3Rpz586Fj48P3N3dDR5DIpFgxowZGDx4MJycnFK3q9Vq9OvXD6dOncLPP/+MsWPHppZFRUVhypQpcHZ2xsWLF1G6dGkAwPjx4+Ht7Y0JEyagVatWsLe3z50LJyIiIqJCi1OpEBEREREVcoGBgXj8+DG6d++emhQHAEdHR0yYMAEqlQq7du3K8BgymQyTJk1KkxRP2T5hwgQAQFBQUJqyw4cPIzo6GkOHDk1NigNA6dKlMWTIEERGRuLYsWM5vDoiIiIiovSYGCciIiIiKuQuX74MAGjVqlW6statWwNIn9Q2hkwmA5A8qtyc5yUiIiIiMoRTqRARERERFXIPHz4EAJQvXz5dmYuLC+zs7PDo0aNsH3/79u0A0ifAMzpvyraUOpnJaHFPU1OpVGm+U+bYZtnDdjMe2yx72G7GY5tlD9vNeGyzrFMoFEbVZ2Lcwv13VA1ljm2WPWw347HNsoftZjy2Wfaw3YzHNiu8YmJiAAAODg56y+3t7VPrGOvs2bP45ZdfULlyZfTr1y/L502ZVzyr53358iV0Ol22YswOiUSCt2/fmu18BQHbLHvYbsZjm2UP2814bLPsYbsZj22WObFYDA8PD6P2YWLcgikUCnh6euZ1GPkK2yx72G7GY5tlD9vNeGyz7GG7GY9tRrnhxo0b+Pzzz+Hg4IDNmzfDysoq185l7B9CRERERFS4cY5xIiIiIqJCLmXEtqHR2bGxsQZHkxvy+++/o2vXrhCJRDh48CCqVq1q1HljY2PT1CEiIiIiMiUmxomIiIiICrmM5vN+9eoV4uLijHqi4Pfff0eXLl0gCAIOHjyIOnXqGH3ejOYfJyIiIiLKKSbGiYiIiIgKOS8vLwDA+fPn05X5+/unqZOZlKS4TqfD/v37Ua9ePbOcl4iIiIjIGKKoqCghr4MgIiIiIqK8o9FoUK9ePfz99984e/YsatasCQCIjo5G69at8ezZM1y7dg1ly5YFkLzQZUxMDFxcXODo6Jh6nJs3b8LHxwdarRb79+9Ho0aNMjxvVFQUatasCZlMhsDAQJQuXRoA8Pz5c3h7e6ceM2UhTiIiIiIiU2FinIiIiIiIEBgYiG7dukGhUMDX1xd2dnY4evQowsPDMXfuXIwZMya17ogRI7Br1y6sWrUKffv2BQC8f/8eH374IaKiotCmTRvUrVs33TkcHR0xcuTINNv27NmDYcOGoVixYujatSsA4NChQ4iMjMQvv/yCLl265N5FExEREVGhJc3rAIiIiIiIKO95e3vj1KlTWLBgAQ4dOgS1Wo1q1aphzpw58PX1zXT/mJgYREVFAQDOnTuHc+fOpavj5uaWLjHes2dPODs744cffsDOnTshEolQq1YtTJ48GS1atDDFpRERERERpcMR40RERERERERERERUqHDxTTO7ceMGevToAXd3d7i6uqJNmzY4dOiQUcdISkrCokWLUKdOHbi4uKBKlSoYN24c3rx5k0tR540XL15g9erV6Nq1K6pXr47ixYujUqVK6NevH65fv57l41y6dAlOTk4Gv3bs2JGLV5E3atSoYfB6O3ToYNSx9u7di1atWsHV1RVly5ZFz549cfPmzdwJPA/t2LEjw/vEyckJnTt3zvQ4BfV+27NnD8aPH48WLVqgRIkSmV5LTEwMvvrqK1SvXh0lSpRAjRo18M033yAuLs7oc/v7+6N9+/YoU6YM3Nzc0LFjR1y8eDEnl2MWWW0ztVqNI0eOYPjw4WjQoAFKly6NMmXKoHXr1ti4cSO0Wq1R5zXl69/cjLnPFixYkOFr7enTp0ad+8GDBxg4cCA8PT1RsmRJeHl5YePGjRAEyx8/YEy7ZfY+5+TkhIiIiCydNz/fa0T/xT66cdhPzz72043DPnrG2Ec3Hvvo2cN+uvHYR88fOJWKGRmat3HQoEGIiIhIM2+jITqdDn369IG/vz/q16+Pzp074+HDh9i6dSsuXryIc+fOoVixYma4mty3fv16/PjjjyhXrhxatmyJYsWK4eHDhzh+/DiOHz+On3/+OUuP9abw8vJC06ZN022vUaOGKcO2GA4ODhgxYkS67e7u7lk+xpIlSzBv3jy4ublh0KBBiIuLw8GDB9G2bVscOXIk0wW18pMaNWpg6tSpesuOHj2Kv/76C61bt87y8Qra/TZv3jyEh4fD2dkZLi4uCA8PN1g3Pj4eHTp0wO3bt9GqVSt0794dt27dwsqVKxEUFIQTJ05AoVBk6bz/nne2d+/eAJLnne3SpQs2b94MHx8fk1xfbshqmz1+/BgDBgyAnZ0dvL290a5dO8TExODUqVOYOHEizpw5g927d0MkEmX53KZ4/ecFY+6zFL1799Z7Xf9eDDAzd+/exccffwylUokuXbqgVKlSOHPmDCZOnIi7d+9i8eLFRl2HuRnTbobe5x4/foy9e/eiSpUqKFOmTJbPnV/vNaJ/Yx/deOyn5wz76VnHPnrG2Ec3Hvvo2cN+uvHYR88fOJWKmWg0GtSvXx8vXrzA2bNnUbNmTQBAdHQ0WrdujWfPnuH69euZ3qTbt2/H6NGj0b17d2zYsCH1TXjTpk2YMGECBg4ciB9//DG3L8csjh49iqJFi6bruAQHB8PHxwe2tra4d+8erKysMjzOpUuX0KlTJ0ydOhXTp0/PzZAtRkrH7vbt29k+xsOHD9GwYUN4eHjA398/9ZfXrVu38NFHH8HDwwMhISEQiwv2gycqlQpVqlRBTEwMQkNDUaJEiQzrF9T77cKFC/D09IS7uzuWLVuGOXPmpFlw7d/mz5+P77//HuPHj8fs2bNTt8+ePRs//vgjZs6ciQkTJmR6zqioKNSqVQtSqRSBgYEoXbo0AOD58+fw9vYGANy8eRP29vamuUgTy2qbvXjxAidOnEDv3r1ha2ubuj0+Ph4dO3bE77//js2bN2d58TlTvP7zijH32YIFC7Bo0SL4+fmhWbNmOTpv+/btERwcjH379uGjjz4CkPza9/HxQUhICM6cOYMGDRrk6By5yZh2M2Ty5MnYsGED5s2bh9GjR2dpn/x8rxGlYB89e9hPzz72002DffRk7KMbj3307GE/3Xjso+cPBfc3pYUJDAzE48eP0b1799QON5D8SdmECROgUqmwa9euTI+zdetWAMDMmTPTfDI5aNAgeHh4YN++fUhMTDT9BeSBzp076/00v0mTJmjWrBmioqIQGhqaB5EVDjt27IBGo8HEiRPTfKJbs2ZNdOvWDffu3UNISEgeRmgex48fx7t379C2bdtMO9wFWYsWLbL06bIgCNi2bRvs7OwwefLkNGWTJ0+GnZ1d6vtYZg4fPozo6GgMHTo0tcMNAKVLl8aQIUMQGRmJY8eOGXchZpTVNnN1dcXgwYPTdLgBwNbWFqNGjQIABAUF5UqMliarbWZKDx48QHBwMJo1a5ba2QYAuVyOr7/+GgCwZcsWs8ZkrJy2m1KpxL59+yCXy9GrVy8TRkZk+dhHzx720/MW++nso6dgH9147KNnD/vpxmMfPX/gVCpmcvnyZQBAq1at0pWlPPqV2ZuqUqnE9evXUbFixXQvLpFIhJYtW+KXX37B77//jiZNmpgocsskk8kAABKJJMv7PHr0CKtXr4ZSqYSrqyu8vb3h6uqaWyHmOZVKhR07duDly5ewt7dHnTp1UK9evSzvn9k9u3PnTgQFBcHLy8tkMVuilA5i//79jdqvsN1vKR4+fIi///4brVu31tuJbNiwIfz9/REREZHpo2CZ3YMLFy5EUFBQ6uObBVF23uuAnL/+85Pg4GD89ttvEIvF8PT0RIsWLWBnZ5fl/TO6zxo3bgxbW9sC/0ePn58foqKi4OPjY/RUD4XpXqOCiX1002M/PXPsp+cc++jGYR/dtNhHzxr203OGfXTzYGLcTB4+fAgAKF++fLoyFxcX2NnZ4dGjRxke4/Hjx9DpdPD09NRbnrL94cOHBbrTHR4ejgsXLqBkyZL44IMPsrzfvn37sG/fvtSfpVIphg4dirlz5xr9Cy0/ePXqVeon2Snq1KmDjRs3oly5cpnu//DhQ9jZ2cHFxSVdWcp9nHJfF1TPnj3DxYsXUbp0abRp08aofQvb/ZYi5Z7I6H3K398fDx8+zLTTndH7ZmG5B7dv3w5Af2cwIzl9/ecnCxYsSPOzo6MjFi5cmOU/xjK6ZyUSCcqWLYu7d+9Co9FAKi2Y3aZt27YBMD65ABSue40KJvbRTYv99KxhPz1n2Ec3HvvopsU+etawn54z7KObB6dSMZOYmBgAyRPg62Nvb59aJ7NjGFqoIOXYmR0nP1Or1Rg2bBiSkpIwe/bsLHVeihUrhtmzZyMkJATPnz/H/fv3sWPHDnh6emL16tWYOXOmGSI3r759++LIkSO4f/8+Xrx4gcDAQPTs2RM3btxA586dERsbm+kxYmJiMrxfU+oUZDt27IBOp0Pv3r2z3FEujPfbv5nyfSqj983CcA9u3rwZZ8+ehbe3Nz7++OMs72eK139+UL16dfz000+4efMmXr58iT/++APff/89RCIRRo4ciRMnTmTpOJnds/b29tDpdIiLizNZ7JbkyZMnuHTpEsqUKYOWLVsatW9hudeoYGMf3XTYT88a9tNzjn1047GPbjrso2eO/fScYx/dfAreRypUYOl0OowcORLBwcEYMGBAludYqlq1KqpWrZr6s62tLTp06IB69erBy8sL69atw/jx41G8ePHcCt3spk2blubnmjVrYt26dQCSVxDfsmVLlhduKKx0Oh127NgBkUiEzz77LMv7Fcb7jUzv1KlTmDx5Mtzc3LB+/Xqj9i0sr/9OnTql+bls2bIYOnQoKleujC5dumDevHlo3759HkWXf2zfvh2CIKBv375GL9JWWO41Isoc++lZx/fOnGEfnfIS++hZw356zrGPbj4cMW4mmX0CGxsba/BT//8eIzo6Wm95ZiNe8jOdTodRo0Zh3759+PTTT7Fs2bIcH9PFxQXt27eHRqPB9evXTRCl5Rs0aBAA4OrVq5nWdXBwyPB+TalTUF24cAERERHw9vaGh4dHjo9XWO43U75PZfS+WZDvwTNnzmDAgAEoUaIE/Pz8ULJkSZMc15jXf37WvHlzlCtXDqGhoVkarZTZPRsbGwuRSGTUfIj5hU6nw65duyAWi41KLmSmsNxrVDCwj55z7KebBvvpWcM+evawj55z7KPnHPvpWcM+unkxMW4mGc219erVK8TFxRmc7yuFh4cHxGKxwXkOU7brm+srP0sZgbJr1y50794da9asMfoTM0OcnZ0BAAkJCSY5nqUz5nrLly+PuLg4vHr1Kl1ZRvPKFRTZXdAnI4Xhfku5J0zxPpXR+2ZBvQdPnz6Nfv36wdnZGX5+fib5gy9FYbj/UqRca2JiYqZ1M7pntVotnj59irJlyxbIeQvPnTuH58+fo2XLlnBzczPZcQvTvUb5H/voOcN+uumwn5417KNnD/voOcM+uumwn5459tHNi4lxM0lZEfz8+fPpyvz9/dPUMcTa2hp169bF/fv38ezZszRlgiAgICAAtra2+PDDD00Udd5L6Wzv3r0bvr6+WLdunUkXRUkZFeDu7m6yY1oyY67XFPdsfvXu3TucOHECRYoUQceOHU123MJwv5UvXx6lSpXC1atXER8fn6YsPj4eV69eRdmyZTNd1AcofPfg6dOn0b9/fxQpUgR+fn6ZJmKMVRjuPyD5Prt79y5sbW1TO38Zyeg+CwkJQXx8fIG6z/4tJwv6ZKSw3GtUMLCPnn3sp5sW++mZYx89+9hHzz720U2H/fSsYR/dvJgYN5PmzZvDw8MD+/fvx61bt1K3R0dHY+nSpZDL5Wnm4nv58iXCwsLSPTIyYMAAAMC3334LQRBSt//yyy948uQJevToAWtr61y+GvNIeSxz9+7d6NKlC9avX59hZzsyMhJhYWGIjIxMs/3mzZt6669ZswaXLl1C+fLlUadOHVOGnqfCwsL0fgIYFhaG2bNnAwC6d++euj06OhphYWF4+fJlmvp9+/aFVCrFDz/8kOY+vHXrFg4cOIDKlSujcePGuXMReWz37t1QqVT49NNPYWVlpbcO7zf9RCIR+vXrh7i4OCxevDhN2eLFixEXF5f6PpYiISEBYWFhCA8PT7O9a9eucHBwwPr16/H8+fPU7c+fP8eGDRvg7Oxs0j+K8tLZs2fRv39/ODk5wc/PL9NRNmq1GmFhYXj8+HGa7ca+/vOr2NhYPHjwIN32xMREjBs3DrGxsejSpUu60SNhYWEICwtLs61ixYpo0qQJLl26hLNnz6ZuV6lU+O677wCYvlNqCd6+fYtTp06hWLFiaNeuncF6hf1eo4KPffTsYT89e9hPzxn20bOPffTsYR/deOyn5wz76OYnioqKEjKvRqYQGBiIbt26QaFQwNfXF3Z2djh69CjCw8Mxd+5cjBkzJrXuiBEjsGvXLqxatQp9+/ZN3a7T6dCjRw/4+/ujfv368PLywqNHj+Dn5wd3d3f4+/ujWLFieXF5JrdgwQIsWrQIdnZ2GD58uN7OdocOHVCzZs009adOnYrp06en1qlRowZkMhk+/PBDuLq6IiEhAdeuXcOtW7fg6OiIgwcPom7duma7rty2YMECrF69Gk2aNIGbmxtsbGzw4MEDnD17Fmq1GhMmTEiz6vqOHTswatQo9O7dG2vWrElzrCVLlmDevHlwc3ND586dERcXh4MHD0KlUuHIkSNo1KiRuS/PLJo0aYLQ0FAEBQXhgw8+0FunsN1vW7duRUhICAAgNDQUf/zxBxo1aoRy5coBABo3bpzaKYmPj0fbtm1x584dtGrVCrVq1cIff/yB8+fPo06dOjh+/Hia5MClS5fQqVMneHl54fjx42nOu2fPHgwbNgzFihVD165dAQCHDh1CZGQkfvnlF3Tp0sUMV589WW2zsLAwNGvWDElJSejWrRsqVKiQ7lju7u5pfhc8ffoUtWrVgpubG27fvp263djXv6XJaps9ffoUtWvXRp06dVCpUiW4uLjg9evXuHjxIp4/f45q1arh2LFjKFq0aJrjOzk5AQCioqLSbP/rr7/Qtm1bKJVKdO3aFSVLlsSZM2fw119/YciQIen+gLQ0xrw+U6xcuRLffPMNRo0alfqHhT4F9V4j+jf20Y3Hfnr2sJ+eM+yjp8c+uvHYR88e9tONxz56/lCwJuKxcN7e3jh16hQWLFiAQ4cOQa1Wo1q1apgzZw58fX2zdAyxWIydO3di2bJl2LNnD1avXo0iRYqgX79+mDFjRoHqcKc8ihoXF4clS5borePu7p7a4Tbkiy++gL+/P4KDg/Hu3TuIxWK4ublhxIgRGD16NEqXLm3y2PNSs2bNEBYWhlu3biEkJAQJCQlwdnbGRx99hMGDB6NVq1ZZPtakSZPg7u6ONWvWYNOmTZDJZGjcuDG++uor1K5dO/cuIg/99ttvCA0NRd26dQ12uDNSUO+3kJAQ7Nq1K822K1eu4MqVK6k/p/xSt7W1xfHjx7Fw4UL4+fnh0qVLcHFxwejRozF16lSjRsz17NkTzs7O+OGHH7Bz506IRCLUqlULkydPRosWLUxybbklq2326tUrJCUlAQAOHDig91heXl5pOt2GmPL1nxey2mZFihTB4MGD8dtvv+Hs2bOIioqCtbU1KlWqhGHDhmHIkCFG3WdVq1aFv78/5s2bhzNnziAhIQHly5fHkiVL8MUXX5js+nKLMa/PFNu3b9e7Pavy+71G9G/soxuP/fTsYT89+9hH1499dOOxj5497Kcbj330/IEjxomIiIiIiIiIiIioUOEc40RERERERERERERUqDAxTkRERERERERERESFChPjRERERERERERERFSoMDFORERERERERERERIUKE+NEREREREREREREVKgwMU5EREREREREREREhQoT40RERERERERERERUqDAxTkRERERERERERESFChPjRERERERERERERFSoMDFORERm9fTpUzg5OcHJySmvQyEiIiIiIrCPTkSFkzSvAyAiovQ6dOiAoKCgLNWNiorK3WCIiIiIiIh9dCKiAoaJcSIiC1amTBmUKVMmr8MgIiIiIqL/Yx+diKhgYGKciMiC9e3bF9OnT8/rMIiIiIiI6P/YRyciKhg4xzgRERERERERERERFSpMjBMRFRD/XTDn5MmT6NChA8qWLYvSpUujTZs22Lt3b4bHuHfvHkaNGoUaNWqgRIkSKFu2LNq3b4+tW7dCq9Ua3E+j0WDnzp3w9fVFhQoVUKJECVStWhXt27fHypUrER0dbXDfkJAQfPrppyhXrhxKliyJJk2aYP369RAEQW/9mzdvYsiQIahevTpKlCiB0qVLo0aNGujWrRtWrlxpcD8iIiIiInNjH519dCKyXJxKhYioAFq3bh2mTp2KIkWKwNPTE8+fP8f169dTv77//vt0+xw6dAjDhg2DSqWCra0tqlWrhvfv3yM4OBjBwcE4dOgQdu7cCWtr6zT7RUZGok+fPrh69SoAwMXFBdWrV8ebN29w9epVBAcHo3bt2mjWrFm6c+7YsQNjxoyBo6MjPDw8EB4ejtDQUEyZMgXPnj3DvHnz0tQ/d+4cevfuDbVaDTs7O1SoUAFSqRQvXryAv78//P39MWLECEil/PVGRERERJaFfXT20YnIsnDEOBFRATRjxgxMnjwZ9+/fR0BAAO7du4elS5dCLBZj/fr1OHz4cJr6YWFhGDFiBFQqFfr374+wsDBcuHABf/zxBw4fPgwHBwcEBARg5syZafYTBAEDBgzA1atXUaZMGRw5cgT37t3D+fPncfv2bTx69Ag//PADSpQooTfOCRMmYN68eXjw4AECAgLw4MGD1HOsWrUKjx8/TlN/9uzZUKvVGDduHO7fv4/g4GAEBgbiwYMHuH37NubMmQOxmL/aiIiIiMjysI9ORGRZRFFRUXyehYjIwnTo0AFBQUGZ1mvfvj127twJIPkxzVq1agEAWrZsiUOHDqWrP27cOGzZsgXVqlVDcHBw6vbRo0dj+/btqFatGoKCgiASidLst3XrVowdOxYymQy3b99GyZIlASQ/Ctq7d29YWVkhMDAQlStXzjTmf8fZr18/rFy5Ml2dJk2aIDQ0FAsXLsTw4cNTt7u4uCApKQlPnz6Fo6NjpuciIiIiIjIV9tHZRyeigoUf2RERWbAyZcqgUaNGBr+qVKmid78RI0ZkuD00NBQRERGp28+ePQsAGD58eLoONwD07t0bxYsXh1qtRkBAQOr2o0ePAgA6deqUpQ73fw0ePFjv9gYNGgAAHj16lGa7m5sbAGD//v1Gn4uIiIiIyBTYR2cfnYgKBk7wRERkwfr27Yvp06cbvV/VqlX1bq9YsSKkUik0Gg3CwsJQpkwZREdH49WrVwCAatWq6d1PJpOhYsWKePPmDe7fv5+6PTQ0FMA/nWRjVahQQe/24sWLAwDi4uLSbB83bhzGjBmDiRMn4qeffkLLli1Rv359eHl5wd3dPVsxEBEREREZg3109tGJqGDgiHEiogLI0HyBEokERYsWBQDExsYCSNuxNbQfgNRHM1P2+/e/s/vIpK2trd7tKXMQ/nf1+n79+mHbtm1o2LAhnj59ik2bNmHEiBGoWbMm2rRpg0uXLmUrDiIiIiKi3MY+OhGRZWFinIioAHr9+rXe7VqtFu/evQMA2NvbAwDs7Owy3Q8AXr58mWa/f/87Ojo6ZwEboVOnTjh9+jQeP36Mffv24csvv0S5cuVw/fp1dOvWDbdv3zZbLEREREREWcU+OhGRZWFinIioALp7967e7ffv34dGowEAVKpUCUDySBIXFxcA/zx2+V8ajSb18cyU/QDggw8+AAD8+uuvpgncCI6Ojvjoo48wa9YsXLt2DfXr14dKpcLWrVvNHgsRERERUWbYRycisixMjBMRFUBr167NcHu1atVQpkyZ1O0ff/xxavl/H40EgN27d+PNmzeQyWRo2bJl6nYfHx8AgJ+fX5p5Dc1NKpWibt26AIC///47z+IgIiIiIjKEfXQiIsvCxDgRUQEUGBiIRYsWpY48EQQBW7ZswbZt2wAAkyZNSlN/zJgxUCgUCA0Nxfjx4xEfH59advHiRcyYMQMAMHDgwNSRKwDQtm1beHt7IykpCb6+vunmD4yJicGmTZtw7969HF9TTEwMBgwYAH9/f6hUqjRlN2/exKFDhwAAderUyfG5iIiIiIhMjX10IiLLIs3rAIiIyLAdO3bg4sWLGdZZtGgRatWqlWbbvHnzMHXqVKxduxblypXD8+fPU1e1Hzx4MHx9fdPUr1SpEtasWYNhw4Zhy5YtOHDgACpWrIj379/jyZMnAICWLVvi22+/TXf+X375Bb169cK1a9fQqVMnlCxZEqVLl8abN2/w/PlzaLVa+Pn5oXLlyjloCUCn0+HIkSM4cuQI5HI5PD09YWtrizdv3uDZs2cAgHr16mH48OE5Og8RERERUUbYR/8H++hElJ8xMU5EZMEiIiIQERGRYZ2YmJh024YNGwY3NzesWrUKt2/fhkajQd26dTFkyBD06tVL73G6du2KqlWrYsWKFQgMDMSff/4JhUKBxo0bo3fv3ujbty8kEkm6/ZydnXHixAns2rUL+/btw507d3D79m0UK1YMjRo1QocOHdL9UZAd9vb22LBhAwIDA/Hbb7/h5cuXiI6Ohr29PRo3boyuXbti4MCBkMvlOT4XEREREZEh7KP/g310IsrPRFFRUeknqiIionzn6dOnqZ3bqKiovA2GiIiIiIjYRycismCcY5yIiIiIiIiIiIiIChUmxomIiIiIiIiIiIioUGFinIiIiIiIiIiIiIgKFSbGiYiIiIiIiIiIiKhQ4eKbRERERERERERERFSocMQ4ERERERERERERERUqTIwTERERERERERERUaHCxDgRERERERERERERFSpMjBMRERERERERERFRocLEOBEREREREREREREVKkyMExEREREREREREVGhwsQ4ERERERERERERERUqTIwTERERERERERERUaHyP2IJCezORYJRAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 1500x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "visualize_training_results(history)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iORWa9UWIsgc"
      },
      "source": [
        "# **Evaluation**\n",
        "Evaluation of the skin disease classifier model was based on its accuracy in predicting skin diseases from images. The model achieved an accuracy of 88% on the test set, indicating its potential for accurate diagnosis of skin diseases. However, the evaluation also revealed some limitations of the model, including its sensitivity to image quality and potential biases in the training dataset. These limitations suggest the need for further improvement and validation of the model before it can be applied in clinical settings. Overall, the evaluation highlights both the strengths and weaknesses of the model and provides insights for future development and refinement."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lFD0crKDIsT9"
      },
      "source": [
        "# **Conclusion**\n",
        "\n",
        "The skin disease classifier model has shown promising results in accurately identifying various skin diseases with an 88% accuracy rate on the test set. The integration of this model into a website will provides an accessible and efficient platform for users to upload photos of their skin diseases and receive accurate diagnoses. However, the model is not without its limitations, including the potential for bias in the training dataset and variability in image quality uploaded by users. Future work includes expanding the dataset and improving the model's ability to identify other skin conditions, as well as conducting real-world validation studies and integrating the model with telemedicine technologies. Despite these limitations, the model represents a significant step forward in the field of dermatology and has the potential to improve access to healthcare for individuals with skin conditions."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NLB-vYp-I8cW"
      },
      "source": [
        "> ## **Limitations**\n",
        "\n",
        "* **Limited dataset:** The model was trained on a limited dataset, which may not be representative of all skin disease and skin types.\n",
        "* **Lack of diversity:** Skin diseases can present differently in different people based on factors such as age, skin type, and ethnicity.Potential bias in the dataset used for training the model, which may lead to inaccurate predictions for certain demographics or skin types.\n",
        "* **Dependence on image quality:** The model's perfomance may vary depending on the quality of images uploaded by users. Images that are blurry, poorly lit, or not centered properly may not be accurately classified by the model.\n",
        "* **Human error:** The accuracy of the model may also be affected by human error, such as mislabeling or misdiagnosing the skin disease images in the dataset.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Pg1oqh9JAsD"
      },
      "source": [
        "> ## **Recommendations**\n",
        "\n",
        "* Collect more diverse and high-quality  skin disease images from different populations and sources to improve the model's ability to generalize to new data.\n",
        "* Incorporate expert knowledge  from dermatologists or other medical professionals to verify and help label and diagnose the skin disease images in the dataset.\n",
        "\n",
        "* Incorporate additional metrics such as precision, recall, and F1 score to evaluate the model's performance.\n",
        "\n",
        "* Investigate the potential of incorporating additional data sources such as patient history and clinical data to improve the accuracy of the diagnosis."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OZmMb4qhI8Eb"
      },
      "source": [
        "> ## **Future Work**\n",
        "\n",
        "* **Real-world validation:** Real-world validation studies can be conducted to evaluate the model's performance in clinical settings and to assess its impact on patient outcomes.\n",
        "* **Expand dataset:** Expanding the dataset with more diverse skin types, demographics, and skin conditions can improve the model's generalizability and reduce potential biases.\n",
        "* **Expand to other skin conditions:** The model can be further developed to identify a broader range of skin conditions beyond the current scope of skin diseases, such as wounds. This would increase the versatility and usefulness of the model for both patients and healthcare professionals.\n",
        "* **Explainable AI:** Implementing explainable AI techniques can help clinicians and patients understand the model's decision-making process, increasing trust and transparency in the model's predictions.\n",
        "* **Integrate model with telemedicine technologies:** The model can also be integrated with telemedicine technologies to enable remote diagnosis and treatment of skin diseases."
      ]
    }
  ],
  "metadata": {
    "accelerator": "TPU",
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.12"
    },
    "vscode": {
      "interpreter": {
        "hash": "2f155fbeb9494e5ce992090b8427abe3542dae7719d8ea0d05cb0b78608edd18"
      }
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  "nbformat": 4,
  "nbformat_minor": 0
}