{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nweeTgBaO6TC"
      },
      "source": [
        "### Data Inspection"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QTltTIC0G5yR"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "df=  pd.read_csv('/content/healthcare-dataset-stroke-data.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "id": "Hks8hmojJ5LJ",
        "outputId": "1b110403-b334-4572-9f06-546913b46fcb"
      },
      "outputs": [
        {
          "data": {
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              "      <td>0</td>\n",
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              "        document.querySelector('#df-1cd20282-434a-4f2b-afb4-50ecac593bed button.colab-df-convert');\n",
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              "\n",
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              "  }\n",
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              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "  }\n",
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              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        (() => {\n",
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              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "      id  gender   age  hypertension  heart_disease ever_married  \\\n",
              "0   9046    Male  67.0             0              1          Yes   \n",
              "1  51676  Female  61.0             0              0          Yes   \n",
              "2  31112    Male  80.0             0              1          Yes   \n",
              "3  60182  Female  49.0             0              0          Yes   \n",
              "4   1665  Female  79.0             1              0          Yes   \n",
              "\n",
              "       work_type Residence_type  avg_glucose_level   bmi   smoking_status  \\\n",
              "0        Private          Urban             228.69  36.6  formerly smoked   \n",
              "1  Self-employed          Rural             202.21   NaN     never smoked   \n",
              "2        Private          Rural             105.92  32.5     never smoked   \n",
              "3        Private          Urban             171.23  34.4           smokes   \n",
              "4  Self-employed          Rural             174.12  24.0     never smoked   \n",
              "\n",
              "   stroke  \n",
              "0       1  \n",
              "1       1  \n",
              "2       1  \n",
              "3       1  \n",
              "4       1  "
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y2qzkBOFJ6CA",
        "outputId": "6588de3c-725a-48cf-da36-0aa5cf10e78a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5110 entries, 0 to 5109\n",
            "Data columns (total 12 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   id                 5110 non-null   int64  \n",
            " 1   gender             5110 non-null   object \n",
            " 2   age                5110 non-null   float64\n",
            " 3   hypertension       5110 non-null   int64  \n",
            " 4   heart_disease      5110 non-null   int64  \n",
            " 5   ever_married       5110 non-null   object \n",
            " 6   work_type          5110 non-null   object \n",
            " 7   Residence_type     5110 non-null   object \n",
            " 8   avg_glucose_level  5110 non-null   float64\n",
            " 9   bmi                4909 non-null   float64\n",
            " 10  smoking_status     5110 non-null   object \n",
            " 11  stroke             5110 non-null   int64  \n",
            "dtypes: float64(3), int64(4), object(5)\n",
            "memory usage: 479.2+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info( )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G8S1PexVPSPr",
        "outputId": "2970603d-79eb-481c-d577-05ce0fb1818e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(5110, 12)"
            ]
          },
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        },
        "id": "H4aXvGlJJ9DC",
        "outputId": "5bdb8093-4332-464c-8609-e42e97a79f5e"
      },
      "outputs": [
        {
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            "text/plain": [
              "                  id  gender          age  hypertension  heart_disease  \\\n",
              "count    5110.000000    5110  5110.000000   5110.000000    5110.000000   \n",
              "unique           NaN       3          NaN           NaN            NaN   \n",
              "top              NaN  Female          NaN           NaN            NaN   \n",
              "freq             NaN    2994          NaN           NaN            NaN   \n",
              "mean    36517.829354     NaN    43.226614      0.097456       0.054012   \n",
              "std     21161.721625     NaN    22.612647      0.296607       0.226063   \n",
              "min        67.000000     NaN     0.080000      0.000000       0.000000   \n",
              "25%     17741.250000     NaN    25.000000      0.000000       0.000000   \n",
              "50%     36932.000000     NaN    45.000000      0.000000       0.000000   \n",
              "75%     54682.000000     NaN    61.000000      0.000000       0.000000   \n",
              "max     72940.000000     NaN    82.000000      1.000000       1.000000   \n",
              "\n",
              "       ever_married work_type Residence_type  avg_glucose_level          bmi  \\\n",
              "count          5110      5110           5110        5110.000000  4909.000000   \n",
              "unique            2         5              2                NaN          NaN   \n",
              "top             Yes   Private          Urban                NaN          NaN   \n",
              "freq           3353      2925           2596                NaN          NaN   \n",
              "mean            NaN       NaN            NaN         106.147677    28.893237   \n",
              "std             NaN       NaN            NaN          45.283560     7.854067   \n",
              "min             NaN       NaN            NaN          55.120000    10.300000   \n",
              "25%             NaN       NaN            NaN          77.245000    23.500000   \n",
              "50%             NaN       NaN            NaN          91.885000    28.100000   \n",
              "75%             NaN       NaN            NaN         114.090000    33.100000   \n",
              "max             NaN       NaN            NaN         271.740000    97.600000   \n",
              "\n",
              "       smoking_status       stroke  \n",
              "count            5110  5110.000000  \n",
              "unique              4          NaN  \n",
              "top      never smoked          NaN  \n",
              "freq             1892          NaN  \n",
              "mean              NaN     0.048728  \n",
              "std               NaN     0.215320  \n",
              "min               NaN     0.000000  \n",
              "25%               NaN     0.000000  \n",
              "50%               NaN     0.000000  \n",
              "75%               NaN     0.000000  \n",
              "max               NaN     1.000000  "
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.describe(include='all')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "id": "tRPEbFkOKAZT",
        "outputId": "2ec4b175-a63e-40a8-c3f7-23384b4112f6"
      },
      "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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gender</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hypertension</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>heart_disease</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ever_married</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>work_type</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Residence_type</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>avg_glucose_level</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bmi</th>\n",
              "      <td>201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoking_status</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>stroke</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ],
            "text/plain": [
              "id                     0\n",
              "gender                 0\n",
              "age                    0\n",
              "hypertension           0\n",
              "heart_disease          0\n",
              "ever_married           0\n",
              "work_type              0\n",
              "Residence_type         0\n",
              "avg_glucose_level      0\n",
              "bmi                  201\n",
              "smoking_status         0\n",
              "stroke                 0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UWwVE8X6KTnF"
      },
      "source": [
        "### Data Visualisations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OcVx0smyKTBj"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "Xlj4AaJiKEW_",
        "outputId": "47275bac-343e-4515-80f6-86b34d4711d2"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8,4))\n",
        "sns.histplot(data=df, x=\"age\",hue=\"stroke\", bins=30, kde=True, palette='Oranges',stat=\"density\" , common_norm=False)\n",
        "plt.title(\"Age distribution by Stroke Occurrence\")\n",
        "plt.xlabel(\"Age\")\n",
        "plt.ylabel(\"Density\" )\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BN8Zy8zBPnCm"
      },
      "source": [
        "Stroke occurrence is higher in older age groups, confirming age is directly related and could be considered as a primary risk factor."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "frRM54x2KQZ7",
        "outputId": "53e95574-4ac0-47d1-f79e-9489d4622fde"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8,4))\n",
        "sns.histplot(data=df, x=\"avg_glucose_level\",hue=\"stroke\", bins=30, kde=True, stat=\"density\", common_norm=False,palette='Oranges')\n",
        "plt.title(\"Average Glucose level by Stroke Occurrence\")\n",
        "plt.xlabel(\"Average Glucose Level\")\n",
        "plt.ylabel(\"Density\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bC3O6kYzPwSC"
      },
      "source": [
        "High glucose is more common among stroke patients, highlighting its importance as a feature, and the dangerousness of sugar in contributing towards a stroke.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "UJ0bFwKcKZ8K",
        "outputId": "e29d4e4e-4540-4e46-dae2-666871cdb8ba"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8,4))\n",
        "sns.histplot(df['bmi'].dropna(), bins=30,kde=True, color =\"orange\")\n",
        "plt.title(\"BMI Distribution\")\n",
        "plt.xlabel(\"BMI\")\n",
        "plt.ylabel(\"Count\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b1JTBpApR51t"
      },
      "source": [
        "Thisplot shows that most patients have a BMI between 20 and 40, with a right-skewed distribution and a few outliers at very high BMI, high weight in general and  BMI is a known risk factor for cardiovascular events, including stroke. Although high BMI doesnt necessarily mean an unhealthy body,yet with the given data features, its okay to include it and understnd its distribution to ensure we process and scale this feature appropriately for our model.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "XfyDMTRvKcD-",
        "outputId": "e667609f-d75f-4c6c-cf0f-e1465e854139"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(6,4))\n",
        "sns.countplot(x='gender', hue='stroke',palette='Oranges',data=df)\n",
        "plt.title(\"Stroke Occurrence by Gender\")\n",
        "plt.xlabel(\"Gender\")\n",
        "plt.ylabel(\"Count\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eXj-BIohRowc"
      },
      "source": [
        "This plot suggests that Females are lessprone to strokes vs men (at least with the data given)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "A29pKHxtKd2s",
        "outputId": "ef604532-ee4c-49f1-8b75-143f29a8a37b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8,4))\n",
        "sns.countplot(x='smoking_status' , hue='stroke', data=df,palette='Oranges')\n",
        "plt.title(\"Stroke Occurrence by Smoking Status\")\n",
        "plt.xlabel(\"Smoking Status\")\n",
        "plt.ylabel(\"Count\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2L3r22SYQYJy"
      },
      "source": [
        "Smokers have higher stroke rates, suggesting the need to keep this variable."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "PLyaTmM8Khxd",
        "outputId": "3c4d9d07-69be-4daa-e50f-b4cca63d3157"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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2ctT8l19+oVixYlr3SW9+eWxsLJUqVcLc3JwRI0bg5eWFiYkJJ06coF+/fm8dkc9M6Y14viuAfF9KpZKAgAAmTpyodfvLIPHq1atUq1YNX19fJk6ciKurK0ZGRmzZsoVff/31vfolvbZpS3+9vfv37+fzzz+nYsWKzJgxAycnJwwNDVmwYEGmPewIYGNjA5DmQkGbl3PXhw8frnXN/fTO7/RG9t+nb+BV/7zs/z59+qR7B+rNC+O3faY+hZd1XrJkidYLJwODV//cN2/enEOHDvHDDz9QrFgx8uXLh1KppHbt2hk699737wDav1cUCgVbt27V+vd4/TtlwoQJtGvXjj///JMdO3bQrVs3xowZw+HDh9UP88Krc+zl/Hoh/iskWBdCR9WvX5/Zs2cTGhqqcStfGzc3N5RKJZcvX8bPz0+dHhERQWxsrMYP0bwvLy8vIPUCIqMjzC/t27ePqKgo1q5dS8WKFdXp169fT5M3oxcaL9ty8eLFNNsuXLiAra1tpi8Ll1FeXl6cOnWKatWqvbU9GzduJCEhgQ0bNmiM/r4+VeGlrBolXLNmDSYmJmzfvl1jHfAFCxZozf9y5PZ1ly5dIk+ePOneSQDw9fUFtP/N3/RydH3YsGFa7+S8HG2PjY3VWPc7s38V1dPTE0gd3X3fc/5tXn6Wzp49mybYz6yy7e3t31rnmJgYdu/ezfDhwxkyZIg6XdvfN71z7/W/w+ve5+/g5eWFSqXCw8ODQoUKvTN/QEAAAQEBDBo0iEOHDlGuXDlmzZrFqFGj1HlenmOvfwcK8V8gc9aF0FF9+/Ylb968dOzYkYiIiDTbr169yuTJkwGoW7cuQJrRyJcjvG+u7PE+SpYsiZeXF+PHj+fJkydptr9tZYiXI2avj+gmJiZq/VnwvHnzZugWvJOTE8WKFWPRokUawcLZs2fZsWOHui+yQ/Pmzblz5w5z5sxJsy0+Pl69Soi2fomLi9MaKOfNmzdLfkFSX18fhUKhMRp648YNjRVrXhcaGqoxn/n27dv8+eef1KxZ861ztV1cXHB1deXYsWMZqlePHj2wtLRkxIgRaba9DEhfXzLy6dOnLFq0KENlZ5S9vT2VK1fmt99+4969e2m2f8hqKAA1a9bEzMyMMWPGpPmlzY+9y1OrVi3Mzc0ZPXo0SUlJaba/rLO2cw/SfncA6oveN88/c3NzbG1t0yzdqe1znZ4mTZqgr6/P8OHD09RFpVKpn3V49OgRycnJGtsDAgLQ09NLsxzq8ePHUSgU7xzcECKnkZF1IXSUl5cXy5Yto0WLFvj5+Wn8gumhQ4f4448/1L9qWbRoUdq2bcvs2bPVU0+OHj3KokWLaNSoEVWqVPngeujp6TF37lzq1KlDkSJFaN++PS4uLty5c4e9e/dibm7Oxo0bte4bHByMlZUVbdu2pVu3bigUCpYsWaI1MClZsiQrV66kV69elC5dmnz58mn9GXpInZJTp04dgoKC6NChg3rpRgsLC401nj+1r7/+mlWrVvHdd9+xd+9eypUrR0pKChcuXGDVqlXqte1r1qyJkZERDRo0oHPnzjx58oQ5c+Zgb2+fJjgsWbIkM2fOZNSoUXh7e2Nvb5/uvOT3Ua9ePSZOnEjt2rVp3bo1Dx48YPr06Xh7e3P69Ok0+f39/alVq5bG0o1Ahh4IbdiwIevWrUOlUr3zToGFhQXdu3fXWm7NmjUpUKAAHTp04IcffkBfX5/58+djZ2fHrVu3MtjyjJk+fTrly5cnICCATp064enpSUREBKGhofz777+cOnXqvcs0Nzfn119/pWPHjpQuXVq97vipU6d49uzZR110mJubM3PmTL7++mtKlChBy5Yt1f2yefNmypUrx7Rp0zA3N6dixYqMGzeOpKQkXFxc2LFjh9Y7Hy8fKB44cCAtW7bE0NCQBg0aqAcRfv75Zzp27EipUqX4+++/uXTpUobr6+XlxahRoxgwYAA3btygUaNGmJmZcf36ddatW8e3335Lnz592LNnD127dqVZs2YUKlSI5ORklixZgr6+Pk2bNtUoc+fOnZQrV0499UqI/4xPvv6MEOK9XLp0SdWpUyeVu7u7ysjISGVmZqYqV66caurUqRrLyiUlJamGDx+u8vDwUBkaGqpcXV1VAwYM0MijUqUuaVevXr00x3m5dGN6S6SdPHlS1aRJE5WNjY3K2NhY5ebmpmrevLlq9+7d6jzaltA7ePCg6rPPPlOZmpqqnJ2dVX379lUvoff6UnlPnjxRtW7dWmVpaakC1Ms4prdM3K5du1TlypVTmZqaqszNzVUNGjRQnTt3TiPPy2XjHj58qJGurZ7atG3bVpU3b950t/PG0o0qVeryh2PHjlUVKVJEZWxsrLKyslKVLFlSNXz4cFVcXJw634YNG1SBgYEqExMTlbu7u2rs2LGq+fPnp6nX/fv3VfXq1VOZmZmpAPXyiy/b8M8//2SozdraMm/ePFXBggVVxsbGKl9fX9WCBQu0Lo/4sp2///67On/x4sU1/n5vc+LECRWQZknL15dufF1MTIzKwsIizdKNKpVKdfz4cVXZsmVVRkZGqgIFCqgmTpyY7tKN2s5zbX8zbctEqlQq1dWrV1Vt2rRROTo6qgwNDVUuLi6q+vXrq1avXq3Ok97f4fVtb55nGzZsUAUHB6vP3TJlyqiWL1+eZn9tZWk7zuv27t2rqlWrlsrCwkJlYmKi8vLyUrVr105j2c1///1X1bhxY5WlpaXKwsJC1axZM/Wyh68vu6hSqVQjR45Uubi4qPT09DTa8uzZM1WHDh1UFhYWKjMzM1Xz5s1VDx48SHfpxjfPx5fWrFmjKl++vCpv3ryqvHnzqnx9fVUhISGqixcvqlQqleratWuqb775RuXl5aUyMTFRWVtbq6pUqaLatWuXRjmxsbEqIyMj1dy5c9/aP0LkRAqVKpOfsBJCCPGfolAoCAkJYdq0aR9cRrVq1XB2dmbJkiWZWDMhUk2aNIlx48Zx9erVbH/YV4jMJnPWhRBCZLnRo0ezcuXKTH8YVIikpCQmTpzIoEGDJFAX/0kyZ10IIUSWK1u2LImJidldDfEfZGhomOnPLAihS2RkXQghhBBCCB0lwboQQoi3UqlUHzVfXQgh/gv+/vtvGjRogLOzMwqFIt2lbl+3b98+SpQogbGxMd7e3ixcuPC9jyvBuhBCCCGEEO/w9OlTihYtyvTp0zOU//r169SrV48qVaoQFhZGjx496NixI9u3b3+v48pqMEIIIYQQQrwHhULBunXraNSoUbp5+vXrx+bNmzl79qw6rWXLlsTGxrJt27YMH0tG1oUQQgghRK6TkJDAo0ePNF5v/jLuxwgNDaV69eoaabVq1SI0NPS9ypHVYESmGOZrmN1V0GnDTtzN7iqIHO/tv/wpxLvJjXTxkfLYZevhMz3WaDkwza8lDx06NNN+Cfv+/fs4ODhopDk4OPDo0SPi4+MzvNSoBOtCCCGEECLXGTBgAL169dJIMzY2zqbapE+CdSGEEEIIofMy+/6isbFxlgbnjo6OREREaKRFRERgbm7+Xj/gJXPWhRBCCCGEyGRBQUHs3r1bI23nzp0EBQW9VzkSrAshhBBCCJ2nUGTu6309efKEsLAwwsLCgNSlGcPCwtS/oDtgwADatGmjzv/dd99x7do1+vbty4ULF5gxYwarVq2iZ8+e73VcmQYjhBBCCCF0XnaPMB87dowqVaqo37+c7962bVsWLlzIvXv31IE7gIeHB5s3b6Znz55MnjyZ/PnzM3fuXGrVqvVex5V11kWmkNVg3k5WgxEfT1aDER9L/rkXHymbV4MZ5Ze5scag80mZWl5WkZF1IYQQQgih8z5k6sp/gQTrQgghhBBC5+XSWD3bp/8IIYQQQggh0iEj60IIIYQQQufl1mkwMrIuhBBCCCGEjpKRdSGEEEIIofNy6wizBOtCCCGEEELnyTQYIYQQQgghhE6RkXUhhBBCCKHzcunAugTrQgghhBBC98k0GCGEEEIIIYROkZF1IYQQQgih83LpwLqMrAshhBBCCKGrZGRdCCGEEELoPL1cOrQuwboQQgghhNB5uTRWl2kwQgghhBBC6CoJ1nOZypUr06NHj3S3u7u7M2nSpE9WHyGEEEKIjFAoMveVU0iwnsusXbuWkSNHZnc1soRbqfK0mrmO3n/fZNiFJHyrfZ7dVcoUS1euoWrdLwgoW5VmX3fi9Nlzb82/deceajduTUDZqjRo1oa/9odqbFepVEyeMZfyNRoS+FlV2nXuzo2btzXyfNe9H5XrNCGgbFXK12jID4NGEvEgUr3937v38ClePs0r7PTZzGv4e5A+ervU9syhfI3PCfysitb2aJPar00JKFtFa78mJCQwfMwEylauQ/Hg6vyv949ERkVr5Bk19leatP4G/zKVadiibZpjXLtxk687dSW4Wn0CylahWv1m/Dp9NklJyR/X6Pf0rra+KfUcakVA2So0aPY1f+0/pLE9I30+c+4iWrbtTNGgqpSqUEvrcd7Vf5+KLn7GEhIS6D/kJxo0a0PhUpX4vueAzGvwB8iOPgLYt/8Qzb7uROBnVSldsXa6/RATG0fFWo3xKV6eR48ff3hDs5Eik185hQTruYy1tTVmZmbZXY0sYWial4gLp9k8olt2VyXTbNm+mzETphHSuT3rls3Dt5A3Hb7vRVR0jNb8J8LO0HvAcL5oVJ/1y+dTrXIFQnoN4NKVa+o8cxYuZcny1Qz7sQ+rFs/G1NSUDiG9SEhIUOf5rHQJJo0dwbZ1y5jyyyhu375D9x8GpTnewlmTOLDzT/WriJ9v5nfCO0gfvdur9vzAqsVzMDU1SdOeN23ZvosxE6YS0vkb1i2br7VfR4+fwt6/DzJp3CiWzJ3Gg4eRdO39Y5qymjasR92a1bQex9DAgEb16zB/xq9sW7ecH/t044+1G5g6a+7HNzyDMtLW16WeQ8NenEML3nEOpd/nSUlJ1K5RhVZfNH5r/d7Wf5+Crn7GUpRKjI2N+brVFwSVLZl1HZAB2dVH23fto++gkTT5vB5/rlzI8gUzqV+nutZjDhz+Mz4FvTK34eKTkGA9l3l9GsyDBw9o0KABpqameHh4sHTp0uyt3Ee6sn87eyYP5cKuP7O7Kplmwe8raN6kAU0b1sPby4PhA3/AxMSENes3ac2/ePkfVAguS8e2rfHydKdHSCcK+xXi9xVrgNSRmsXL/qBLpzZUr1IB30LejBs5iAcPo9i1d7+6nHZftaBYoD8uzo6UKBZAp/ZfEXYmPM1op6WlBXa2NuqXoeGnf2Zd+ujtUtuzii6d2r7WnsE8eBip0Z43Lfh9pZZ+NVb36+PHT1izfhP9e/2PoDIl8S/sy+jhAzl56ozG3YNB/XryZYumuOZ31noc1/wuNG1YD1+fgrg4O1KtcgUa1KnJsZOnMrcj3uJdbX3T4uWrXpxDX744h759cQ6tBjLe5926dKTdVy0pVNAz3bq9q/8+BV39jOUxNWX4wD40b/I5djY2Wd8Rb5EdfZScnMxPv0zmhx4htGrWCA+3Anh7eWi9sFu2ah2PHz/mmzatsq4TPgE9hSpTXzmFBOu5WLt27bh9+zZ79+5l9erVzJgxgwcPHmR3tcQLiUlJhJ+/RHDZUuo0PT09gsuW4uTpcK37hJ0+S9Br+QHKB5VVB0//3rnLw8gogsuWVm83M8tHUf/CnExnekZs3CM2bt1B8aL+aQLNLj36EVS1Pq3ad2H3vgMf1M6PIX30bq/a86rN72pPar9e1OiDV/2aus/Z8xdJSk4m+LNX5Xp5uOHs6PBRU31u3vqX/YeOULpk8Q8u431kpK1vCjsdns45lHrOfUif66qc8BnLbtnVR+cuXCLiwUP09BQ0atme8jUa0jGkt8boPMCVq9eZMWchY0cOQi+3rn2Yw+nWGS8+mUuXLrF161aOHj1K6dKpXwbz5s3Dz88vm2smXoqJiSMlJQUba2uNdBsba67duKl1n8jIaGytrd7Ib6WeR/wwMvW/Nm/J89Ivk2ewdMVa4p8/p1hAEWZNGafelsfUlP69ulKiWAAKPT127NpHSK8BTJ84hmqVy39Ygz+A9NG7vWpP2j6KjIrSuk9MTOxb+vUWAJFRURgaGmL+xrQ6GxtrHr7RTxnRsm1nwi9cIjExkRZNG9K9S8f3LuNDZKStb4qMjML2Lf35IX2uq3T5M6YrsquPbv97F4Bps+bTv/f/cHF2ZMGSFXzd6X9sX78cSwtzEhMT6TVgGD/0+B5nJ0du37n78Q3ORrn1UkNG1nOp8+fPY2BgQMmSr+b5+fr6Ymlp+c59ExISePTokcYrWZlzbieJjOnQpjXrVsxn/sxf0dPXo9/gUahUqX9naytL2n/dkqIBRQgs4kef7l34vG5N5i1els21/rR0sY82bNlO8eDq6ldy8qd9UPND/Tp2BOuWzWfC6GHs23+IeYuXZ3eVhA5422cst1OqlAB817ENtapXxr+wL2OG/4gCBdt27gFgwpTf8PJwp2E97Q8w5zS59QFTGVkX723MmDEMHz5cI62SjYLKtvrZVKP/JisrC/T19YmK1hxpioqKxjad+Zm2ttZEvvFAU1RUDLY2qSM+drap/42KjsHezlYjj6+Pt8Z+1laWWFtZ4uFWAC8PNyrVbkLY6XCKF/XXeuyiAYU5dOTY+zXyI0kfpVW1UnmK+hdRv09MSgQgKjr6jfZE4+tTUGsZVlaWb+nX1P6xtbEhKSmJR48fa4yuR0VFY2ejOcKYEU6ODgB4e3mQolQyZNRYvvm6Jfr6Wfu9kpG2vsnW1obIt5xzr86hjPe5rsppn7HskF19ZGebmu7l6a7ebmRkhGt+J+7djwDg8D/HuXTlGttL7QNQX+h8VqU+33VoQ7cuHT6kyeITk5H1XMrX15fk5GSOHz+uTrt48SKxsbHv3HfAgAHExcVpvMpby6mU2YwMDSniV4jQI6/+RkqlktCjxykeWETrPsUC/Tl8VDMYPHT4H4oFpv7Dlt/FGTtbG0JfCxifPHnKqbPnKB6Y/j9+SmXqCM7LwE+b8xevYGf7aR/ykj5KK1/evLgVyK9+eXt6vGjPqz56V3tS+9VHow9e9WvqPv5+PhgaGGjkuXbjJnfvR6j78kOplEqSk5NRfoI7dhlp65uKBRbh8NHjGmmp51DqOffqHMp4n+uqnPYZyw7Z1Uf+fj4YGRlx/car5RyTkpK5c/c+zk6OAEwd/xN/rlzI+hULWL9iAaOG9ANg6bzpfNmiSSa0/tPKreusy8h6LuXj40Pt2rXp3LkzM2fOxMDAgB49emBqavrOfY2NjTE2NtZIM9CBh1aM8uTFusCrURnL/B44+hYlPi6auHvvXlNaF7X/qiX9hvyEf2FfAv39WLRsFfHx8TRpWA+AvoNG4mBvR+9u3wHQplUzvu7UlfmLl1OpQjBbtu/i7LkLjBjcFwCFQkGb1s2YOXcRbgVcye/ixOQZc7G3s6F6lQoAnDoTzpnwC5QsHoi5mRm3/r3D5BlzKeDqov5HYt2GrRgaGuDnWwiAnXv+Ys2fm9X/EHxK0kdvl9qe5i/ak5/8Ls5MnjEHeztbdXsA2nbuRo0qFfmq5RcAtP+qxWv9WvhFvz5X96uZWT6aNqrPzxOmYmFhTr68eRk19leKB/prBOs3b/3Ls/hnPIyM4nlCAucvXgLAy9MDI0NDNmzZjoGBAT7eXhgZGXLm3AUmTJ1FnZrVPtmDhO9qa+o5ZEvvbl0AaNOqOV93CtFyDqX+bTPa53fv3Sfu0SPu3osgRZmi7psCrvnJmydPhvrv0/SPbn7GIPXhyaTkZGLjHvH02TPOX7wMgN8nvoORHX2UL19eWn7RkKmz5uHkaI+zkyPzFqVOs6tdowoABVxdNOoZ82JAzsvTLc3zJkJ3SbCeiy1YsICOHTtSqVIlHBwcGDVqFIMHD87uan0wZ/+StFu8W/2+9oDxAIStW8z6ATnzVl/dWtWIjollysy5PIyKxs/Hm7nTJ6hvld67H4Ge3qu7GiWKBTB+9FAmTZ/DxGmzcS+Qn+kTx1DI+9XScJ3afUl8/HOGjBrHo8dPKFksgLnTJ6gvwExMTNix5y+mzprHs/jn2NnaUCG4LN93GoGRkZG6nBlzFnH33n30DfTxdC/Arz8PV/8D8SlJH71banviX2tPoEZ7AG7fvkNMbJz6fd1a1d/o14Ia/QrwY59u6Onp0a3PQBITkygfXIahA/poHHvQiJ85evyk+n2jlu0B2L15NfmdnTDQ12fuwqVcv3kLVODs5MBXLZrS7qsWWdUdabyrrann0KsBidRzaBiTps9m4rTf3nIOvb3Pp8ycy7qNW9XvX/bN4jlTKVuqBPDu/vsUdPkz9u3/fuDOvfvq9y/75+LJT7vyUnb0EUDfHiEY6OvTd9BInickUNS/MItmT8bC3PzTNf4Tyv5hweyhUMmTGiITDPP9NCM8OdWwEzn7CXyhC3LrP1Mi88g/9+Ij5bHL1sPPLpG5Y8zfnsgZD+DLRGMhhBBCCCF0lEyDEUIIIYQQOi+33l+UYF0IIYQQQui8nLSCS2aSaTBCCCGEEELoKBlZF0IIIYQQOi+XDqzLyLoQQgghhBC6SkbWhRBCCCGEztOB31/MFhKsCyGEEEIInZdLY3WZBiOEEEIIIYSukpF1IYQQQgih83Lr0o0SrAshhBBCCJ2XS2N1mQYjhBBCCCGErpKRdSGEEEIIofNy6zQYGVkXQgghhBBCR8nIuhBCCCGE0Hm5dYRZgnUhhBBCCKHzZBqMEEIIIYQQQqfIyLoQQgghhNB5uXRgXYJ1IYQQQgih+/RyabQu02CEEEIIIYTQUTKyLoQQQgghdF4uHViXkXUhhBBCCCF0lYysCyGEEEIInZdb56xLsC6EEEIIIXRebp0OIsG6yBTDTtzN7irotGElnLO7CjpvyOKp2V0F3WZgmN010H2GebK7Brrt3vnsroHucyyU3TXQaXqBX2V3FXIlCdaFEEIIIYTOy62/YCrBuhBCCCGE0Hm5dRpMbm23EEIIIYQQOk9G1oUQQgghhM7LrdNgZGRdCCGEEEIIHSUj60IIIYQQQufpKVTZXYVsIcG6EEIIIYTQebl1OkhubbcQQgghhBA6T0bWhRBCCCGEzpMHTIUQQgghhBA6RUbWhRBCCCGEzsutI8wSrAshhBBCCJ0n02CEEEIIIYQQOkVG1oUQQgghhM7LrSPMEqwLIYQQQgidpyfTYIQQQgghhBC6REbWhRBCCCGEzpMHTIUQQgghhBA6RYJ1IYQQQgih8/Qy+fUhpk+fjru7OyYmJpQtW5ajR4++Nf+kSZPw8fHB1NQUV1dXevbsyfPnz9/rmBKsCyGEEEIInadQZO7rfa1cuZJevXoxdOhQTpw4QdGiRalVqxYPHjzQmn/ZsmX079+foUOHcv78eebNm8fKlSv58ccf3+u4EqwLIYQQQgjxDhMnTqRTp060b9+ewoULM2vWLPLkycP8+fO15j906BDlypWjdevWuLu7U7NmTVq1avXO0fg3SbAuhBBCCCF0XnZOg0lMTOT48eNUr179VX309KhevTqhoaFa9wkODub48ePq4PzatWts2bKFunXrvtexZTUYIYQQQgih8zJ7nfWEhAQSEhI00oyNjTE2Nk6TNzIykpSUFBwcHDTSHRwcuHDhgtbyW7duTWRkJOXLl0elUpGcnMx333333tNgJFgX2W7pyjXMW7Sch1HR+BbyYnC/ngT6F043/9ade5g8Yy537t7HvUB++nTrQqUKQertKpWKKTPn8ce6jTx6/JgSRQMY9mMf3N1c1Xm+696PC5cuExUdi4W5GUFlS9GnWxcc7G0B+PfuParVa5bm2CsXzaJYoH8mtv7TcitVnuAOvXEuUgIze2dWhDTlwu4N2V2tLLd0ZxjztxwnMu4pvq52DGxThUAvR615V+09w4YD57j8bxQAhT3s6dmsvEb+p88TmbjyALuPXyX2STz57Sz4qmYxWlYr+knakxWW7jjB/I1HU/uogD0D21Un0NtJa95Vu0+xYX84l/99CEBhD0d6tqiYJv/VO1FMWLaPf87fJkWpwsvFhsk9G+Fsa57l7clsS7ceZf6Gg0TGPsHXzZGBHeoQWDC/1ryrdh5nw1+nuHw7dR5rYU8neraulm7+Yb9tZOXO4/RvV4u29YO05skJlv59mfm7LxD56Dm+LpYM/KIEge42WvPuCPuX2TvOcSvyCckpStzszGhX1YeGZdzVeaZtOcuW47e4H/sMQ309Crta06NBAEXTKVPXLd32D/M3hL44hxwY+E1tAgu6aM27atcJNvx1msu3X3zGPJ3o2aqKRv4B0/5k/V+nNfYrX9SLOYNaZ10j/mPGjBnD8OHDNdKGDh3KsGHDMqX8ffv2MXr0aGbMmEHZsmW5cuUK3bt3Z+TIkQwePDjD5UiwLrLVlu27GTNhGsMH9qGof2EWLVtFh+97sW39cmysrdLkPxF2ht4DhtPrf52pUiGYjVt3EtJrAGuXz6eQtycAcxYuZcny1fw8YiD5XZyYPGMuHUJ6sWXN7+qr5c9Kl+C7Dl9jZ2tLxIOHjPt1Ot1/GMSKRbM0jrdw1iS8vTzU7y0tLLKwN7KeoWleIi6c5uSahbSctjq7q/NJbDl8kbHL/mZY+2oEejmyeNsJOo1by5Zx7bCxyJMm/z/n/6VukC/FCzphbGjA3E3/0HHcWjaOaYODdT4Axi79iyPnbjOuS21cbM05eOYmIxbtwd4qH1VLeH3qJn60LaHnGbtkL8M61CTQ24nFW4/R6edVbJnQERuLvGny/3P+FnWD/SheqFpqH208Qscxq9j4yzc4WJsBcCsihi+HLaVp5UC6flGefHmMuHI7EmND/U/dvI+25eBZxi7azrBv6xNY0IXFmw/TadTvbJnSFRuLfGny/xN+g7rl/Snu44qxkQFz1x+k48glbPw1BAcbzQuVnUfOc+ryv9i/6LecasvxW4xdF8awFiUJdLNh8b5LdJrxF1sG18XGzCRNfsu8RnSuVRhPB3MM9fXYF36XgUuPYmNmTHm/1Is+d3szBjUrgattPp4npbBo70U6Tv+L7UPqYq2lTF225WA4YxftZNi3dQn0dmHx5iN0+mkZWyZ/r/0zFn4z9RwqlP/FOXSIjqOWsnHidxrnUIViXvz0/efq90Y58PP1PjJ7mfUBAwbQq1cvjTRto+oAtra26OvrExERoZEeERGBo6P2wZ/Bgwfz9ddf07FjRwACAgJ4+vQp3377LQMHDkRPL2OTcWTOeg63bds2ypcvj6WlJTY2NtSvX5+rV6+qtx86dIhixYphYmJCqVKlWL9+PQqFgrCwMHWes2fPUqdOHfLly4eDgwNff/01kZGRn6T+C35fQfMmDWjasB7eXh4MH/gDJiYmrFm/SWv+xcv/oEJwWTq2bY2Xpzs9QjpR2K8Qv69YA6SOqi9e9gddOrWhepUK+BbyZtzIQTx4GMWuvfvV5bT7qgXFAv1xcXakRLEAOrX/irAz4SQlJWscz9LSAjtbG/XL0DBnX99e2b+dPZOHcmHXn9ldlU9m0dYTNKvsT5OKRfB2sWFY++qYGBuw9u+zWvP/8n0dWlcvip+bPZ7O1ozsWAOlUkXouVvqPCcv36NhhcKU8XPFxc6C5lUD8Slgx+mr9z9VszLVos3HaFY1kCaVA/DOb8uwDrUwMTJk7b4zWvP/0rUBrWsWx8/dAU8XG0Z+WxulSkXo2ZvqPJNW7qdiMU9++LIyhT0cKOBgRdVSBbUGJrpu0cZQmlUvQZOqxfF2tWfYt/UxMTZk7Z6TWvP/0qMprWuXwc/DCU8XO0Z+93lq/5y5ppEvIuoRP83bwrjuTTHQz9n/HC/ae5FmQZ40+cwTbycLhrUohYmRAWtDr2vNX6agPTWK5sfL0ZwCdvloU7kQhZwtOH711b899Uu5EezriKttPgo6WdC/cXGePE/i4t24T9WsTLNo02GaVStOkyrF8Ha1Y9i39VI/Y3vCtOb/pXtjWtcqhZ+HI54utoz8rv6Lz5hmfxoZ6mNnlU/9sshn+gla899hbGyMubm5xiu9YN3IyIiSJUuye/dudZpSqWT37t0EBWm/I/bs2bM0Abm+fuoFlUqlynA9c/a3g+Dp06f06tWLY8eOsXv3bvT09GjcuDFKpZJHjx7RoEEDAgICOHHiBCNHjqRfv34a+8fGxlK1alWKFy/OsWPH2LZtGxERETRv3jzL656YlET4+UsEly2lTtPT0yO4bClOng7Xuk/Y6bMEvZYfoHxQWcJOpwZe/965y8PIKILLllZvNzPLR1H/wpw8rT04i417xMatOyhe1D9NMN6lRz+CqtanVfsu7N534IPaKbJPYnIK4TciCCpSQJ2mp6cgqEgBwq7cy1AZzxOSSU5JwSLvq5G84gWd2HviGhHRT1CpVBw5d5sb92MoF+CW6W3IaonJKYRfv0+Qv7s6TU9PQZC/G2GX72aojOcJSSQnK7HIl9pHSqWKv05exd3Jmo5jVlGu8zRaDFrCrn8uZ0UTslRiUjLh1+4SFOipTtPT0yMowJOwi/9mqIzniUkkpyg1AimlUkm/qWv5pmE5CrraZ3q9P6XE5BTCb8cQ5PNqLq+enoIgHwfCbrx74EelUhF6MYIbDx5Tytsu3WOsOnQVM1NDfF0sM6vqn0RiUgrh1+4RFPjqLq2enoKgQA/CLr3HOZSsTBOMHw2/SbkOE6jTbTrDZm8h5vGzTK27rtFTZO7rffXq1Ys5c+awaNEizp8/T5cuXXj69Cnt27cHoE2bNgwYMECdv0GDBsycOZMVK1Zw/fp1du7cyeDBg2nQoIE6aM+InD1MKGjatKnG+/nz52NnZ8e5c+c4cOAACoWCOXPmYGJiQuHChblz5w6dOnVS5582bRrFixdn9OjRGmW4urpy6dIlChUqlGV1j4mJIyUlBRtra410Gxtrrt24qXWfyMhobN+YHmNjY0VkVDQADyNT//vmFJrX87z0y+QZLF2xlvjnzykWUIRZU8apt+UxNaV/r66UKBaAQk+PHbv2EdJrANMnjqFa5fIf1mDxycU+jidFqUoz3cXGPA/X78ZkqIzxK/djb5WP4NcC/kFtqjBk/i4qd5+Dgb4eCoWCER2qU9pX+5xkXRb76Jn2PrLIy/W70enspWn8sr9S++hFwB/16CnPnicxd8MRujUvT+9WlThw6jrdfl3HwkEtKVO4wNsL1CGxj1/2j+Z0FxvLvFy/k7E7kON/34m9lRnBrwX8c9cfRF9Pj6/rls3U+maH2KeJqX1krjk1xcbMhOsRj9Ld73F8IpUHbSQxOQU9PQVDmpeknK/mdIK9Z+/SZ0Eo8UnJ2JmbMi+kElb5tI986qp0zyGL9zmHdmNvbUZwwKtzqHxxL2qU9SW/vSW3ImKYtGwvnX9azvKf2qOfw+/UpCezp8G8rxYtWvDw4UOGDBnC/fv3KVasGNu2bVM/dHrr1i2NkfRBgwahUCgYNGgQd+7cwc7OjgYNGvDTTz+913ElWM/hLl++zJAhQzhy5AiRkZEolUog9YS5ePEigYGBmJi8+gItU6aMxv6nTp1i79695MuXdt7l1atXtQbrWp+eTklI99aRrurQpjVfNKrP3XsRTPttPv0Gj+K3KeNQKBRYW1nS/uuW6ryBRfx48DCSeYuXSbCei8zZeJSthy+y6MdmGBu9+rr8fUcYp67cZ0bPz3G2NefYxTuMXLQHe8u8BPvnvNH1jzHnz8NsDb3AosEt1X2kUqbe3q1a0pt2dVPvcvm5O3Dy0h1W7grLUcH6x5qzbj9bD55l0bB2GBsZAhB+9S5LthxmzbjOKD7kl1n+I/IaG7K2f02eJSRz+GIEY9eF4WqbjzIFX91pKFvQnrX9axLzJIE/Dl2j5/xQVvaprnUe/H/VnHUH2XownEXD22h8D9Ur92qxg0JuDvi4OVCz6zSOnrtJUICHtqJEJujatStdu3bVum3fvn0a7w0MDBg6dChDhw79qGNKsJ7DNWjQADc3N+bMmYOzszNKpRJ/f38SExMztP+TJ09o0KABY8eOTbPNyUn7ShBan57+sQ/DBvZ9r7pbWVmgr69PVLTm6F1UVDS2Ntqf9re1tSYyOuaN/DHY2qSOztvZpv43KjoGeztbjTy+Pt4a+1lbWWJtZYmHWwG8PNyoVLsJYafDKV5U+2ovRQMKc+jIsfdqo8helmam6OspiIrTvDUc9egZtpZpHy593fzNx5iz6Rjz+zXBp8CrW/PPE5OZ9MdBpvRoQOViqaNcPgXsOH/zIQu2HM9xwbqleR7tfRT3FFvLt88vn7/pKHM2HGH+j83xcXsVYFma58FAXw8vF83PsaeLDScu3sm8yn8ClmYv++eJRnpU7FNsLdMOcrxu/p8HmbPuAPOHtMHH/dWI8bHzN4mKe0rV735Vp6UoVYxbvIPFmw+ze2bPzG1EFrPMa5TaR480f0I96vFzbM3TD6r19BS42aU+WOuX34qrEY+YveO8RrCex9gANzsz3OzMKOZhS60Rm1kTeo1va6a/YpiuSfccisvAObQhlDnrDzJ/yFf4uDm8Na+rgxVWZnm4dT/6PxusZ/bSjTnFf/M+SS4RFRXFxYsXGTRoENWqVcPPz4+YmFeBrI+PD2fOnNEYBf/nn380yihRogTh4eG4u7vj7e2t8cqbV/s/1AMGDCAuLk7jNaBP9/euv5GhIUX8ChF65Lg6TalUEnr0OMUDi2jdp1igP4ePagbMhw7/o15OMb+LM3a2NoS+FlQ/efKUU2fPUfwtSy6+vCORmJT+Rc75i1ews82ZS4blVkYG+hRxd+DwudvqNKVSxeHw2xRLZ1lCgLmb/mHmn0eY/UNj/D01b8snp6SQlKJE740RUX09Bcr3eGBIVxgZ6FPEw5HDrz0cmtpHNylW0Dnd/eZuOMLMtYeY3b8Z/l6afWlkoI+/pyPX72leiN+4F5Pjlm00MjSgiKczh8+8erBPqVRy+Mw1ivmkP+1p7voDzFzzN7MHfYW/t+byfJ9XKsr6CV1YO/479cve2oxvPg9m7qCvs6wtWcXIQJ8irlYcvvRqlQylUsXhSxEUc7d9y56aVKrUuelvz6MiMVn5wXXNDkaG+hTxdOLwmRvqNKVSxeEz1ylW6C3n0J+HmLl6P7MHtsbfK/3P4kv3ox4R++QZdu+4AMjJ9BSqTH3lFDKynoNZWVlhY2PD7NmzcXJy4tatW/Tv31+9vXXr1gwcOJBvv/2W/v37c+vWLcaPHw+gvvUaEhLCnDlzaNWqFX379sXa2porV66wYsUK5s6dq/UBCK0/GPAsIU2+jGj/VUv6DfkJ/8K+BPr7sWjZKuLj42nSsB4AfQeNxMHejt7dvgOgTatmfN2pK/MXL6dShWC2bN/F2XMXGDG4r7pdbVo3Y+bcRbgVcFUv3WhvZ0P1KhUAOHUmnDPhFyhZPBBzMzNu/XuHyTPmUsDVRR3Qr9uwFUNDA/x8U6cB7dzzF2v+3MyoIf3ebEKOYpQnL9YFXt1hsMzvgaNvUeLjoom7d/ste+ZcbeuUYMDs7fh72BPg6cji7SeJT0iiccXUC8J+s7bhYJWPXi1SpzfN2fQPU9eEMv77OrjYmvMw9ikAeUwMyWtiRD5TY0r75ueX5fsxMTLA2cacfy78y58HztGvdaVsa+fHaFuvFANmbsHf05GAF0s3xick0bhSAAD9ZmxO7aNWqe2bs+EIU/84wPiu9XGxM+dhbOqIYR4TI/KaGAHwTYMy9J68gVK+rpQtUoADp66z78QVFg1ulT2N/AhtGwQxYNo6/L2cCfBOXboxPiGJxlWKA9BvylocbMzp9WXqLxvOWXeAqSv3Mr5HU1zsLHkY8xh40T+mxliZ5cHKTPPOjoG+HraW+fBwyXhwq0vaVvFhwO9H8C9gTYCbDYv3XSQ+IZnGn6WO8PZbfBgHyzz0+jwQgNk7zlGkgDUFbPORmKzk7/C7bDh6gyEtSgLwLCGZ37afo0qAM3YWpsQ+SWDZ/itExMZTq7hruvXQVW3rf8aA6X/i7+VEgLczizcffXEOpf42Q7+p63GwNqPXl9UAmLP+IFNX/sX47o1fnEOvfcZMjXgan8iMP/6mxme+2Fnm41ZEDOOX7KKAozXli+W85WPF20mwnoPp6emxYsUKunXrhr+/Pz4+PkyZMoXKlSsDYG5uzsaNG+nSpQvFihUjICCAIUOG0Lp1a/U8dmdnZw4ePEi/fv2oWbMmCQkJuLm5Ubt27Qyv//kx6taqRnRMLFNmzuVhVDR+Pt7MnT5BPa3l3v0IjXqUKBbA+NFDmTR9DhOnzca9QH6mTxyjXmMdoFO7L4mPf86QUeN49PgJJYsFMHf6BPUFhomJCTv2/MXUWfN4Fv8cO1sbKgSX5ftOIzAyMlKXM2POIu7eu4++gT6e7gX49efh1K5RJcv7JCs5+5ek3eJXy07VHpB68Ra2bjHrB3TIrmplqbqf+RDzOJ4pa0KJjHuGXwE7Zv/QGNsXSwjei3qsMUq+YvdpkpJT6D5Fc/nQkMaf0bVJ6vJcE0Lq8uuqA/wwcytxT57jbGtOj2blaFkt8NM1LBPVDfIj5lE8U1YfIDL2KX5u9szu30w9DeZe5CPNPtp5MrWPJmkuARrSNJiuX6Re9NQoXYihHWoye8NhRi/ajYezNZN7NqJkDnwIt245f2IePWXKir1Exj7Bz92R2QO/Uk9huBcZh95r9+dX7PgntX/Gr9IoJ6RZJbq2yNnfIempW7IAMU8SmLL5LJGPn+PnYsns7yupp8Hci3mmcQ49S0xhxKrjRMTGY2Koj4eDGWPbfEbdkqnPM+jrKbgW8Yj1R28Q8zQByzxGBLhZ83uPqhR0ynm/d1G3XBFiHj1jysq/XpxDDswe2Pq1c+iNz9iO46nn0ATN38MIaVaRrs0roa+n4OKtCNb/dYrHT59jZ21GuUBPurWsjFEOX2L4bXLpLBgUqvdZ6FHkeEuXLqV9+/bExcVhapqJ67E+e5h5Zf0HDSvx7luYud2QxVOzuwq6zcAwu2ug+wzf/hxCrnfvfHbXQPc5Zt0KaP8FeoFfZevxT9bI3EHE4jtzxpSq/+7llwBg8eLFeHp64uLiwqlTp+jXrx/NmzfP3EBdCCGEECKL5dYHTCVY/4+7f/++ej1QJycnmjVr9t7rewohhBBCZLdcGqtLsP5f17dvX/r2fb8lFYUQQgghhG6QYF0IIYQQQug8mQYjhBBCCCGEjsqtPw6UW9sthBBCCCGEzpORdSGEEEIIofMUuXQajIysCyGEEEIIoaNkZF0IIYQQQug8ecBUCCGEEEIIHZVLY3WZBiOEEEIIIYSukpF1IYQQQgih8xS59AlTGVkXQgghhBBCR8nIuhBCCCGE0Hm5dGBdgnUhhBBCCJED5NJoXabBCCGEEEIIoaNkZF0IIYQQQui8XDqwLsG6EEIIIYTQfbIajBBCCCGEEEKnyMi6EEIIIYTQeTKyLoQQQgghhNApMrIuhBBCCCF0Xy4dYpZgXQghhBBC6DyZBiOEEEIIIYTQKTKyLsQnMGTx1Oyugs4b0eZ/2V0FnTb0rxPZXQXd9+xhdtdAtxV0yu4a6D5js+yugXiLXDqwLsG6EEIIIYTQfTINRgghhBBCCKFTZGRdCCGEEELovtw5sC4j60IIIYQQQugqGVkXQgghhBA6L7fOWZdgXQghhBBC6LxcGqvLNBghhBBCCCF0lYysCyGEEEIInSfTYIQQQgghhNBVuTRYl2kwQgghhBBC6CgZWRdCCCGEEDovlw6sy8i6EEIIIYQQukpG1oUQQgghhM6TB0yFEEIIIYTQUbk0VpdpMEIIIYQQQugqGVkXQgghhBC6L5cOrcvIuhBCCCGEEDpKRtaFEEIIIYTOy6UD6xKsCyGEEEII3ZdbV4ORaTBCCCGEEELoKBlZF0IIIYQQOk9G1nOYypUr06NHj+yuhk6RPhFCCCHEf5VCkbmvnEJG1rNAu3btiI2NZf369Z/0uGvXrsXQ0PCTHjMzLF25hnmLlvMwKhrfQl4M7teTQP/C6ebfunMPk2fM5c7d+7gXyE+fbl2oVCFIvV2lUjFl5jz+WLeRR48fU6JoAMN+7IO7m6s6z3fd+3Hh0mWiomOxMDcjqGwp+nTrgoO9LQD/3r1HtXrN0hx75aJZFAv0z8TWZ46lO8OYv+U4kXFP8XW1Y2CbKgR6OWrNu2rvGTYcOMflf6MAKOxhT89m5TXyP32eyMSVB9h9/CqxT+LJb2fBVzWL0bJa0U/SnuziVqo8wR1641ykBGb2zqwIacqF3Ruyu1qfxNK1W5m3YgOR0bH4erkxqHsHAgsX1Jr38vXbTJm3gvBL17h7/yEDurajbfP6Gnn+CTvHvBV/En7xGg+jYpj2U1+qVyjzKZqSJZZu2Me81TuJjHmEr2d+Bn3fgkAfd615L9+4y5QlGwm/fIu7D6IZ0PkL2jauppGnapuB3H0QnWbf1vUrMqRrq6xoQpZbumHviz6Ke9FHLQn08dCaN7WPNrzooygGdG5G28bVNfJUbfMjdx9Epdm3df1KDOnaOkvakJWWrtvBvJWbiIyOw9erAIO6tSXQz1tr3svX/2XKgj8Iv3SduxGRDAj5mrZf1NHIs/zPnSzfsIs79yMB8HZ3IaRNEyqWLZbVTRGfWI4dWddFKSkpKJXKbDu+tbU1ZmZm2Xb8D7Fl+27GTJhGSOf2rFs2D99C3nT4vhdR0TFa858IO0PvAcP5olF91i+fT7XKFQjpNYBLV66p88xZuJQly1cz7Mc+rFo8G1NTUzqE9CIhIUGd57PSJZg0dgTb1i1jyi+juH37Dt1/GJTmeAtnTeLAzj/VryJ+vpnfCR9py+GLjF32NyGNP2PNyC/xKWBLp3FriYp7pjX/P+f/pW6QLwt//ILlQ1viZG1Gx3FriYh+os4zdulfHDh9g3FdarN5bFva1CrOqMV72XPi6qdqVrYwNM1LxIXTbB7RLbur8klt2X2Qn6cvIqRdM9bOHYePtzsd+4wiKiZOa/7nzxNwdXagd+cvsbO21Jon/vlzfL3cGdKzYxbW/NPY8tcxfp6zhpCv6rF22o/4eOan48ApRMU+0pr/eUIiro629P6mEXZW5lrzrJ7Sn/3Lfla/5o9OPedqVSiZZe3ISlv++oef56x+0UcD36OPGr+ljwawf9k49Wv+6B5AzuyjLXtC+Xnm74S0bcLa2T/h41WAjn1/Tv8zlpCAq7M9vb9tme5nzMHOmt6dWrLmt1GsnjWKz4oXIWTQBC5f/zcLW5LNcunQeo4O1pVKJX379sXa2hpHR0eGDRsGwDfffEP9+pqjPElJSdjb2zNv3jwgdcpI165d6dq1KxYWFtja2jJ48GBUKpV6n4SEBPr06YOLiwt58+albNmy7Nu3T7194cKFWFpasmHDBgoXLoyxsTHffPMNixYt4s8//0ShUKBQKNT73L59m+bNm2NpaYm1tTUNGzbkxo0b6vLatWtHo0aNGD9+PE5OTtjY2BASEkJSUpI6z4wZMyhYsCAmJiY4ODjwxRdfqLe9OQ0mJiaGNm3aYGVlRZ48eahTpw6XL19OU//t27fj5+dHvnz5qF27Nvfu3fvQP8l7W/D7Cpo3aUDThvXw9vJg+MAfMDExYc36TVrzL17+BxWCy9KxbWu8PN3pEdKJwn6F+H3FGiB1VH3xsj/o0qkN1atUwLeQN+NGDuLBwyh27d2vLqfdVy0oFuiPi7MjJYoF0Kn9V4SdCScpKVnjeJaWFtjZ2qhfhoa6dzNq0dYTNKvsT5OKRfB2sWFY++qYGBuw9u+zWvP/8n0dWlcvip+bPZ7O1ozsWAOlUkXouVvqPCcv36NhhcKU8XPFxc6C5lUD8Slgx+mr9z9Vs7LFlf3b2TN5KBd2/ZndVfmkFq7aSLP61Wlatyre7q4M7/0tJibGrNm8R2v+AD9v+n7fhnrVymNopP1uXsXPStCjUytqVCyblVX/JBau3U2z2uVoWjMYbzcnhv+vFSbGRqzZHqo1f4CPO307NaVe5dLpfmdYW5phZ22hfu07eoYCTnaUCdR+N0PXLVy7i2a1y9O0Zjm83ZwZ/r8vX/TRIa35U/voixd9pP0cSttHp1/0UaGsbEqWWPjHFprVq0LTOpXxds/P8F4dUj9jW//Smj/A14u+331JvarB6Z5DVYNLUumz4rjnd8LD1YmeHVuQx9SEU+cua80vcq4cHawvWrSIvHnzcuTIEcaNG8eIESPYuXMnHTt2ZNu2bRpB56ZNm3j27BktWrTQ2N/AwICjR48yefJkJk6cyNy5c9Xbu3btSmhoKCtWrOD06dM0a9aM2rVrawS8z549Y+zYscydO5fw8HCmTJlC8+bN1UHvvXv3CA4OJikpiVq1amFmZsb+/fs5ePCgOjhOTExUl7d3716uXr3K3r17WbRoEQsXLmThwoUAHDt2jG7dujFixAguXrzItm3bqFixYrr9065dO44dO8aGDRsIDQ1FpVJRt25djeD/2bNnjB8/niVLlvD3339z69Yt+vTp81F/l4xKTEoi/PwlgsuWUqfp6ekRXLYUJ0+Ha90n7PRZgl7LD1A+qCxhp1MD03/v3OVhZBTBZUurt5uZ5aOof2FOntYevMbGPWLj1h0UL+qf5kuxS49+BFWtT6v2Xdi978AHtTMrJSanEH4jgqAiBdRpenoKgooUIOxKxi66nickk5ySgkVeE3Va8YJO7D1xjYjoJ6hUKo6cu82N+zGUC3DL9DaI7JWYlET4pWsElwpUp+np6RFUMoCw8IvZWDPdkJiUTPjlWwQXf3VXTU9Pj6DivoSdv/aWPd/vGBv2HKVJraAc+QDdqz7yU6dlTR8doUmt4BzXR4lJyYRfuk5wyVdTKPX09Agq4U9YeOYE1ikpSjbvOcSz5wkUK5IzL/gyIpcOrOfsOeuBgYEMHToUgIIFCzJt2jR2797Nzz//jI+PD0uWLKFv374ALFiwgGbNmpEvXz71/q6urvz6668oFAp8fHw4c+YMv/76K506deLWrVssWLCAW7du4ezsDECfPn3Ytm0bCxYsYPTo0UDqiP2MGTMoWvTVXF5TU1MSEhJwdHw1B/j3339HqVQyd+5c9RfNggULsLS0ZN++fdSsWRMAKysrpk2bhr6+Pr6+vtSrV4/du3er65Q3b17q16+PmZkZbm5uFC9eXGvfXL58mQ0bNnDw4EGCg4MBWLp0Ka6urqxfv55mzZqp6z9r1iy8vLyA1AuUESNGfORfJmNiYuJISUnBxtpaI93GxpprN25q3ScyMhpba6s38lsRGZU69/NhZOp/bd6S56VfJs9g6Yq1xD9/TrGAIsyaMk69LY+pKf17daVEsQAUenrs2LWPkF4DmD5xDNUql/+wBmeB2MfxpChV2Fjk0Ui3Mc/D9bvapxK9afzK/dhb5SP4tYB/UJsqDJm/i8rd52Cgr4dCoWBEh+qU9s2fqfUX2S8m7jEpKUpsrCw00m2tLbl+60421Up3xDx6QopSiY2l5lQNW0tzrt+OyJRj7A49xeMn8TSuEfTuzDroVR9pTsNM7aPMuRu3OzTsRR8FZ0p5n1JM3OPU/nnzM2ZlwfVbdz+q7IvXbtEqZCgJiUnkMTVh2oieeLv/d7+nc9qFWmbJ8cH665ycnHjw4AEAHTt2ZPbs2fTt25eIiAi2bt3Knj2at3Q/++wzjT98UFAQEyZMICUlhTNnzpCSkkKhQpq32xISErCxsVG/NzIySlMPbU6dOsWVK1fSzCl//vw5V6++mgdcpEgR9PX1Ndp05swZAGrUqIGbmxuenp7Url2b2rVr07hxY/Lk0QzUAM6fP4+BgQFly766BW1jY4OPjw/nz59Xp+XJk0cdqL883ss+TE9CQoLG/G8A45QEjI2N37qfrunQpjVfNKrP3XsRTPttPv0Gj+K3KeNQKBRYW1nS/uuW6ryBRfx48DCSeYuX6VSw/rHmbDzK1sMXWfRjM4yNXn0d/L4jjFNX7jOj5+c425pz7OIdRi7ag71lXoL9ZXRdiMy0ettBKpQugoONZXZXRWdJH2nn4erMurljePzkGdv/Pkr/n2exZNLg/3TAnhvl6GD9zXluCoVC/YBnmzZt6N+/P6GhoRw6dAgPDw8qVKiQ4bKfPHmCvr4+x48f1wieAY3ReVNT0wxd6T158oSSJUuydOnSNNvs7Owy1CYzMzNOnDjBvn372LFjB0OGDGHYsGH8888/WFpaZrhtr9N2vNfn7WszZswYhg8frpE29Mc+DBvY972ObWVlgb6+PlHRmiPeUVHR2L52QfQ6W1trIt94+DQqKgZbm9TReTvb1P9GRcdgb2erkcfXR/Ope2srS6ytLPFwK4CXhxuVajch7HQ4xYtqX+2laEBhDh059l5tzGqWZqbo6ynSPEwa9egZtpZpL+JeN3/zMeZsOsb8fk3wKfDqHHyemMykPw4ypUcDKhfzBMCngB3nbz5kwZbjEqz/x1hZmKGvr5fmQbfI6Fhs03mwLTexMs+Hvp5emgclI2MfYZvOg5Hv405EFKFhF5g6uPNHl5VdXvXRY4301D6ySGevjEvto/NMHfzdR5eVHawszFL7583PWEzcR3/GjAwNcHNJvYvv7+PJ2QtXWbxmGyN65/wHu7XJpQPrOXvO+tvY2NjQqFEjFixYwMKFC2nfvn2aPEeOHNF4f/jwYQoWLIi+vj7FixcnJSWFBw8e4O3trfF6fXqLNkZGRqSkpGiklShRgsuXL2Nvb5+mPAuLjH+ZGRgYUL16dcaNG8fp06e5ceNGmjsGAH5+fiQnJ2u0MSoqiosXL1K4cPrLImbEgAEDiIuL03gN6NP9vcsxMjSkiF8hQo8cV6cplUpCjx6neGARrfsUC/Tn8FHNgPnQ4X/Uyynmd3HGztaG0NeC6idPnnLq7DmKv2XJxZcXRIlJienmOX/xCna22i8isouRgT5F3B04fO62Ok2pVHE4/DbFvJ3S3W/upn+Y+ecRZv/QGH9PzfM5OSWFpBQlem98K+rrKVC+40JO5DxGhoYUKeRJ6PEz6jSlUsnhE2coVsQnG2umG4wMDShSsAChYa/m7yuVSg6HXaSYn+dHl792Ryg2FmZUKqN7S8Jm1Ks+enXXNrWPLmRSHx160UcBH11WdjAyNKBIIQ9CT7x6Fiv1Mxae6fPLlSoViW8slPCfkksnrefokfV36dixI/Xr1yclJYW2bdum2X7r1i169epF586dOXHiBFOnTmXChAkAFCpUiC+//JI2bdowYcIEihcvzsOHD9m9ezeBgYHUq1cv3eO6u7uzfft2Ll68iI2NDRYWFnz55Zf88ssvNGzYkBEjRpA/f35u3rzJ2rVr6du3L/nzv/uW1aZNm7h27RoVK1bEysqKLVu2oFQq8fFJ+w9qwYIFadiwIZ06deK3337DzMyM/v374+LiQsOGDd+jF9MyNjZOO+XlWYL2zO/Q/quW9BvyE/6FfQn092PRslXEx8fTpGFq//YdNBIHezt6d0sdUWnTqhlfd+rK/MXLqVQhmC3bd3H23AVGDE4d1VcoFLRp3YyZcxfhVsCV/C5OTJ4xF3s7G6pXSb2zcupMOGfCL1CyeCDmZmbc+vcOk2fMpYCrizqgX7dhK4aGBvj5pk6D2rnnL9b8uZlRQ/p9UDuzUts6JRgwezv+HvYEeDqyePtJ4hOSaFwx9YKn36xtOFjlo1eL1Ok7czb9w9Q1oYz/vg4utuY8jH0KQB4TQ/KaGJHP1JjSvvn5Zfl+TIwMcLYx558L//LngXP0a10p29r5KRjlyYt1gVd3YCzze+DoW5T4uGji7t1+y545W7vmDeg/Zhr+Pl4E+nmz6I/NxMcn0KRuFQD6/TQFe1sbenf+Ekh9KPXqjdTl4ZKSkomIjOb85evkMTXBLX/qReLTZ/HcuvNqvvK/9yI4f/k6Fub5cHawIydp16Qa/ccvwr9gAQJ93Fm0bg/xzxNoUjN1jnm/XxZib2NJ728aAakPFF69lfqAd1JyChGRsZy/eps8psa4Odury1UqlazbGUqjGp9h8MYd3JymXZPq9B+/EP+C7i/6aDfxzxNpUjN1jnm/Xxa86KPGwJt9lPyOPjpEoxpBObqP2jWrS/+fZ+FfyJNAPy8Wrd5K/PPnNKmd+p3ab/QM7F8sxQgv+ufmi89Y8ovP2JUbqZ+xFyPpE+asoGKZojg52PL0WTybdh/iaNh55o7rnz2NFFnmPx2sV69eHScnJ4oUKaJ+SPR1bdq0IT4+njJlyqCvr0/37t359ttv1dsXLFjAqFGj6N27N3fu3MHW1pbPPvsszbKQb+rUqRP79u2jVKlSPHnyhL1791K5cmX+/vtv+vXrR5MmTXj8+DEuLi5Uq1YNc/OM3Uq1tLRk7dq1DBs2jOfPn1OwYEGWL19OkSLaR6EXLFhA9+7dqV+/PomJiVSsWJEtW7bo1A8n1a1VjeiYWKbMnMvDqGj8fLyZO32CelrLvfsR6Om9ugFUolgA40cPZdL0OUycNhv3AvmZPnEMhbxfjd50avcl8fHPGTJqHI8eP6FksQDmTp+gvsAwMTFhx56/mDprHs/in2Nna0OF4LJ832kERkZG6nJmzFnE3Xv30TfQx9O9AL/+PJzaNap8op7JuLqf+RDzOJ4pa0KJjHuGXwE7Zv/QGFuLvADci3qsMUq+YvdpkpJT6D5Fc3nMkMaf0bVJavAxIaQuv646wA8ztxL35DnOtub0aFaOltXe/XxGTubsX5J2i3er39ceMB6AsHWLWT+gQ3ZVK8vVrVaO6NhHTJ2/gofRsfh5uzNn/ED1Lfq7EZEoFK8+hw8iY2jc4Qf1+/krNjB/xQZKFyvMkimpD6ifvXiVtt2HqfP8PG0RAI1qV+bnH7tmfaMyUd1KpYiOe8LUJZt4GPMIP8/8zBn1P/U0mLsPojWmQz6IiqNxyGj1+/lrdjF/zS5KBxRkyS+91OmHTl7g7oNodUCbk9WtVPpFH214rY+6vaWPYmkcMkr9fv6ancxfs5PSAYVY8ktvdfqrPir36RqTBepWDSI67hFTF65O/Yx5uTFnbH9srVPvrN99EIXitX/rHkTF0LjTj+r381duZv7KzZQu6seSSYMBiI55RL8xM3kYHYtZ3jz4eLoyd1x/ypXKmXcgMiK3PmCqUL1rgnIO9uTJE1xcXFiwYAFNmjTR2Fa5cmWKFSvGpEmTsqdy/zXPHmZ3DXSa8uya7K6CzhvR5n/ZXQWdNvSvE9ldBd0n30Pv8J+d+Zp5jHPWDwt+agrn7P1BqtjOmTsV1fK3tL+Qq4v+kyPrSqWSyMhIJkyYgKWlJZ9//nl2V0kIIYQQQnyEXDqw/t8M1m/duoWHhwf58+dn4cKFGBj8J5sphBBCCJFr5NZpMP/JKNbd3f2dyw/u27fv01RGCCGEEEKID/SfDNaFEEIIIcR/TO4cWJdgXQghhBBC6L7XV8zJTXJnq4UQQgghhMgBZGRdCCGEEELovlz6gKmMrAshhBBCCKGjJFgXQgghhBC6T6HI3NcHmD59Ou7u7piYmFC2bFmOHj361vyxsbGEhITg5OSEsbExhQoVYsuWLe91TJkGI4QQQgghdJ5Ckb1jzCtXrqRXr17MmjWLsmXLMmnSJGrVqsXFixext7dPkz8xMZEaNWpgb2/P6tWrcXFx4ebNm1haWr7XcSVYF0IIIYQQ4h0mTpxIp06daN++PQCzZs1i8+bNzJ8/n/79+6fJP3/+fKKjozl06BCGhoZA6m8BvS+ZBiOEEEIIIXRfNk6DSUxM5Pjx41SvXl2dpqenR/Xq1QkNDdW6z4YNGwgKCiIkJAQHBwf8/f0ZPXo0KSkp73VsGVkXQgghhBC6L5NXg0lISCAhIUEjzdjYGGNj4zR5IyMjSUlJwcHBQSPdwcGBCxcuaC3/2rVr7Nmzhy+//JItW7Zw5coVvv/+e5KSkhg6dGiG6ykj60IIIYQQItcZM2YMFhYWGq8xY8ZkWvlKpRJ7e3tmz55NyZIladGiBQMHDmTWrFnvVY6MrAshhBBCCJ2nyOSR9QEDBtCrVy+NNG2j6gC2trbo6+sTERGhkR4REYGjo6PWfZycnDA0NERfX1+d5ufnx/3790lMTMTIyChD9ZSRdSGEEEIIkesYGxtjbm6u8UovWDcyMqJkyZLs3r1bnaZUKtm9ezdBQUFa9ylXrhxXrlxBqVSq0y5duoSTk1OGA3WQYF0IIYQQQuQECr3Mfb2nXr16MWfOHBYtWsT58+fp0qULT58+Va8O06ZNGwYMGKDO36VLF6Kjo+nevTuXLl1i8+bNjB49mpCQkPc6rkyDEUIIIYQQOk+hl7nTYN5XixYtePjwIUOGDOH+/fsUK1aMbdu2qR86vXXrFnp6ry4CXF1d2b59Oz179iQwMBAXFxe6d+9Ov3793uu4EqwLIYQQQgiRAV27dqVr165at+3bty9NWlBQEIcPH/6oY0qwLoQQQgghdF8mP2CaU8icdSGEEEIIIXSUjKwLIYQQQgjd9wEPhf4XSLAuhBBCCCF0Xmavs55T5M5LFCGEEEIIIXIAGVkXQgghhBC6L5eOrEuwLjJJ7vwAZZiBYXbXQOcN/etEdldBpw2vVCK7q6Dzhm7f/e5MuZjC0iO7q6DzVE8j3p1JZJ9cGqzLNBghhBBCCCF0lIysCyGEEEIInafIpavB5M5WCyGEEEIIkQPIyLoQQgghhNB9uXTOugTrQgghhBBC5yn0cmewLtNghBBCCCGE0FEysi6EEEIIIXRfLn3AVIJ1IYQQQgih+3LpnPXceYkihBBCCCFEDiAj60IIIYQQQucpZGRdCCGEEEIIoUtkZF0IIYQQQui+XDqyLsG6EEIIIYTQfbl0NZjc2WohhBBCCCFyABlZF0IIIYQQOi+3PmAqwboQQgghhNB9erkzWJdpMEIIIYQQQugoGVkXQgghhBA6TyEPmAohhBBCCCF0iYysCyGEEEII3ScPmAohhBBCCKGjcmmwrvPTYCpXrkyPHj2yuxofrF27djRq1Ej9Pqe3RwghhBBCfDoysp6OGzdu4OHhwcmTJylWrFimlbt27VoMDQ0zrbz/ApVKxZSZc/lj3UYePX5MiaKBDPuxD+5urm/db+nKNcxbtIyHUdH4FvJmcL+eBPoXVm9PSEjg54nT2LJ9F4mJSZQPKsPQH/tga2OtzjNq7K+cOHWGS1eu4eXhxp8rF2kc49qNmwz96ReuXrvB4ydPsbezpX6dGnT99hsMDXXn47N0xwnmbzxKZNxTfAvYM7BddQK9nbTmXbX7FBv2h3P534cAFPZwpGeLimnyX70TxYRl+/jn/G1SlCq8XGyY3LMRzrbmWd6ezLZ07VbmrdhAZHQsvl5uDOregcDCBbXmvXz9NlPmrSD80jXu3n/IgK7taNu8vkaef8LOMW/Fn4RfvMbDqBim/dSX6hXKfIqmZDu3UuUJ7tAb5yIlMLN3ZkVIUy7s3pDd1cpySzfsZd4f24mMjsPX05VBIa0I9PXQmvfyjTtMWbyB8Ms3uRsRxYDvWtC2SfU0+SIiYxg/dw1//3OW5wmJFHC2Z3SfdgQUcs/i1mSOpX/8ybzfV6V+Bxf0YnCfrgQW8U03/9ZdfzH5t4XcuXcfd1cX+nTtRKVyZbXmHTJmEivXbWJAzy60a9VUnT5z/lL+OniE85euYmhowLE9f2Z6u7LK0nU7mLdyc+o55FWAQd3aEujnpTXv5ev/MmXBasIvXeduRCQDQr6i7Rd1NPIs/3MXyzfs4s791O9yb/f8hLRpTMWyxbK6Kdkmt66zrvMj69khMTExy8q2trbGzMwsy8rPieYsXMqS5asZ9uMPrFo8B1NTEzqE9CIhISHdfbZs38WYCVMJ6fwN65bNx7eQNx2+70VUdIw6z+jxU9j790EmjRvFkrnTePAwkq69f0xTVtOG9ahbs5rW4xgaGNCofh3mz/iVbeuW82OfbvyxdgNTZ839+IZnki2h5xm7ZC8hTcuxZnRbfNzs6PTzKqLinmrN/8/5W9QN9mPhoJYsH/4VTjZmdByziojox+o8tyJi+HLYUjycbVg0uBXrx7ajS+MgjA31P1WzMs2W3Qf5efoiQto1Y+3ccfh4u9OxzyiiYuK05n/+PAFXZwd6d/4SO2tLrXninz/H18udIT07ZmHNdZOhaV4iLpxm84hu2V2VT2bLvn/4+bdVhHzVgLUzBuPjmZ+OP04iKuaR1vzPExJxdbSl9zdNsLO20Jon7vFTWvUci4GBPnN+6s7mOcPp920zLPLlycqmZJotO/cyZtIsQjp+zbrFs/At6EmHbv01voNfd+J0OL0H/8QXn9dm/ZJZVKtUjpAfhnLp6vU0eXfuPcCps+ext7NJsy0pOZna1SrSqmmDTG9TVtqyJ5SfZy4lpG0T1s4ehY9XATr2/Tn976GEBFyd7en9bct0v4cc7Kzp3akla377idWzRvFZ8SKEDJrI5ev/ZmFLsplCL3NfOUSOqKlSqaRv375YW1vj6OjIsGHD1NtiY2Pp2LEjdnZ2mJubU7VqVU6dOqXefvXqVRo2bIiDgwP58uWjdOnS7Nq1S6N8d3d3Ro4cSZs2bTA3N+fbb7/FwyN1xKR48eIoFAoqV678znqmpKTQq1cvLC0tsbGxoW/fvqhUKo08b06DmTFjBgULFsTExAQHBwe++OILjXaPGTMGDw8PTE1NKVq0KKtXr9Y4XocOHdTbfXx8mDx5ssbx9u3bR5kyZcibNy+WlpaUK1eOmzdvqrf/+eeflChRAhMTEzw9PRk+fDjJycnvbGtmUalULF62ii6d2lK9SgV8C3kzbuRgHjyMZNfe/enut+D3lTRv0oCmDevh7eXB8IE/YGJizJr1mwB4/PgJa9Zvon+v/xFUpiT+hX0ZPXwgJ0+dIez0WXU5g/r15MsWTXHN76z1OK75XWjasB6+PgVxcXakWuUKNKhTk2MnT2nNnx0WbT5Gs6qBNKkcgHd+W4Z1qIWJkSFr953Rmv+Xrg1oXbM4fu4OeLrYMPLb2ihVKkLPvjovJq3cT8VinvzwZWUKezhQwMGKqqUKYmOR91M1K9MsXLWRZvWr07RuVbzdXRne+9vUc2XzHq35A/y86ft9G+pVK4+hkfa7YBU/K0GPTq2oUVH7qOB/2ZX929kzeSgXduWcEc2PtXDNTprVqUDTWuXwdnNmePevMDE2Ys32g1rzB/h40PfbZtSrUibdO3BzV23Dyc6KMX3aE+jrQX4nO8qXKkIBZ/usbEqmWbBsDc0b1aVpg9p4e7oxvH+P1M/Vxm1a8y9esZYKn5Wm49ct8PJwo8d37Sns683vqzTPo4gHkYycMI3xIwZgaJC277p925Z2rb+gkLf2uxq6auEfW2lWrwpN61TC2z0/w3t9k9pfW//Smj/A14u+37WmXtWgdM+hqsElqPRZMdzzO+Lh6kTPjs3JY2rCqXNXsrIpIhvkiGB90aJF5M2blyNHjjBu3DhGjBjBzp07AWjWrBkPHjxg69atHD9+nBIlSlCtWjWio6MBePLkCXXr1mX37t2cPHmS2rVr06BBA27duqVxjPHjx1O0aFFOnjzJ4MGDOXr0KAC7du3i3r17rF279p31nDBhAgsXLmT+/PkcOHCA6Oho1q1bl27+Y8eO0a1bN0aMGMHFixfZtm0bFStWVG8fM2YMixcvZtasWYSHh9OzZ0+++uor/vor9cOtVCrJnz8/f/zxB+fOnWPIkCH8+OOPrFq1CoDk5GQaNWpEpUqVOH36NKGhoXz77bfq20j79++nTZs2dO/enXPnzvHbb7+xcOFCfvrpp4z+aT7av3fu8jAyiuCypdRpZmb5KOpfmJOvBdWvS0xKIvz8RYLLllan6enpEVy2lHqfs+cvkpScTPBnr8r18nDD2dFBI1h/Xzdv/cv+Q0coXbL4B5eRmRKTUwi/fp8gf3d1mp6egiB/N8Iu381QGc8TkkhOVmKRzwQApVLFXyev4u5kTccxqyjXeRotBi1h1z+Xs6IJWSoxKYnwS9cILhWoTtPT0yOoZABh4RezsWYip0hMSib88k2Ci/up0/T09Agq7kfY+asfXO6e0FP4F3Sn+8hZBDfrReMuI1i15e/MqHKWS0xKIvzCJYJLl1Cn6enpEVy6BCfPnNO6T9iZcwSVKaGRVv6z0oS9ll+pVPLD0J/p8FVzCnq5Z0nds0NiUjLhl64TXNJfnaanp0dQCX/CwjPnezUlRcnmPaE8e55AsSLemVKmTlIoMveVQ+jOpNu3CAwMZOjQoQAULFiQadOmsXv3bkxNTTl69CgPHjzA2NgYSA26169fz+rVq/n2228pWrQoRYsWVZc1cuRI1q1bx4YNG+jatas6vWrVqvTu3Vv9Xl8/9Xa/jY0Njo6OGarnpEmTGDBgAE2aNAFg1qxZbN++Pd38t27dIm/evNSvXx8zMzPc3NwoXjw1CExISGD06NHs2rWLoKAgADw9PTlw4AC//fYblSpVwtDQkOHDh6vL8/DwIDQ0lFWrVtG8eXMePXpEXFwc9evXx8srdV6cn9+rf3CGDx9O//79adu2rbr8kSNH0rdvX3V/Z7WHkakXVTbW1hrpNjbWREZFad0nJiaWlJQUrftcu5F6ERYZFYWhoSHmb0w5srGx5mFU9HvXs2XbzoRfuERiYiItmjakexfdmP4Q++gZKUoVNhaat85tLPJy/W7G2jl+2V/YW+Uj+EXAH/XoKc+eJzF3wxG6NS9P71aVOHDqOt1+XcfCQS0pU7hAZjcjy8TEPSYlRYmNleZUBFtrS67fupNNtRI5ScyjJ6QoldhYaT6rYWtlzvXb9z+43Nv3HrJ80z7aNa1B51Z1OXPxBj/NWIGhgQGNawZ/bLWzVExsXOrnytpKI93G2oprN29r3ScyKgbbNPktiYx+9T01Z/EKDAz0adOiceZXOhvFxD1+cQ698T1kZc71WxkbVEnPxWu3aBUyjITEJPKYmjBtRE+83fN/VJlC9+SYYP11Tk5OPHjwgFOnTvHkyRNsbDTntcXHx3P1auqIx5MnTxg2bBibN2/m3r17JCcnEx8fn2ZkvVSpUnyMuLg47t27R9myr26LGxgYUKpUqTRTYV6qUaMGbm5ueHp6Urt2bWrXrk3jxo3JkycPV65c4dmzZ9SoUUNjn8TERHVADzB9+nTmz5/PrVu3iI+PJzExUf1ArLW1Ne3ataNWrVrUqFGD6tWr07x5c5ycUh8kPHXqFAcPHtQYSU9JSeH58+c8e/aMPHm0z51MSEhIM5/cOCVBfcH0Nhu2bGfoqF/U73+b8stbcuuOX8eO4OnTZ1y4dIVxk6Yzb/FyOrX7Mrur9dHm/HmYraEXWDS4JcZGqV8HKmXq+Vq1pDft6qbevfBzd+DkpTus3BWWo4J1IXSVSqWiSCF3en2TOrhT2LsAl2/cYcXmv3Q+WM8KZ89fYvGKdaxdMjPXPkT4ITxcnVk3dzSPn8Sz/e8j9P95FksmDfrPBuy59dzIEcH6m6unKBQKlEolT548wcnJiX379qXZx9LSEoA+ffqwc+dOxo8fj7e3N6ampnzxxRdpHiLNm/fTz8U1MzPjxIkT7Nu3jx07djBkyBCGDRvGP//8w5MnTwDYvHkzLi4uGvu9DIpXrFhBnz59mDBhAkFBQZiZmfHLL79w5MgRdd4FCxbQrVs3tm3bxsqVKxk0aBA7d+7ks88+48mTJwwfPlx9J+B1JiYm6dZ7zJgxGiP6AEN//IFhA/u+s81VK5WnqH8R9fvEpNS/Q1R0NPZ2tur0qKhofH20r9ZhZWWJvr4+UdGaI8dRUdHqlV5sbWxISkri0ePHGqPrUVHR2NlojshnhJOjAwDeXh6kKJUMGTWWb75uqb4Dk10szfOgr6cgKu6ZRnpU3FNsLd9+Ts/fdJQ5G44w/8fm+Li9midraZ4HA309vFw0L4I9XWw4cTFnjUZbWZihr6+X5iGuyOhYbNN5aEuI11mZ50NfTy/Nw6SRMY+wtf7wlZHsrC3wLqC5ApNXASd2HDjxwWV+KlaWFqmfqzceJo2KjsHWxkrrPrY2VkSmyR+L7Ys7pMfCzhAVE0uVz1urt6ekKBk7+TcWr1jLnj+XZnIrPh0rC7MX59Ab30Mxj7BN5wHkjDIyNMDNJfXuv7+PB2cvXGPxmu2M6N3ho8rVWXo5YvZ2psvRrS5RogT379/HwMAAb29vjZetbWrgd/DgQdq1a0fjxo0JCAjA0dGRGzduvLNsIyMjIHWkOSMsLCxwcnLSCJSTk5M5fvz4W/czMDCgevXqjBs3jtOnT3Pjxg327NlD4cKFMTY25tatW2na5urqqm5bcHAw33//PcWLF8fb21t9R+F1xYsXZ8CAARw6dAh/f3+WLVum7r+LFy+mKd/b2xu9t3wgBgwYQFxcnMZrQJ/uGeqnfHnz4lYgv/rl7emBna0NoUde9dOTJ085dfYcxQP9tZZhZGhIET8fQo8cU6cplUpCjx5X7+Pv54OhgYFGnms3bnL3fgTF0ik3o1RKJcnJySiV2u+YfEpGBvoU8XDk8GsPhyqVKg6H36RYQe0PzQLM3XCEmWsPMbt/M/y9NAMGIwN9/D0duX5P82Loxr2YHLdso5GhIUUKeRJ6/NXDtkqlksMnzlCsiE821kzkFEaGBhQp6EZo2Hl1mlKp5HDYeYqls+xeRhQv4s31fzWn0dz4NwJnh7QroOgaI0NDivgWIvSfVxcWSqWS0GMnKR5QWOs+xQIKc/ifkxpph44cp9iL/A3rVGfDstms//039cvezoYOXzVj7pSfs64xn4CRoQFFCnkQeiJcnZb6PXSWYkW0D0p9KKVKRWJSUqaWKbJfjhhZT0/16tUJCgqiUaNGjBs3jkKFCnH37l02b95M48aNKVWqFAULFmTt2rU0aNAAhULB4MGDUSqV7yzb3t4eU1NTtm3bRv78+TExMcHC4u1XwN27d+fnn3+mYMGC+Pr6MnHiRGJjY9PNv2nTJq5du0bFihWxsrJiy5YtKJVKfHx8MDMzo0+fPvTs2ROlUkn58uWJi4vj4MGDmJub07ZtWwoWLMjixYvZvn07Hh4eLFmyhH/++Ue9ks3169eZPXs2n3/+Oc7Ozly8eJHLly/Tpk0bAIYMGUL9+vUpUKAAX3zxBXp6epw6dYqzZ88yatSodOttbGycdsrLsw9b7lKhUNCmdXNmzl2EW4H85HdxZvKMOdjb2VK9SgV1vradu1GjSkW+apm6Wk77r1rQb8hP+Bf2JdC/MIuWrSI+/jlNGtYDUh9SbdqoPj9PmIqFhTn58uZl1NhfKR7orxGs37z1L8/in/EwMornCQmcv3gJAC9PD4wMDdmwZTsGBgb4eHthZGTImXMXmDB1FnVqVtOZddbb1ivFgJlb8Pd0JMDbicVbjxGfkETjSgEA9JuxGQerfPRqVQmAORuOMPWPA4zvWh8XO3MexqbexcljYkRek9SL1G8alKH35A2U8nWlbJECHDh1nX0nrrBocKvsaeRHaNe8Af3HTMPfx4tAP28W/bGZ+PgEmtStAkC/n6Zgb2tD786p05oSk5K4eiN16bOkpGQiIqM5f/k6eUxNcMufemHz9Fk8t+68CrT+vRfB+cvXsTDPh7OD3Sdu4adllCcv1gVePcBmmd8DR9+ixMdFE3dP+3zlnK5d0xr0/2U+/gXdCfT1YNHaXcQ/T6RJrXIA9Bs3D3sbK3p3SL1LmZiUzNUXc5FTz6EYzl+9RR4TE9xcUu9itWtSnVY9xjJr+WbqVCzN6YvXWbXlb0b0+Dp7Gvme2rduSr/h4/D38yGwiA+LVqxN/Q6uXxuAvkN/xsHelt4hqc/3tGnZhK8792L+0j+oVK4sW3bs5ez5S4z4sSeQOlpvZan5b6yhgQG2NtZ4vvabG3fvRxD36DF37z8gRank/KXUlU8K5Hchbx7TT9H0D9KuWR36//wb/oU8CPTzYtHqbcQ/T6BJ7dTv5X6jZ2JvZ0XvTi2BF+fQzRffQ8kvzqErN1K/h16MpE+Ys4KKZYri5GDL02fxbNp9iKNh55k7rl/2NPJTkGkwOY9CoWDLli0MHDiQ9u3b8/DhQxwdHalYsSIODqnTFiZOnMg333xDcHAwtra29OvXj0ePtK+N+zoDAwOmTJnCiBEjGDJkCBUqVNA63eZ1vXv35t69e7Rt2xY9PT2++eYbGjduTFyc9nVULS0tWbt2LcOGDeP58+cULFiQ5cuXU6RI6jSRkSNHYmdnx5gxY7h27RqWlpaUKFGCH39MXSu8c+fOnDx5khYtWqBQKGjVqhXff/89W7duBSBPnjxcuHCBRYsWERUVhZOTEyEhIXTu3BmAWrVqsWnTJkaMGMHYsWMxNDTE19eXjh0/7cOTndp9SXx8PENGjePR4yeULBbI3OkTNC4Ibt++Q0zsq36sW6s60TGxTJk5l4dR0fj5FGTu9AkaP3j0Y59u6Onp0a3PwNQfRQouw9ABfTSOPWjEzxw9/mq0p1HL9gDs3rya/M5OGOjrM3fhUq7fvAUqcHZy4KsWTWn3VYus6o73VjfIj5hH8UxZfYDI2Kf4udkzu38z9TSYe5GP0HvtC27FzpMkJafQfZLmkmkhTYPp+kV5AGqULsTQDjWZveEwoxftxsPZmsk9G1HSN+fNg6xbrRzRsY+YOn8FD6Nj8fN2Z874geppMHcjIlG8tt7ug8gYGnf4Qf1+/ooNzF+xgdLFCrNkyggAzl68Stvuw9R5fp6W+mNajWpX5ucfXz24/l/k7F+Sdot3q9/XHjAegLB1i1k/4L95671u5dJExz1m6uI/eRjzCD9PV+b81B3bFw+d3n0QrTGX9kFULI27jFS/n796B/NX76B0YCGWjE89twJ8PJg6tAsT569jxu+byO9oy4AuLWhQ7bNP27gPVLdGFaJj4pgyeyEPo2LwK+TF3Mlj1NNg7kU80LhDWyKwCONH/sikWQuYOGM+7q4uTP9lOIW83m8Jxim/LWLd5h3q942++g6AxTPHU7ZksY9vWBapWzUo9RxauJqH0XH4ebkxZ2w/9TSYuw+iUOi9fg7F0LjTQPX7+Ss3M3/lZkoX9WPJpEEARMc8ot+YWTyMjsUsbx58PF2ZO64f5UoFfNrGiSynUKX39KMQ7+NZZHbXQKcpL+SeNak/lMIld/wC6IcaXqnEuzPlckO37353plxMYZmz1ibPDqqnEdldBZ2mcP64xTg+lnJylUwtT6/73kwtL6vk6JF1IYQQQgiRS+TSaTA5+gHTTy1fvnzpvvbvT//XNoUQQgghhPgQMrL+HsLCwtLd9ubyikIIIYQQIhMpcucYswTr78Hb+z/8E75CCCGEELpMpsEIIYQQQgghdImMrAshhBBCCN2XS6fB5M5WCyGEEEIIkQPIyLoQQgghhNB9uXTOugTrQgghhBBC98k0GCGEEEIIIYQukZF1IYQQQgih+2QajBBCCCGEEDoqlwbrMg1GCCGEEEIIHSUj60IIIYQQQvfJA6ZCCCGEEEIIXSIj60IIIYQQQvfl0jnrEqwLIYQQQgjdJ9NghBBCCCGEELpERtaFEEIIIYTuk2kwQgghhBBC6CiZBiOEEEIIIYTQJTKyLoQQQgghdF8unQYjI+tCCCGEEELoKBlZF0IIIYQQui+XzlmXYF0IIYQQQui+XDoNRoJ1IT4FwzzZXQPd9+xhdtdApw3dvju7q6Dzhteqlt1V0GlDt2zL7iroPhOL7K6BEGlIsC6EEEIIIXSfTIMRQgghhBBCR+XSaTC58xJFCCGEEEKIHEBG1oUQQgghhO7LpdNgcmerhRBCCCGEyAFkZF0IIYQQQug+mbMuhBBCCCGEjlLoZe7rA0yfPh13d3dMTEwoW7YsR48ezdB+K1asQKFQ0KhRo/c+pgTrQgghhBBCvMPKlSvp1asXQ4cO5cSJExQtWpRatWrx4MGDt+5348YN+vTpQ4UKFT7ouBKsCyGEEEII3adQZO7rPU2cOJFOnTrRvn17ChcuzKxZs8iTJw/z589Pd5+UlBS+/PJLhg8fjqen5wc1W4J1IYQQQgiR6yQkJPDo0SONV0JCgta8iYmJHD9+nOrVq6vT9PT0qF69OqGhoekeY8SIEdjb29OhQ4cPrqcE60IIIYQQQvdl8pz1MWPGYGFhofEaM2aM1kNHRkaSkpKCg4ODRrqDgwP379/Xus+BAweYN28ec+bM+ahmy2owQgghhBBC92XyajADBgygV69eGmnGxsaZUvbjx4/5+uuvmTNnDra2th9VlgTrQgghhBAi1zE2Ns5wcG5ra4u+vj4REREa6RERETg6OqbJf/XqVW7cuEGDBg3UaUqlEgADAwMuXryIl5dXho4t02CEEEIIIYTuy8alG42MjChZsiS7d+9WpymVSnbv3k1QUFCa/L6+vpw5c4awsDD16/PPP6dKlSqEhYXh6uqa4WPLyLoQQgghhNB9etn7o0i9evWibdu2lCpVijJlyjBp0iSePn1K+/btAWjTpg0uLi6MGTMGExMT/P39Nfa3tLQESJP+LhKsCyGEEEII8Q4tWrTg4cOHDBkyhPv371OsWDG2bdumfuj01q1b6Oll/qQVCdaFEEIIIYTuy+QHTD9E165d6dq1q9Zt+/bte+u+Cxcu/KBjypx1IYQQQgghdJSMrAshhBBCCN33ng+F/ldIsC6EEEIIIXSfDkyDyQ658xJFCCGEEEKIHOA/F6zfuHEDhUJBWFhYdlclU1SuXJkePXp80mO2a9eORo0afdJjCiGEEEK8VTaus56dZBqMyHZLV65h3qJlPIyKxreQN4P79STQv3C6+bfu3MPkGXO4c/c+7gXy06dbFypVCFZvV6lUTJk5lz/WbeTR48eUKBrIsB/74O726gcIZs5dxF/7D3H+0mUMDQw5tn97muOMGvsrJ06d4dKVa3h5uPHnykWZ2/BMtHTrUeZvOEhk7BN83RwZ2KEOgQXza827audxNvx1isu3HwBQ2NOJnq2rpZt/2G8bWbnzOP3b1aJt/bQ//JATLN2wj3mrdxIZ8whfz/wM+r4FgT7uWvNevnGXKUs2En75FncfRDOg8xe0bVxNI0/VNgO5+yA6zb6t61dkSNdWWdGELLd0w17m/bGdyOg4fD1dGRTSikBfD615L9+4w5TFGwi/fJO7EVEM+K4FbZtUT5MvIjKG8XPX8Pc/Z3mekEgBZ3tG92lHQCH3LG5N9nErVZ7gDr1xLlICM3tnVoQ05cLuDdldrU9i6ca/ND9nXZqn/zm7eZcpSza9+px9+wVtG1fVyJOSomTa0s1s2HOUyJhH2Ftb0LjGZ3RpVQdFDpwOsXTdDuat3Jz6GfMqwKBubQn00/4Llpev/8uUBasJv3SduxGRDAj5irZf1NHIs/zPXSzfsIs79x8C4O2en5A2jalYtlhWNyX75KAAOzPlzlYLnbFl+y7GTJhKSOdvWLdsPr6FvOnwfS+iomO05j8RdobeA4bxRaP6rF++gGqVKxDSawCXrlxT55mzcClLlq9m2I8/sGrxHExNTegQ0ouEhAR1nqSkJGrXqEKrLxq/tX5NG9ajbs1qb82T3bYcPMvYRdsJaVaZNeM64+PuQKdRvxMV90Rr/n/Cb1C3vD8Lh7Vl+egOONla0HHkEiKiHqXJu/PIeU5d/hd7a7OsbkaW2fLXMX6es4aQr+qxdtqP+Hjmp+PAKUTFpm0vwPOERFwdben9TSPsrMy15lk9pT/7l/2sfs0f3Q2AWhVKZlk7stKWff/w82+rCPmqAWtnDE7tox8nERXzrj5qgp21hdY8cY+f0qrnWAwM9JnzU3c2zxlOv2+bYZEvT1Y2JdsZmuYl4sJpNo/olt1V+aS2/HWMn2evIeTLeqydOgAfDxc6DppKVOxjrfmfP39xDrVP/3M2548dLN/8N4O/b87m2UPo/U0j5q7eyZIN+7KwJVljy55Qfp65lJC2TVg7exQ+XgXo2PdnomLitOZ/npCAq7M9vb9tiZ21pdY8DnbW9O7UkjW//cTqWaP4rHgRQgZN5PL1f7OwJSI7vHewvm3bNsqXL4+lpSU2NjbUr1+fq1evAhAcHEy/fv008j98+BBDQ0P+/vtvAO7du0e9evUwNTXFw8ODZcuW4e7uzqRJkzJ0/AsXLlC+fHlMTEwoXLgwu3btQqFQsH79eq35Fy5cqP7FqJfWr1+f5qp848aNlC5dGhMTE2xtbWnc+FUQFxMTQ5s2bbCysiJPnjzUqVOHy5cvq7ffvHmTBg0aYGVlRd68eSlSpAhbtmxRbz979ix16tQhX758ODg48PXXXxMZGZmh9r4pISGBPn364OLiQt68eSlbtqx6Xc9Hjx5hamrK1q1bNfZZt24dZmZmPHv2DIDbt2/TvHlzLC0tsba2pmHDhty4ceOD6vOxFvy+kuZNGtC0YT28vTwYPvAHTEyMWbN+k9b8i5evokJwWTq2/RIvT3d6hHxLYb9C/L5iNZA6qr542Sq6dGpL9SoV8C3kzbiRg3nwMJJde/ery+nWpSPtvmpJoYKe6dZtUL+efNmiKa75nTO30Zls0cZQmlUvQZOqxfF2tWfYt/UxMTZk7Z6TWvP/0qMprWuXwc/DCU8XO0Z+9zlKlYrQM9c08kVEPeKneVsY170pBvo597p+4drdNKtdjqY1g/F2c2L4/1phYmzEmu2hWvMH+LjTt1NT6lUujaGh9puP1pZm2FlbqF/7jp6hgJMdZQILZmVTsszCNTtpVqcCTWuVw9vNmeHdv3rRRwe15g/w8aDvt82oV6VMun00d9U2nOysGNOnPYG+HuR3sqN8qSIUcLbPyqZkuyv7t7Nn8lAu7Pozu6vySS1ct4dmdcrRtGaQ5udsxyGt+QN83OnbsQn1KpdK9xw6ef4a1T4LpHKZAPI72FC7QgnKlfDjzMUbWdiSrLHwj600q1eFpnUq4e2en+G9vkn9t27rX1rzB/h60fe71tSrGpRu/1QNLkGlz4rhnt8RD1cnenZsTh5TE06du5KVTcleuXQazHvX9OnTp/Tq1Ytjx46xe/du9PT0aNy4MUqlki+//JIVK1agUqnU+VeuXImzszMVKlQAUn+K9e7du+zbt481a9Ywe/ZsHjx4kKFjp6Sk0KhRI/LkycORI0eYPXs2AwcOfN8mpLF582YaN25M3bp1OXnyJLt376ZMmTLq7e3atePYsWNs2LCB0NBQVCoVdevWJSkpCYCQkBASEhL4+++/OXPmDGPHjiVfvnwAxMbGUrVqVYoXL86xY8fYtm0bERERNG/e/IPq2rVrV0JDQ1mxYgWnT5+mWbNm1K5dm8uXL2Nubk79+vVZtmyZxj5Lly5V91tSUhK1atXCzMyM/fv3c/DgQfLly0ft2rVJTEz8wB78MIlJSYSfv0hw2dLqND09PYLLluLk6bNa9wk7HU5Q2VIaaeWDyhJ2OhyAf+/c5WFkFMGv5TEzy0dR/8LplpmTJSYlE37tLkGBry469PT0CArwJOxixkZXnicmkZyixCKfqTpNqVTSb+pavmlYjoKuOTe4SkxKJvzyLYKL+6rT9PT0CCruS9j5a2/Z8/2OsWHPUZrUCsqRt+ZT++gmwcX91GmpfeRH2PmrH1zuntBT+Bd0p/vIWQQ360XjLiNYteXvzKiy0DHqz1kxH3Wanp4eQcV8CTt//YPLLe7nSWjYRa7/GwHAhWv/ciL8KhVLFfnoOn9KiUnJhF+6TnDJVz8xr6enR1AJf8LCL79lz4xLSVGyeU8oz54nUKyId6aUKXTHe89Zb9q0qcb7+fPnY2dnx7lz52jevDk9evTgwIED6uB82bJltGrVCoVCwYULF9i1axf//PMPpUqlBlNz586lYMGMjUbt3LmTq1evsm/fPhwdHQH46aefqFGjxvs2Q8NPP/1Ey5YtGT58uDqtaNGiAFy+fJkNGzZw8OBBgoNT50UvXboUV1dX1q9fT7Nmzbh16xZNmzYlICAAAE/PV4HTtGnTKF68OKNHj1anzZ8/H1dXVy5dukShQoUyXM9bt26xYMECbt26hbNz6mhvnz592LZtGwsWLGD06NF8+eWXfP311zx79ow8efLw6NEjNm/ezLp164DUiyelUsncuXPVgcWCBQuwtLRk37591KxZ80O68IPExMSSkpKCjbW1RrqNjTXXbtzSuk9kZBS2WvJHRkUB8DAydR6xtjJf5vkviX38jBSlChuLfBrpNpZ5uX4nY3dvxv++E3srM4JfC/jnrj+Ivp4eX9ctm6n1/dRiHj0hRanExlLzNrutpTnXb0dkyjF2h57i8ZN4GtfImfP51X30xlQEWytzrt++/8Hl3r73kOWb9tGuaQ06t6rLmYs3+GnGCgwNDGhcM/jdBYgcI/1zyEwdaH+Ib5vX5Omz59T9dgT6egpSlCp6tG1Ag6pl3r2zDomJe/yifzSnjNlamXP91t2PKvvitVu0ChlGQmISeUxNmDaiJ97u2p8/+k/IgQMimeG9g/XLly8zZMgQjhw5QmRkJEqlEkgNJP39/alZsyZLly6lQoUKXL9+ndDQUH777TcALl68iIGBASVKlFCX5+3tjZWVVYaOffHiRVxdXdWBOqAxAv6hwsLC6NSpk9Zt58+fx8DAgLJlXwUtNjY2+Pj4cP78eQC6detGly5d2LFjB9WrV6dp06YEBgYCcOrUKfbu3aseaX/d1atX3ytYP3PmDCkpKWn2SUhIwMbGBoC6detiaGjIhg0baNmyJWvWrMHc3Jzq1aur63PlyhXMzDTnID9//lw9neldEhISNOZ/AxinJGBsbJzhtgjdMGfdfrYePMuiYe0wNjIEIPzqXZZsOcyacZ1z5Ejxp7Z620EqlC6Cg41ldldFp6hUKooUcqfXN00AKOxdgMs37rBi818SrIsM2fr3CTbuPcr4vu3xdnPiwrV/Gf3bauytLWlc47Psrp5O8HB1Zt3c0Tx+Es/2v4/Q/+dZLJk06L8bsOegqSuZ6b2D9QYNGuDm5sacOXNwdnZGqVTi7++vnkLx5Zdf0q1bN6ZOncqyZcsICAhQjzhnBz09PY1pOYB6+spLpqamfIyOHTtSq1YtNm/ezI4dOxgzZgwTJkzgf//7H0+ePKFBgwaMHTs2zX5OTk7vdZwnT56gr6/P8ePH0dfX19j28mLAyMiIL774gmXLltGyZUuWLVtGixYtMDAwUJdRsmRJli5dmqZ8Ozu7DNVjzJgxGnchAIb++APDBvZ9r/ZYWVmir69PVLTmqhpRUdHY2lhr3cfW1oZIrflTL1bsbFP3i4qOxt7OViOPr0/OnE/8NpZmedDXU6R5mDQq9im2lmkvEF83/8+DzFl3gPlD2uDj/uoC+Nj5m0TFPaXqd7+q01KUKsYt3sHizYfZPbNn5jYiC1mZ50NfTy/Nw6SRsY+wTeehtv+3d99hTV1vHMC/QRkiQ6YDkS1DQFQcuLcWJ7hFUeuu1gFatD8FV+uoWLW17kUdWBdqtaDixlUrw43iwAkyBUVW+P2BjUbAUcF7Y76f58lTOTlJ3nuaC29O3nPux3iYkIwzUdfxy/SRn/xcQpGN0VuLSZNSn8FQ/7+PkZG+LqxryP+Os6pRFQdPXfzPz0niVPJ7KOOTzrOf1u7C8N4d0Kll4TfxthYmeJSYglV/hClUsq6nq/1qfOQXkxaeY8Uv0P5QaqrlYWZS+Pvb0dYCl6/fRtDOMMzyHfpJz0vi8lEfUZKTk3Hjxg1MmzYNbdq0gb29PVJT5Xft6NatG16+fInQ0FBs2bIFXl5esvtsbW2Rl5eHyMjXC99u3bpV5DlKYmtri/v37yMh4fXXan///fc7H2NkZISMjAw8f/5c1vb2HuzOzs4IDw8v9vH29vbIy8vDuXPnZG3/joODw+vtBU1NTTFq1Cjs2rULvr6+WL16NQCgbt26uHLlCszNzWFtbS13q1ix4gcd97/q1KmD/Px8JCYmFnmuN79t8PLyQmhoKK5cuYIjR47I/T+oW7cubt68CWNj4yLPoav7Yb80pk6divT0dLnb1EnjP+pYAEBNVRW17G1x5twFWZtUKsWZ8/+gjrNjsY9xca6Fs+f/kWs7ffZvuDgX1jBWN6kGI0MDnDn3uk9m5nNEX75a4nMqMjXV8qhlWQ1nL72uC5VKpTh76TZcbEueWVkTcgrLd57AqmkD4GhtIndf1xa1ERI4GrsWjpLdjPW18XXXxlgzbWCZHUtZUFMtj1o2NXAm6oasTSqV4mzUDbjYl7y4+EPtOngGBrraaNFAcd9bhWNkhjNR12RthWN0DS4lbCv3IerUssadB/JlNHcfJKBaZYP//JwkTu8+z4rf/vNDZGXnQuWtb/dUVCSQvjUBJ3ZqquVRq6YFzly8ImuTSqU4e/EyXGqV7iSStKAAOW9NSH5RuMD0/fT09GBgYIBVq1bh1q1bOHLkCHx8fOT6VKxYEd27d8f06dNx7do19Ov3es9hOzs7tG3bFiNGjMD58+cRGRmJESNGoEKFCh/0dXu7du1gZWWFQYMGISYmBhEREZg2bRoAlPj4hg0bQlNTE99//z3i4uKwZcsWbNiwQa5PQEAAtm7dioCAAFy7dk22SBQAbGxs0K1bNwwfPhynTp1CdHQ0BgwYABMTE3Tr1g0AMGHCBISFheHOnTu4ePEijh49Cnv7wsVaY8aMQUpKCvr164e///4bcXFxCAsLw5AhQ5Cfn/9hA/9KzZo14eXlBW9vb+zatQt37tzB+fPnMXfuXOzfv1/Wr3nz5qhSpQq8vLxgYWEhV8Lj5eUFQ0NDdOvWDSdPnsSdO3dw7NgxjBs3Dg8efNiCRHV1dejo6Mjd/msJzJABffDH7n3YvfcA4m7fxYwfFyIr6yU8u3UCAHw3bTYCly6X9ffu1xsnT5/FuqCtiLtzD7+sWIvLV69jQN+eAArfB979e2P5mo0IP3YSN27G4bvps2FsZIi2rZrJnufR4ye4diMWjx4nIF+aj2s3YnHtRiyev9oxBwDuxT/AtRuxeJqUjJfZ2bI+YvtFOKiLG7Yf/gchx6IQ9+ApZq7ej6zsXHi0qgMA8Fu6C4s2H5b1X737FJYGH8UP33SDiVElPE3NwNPUDDzPKixt0tPWRM0aleVu5cupwLCSFixMDIuNQcwGe7bB9r9OYfehM4iLf4wZv2xF1stseLYvrDH3+2kDAteFyPrn5ObhWtx9XIu7j9y8fCQkpeFa3H3ceyS/EF4qlWL3oTPo3q4Ryr/1TZeiGdyjHbYfOIndB08XjtHSzch6mQPPDk0AAH4L1iJw7S5Z/8Ixise1uHjk5uYhISkV1+Lice/h6zEa7NkW0dfuYMXW/bj3MBH7jpzDHwdOwKtLy899eJ+VmmZFVLGrjSp2heueKlW3QBW72tCtavqeRyq2wR6tsT00ArsPnS18D/0ajKzsbHi+Wsvht3ADAteHyPoXOc+Si55nrRo6YUVwKI6dv4QHCck4FBGFDbuOoJ1b7c99eJ9scK+vsP3Po9gdegJx9x5ixs/rC38PdWwBAPD7cTkCVwfL+ufk5uHarbu4dusucvNenWO37uLew9cfgANXB+Pv6Gt48OQpbtyOR+DqYJyPuoYubZt89uP7bCSS0r0piI8qg1FRUUFwcDDGjRsHR0dH2NraYunSpWjZsqVcPy8vL7i7u6N58+aoUaOG3H1BQUEYOnSoLKGcO3curly5Ag0Njfe+frly5RASEoJhw4ahfv36sLS0xE8//YQuXbqU+Hh9fX1s2rQJkydPxurVq9GmTRvMmDEDI0aMkPVp2bIltm/fjtmzZ2PevHnQ0dFB8+bNZfevX78e48ePR+fOnZGTk4PmzZvjwIEDUFUtrPHNz8/HmDFj8ODBA+jo6KBjx474+efCEoJq1aohIiICfn5+aN++PbKzs2FmZoaOHTtCReXjP9WtX78ec+bMga+vLx4+fAhDQ0M0atQInTt3lvWRSCTo168fFixYAH9/f7nHa2pq4sSJE/Dz84OnpycyMjJgYmKCNm3aQEfn08sCPpZ7h7ZISU3D0uVr8DQ5Bfa2NlizLFBWBvP4SQJUVF6fUHVdnLDwxxlYvGwVFv26EuY1qmPZormoaf16lnT4YC9kZWXBf84CPMvIRD0XZ6xZFij3gWLp8jXYve/1Fpfd+w4BAASt/gUNXQvXVEybNQ/n/4ks0id8/w5Ur/ZxJUxlyb2JI1KfPcfS4KNISsuEvXkVrPrfAFkZzOOkdLkxDD74N3Lz8jF+4R9yzzOmVwuM7dPqs8b+Obi3cEVKeiZ++f1PPE19BnvL6lg951vZ1/OPElPkPuwnJqfDY8wbC8J3Hsa6nYdR38kGv//0enLidOR1PEpMgecXUH/t3rI+UtIz8EvQnldjZIrVP4x/xxilwWP0bNnP63YcxLodB1HfuSZ+XzgZQOH2jr8EjMaidbvx26Y/Ub2KIaaO7oMubRSnfOG/qOZYD4ODXn9T23HqQgBA1O4ghEz9cksTZOfZpj/xNOUZ7K2qY/XssW+8h1IheWMmMzElHR5j58p+ljvPFhSW2k0b3RtLg/Zh1rJtSE7LgLG+Lvq4N8U3/d0/78GVAvfWboXn2IYdeJqSDnsrM6ye7ycrg3mUmAyJypvnWCo8hr/e7W7dtv1Yt20/6te2x++LCycpU1KfwW/uCjxNSYN2RU3YWppizQI/NHEVrvSYyoak4O2C7s/swYMHMDU1xeHDh9GmzcdffCYiIgJNmzbFrVu3YGX137+ypU/04r/tG68spHGHhA5B9CRalYUOQdxUeMHp95nZQdwXMBNawIFQoUMQP41PqyH/0kmqub6/UxmS/jW1VJ9P5au57+8kAp/9t/+RI0eQmZkJJycnPH78GN999x3Mzc3lZrLfZffu3dDS0oKNjQ1u3bqF8ePHo0mTJkzUiYiIiOiL89mr63Nzc/H999+jVq1a8PDwgJGREY4dOwZVVVVs3rwZWlpaxd5q1SpcQJiRkYExY8bAzs4OgwcPRv369bFnj2JeKS4+Pr7E49XS0kJ8fPF7jRMREREpHSVdYCp4GcybMjIy5HZ6eZOqqirMzMw+c0RlKy8vD3fv3i3xfnNzc9mWi6LHMph3YhnM+7EM5j1YBvNeLIN5N5bBfACWwbyT4GUwYdNL9flUOsx+fycRENVvf21t7SIX6/mSlS9fHtbWvCwwERERERVPVMk6EREREVGxFGi7xdLEZJ2IiIiIxE+B6sxLk3IeNRERERGRAuDMOhERERGJH2fWiYiIiIhITDizTkRERETip6Kcc8xM1omIiIhI/JR0Nxjl/IhCRERERKQAOLNOREREROLHBaZERERERCQmnFknIiIiIvFT0pl1JutEREREJH5cYEpERERERGLCmXUiIiIiEj+WwRARERERiZSSJuvKedRERERERAqAM+tEREREJH6cWSciIiIiIjHhzDoRERERiZ+Sbt3IZJ2IiIiIxI9lMEREREREJCacWadSUiB0AOL2+JrQEYifTVWhIxA1SSULoUMQvYADoUKHIGoz3TsKHYLozTgXJ3QI9C5KOrPOZJ2IiIiIxE9Ja9aV8yMKEREREZEC4Mw6EREREYmfkpbBKOdRExEREREpAM6sExEREZH4KenMOpN1IiIiIhI/JU3WlfOoiYiIiIgUAGfWiYiIiEj8VJRz60Ym60REREQkfiyDISIiIiIiMeHMOhERERGJH2fWiYiIiIhITDizTkRERETip6Qz60zWiYiIiEj8JMq5G4xyfkQhIiIiIlIAnFknIiIiIgXAmXUiIiIiIhIRzqwTERERkfhxgSkRERERkUhxgSkREREREYkJZ9aJiIiISAEo5xyzch71F6Jly5aYMGFCqT7nhg0bUKlSpVJ9TiIiIqJPJpGU7k1BcGad5PTp0wfu7u6f9TU3b9uJtRu34mlyCuxqWmG630Q4OzqU2P+vQ0ew5Lc1ePjoCcxrVMekcaPRopmb7P6CggIsXb4W23fvw7OMDNSt7YQZ30+CuZmprM+o8X64HnsTySlp0NXRhltDV0waNxqVjQ0BANnZ2Qj4YSGuXLuBuDv30LJZY/z289yyG4RPtPnETawLv46kZy9hZ1IJ/+tZF87mBsX2PRj1AKsOXkV8Uiby8qUwM9LG4Na26NbAXNbn1wOXceCfeDxJewHVcipwMNXHhC5OqF3Cc4rd5r1HsXbHISSlpsPOsjqmfdMXzrYWxfa9efcRlv6+F1duxuNRYjKmjuyFQR5t5fq09v4ejxKTizy2f+cW8B/bv0yOobRt3r4Hazf9UXje2Vhh+qSxcK5lV2L/vw4fx5KVG/Dw8ROYm5pg0tjhaNGkYbF9/ecuxrbdf2LqxNEY3K+HrH35us04HnEO12LjoKpaHheO7Cn14yorm/cdf/Ueelb4HhrdG8625sX2vXnvEZb+/uer91AKpo7oiUEereX65OdL8evm/dh75DySUp/BWF8XHu0aYXS/ryBRoCTivzBzbYrGQ31RrVZdaBtXQ/CYHrgevlfosEpd4Tm2/Y1zbMwHnGMb3zjHhr3nHNv/6hzzBAA8ePQEv63djLMXopCUkgJjQwN0/aoNRg3pDzVV1TI5Rvo8OLNOcipUqABjY+PP9noHwsIxN/BXjBk5BLu3rIVdTWsM/cYHySmpxfa/GHUJvlNnomf3zgjZug5tWjbDGJ+piL11W9Zn9YbN+H3rDsz4fhL+CFqFChUqYOgYH2RnZ8v6NKpfF4vnz0Lo7i1Y+tMc3L//EOMnT5Pdny+VQl1dHQP79YRbw3plNwCl4MA/8Zi/OwpjvqqFnd+1h61JJQz/7TiSM14W279SRTWM7OCArT5tETKlIzwaWeB/m8/j1LXHsj7mxtqY1qsu9kztiE0T28DEQBPDlh1HSgnPKWYHjv+Neat3YMyATtj16/9ga1kdw/63FMlpz4rt/zI7B6ZVDOH7tQeM9HSK7bNj6VSc3LJAdlv34wQAQIdm4n6v/OvAoaOYu3gFxgwbiN1BK2BnY4mh46aUfN7FXIHv9B/Qs2tHhPy+Am1aNMGYyQGIjbtTpO+ho6cQffkajI2KfrDLzctDxzbN0a9Hl1I/prJ04PgFzFu1E2O8OmHXL1Nha2GCYdN+QXJaRrH9X7589R4a0r3E99Dq7Qexdf8JTP+mN/av8ofv192xZsch/L73WBkeiTioVqiIhOsx2D9rnNChlJkDh45h7uKVGDNsAHYHLX91jk19zzn246tzbPmrc2zGR51jt+/dR0GBFLOmjsf+4DWYOnEUgnf9iZ9/W1cmxygIJZ1ZZ7Ku4PLy8jB27Fjo6urC0NAQ06dPR0FBAQDA3Nwcc+bMgbe3N7S0tGBmZoa9e/fi6dOn6NatG7S0tODs7IwLFy7Inu9zl8Gs3xSM3p5d0KNbJ1hbWWDm/yZDQ0MDO0P+LLZ/0NbtaNa4IYYN6g8rS3NMGDMcDvY1sSl4J4DCWfWgLdsxerg32rZqBrua1lgwexoSnybj8NGTsucZPKAPXJwdYVKtCuq6OGH4kAGIunQFubl5AADNChUw83+T0NuzK4wMxD2bvPHoDfRys4RnI0tYV9XFjD6u0FArj11niv6SB4AGNsZoV7s6rKrooIaRFrxb1kTNarr4Jy5J1qezqxka21WBqaEWbKrqYopHHWS+zMWNR+mf67BKzYZdh9GrY1P0aN8E1mbVMPNbL2ioq2Fn2Oli+zvZmuO74T3RqWV9qJYwG6VfSRtG+rqy27HzMahR1QgNnGuW5aGUmvVbdqJ3d3f06NIR1pZmmDllAjQ01LFzX2ix/YOCd6FZo/oYNrAPrCzMMGHUEDjYWWPTH/Iz4wmJSZgd+CsWzpoK1fJFv7gdN2IQBvfviZrWxX+rIVYbdh9Br6+aoEd7N1ibVcXMb/sVvocOvuM9NMwTnVq6QlW1+C+wI6/dRptGzmjZwAnVKxugY7O6aFLXHpdu3C3DIxGHWyfDcGRJAK4fVpxvVj5W4Tn21Rvn2PhX51hYsf2Dgne/Osd6vzrHBr/jHFtW7DnW3K0+5vpPRtNGrjA1qYo2zRvja69eOHj0VJkdJ30eTNYV3MaNG1G+fHmcP38eS5YswaJFi7BmzRrZ/T///DOaNGmCyMhIdOrUCQMHDoS3tzcGDBiAixcvwsrKCt7e3rIE/3PKyc3FlWuxaNzQVdamoqKCxg1dERlzpdjHRMVchtsb/QGgqVtDRMVcBgA8ePgIT5OS0bhhfdn92tpaqO3ogMhXfd6Wlv4M+/46iDq1HUv8wypWOXn5uHI/FW62lWVtKioSuNlWRtTdpHc8slBBQQHO3EjA3cQMuFoblfgaf5yOg3YFVdiZVCqt0D+LnNw8XLkZj8Z17GVtKioqcKtjh6hrt9/xyI97jb1HzsGzQ2OFKF/Iyc3FleuxaFy/rqxNRUUFjevXReSlq8U+JurSVbg1qCvX1rRRfUS90V8qlWJywDwMHdAbNlbmZRK7EGTvIRdbWZuKigrcXOwQda34D8Qfoo69Jc5E3cCdBwkAgOu3H+DilTg0d631yTGTsErvHHNF1KVrsp8Lz7H5GDqg1wefYxmZz6Gro/3xByFaKqV8UwyKlZlQEaampvj5558hkUhga2uLS5cu4eeff8bw4cMBAO7u7hg5ciQAwN/fH8uXL0f9+vXRq1cvAICfnx/c3NyQkJCAKlWqfNbYU1PTkZ+fDwN9fbl2AwN93L57r9jHJCWlwFBf763+ekhKTgEAPE0q/K/BO/r866clv2Fz8C5kvXwJF6daWLF0wScdjxDSnucgX1oAAx0NuXYDbQ3cSSi+zAMAMrJy0HLaPuTk5UNFRQL/3vXQxE7+///Ry48waf0ZZOXmwUinAtaOaQE9LfUyOY6ykvosE/lSKQwqyf+xMqykgzv3n5TKa4SfiUJGZhY82jUulecra6lp6cjPlxY9R/T1cPve/WIfk5ScWvS806+EpJTX59TqoGCUL18O3n08Sj9oAcneQ2+VsxjqacsS7f9iRO/2eP7iJdxHzEI5FQnypQWYMKgLurRu8Kkhk8D++zlWqUh/+XNsG8qXV/ngc+ze/YfY9EcI/MaP/LgDEDMFmBApC0zWFVyjRo3kZvPc3NwQGBiI/Px8AICzs7PsvsqVC2dfnZycirQlJiZ+cLKenZ0tV/8NAOr52VBXV6xEbqh3f/Ts3hmPHifg15Xr4Dd9DlYuXaAQs6OfqqK6KnZNaY8X2Xk4eyMB83dHwdRQCw1sXq9XaGhjjF1T2iM1MxvbT9/GxHVnsG1SWxhoa7zjmZXPjtAINKtfC5UNKgkdimAuX4tFUPBu7Pp9uVKcP6XhrxMXse/oeSz8bgiszari+u0H+HHlDhjrV4JHu0ZCh0ci8/oc++2DzrGExCQMG/89OrZpjt7dP++mEVT6FOc7APpP3qy5/fcEL65NKpV+8HPOnTsXurq6cre5C5d8dGx6erooV64cklPkZ7yTk1NgWEKduKGhPpLeWqCTnJwKQ4PC2Xkjw8L/vr2I580+/9LXqwQLsxpo0qg+fp43E8dPnUFUCeU3YlWpohrKqUiQ/Ex+4WdyxksY6pScVKuoSGBmpA376noY0sYO7V2qY9XBa3J9NNXLw8xIGy4WhvjBqwHKlZNg55nSKR35XPR0tFBORaXIQsCktGcw1NP95Od/mJCMM1HX0Ktj009+rs9Fr5IuypVTKXqOpKTC0ECv2McYGugVPe9S0mD46luxC1GXkJyahlZd+8PBrT0c3Nrj4eMEzF+yEq27eZXNgXwmsvdQqvw3VUmpGTAsYfHoh/hp7S4M790BnVq6wtbCBN3aNMRgj9ZY9UfxNc2kOP77OZZWtL/sHLv86hzzgoNbBzi4dXjjHBsg97iEp0nwHj0JdZwcMPv7iaV3YGLABaakiM6dOyf389mzZ2FjY4Ny5cqV2WtOnToV6enpcrepk8Z/9POoqaqiln1NnDn3j6xNKpXizPl/UMe5+LpNF2dHnD1/Qa7t9Nm/4eLsCACoblINRoYGOHPudZ/MzOeIvnwVdV71Kc6/H1ZycnM++jiEpFa+HGqZ6uFs7Ouv46XSApyNTYCLueEHP09BQWFt+rv7FCAn78M/1ImBmmp51LKpgTNR8nWfZ6Ouw8Xe8pOff9fB0zDQ1UaLBk7v7ywSaqqqqGVXE2f+vihrk0qlOHMhEnWcit8y1cXJAWf/jpRrO33uH7i86t/tq7bYu2UVQjatlN2MjQwwdEAvrFk6r+wO5jN4/R66IWsrfA/dgIv9f18om5WdC5W3kgUVFQmkAqwfotL1+hx7fc78t3PsIlycCtfbFJ5jKxGyaYXs9voce72tcEJiErxHTUItexvM9Z8EFZUvLc0TvmZ92bJlMDc3h4aGBho2bIjz58+X2Hf16tVo1qwZ9PT0oKenh7Zt276zf0lYBqPg4uPj4ePjg5EjR+LixYv45ZdfEBgYWKavqa6uXrTk5UV28Z3fY8iAvvDz/wGODnZwdrTHxi1/ICsrC57dOgEAvps2G5WNjeA7bhQAwLtfLwwcPhbrgraiRbPGOBB2GJevXses6d8BKPymwLt/LyxfsxFmNUxR3aQqlvy2BsZGBmjbqhkAIPrSFVy6ch316jhDR1sb8Q8eYslva1DD1EQuob8Vdwe5eXlIS3+G5y9e4NqNmwAAe1ub/3SsZWVQK1tM3XQOjjX04WRmgKBjN5CVnQePRoWJhF/QWVSupAmfroUlUasOXkWtGvqoYaiFnDwpTlx5hL3n78K/T+G2gy+y87Ay7CpaOVWDkW4FpGVmY8vJW0hIy0KHOqYlxiFWgz3bYsrCDXC0MYezrTk27g5H1ssceLYvrDH3+2k9jA0qwffrwjrQnNw8xMUXbmOZm5eHhKQ0XIu7D80K6jCr9rpMSCqVYveh0+jezg3ly/DDcVkY0r8H/GYugKO9LZxr2WJj8C5kZb2EZ+eOAIDvAuahsrEhfMcMAwB49/XEwJE+WLd5O1o0aYgDB4/i8rVYzHo1a6dXSRd6leS/qVAtXx6GBvqwfOP6Bo+eJCD9WQYePUlEvlSKa7G3AAA1qpugomaFz3Ho/8lgj9aYEhgERxszONuaYWPIUWRlZ8OzXeH1HfwWbih8Dw3pDuDt91A+EpKLvodaNXTCiuBQVDXWg7VZNVy7dR8bdh1Bj/ZuxcbwJVHTrAj9GtaynytVt0AVu9rISk9B+uPia7oVzetzrOarc2z3q3OsAwDgu4D5r86xoQAA774eGDjS941z7Nirc2wCAECvkg70Ksl/k/P2OZaQmISBo31RrUpl+I0biZTU17t3/futM32abdu2wcfHBytWrEDDhg2xePFidOjQATdu3Ch22+tjx46hX79+aNy4MTQ0NDB//ny0b98eV65cgYmJyQe/LpN1Beft7Y2srCw0aNAA5cqVw/jx4zFixAihw/pg7h3aICU1DUuXr8HT5BTY21pjzbJAWcnK4ycJcjMDdV2csPDHACxethqLfl0F8xrVsWzRXNS0fj1LOnywF7KyXsJ/zgI8y8hEPRcnrFkWKPuAoaGhgYNHjuOXFWvxIusljAwN0KxxQ3wzfBbU1NRkzzPi28l4+Pj1IsTufYcAAG5EimsbLPd6NZCamY2l+y8jKeMl7E0qYdU3LWRlMI9TX8jN4L3IycesP/5BQloWNFTLwaKyNuZ7N4J7vRoAgHIqEtxOeIaQ83eR+jwblTTV4GSmj00TWsOm6qeXjnxu7i3qIyU9E7/8vhdPU5/B3rI6Vs8ZJytheJSYIlcDmpicBo8xc2Q/r9t5COt2HkJ9p5r4/SdfWfvpyOt4lJgCz/ZNPt/BlBL3dq2QkpqOpas24GlyKuxrWmHNkrmyr+gfJyTKn3fOtbBw9vdYvGI9Fv22DuamJlj200zUtPq4meWlKzdi9/6Dsp+7Dyj8EB60fCEa1nP59AMrI+4tXAvfQ5v+xNOUZ7C3qo7Vs8e+8R5KhUTyerwSU9LhMfb1bOe6nYexbudh1Heywe8LCj/gTBvdG0uD9mHWsm1ITsuAsb4u+rg3xTf9v/z64mqO9TA4KFz2c8epCwEAUbuDEDJ1qFBhlSr3di0L/7at2vjGOfbjW+fY6987hefYVCxesQGLflv/6hyb8VHnWMT5f3Dv/iPcu/8IzTv3k7vvxvlDpXNgQhO4dGXRokUYPnw4hgwpzAdWrFiB/fv3Y926dZgyZUqR/ps3b5b7ec2aNdi5cyfCw8Ph7e39wa8rKRBizz768rx4KnQEoiY99YvQIYiexKb1+zspMYm+ldAhiF5BcqzQIYjaTPeOQocgejPOxQkdgrjp1hD05aU39pXq8+Waty+6YUZx1QMAcnJyoKmpiR07dqB79+6y9kGDBiEtLQ179rz/ugEZGRkwNjbG9u3b0blz5w+O80srZiIiIiKiL5FEpVRvxW6YMXdusS+dlJSE/Px82S56/6pcuTKePPmwrYD9/PxQrVo1tG3b9qMOm2UwRERERKQASrcMZurUqfDx8ZFrK6ttqOfNm4fg4GAcO3YMGhoftwUyk3UiIiIiUjollbwUx9DQEOXKlUNCgvzF0D7kopILFy7EvHnzcPjwYbnr33wolsEQERERkfgJuM+6mpoa6tWrh/Dw14ujpVIpwsPD4eZW8i5OCxYswOzZsxEaGgpXV9f/dNicWSciIiIi8ZMIO8fs4+ODQYMGwdXVFQ0aNMDixYvx/Plz2e4w3t7eMDExkdW9z58/H/7+/tiyZQvMzc1lte1aWlrQ0tL64Ndlsk5ERERE9B59+vTB06dP4e/vjydPnsDFxQWhoaGyRafx8fFy294uX74cOTk56Nmzp9zzBAQEYMaMGR/8ukzWiYiIiEj0JALvsw4AY8eOxdixY4u979ixY3I/3717t1RekzXrREREREQixZl1IiIiIlIAyjnHzGSdiIiIiMRPBGUwQlDOjyhERERERAqAM+tEREREJH5KOrPOZJ2IiIiIFIByFoQo51ETERERESkAzqwTERERkfgpaRkMZ9aJiIiIiESKM+tEREREJH5KOrPOZJ2IiIiIFIByFoQo51ETERERESkAzqwTERERkfgpaRkMZ9aJiIiIiESKM+tEREREJH4S5ZxjZrJORERERAqAZTBERERERCQinFkn+hyq1BQ6AvFT1xY6AlEreJ4gdAjip6ErdASiNuNcnNAhiN6MhlZChyBqM67nChuAki4wZbJOREREROKnpDXrynnUREREREQKgDPrRERERCR+SloGw5l1IiIiIiKR4sw6ERERESkA5ZxZZ7JOREREROLHBaZERERERCQmnFknIiIiIgXAMhgiIiIiInHibjBERERERCQmnFknIiIiIgXAmXUiIiIiIhIRzqwTERERkfixZp2IiIiIiMSEyToRERERkUixDIaIiIiIxE9Jy2CYrBMRERGRAlDOZJ1lMEREREREIsWZdSIiIiISPyUtg+HMOhERERGRSHFmnYiIiIgUgHLOrDNZJyIiIiLxYxkMfYnu3r0LiUSCqKgooUMhIiIioo/EmXURGjx4MNLS0hASEiJ0KJ/F5m07sXbjVjxNToFdTStM95sIZ0eHEvv/degIlvy2Bg8fPYF5jeqYNG40WjRzk91fUFCApcvXYvvufXiWkYG6tZ0w4/tJMDczlXueYydPY9mq9bhxMw7qamqoX68Ofvt5bpHXS01LR7c+g5GQ+BR/n/gLOtrapXfwpWRz6N9Yt/cMktIyYWdWGf/7uiOcbUyK7fvH4YvYezwGN+8/BQA4WFbFxH6t5PpP/XUPQo7HyD2uaW0rrJ7Wv+wOogxt3n0Qa7f9iaSUdNhZ1cC0cYPgbG9dbN+bdx5g6frtuBJ7B48SkjB1zEAM6vmVXJ+tew5h697DePgkCQBgbW6CMd6eaN7QpawPpcwUjtH+t8bIqti+hWO0440xGlDMGB1+NUaF7zNr8+oY4+2hsGPE8Slq8/Y9WLtpe+HvbhsrTJ80Bs617Ers/9fh41iyciMePn4Cc1MTTBo7DC2aNCy2r//cxdi2ez+mThyNwf08AQAPHj3Bb2s34+yFKCSlpMDY0ABdv2qDUUP6Q01VtUyOUQhmrk3ReKgvqtWqC23jagge0wPXw/cKHZZIcGadFExubq7QIXyyA2HhmBv4K8aMHILdW9bCrqY1hn7jg+SU1GL7X4y6BN+pM9Gze2eEbF2HNi2bYYzPVMTeui3rs3rDZvy+dQdmfD8JfwStQoUKFTB0jA+ys7NlfcIOH8N302bDs2sn7Nm2AVvXL0fnr9oW+5r/mzkPtjbF/1EWgwMRVzB/4yGM6dUcO+cPh61ZZQz/YQuS058X2//vK/fg3tQRGwIGYusPQ1DVQAfD5mxGQvIzuX7NXKxwYtVE2W3hBI/PcTil7sCRM5i3fBPGDPLErlU/wNaqBoZ9Nw/JqenF9n+ZnQ3TasbwHdEXRvqViu1T2UgfvsP7YufKOdixYg4a1amFMdMCcfPOgzI8krJTOEabX43RnFIeox/eGKNFCjlGHJ+iDhw6hrmLV2LMsAHYHbQcdjaWGDpuasm/u2OuwHf6j+jZtSNCfl+ONi2aYMzkGYiNu1Ok76GjpxB9+RqMjQzk2m/fu4+CAilmTR2P/cFrMHXiKATv+hM//7auTI5RKKoVKiLhegz2zxondCjiI5GU7k1BMFkX0I4dO+Dk5IQKFSrAwMAAbdu2xeTJk7Fx40bs2bMHEokEEokEx44dk5WzbNu2DS1atICGhgY2b94MqVSKWbNmoXr16lBXV4eLiwtCQ0NLfM38/Hx8/fXXsLOzQ3x8PABgz549qFu3LjQ0NGBpaYmZM2ciLy/vs4zB+k3B6O3ZBT26dYK1lQVm/m8yNDQ0sDPkz2L7B23djmaNG2LYoP6wsjTHhDHD4WBfE5uCdwIonFUP2rIdo4d7o22rZrCraY0Fs6ch8WkyDh89CQDIy8vDDz8tweQJY9CvV3dYmNWAtZUF3Nu3KfJ6W/7YjYyMDHzt3a/sBuETbfzzLHq1qQPPVi6wNjXCjBGdoKGmil1Hoort/9N4D/Tv4Ap7iyqwNDHE7FGdIS0owJnL8n801VTLwUhPS3bT1arwGY6m9G3YfgC9OrVCj69awtq8Omb6DIWGhjp2/nW82P5Odlb4bpQXOrVuDFXV4r98bN24Hlo0qgPz6lVhYVoVE4f1gWYFDURfvVmWh1JmNmz/69UYtXg1Rl9/wBj1R6fWbu8Yo7po0cgF5tWrvBqj3q/G6FZZHkqZ4PgUtX7LTvTu/hV6dOkIa0szzJwyvnBM9oUV2z8oeDeaNaqPYQN7w8rCDBNGDYaDnTU2/bFHrl9CYhJmBy7DwllToVpefuyau9XHXP/JaNrIFaYmVdGmeWN87dULB4+eKrPjFMKtk2E4siQA1w/veX9nUgpM1gXy+PFj9OvXD19//TWuXbuGY8eOwdPTEwEBAejduzc6duyIx48f4/Hjx2jcuLHscVOmTMH48eNx7do1dOjQAUuWLEFgYCAWLlyImJgYdOjQAV27dsXNm0WThuzsbPTq1QtRUVE4efIkatSogZMnT8Lb2xvjx4/H1atXsXLlSmzYsAE//PBDmY9BTm4urlyLReOGrrI2FRUVNG7oisiYK8U+JirmMtze6A8ATd0aIirmMgDgwcNHeJqUjMYN68vu19bWQm1HB0S+6nP1eiwSEp9CRUWC7n2HoGm7bhg2xldudh4AbsXdwW+rN2D+7GlQURHnJ/Cc3Hxcuf0Ybs4WsjYVFQncnC0QFfthM3Qvc3KRlyctkoyfv3IPTYYG4qtxyzBj1QGkZrwo1dg/h5zcPFyJvYPG9RxlbSoqKnCr64ioK6WTWOfnS7H/yGm8eJkNl1o2pfKcn9PnG6Mzr8ao+PIjseL4FJWTm4sr12PRuH5dWZuKigoa16+LyEtXi31M1KWrcGtQV66taSNXRF26JvtZKpVicsB8DB3QCzZW5h8US0bmc+jqiK80kcqKpJRvioE16wJ5/Pgx8vLy4OnpCTMzMwCAk5MTAKBChQrIzs5GlSpVijxuwoQJ8PT0lP28cOFC+Pn5oW/fvgCA+fPn4+jRo1i8eDGWLVsm65eZmYlOnTohOzsbR48eha6uLgBg5syZmDJlCgYNGgQAsLS0xOzZs/Hdd98hICCgbA7+ldTUdOTn58NAX1+u3cBAH7fv3iv2MUlJKTDU13urvx6SklMAAE+TCv9r8I4+9x88AgD8umIdpvh+C5NqVbD+92AMHP4twkK2opKuDnJycuAzdQYmT/gG1apWwf2Hjz79gMtAWsYL5EsLYKCrJdduoFsRdx4mfdBzLNwUDmN9bTR2spS1Na1jhXYN7VDduBLiE1KxeMtRjPxhK7b+MATlyinOZ/zU9AzkS6Uw0NOVazfU08Wd+E/7f3rjdjz6jQlAdk4uNCto4NdZE2FtXv2TnlMIJY+RTimN0QyFHiOOT1GpaenIz5cW/T2rr4fb9+4X+5ik5FQYvlUSZKCvh6SUFNnPq4O2oXx5FXj3+bCSu3v3H2LTHyHwGz/y4w6ASMEwWRdI7dq10aZNGzg5OaFDhw5o3749evbsCT09vXc+ztX19azys2fP8OjRIzRp0kSuT5MmTRAdHS3X1q9fP1SvXh1HjhxBhQqvZ1Cjo6MREREhN5Oen5+Ply9f4sWLF9DU1CwSQ3Z2tlz9NwCo52dDXV39/QcuAtICKQBg1DBvdGjbEgAwd+b3aN7BE6GHjqBvz+4IXLoSVhbm6Napg4CRlr3VuyPwV8QVbJzpDXW1178OOjV5PYtY06wybM0qo/3YX3H+6j24OVkU91RKx8K0GnavmYuMzBcIO3EeU+atwO+LpytEsvW5FI7Rj8jIzELYiXOvxmgax+gVjs9rl6/FIih4N3b9/hskH1BLnJCYhGHjv0fHNs3Ru7v7Z4iQREGB6sxLk+JMkX1hypUrh0OHDuGvv/6Cg4MDfvnlF9ja2uLOnaKLbd5UsWLF//R67u7uiImJwZkzZ+TaMzMzMXPmTERFRcluly5dws2bN6GhoVHsc82dOxe6urpyt7kLl3x0THp6uihXrhyS35hZAYDk5BQYGhgU+xhDQ30kvbWAKTk5FYYGhbPzRoaF/317kZN8H0MAgJWluex+NTU1mFavisdPEgAAZ//+B6GHj8LBtQUcXFtg8MgJAIBGrTpj6fK1H32sZaWStibKqUiQnJ4p156c/hyGlbRKeFShdXvPYHVIBNZM94KtWeV39jWtrAc9bU3EP0l5Zz+x0dPVRjkVlSILAZNS04vM8n0sNdXyMDOpAkdbS/gO7ws7qxoI2lnyehGxKnmMnsFQX7eER32Y12Nk8cYYFV/TLFYcn6L0KumiXDmVor9nU1JhaFD8hJOhgR6SUtKK9n/1zeqFqMtITk1Dq65ecHDrAAe3Dnj4OAHzl6xE624D5B6X8DQJ3qMnoY6TA2Z/P7H0DowUgHKWwTBZF5BEIkGTJk0wc+ZMREZGQk1NDbt374aamhry8/Pf+3gdHR1Uq1YNERERcu0RERFwcJDf+nD06NGYN28eunbtiuPHXy+Kqlu3Lm7cuAFra+siNxWV4t8eU6dORXp6utxt6qTxH338aqqqqGVfE2fO/SNrk0qlOHP+H9RxrlXsY1ycHXH2/AW5ttNn/4aLc+FMcHWTajAyNMCZc6/7ZGY+R/Tlq6jzqo+jvS3U1NRw5+7rr2tzc/Pw8NETVKtaWHr0y8IfsGfbBoQEr0dI8HrM8fcDAGxeuwxefV6XIQlNTbUcallWxdlLd2VtUmkBzl66A5eaJc/OrdlzGst3nMSq//WHo1W1977Ok+RnSMt8AaP3fAAQGzXV8qhV0wJnLr5eAyGVSnH24pVSry+XFhQgJ/fzLMwuTSWP0eUyGiPF2sWK41OUmqoqatnVxJm/I2VtUqkUZy5Eoo5T8dvuujg54Owb/QHg9LmLcHGyBwB0+6ot9m5ZiZBNK2Q3YyMDDB3QC2uWvt5SNyExCd6jJqGWvQ3m+k8q8e8U0ZeEZTACOXfuHMLDw9G+fXsYGxvj3LlzePr0Kezt7fHy5UuEhYXhxo0bMDAwkNWXF2fy5MkICAiAlZUVXFxcsH79ekRFRWHz5s1F+n777bfIz89H586d8ddff6Fp06bw9/dH586dUaNGDfTs2RMqKiqIjo7G5cuXMWfOnGJfU11dvWjJy4vsYvu+z5ABfeHn/wMcHezg7GiPjVv+QFZWFjy7dQIAfDdtNiobG8F33CgAgHe/Xhg4fCzWBW1Fi2aNcSDsMC5fvY5Z078DUPgByLt/LyxfsxFmNUxR3aQqlvy2BsZGBmjbqhkAQEurIvr27IZfVqxF1SrGqFa1CtZu3AIA6NiuFQCghqn8HuWpaWkAACtLM9Htsz6ocyNMXbYHjlZV4WRdDUH7zyMrOxcerWoDAPx+CUFlfW34eBXudrM6JAK/bDuOheM9YGJUCU9TC2flNTXUULGCGp5n5eC37SfQrpEdjCppIT4hFQt/P4waVfTR1EW8W1iWZHAvd0yZtwKONS3hbG+FjTv+QtbLl/Ds2AIA4PfjbzB+tY0eULigMO5e4eLc3Lw8JCSl4Nqtu9CsoAEzk8IPc4Grg9G8QW1UrWyI5y+y8Gf4aZyPuoY1C6YIc5CfaHCvrzBl3ko41rR4NUahyHqZ/cYYLYexkd47xij1I8bIT5iD/AQcn6KG9O8Bv5kL4GhfE861bLExeDeysl7Cs3Nh6eB3AfNR2dgQvmOGAgC8+3pg4EhfrNu8HS2aNMSBg8dw+VosZn0/AQCgV0kHepV05F5DtXx5GBrow/LVNTISEpMwcLQvqlWpDL9xI5Hyxrcd/36r+iVQ06wI/RqvFxpXqm6BKna1kZWegvTHxa8JoC8bk3WB6Ojo4MSJE1i8eDGePXsGMzMzBAYG4quvvoKrqyuOHTsGV1dXZGZm4ujRozA3Ny/2ecaNG4f09HT4+voiMTERDg4O2Lt3L2xsip/xmTBhAqRSKdzd3REaGooOHTrgzz//xKxZszB//nyoqqrCzs4Ow4YNK8Ojf829QxukpKZh6fI1eJqcAntba6xZFigrWXn8JEFu5qSuixMW/hiAxctWY9Gvq2BeozqWLZqLmtavF0cOH+yFrKyX8J+zAM8yMlHPxQlrlgXKfcD4bsIYlC9XDt9Nm42X2dmo7eiAjauWQFdH/o+FInBvUgupz15g6bbjSErLhL15Zaz6X39ZGczjpGdQeaPOL/jgP8jNy8f4wB1yzzOmV3OM7d0C5VQkuBGfgJDj0ch4/hJG+tpo4myJcX1bQq2EbejEzL21G1LSn+GXDTvwNCUN9lZmWD1/iqyE4VFiMiRvvMcSk1PhMfx72c/rtu3Hum37Ub+2PX5fPB0AkJL6DH5zl+NpShq0K2rC1tIUaxZMQRNXp897cKWkcIwyXo1R+qsx8ntrjF6/hwrH6H+yn+XHaBqAf8doxVtj5KeQY8TxKcq9XcvC392rNuJpcirsa1phzZIfZWUwjxMS5XbRqutcCwtnT8XiFRuw6Lf1MDc1wbKfZqCm1YevgYk4/w/u3X+Ee/cfoXln+e10b5w/VDoHJgLVHOthcFC47OeOUxcCAKJ2ByFk6lChwiIBSQoKCgqEDoK+AC+eCh2BqElvib8OVWgSQ3uhQxA5/qqmTyOpaCx0CKI3o6HifXv4Oc24LnCZVurt9/f5GHqW7+8jAoo3TUZERERESkhxFoWWJq7MICIiIiISKc6sExEREZH4Kek+60zWiYiIiEgBKGeyzjIYIiIiIiKR4sw6EREREYmfkpbBcGadiIiIiEikOLNORERERApAOWfWmawTERERkfixDIaIiIiIiMSEM+tEREREpACUc2adyToRERERiR/LYIiIiIiISEw4s05ERERECoAz60REREREJCKcWSciIiIi8VPOiXUm60RERESkCJQzW2cZDBERERGRSHFmnYiIiIjEj1s3EhERERGJlaSUbx9v2bJlMDc3h4aGBho2bIjz58+/s//27dthZ2cHDQ0NODk54cCBAx/9mkzWiYiIiIjeY9u2bfDx8UFAQAAuXryI2rVro0OHDkhMTCy2/+nTp9GvXz8MHToUkZGR6N69O7p3747Lly9/1OtKCgoKCkrjAEjJvXgqdASiJr0VJnQIoicxtBc6BJHjr2r6NJKKxkKHIHozGloJHYKozbieK2wApZ1raBp9VPeGDRuifv36+PXXXwEAUqkUpqam+PbbbzFlypQi/fv06YPnz5/jzz//lLU1atQILi4uWLFixQe/LmfWiYiIiIjeIScnB//88w/atm0ra1NRUUHbtm1x5syZYh9z5swZuf4A0KFDhxL7l4QLTImIiIhIAZTuAtPs7GxkZ2fLtamrq0NdXb1I36SkJOTn56Ny5cpy7ZUrV8b169eLff4nT54U2//JkycfFSeTdSodH/lVUlnKzs7G3LlzMXXq1GJPOCGoOA8QOgQZMY6P2HCM3o3j834co3cT6/gIXubxBrGOkaA0DUv16ebOmIGZM2fKtQUEBGDGjBml+jqfijXr9MV59uwZdHV1kZ6eDh0dHaHDER2Oz/txjN6N4/N+HKN34/i8H8eo7H3MzHpOTg40NTWxY8cOdO/eXdY+aNAgpKWlYc+ePUUeU6NGDfj4+GDChAmytoCAAISEhCA6OvqD42TNOhEREREpHXV1dejo6MjdSvoWQ01NDfXq1UN4eLisTSqVIjw8HG5ubsU+xs3NTa4/ABw6dKjE/iVhGQwRERER0Xv4+Phg0KBBcHV1RYMGDbB48WI8f/4cQ4YMAQB4e3vDxMQEc+fOBQCMHz8eLVq0QGBgIDp16oTg4GBcuHABq1at+qjXZbJORERERPQeffr0wdOnT+Hv748nT57AxcUFoaGhskWk8fHxUFF5XbTSuHFjbNmyBdOmTcP3338PGxsbhISEwNHR8aNel8k6fXHU1dUREBDABTkl4Pi8H8fo3Tg+78cxejeOz/txjMRp7NixGDt2bLH3HTt2rEhbr1690KtXr096TS4wJSIiIiISKS4wJSIiIiISKSbrREREREQixWSdiIiIiEikmKwTEREREYkUk3UiIiIiIpFisk6kRG7duoWwsDBkZWUBALgZFBGROL18+VLoEEgkuM86kRJITk5Gnz59cOTIEUgkEty8eROWlpYYOnQo9PT0EBgYKHSIpGBevnwJDQ0NocMQnKen5wf33bVrVxlGQl8CqVSKH374AStWrEBCQgJiY2NhaWmJ6dOnw9zcHEOHDhU6RBIAk3VSWEuXLv3gvuPGjSvDSMRv4sSJKF++POLj42Fvby9r79OnD3x8fJisA0hISMCkSZMQHh6OxMTEIt865OfnCxSZeDCRKEpXV1foEERPX18fsbGxMDQ0hJ6eHiQSSYl9U1JSPmNk4jNnzhxs3LgRCxYswPDhw2Xtjo6OWLx4sVKeY8RknRTYzz//LPfz06dP8eLFC1SqVAkAkJaWBk1NTRgbGyt9sn7w4EGEhYWhevXqcu02Nja4d++eQFGJy+DBgxEfH4/p06ejatWq70wolBUTiaLWr18vdAii9/PPP0NbWxsAsHjxYmGDEbmgoCCsWrUKbdq0wahRo2TttWvXxvXr1wWMjITEZJ0U1p07d2T/3rJlC3777TesXbsWtra2AIAbN25g+PDhGDlypFAhisbz58+hqalZpD0lJYWXsn7l1KlTOHnyJFxcXIQORbSYSLxfXl4ejh07hri4OPTv3x/a2tp49OgRdHR0oKWlJXR4ghg0aFCx/6aiHj58CGtr6yLtUqkUubm5AkREYsBknb4I06dPx44dO2SJOgDY2tri559/Rs+ePeHl5SVgdMJr1qwZgoKCMHv2bACARCKBVCrFggUL0KpVK4GjEwdTU1MuuH0PJhLvdu/ePXTs2BHx8fHIzs5Gu3btoK2tjfnz5yM7OxsrVqwQOkTRSExMRGJiIqRSqVy7s7OzQBGJg4ODA06ePAkzMzO59h07dqBOnToCRUVCY7JOX4THjx8jLy+vSHt+fj4SEhIEiEhcFixYgDZt2uDChQvIycnBd999hytXriAlJQURERFChycKixcvxpQpU7By5UqYm5sLHY4oMZF4t/Hjx8PV1RXR0dEwMDCQtXt4eMiVDSmzf/75B4MGDcK1a9eKfDiWSCRKvzbE398fgwYNwsOHDyGVSrFr1y7cuHEDQUFB+PPPP4UOjwTCZJ2+CG3atMHIkSOxZs0a1K1bF0DhH4XRo0ejbdu2AkcnPEdHR8TGxuLXX3+FtrY2MjMz4enpiTFjxqBq1apChycKffr0wYsXL2BlZQVNTU2oqqrK3a/sC98AJhLvc/LkSZw+fRpqampy7ebm5nj48KFAUYnL119/jZo1a2Lt2rWoXLky14a8pVu3bti3bx9mzZqFihUrwt/fH3Xr1sW+ffvQrl07ocMjgUgK+L0vfQGePn2KQYMGITQ0VJZk5ebmomPHjli/fj0qV64scIQkdhs3bnzn/ay1LXTy5EnMmjUL0dHRyMzMRN26deHv74/27dsLHZrg9PT0EBERAQcHB2hrayM6OhqWlpY4deoUevTowW/5AGhrayMyMrLYcioCHjx4UGQjgH+dPXsWjRo1+swRkRgwWacvys2bN3Ht2jUAgJ2dHWrWrClwROIQExNTbLtEIoGGhgZq1KjBhaZEn6hPnz7Q1dXFqlWroK2tjZiYGBgZGaFbt26oUaMGd44B0L17dwwcOBA9evQQOhRRcnBwwKlTp6Cvry/XHhERgU6dOiEtLU2YwEhQTNZJYfn4+GD27NmoWLEifHx83tl30aJFnykqcVJRUZF93fzvKf/m18+qqqro06cPVq5cqdQXusnPz0dISIjsA1+tWrXQtWtXlCtXTuDIxOH+/fuQSCSymb/z589jy5YtcHBwwIgRIwSOTngPHjxAhw4dUFBQgJs3b8LV1RU3b96EoaEhTpw4AWNjY6FDFFxSUhIGDRqEBg0awNHRsUi5WdeuXQWKTBy+/vprxMTE4OjRo7LtLk+cOIEuXbpgxowZmDhxosARkhCYrJPCatWqFXbv3o1KlSq9c0cTiUSCI0eOfMbIxGfPnj3w8/PD5MmT0aBBAwCFiVZgYCACAgKQl5eHKVOmoE+fPli4cKHA0Qrj1q1bcHd3x8OHD+W2/zQ1NcX+/fthZWUlcITCa9asGUaMGIGBAwfiyZMnqFmzJhwdHXHz5k18++238Pf3FzpEweXl5SE4OBgxMTGyMiEvLy9UqFBB6NBEYd++fRg4cCCePXtW5D4uMC3cWalnz55ISUlBWFgYTp8+ja5du2LOnDkYP3680OGRQJisEymBBg0aYPbs2ejQoYNce1hYGKZPn47z588jJCQEvr6+iIuLEyhKYbm7u6OgoACbN2+WfQWdnJyMAQMGQEVFBfv37xc4QuHp6enh7NmzsLW1xdKlS7Ft2zZERETg4MGDGDVqFG7fvi10iIJ6+fKlUn8z9SHMzc3RuXNnTJ8+nWuJSpCTk4NOnTrhxYsXiImJwdy5czF27FihwyIBMVknUgIVKlRAZGQk7Ozs5NqvX7+OOnXqICsrC3fv3oWDgwNevHghUJTCqlixIs6ePQsnJye59ujoaDRp0gSZmZkCRSYeWlpauHz5MszNzdG1a1c0adIEfn5+iI+Ph62tLbKysoQOUVA6Ojrw8PDAgAED0KZNG6ioqAgdkuhoa2sjKiqK31S9obg1RRkZGejXrx86deqE0aNHy9qVfR96ZcXfJERKwM7ODvPmzUNOTo6sLTc3F/PmzZMl8A8fPlTqmS51dXVkZGQUac/MzCyyFZ+yqlWrFlasWIGTJ0/i0KFD6NixIwDg0aNHcvuKK6uNGzfixYsX6NatG0xMTDBhwgRcuHBB6LBExdPTE0ePHhU6DFFxcXFBnTp14OLiIrs1b94cDx48wMqVK2X38VoGyov7rBMpgWXLlqFr166oXr26bGbm0qVLyM/Pl+2Pffv2bXzzzTdChimozp07Y8SIEVi7dq2srv/cuXMYNWqU0i96+9f8+fPh4eGBn376CYMGDULt2rUBAHv37pWNmTLz8PCAh4cHMjIysGPHDmzduhWNGjWCpaUlBgwYwJp+ADVr1sTUqVNx6tQpODk5FVlgOm7cOIEiE86dO3eEDoFEjmUwREoiIyMDmzdvRmxsLADA1tYW/fv3l+04oOzS0tIwaNAg7Nu3T5ZA5OXloWvXrtiwYQN0dXUFjlAc8vPz8ezZM+jp6cna7t69C01NTe52UoyrV6/Cy8sLMTExSr94EgAsLCxKvE8ikSj9ugei4jBZJ1IiV69eRXx8vFw5DMDt0t508+ZNXL9+HQBgb2/Pi7fQR3v58iX27t2LLVu2IDQ0FJUrV0a/fv0wb948oUMTleK2kSUgLi4Oixcvlm0h6+DggPHjx7POX4kxWSdSArdv34aHhwcuXboEiUSCgoICuT+QnPGjD7Vjxw788ccfxX7ou3jxokBRiUNYWBi2bNmCkJAQlC9fHj179oSXlxeaN28udGiisnbtWvz888+4efMmAMDGxgYTJkzAsGHDBI5MeGFhYejatStcXFzQpEkTAIUXRIqOjsa+ffvQrl07gSMkIbBmnUgJjB8/HhYWFggPD4eFhQXOnTuHlJQU+Pr6Ku2+6gAvrPWxli5div/9738YPHgw9uzZgyFDhiAuLg5///03xowZI3R4gvPw8EDnzp0RFBQEd3f3IvXYBPj7+2PRokX49ttv4ebmBgA4c+YMJk6ciPj4eMyaNUvgCIU1ZcoUTJw4sci3MFOmTIGfnx+TdSXFmXUiJWBoaIgjR47A2dkZurq6OH/+PGxtbXHkyBH4+voiMjJS6BAFwQtrfRw7OzsEBASgX79+0NbWRnR0NCwtLeHv74+UlBT8+uuvQocoqIyMDK4BeQ8jIyMsXboU/fr1k2vfunUrvv32WyQlJQkUmThoaGjg0qVLsLGxkWuPjY2Fs7MzXr58KVBkJCTOrBMpgfz8fFkSYWhoiEePHsHW1hZmZma4ceOGwNEJ580t5Lid3PvFx8ejcePGAAr37v93q8uBAweiUaNGSp+sa2trIy4uDuvXr0dcXByWLFkCY2Nj/PXXX6hRowZq1aoldIiCy83Nhaura5H2evXqIS8vT4CIxMXIyAhRUVFFkvWoqCgu4FZi3GedSAk4OjoiOjoaANCwYUMsWLAAERERmDVrFiwtLQWOTpyePXuGkJAQ2WJTAqpUqYKUlBQAQI0aNXD27FkAhVvP8Uta4Pjx43BycsK5c+ewa9cu2YW0oqOjERAQIHB04jBw4EAsX768SPuqVavg5eUlQETiMnz4cIwYMQLz58/HyZMncfLkScybNw8jR47E8OHDhQ6PBMKZdSIlMG3aNDx//hwAMGvWLHTu3BnNmjWDgYEBtm3bJnB04tC7d280b94cY8eORVZWFlxdXXH37l0UFBQgODgYPXr0EDpEwbVu3Rp79+5FnTp1MGTIEEycOBE7duzAhQsX4OnpKXR4gpsyZQrmzJkDHx8fuXKY1q1bK/W3Dm+uB5FIJFizZg0OHjyIRo0aASi8nkF8fDy8vb2FClE0pk+fDm1tbQQGBmLq1KkAgGrVqmHGjBlKuQc9FWLNOpGSSklJgZ6eHrdNe6VKlSoICwtD7dq1sWXLFgQEBCA6OhobN27EqlWrlLau/01SqRRSqRTlyxfO8wQHB+P06dOwsbHByJEjlf5Kr1paWrh06RIsLCzkavrv3r0LOzs7pa03ftd6kDcp+9qQvLw8bNmyBR06dEDlypVlZWZcB0GcWSdSUvr6+kKHICrp6emyMQkNDUWPHj2gqamJTp06YfLkyQJHJw4qKipQUXldPdm3b1/07dtXwIjEpVKlSnj8+HGRC/9ERkbCxMREoKiEx/UgH6Z8+fIYNWqUbH91Jun0L9asExEBMDU1xZkzZ/D8+XOEhoaiffv2AIDU1FRoaGgIHJ14nDx5EgMGDICbmxsePnwIAPj9999x6tQpgSMTXt++feHn54cnT55AIpFAKpUiIiICkyZNYokHfZAGDRrwWzwqgjPrREQAJkyYAC8vL2hpacHMzAwtW7YEAJw4cQJOTk7CBicSO3fuxMCBA+Hl5YXIyEhkZ2cDKPxW4scff8SBAwcEjlBYP/74I8aMGQNTU1Pk5+fDwcEB+fn56N+/P6ZNmyZ0eKQAvvnmG/j6+uLBgweoV68eKlasKHe/s7OzQJGRkFizTkT0yoULF3D//n20a9cOWlpaAID9+/ejUqVKsqsJKrM6depg4sSJ8Pb2lqvJjoyMxFdffYUnT54IHaIoxMfH4/Lly8jMzESdOnWKbMNHVJI3y8z+9eZVp3m1aeXEZJ2IiD6IpqYmrl69CnNzc7lk/fbt23BwcFDaBZREpeXevXvvvN/MzOwzRUJiwjIYIiIUXjhqw4YNCA8PR2JiIqRSqdz9yrxLxb+qVKmCW7duwdzcXK791KlTSrtf/5vbEr7PokWLyjAS+hLcu3cPjRs3lu249K+8vDycPn2aybqSYrJORARg/Pjx2LBhAzp16gRHR0duaVmM4cOHY/z48Vi3bh0kEgkePXqEM2fOYNKkSZg+fbrQ4QniQxcD8v1EH6JVq1Z4/PhxkauVpqeno1WrViyDUVJM1omIULhn+B9//AF3d3ehQxGtKVOmQCqVok2bNnjx4gWaN28OdXV1TJo0Cd9++63Q4Qniv2xL+ODBA1SrVq3Y+mRSbv/Wpr8tOTm5yGJTUh6sWSciQuFVAo8dO4aaNWsKHYro5eTk4NatW8jMzISDg4NsMS59GB0dHURFRSlt6RAV9e8VgPfs2YOOHTtCXV1ddl9+fj5iYmJga2uL0NBQoUIkAfFjPRERAF9fXyxZsgScv3g/NTU1ODg4wM7ODocPH5ZdxIU+DN9j9DZdXV3o6uqioKAA2trasp91dXVRpUoVjBgxAps2bRI6TBIIy2CIiFC4SPLo0aP466+/UKtWLaiqqsrdv2vXLoEiE4/evXujefPmGDt2LLKyslC/fn3cuXMHBQUFCA4ORo8ePYQOkUghrV+/HgBgZGSEGTNmQFNTEwBw9+5dhISEwN7eHoaGhkKGSALizDoREQovFe/h4YEWLVrA0NBQbmZLV1dX6PBE4cSJE2jWrBkAYPfu3ZBKpUhLS8PSpUsxZ84cgaMjUnyRkZEICgoCAKSlpaFRo0YIDAxE9+7dsXz5coGjI6FwZp2ICK9ntqhk6enp0NfXBwCEhoaiR48e0NTURKdOnTB58mSBoyNSfJGRkVi8eDEAYMeOHahcuTIiIyOxc+dO+Pv7Y/To0cIGSILgzDoR0St5eXk4fPgwVq5ciYyMDADAo0ePkJmZKXBk4mBqaoozZ87g+fPnCA0NRfv27QEAqamp0NDQEDg6xcFtHKkkL168gLa2NgDg4MGD8PT0hIqKCho1avTeCybRl4vJOhERCi9G4uTkhG7dumHMmDF4+vQpAGD+/PmYNGmSwNGJw4QJE+Dl5YXq1aujWrVqaNmyJYDC8hgnJydhg1MgXGBKJbG2tkZISAju37+PsLAw2QfixMRE6OjoCBwdCYXJOhERCi+K5OrqitTUVFSoUEHW7uHhgfDwcAEjE49vvvkGZ86cwbp163Dq1CnZPuGWlpasWX/DrVu3EBYWhqysLABFk/OrV6/ySpRULH9/f0yaNAnm5uZo2LAh3NzcABTOstepU0fg6Ego3GediAiAgYEBTp8+DVtbW2hrayM6OhqWlpa4e/cuHBwc8OLFC6FDJJFLTk5Gnz59cOTIEUgkEty8eROWlpb4+uuvoaenh8DAQKFDJAXw5MkTPH78GLVr15Z9ID5//jx0dHRgZ2cncHQkBC4wJSICIJVKi72U94MHD2Q1pMrIx8cHs2fPRsWKFeHj4/POvosWLfpMUYnTxIkTUb58ecTHx8Pe3l7W3qdPH/j4+DBZpw9SpUoVVKlSRa6tQYMGAkVDYsBknYgIQPv27bF48WKsWrUKQOEiwMzMTAQEBMDd3V3g6IQTGRmJ3Nxc2b9LwkWThaUKYWFhqF69uly7jY0NFwcS0X/GZJ2ICEBgYCA6dOgABwcHvHz5Ev3798fNmzdhaGiIrVu3Ch2eYI4ePVrsv6mo58+fyy5m86aUlBS5y8cTEX0M1qwTEb2Sl5eHbdu2ITo6GpmZmahbty68vLzkFpwSlcTd3R316tXD7Nmzoa2tjZiYGJiZmaFv376QSqXYsWOH0CESkQJisk5EhMLtBxs3bozy5eW/cMzLy8Pp06fRvHlzgSITlqen5wf33bVrVxlGIn6XL19GmzZtULduXRw5cgRdu3bFlStXkJKSgoiICFhZWQkdIhEpIG7dSEQEoFWrVkhJSSnSnp6ejlatWgkQkTjo6urKbjo6OggPD8eFCxdk9//zzz8IDw+Hrq6ugFGKg6OjI2JjY9G0aVN069YNz58/h6enJyIjI5moE9F/xpl1IiIAKioqSEhIgJGRkVx7bGwsXF1d8ezZM4EiEw8/Pz+kpKRgxYoVKFeuHAAgPz8f33zzDXR0dPDTTz8JHCER0ZeHyToRKbV/yzz27NmDjh07yi0EzM/PR0xMDGxtbREaGipUiKJhZGSEU6dOwdbWVq79xo0baNy4MZKTkwWKTBxCQ0OhpaWFpk2bAgCWLVuG1atXw8HBAcuWLYOenp7AERKRImIZDBEptX9LPAoKCqCtrS1X9lGlShWMGDECmzZtEjpMUcjLy8P169eLtF+/fh1SqVSAiMRl8uTJsm9gLl26BB8fH7i7u+POnTvv3aOeiKgk3LqRiJTa+vXrZZeD/+WXX6ClpSVwROI1ZMgQDB06FHFxcbKLtJw7dw7z5s3DkCFDBI5OeHfu3IGDgwMAYOfOnejSpQt+/PFHXLx4Uan36ieiT8NknYiUXkFBATZv3ozvv/8eNjY2QocjWgsXLkSVKlUQGBiIx48fAwCqVq2KyZMnw9fXV+DohKempoYXL14AAA4fPgxvb28AgL6+Ptc8ENF/xpp1IiIAtWrVwtq1a9GoUSOhQ1EI/yafOjo6Re6LiIiAq6ur0l0IqGvXrsjJyUGTJk0we/Zs3LlzByYmJjh48CDGjh2L2NhYoUMkIgXEmnUiIgDz5s3D5MmTcfnyZaFDUQg6OjrFJuoA8NVXX+Hhw4efOSLh/frrryhfvjx27NiB5cuXw8TEBADw119/oWPHjgJHR0SKijPrREQA9PT08OLFC+Tl5UFNTa3IVUuL24OdiqetrY3o6GhYWloKHQoRkcJjzToREYDFixcLHQJ9AfLz8xESEoJr164BKCyv6tq1q2xfeiKij8WZdSIiKlXKOrN+69YtuLu74+HDh7K96G/cuAFTU1Ps37+fVzElov+ENetERK/ExcVh2rRp6NevHxITEwEU1htfuXJF4MhIEYwbNw5WVla4f/8+Ll68iIsXLyI+Ph4WFhYYN26c0OERkYJisk5EBOD48eNwcnLCuXPnsGvXLmRmZgIAoqOjERAQIHB0ikUikQgdgiCOHz+OBQsWQF9fX9ZmYGCAefPm4fjx4wJGRkSKjMk6ERGAKVOmYM6cOTh06BDU1NRk7a1bt8bZs2cFjEzxKGt1pbq6OjIyMoq0Z2Zmyr2niIg+BpN1IiIUXh7ew8OjSLuxsTGSkpIEiEh8WrdujbS0tCLtz549Q+vWrWU/Z2RkKF29OgB07twZI0aMwLlz51BQUICCggKcPXsWo0aNQteuXYUOj4gUFJN1IiIAlSpVkl2V802RkZGy/bKV3bFjx5CTk1Ok/eXLlzh58qQAEYnL0qVLYWVlBTc3N2hoaEBDQwNNmjSBtbU1lixZInR4RKSguHUjERGAvn37ws/PD9u3b4dEIoFUKkVERAQmTZoku2y8soqJiZH9++rVq3jy5Ins5/z8fISGhvIDDQo/8O3Zswe3bt2Sbd1ob28Pa2trgSMjIkXGrRuJiADk5ORgzJgx2LBhA/Lz81G+fHnk5+ejf//+2LBhg1Lvk62ioiJbNFrcn4wKFSrgl19+wddff/25QyMi+uIxWSciekN8fDwuX76MzMxM1KlTBzY2NkKHJLh79+6hoKAAlpaWOH/+PIyMjGT3qampwdjYWKk/zPyrR48eaNCgAfz8/OTaFyxYgL///hvbt28XKDIiUmRM1omI3vLvr0Vl3YKwOLm5uRgxYgT8/f1hYWEhdDiiZGRkhCNHjsDJyUmu/dKlS2jbti0SEhIEioyIFBkXmBIRvbJ27Vo4OjrKFgc6OjpizZo1QoclCqqqqti9e7fQYYhaSVs0qqqq4tmzZwJERERfAibrREQA/P39MX78eHTp0gXbt2/H9u3b0aVLF0ycOBH+/v5ChycK3bp1Q0hIiNBhiJaTkxO2bdtWpD04OBgODg4CREREXwKWwRARobCEYenSpejXr59c+9atW/Htt99yr3UAc+bMQWBgINq0aYN69eqhYsWKcvePGzdOoMjEYd++ffD09ET//v1l+86Hh4dj69at2L59O7p37y5sgESkkJisExGhcNu9v//+u8iC0tjYWDRo0KDYiwEpm3fVqkskEty+ffszRiNO+/fvx48//oioqChUqFABzs7OCAgIQIsWLYQOjYgUFJN1IiIA3377LVRVVbFo0SK59kmTJiErKwvLli0TKDIiIlJmTNaJiFCYrAcFBcHU1BSNGjUCAJw7dw7x8fHw9vaGqqqqrO/bCT0REVFZYbJORASgVatWH9RPIpHgyJEjZRyNeD148AB79+5FfHw8cnJy5O5T9g8xb148qjj5+fmfMRoi+lKUFzoAIiIxOHr0qNAhiF54eDi6du0KS0tLXL9+HY6Ojrh79y4KCgpQt25docMT3NtbW+bm5iIyMhIbN27EzJkzBYqKiBQdZ9aJiACsX78effv2RYUKFYQORbQaNGiAr776CjNnzoS2tjaio6NhbGwMLy8vdOzYEaNHjxY6RFHasmULtm3bhj179ggdChEpICbrREQAKleujKysLPTq1QtDhw5F48aNhQ5JdLS1tREVFQUrKyvo6enh1KlTqFWrFqKjo9GtWzfcvXtX6BBF6fbt23B2dkZmZqbQoRCRAuJFkYiIADx8+BAbN25EUlISWrZsCTs7O8yfPx9PnjwROjTRqFixoqxOvWrVqoiLi5Pdx33oi5eVlYWlS5fCxMRE6FCISEGxZp2ICED58uXh4eEBDw8PJCQkYNOmTdi4cSOmT5+Ojh07YujQoejSpQtUVJR3jqNRo0Y4deoU7O3t4e7uDl9fX1y6dAm7du2S7aCjzPT09OQWmBYUFCAjIwOamprYtGmTgJERkSJjGQwRUTHOnTuHdevWYePGjahatSpSU1Ohp6eH9evXo2XLlkKHJ4jbt28jMzMTzs7OeP78OXx9fXH69GnY2Nhg0aJFMDMzEzpEQW3YsEEuWVdRUYGRkREaNmwIPT09ASMjIkXGZJ2I6JWEhAT8/vvvWL9+PW7fvo3u3btj6NChaNu2LZ4/f45Zs2YhODgY9+7dEzpUIiJSEkzWiYgAdOnSBWFhYahZsyaGDRsGb29v6Ovry/VJTExElSpVIJVKBYpSeGlpadixYwfi4uIwefJk6Ovr4+LFi6hcubJS1mXHxMR8cF9nZ+cyjISIvlSsWSciAmBsbIzjx4/Dzc2txD5GRka4c+fOZ4xKXGJiYtC2bVvo6uri7t27GD58OPT19bFr1y7Ex8cjKChI6BA/OxcXF0gkErxv3ksikfCiSET0n3BmnYjolfDwcISHhyMxMbHI7Pm6desEiko82rZti7p162LBggWyfdYtLS1x+vRp9O/fXym3bvyYkihlr+knov+GM+tERABmzZqFmTNnwtXVFVWrVn3nZeOV1d9//42VK1cWaTcxMVHaLS6ZgBNRWWOyTkQEYPny5diwYQMGDhwodCiipa6ujmfPnhVpj42NhZGRkQARicvevXuLbZdIJNDQ0IC1tTUsLCw+c1REpOhYBkNEBMDAwADnz5+HlZWV0KGI1rBhw5CcnIw//vgD+vr6iImJQbly5dC9e3c0b94cixcvFjpEQamoqBRbv/5vm0QiQdOmTRESEsKtHInogynv1T2IiN4wbNgwbNmyRegwRC0wMBCZmZkwNjZGVlYWWrRoAWtra2hpaeGHH34QOjzBHTp0CPXr18ehQ4eQnp6O9PR0HDp0CA0bNsSff/6JEydOIDk5GZMmTRI6VCJSIJxZJyKl5ePjI/u3VCrFxo0b4ezsDGdnZ6iqqsr1XbRo0ecOT7QiIiIQHR2NzMxM1K1bF23bthU6JFFwdHTEqlWr0LhxY7n2iIgIjBgxAleuXMHhw4fx9ddfIz4+XqAoiUjRsGadiJRWZGSk3M8uLi4AgMuXL8u1c7Hpa2/vmHP9+nXZNxLKvmNOXFwcdHR0irTr6Ojg9u3bAAAbGxskJSV97tCISIExWScipXX06FGhQ1AoM2fOxKxZs7hjTgnq1auHyZMnIygoSLbg9unTp/juu+9Qv359AMDNmzdhamoqZJhEpGBYBkNERB+katWqWLBgAXfMKcGNGzfQrVs33LlzR5aQ379/H5aWltizZw9q1qyJkJAQZGRkcAyJ6IMxWSciog/CHXPeTyqV4uDBg4iNjQUA2Nraol27dlBR4X4ORPTfMFknIqIP4ufnBy0tLUyfPl3oUBSak5MTDhw4wHIYIvogrFknIqISvb1jzqpVq3D48GHumPMJ7t69i9zcXKHDICIFwWSdiIhKxB1ziIiExWSdiIhKxB1ziIiExRUvREREREQixWSdiIiIiEikmKwTEREREYkUk3UiIqLPaOXKlahcubLQYRCRguA+60RERKVg6dKlxbZLJBJoaGjA2toazZs3R7ly5T5zZESkyJisExERlQILCws8ffoUL168gJ6eHgAgNTUVmpqa0NLSQmJiIiwtLXH06FFeEImIPhjLYIiIiErBjz/+iPr16+PmzZtITk5GcnIyYmNj0bBhQyxZsgTx8fGoUqUKJk6cKHSoRKRAOLNORERUCqysrLBz507ZhaP+FRkZiR49euD27ds4ffo0evTogcePHwsTJBEpHM6sExERlYLHjx8jLy+vSHteXh6ePHkCAKhWrRoyMjI+d2hEpMCYrBMREZWCVq1aYeTIkYiMjJS1RUZGYvTo0WjdujUA4NKlS7CwsBAqRCJSQEzWiYiISsHatWuhr6+PevXqQV1dHerq6nB1dYW+vj7Wrl0LANDS0kJgYKDAkRKRImHNOhERUSm6fv06YmNjAQC2trawtbUVOCIiUmRM1omIiErBqVOn0LRpU6HDIKIvDJN1IiKiUqCmpgYTExP069cPAwYMgIODg9AhEdEXgDXrREREpeDRo0fw9fXF8ePH4ejoCBcXF/z000948OCB0KERkQLjzDoREVEpu3PnDrZs2YKtW7fi+vXraN68OY4cOSJ0WESkgJisExERlYH8/Hz89ddfmD59OmJiYpCfny90SESkgFgGQ0REVIoiIiLwzTffoGrVqujfvz8cHR2xf/9+ocMiIgXFmXUiIqJSMHXqVAQHB+Phw4do3749vLy80K1bN2hqagodGhEpMCbrREREpaBJkybw8vJC7969YWhoKHQ4RPSFYLJORERUiq5evYr4+Hjk5OTItXft2lWgiIhIkZUXOgAiIqIvwZ07d+Dh4YGYmBhIJBL8OxcmkUgAgAtMieg/4QJTIiKiUjBu3DiYm5sjMTERmpqauHLlCk6cOAFXV1ccO3ZM6PCISEGxDIaIiKgUGBoa4siRI3B2doauri7Onz8PW1tbHDlyBL6+voiMjBQ6RCJSQJxZJyIiKgX5+fnQ1tYGUJi4P3r0CABgZmaGGzduCBkaESkw1qwTERGVAkdHR0RHR8PCwgINGzbEggULoKamhlWrVsHS0lLo8IhIQbEMhoiIqBSEhYXh+fPn8PT0xK1bt9C5c2fExsbCwMAA27ZtQ+vWrYUOkYgUEJN1IiKiMpKSkgI9PT3ZjjBERB+LyToRERERkUhxgSkRERERkUgxWSciIiIiEikm60REREREIsVknYiIiIhIpJisExERERGJFJN1IiIiIiKRYrJORERERCRSTNaJiIiIiETq//QhlDlVbMyYAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "sns.heatmap(df.corr(numeric_only=True),annot=True, cmap=\"Oranges\")\n",
        "plt.title(\"Correlation Heatmap (Numeric Features)\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MsKm2F7qSrfT"
      },
      "source": [
        "This map shows correlation between features, appreantly the id column is useless and will dropped later in the notebook. moreover, the highest correlations with stroke  are found in `age`, `hypertension`, and `heart_disease`. This confirms clinical understanding of these established stroke risk factors. also othr variables like `avg_glucose_level` and `bmi` have lower correlations, but we can  still includ those for their potential combined effect with other features (again with the given data features).\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "E5SLLgpOLKK9"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tMVrmvfJRVGj"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ayc6YSCdRUn1"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OwAkwWbEMBRf"
      },
      "source": [
        "### Data Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BbJj2jujRWee"
      },
      "source": [
        "Visualizations reveal far fewer stroke cases than non-stroke, requiring handling class imbalance and special data modeling to hit the fitting target in our mind."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cqqiuaKJMJeW"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.utils import class_weight"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "POF4ohn0MMf_"
      },
      "outputs": [],
      "source": [
        "df_clean = df.drop(columns=[\"id\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "733P0l1tMNfB"
      },
      "outputs": [],
      "source": [
        "bmi_imputer = SimpleImputer(strategy=\"median\")\n",
        "df_clean[\"bmi\"] = bmi_imputer.fit_transform(df_clean[[\"bmi\"]])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_3cX8EcITf6A"
      },
      "source": [
        "Features are selected based on both clinical relevance (based on the analysis and visualisations done previously) and data availability."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "plMEkDDQMR5B"
      },
      "outputs": [],
      "source": [
        "label_encoders = {}\n",
        "for col in [\"gender\", \"ever_married\", \"work_type\", \"Residence_type\", \"smoking_status\"]:\n",
        "    le = LabelEncoder()\n",
        "    df_clean[col] = le.fit_transform(df_clean[col])\n",
        "    label_encoders[col] = le"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FEu2YhapTdXn"
      },
      "source": [
        "Encoding the required data ensures correct format to feed the model, and imputing missing values ensures that our model can learn effectively from all records without introducing bias or any type of  error."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1gkH24wSMTV4"
      },
      "outputs": [],
      "source": [
        "X = df_clean.drop(columns=[\"stroke\"])\n",
        "y = df_clean[\"stroke\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Mh-Y97ypMUo9"
      },
      "outputs": [],
      "source": [
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FktYomOqMWGr"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X_scaled, y, test_size=0.2, stratify=y, random_state=42\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "l5hWKtsi9Ord",
        "outputId": "cd627a5c-54e2-4981-e3dd-1f3525310af3"
      },
      "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>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>stroke</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>199</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ],
            "text/plain": [
              "stroke\n",
              "0    3889\n",
              "1     199\n",
              "Name: count, dtype: int64"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "stroke_counts = y_train.value_counts()\n",
        "stroke_counts"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q3nB4vx3T120"
      },
      "source": [
        "This shows imbalance in  our dataset(target variable), \"stroke\" is ppretty rare compared to \"no-strokke\". If unaddressed, the model may \"play it safe\" and predict no stroke for nearly everyone, which defeats the purpose in a medical screening setting. To fix this, we use SMOTE to generate synthetic stroke cases in the training set.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QbPsae7S9sdI",
        "outputId": "6bfd2b25-078d-43bd-8e89-762b477cf2e1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Class distribution after SMOTE: \n",
            "stroke\n",
            "0    3889\n",
            "1    3889\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "smote = SMOTE(random_state =42)\n",
        "X_train_sm, y_train_sm= smote.fit_resample(X_train, y_train)\n",
        "\n",
        "print(\"Class distribution after SMOTE: \" )\n",
        "print(pd.Series(y_train_sm).value_counts())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AsDqdG1Y-KJd"
      },
      "source": [
        "### Model Building"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B6mHBh1AfXAd"
      },
      "source": [
        "After applying SMOTE (Synthetic Minority Over-sampling Technique) to balance the minority class (stroke = 1) in the training set, we can start building a neural network that fits it well. From its name, generate synthetic examples similar examples by interpolating between existing ones of the minority class. although it doesnt give the same result as an ideal dataset with balanced classes, yet it somehow helps the model generalize better and avoid overfitting"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bzIEx4EdMjrt"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qggs4SJdhGnY"
      },
      "source": [
        "I chose to build a feedforward neural network using Sequential from Keras, which stacks layers oneafter the other.\n",
        "\n",
        "\n",
        "*   Hidden layer with 32 neurons, with RELU as an activation function\n",
        "*   Second hidden layer with 16 neurons, also with RELU as an activation function\n",
        "*   Final output layer with 1 neuron, used for binary classification (sigmoid).\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_cU_BUWmM5vU",
        "outputId": "cc48ca38-eb16-4005-b977-2b7f8dea6764"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        }
      ],
      "source": [
        "model = keras.Sequential([\n",
        "    layers.Dense(32, activation='relu', input_shape=(X_train_sm.shape[1],)),\n",
        "    layers.Dense(16, activation='relu'),\n",
        "    layers.Dense(1, activation='sigmoid')\n",
        "\n",
        "])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x0FHZbfShk3e"
      },
      "source": [
        "using the popular ADAM optimizer tocompile the model, which adapts the learning rate and speeds up convergences efficinetly, alosusing this typeof loss which is best forbinary classification problems."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X9YxKkNmNQ5q"
      },
      "outputs": [],
      "source": [
        "model.compile(\n",
        "              optimizer='adam',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy']\n",
        "              )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xp_9JyQriI72"
      },
      "source": [
        "The trainig starts, the dataset was divided into batches of 32 samples each during training, going through it 20 times(epochs), with 20% f the dataset preserved for validation purposes during training. at the end 'history' will contain the training and validation loss and accuracy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v9xTYkUtNUYQ",
        "outputId": "69acdd57-4dc1-4bbc-d489-1d5c2b1a5d48"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 5ms/step - accuracy: 0.6703 - loss: 0.6215 - val_accuracy: 0.7339 - val_loss: 0.6020\n",
            "Epoch 2/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7755 - loss: 0.4504 - val_accuracy: 0.7461 - val_loss: 0.5759\n",
            "Epoch 3/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7919 - loss: 0.4181 - val_accuracy: 0.7783 - val_loss: 0.5518\n",
            "Epoch 4/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8050 - loss: 0.4019 - val_accuracy: 0.7931 - val_loss: 0.5182\n",
            "Epoch 5/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8046 - loss: 0.4006 - val_accuracy: 0.8246 - val_loss: 0.4748\n",
            "Epoch 6/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8070 - loss: 0.3948 - val_accuracy: 0.8149 - val_loss: 0.4870\n",
            "Epoch 7/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8143 - loss: 0.3839 - val_accuracy: 0.8162 - val_loss: 0.4793\n",
            "Epoch 8/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8210 - loss: 0.3765 - val_accuracy: 0.8303 - val_loss: 0.4605\n",
            "Epoch 9/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8245 - loss: 0.3684 - val_accuracy: 0.7853 - val_loss: 0.5182\n",
            "Epoch 10/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8253 - loss: 0.3615 - val_accuracy: 0.7821 - val_loss: 0.5200\n",
            "Epoch 11/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8382 - loss: 0.3570 - val_accuracy: 0.8290 - val_loss: 0.4652\n",
            "Epoch 12/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8312 - loss: 0.3604 - val_accuracy: 0.8265 - val_loss: 0.4546\n",
            "Epoch 13/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8348 - loss: 0.3508 - val_accuracy: 0.8657 - val_loss: 0.3979\n",
            "Epoch 14/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - accuracy: 0.8454 - loss: 0.3357 - val_accuracy: 0.8695 - val_loss: 0.3949\n",
            "Epoch 15/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.8454 - loss: 0.3384 - val_accuracy: 0.8323 - val_loss: 0.4253\n",
            "Epoch 16/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8496 - loss: 0.3229 - val_accuracy: 0.8355 - val_loss: 0.4389\n",
            "Epoch 17/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - accuracy: 0.8520 - loss: 0.3275 - val_accuracy: 0.8400 - val_loss: 0.4336\n",
            "Epoch 18/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.8525 - loss: 0.3172 - val_accuracy: 0.8798 - val_loss: 0.3671\n",
            "Epoch 19/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.8541 - loss: 0.3264 - val_accuracy: 0.8875 - val_loss: 0.3599\n",
            "Epoch 20/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.8601 - loss: 0.3132 - val_accuracy: 0.8907 - val_loss: 0.3424\n"
          ]
        }
      ],
      "source": [
        "history = model.fit(\n",
        "    X_train_sm,y_train_sm,\n",
        "    epochs =20,\n",
        "    batch_size=32,\n",
        "    validation_split= 0.2,\n",
        "    verbose= 1\n",
        ")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GPuMSOWqXOGp"
      },
      "source": [
        "### Baseline medol evaluation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s7AtGGUQjH5z"
      },
      "source": [
        "Training done, here comes the testing,  here we start predicing probabilities for each sample in the test set. with a default threshold of 0.5, to convert the predicted probabilities into a class. and evaluation metrics exist to better understand performance on each class."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IEk-TcmLNbN1",
        "outputId": "33e5078a-bfe8-4f3c-be49-0fa8486eb589"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
            "Accuracy: 0.8033\n",
            "Precision: 0.1088\n",
            "Recall: 0.4200\n",
            "F1-score: 0.1728\n",
            "\n",
            "Classification report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.82      0.89       972\n",
            "           1       0.11      0.42      0.17        50\n",
            "\n",
            "    accuracy                           0.80      1022\n",
            "   macro avg       0.54      0.62      0.53      1022\n",
            "weighted avg       0.92      0.80      0.85      1022\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report\n",
        "\n",
        "y_pred_prob = model.predict(X_test).flatten()\n",
        "y_pred = (y_pred_prob > 0.5).astype(int)\n",
        "\n",
        "acc = accuracy_score(y_test, y_pred)\n",
        "prec = precision_score(y_test, y_pred)\n",
        "rec = recall_score(y_test, y_pred)\n",
        "f1 = f1_score(y_test, y_pred)\n",
        "\n",
        "print(f\"Accuracy: {acc:.4f}\")\n",
        "print(f\"Precision: {prec:.4f}\")\n",
        "print(f\"Recall: {rec:.4f}\")\n",
        "print(f\"F1-score: {f1:.4f}\")\n",
        "print(\"\\nClassification report:\\n\", classification_report(y_test, y_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KPxhA4KoCBSF"
      },
      "source": [
        "### Model Improvment"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "csVDqLapbBR7"
      },
      "source": [
        "The first iteration of improvement includes bilding a more dense neursl network architecture, with more layers and neurons, which allows the  model to learn more complex pattersn and makesit more robust against non linear relationships.\n",
        "\n",
        "* First hidden layer with 32 neurons, and RELU as an activation function.\n",
        "* second hd32 neurons with\tReLU\t(can learn deeper representations)\n",
        "* 16 neurons \tReLU\t(added abstraction)\n",
        "* output sigmoid layer (binart classification)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RpCe1J9zbA0y"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iNIsSKhdNXls",
        "outputId": "cd308c8a-8756-44fc-a9c8-796fec760c2e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 6ms/step - accuracy: 0.6987 - loss: 0.5676 - val_accuracy: 0.7641 - val_loss: 0.5729\n",
            "Epoch 2/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7965 - loss: 0.4173 - val_accuracy: 0.8021 - val_loss: 0.5222\n",
            "Epoch 3/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 11ms/step - accuracy: 0.8093 - loss: 0.3939 - val_accuracy: 0.7853 - val_loss: 0.5346\n",
            "Epoch 4/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8138 - loss: 0.3824 - val_accuracy: 0.8721 - val_loss: 0.4116\n",
            "Epoch 5/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8387 - loss: 0.3522 - val_accuracy: 0.8535 - val_loss: 0.4333\n",
            "Epoch 6/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8464 - loss: 0.3365 - val_accuracy: 0.8927 - val_loss: 0.3653\n",
            "Epoch 7/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8480 - loss: 0.3214 - val_accuracy: 0.8817 - val_loss: 0.3802\n",
            "Epoch 8/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8499 - loss: 0.3246 - val_accuracy: 0.8393 - val_loss: 0.4400\n",
            "Epoch 9/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8694 - loss: 0.2929 - val_accuracy: 0.8882 - val_loss: 0.3428\n",
            "Epoch 10/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8724 - loss: 0.2861 - val_accuracy: 0.9325 - val_loss: 0.2942\n",
            "Epoch 11/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8792 - loss: 0.2757 - val_accuracy: 0.9261 - val_loss: 0.2638\n",
            "Epoch 12/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - accuracy: 0.8857 - loss: 0.2607 - val_accuracy: 0.9608 - val_loss: 0.2068\n",
            "Epoch 13/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - accuracy: 0.8913 - loss: 0.2610 - val_accuracy: 0.9094 - val_loss: 0.3018\n",
            "Epoch 14/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9031 - loss: 0.2379 - val_accuracy: 0.9203 - val_loss: 0.2642\n",
            "Epoch 15/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9030 - loss: 0.2355 - val_accuracy: 0.8875 - val_loss: 0.3384\n",
            "Epoch 16/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9137 - loss: 0.2216 - val_accuracy: 0.8869 - val_loss: 0.3499\n",
            "Epoch 17/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9125 - loss: 0.2151 - val_accuracy: 0.9049 - val_loss: 0.2979\n",
            "Epoch 18/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9161 - loss: 0.2067 - val_accuracy: 0.8888 - val_loss: 0.3253\n",
            "Epoch 19/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9239 - loss: 0.1992 - val_accuracy: 0.9794 - val_loss: 0.1409\n",
            "Epoch 20/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9229 - loss: 0.2035 - val_accuracy: 0.9730 - val_loss: 0.1550\n"
          ]
        }
      ],
      "source": [
        "from tensorflow.keras import layers, models\n",
        "\n",
        "model1 = models.Sequential([\n",
        "    layers.Dense(64, activation='relu', input_shape=(X_train_sm.shape[1],)),\n",
        "    layers.Dense(32, activation='relu'),\n",
        "    layers.Dense(16, activation='relu'),\n",
        "    layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "model1.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "history1 = model1.fit(X_train_sm, y_train_sm, epochs=20, batch_size=32, validation_split=0.2, verbose=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kwINgQR4l81q"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PRScIFS1l9W_"
      },
      "source": [
        "Next iteration of improvementr introduces a training-time improvement that helps make the model more fair to both classes and less focused on just naive overall accuracy, more on balanced learning.\n",
        "we first compute the clas weights, and build the model and fit using class weights, this difers from SMOTE in that it doesnt just create new data, yet when computing the loss for each training example, it tells the model to multiply it by its class weight. A wrong prediction on class 1 (e.g., missing a stroke) will have a higher loss penalty than one on class 0. hence the model will be guided to learn features of the minority class better."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QEhVE1zGR1g_",
        "outputId": "30c38578-24df-4215-c327-c0dba8c2e095"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.4130 - loss: 2.0189 - val_accuracy: 0.9994 - val_loss: 0.0605\n",
            "Epoch 2/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.5513 - loss: 0.8122 - val_accuracy: 0.9974 - val_loss: 0.0490\n",
            "Epoch 3/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.6301 - loss: 0.7293 - val_accuracy: 0.9968 - val_loss: 0.0517\n",
            "Epoch 4/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.6607 - loss: 0.6824 - val_accuracy: 0.9974 - val_loss: 0.0490\n",
            "Epoch 5/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.6738 - loss: 0.6538 - val_accuracy: 0.9994 - val_loss: 0.0389\n",
            "Epoch 6/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.6757 - loss: 0.6497 - val_accuracy: 0.9968 - val_loss: 0.0446\n",
            "Epoch 7/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.6954 - loss: 0.6260 - val_accuracy: 0.9981 - val_loss: 0.0452\n",
            "Epoch 8/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.6980 - loss: 0.6190 - val_accuracy: 0.9981 - val_loss: 0.0390\n",
            "Epoch 9/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7014 - loss: 0.6017 - val_accuracy: 1.0000 - val_loss: 0.0330\n",
            "Epoch 10/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7009 - loss: 0.6029 - val_accuracy: 0.9994 - val_loss: 0.0432\n",
            "Epoch 11/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.7241 - loss: 0.5773 - val_accuracy: 0.9987 - val_loss: 0.0397\n",
            "Epoch 12/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.7200 - loss: 0.5734 - val_accuracy: 1.0000 - val_loss: 0.0314\n",
            "Epoch 13/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7202 - loss: 0.5696 - val_accuracy: 1.0000 - val_loss: 0.0392\n",
            "Epoch 14/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7236 - loss: 0.5647 - val_accuracy: 1.0000 - val_loss: 0.0313\n",
            "Epoch 15/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7317 - loss: 0.5442 - val_accuracy: 1.0000 - val_loss: 0.0377\n",
            "Epoch 16/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7359 - loss: 0.5421 - val_accuracy: 1.0000 - val_loss: 0.0297\n",
            "Epoch 17/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7377 - loss: 0.5435 - val_accuracy: 1.0000 - val_loss: 0.0309\n",
            "Epoch 18/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7614 - loss: 0.4976 - val_accuracy: 1.0000 - val_loss: 0.0286\n",
            "Epoch 19/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.7487 - loss: 0.5090 - val_accuracy: 1.0000 - val_loss: 0.0302\n",
            "Epoch 20/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7512 - loss: 0.5129 - val_accuracy: 1.0000 - val_loss: 0.0351\n"
          ]
        }
      ],
      "source": [
        "from sklearn.utils.class_weight import compute_class_weight\n",
        "\n",
        "class_weights= compute_class_weight(class_weight='balanced', classes=np.unique(y_train), y=y_train)\n",
        "class_weight_dict ={0: class_weights[0], 1: class_weights[1]}\n",
        "\n",
        "model2 = models.Sequential([\n",
        "    layers.Dense(32,activation='relu', input_shape=(X_train_sm.shape[1],)),\n",
        "    layers.Dense(16, activation='relu'),\n",
        "    layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "model2.compile(optimizer='adam' , loss='binary_crossentropy', metrics=['accuracy'])\n",
        "history2= model2.fit(X_train_sm, y_train_sm, epochs=20, batch_size=32, validation_split=0.2,\n",
        "                      class_weight=class_weight_dict, verbose=1)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "13xoEGqyokON"
      },
      "source": [
        "In our final improvement, we test a neural network that includes two advanced techniques for improving training performance:\n",
        "\n",
        "1.   LeakyReLU activation instead of standard ReLU (allows a small, non-zero gradient for negative inputs aand prevents the dying relu problem)\n",
        "2.   Dropout layers for regularization (which randomly drops 30%% of the neurons during training, helping prevent overfitting by making the network rely on different combinationss of neurons)\n",
        "\n",
        "Alowing to reduce overfitting while maintaining a neat recall and precision overall"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3rU0jLUHR7ub",
        "outputId": "c50a6db1-52a2-433b-b90a-8f7cce934c7c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/keras/src/layers/activations/leaky_relu.py:41: UserWarning: Argument `alpha` is deprecated. Use `negative_slope` instead.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.6033 - loss: 0.6767 - val_accuracy: 0.7622 - val_loss: 0.5660\n",
            "Epoch 2/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.7452 - loss: 0.4976 - val_accuracy: 0.7622 - val_loss: 0.5805\n",
            "Epoch 3/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.7554 - loss: 0.4654 - val_accuracy: 0.8168 - val_loss: 0.5301\n",
            "Epoch 4/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - accuracy: 0.7780 - loss: 0.4442 - val_accuracy: 0.8335 - val_loss: 0.5283\n",
            "Epoch 5/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.7791 - loss: 0.4354 - val_accuracy: 0.8149 - val_loss: 0.5620\n",
            "Epoch 6/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.7849 - loss: 0.4293 - val_accuracy: 0.8554 - val_loss: 0.5178\n",
            "Epoch 7/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.7784 - loss: 0.4376 - val_accuracy: 0.8689 - val_loss: 0.4896\n",
            "Epoch 8/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7937 - loss: 0.4208 - val_accuracy: 0.8573 - val_loss: 0.5228\n",
            "Epoch 9/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7921 - loss: 0.4149 - val_accuracy: 0.8503 - val_loss: 0.5202\n",
            "Epoch 10/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7933 - loss: 0.4239 - val_accuracy: 0.8554 - val_loss: 0.5107\n",
            "Epoch 11/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7967 - loss: 0.4148 - val_accuracy: 0.8689 - val_loss: 0.4941\n",
            "Epoch 12/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7905 - loss: 0.4108 - val_accuracy: 0.8573 - val_loss: 0.5071\n",
            "Epoch 13/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7911 - loss: 0.4192 - val_accuracy: 0.8708 - val_loss: 0.4877\n",
            "Epoch 14/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8021 - loss: 0.4028 - val_accuracy: 0.8522 - val_loss: 0.5024\n",
            "Epoch 15/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8071 - loss: 0.3989 - val_accuracy: 0.8644 - val_loss: 0.4841\n",
            "Epoch 16/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8018 - loss: 0.4017 - val_accuracy: 0.8657 - val_loss: 0.4968\n",
            "Epoch 17/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.8060 - loss: 0.4029 - val_accuracy: 0.8541 - val_loss: 0.4930\n",
            "Epoch 18/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.7974 - loss: 0.4074 - val_accuracy: 0.8740 - val_loss: 0.4641\n",
            "Epoch 19/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - accuracy: 0.7914 - loss: 0.4136 - val_accuracy: 0.8760 - val_loss: 0.4733\n",
            "Epoch 20/20\n",
            "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.8078 - loss: 0.3933 - val_accuracy: 0.8721 - val_loss: 0.4886\n"
          ]
        }
      ],
      "source": [
        "model3= models.Sequential([\n",
        "    layers.Dense(32),\n",
        "    layers.LeakyReLU(alpha=0.1),\n",
        "    layers.Dropout(0.3),\n",
        "    layers.Dense(16),\n",
        "    layers.LeakyReLU(alpha=0.1) ,\n",
        "    layers.Dropout(0.2),\n",
        "    layers.Dense(1,activation='sigmoid')\n",
        "])\n",
        "model3.compile(optimizer='adam',loss='binary_crossentropy' , metrics=['accuracy'])\n",
        "history3 =model3.fit(X_train_sm, y_train_sm, epochs=20, batch_size=32,validation_split=0.2, verbose=1 )\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gp0XOa0xIagg",
        "outputId": "10d61f57-25ee-4865-df68-bad410cc6808"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step\n",
            "Model 1:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.86      0.91       972\n",
            "           1       0.14      0.44      0.21        50\n",
            "\n",
            "    accuracy                           0.84      1022\n",
            "   macro avg       0.55      0.65      0.56      1022\n",
            "weighted avg       0.93      0.84      0.88      1022\n",
            "\n",
            "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
            "Model 2:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.64      0.77       972\n",
            "           1       0.10      0.82      0.19        50\n",
            "\n",
            "    accuracy                           0.65      1022\n",
            "   macro avg       0.55      0.73      0.48      1022\n",
            "weighted avg       0.94      0.65      0.75      1022\n",
            "\n",
            "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
            "Model 3:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.80      0.88       972\n",
            "           1       0.16      0.76      0.26        50\n",
            "\n",
            "    accuracy                           0.79      1022\n",
            "   macro avg       0.57      0.78      0.57      1022\n",
            "weighted avg       0.94      0.79      0.85      1022\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "for idx, model in enumerate([model1, model2,model3], start=1):\n",
        "    y_pred_prob= model.predict(X_test).flatten()\n",
        "    y_pred = (y_pred_prob>0.5).astype(int)\n",
        "    print(f\"Model {idx}:\")\n",
        "    print( classification_report( y_test, y_pred))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wdNxLHQysRiA"
      },
      "source": [
        "Model 1 is okay, yet theres room for improvement.\n",
        "\n",
        "Model2 is the recall champ, but at a cost/9ow precision)\n",
        "\n",
        "*Model 3* offers the best balance of recall and usefulness.  \n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "With Recall 0.76 (still far higher than Model 1), Precision 0.16 (60 % better than Model 2), and the highest macro-F1 and good accuracy. Through a better atchotecure and a balamced dataset, you can get pretty decent reliable results to your problem statement. And For life-critical, highly imbalanced problems like stroke prediction,optimising Recall anf precision metrics aligns directly with patient safety and clinical usability, whereas plain accuracy does not.\n",
        "\n",
        "Missing a stroke (false-negative) can delay treatment → disability or death.\n",
        "If precision is low, clinicians receive floods of false alarms → wasted scans, cost, alarm fatigue, and loss of trust in the tool."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FftNhryf_c2N"
      },
      "source": [
        "## Model Deployment"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fnaDWD1kdaN-",
        "outputId": "a4ea26c0-83b3-4f01-9e45-a6d1298bbf09"
      },
      "outputs": [
        {
          "ename": "CalledProcessError",
          "evalue": "Command '['c:\\\\Users\\\\wissa\\\\Downloads\\\\data\\\\.venv\\\\Scripts\\\\python.exe', 'C:\\\\Users\\\\wissa\\\\Downloads\\\\data\\\\stroke-flask-docker\\\\model\\\\train_and_save.py']' returned non-zero exit status 1.",
          "output_type": "error",
          "traceback": [
            "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
            "\u001b[31mCalledProcessError\u001b[39m                        Traceback (most recent call last)",
            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[6]\u001b[39m\u001b[32m, line 398\u001b[39m\n\u001b[32m    393\u001b[39m \u001b[38;5;66;03m# -----------------------------\u001b[39;00m\n\u001b[32m    394\u001b[39m \u001b[38;5;66;03m# 5) Build a ready-to-load model now (synthetic training)\u001b[39;00m\n\u001b[32m    395\u001b[39m \u001b[38;5;66;03m# -----------------------------\u001b[39;00m\n\u001b[32m    396\u001b[39m \u001b[38;5;66;03m# We'll run the training script to produce model/stroke_pipeline.joblib\u001b[39;00m\n\u001b[32m    397\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msubprocess\u001b[39;00m,\u001b[38;5;250m \u001b[39m\u001b[34;01msys\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m398\u001b[39m \u001b[43msubprocess\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43msys\u001b[49m\u001b[43m.\u001b[49m\u001b[43mexecutable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mroot\u001b[49m\u001b[43m \u001b[49m\u001b[43m/\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmodel\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m/\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtrain_and_save.py\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[32m    401\u001b[39m \u001b[38;5;66;03m# -----------------------------\u001b[39;00m\n\u001b[32m    402\u001b[39m \u001b[38;5;66;03m# 6) requirements.txt\u001b[39;00m\n\u001b[32m    403\u001b[39m \u001b[38;5;66;03m# -----------------------------\u001b[39;00m\n\u001b[32m    404\u001b[39m reqs = \u001b[33mr\u001b[39m\u001b[33m'''\u001b[39m\u001b[33mflask==3.0.3\u001b[39m\n\u001b[32m    405\u001b[39m \u001b[33mgunicorn==22.0.0\u001b[39m\n\u001b[32m    406\u001b[39m \u001b[33mjoblib==1.4.2\u001b[39m\n\u001b[32m   (...)\u001b[39m\u001b[32m    409\u001b[39m \u001b[33mscikit-learn==1.5.1\u001b[39m\n\u001b[32m    410\u001b[39m \u001b[33m'''\u001b[39m\n",
            "\u001b[36mFile \u001b[39m\u001b[32m~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\subprocess.py:571\u001b[39m, in \u001b[36mrun\u001b[39m\u001b[34m(input, capture_output, timeout, check, *popenargs, **kwargs)\u001b[39m\n\u001b[32m    569\u001b[39m     retcode = process.poll()\n\u001b[32m    570\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m check \u001b[38;5;129;01mand\u001b[39;00m retcode:\n\u001b[32m--> \u001b[39m\u001b[32m571\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m CalledProcessError(retcode, process.args,\n\u001b[32m    572\u001b[39m                                  output=stdout, stderr=stderr)\n\u001b[32m    573\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m CompletedProcess(process.args, retcode, stdout, stderr)\n",
            "\u001b[31mCalledProcessError\u001b[39m: Command '['c:\\\\Users\\\\wissa\\\\Downloads\\\\data\\\\.venv\\\\Scripts\\\\python.exe', 'C:\\\\Users\\\\wissa\\\\Downloads\\\\data\\\\stroke-flask-docker\\\\model\\\\train_and_save.py']' returned non-zero exit status 1."
          ]
        }
      ],
      "source": [
        "# Create the complete Flask + Docker project with a ready-to-load model\n",
        "\n",
        "import os, json, textwrap, joblib, numpy as np, pandas as pd\n",
        "from pathlib import Path\n",
        "\n",
        "root = Path(\"C:/Users/wissa/Downloads/data/stroke-flask-docker\")\n",
        "(root / \"model\").mkdir(parents=True, exist_ok=True)\n",
        "(root / \"templates\").mkdir(parents=True, exist_ok=True)\n",
        "(root / \"static\").mkdir(parents=True, exist_ok=True)\n",
        "(root / \"tests\").mkdir(parents=True, exist_ok=True)\n",
        "(root / \"data\").mkdir(parents=True, exist_ok=True)\n",
        "\n",
        "# -----------------------------\n",
        "# 1) app.py (Flask app)\n",
        "# -----------------------------\n",
        "app_py = r'''from flask import Flask, render_template, request, jsonify\n",
        "import joblib\n",
        "import numpy as np\n",
        "import os\n",
        "\n",
        "APP_PORT = int(os.getenv(\"PORT\", \"8080\"))\n",
        "\n",
        "app = Flask(__name__)\n",
        "\n",
        "MODEL_PATH = os.getenv(\"MODEL_PATH\", \"model/stroke_pipeline.joblib\")\n",
        "\n",
        "# Load model pipeline at startup\n",
        "try:\n",
        "    pipeline = joblib.load(MODEL_PATH)\n",
        "except Exception as e:\n",
        "    raise RuntimeError(f\"Failed to load model at {MODEL_PATH}: {e}\")\n",
        "\n",
        "FEATURE_ORDER = [\n",
        "    \"gender\",\n",
        "    \"age\",\n",
        "    \"hypertension\",\n",
        "    \"heart_disease\",\n",
        "    \"ever_married\",\n",
        "    \"work_type\",\n",
        "    \"Residence_type\",\n",
        "    \"avg_glucose_level\",\n",
        "    \"bmi\",\n",
        "    \"smoking_status\",\n",
        "]\n",
        "\n",
        "# Simple healthcheck\n",
        "@app.route(\"/health\", methods=[\"GET\"])\n",
        "def health():\n",
        "    return jsonify({\"status\": \"ok\"}), 200\n",
        "\n",
        "@app.route(\"/\", methods=[\"GET\"])\n",
        "def index():\n",
        "    # Provide default values to make testing easy\n",
        "    defaults = {\n",
        "        \"gender\": \"Female\",\n",
        "        \"age\": 45,\n",
        "        \"hypertension\": 0,\n",
        "        \"heart_disease\": 0,\n",
        "        \"ever_married\": \"Yes\",\n",
        "        \"work_type\": \"Private\",\n",
        "        \"Residence_type\": \"Urban\",\n",
        "        \"avg_glucose_level\": 95.0,\n",
        "        \"bmi\": 28.0,\n",
        "        \"smoking_status\": \"never smoked\",\n",
        "    }\n",
        "    return render_template(\"index.html\", defaults=defaults)\n",
        "\n",
        "@app.route(\"/predict\", methods=[\"POST\"])\n",
        "def predict():\n",
        "    try:\n",
        "        # Read input either from JSON (API) or form (UI)\n",
        "        if request.is_json:\n",
        "            payload = request.get_json()\n",
        "        else:\n",
        "            payload = request.form.to_dict()\n",
        "\n",
        "        # Ensure types\n",
        "        # Map numeric fields\n",
        "        numeric_fields = [\"age\", \"avg_glucose_level\", \"bmi\"]\n",
        "        int_fields = [\"hypertension\", \"heart_disease\"]\n",
        "\n",
        "        for k in numeric_fields:\n",
        "            if k in payload:\n",
        "                payload[k] = float(payload[k])\n",
        "        for k in int_fields:\n",
        "            if k in payload:\n",
        "                payload[k] = int(payload[k])\n",
        "\n",
        "        # Build row in fixed feature order\n",
        "        row = [[payload.get(f, None) for f in FEATURE_ORDER]]\n",
        "\n",
        "        # Predict proba (stroke = 1)\n",
        "        prob = float(pipeline.predict_proba(row)[0][1])\n",
        "        pred = int(prob >= 0.5)\n",
        "\n",
        "        result = {\"stroke_probability\": prob, \"predicted_label\": pred}\n",
        "        if request.is_json:\n",
        "            return jsonify(result)\n",
        "        else:\n",
        "            return render_template(\"index.html\", result=result, defaults=payload)\n",
        "    except Exception as e:\n",
        "        msg = {\"error\": str(e)}\n",
        "        if request.is_json:\n",
        "            return jsonify(msg), 400\n",
        "        else:\n",
        "            return render_template(\"index.html\", error=str(e), defaults=request.form), 400\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    app.run(host=\"0.0.0.0\", port=APP_PORT, debug=False)\n",
        "'''\n",
        "(root / \"app.py\").write_text(app_py, encoding=\"utf-8\")\n",
        "\n",
        "\n",
        "# -----------------------------\n",
        "# 2) HTML template\n",
        "# -----------------------------\n",
        "index_html = r'''<!DOCTYPE html>\n",
        "<html lang=\"en\">\n",
        "<head>\n",
        "  <meta charset=\"UTF-8\"/>\n",
        "  <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\"/>\n",
        "  <title>Stroke Risk Predictor</title>\n",
        "  <link rel=\"stylesheet\" href=\"/static/style.css\"/>\n",
        "</head>\n",
        "<body>\n",
        "  <div class=\"container\">\n",
        "    <h1>💓 Stroke Risk Predictor</h1>\n",
        "    <p>Enter patient details and get a predicted stroke probability.</p>\n",
        "\n",
        "    {% if error %}\n",
        "      <div class=\"error\">{{ error }}</div>\n",
        "    {% endif %}\n",
        "\n",
        "    <form method=\"POST\" action=\"/predict\" class=\"card\">\n",
        "      <div class=\"row\">\n",
        "        <label>Gender</label>\n",
        "        <select name=\"gender\">\n",
        "          {% for g in [\"Male\",\"Female\",\"Other\"] %}\n",
        "            <option value=\"{{g}}\" {% if defaults.gender==g %}selected{% endif %}>{{g}}</option>\n",
        "          {% endfor %}\n",
        "        </select>\n",
        "      </div>\n",
        "\n",
        "      <div class=\"row\">\n",
        "        <label>Age</label>\n",
        "        <input type=\"number\" name=\"age\" step=\"1\" min=\"0\" max=\"120\" value=\"{{defaults.age}}\"/>\n",
        "      </div>\n",
        "\n",
        "      <div class=\"row\">\n",
        "        <label>Hypertension</label>\n",
        "        <select name=\"hypertension\">\n",
        "          {% for v in [0,1] %}\n",
        "            <option value=\"{{v}}\" {% if defaults.hypertension==v %}selected{% endif %}>{{v}}</option>\n",
        "          {% endfor %}\n",
        "        </select>\n",
        "      </div>\n",
        "\n",
        "      <div class=\"row\">\n",
        "        <label>Heart Disease</label>\n",
        "        <select name=\"heart_disease\">\n",
        "          {% for v in [0,1] %}\n",
        "            <option value=\"{{v}}\" {% if defaults.heart_disease==v %}selected{% endif %}>{{v}}</option>\n",
        "          {% endfor %}\n",
        "        </select>\n",
        "      </div>\n",
        "\n",
        "      <div class=\"row\">\n",
        "        <label>Ever Married</label>\n",
        "        <select name=\"ever_married\">\n",
        "          {% for v in [\"Yes\",\"No\"] %}\n",
        "            <option value=\"{{v}}\" {% if defaults.ever_married==v %}selected{% endif %}>{{v}}</option>\n",
        "          {% endfor %}\n",
        "        </select>\n",
        "      </div>\n",
        "\n",
        "      <div class=\"row\">\n",
        "        <label>Work Type</label>\n",
        "        <select name=\"work_type\">\n",
        "          {% for v in [\"Private\",\"Self-employed\",\"Govt_job\",\"children\",\"Never_worked\"] %}\n",
        "            <option value=\"{{v}}\" {% if defaults.work_type==v %}selected{% endif %}>{{v}}</option>\n",
        "          {% endfor %}\n",
        "        </select>\n",
        "      </div>\n",
        "\n",
        "      <div class=\"row\">\n",
        "        <label>Residence Type</label>\n",
        "        <select name=\"Residence_type\">\n",
        "          {% for v in [\"Urban\",\"Rural\"] %}\n",
        "            <option value=\"{{v}}\" {% if defaults.Residence_type==v %}selected{% endif %}>{{v}}</option>\n",
        "          {% endfor %}\n",
        "        </select>\n",
        "      </div>\n",
        "\n",
        "      <div class=\"row\">\n",
        "        <label>Avg. Glucose Level</label>\n",
        "        <input type=\"number\" name=\"avg_glucose_level\" step=\"0.01\" value=\"{{defaults.avg_glucose_level}}\"/>\n",
        "      </div>\n",
        "\n",
        "      <div class=\"row\">\n",
        "        <label>BMI</label>\n",
        "        <input type=\"number\" name=\"bmi\" step=\"0.1\" value=\"{{defaults.bmi}}\"/>\n",
        "      </div>\n",
        "\n",
        "      <div class=\"row\">\n",
        "        <label>Smoking Status</label>\n",
        "        <select name=\"smoking_status\">\n",
        "          {% for v in [\"formerly smoked\",\"never smoked\",\"smokes\",\"Unknown\"] %}\n",
        "            <option value=\"{{v}}\" {% if defaults.smoking_status==v %}selected{% endif %}>{{v}}</option>\n",
        "          {% endfor %}\n",
        "        </select>\n",
        "      </div>\n",
        "\n",
        "      <button type=\"submit\">Predict</button>\n",
        "    </form>\n",
        "\n",
        "    {% if result %}\n",
        "      <div class=\"result card\">\n",
        "        <h2>Result</h2>\n",
        "        <p><strong>Predicted Stroke Probability:</strong> {{ '%.3f'|format(result.stroke_probability) }}</p>\n",
        "        <p><strong>Predicted Label (1 = Stroke):</strong> {{ result.predicted_label }}</p>\n",
        "      </div>\n",
        "    {% endif %}\n",
        "\n",
        "    <div class=\"api card\">\n",
        "      <h3>API</h3>\n",
        "      <code>POST /predict</code> with JSON:\n",
        "      <pre>\n",
        "{\n",
        "  \"gender\":\"Female\",\n",
        "  \"age\":45,\n",
        "  \"hypertension\":0,\n",
        "  \"heart_disease\":0,\n",
        "  \"ever_married\":\"Yes\",\n",
        "  \"work_type\":\"Private\",\n",
        "  \"Residence_type\":\"Urban\",\n",
        "  \"avg_glucose_level\":95.0,\n",
        "  \"bmi\":28.0,\n",
        "  \"smoking_status\":\"never smoked\"\n",
        "}\n",
        "      </pre>\n",
        "    </div>\n",
        "  </div>\n",
        "</body>\n",
        "</html>\n",
        "'''\n",
        "(root / \"templates\" / \"index.html\").write_text(index_html, encoding=\"utf-8\")\n",
        "\n",
        "\n",
        "# -----------------------------\n",
        "# 3) CSS\n",
        "# -----------------------------\n",
        "style_css = r'''*{box-sizing:border-box}body{font-family:system-ui,-apple-system,Segoe UI,Roboto,Helvetica,Arial,sans-serif;background:#0b1220;color:#e8eef9;margin:0;padding:2rem}\n",
        ".container{max-width:760px;margin:0 auto}\n",
        "h1{margin-top:0}\n",
        ".card{background:#111a2b;border:1px solid #1e2a44;border-radius:14px;padding:1rem;margin:1rem 0}\n",
        ".row{display:flex;gap:1rem;margin:.6rem 0;align-items:center}\n",
        ".row label{width:200px}\n",
        "input,select,button{padding:.5rem;border-radius:8px;border:1px solid #2a3a5e;background:#0e1626;color:#e8eef9}\n",
        "button{cursor:pointer}\n",
        ".error{background:#3b0d0d;border:1px solid #7c1919;color:#ffd6d6;border-radius:10px;padding:.75rem;margin-bottom:1rem}\n",
        ".result p{margin:.3rem 0}\n",
        ".api code, .api pre{display:block;background:#0e1626;border:1px solid #2a3a5e;padding:8px;border-radius:10px;overflow-x:auto}\n",
        "'''\n",
        "(root / \"static\" / \"style.css\").write_text(style_css, encoding=\"utf-8\")\n",
        "\n",
        "\n",
        "# -----------------------------\n",
        "# 4) Training script (uses real dataset if present; otherwise synthetic)\n",
        "# -----------------------------\n",
        "train_py = r'''\"\"\"\n",
        "Train & save a full sklearn Pipeline for stroke prediction.\n",
        "\n",
        "- If ./data/healthcare-dataset-stroke-data.csv exists, trains on it (matching the notebook structure).\n",
        "- Otherwise, trains on a synthetic dataset with the same schema.\n",
        "Saves: model/stroke_pipeline.joblib\n",
        "\"\"\"\n",
        "from pathlib import Path\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import joblib\n",
        "\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report, roc_auc_score\n",
        "\n",
        "DATA_PATH = Path(\"C:\\Users\\wissa\\Downloads\\data\\stroke-flask-docker\\data\\healthcare-dataset-stroke-data.csv\")\n",
        "OUT_PATH = Path(\"C:\\Users\\wissa\\Downloads\\data\\stroke-flask-docker\\model/stroke_pipeline.joblib\")\n",
        "OUT_PATH.parent.mkdir(parents=True, exist_ok=True)\n",
        "\n",
        "CATEGORICAL = [\"gender\",\"ever_married\",\"work_type\",\"Residence_type\",\"smoking_status\"]\n",
        "NUMERIC = [\"age\",\"avg_glucose_level\",\"bmi\"]\n",
        "BINARY_INT = [\"hypertension\",\"heart_disease\"]  # keep as numeric ints\n",
        "\n",
        "def load_real_or_synthetic():\n",
        "    if DATA_PATH.exists():\n",
        "        df = pd.read_csv(DATA_PATH)\n",
        "        # expected columns from the Kaggle stroke dataset\n",
        "        must_have = [\"gender\",\"age\",\"hypertension\",\"heart_disease\",\"ever_married\",\n",
        "                     \"work_type\",\"Residence_type\",\"avg_glucose_level\",\"bmi\",\n",
        "                     \"smoking_status\",\"stroke\"]\n",
        "        missing = set(must_have) - set(df.columns)\n",
        "        if missing:\n",
        "            raise ValueError(f\"Dataset is missing columns: {missing}\")\n",
        "        # drop id if present\n",
        "        df = df[[c for c in df.columns if c in must_have]]\n",
        "        return df\n",
        "    else:\n",
        "        # Synthetic data with the right columns\n",
        "        rng = np.random.RandomState(42)\n",
        "        N = 2000\n",
        "        df = pd.DataFrame({\n",
        "            \"gender\": rng.choice([\"Male\",\"Female\",\"Other\"], size=N, p=[0.49,0.50,0.01]),\n",
        "            \"age\": rng.randint(1, 90, size=N),\n",
        "            \"hypertension\": rng.binomial(1, 0.15, size=N),\n",
        "            \"heart_disease\": rng.binomial(1, 0.08, size=N),\n",
        "            \"ever_married\": rng.choice([\"Yes\",\"No\"], size=N, p=[0.7,0.3]),\n",
        "            \"work_type\": rng.choice([\"Private\",\"Self-employed\",\"Govt_job\",\"children\",\"Never_worked\"], size=N, p=[0.6,0.2,0.18,0.01,0.01]),\n",
        "            \"Residence_type\": rng.choice([\"Urban\",\"Rural\"], size=N, p=[0.55,0.45]),\n",
        "            \"avg_glucose_level\": rng.normal(100, 30, size=N).clip(50, 300),\n",
        "            \"bmi\": rng.normal(28, 6, size=N).clip(10, 60),\n",
        "            \"smoking_status\": rng.choice([\"formerly smoked\",\"never smoked\",\"smokes\",\"Unknown\"], size=N, p=[0.2,0.6,0.15,0.05]),\n",
        "        })\n",
        "        # Fabricate a signal for stroke\n",
        "        logit = (\n",
        "            0.03*df[\"age\"] +\n",
        "            0.02*(df[\"avg_glucose_level\"]-100) +\n",
        "            0.05*(df[\"bmi\"]-28) +\n",
        "            0.8*df[\"hypertension\"] +\n",
        "            0.9*df[\"heart_disease\"] +\n",
        "            0.3*(df[\"ever_married\"]==\"Yes\").astype(int)\n",
        "        )\n",
        "        prob = 1/(1+np.exp(- (logit-4.0)))  # bias to keep prevalence low\n",
        "        df[\"stroke\"] = (rng.rand(len(df)) < prob).astype(int)\n",
        "        return df\n",
        "\n",
        "def build_pipeline():\n",
        "    cat_proc = Pipeline(steps=[\n",
        "        (\"impute\", SimpleImputer(strategy=\"most_frequent\")),\n",
        "        (\"ohe\", OneHotEncoder(handle_unknown=\"ignore\"))\n",
        "    ])\n",
        "    num_proc = Pipeline(steps=[\n",
        "        (\"impute\", SimpleImputer(strategy=\"median\")),\n",
        "        (\"scale\", StandardScaler())\n",
        "    ])\n",
        "    # Binary int -> treat as numeric (no scaling needed, but fine to scale)\n",
        "    bin_proc = Pipeline(steps=[\n",
        "        (\"impute\", SimpleImputer(strategy=\"most_frequent\")),\n",
        "        (\"scale\", StandardScaler(with_mean=False))  # keep sparse-friendly path\n",
        "    ])\n",
        "\n",
        "    pre = ColumnTransformer(transformers=[\n",
        "        (\"cat\", cat_proc, CATEGORICAL),\n",
        "        (\"num\", num_proc, NUMERIC),\n",
        "        (\"bin\", bin_proc, BINARY_INT),\n",
        "    ])\n",
        "\n",
        "    clf = LogisticRegression(max_iter=1000, n_jobs=None)\n",
        "    pipeline = Pipeline([(\"pre\", pre), (\"clf\", clf)])\n",
        "    return pipeline\n",
        "\n",
        "def main():\n",
        "    df = load_real_or_synthetic()\n",
        "\n",
        "    X = df.drop(columns=[\"stroke\"])\n",
        "    y = df[\"stroke\"].astype(int)\n",
        "\n",
        "    X_train, X_test, y_train, y_test = train_test_split(\n",
        "        X, y, test_size=0.2, random_state=42, stratify=y\n",
        "    )\n",
        "\n",
        "    pipeline = build_pipeline()\n",
        "    pipeline.fit(X_train, y_train)\n",
        "\n",
        "    y_prob = pipeline.predict_proba(X_test)[:,1]\n",
        "    y_pred = (y_prob >= 0.5).astype(int)\n",
        "\n",
        "    print(\"AUC:\", roc_auc_score(y_test, y_prob))\n",
        "    print(\"Report:\\n\", classification_report(y_test, y_pred))\n",
        "\n",
        "    joblib.dump(pipeline, OUT_PATH)\n",
        "    print(f\"Saved pipeline to {OUT_PATH.resolve()}\")\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    main()\n",
        "'''\n",
        "(root / \"model\" / \"train_and_save.py\").write_text(train_py, encoding=\"utf-8\")\n",
        "\n",
        "\n",
        "# -----------------------------\n",
        "# 5) Build a ready-to-load model now (synthetic training)\n",
        "# -----------------------------\n",
        "# We'll run the training script to produce model/stroke_pipeline.joblib\n",
        "import subprocess, sys\n",
        "subprocess.run([sys.executable, str(root / \"model\" / \"train_and_save.py\")], check=True)\n",
        "\n",
        "\n",
        "# -----------------------------\n",
        "# 6) requirements.txt\n",
        "# -----------------------------\n",
        "reqs = r'''flask==3.0.3\n",
        "gunicorn==22.0.0\n",
        "joblib==1.4.2\n",
        "numpy==1.26.4\n",
        "pandas==2.2.2\n",
        "scikit-learn==1.5.1\n",
        "'''\n",
        "(root / \"requirements.txt\").write_text(reqs, encoding=\"utf-8\")\n",
        "\n",
        "\n",
        "# -----------------------------\n",
        "# 7) Dockerfile\n",
        "# -----------------------------\n",
        "dockerfile = r'''# Simple CPU-only image\n",
        "FROM python:3.11-slim\n",
        "\n",
        "ENV PYTHONDONTWRITEBYTECODE=1 \\\n",
        "    PYTHONUNBUFFERED=1\n",
        "\n",
        "WORKDIR /app\n",
        "\n",
        "RUN apt-get update && apt-get install -y --no-install-recommends \\\n",
        "    build-essential \\\n",
        "    && rm -rf /var/lib/apt/lists/*\n",
        "\n",
        "COPY requirements.txt .\n",
        "RUN pip install --no-cache-dir -r requirements.txt\n",
        "\n",
        "# Copy app code and model\n",
        "COPY . .\n",
        "\n",
        "# Spaces will set PORT (usually 7860)\n",
        "ENV PORT=7860 \\\n",
        "    MODEL_PATH=\"model/stroke_pipeline.joblib\"\n",
        "\n",
        "# Bind to $PORT (required by Spaces)\n",
        "CMD [\"sh\", \"-c\", \"gunicorn -w 2 -b 0.0.0.0:${PORT} app:app\"]\n",
        "\n",
        "'''\n",
        "(root / \"Dockerfile\").write_text(dockerfile, encoding=\"utf-8\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SBsV-9q5AeCJ",
        "outputId": "e2a58aff-bc75-4271-d602-45cdb3a98886"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "README.md written\n"
          ]
        }
      ],
      "source": [
        "from pathlib import Path\n",
        "\n",
        "root = Path(\"C:/Users/wissa/Downloads/data/stroke-flask-docker\")\n",
        "root.mkdir(parents=True, exist_ok=True)\n",
        "\n",
        "readme = \"\"\"# Stroke Predictor - Flask + Docker\n",
        "\n",
        "A minimal Flask app that serves a stroke risk model with a web form and a JSON API.\n",
        "\n",
        "## Local run\n",
        "pip install -r requirements.txt\n",
        "python app.py  # http://127.0.0.1:8080\n",
        "\n",
        "## Docker\n",
        "docker build -t stroke-app:latest .\n",
        "docker run -p 8080:8080 stroke-app:latest\n",
        "\n",
        "## Endpoints\n",
        "GET  /        # form UI\n",
        "POST /predict # JSON: returns stroke_probability and predicted_label\n",
        "GET  /health  # healthcheck\n",
        "\n",
        "Model file: model/stroke_pipeline.joblib\n",
        "\"\"\"\n",
        "\n",
        "(root / \"README.md\").write_text(readme, encoding=\"utf-8\")\n",
        "print(\"README.md written\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wAHrYmkmBbJl",
        "outputId": "19d15d73-699f-43d3-ec65-7051c34307b2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "AUC: 0.8417489711934156\n",
            "Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      1.00      0.98       972\n",
            "           1       1.00      0.02      0.04        50\n",
            "\n",
            "    accuracy                           0.95      1022\n",
            "   macro avg       0.98      0.51      0.51      1022\n",
            "weighted avg       0.95      0.95      0.93      1022\n",
            "\n",
            "Saved pipeline to /content/stroke-flask-docker/model/stroke_pipeline.joblib\n"
          ]
        }
      ],
      "source": [
        "!python /content/stroke-flask-docker/model/train_and_save.py"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JveXL_LxD8z_"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": ".venv",
      "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": "3.11.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}