models.ipynb

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      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import metrics\n",
        "\n",
        "import re\n",
        "import string\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.preprocessing import sequence\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Embedding\n",
        "from tensorflow.keras.layers import SimpleRNN, LSTM\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences"
      ]
    },
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        "df=pd.read_csv(\"/content/clean.csv\")\n",
        "df"
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        "data = {\n",
        "    'Label': [\"Non-mental-health\", \"Mental-health\"],\n",
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        "}\n",
        "\n",
        "dr = pd.DataFrame(data)\n",
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              "               Label  Label Encoded\n",
              "0  Non-mental-health              0\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-36a2b98e-840f-4b8f-9ccd-a198640bd3ea button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-36a2b98e-840f-4b8f-9ccd-a198640bd3ea');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\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",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-fecc1fea-e97d-4322-a7d6-19a09d109494 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_fea1f271-b000-4b90-b3bb-c2c61044859b\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('dr')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_fea1f271-b000-4b90-b3bb-c2c61044859b button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('dr');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dr",
              "summary": "{\n  \"name\": \"dr\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Label\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Mental-health\",\n          \"Non-mental-health\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Label Encoded\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_names=dr.Label.to_list()\n",
        "class_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rC4_w6PFHKsC",
        "outputId": "fdeee174-2f33-40d0-e7fd-3ba6757d31fe"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Non-mental-health', 'Mental-health']"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('total size: ', df.shape[0])\n",
        "\n",
        "# Do we have duplicates?\n",
        "print(' Duplicates:', len(df[df.duplicated()]))\n",
        "\n",
        "# Do we have missing values?\n",
        "missing_values = df.isnull().sum()\n",
        "print('Missing Values by column:\\n',missing_values)\n",
        "\n",
        "print('Missing Values:', df.isnull().sum().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PRebaPlUIv5l",
        "outputId": "ec5e87c5-cd1e-47c6-9e57-42833344b0fe"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total size:  27972\n",
            " Duplicates: 1\n",
            "Missing Values by column:\n",
            " label      0\n",
            "c_text3    2\n",
            "dtype: int64\n",
            "Missing Values: 2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.replace(\"\", np.nan, inplace=True)\n",
        "missing_values = df.isnull().sum()\n",
        "print('Missing Values and Empty Spaces by column:\\n',missing_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uhuhWDigI0zU",
        "outputId": "82b1ab77-8c48-435c-b83e-e41dc6b80024"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Missing Values and Empty Spaces by column:\n",
            " label      0\n",
            "c_text3    2\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "duplicate_rows = df[df.duplicated(keep=False)]\n",
        "\n",
        "# Then sort the dataframe on all columns to ensure duplicates are adjacent\n",
        "sorted_duplicates = duplicate_rows.sort_values(by=list(duplicate_rows.columns))\n",
        "\n",
        "# Now, if we want to see 5 pairs of duplicates (10 rows), we can simply:\n",
        "top_5_duplicate_pairs = sorted_duplicates.head(20)\n",
        "\n",
        "top_5_duplicate_pairs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "id": "PdkGse5UI5KZ",
        "outputId": "efaecf81-ad3d-49d2-d873-7c5fa3763ce9"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       label                                   c_text3\n",
              "229        0  day asking day day people woke great day\n",
              "21705      0  day asking day day people woke great day"
            ],
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              "  <div id=\"df-599c2973-bf49-4588-a52b-80bf7e713ed7\" class=\"colab-df-container\">\n",
              "    <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",
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              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>c_text3</th>\n",
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              "      <th>229</th>\n",
              "      <td>0</td>\n",
              "      <td>day asking day day people woke great day</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21705</th>\n",
              "      <td>0</td>\n",
              "      <td>day asking day day people woke great day</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-599c2973-bf49-4588-a52b-80bf7e713ed7')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-599c2973-bf49-4588-a52b-80bf7e713ed7 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-599c2973-bf49-4588-a52b-80bf7e713ed7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "            title=\"Suggest charts\"\n",
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              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\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",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a9e451bf-1ee6-46a6-a9be-d67ecb22504a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_3b68dae8-32cf-4556-ad97-da32f6601f64\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('top_5_duplicate_pairs')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_3b68dae8-32cf-4556-ad97-da32f6601f64 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('top_5_duplicate_pairs');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "top_5_duplicate_pairs",
              "summary": "{\n  \"name\": \"top_5_duplicate_pairs\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"c_text3\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"day asking day day people woke great day\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop_duplicates()\n",
        "print('Duplicates:', len(df[df.duplicated()]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jc9389jKI5Mx",
        "outputId": "02caf5e5-6a29-4f0f-d1e6-3e3fa3d13d11"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Duplicates: 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.dropna()\n",
        "print('Missing Values:', df.isnull().sum().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G5QXpA5jI5PQ",
        "outputId": "e556d48e-7f92-48a3-cd4d-dd7d6e3cd775"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Missing Values: 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df['c_text3'].to_numpy()\n",
        "y = df['label'].to_numpy()"
      ],
      "metadata": {
        "id": "kUMW7O1cH_sU"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.10, stratify=y, random_state=42)\n",
        "X_train.shape, X_valid.shape, y_train.shape, y_valid.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pgwwT4EMH_u5",
        "outputId": "7c2c0531-73aa-45fe-ac7f-990fcb560a54"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((25172,), (2797,), (25172,), (2797,))"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text_lengths = [len(str(text).split()) for text in X_train]\n",
        "\n",
        "# Find the 95th percentile\n",
        "percentile_95 = np.percentile(text_lengths, 95)\n",
        "\n",
        "print(f\"95th Percentile of Text Lengths: {percentile_95}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dyeFboduH_xb",
        "outputId": "47c79ba9-8b3a-45a8-cf7c-8bfc47f691c1"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "95th Percentile of Text Lengths: 231.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_text_length = max(text_lengths)\n",
        "print(f\"Maximum Text Length: {max_text_length}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hWzLzL0wH_0Z",
        "outputId": "b1661bbd-c6b0-4030-8cf8-9901dc65b002"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Maximum Text Length: 2556\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.layers import TextVectorization\n",
        "\n",
        "# Setup text vectorization with custom variables\n",
        "max_vocab_length = None  # max number of words to have in our vocabulary\n",
        "max_length = int(percentile_95)  # max length our sequences will be (e.g. how many words from a text does our model see?)\n",
        "\n",
        "text_vectorizer = TextVectorization(max_tokens=max_vocab_length,\n",
        "                                    output_mode=\"int\",\n",
        "                                    output_sequence_length=max_length)"
      ],
      "metadata": {
        "id": "Jrf0eB2xH_2d"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text_vectorizer.adapt(X_train)"
      ],
      "metadata": {
        "id": "1QBEG4ytH_5w"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "words_in_vocab = text_vectorizer.get_vocabulary()\n",
        "top_5_words = words_in_vocab[:5] # most common tokens (notice the [UNK] token for \"unknown\" words)\n",
        "bottom_5_words = words_in_vocab[-5:] # least common tokens\n",
        "print(f\"Number of words in vocab: {len(words_in_vocab)}\")\n",
        "print(f\"Top 5 most common words: {top_5_words}\")\n",
        "print(f\"Bottom 5 least common words: {bottom_5_words}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EWV3AqpiJYu0",
        "outputId": "6dafaccd-6ad6-4eac-eebf-23eadae470aa"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of words in vocab: 62781\n",
            "Top 5 most common words: ['', '[UNK]', 'im', 'like', 'want']\n",
            "Bottom 5 least common words: ['aaaaaaaaaaaaaaaaaaaa', 'aaaaaaaaaaaaaaaaaa', 'aaaaaaaaaaaaaaaaa', 'aaaaaaaaaaaaa', 'aaaaa']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_vocab_length=len(words_in_vocab)"
      ],
      "metadata": {
        "id": "XatCbF0VJgH8"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ***pipeline model***"
      ],
      "metadata": {
        "id": "A-EeagNvKJse"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Naive Bayes tends to perform well because it relies on probability distributions\n",
        "# rather than complex optimization processes like other classifiers (e.g., SVMs or neural networks).\n",
        "\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.pipeline import Pipeline\n",
        "\n",
        "# Create tokenization and modelling pipeline\n",
        "model_0 = Pipeline([\n",
        "                    (\"tfidf\", TfidfVectorizer()), # convert words to numbers using tfidf\n",
        "                    (\"clf\", MultinomialNB()) # model the text\n",
        "])\n",
        "\n",
        "# Now fit the model\n",
        "model_0.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "id": "7kfXsL6wJgTq",
        "outputId": "ec58d8e4-dbb5-4123-fd35-87d0a63f08be"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('tfidf', TfidfVectorizer()), ('clf', MultinomialNB())])"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;tfidf&#x27;, TfidfVectorizer()), (&#x27;clf&#x27;, MultinomialNB())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;tfidf&#x27;, TfidfVectorizer()), (&#x27;clf&#x27;, MultinomialNB())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;TfidfVectorizer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html\">?<span>Documentation for TfidfVectorizer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>TfidfVectorizer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;MultinomialNB<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.naive_bayes.MultinomialNB.html\">?<span>Documentation for MultinomialNB</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>MultinomialNB()</pre></div> </div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "\n",
        "with open('model_nb.pkl', 'wb') as f:\n",
        "    pickle.dump(model_0, f)\n",
        "\n",
        "print(\"model saved as \",f)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yXa2ValML4vF",
        "outputId": "fbf4fa09-bac9-4896-899e-8b23325cb317"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "model saved as  <_io.BufferedWriter name='model_nb.pkl'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "baseline_score = model_0.score(X_valid, y_valid)\n",
        "print(f\"Our baseline model achieves an accuracy of: {baseline_score*100:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WDrkN9r2JgXE",
        "outputId": "330d9230-a9cd-42ff-ab5c-a1deb2990f9d"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Our baseline model achieves an accuracy of: 85.13%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "baseline_preds = model_0.predict(X_valid)\n",
        "baseline_preds[:20]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R7qVe2mdKUed",
        "outputId": "5cc93537-b389-4364-aad7-e37a3e5e3f9c"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, precision_recall_fscore_support\n",
        "\n",
        "def calculate_results(y_true, y_pred):\n",
        "  # Calculate model accuracy\n",
        "  model_accuracy = accuracy_score(y_true, y_pred) * 100\n",
        "  # Calculate model precision, recall and f1 score using \"weighted\" average\n",
        "  model_precision, model_recall, model_f1, _ = precision_recall_fscore_support(y_true, y_pred, average=\"weighted\")\n",
        "  model_results = {\"accuracy\": model_accuracy,\n",
        "                  \"precision\": model_precision,\n",
        "                  \"recall\": model_recall,\n",
        "                  \"f1\": model_f1}\n",
        "  return model_results\n",
        "\n",
        "baseline_results = calculate_results(y_true=y_valid, y_pred=baseline_preds)\n",
        "baseline_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v73VwkmQKUhX",
        "outputId": "49e48f16-a9c5-42b6-d96b-58221c5b2b90"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 85.12692170182338,\n",
              " 'precision': 0.8776854575982233,\n",
              " 'recall': 0.8512692170182338,\n",
              " 'f1': 0.8488621374386252}"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "import pandas as pd\n",
        "\n",
        "# Load the model using pickle\n",
        "with open('model_nb.pkl', 'rb') as f:\n",
        "    model_0_loaded = pickle.load(f)\n",
        "\n",
        "# Use the loaded model to predict new sentences\n",
        "new_sentences = [\"She's been very depressed and upset about this whole situation\"]\n",
        "predictions = model_0_loaded.predict(new_sentences)\n",
        "\n",
        "if(predictions==0):\n",
        "  print(\"0 - Non-mental-health\")\n",
        "else:\n",
        "  print(\"1 - Mental-health\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1tDC83RTM1KI",
        "outputId": "8ecf03ed-f674-4dc1-9555-8fcd90635d95"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1 - Mental-health\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ***dense model***"
      ],
      "metadata": {
        "id": "0Do9s__0KmfS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow_hub as hub\n",
        "from tensorflow.keras import layers"
      ],
      "metadata": {
        "id": "RmysyMtuKUk8"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# embedding layer in a neural network, commonly used in NLP tasks.\n",
        "# The layers.Embedding layer is specifically designed to convert words or tokens into dense, continuous-valued vectors\n",
        "# (called embeddings) that represent their semantic meaning.\n",
        "\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "embedding = layers.Embedding(input_dim=max_vocab_length, # set input shape\n",
        "                             output_dim=300, # set size of embedding vector\n",
        "                             embeddings_initializer=\"uniform\", # default, intialize randomly\n",
        "                             input_length=max_length, # how long is each input\n",
        "                             name=\"embedding_1\")\n",
        "\n",
        "embedding"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aJJYvWRVKsiO",
        "outputId": "267890d8-416d-459f-be23-71ecaaaabf0f"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/embedding.py:90: UserWarning: Argument `input_length` is deprecated. Just remove it.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Embedding name=embedding_1, built=False>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras import layers\n",
        "\n",
        "inputs = layers.Input(shape=(1,), dtype=\"string\") # inputs are 1-dimensional strings\n",
        "x = text_vectorizer(inputs) # turn the input text into numbers\n",
        "x = embedding(x) # create an embedding of the numerized numbers\n",
        "x = layers.GlobalAveragePooling1D()(x) # lower the dimensionality of the embedding (try running the model without this layer and see what happens)\n",
        "outputs = layers.Dense(1, activation=\"sigmoid\")(x) # create the output layer, want binary outputs so use sigmoid activation\n",
        "model_dense = tf.keras.Model(inputs, outputs, name=\"model_dense\") # construct the model"
      ],
      "metadata": {
        "id": "EDpIs28CKskz"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_dense.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 327
        },
        "id": "4aPCeOdhKsoe",
        "outputId": "9d63bbc6-4656-448e-8714-fcda3e162e61"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"model_dense\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"model_dense\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer (\u001b[38;5;33mInputLayer\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ text_vectorization                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m231\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
              "│ (\u001b[38;5;33mTextVectorization\u001b[0m)                  │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ embedding_1 (\u001b[38;5;33mEmbedding\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m231\u001b[0m, \u001b[38;5;34m300\u001b[0m)            │      \u001b[38;5;34m18,834,300\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ global_average_pooling1d             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m300\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
              "│ (\u001b[38;5;33mGlobalAveragePooling1D\u001b[0m)             │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │             \u001b[38;5;34m301\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ text_vectorization                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">231</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TextVectorization</span>)                  │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ embedding_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">231</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">300</span>)            │      <span style=\"color: #00af00; text-decoration-color: #00af00\">18,834,300</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ global_average_pooling1d             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">300</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePooling1D</span>)             │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">301</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m18,834,601\u001b[0m (71.85 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">18,834,601</span> (71.85 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m18,834,601\u001b[0m (71.85 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">18,834,601</span> (71.85 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_dense.compile(loss=\"binary_crossentropy\",\n",
        "                optimizer=tf.keras.optimizers.Adam(),\n",
        "                metrics=[\"accuracy\"])"
      ],
      "metadata": {
        "id": "z1suJw1RLPrb"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_dense_history = model_dense.fit(X_train, # input sentences can be a list of strings due to text preprocessing layer built-in model\n",
        "                              y_train,\n",
        "                              epochs=10,\n",
        "                              validation_data=(X_valid, y_valid),)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fIgZ_OlaLhod",
        "outputId": "bbccab13-9f06-455a-96ce-d3948e118021"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m278s\u001b[0m 351ms/step - accuracy: 0.7232 - loss: 0.5392 - val_accuracy: 0.8845 - val_loss: 0.3197\n",
            "Epoch 2/10\n",
            "\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m299s\u001b[0m 322ms/step - accuracy: 0.8730 - loss: 0.3107 - val_accuracy: 0.8867 - val_loss: 0.2754\n",
            "Epoch 3/10\n",
            "\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m259s\u001b[0m 318ms/step - accuracy: 0.8974 - loss: 0.2570 - val_accuracy: 0.9042 - val_loss: 0.2584\n",
            "Epoch 4/10\n",
            "\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m268s\u001b[0m 326ms/step - accuracy: 0.9098 - loss: 0.2295 - val_accuracy: 0.9106 - val_loss: 0.2506\n",
            "Epoch 5/10\n",
            "\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m259s\u001b[0m 322ms/step - accuracy: 0.9171 - loss: 0.2114 - val_accuracy: 0.9181 - val_loss: 0.2484\n",
            "Epoch 6/10\n",
            "\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m259s\u001b[0m 318ms/step - accuracy: 0.9244 - loss: 0.1975 - val_accuracy: 0.9188 - val_loss: 0.2501\n",
            "Epoch 7/10\n",
            "\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m265s\u001b[0m 322ms/step - accuracy: 0.9306 - loss: 0.1866 - val_accuracy: 0.9206 - val_loss: 0.2551\n",
            "Epoch 8/10\n",
            "\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m258s\u001b[0m 317ms/step - accuracy: 0.9344 - loss: 0.1781 - val_accuracy: 0.9196 - val_loss: 0.2644\n",
            "Epoch 9/10\n",
            "\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m263s\u001b[0m 317ms/step - accuracy: 0.9366 - loss: 0.1718 - val_accuracy: 0.9106 - val_loss: 0.2784\n",
            "Epoch 10/10\n",
            "\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m258s\u001b[0m 312ms/step - accuracy: 0.9386 - loss: 0.1670 - val_accuracy: 0.9003 - val_loss: 0.2967\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "\n",
        "text_vectorizer.adapt(X_train)\n",
        "\n",
        "# Get the vectorizer configuration and weights\n",
        "vectorizer_config = text_vectorizer.get_config()\n",
        "vectorizer_weights = text_vectorizer.get_weights()\n",
        "\n",
        "# Save the config and weights as a tuple\n",
        "with open(\"text_vectorizer.pkl\", \"wb\") as f:\n",
        "    pickle.dump((vectorizer_config, vectorizer_weights), f)"
      ],
      "metadata": {
        "id": "4n7QazQvLht3"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_dense.evaluate(X_valid, y_valid)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d6nPfqciLhxi",
        "outputId": "db006738-8493-4814-8861-3c13c26edb82"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 20ms/step - accuracy: 0.8945 - loss: 0.3339\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.29673081636428833, 0.900250256061554]"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_dense_pred_probs = model_dense.predict(X_valid)\n",
        "model_dense_pred_probs[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z8e1i0xKZunI",
        "outputId": "abadf343-5c91-46cb-cbf5-d6611e039702"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 17ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.986857  ],\n",
              "       [0.5816667 ],\n",
              "       [0.9777785 ],\n",
              "       [0.99999666],\n",
              "       [0.9984303 ],\n",
              "       [0.03459587],\n",
              "       [0.15774393],\n",
              "       [0.8452648 ],\n",
              "       [0.3946647 ],\n",
              "       [0.22247191]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_dense_preds = tf.squeeze(tf.round(model_dense_pred_probs))\n",
        "model_dense_preds[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eh0eQyNlZuqx",
        "outputId": "9cc54bc5-2775-47e3-ba5a-d83f3fe55b66"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10,), dtype=float32, numpy=array([1., 1., 1., 1., 1., 0., 0., 1., 0., 0.], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_dense_results = calculate_results(y_true=y_valid,\n",
        "                                    y_pred=model_dense_preds)\n",
        "model_dense_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rHurQ7LIdZZR",
        "outputId": "36aa7364-6ef1-44a2-8482-c011df607892"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 90.02502681444405,\n",
              " 'precision': 0.905464874180341,\n",
              " 'recall': 0.9002502681444404,\n",
              " 'f1': 0.8999934965873262}"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_true = y_valid.tolist()  # Convert labels to a list\n",
        "preds = model_dense.predict(X_valid)\n",
        "y_probs = preds.squeeze().tolist()  # Store the prediction probabilities as a list\n",
        "y_preds = tf.round(y_probs).numpy().tolist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6OFSJRcXz_U0",
        "outputId": "37d568b0-f5cc-4dc1-a7b6-8ab71ec736a0"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import itertools\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "def make_confusion_matrix(y_true, y_pred, classes=None, figsize=(10, 10), text_size=15):\n",
        "\n",
        "  # Create the confustion matrix\n",
        "  cm = confusion_matrix(y_true, y_pred)\n",
        "  cm_norm = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis] # normalize it\n",
        "  n_classes = cm.shape[0] # find the number of classes we're dealing with\n",
        "\n",
        "  # Plot the figure and make it pretty\n",
        "  fig, ax = plt.subplots(figsize=figsize)\n",
        "  cax = ax.matshow(cm, cmap=plt.cm.Blues) # colors will represent how 'correct' a class is, darker == better\n",
        "  fig.colorbar(cax)\n",
        "\n",
        "  # Are there a list of classes?\n",
        "  if classes:\n",
        "    labels = classes\n",
        "  else:\n",
        "    labels = np.arange(cm.shape[0])\n",
        "\n",
        "  # Label the axes\n",
        "  ax.set(title=\"Confusion Matrix\",\n",
        "         xlabel=\"Predicted label\",\n",
        "         ylabel=\"True label\",\n",
        "         xticks=np.arange(n_classes), # create enough axis slots for each class\n",
        "         yticks=np.arange(n_classes),\n",
        "         xticklabels=labels, # axes will labeled with class names (if they exist) or ints\n",
        "         yticklabels=labels)\n",
        "\n",
        "  # Make x-axis labels appear on bottom\n",
        "  ax.xaxis.set_label_position(\"bottom\")\n",
        "  ax.xaxis.tick_bottom()\n",
        "\n",
        "  # Set the threshold for different colors\n",
        "  threshold = (cm.max() + cm.min()) / 2.\n",
        "\n",
        "  # Plot the text on each cell\n",
        "  for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "    plt.text(j, i, f\"{cm[i, j]} ({cm_norm[i, j]*100:.1f}%)\",\n",
        "             horizontalalignment=\"center\",\n",
        "             color=\"white\" if cm[i, j] > threshold else \"black\",\n",
        "             size=text_size)"
      ],
      "metadata": {
        "id": "o3wLDNifz-7M"
      },
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E0PjRmlEdr_B",
        "outputId": "2d679edb-9775-45f7-a39a-a43f3c50ec8b"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Non-mental-health', 'Mental-health']"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import itertools\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "def make_confusion_matrix(y_true, y_pred, classes=None, figsize=(10, 10), text_size=15):\n",
        "\n",
        "  # Create the confustion matrix\n",
        "  cm = confusion_matrix(y_true, y_pred)\n",
        "  cm_norm = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis] # normalize it\n",
        "  n_classes = cm.shape[0] # find the number of classes we're dealing with\n",
        "\n",
        "  # Plot the figure and make it pretty\n",
        "  fig, ax = plt.subplots(figsize=figsize)\n",
        "  cax = ax.matshow(cm, cmap=plt.cm.Blues) # colors will represent how 'correct' a class is, darker == better\n",
        "  fig.colorbar(cax)\n",
        "\n",
        "  # Are there a list of classes?\n",
        "  if classes:\n",
        "    labels = classes\n",
        "  else:\n",
        "    labels = np.arange(cm.shape[0])\n",
        "\n",
        "  # Label the axes\n",
        "  ax.set(title=\"Confusion Matrix\",\n",
        "         xlabel=\"Predicted label\",\n",
        "         ylabel=\"True label\",\n",
        "         xticks=np.arange(n_classes), # create enough axis slots for each class\n",
        "         yticks=np.arange(n_classes),\n",
        "         xticklabels=labels, # axes will labeled with class names (if they exist) or ints\n",
        "         yticklabels=labels)\n",
        "\n",
        "  # Make x-axis labels appear on bottom\n",
        "  ax.xaxis.set_label_position(\"bottom\")\n",
        "  ax.xaxis.tick_bottom()\n",
        "\n",
        "  # Set the threshold for different colors\n",
        "  threshold = (cm.max() + cm.min()) / 2.\n",
        "\n",
        "  # Plot the text on each cell\n",
        "  for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "    plt.text(j, i, f\"{cm[i, j]} ({cm_norm[i, j]*100:.1f}%)\",\n",
        "             horizontalalignment=\"center\",\n",
        "             color=\"white\" if cm[i, j] > threshold else \"black\",\n",
        "             size=text_size)"
      ],
      "metadata": {
        "id": "kCT1aUp4dZc0"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "make_confusion_matrix(y_true=y_true,\n",
        "                      y_pred=y_preds,\n",
        "                      classes=class_names,\n",
        "                      figsize=(15, 15),\n",
        "                      text_size=10)\n",
        "\n",
        "# 1195: True Negatives (TN) — The model correctly predicted Non-mental-health for 1195 instances.\n",
        "# 218: False Positives (FP) — The model incorrectly predicted Mental-health for 218 instances that were actually Non-mental-health.\n",
        "# 61: False Negatives (FN) — The model incorrectly predicted Non-mental-health for 61 instances that were actually Mental-health.\n",
        "# 1323: True Positives (TP) — The model correctly predicted Mental-health for 1323 instances."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 941
        },
        "id": "GaqXmLqbdu_5",
        "outputId": "f1b7984e-29cf-492a-ef28-8862a0ed8786"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1500 with 2 Axes>"
            ],
            "image/png": 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Hj88TTzyR//u//8uee+6ZJFlppZWyxx575LLLLsuIESOy3nrr5cknn0z//v2z3Xbb1dkR9z/VvXv3HH300fnVr36V3/3udxkzZkwuueSSLL300nU2jzjllFPyyCOPZMstt8xiiy2WTz/9NBdffHEWWWSRrL322jN8/tlnn53NN988a6yxRvbZZ59888036devX1q2bJnevXvPtjq+r6qqKscff/yPjttqq61yyimnZK+99sqaa66ZF198Mdddd10WX3zxOuOWWGKJtGrVKpdeemmaN2+eZs2aZfXVV0/nzp1naV4DBgzIxRdfnJNOOimrrLJKkuSqq67K+uuvnxNOOCFnnXXWLD0PAOB/mY49AKBibLPNNnnhhRfym9/8Jv/4xz9y8MEH55hjjsm7776bc889N3379q0de8UVV+Tkk0/OU089lUMOOSQDBgzIsccemxtvvHG2zmmBBRbI3//+9zRt2jRHHXVU+vfvnz59+mTrrbeeZu6LLrpo/vKXv+Tggw/On/70p6y77roZMGBAWrZsOcPnb7TRRrnnnnuywAIL5MQTT8w555yTX/ziF3n88cdnORSbE/7whz/k8MMPz7333pvf//73efbZZ3PXXXelY8eOdcbNM8886d+/fxo0aJADDjggO++8c/71r3/N0md99dVX2XvvvbPyyivnuOOOqz2/zjrr5Pe//33OPffc/Pvf/54tdQEA/C8opszKSskAAAAAVIRRo0alZcuWabTRGSkaNp7b05kjpkwcm3EP/CEjR45MixYt5vZ0ZjsdewAAAABQQoI9AAAAACghwR4AAAAAlJBgDwAAAKBeq0qKCj1+QvT1yCOPZOutt06HDh1SFEVuu+222msTJkzI0Ucfna5du6ZZs2bp0KFDdt9993z88cd1njF8+PD06NEjLVq0SKtWrbLPPvtk9OjRdca88MILWWedddK4ceN07NgxZ5111k/5kwMAAAAAkuTrr7/OSiutlD/96U/TXBszZkyeffbZnHDCCXn22Wdz66235rXXXss222xTZ1yPHj0yZMiQ3H///bnzzjvzyCOPpGfPnrXXR40alU022SSLLbZYnnnmmZx99tnp3bt3Lrvsslmaq11xAQAAAOqhb3fF/WOKeSp0V9wJYzPugWN+8q64RVHk73//e7bbbrsZjnnqqafy85//PO+9914WXXTRvPLKK1luueXy1FNPZbXVVkuS3HPPPdliiy3y4YcfpkOHDrnkkkty3HHHZejQoZl33nmTJMccc0xuu+22vPrqqzM9v4azXBH8F02ePDkff/xxmjdvnqIo5vZ0AAAAqHBTpkzJV199lQ4dOqSqyouO/LiRI0emKIq0atUqSTJw4MC0atWqNtRLko022ihVVVUZNGhQfvWrX2XgwIFZd911a0O9JNl0001z5pln5ssvv0zr1q1n6rMFe/xP+/jjj9OxY8e5PQ0AAADqmQ8++CCLLLLI3J4Gs8moUaPqfN2oUaM0atToP37u2LFjc/TRR2fnnXeu7QgcOnRo2rRpU2dcw4YNM//882fo0KG1Yzp37lxnTNu2bWuvCfaoCM2bN0+SzLv+ySkaVmZbMADMKU9etufcngIAlM7or77KWistWfv7aL1QFNVHJZpa1/ebhk466aT07t37P3r0hAkTsuOOO2bKlCm55JJL/qNn/VSCPf6n1bx+WzRsXLHv+wPAnNK8+ayvIwMAVLMcVGX54IMP6qyx959269WEeu+9914GDBhQ59nt2rXLp59+Wmf8xIkTM3z48LRr1652zLBhw+qMqfm6ZszM8LI4AAAAABWtRYsWdY7/JNirCfXeeOONPPDAA1lggQXqXF9jjTUyYsSIPPPMM7XnBgwYkMmTJ2f11VevHfPII49kwoQJtWPuv//+LLPMMjP9Gm4i2AMAAACAWqNHj87gwYMzePDgJMk777yTwYMH5/3338+ECRPym9/8Jk8//XSuu+66TJo0KUOHDs3QoUMzfvz4JEmXLl2y2WabZb/99suTTz6Zxx9/PL169Ur37t3ToUOHJMkuu+ySeeedN/vss0+GDBmSm266KRdeeGEOO+ywWZqrV3EBAAAA6rOiSIoK7f36Ca9UP/3009lggw1qv64J2/bYY4/07t07t99+e5KkW7dude576KGHsv766ydJrrvuuvTq1Ssbbrhhqqqqsv3226dv3761Y1u2bJn77rsvBx98cFZdddUsuOCCOfHEE9OzZ89ZmqtgDwAAAACmWn/99TNlypQZXv+hazXmn3/+XH/99T84ZsUVV8yjjz46y/P7rgqNYwEAAACgsgn2AAAAAKCEBHsAAAAAUELW2AMAAACoz4qqCt48o0LrmqqyqwMAAACACiXYAwAAAIASEuwBAAAAQAlZYw8AAACgPiuK6qMSVWpdU+nYAwAAAIASEuwBAAAAQAkJ9gAAAACghAR7AAAAAFBCNs8AAAAAqM+KquqjElVqXVNVdnUAAAAAUKEEewAAAABQQoI9AAAAACgha+wBAAAA1GdFUX1UokqtayodewAAAABQQoI9AAAAACghwR4AAAAAlJBgDwAAAABKyOYZAAAAAPVZUVV9VKJKrWuqyq4OAAAAACqUYA8AAAAASkiwBwAAAAAlZI09AAAAgPqsKKqPSlSpdU2lYw8AAAAASkiwBwAAAAAlJNgDAAAAgBIS7AEAAABACdk8AwAAAKAeK4oiRaVuMlGpdU2lYw8AAAAASkiwBwAAAAAlJNgDAAAAgBKyxh4AAABAPWaNvfLSsQcAAAAAJSTYAwAAAIASEuwBAAAAQAkJ9gAAAACghGyeAQAAAFCfFVOPSlSpdU2lYw8AAAAASkiwBwAAAAAlJNgDAAAAgBKyxh4AAABAPVYURYqiQhejq9S6ptKxBwAAAAAlJNgDAAAAgBIS7AEAAABACQn2AAAAAKCEbJ4BAAAAUI/ZPKO8dOwBAAAAQAkJ9gAAAACghAR7AAAAAFBC1tgDAAAAqMessVdeOvYAAAAAoIQEewAAAABQQoI9AAAAACghwR4AAAAAlJDNMwAAAADqMZtnlJeOPQAAAAAoIcEeAAAAAJSQYA8AAAAASsgaewAAAAD1WTH1qESVWtdUOvYAAAAAoIQEewAAAABQQoI9AAAAACghwR4AAAAAlJDNMwAAAADqsaIoUhQVustEpdY1lY49AAAAACghwR4AAAAAlJBgDwAAAABKyBp7AAAAAPVYUaSC19ib2xOYs3TsAQAAAEAJCfYAAAAAoIQEewAAAABQQoI9AAAAACghm2cAAAAA1GNFisrdPKPCd8/QsQcAAAAAJSTYAwAAAIASEuwBAAAAQAkJ9gAAAACghGyeAQAAAFCPFUUFb55RqXVNpWMPAAAAAEpIsAcAAAAAJSTYAwAAAIASssYeAAAAQH1WTD0qUaXWNZWOPQAAAAAoIcEeAAAAAJSQYA8AAAAASkiwBwAAAAAlZPMMAAAAgPqsKFIUlbnLxJQKrauGjj0AAAAAKCHBHgAAAACUkGAPAAAAAErIGnsAAAAA9VhRwWvsVWpdNXTsAQAAAEAJCfYAAAAAoIQEewAAAABQQoI9AAAAACghm2cAAAAA1GM2zygvHXsAAAAAUEKCPQAAAAAoIcEeAAAAAJSQNfYAAAAA6rNi6lGJKrWuqXTsAQAAAEAJCfYAAAAAoIQEewAAAABQQoI9AAAAACghm2cAAAAA1GNFUaQoKnOXiUqtq4aOPQAAAAAoIcEeAAAAAJSQYA8AAAAASsgaewAAAAD1mDX2ykvHHgAAAACUkGAPAAAAAEpIsAcAAAAAJSTYAwAAAIASsnkGAAAAQD1m84zy0rEHAAAAACUk2AMAAACAEhLsAQAAAEAJWWMPAAAAoB6zxl556dgDAAAAgBIS7AEAAABACQn2AAAAAKCEBHsAAAAAUEI2zwAAAACoz4qpRyWq1Lqm0rEHAAAAACUk2AMAAACAEhLsAQAAAEAJWWMPAAAAoB4riiJFUZmL0VVqXTV07AEAAABACQn2AAAAAKCEBHsAAAAAUEKCPQAAAAAoIZtnAAAAANRjNs8oLx17AAAAAFBCgj0AAAAAKCHBHgAAAACUkDX2AAAAAOoxa+yVl449AAAAACghwR4AAAAAlJBgDwAAAABKSLAHAAAAACVk8wwAAACA+qyYelSiSq1rKh17AAAAAFBCgj0AAAAAKCHBHgAAAACUkDX2AAAAAOqxoihSFJW5GF2l1lVDxx4AAAAAlJBgDwAAAABKSLAHAAAAACUk2AMAAACAErJ5BgAAAEA9ZvOM8tKxBwAAAAAlJNgDAAAAgBIS7AEAAABACVljDwAAAKAeK1LBa+ylMuuqoWMPAAAAAEpIsAcAAAAAJSTYAwAAAIASEuwBAAAAQAnZPAMAAACgHiuKCt48o0LrqqFjDwAAAABKSLAHAAAAACUk2AMAAACAErLGHgAAAEB9Vkw9KlGl1jWVjj0AAAAAKCHBHgAAAACUkGAPAAAAAEpIsAcAAAAAJWTzDAAAAIB6rCiKFEVl7jJRqXXV0LEHAAAAACUk2AMAAACAqR555JFsvfXW6dChQ4qiyG233Vbn+pQpU3LiiSemffv2adKkSTbaaKO88cYbdcYMHz48PXr0SIsWLdKqVavss88+GT16dJ0xL7zwQtZZZ500btw4HTt2zFlnnTXLcxXsAQAAAMBUX3/9dVZaaaX86U9/mu71s846K3379s2ll16aQYMGpVmzZtl0000zduzY2jE9evTIkCFDcv/99+fOO+/MI488kp49e9ZeHzVqVDbZZJMstthieeaZZ3L22Wend+/eueyyy2ZprtbYAwAAAKjHrLFX1+abb57NN998utemTJmSCy64IMcff3y23XbbJMk111yTtm3b5rbbbkv37t3zyiuv5J577slTTz2V1VZbLUnSr1+/bLHFFjnnnHPSoUOHXHfddRk/fnz+8pe/ZN55583yyy+fwYMH57zzzqsTAP4YHXsAAAAAMBPeeeedDB06NBtttFHtuZYtW2b11VfPwIEDkyQDBw5Mq1atakO9JNloo41SVVWVQYMG1Y5Zd911M++889aO2XTTTfPaa6/lyy+/nOn56NgDAAAAoKKNGjWqzteNGjVKo0aNZvk5Q4cOTZK0bdu2zvm2bdvWXhs6dGjatGlT53rDhg0z//zz1xnTuXPnaZ5Rc61169YzNR8dewAAAABUtI4dO6Zly5a1R58+feb2lGYLHXsAAAAAVLQPPvggLVq0qP36p3TrJUm7du2SJMOGDUv79u1rzw8bNizdunWrHfPpp5/WuW/ixIkZPnx47f3t2rXLsGHD6oyp+bpmzMzQsQcAAABQjxVFZR9J0qJFizrHTw32OnfunHbt2uXBBx+sPTdq1KgMGjQoa6yxRpJkjTXWyIgRI/LMM8/UjhkwYEAmT56c1VdfvXbMI488kgkTJtSOuf/++7PMMsvM9Gu4iWAPAAAAAGqNHj06gwcPzuDBg5NUb5gxePDgvP/++ymKIoccckhOO+203H777XnxxRez++67p0OHDtluu+2SJF26dMlmm22W/fbbL08++WQef/zx9OrVK927d0+HDh2SJLvsskvmnXfe7LPPPhkyZEhuuummXHjhhTnssMNmaa5exQUAAACAqZ5++ulssMEGtV/XhG177LFHrr766hx11FH5+uuv07Nnz4wYMSJrr7127rnnnjRu3Lj2nuuuuy69evXKhhtumKqqqmy//fbp27dv7fWWLVvmvvvuy8EHH5xVV101Cy64YE488cT07NlzluZaTJkyZcp/WC/MMaNGjUrLli3TaKMzU8zT+MdvAABqvXzNrP2PIQCQfPXVqKy0eNuMHDmyzppslajmd+7OvW5JVaOmc3s6c8TkcWPyzkW/qdg/Tx17AAAAAPVY9Vp0xdyexhxRoWXVssYeAAAAAJSQYA8AAAAASkiwBwAAAAAlJNgDAAAAgBKyeQYAAABAfVZU8CYTlVrXVDr2AAAAAKCEBHsAAAAAUEKCPQAAAAAoIWvsAQAAANRjRVGkqNBF9iq1rho69gAAAACghAR7AAAAAFBCgj0AAAAAKCHBHgAAAACUkM0zAAAAAOqxoqg+KlGl1lVDxx4AAAAAlJBgDwAAAABKSLAHAAAAACVkjT0AAACAeqyqqkhVVWUuRjelQuuqoWMPAAAAAEpIsAcAAAAAJSTYAwAAAIASEuwBAAAAQAnZPAMAAACgHiuK6qMSVWpdNXTsAQAAAEAJCfYAAAAAoIQEewAAAABQQtbYAwAAAKjHiqJIUaGL0VVqXTV07AEAAABACQn2AAAAAKCEBHsAAAAAUEKCPQAAAAAoIZtnAAAAANRjRVF9VKJKrauGjj0AZtr6Ky2S5y7dNVVV5fzpuO8WK+SWk7ae29MAoMK9/ebr+flynTJ69Fdzeyo/yXVXX559e2w/t6cBwEwQ7AH8j1hrhQ655aSt8/a1e+ebf/4uW6+x+DRjtl1zidxx2nb58Mb98s0/f5cVF19wmjGd27XMTcdvmfdv2C/Dbjkgfz1287Rp1aTOmFev2jPf/PN3dY4jdlj1R+d4+t5r58wbn8rkyVNqz3Vff5kMumjnfHHrgXn7r/vk0kM2zPzNG0/3/h3WXSrf/PN3ufmELX/0s+Zt2CC9d18jr129Z0b84+C8etWe2X3j5Wqv/3Lljnnh8t0y7JYDcuURm2Seht/+SGvRdN68cPluWbRN8zrP7H/fy1l5iYWy1vIdfvTzASiviy84O9tuvFa6dlooP+uyaPbffYe8/ebrdcbccM2V2XnbTbJi5zZZfKEmGTVyxDTPefutN9Jztx2y6jKLZMXObbLDlr/MwMf+9aOff9ZpJ2SPfQ/MfPNV/xwaN3Zsjuy1XzZbd7Us1W6+7L/7DtPc8+/HH8niCzWZ5vhs2NCZqvndt99K104LZaUl2tU5/+jDD+aXq3fNip3b5LCD9s748eNrr40aNTK/XL1rPvrgvTr37LDLHhnywnN5cuBjM/XZAMw9gj2A/xHNGs+TF9/5LIdc/PAMxzRtPE+eGPJxjr/qielfb9Qwd56+XaZMmZLNj701vzzi/zJvw6r87aStp2lBP/nagenU44ra4+Lbn//B+a25XPss3r5l/v74m7Xn1liufa44fOP0v+/lrHLgddn1jH9mtaXb5eLf/XKa+xdt0zx99l0nj7300Q9+To2/Hrt5NujWMQdc8GBW3O+a7HHmPXnjoy+TVLfTX33UZrniny9l/cNuzipLtsk+m61Qe++pe62ZK/75Ut7/tG6nxISJk3PTw6/loG1Wmqk5AFBOTz7xaHbb+4D87Z5/5Zr/uzMTJkzM7jtslTFff1075psxY7LuLzfOgYccOcPn7LvLrzNx4sT89da7848HnkiX5VfMvj1+/YNh20cfvp+H7rs72++8a+25SZMmpVGTJtlzv4Oy1rrT/oz8rgf+/UIGvfRO7bHAQm1+tN4JEybk9/vvntV+sVad85MnT86hB+yZXfbcN7fc/XBeHPxsbrzmytrrZ516QnbZc98s3HGxOvfNO++82Wb7ndL/8ot/9LMBmLussQfwP+K+p9/LfU+/94NjbhjwapJM04lWY43lOmSxNs3zi1435Ktvqv9Gft9z788nN++f9VfqmIcGf1A7dvSYCRn25ZiZnt8O6y2dAc+9n3ETJtWeW33Zdnnv069qQ8H3ho3KlXe/mMN3WK3OvVVVRa4+atOc+td/Z63lO6TVfI1+8LM2XnWxrNN14Sy399X5cvS4JKkT0i3YokkWatkkf77zhYybMCl3DXo7yyw6f5LkF13aZdWl2+bQS6bfUXHXk+/krtN/lcbzNsjY8ZOmOwaAcrv65tvrfH12v8vysy6L5qXnn8vP11w7SbL3Ab9NUt0pNz3Dv/g87779Zv54wSXpsnzXJMlRJ56av17157z26stZqG276d73z3/8Lcsu3zXt2i9ce65ps2Y57ey+SZJnBg3MqFEjZjj3BRdcKC1atpqpOmuc26d3llhqmay5zgZ59ql/16lh+BefZ7e99k+jxo2z4WZb5s3XX6uex5MD88Jzz+TkP54/3WduuMmW2X2HLTP2m2/SuEmT6Y4BKkdRFCkqdDG6Sq2rho49gArSaJ4GmZLUCd/Gjp+UyVOmZM3vvX56+A6r5sMb98vAfjvn0O1XSYMfWTdvzeU75Nk3Pq1zbtCrQ7PIgvNl09Wq/6a/Tasm+dXaS+Wep96tM+4PO/88n434Jv3ve3mm6thy9c559o1hOew3q+ata/bOC5fvlj77rJ3G8zZIknw28pt88sXobLTKomnSqGHWWn7hvPTO52nYoCoXHrxBfttvQJ3Xhb/r2Tc+TcOqIj9bZvq/kAFQeb4aNSpJ0rJ165m+p/X8C2TxJZfO32++PmO+/joTJ07MDf2vyAILtUnXlVae4X1P/fuJdO3248tbzMiWG6ye1ZfvnN1+s2WeHjT9Dv3veuLRh3P37bfm5DMvmObaAgsulDZt2+XRhx/IN2PG5Ol/P55ll18hEyZMyAlH/T6nn9svDRo0mO5zu3ZbJRMnTszgZ5/6ybUAMOcJ9krk6quvTqtWrX5wzJ577pnttttujs9l/fXXzyGHHPKDY2ZmvsDs9eSrQ/P12Ak5fe8106RRwzRt1DB/3HftNGxQlXatm9aOu/j257P7mfdks2NuzZV3v5gjd1wtZ+yz9g8+e9E2LfLx8K/rnBv48ifZ6+x7c+0xm2fU7Qfnvev3y8ivx9V5nXjN5dpnz02Xz0F9H5zpOjq3a5k1l++Q5TotkJ1OuytH/vmR/GrtJXPhwRvUjtm1z905duef59lLemTw25+l/30v54gdV80jL3yYseMnZcA5v8nzl+2WA7Zasc6zvxk3MSPHjJ9h1yMAlWXy5Mk59fgjs+rP18gyXZaf6fuKosi1f7srQ158Pl07L5Qui7TKlZf0zdU3/iMtW804IPzog/fTtl37WZ5nm7btcto5/XLxVTfk4quuT/sOi2SX7TbNS88/N8N7vhz+RY787X45u+/lad68xXRr6HfFX9Pv3D7ZdJ1VstwK3bLDLnvk0r7n5BdrrZtGjRpnhy02yIa/WDHXXHFJnXubNG2a5i1a5qMP3p/lWgD475mrr+Luueee6d+/f/r06ZNjjjmm9vxtt92WX/3qV5kyZfrdFmXXu3fv3HbbbRk8ePDcnsps06lTpxxyyCE/GvYBc9bno75JjzPuTt9eG+Sgbbpl8pQpuflfr+fZNz7N5O/8N7Xv37/9JeGld7/I+AmTc9FvN8gJVz2R8ROn/3pqk0YNM278xDrnlu04f87Zf930ueHJ3P/Me2k3f7Ocsc/a6ddrgxx44YOZr8k8ufKITXJQ3wfzxaixM11HVVWRKVOSvc66N6PGVL9SfPTlj+b6P2yR3//poYwdPylPvPxJ1j7kptp7lly4VXr8skt+8dsbcv9Z2+dP/xic+55+L09f0iOPvfRRXnr3i9qxY8dNTNNG88z0fAAorxOPPiSvvzokN98583/BlCRTpkzJSUcfmgUWXCg33fFAGjdukpuuuzr77bp9brvvsbSZQXg3duw3adToh5ecmJ7Fl1w6iy+5dO3Xq/58jbz/7tv5y5/75byL/zLde4497KBs8+udal8vnp6f/WKt/OP+x2u/fvutN3LrTdflzgH/TvdtNs6ePQ/Oehtuks3XXS0/W2Pt2teOk6Rx48b55puZX7YDgP++ub7GXuPGjXPmmWdm//33T+tZaI0HYPoefO79LL9P/yzQonEmTpqckV+Pzzt/3SfvDh01w3ueem1o5mnYIIu1bZ43Phox3TFfjPxmmrXxjtxptQx8+ZOc/7dnk1SHhGPGPpQHz9khJ18zMG1aN02ndi3zt5O2rr2nauoaF1/d0Ssr7ndt3hk6cprPGjr863z8xejaUC9JXv1geKqqiiy84Hx56+Np77mo1y9zzBWPpqoosvKSbXLrY2/mm3ET89iLH2WdrgvXCfZaN2+cz0d+M8PvBwCV4aSjD8lD9/0zN97+QNp3WGSW7n3i0Ycz4L5/5rk3P6nthlthpZXz2MMP5m83/TUH/n76m27MP/8CGTlixH8482orrbLaD76OO/DRf+XBe+7KFRdfkKQ6jJw8eXKWajdfTj/3T9mxxx7T3HP84b1y3Cl/zOQpkzPkxcHZYptfp0nTpvn5GmvnyScerRPsjRjxZeZfYMHZUgsAc8ZcfxV3o402Srt27dKnT58Zjvnb3/6W5ZdfPo0aNUqnTp1y7rnn1rneqVOnnHHGGdl7773TvHnzLLroornssst+8HMffvjhFEWRe++9NyuvvHKaNGmSX/7yl/n0009z9913p0uXLmnRokV22WWXjBnz7d9STZ48OX369Ennzp3TpEmTrLTSSrnlllumee6DDz6Y1VZbLU2bNs2aa66Z116rXqT26quvzsknn5znn3++dnHKq6++Okly3nnnpWvXrmnWrFk6duyYgw46KKNHj57Vb2mS5Jxzzkn79u2zwAIL5OCDD86ECRNqr40bNy5HHHFEFl544TRr1iyrr756Hn744drrX3zxRXbeeecsvPDCadq0abp27Zobbrhhhp+1/vrr57333suhhx463QU377333nTp0iXzzTdfNttss3zyySc/qSZg1nwxamxGfj0+6620SNq0apo7//32DMeutPhCmTRpcj77gbBr8NufpcvUDSpqNG3UsE4nYJJMmrq2XVEUee2DL7PqgX/N6r2urz3uGvR2/vXCh1m91/X58PO6u9bWGPjyx2k/f7M0a/xtV91SC7fOpEmT89Hn0/53cY9NlsuXo8fmrkHvpEGD6v8GzdOg+kfcPA2r0qDq2x93ndu1TJNGDTP4rc9mWCsA5VbdbXdI7vvn7fnrrfek42KdZvkZY6d2qlUVdX9lqqqqmuE6rkmyXNeV8ubrr8zy503Pyy+9MMNNOpLkb3c/nDsfGlR7HHr0iZlvvua586FB2XTLbaYZf9Nfr07LVq2z0WZbZfKk6g79CROrf0+YOHFCJk36tmv/vXfezrixY7N8126zpRbgf1vN7/KVelSyuR7sNWjQIGeccUb69euXDz/8cJrrzzzzTHbcccd07949L774Ynr37p0TTjihNgyrce6552a11VbLc889l4MOOigHHnhgbZj2Q3r37p2LLrooTzzxRD744IPsuOOOueCCC3L99dfnrrvuyn333Zd+/frVju/Tp0+uueaaXHrppRkyZEgOPfTQ7LrrrvnXv+ruvnjcccfl3HPPzdNPP52GDRtm7733TpLstNNOOfzww7P88svnk08+ySeffJKddtopSfX/JPTt2zdDhgxJ//79M2DAgBx11FGz+i3NQw89lLfeeisPPfRQ+vfvn6uvvrrO96tXr14ZOHBgbrzxxrzwwgvZYYcdstlmm+WNN95IkowdOzarrrpq7rrrrrz00kvp2bNndttttzz55JPT/bxbb701iyyySE455ZTammqMGTMm55xzTq699to88sgjef/993PEEUfMcO7jxo3LqFGj6hxQXzRrPE9WXHzBrLh49d+Md2rbIisuvmA6LjRf7ZjW8zXKiosvWBuwLb1I66y4+IJp+53183bbuEt+vky7dG7XMt03WCbXHbt5+t32XG0n3urLtkuvbbula+cF06ldi3Rff5mc2XOd3PDQaxkxdQfa6Xngmfeyxvc24Lhr0DvZds0lst8WXdOpXYussVz7nHvAennqtaH5ZPjXGTdhUl5+b3idY8TocRn9zfi8/N7wTJg4OUlyyp5r5orDN6597k0Pv57hX43NZYdulGU7zp+1VuiQM/ZZK/3vf3manWwXatkkx3T/WQ6bugvuiNHj8sr7w9Nru25Zfdl2WX+ljhn48se149daoUPe/mTkdDsFAagMJx59SG675cZccGn/zDfffPls2NB8Nmxoxn7z7V9gfTZsaF5+8fm89/ZbSZJXX34pL7/4fEZ8OTxJsvJqq6dlq9Y5ote+eeWlF/L2W2+kT+9j8+H77+aXG282w89e95cb59mnn6wTkiXJG6+9Uv38EV/mq1Gj8vKLz+flF5+vvf6XS/vl/rvvyLtvv5XXXhmSU447IgMffTi77X1A7ZhrrrgkPX69ee3XSy69bJbpsnzt0bZ9hxRVVVmmy/LTrAP4+Wef5k/n/zG9/3hekqRlq9ZZcullc9WfL8qzT/07TzzycFZdfY3a8U/9+/Es2qlzFuu8+Mx+2wGYC+b6q7hJ8qtf/SrdunXLSSedlCuvvLLOtfPOOy8bbrhhTjjhhCTJ0ksvnZdffjlnn3129txzz9pxW2yxRQ466KAkydFHH53zzz8/Dz30UJZZZpkf/OzTTjsta621VpJkn332ybHHHpu33noriy9e/QPsN7/5TR566KEcffTRGTduXM4444w88MADWWON6h96iy++eB577LH8+c9/znrrrVf73NNPP73262OOOSZbbrllxo4dmyZNmmS++eZLw4YN065d3b99++76dJ06dcppp52WAw44IBdffPHMfiuTJK1bt85FF12UBg0aZNlll82WW26ZBx98MPvtt1/ef//9XHXVVXn//ffToUP1L+hHHHFE7rnnnlx11VU544wzsvDCC9cJ337729/m3nvvzc0335yf//zn03ze/PPPnwYNGqR58+bT1DRhwoRceumlWWKJJZJUh4qnnHLKDOfep0+fnHzyybNUL1SKVZZqk/vO3L7267N6rpskufb+l9Pz/AeSJFv+YvFcfti3Adi1x1T/z/1p1w3K6dcNSpIsvXDrnLLHmpm/eeO89+monHXT03XW1Bs3YVJ2WG/pHNdj9TSap0HeHTYq/W4bnL63znhx7iS58aHXcvrea2ephVvVhoR/feCVNG8yTw7YesX8cd+1M/LrcXn4+Q9z/FWP/+Czvq9d66bpuNC3m1l8PXZCtjzutpx34Hp5/MKdMvyrsfnbo2+k9zUDp7n3nP3XzYW3PpdPvrOxR8/z7s/lh22cg7ZZKef/7dk8853dfHdcb+lcdc9LszQ/AMrluquq397ZebtN6pw/q+9l+c3Ou1WP6X9F+p59eu217ttsXGfM/AssmKtu+kfOPb13evx680ycMCFLLdslf77m/9JlhbobM33XehtumoYNGuTxfw3Iur/89mf23jtvV2cjiq1++YskydufVYeNEyaMzxknHpOhQz9OkyZNs+xyK+Tav/0za6z97e8Yw4d/kfffnXEH/g855bgjsu+Bv0/bdt/+Jd3Z/S7LEb32S//LL85+vQ7NSiuvVnvtjltvzk677vWTPguA/57/iWAvSc4888z88pe/nKab65VXXsm2225b59xaa62VCy64IJMmTardnn3FFb/94VoURdq1a5dPP63+RW7zzTfPo48+miRZbLHFMmTIkNqx372vbdu2adq0aW2oV3OuplPtzTffzJgxY7Lxxt/+gE6S8ePHZ+WV6255/93ntm9fvbDup59+mkUXXXSG34MHHnggffr0yauvvppRo0Zl4sSJGTt2bMaMGZOmTZvWGfv+++9nueWWq/36D3/4Q/7whz8kSZZffvk629a3b98+L774YpLkxRdfzKRJk7L00kvXed64ceOywAILJEkmTZqUM844IzfffHM++uijjB8/PuPGjZtmDjOjadOmtaFezVxq/lym59hjj81hhx1W+/WoUaPSsWPHWf5cKKNHX/woTbbo+4Nj/vrAK/nrAz/8es8JVz+RE66e8Xo8g9/6LOsddvMsz+/L0eNy6R3P53e/Wjm/veih2vOX3PFCLrnjhZl+Tk1I+WPnXv/wy2x13G0/+rw9zrp3mnNPvz4sKx/w12nOd1l0/qy4+ELZtc/dMzdZAEqpJiz7IYccdXwOOer4HxyzYrdV0///7pilz27YsGEOPOSoXHlJ3zrB3qPP/vDbRPv/9vDs/9vDf3DMj835NzvvVhtcfl/fy66Z5txKq/ws9z8xeJrzr7/6cl4e8kL6XTntz1IA/rf8zwR76667bjbddNMce+yxdTrxZtY889Td3bAoikyeXP2K1xVXXJFvprbdf3/cd78uiuIHn1Oz3t1dd92VhRdeuM647+989f3nJql9zvS8++672WqrrXLggQfm9NNPz/zzz5/HHnss++yzT8aPHz9NqNahQ4c6u+rOP/+36179WA0NGjTIM888Uyf8S5L55qt+3e/ss8/OhRdemAsuuKB2zb9DDjkk48ePz6ya3lx+aLfjRo0a/aRdxID/jjNveio9t1wxRZGUcePydvM3y77n3ldnUw4AmN122WPffDVyZEaP/irzzdf8x2/4H/PpsKE596Ir0qJFy7k9FeC/pCiqj0pUqXXV+J8J9pLkj3/8Y7p161bn9dkuXbrk8cfrvtL1+OOPZ+mll54mmJqR74dwP9Vyyy2XRo0a5f3336/z2u2smnfeeadZc+OZZ57J5MmTc+6556Zq6iLvN988446ahg0bZskll5zlz1555ZUzadKkfPrpp1lnnXWmO+bxxx/Ptttum1133TVJdSD5+uuv1+kQ/L7p1QRUnpFfj8/ZNz89t6fxkz00+IO5PQUA6oGGDRvm4MOOntvT+MnWXu+Xc3sKAMyk/6lgr2vXrunRo0f69v32VbTDDz88P/vZz3Lqqadmp512ysCBA3PRRRfN8rpzs0Pz5s1zxBFH5NBDD83kyZOz9tprZ+TIkXn88cfTokWL7LHHtNvJT0+nTp3yzjvvZPDgwVlkkUXSvHnzLLnkkpkwYUL69euXrbfeOo8//nguvfTS2V7D0ksvnR49emT33XfPueeem5VXXjmfffZZHnzwway44orZcssts9RSS+WWW27JE088kdatW+e8887LsGHDfjDY69SpUx555JF07949jRo1yoILLjjb5w4AAADAt+b6rrjfd8opp9R5ZXWVVVbJzTffnBtvvDErrLBCTjzxxJxyyik/6XXd2eHUU0/NCSeckD59+qRLly7ZbLPNctddd6Vz584z/Yztt98+m222WTbYYIMstNBCueGGG7LSSivlvPPOy5lnnpkVVlgh1113Xfr06TNHarjqqquy++675/DDD88yyyyT7bbbLk899VTt+n/HH398VllllWy66aZZf/31065du2y33XY/+MxTTjkl7777bpZYYokstNBCc2TeAAAAAHyrmPJDC57BXDZq1Ki0bNkyjTY6M8U8jef2dACgVF6+pufcngIAlM5XX43KSou3zciRI9OiRYu5PZ05quZ37hWO+UcaNGo2t6czR0wa93Ve+uO2Ffvn+T/1Ki4AAAAA/11FitqNPytNkcqsq8b/3Ku4AAAAAMCPE+wBAAAAQAkJ9gAAAACghKyxBwAAAFCPFUX1UYkqta4aOvYAAAAAoIQEewAAAABQQoI9AAAAACghwR4AAAAAlJDNMwAAAADqsaIoUlToLhOVWlcNHXsAAAAAUEKCPQAAAAAoIcEeAAAAAJSQNfYAAAAA6rGiqD4qUaXWVUPHHgAAAACUkGAPAAAAAEpIsAcAAAAAJSTYAwAAAIASsnkGAAAAQD1WFEWKCt1lolLrqqFjDwAAAABKSLAHAAAAACUk2AMAAACAErLGHgAAAEA9VhTVRyWq1Lpq6NgDAAAAgBIS7AEAAABACQn2AAAAAKCEBHsAAAAAUEI2zwAAAACox4qiSFGhu0xUal01dOwBAAAAQAkJ9gAAAACghAR7AAAAAFBC1tgDAAAAqM+KpGKXoqvUuqbSsQcAAAAAJSTYAwAAAIASEuwBAAAAQAkJ9gAAAACghGyeAQAAAFCPFUWRokJ3z6jUumro2AMAAACAEhLsAQAAAEAJCfYAAAAAoIQEewAAAABQQjbPAAAAAKjHiqL6qESVWlcNHXsAAAAAUEKCPQAAAAAoIcEeAAAAAJSQNfYAAAAA6rGiKFJU6GJ0lVpXDR17AAAAAFBCgj0AAAAAKCHBHgAAAACUkGAPAAAAAErI5hkAAAAA9VhRVB+VqFLrqqFjDwAAAABKSLAHAAAAACUk2AMAAACAErLGHgAAAEA9VhRFigpdjK5S66qhYw8AAAAASkiwBwAAAAAlJNgDAAAAgBIS7AEAAABACdk8AwAAAKAes3lGeenYAwAAAIASEuwBAAAAQAkJ9gAAAACghKyxBwAAAFCPFUX1UYkqta4aOvYAAAAAoIQEewAAAABQQoI9AAAAACghwR4AAAAAlJDNMwAAAADqsaIoUlToLhOVWlcNHXsAAAAAUEKCPQAAAAAoIcEeAAAAAJSQNfYAAAAA6rGiqD4qUaXWVUPHHgAAAACUkGAPAAAAAEpIsAcAAAAAJSTYAwAAAIASsnkGAAAAQD1WFEWKCt1lolLrqqFjDwAAAABKSLAHAAAAACUk2AMAAACAErLGHgAAAEA9ViSp1KXoKrSsWjr2AAAAAKCEBHsAAAAAUEKCPQAAAAAoIcEeAAAAAJSQzTMAAAAA6rGqokhVhe6eUal11dCxBwAAAAAlJNgDAAAAgBIS7AEAAABACVljDwAAAKAeK4rqoxJVal01dOwBAAAAQAkJ9gAAAACghAR7AAAAAFBCgj0AAAAAKCGbZwAAAADUY0VRpKjQXSYqta4aOvYAAAAAoIQEewAAAABQQoI9AAAAACgha+wBAAAA1GNVRfVRiSq1rho69gAAAACghAR7AAAAAFBCgj0AAAAAKCHBHgAAAACUkM0zAAAAAOqzIimKCt1lokLLqqFjDwAAAABKSLAHAAAAACUk2AMAAACAErLGHgAAAEA9VhTVRyWq1Lpq6NgDAAAAgBIS7AEAAABACQn2AAAAAKCEBHsAAAAAUEI2zwAAAACox4qp/1SiSq2rho49AAAAACghwR4AAAAAlJBgDwAAAABKyBp7AAAAAPVYVVF9VKJKrauGjj0AAAAAKCHBHgAAAACUkGAPAAAAAEpIsAcAAAAAJSTYAwAAAKjHiqKo6GNWTJo0KSeccEI6d+6cJk2aZIkllsipp56aKVOm1I6ZMmVKTjzxxLRv3z5NmjTJRhttlDfeeKPOc4YPH54ePXqkRYsWadWqVfbZZ5+MHj16tvx5fZdgDwAAAACSnHnmmbnkkkty0UUX5ZVXXsmZZ56Zs846K/369asdc9ZZZ6Vv37659NJLM2jQoDRr1iybbrppxo4dWzumR48eGTJkSO6///7ceeedeeSRR9KzZ8/ZPt+Gs/2JAAAAAFBCTzzxRLbddttsueWWSZJOnTrlhhtuyJNPPpmkulvvggsuyPHHH59tt902SXLNNdekbdu2ue2229K9e/e88sorueeee/LUU09ltdVWS5L069cvW2yxRc4555x06NBhts1Xxx4AAAAAJFlzzTXz4IMP5vXXX0+SPP/883nsscey+eabJ0neeeedDB06NBtttFHtPS1btszqq6+egQMHJkkGDhyYVq1a1YZ6SbLRRhulqqoqgwYNmq3z1bEHAAAAUI8VRfVRiWrqGjVqVJ3zjRo1SqNGjaYZf8wxx2TUqFFZdtll06BBg0yaNCmnn356evTokSQZOnRokqRt27Z17mvbtm3ttaFDh6ZNmzZ1rjds2DDzzz9/7ZjZRcceAAAAABWtY8eOadmyZe3Rp0+f6Y67+eabc9111+X666/Ps88+m/79++ecc85J//79/8sznjk69gAAAACoaB988EFatGhR+/X0uvWS5Mgjj8wxxxyT7t27J0m6du2a9957L3369Mkee+yRdu3aJUmGDRuW9u3b1943bNiwdOvWLUnSrl27fPrpp3WeO3HixAwfPrz2/tlFxx4AAAAAFa1FixZ1jhkFe2PGjElVVd24rEGDBpk8eXKSpHPnzmnXrl0efPDB2uujRo3KoEGDssYaayRJ1lhjjYwYMSLPPPNM7ZgBAwZk8uTJWX311WdrXTr2AAAAACDJ1ltvndNPPz2LLrpoll9++Tz33HM577zzsvfeeydJiqLIIYccktNOOy1LLbVUOnfunBNOOCEdOnTIdtttlyTp0qVLNttss+y333659NJLM2HChPTq1Svdu3efrTviJoI9AAAAgHqtqihSVaG7Z8xqXf369csJJ5yQgw46KJ9++mk6dOiQ/fffPyeeeGLtmKOOOipff/11evbsmREjRmTttdfOPffck8aNG9eOue6669KrV69suOGGqaqqyvbbb5++ffvOtrpqFFOmTJky258Ks8moUaPSsmXLNNrozBTzNP7xGwCAWi9f03NuTwEASuerr0ZlpcXbZuTIkXXWZKtENb9zb9Xv4czTZL65PZ05YsI3o3Pnb9ev2D9Pa+wBAAAAQAkJ9gAAAACghKyxBwAAAFCPFUX1UYkqta4aOvYAAAAAoIQEewAAAABQQoI9AAAAACghwR4AAAAAlJDNMwAAAADqsaIoUlToLhOVWlcNHXsAAAAAUEKCPQAAAAAoIcEeAAAAAJSQNfYAAAAA6rGiqD4qUaXWVUPHHgAAAACUkGAPAAAAAEpIsAcAAAAAJSTYAwAAAIASsnkGAAAAQD1WVRSpqtBdJiq1rho69gAAAACghAR7AAAAAFBCgj0AAAAAKCFr7AEAAADUY8XUoxJVal01dOwBAAAAQAkJ9gAAAACghAR7AAAAAFBCgj0AAAAAKCGbZwAAAADUY0VRpCgqc5uJSq2rho49AAAAACghwR4AAAAAlJBgDwAAAABKyBp7AAAAAPVYVVF9VKJKrauGjj0AAAAAKCHBHgAAAACUkGAPAAAAAEpIsAcAAAAAJWTzDAAAAIB6rCiKFEVl7jJRqXXV0LEHAAAAACUk2AMAAACAEhLsAQAAAEAJWWMPAAAAoJ6r8KXoKpaOPQAAAAAoIcEeAAAAAJSQYA8AAAAASkiwBwAAAAAlZPMMAAAAgHqsKIoUFbp7RqXWVUPHHgAAAACUkGAPAAAAAEpIsAcAAAAAJWSNPQAAAIB6rKqoPipRpdZVQ8ceAAAAAJSQYA8AAAAASkiwBwAAAAAlJNgDAAAAgBKyeQYAAABAPVYURYqiMneZqNS6aujYAwAAAIASEuwBAAAAQAkJ9gAAAACghKyxBwAAAFCPFVOPSlSpddXQsQcAAAAAJSTYAwAAAIASEuwBAAAAQAnN1Bp7t99++0w/cJtttvnJkwEAAAAAZs5MBXvbbbfdTD2sKIpMmjTpP5kPAAAAAP9FVUWRqqIyt5mo1LpqzFSwN3ny5Dk9DwAAAABgFvxHa+yNHTt2ds0DAAAAAJgFsxzsTZo0KaeeemoWXnjhzDfffHn77beTJCeccEKuvPLK2T5BAAAAAGBasxzsnX766bn66qtz1llnZd555609v8IKK+SKK66YrZMDAAAAYM4qiso+KtksB3vXXHNNLrvssvTo0SMNGjSoPb/SSivl1Vdfna2TAwAAAACmb5aDvY8++ihLLrnkNOcnT56cCRMmzJZJAQAAAAA/bJaDveWWWy6PPvroNOdvueWWrLzyyrNlUgAAAADAD2s4qzeceOKJ2WOPPfLRRx9l8uTJufXWW/Paa6/lmmuuyZ133jkn5ggAAAAAfM8sd+xtu+22ueOOO/LAAw+kWbNmOfHEE/PKK6/kjjvuyMYbbzwn5ggAAADAHFIURUUflWyWO/aSZJ111sn9998/u+cCAAAAAMyknxTsJcnTTz+dV155JUn1unurrrrqbJsUAAAAAPDDZjnY+/DDD7Pzzjvn8ccfT6tWrZIkI0aMyJprrpkbb7wxiyyyyOyeIwAAAADwPbO8xt6+++6bCRMm5JVXXsnw4cMzfPjwvPLKK5k8eXL23XffOTFHAAAAAOaQoqjso5LNcsfev/71rzzxxBNZZpllas8ts8wy6devX9ZZZ53ZOjkAAAAAYPpmuWOvY8eOmTBhwjTnJ02alA4dOsyWSQEAAAAAP2yWg72zzz47v/3tb/P000/Xnnv66afz+9//Puecc85snRwAAAAAMH0z9Spu69atU3znpeSvv/46q6++eho2rL594sSJadiwYfbee+9st912c2SiAAAAAMC3ZirYu+CCC+bwNAAAAACYG6qKIlUVustEpdZVY6aCvT322GNOzwMAAAAAmAWzvCvud40dOzbjx4+vc65Fixb/0YQAAAAAgB83y5tnfP311+nVq1fatGmTZs2apXXr1nUOAAAAAGDOm+Vg76ijjsqAAQNyySWXpFGjRrniiity8sknp0OHDrnmmmvmxBwBAAAAmEOKorKPSjbLr+Lecccdueaaa7L++utnr732yjrrrJMll1wyiy22WK677rr06NFjTswTAAAAAPiOWe7YGz58eBZffPEk1evpDR8+PEmy9tpr55FHHpm9swMAAAAApmuWg73FF18877zzTpJk2WWXzc0335ykupOvVatWs3VyAAAAAMD0zXKwt9dee+X5559PkhxzzDH505/+lMaNG+fQQw/NkUceOdsnCAAAAABMa5bX2Dv00ENr/32jjTbKq6++mmeeeSZLLrlkVlxxxdk6OQAAAADmrKIoUlToLhOVWleNWQ72vm+xxRbLYostNjvmAgAAAADMpJkK9vr27TvTD/zd7373kycDAAAAAMycmQr2zj///Jl6WFEUgj0AAAAA+C+YqWCvZhdcmFvev+mAtGjRYm5PAwBKpfXPes3tKQBA6UyZNH5uTwFm2n+8xh4AAAAA5VU19ahElVpXjUqvDwAAAAAqkmAPAAAAAEpIsAcAAAAAJWSNPQAAAIB6rCiKFEUxt6cxR1RqXTV+Usfeo48+ml133TVrrLFGPvrooyTJtddem8cee2y2Tg4AAAAAmL5ZDvb+9re/ZdNNN02TJk3y3HPPZdy4cUmSkSNH5owzzpjtEwQAAAAApjXLwd5pp52WSy+9NJdffnnmmWee2vNrrbVWnn322dk6OQAAAABg+mY52Hvttdey7rrrTnO+ZcuWGTFixOyYEwAAAADwI2Z584x27drlzTffTKdOneqcf+yxx7L44ovPrnkBAAAA8F9QFElVhe4xUeF7Z8x6x95+++2X3//+9xk0aFCKosjHH3+c6667LkcccUQOPPDAOTFHAAAAAOB7Zrlj75hjjsnkyZOz4YYbZsyYMVl33XXTqFGjHHHEEfntb387J+YIAAAAAHzPLAd7RVHkuOOOy5FHHpk333wzo0ePznLLLZf55ptvTswPAAAAAJiOWQ72asw777xZbrnlZudcAAAAAPgvq6rgNfYqta4asxzsbbDBBil+YOXBAQMG/EcTAgAAAAB+3CwHe926davz9YQJEzJ48OC89NJL2WOPPWbXvAAAAACAHzDLwd75558/3fO9e/fO6NGj/+MJAQAAAAA/rmp2PWjXXXfNX/7yl9n1OAAAAADgB/zkzTO+b+DAgWncuPHsehwAAAAA/wVFUfzgfgplVql11ZjlYO/Xv/51na+nTJmSTz75JE8//XROOOGE2TYxAAAAAGDGZjnYa9myZZ2vq6qqsswyy+SUU07JJptsMtsmBgAAAADM2CwFe5MmTcpee+2Vrl27pnXr1nNqTgAAAADAj5ilYK9BgwbZZJNN8sorrwj2AAAAACpAVVF9VKJKravGLO+Ku8IKK+Ttt9+eE3MBAAAAAGbSLAd7p512Wo444ojceeed+eSTTzJq1Kg6BwAAAAAw5830q7innHJKDj/88GyxxRZJkm222abOlsFTpkxJURSZNGnS7J8lAAAAAFDHTAd7J598cg444IA89NBDc3I+AAAAAMBMmOlgb8qUKUmS9dZbb45NBgAAAID/rqKoPipRpdZVY5bW2Csq/bsBAAAAACUx0x17SbL00kv/aLg3fPjw/2hCAAAAAMCPm6Vg7+STT07Lli3n1FwAAAAAgJk0S8Fe9+7d06ZNmzk1FwAAAAD+y6qKIlUVuvxapdZVY6bX2LO+HgAAAAD875jpYK9mV1wAAAAAYO6b6VdxJ0+ePCfnAQAAAADMgpnu2AMAAAAA/nfM0uYZAAAAAFSWqlRu51el1lWj0usDAAAAgIok2AMAAACAEhLsAQAAAEAJWWMPAAAAoB4riuqjElVqXTV07AEAAABACQn2AAAAAKCEBHsAAAAAUEKCPQAAAAAoIZtnAAAAANRjVSlSVaG7TFSlMuuqoWMPAAAAAEpIsAcAAAAAJSTYAwAAAIASssYeAAAAQD1WFNVHJarUumro2AMAAACAEhLsAQAAAEAJCfYAAAAAoIQEewAAAABQQjbPAAAAAKjHqorqoxJVal01dOwBAAAAQAkJ9gAAAACghAR7AAAAAFBC1tgDAAAAqMeKIqkqKnMxugotq5aOPQAAAAAoIcEeAAAAAJSQYA8AAAAASkiwBwAAAAAlZPMMAAAAgHqsKCp3k4lKrauGjj0AAAAAKCHBHgAAAACUkGAPAAAAAErIGnsAAAAA9VhVUX1Uokqtq4aOPQAAAAAoIcEeAAAAAJSQYA8AAAAASkiwBwAAAAAlZPMMAAAAgHqsmPpPJarUumro2AMAAACAEhLsAQAAAEAJCfYAAAAAoISssQcAAABQj1UV1UclqtS6aujYAwAAAIASEuwBAAAAQAkJ9gAAAABgqo8++ii77rprFlhggTRp0iRdu3bN008/XXt9ypQpOfHEE9O+ffs0adIkG220Ud544406zxg+fHh69OiRFi1apFWrVtlnn30yevTo2T5XwR4AAAAAJPnyyy+z1lprZZ555sndd9+dl19+Oeeee25at25dO+ass85K3759c+mll2bQoEFp1qxZNt1004wdO7Z2TI8ePTJkyJDcf//9ufPOO/PII4+kZ8+es32+Ns8AAAAAqMdsnvGtM888Mx07dsxVV11Ve65z5861/z5lypRccMEFOf7447PtttsmSa655pq0bds2t912W7p3755XXnkl99xzT5566qmsttpqSZJ+/fpliy22yDnnnJMOHTr854VNpWMPAAAAAJLcfvvtWW211bLDDjukTZs2WXnllXP55ZfXXn/nnXcydOjQbLTRRrXnWrZsmdVXXz0DBw5MkgwcODCtWrWqDfWSZKONNkpVVVUGDRo0W+cr2AMAAACgoo0aNarOMW7cuOmOe/vtt3PJJZdkqaWWyr333psDDzwwv/vd79K/f/8kydChQ5Mkbdu2rXNf27Zta68NHTo0bdq0qXO9YcOGmX/++WvHzC6CPQAAAAAqWseOHdOyZcvao0+fPtMdN3ny5Kyyyio544wzsvLKK6dnz57Zb7/9cumll/6XZzxzrLEHAAAAUI8VRZGiqMxF9mrq+uCDD9KiRYva840aNZru+Pbt22e55Zarc65Lly7529/+liRp165dkmTYsGFp37597Zhhw4alW7dutWM+/fTTOs+YOHFihg8fXnv/7KJjDwAAAICK1qJFizrHjIK9tdZaK6+99lqdc6+//noWW2yxJNUbabRr1y4PPvhg7fVRo0Zl0KBBWWONNZIka6yxRkaMGJFnnnmmdsyAAQMyefLkrL766rO1Lh17AAAAAJDk0EMPzZprrpkzzjgjO+64Y5588slcdtllueyyy5JUdwAecsghOe2007LUUkulc+fOOeGEE9KhQ4dst912Sao7/DbbbLPaV3gnTJiQXr16pXv37rN1R9xEsAcAAAAASZKf/exn+fvf/55jjz02p5xySjp37pwLLrggPXr0qB1z1FFH5euvv07Pnj0zYsSIrL322rnnnnvSuHHj2jHXXXddevXqlQ033DBVVVXZfvvt07dv39k+32LKlClTZvtTYTYZNWpUWrZsmWFfjKzzLjwA8ONa/6zX3J4CAJTOlEnjM+7FyzNyZOX/HlrzO/cpdw5O42bN5/Z05oixX3+VE7fqVrF/njr2AAAAAOqxqqL6qESVWlcNm2cAAAAAQAkJ9gAAAACghAR7AAAAAFBC1tgDAAAAqMeKovqoRJVaVw0dewAAAABQQoI9AAAAACghwR4AAAAAlJBgDwAAAABKyOYZAAAAAPVYVVGkqkJ3majUumro2AMAAACAEhLsAQAAAEAJCfYAAAAAoISssQcAAABQj1UV1UclqtS6aujYAwAAAIASEuwBAAAAQAkJ9gAAAACghAR7AAAAAFBCNs8AAAAAqM+KpKjUTSYqta6pdOwBAAAAQAkJ9gAAAACghAR7AAAAAFBC1tgDAAAAqMeqUqSqQhejq9S6aujYAwAAAIASEuwBAAAAQAkJ9gAAAACghAR7AAAAAFBCNs8AAAAAqMeKovqoRJVaVw0dewAAAABQQoI9AAAAACghwR4AAAAAlJA19gAAAADqsaqi+qhElVpXDR17AAAAAFBCgj0AAAAAKCHBHgAAAACUkGAPAAAAAErI5hkAAAAA9VhVUaSqqMxdJiq1rho69gAAAACghAR7AAAAAFBCgj0AAAAAKCFr7AEAAADUY0VRfVSiSq2rho49AAAAACghwR4AAAAAlJBgDwAAAABKSLAHAAAAACVk8wwAAACAeqwqRaoqdJeJqlRmXTV07AEAAABACQn2AAAAAKCEBHsAAAAAUELW2AMAAACox4qi+qhElVpXDR17AAAAAFBCgj0AAAAAKCHBHgAAAACUkGAPAAAAAErI5hkAAAAA9VhVKrfzq1LrqlHp9QEAAABARRLsAQAAAEAJCfYAAAAAoISssQcAAABQjxVFkaIo5vY05ohKrauGjj0AAAAAKCHBHgAAAACUkGAPAAAAAEpIsAcAAAAAJWTzDAAAAIB6rJh6VKJKrauGjj0AAAAAKCHBHgAAAACUkGAPAAAAAErIGnsAAAAA9VhVUaSqqMzV6Cq1rho69gAAAACghAR7AAAAAFBCgj0AAAAAKCHBHgAAAACUkM0zAAAAAOq5yt5ionLp2AMAAACAEhLsAQAAAEAJCfYAAAAAoISssQcAAABQjxVF9VGJKrWuGjr2AAAAAKCEBHsAAAAAUEKCPQAAAAAoIcEeAAAAAJSQzTMAAAAA6rGiKFJU6C4TlVpXDR17AAAAAFBCgj0AAAAAKCHBHgAAAACUkDX2AAAAAOqxqlRu51el1lWj0usDAAAAgIok2AMAAACAEhLsAQAAAEAJCfYAAAAAoIRsngEAAABQjxVFkaIo5vY05ohKrauGjj0AAAAAKCHBHgAAAACUkGAPAAAAAErIGnsAAAAA9Vgx9ahElVpXDR17AAAAAFBCgj0AAAAAKCHBHgAAAACUkGAPAAAAAErI5hkAAAAA9VhRFCmKytxmolLrqqFjDwAAAABKSLAHAAAAACUk2AMAAACAEhLsAQAAAEAJ2TwDAAAAoB6rSuV2flVqXTUqvT4AAAAAqEiCPQAAAAAoIcEeAAAAAJSQNfYAAAAA6rGiKFIUxdyexhxRqXXV0LEHAAAAACUk2AMAAACAEhLsAQAAAEAJCfYAAAAAoIRsngEAAABQjxVTj0pUqXXV0LEHAAAAACUk2AMAAACAEhLsAQAAAEAJWWMPAAAAoB4riuqjElVqXTV07AEAAABACQn2AAAAAKCEBHsAAAAAUEKCPQAAAAAoIZtnAAAAANRjVSlSlcrcZaJS66qhYw8AAAAASkiwBwAAAAAlJNgDAAAAgBKyxh4AAABAPVYU1UclqtS6aujYAwAAAIASEuwBAAAAQAkJ9gAAAACghAR7AAAAAFBCNs8AAAAAqMeKqf9Uokqtq4aOPQCma/z48Vl+2SUz8Ikn5vZUpvH5559n0Q5t8uGHH87tqQDATDnxoC1z0fE7z+1p/GTX/HGv/H63X87taQDwPYI9gJL76KOPstfuu2bhtgukdfMmWa1b1zzz9NO112/7+63ZavNNsnDbBdJkniLPDx48U8+9/M+XplOnzlljzTWnuTZu3Lisvmq3WXrelClTsu1Wm6fJPEVu/8dtteeHDx+e7bfbOgu2mi+/WG3lDH7uuTr3HfLbg3PB+efWObfgggtml113z2knnzRTnw1AZVtrlSVyywX75+37Ts83z12UrddfcZoxx+2/RQbfenw+f+LcfPyvs3LXpb3ysxUWq72+aPv5c8lJu+SVO3tn+MDzMuT2k3L8AVtknoYNascstVib3HPZ7/LuA2fky3+fn5fv6J2TDtoqDRv+8K9VbRdonoN3Xj9nXXlP7bn5mjbK2Udsn9f+eUqGDzwvD119WFZdbtE691128q755rmL6hz/uOigH/1+dFioZf5y2u758KEzM3zgeXnq5j9kle88+5DdNsx7D/bJew/2mSas+9kKi+Xx645KgwZ1a/rj5ffkqH02TYv5Gv/o5wPw3+NVXIAS+/LLL/PL9dbKeuttkNvuuDsLLbRQ3nzzjbRu3bp2zJivv86aa62d7X+zYw46YL+Zeu6UKVNy6SUX5YSTTpnu9T8cc1Tad+iQF154fqbn2u/CC1IU07bBn9nn9Hz11VcZ+OSzuezPl+TgA/bL44Oqg8lB//53nnpyUM69oO809+2+x15Zc/VVc8aZZ2f++eef6XkAUHmaNWmUF1//KNf8Y2BuOq/ndMe8+d6nOfTM/8s7H36eJo3myW93/WXuuLhXVtj25Hz+5egs07ltqoqq9Drtxrz1wWdZfskO+dMJO6dZk0Y59vy/J0kmTJyU6+58MoNf/SAjvxqTrksvkj+dsHOqqoqcdNEdM5zfnr9aM/9+/p28/8mXtecuOXGXLLdkh+x9fP988tnI7LzFz3PXpb/NKtuflo8/G1k77t7Hh2T/k/5a+/W48RN/8HvRqnmTDLj6sPzrqTeyXa+L89mXo7Pkogvly1FjkiQrLNUhJxy4ZX79+0tTFMmtFx6QBwa+miFvfpwGDarS97ju6XXqDZk0aXKd57781id558PPs/MWP8+fb37kB+cAwH+PYA+gxM49+8wsskjHXHblVbXnOnXuXGfMLrvuliR57913Z/q5zz7zTN5+661svsWW01y795678+AD9+WGm/6We++5e6ae9/zgwbnwgnPz+L+fTueO7etce+3VV7LDjt2z1NJLZ599e+YvV1yWJJkwYUJ+d/ABufjPV6RBgwbTPHO55ZdP+w4dcvttf8+ee+8z07UBUHnue/zl3Pf4yz845qZ7nq7z9dHn3pq9frVmVliqQx5+8vXc/8Qruf+JV2qvv/vRF1l6sTbZb4d1aoO9dz/6Iu9+9EXtmPc/+TLrrrZU1lp5iR/87B02XTWX/99jtV83bjRPttuwW3Y49LI8/uxbSZLT//zPbLHuCtlvh3Vy8sV31o4dP35ihn3x1Y98B751+F4b58OhX2b/3t+Gge99/O2cl+nUNi+98VH+9dTrSZKX3vg4y3RqmyFvfpxD99gojz/7Zp55+f3pPvuuR17MDpuuItiDClQU1UclqtS6angVF6DE7rrz9qyy6mrZpfsOWbRDm/xitZXzlysu/4+f+/hjj2appZdO8+bN65wfNmxYDjpgv1x51bVp2rTpTD1rzJgx2XP3XXJB3z+lXbt201zvuuJKefjhAZk4cWLuv+/erNC1+vWp8845K+ust35WXW21GT57tZ/9PI8/9ugsVAYAyTwNG2SfX6+VEV+NyYuvfzTDcS3ma5LhUzvdpmfxjgtm4zW75NFn3pzhmNYtmqbL4u3y7Mvv1Z5r2KAqDRs2yNjxE+qMHTtuQtb8Xki4zmpL5b0H++T5v5+QC/+wU+Zv2ewHa9tyva559uX3c91Ze+e9B/tk4A1HZ69ffbusxktvfpwlF2uTju1aZ9H2rbPkYm0y5K2P03mRBbP7Nr9I7z/dOcNnP/3Se1lthcUy7zz6QwD+Vwj2SuTqq69Oq1atfnDMnnvume22226Oz2X99dfPIYcc8oNjZma+wH/mnbffzuV/viRLLrlUbr/r3uy3/4E5/NDf5a/X9P+Pnvv++++lffsOdc5NmTIlPffZM/v1POAHw7bvO+rwQ/OLX6yZrbfZdrrXjzjqmDRs2DDLLbNEbv/H33PpZVfmzTfeyF+v7Z9jjzshvz3ogHRZevH02HnHjBw5ss697dt3yPvvvzfd5wLA922+zgr57PFzM2LQ+fntrhtkqwMuyhcjvp7u2MU7LpgDu6+XK295bJprD119WL789/kZcnvvPP7sWznlkrtm+Jkd27VOVVVVPvnO67Wjx4zLv59/O8fut3naL9QyVVVFum/xs6y+Yue0W7BF7bj7n3gl+55wbbbYv1+Ov/AfWWfVJfOPiw5MVdWM2086L7xg9tthnbz5/mfZ5qA/5fL/eyznHvWb9Nh69STJa+8My0kX3ZE7L+mVOy7ulRP73Z7X3hmWi47vnuMuuC0br9klT//fHzLwhqOz1ip1Q8ZPPhuZRvPOk3YLNp/eRwMwFwj2Uh2GFUWRAw44YJprBx98cIqiyJ577jnbPq93797p1q3bbHve/4JOnTrlggsumNvTgHpn8uTJ6bbyKjnltDPSbeWVs89+PbPXPvvl8ssu/Y+eO/abb9K4cd3FsS++qF+++uqrHHn0sTP9nDvvuD0PPzwgZ593wQzHtGzZMv2vvT6vv/Ve7h/wr3RZbrn0Omj/nPHHs3Pj9dflnXfezgtDXkvTJk1zxml11/xr0qRJxoyZcScFAHzXv556Pat375MN9jwv9z3xcv561t5ZqPV804zrsFDL3H7Rwbn1gedy1d+n3R1+t6P/kjV2OTN7HHtVNl9n+Ry6+4Yz/MwmjedNkoz93tp4ex9/TYoiefu+0zNy0AU5eOf1cvM9T2fy5Cm1Y/7v3mdy179ezJA3P84dD7+QX//u0qy2Qqesu9pSM/y8qqoig1/9ICdddEeef+3D/OXWx3PV35/Ifr9Zu3bMFbc8lpV+dWpW+tWpueKWx9Jj69Uz+utxGfTCO7n4xF2y0+GX5+hzb821f9y7TnfeN+Mm1KkJgLlPsDdVx44dc+ONN+abb76pPTd27Nhcf/31WXTRRX/gToC5p1379unSZbk655Zdtks++GD6a+PMrAUWXDBffvllnXMPPzwgg/49MC2bNcp8jRtm+WWXTJKs9YvVsu9ee0z3OQ8/NCBvv/VW2i3YKvM1bpj5Glf/crDzjttnkw3Xn+4911x9VVq2apWtt9k2jzzycLbeZrvMM888+fVvdsij/3q4ztgvhw/PQgst9B/VCkD9MWbs+Lz9wed58sV3c+DJ12fipMnZ41d1d39vv1DL3HP57/PvF97OwafeMN3nfDhsRF59e2huvueZHN/39hy3/xYz7KL7fMToJEmr5nWXsHjnw8+zyb4XZoE1DstSm5+QdXY7J/M0bJB3Pvp8hvN/96Mv8tmXX2WJjjP+2Tf081F55e2hdc69+s7QdGzXerrjF2jVLMf13DyHnfl/+VnXTnnzvU/z1vuf5ZGn30jDhlVZarE2tWPnb1Fdw+dfjp7h5wPw3yXYm2qVVVZJx44dc+utt9aeu/XWW7Poootm5ZVXrj03efLk9OnTJ507d06TJk2y0kor5ZZbbqm9/vDDD6coijz44INZbbXV0rRp06y55pp57bXXklS/nnryySfn+eefT1EUKYoiV199dZLkvPPOS9euXdOsWbN07NgxBx10UEaP/mk/NM8555y0b98+CyywQA4++OBMmPDt+h3jxo3LEUcckYUXXjjNmjXL6quvnocffrj2+hdffJGdd945Cy+8cJo2bZquXbvmhhum/z81SfVrue+9914OPfTQ2pq+6957702XLl0y33zzZbPNNssnn3zyk2oCprXGmmvl9ddfq3PujTdez6KLLvYfPXelbivn9ddezZQp33YNnHt+3zz5zPMZ9PTgDHp6cG67459Jkmuvvym9Tz19us854qhj8tSzL9TeM+jpwUmSs845P5ddcdU04z/77LOccfopOe+CfkmSyZMm1f73a8KECZk0aVKd8UOGvJSVuq08zXMAYGZUFUUafacjrcNCLXPv5b/Pc6+8n54n/bXOz8EZPqOqyDwNG8ww2Hv7g88z8qtv0mXxadeZTarDxqGfj0qr5k2y0ZpdcufDL87wsxZu0yoLtGyWoZ+PmuGYgYPfztLfCeOSZKlF2+T9T4ZPd/xZh2+fftc9lI8+HZEGVUUaNvx2w6qGDRqkwXfqWm7JDvlw6JczfH0ZKK8iRaoq9ChS2btnCPa+Y++9985VV337i+Zf/vKX7LXXXnXG9OnTJ9dcc00uvfTSDBkyJIceemh23XXX/Otf/6oz7rjjjsu5556bp59+Og0bNszee++dJNlpp51y+OGHZ/nll88nn3ySTz75JDvttFOSpKqqKn379s2QIf/f3r3HW1XW+QP/bFEOt3NAkIsgFxVRUFTUSsYmtVRoyHR0UgsVR6tJwRTFSvOKI0crvIbiaIGVSuRtUixveQVKoyg0xWREHFOx+gkaweGyf3/A2XnikjgYrX3e7/1ar9fZaz17rec5vF4s9ofvep5nctNNN+UnP/lJvvSlL230OB5++OHMmzcvDz/8cG666aZMnjy5Eh4myahRozJz5sxMmTIlv/71r/OpT30qQ4cOzW9/+9skqysV995770ybNi1PP/10Pv/5z+e4447Lk08+uc7r3XHHHdluu+0yduzYypgaLVmyJN/4xjfy3e9+N4899lgWLFiQMWPGrLfvy5Yty+LFi5tswPqd+sXRefJnP83XLh2XeS+8kCm33pJv3/hf+Y+TR1ba/PGPf8yvZs/Os8+uXi3w+efn5lezZ+e1115b32mz/wEH5u23385vnnmmsq9Xr17ZdbfdKttOO/VLkuyww47ZbrvtkiSvvPJK9thtlzy15u+Lbt26NfnMrrvtliTp2avXWqv3JslZZ5ye004/Mz169EiS7PtP++XWm7+b5559Nt++8b8y+J/2q7RdsmRJfvmLWfnYwYe8p98dANWjbeuW2b1fj+zeb/X9o0+PTtm9X49KlVqbVi1z0ahD88GBfdJr260zqH/PTLxgeLp36ZA7HvhFkjWh3o2n5eXX/pizL78znbdul66datO101/mkzvm4/vkyIMHZeftu6ZPj0458uBBufjUT+a2+2dlxYpV6+xbuVzOw0/OXWtRjIMG98/B/9Q/vbt3ykc/tEt+fMNpef7F1/OdH86sjGnc6Yev6XPHHPDBfpl6xecz7+XfN1m9996Jp+YLR3+k8v6a7/0kHxy4fc468ZDs0HObHD10n5x45H65/vtrr2T70Q/tkp16d8nENcdmPbMgO/fpmkP2G5ATj9gvK1euyvMvLay032/Qjnnwp8+udR4ANh/LGb3Dsccem7PPPjsvvbR6Ivbp06dnypQplWq2ZcuWZdy4cXnwwQczePDgJMkOO+yQJ554Itdff33233//yrkuueSSyvuvfOUrGTZsWJYuXZrWrVunXbt22XLLLddaHfKdi1H06dMn//mf/5kvfOELufbaazdqHFtvvXW++c1vpkWLFtlll10ybNiwPPTQQ/nc5z6XBQsWZNKkSVmwYEG6d189Mf6YMWPy4x//OJMmTcq4cePSo0ePJuHbqaeemvvuuy9Tp07NBz/4wbWu17Fjx7Ro0SK1tbVrjWn58uWZOHFidtxx9T9kRo0albFjx651jkb19fW56KKLNmq80Jzt84EP5Pu33Znzv3p2xv3n2PTZfvt8ffyV+fRnhlfaTLv7h/n8Z//ynxTHDz8mSfLV8y7IuedfuM7zdurUKZ88/F8z5dabc/El9e+6PyuWL8/zc+fmz3/e+HnvHrj/vsyb90K+fdN3K/tOPmVUfjHr5/nIfh/KPh/4YM4574LKsbt/+N/p2atXPvzhf97oawFQXfYa0Dv333ha5f3XxhyZJPnuD3+az1/wvaxctSo79+maYw/9UDp1aJs/LlqSnz/zUg468YrKY6sf3XeX9O3VJX17dcm8+5tWorceNCpJsmLlqpxxwsHZqXeXlEqlLHj1j7nu+4/lmu/9ZIP9m3TnjFx73qdzzpV3VaoA27drlbGnfjI9unbIHxctyX8/NDsXTLi7EhCuXFXObjv1yPBDP5QOta3z6huL8uDM5zL22nvSsPwv8/Xt0HObdOrwl3kCZ/1mQY4+84aMPfWTOefzH8/8V/6Qs75+e6b86OdN+tSqZqtc8ZVP5bgvf7vSp1cWvpkzvvaDXH/hsWlYviKfO/+7WbpmXr2allvm0AN3z2EjN+67CQDvL8HeO3Tu3DnDhg3L5MmTUy6XM2zYsGyzzTaV4y+88EKWLFmSgw8+uMnnGhoamjyumyS777575edtt902SbJw4cINztf34IMPpr6+Ps8991wWL16cFStWZOnSpVmyZEnatGk6J8eCBQsyYMBf5tU655xzcs455yRJdt1117Ro8ZcS+m233TZz5qwu6Z8zZ05WrlyZfv36NTnfsmXL0qlTpyTJypUrM27cuEydOjWvvPJKGhoasmzZsrX68G60adOmEuo19mXhwoXrbX/22WfnjDPOqLxfvHhxevbsudHXhebkX4Z9Iv8y7BPrPX7ciBNy3IgTNvq8X/7KV/OJjx+cL5/91bRrt/bE4r379Mmfl5f/5r6/tr7jBx8yJAcfMqTJvjZt2uTmW6eus/2Ea67K2V89f4PXAqB5eHzWbyvh27osa1iRY8bcuMFzfO/un+V7d/9sg21uu/8Xue3+X2x0/+6f/pu8+saifGrIXpn641lJktsf+GVuf+CX6/3M0mXL88mRE/7muXcZdsFa+370+NP50eNPb/BzS5ctzx7/evFa+yffOTOT75y51v7jD9s3P3/6pTw5Z/7f7BMAfz+Cvb9y4oknZtSo1f8omDCh6Y20cb67adOmVR4Ta1RTU9Pk/VZbbVX5uXHOuVWr1l2enyTz58/PJz7xiZx88sm55JJL0rFjxzzxxBM56aST0tDQsFao1r1798yePbvyvmPHjuu8duP1G6/99ttvp0WLFpk1a1aT8C9J5Yv717/+9Vx11VW58sorK3P+nX766WloaFhv/9dnXX3Z0FwlNTU1a/0ugc1j4O675z/HXZb5L76Y3QYO3NzdaeL3v/99DvvXI3L0MZ/e3F0BgHdl5MW3Ztedum/ubrxny1eszBmX/WBzdwN4n5RKq7dqVK3jaiTY+ytDhw5NQ0NDSqVShgxpWjkyYMCA1NTUZMGCBU0eu91YLVu2XGsC+FmzZmXVqlUZP358tthi9dSHU6euu0olSbbccsv07dt3o689aNCgrFy5MgsXLsw///O6H1+bPn16DjvssBx77LFJVgeSzz//fJMKwb+2rjEBxfdeKv3+HrbZZpucOWbj5yAFgM3l18+/kl8//8rm7sZ7tq4qPgA2P8HeX2nRokWeffbZys/vVFtbmzFjxmT06NFZtWpVPvzhD2fRokWZPn166urqMmLEiHd1jT59+uTFF1/M7Nmzs91226W2tjZ9+/bN8uXLc8011+TQQw/N9OnTM3HixE0+vn79+mX48OE5/vjjM378+AwaNChvvPFGHnrooey+++4ZNmxYdtppp9x2222ZMWNGtt5661x++eV5/fXXNxjs9enTJ4899liOOeaY1NTUNHmEGQAAAIBNz6q461BXV5e6urp1Hrv44otz3nnnpb6+Pv3798/QoUMzbdq0bL+O1R3X58gjj8zQoUNz4IEHpnPnzrn11luzxx575PLLL89ll12W3XbbLTfffHPq69/9hPUbY9KkSTn++ONz5plnZuedd87hhx+ep556qjL/37nnnpu99torQ4YMyQEHHJBu3brl8MMP3+A5x44dm/nz52fHHXdM586d35d+AwAAAPAXpfKGJjyDzWzx4sVp3759Xv/DovWGrQDAum39gfUvJgAArFt5ZUOWzbkhixZV//fQxu/ct/9sXtq2q93c3Xlf/Ontt3Lkh3as2j9Pj+ICAAAANGMWzyguj+ICAAAAQAEJ9gAAAACggAR7AAAAAFBA5tgDAAAAaMZKa17VqFrH1UjFHgAAAAAUkGAPAAAAAApIsAcAAAAABSTYAwAAAIACsngGAAAAQDO2RWn1Vo2qdVyNVOwBAAAAQAEJ9gAAAACggAR7AAAAAFBA5tgDAAAAaMZKa17VqFrH1UjFHgAAAAAUkGAPAAAAAApIsAcAAAAABSTYAwAAAIACsngGAAAAQDNWKq3eqlG1jquRij0AAAAAKCDBHgAAAAAUkGAPAAAAAArIHHsAAAAAzVgpSSnVORlddY7qL1TsAQAAAMA6XHrppSmVSjn99NMr+5YuXZqRI0emU6dOadeuXY488si8/vrrTT63YMGCDBs2LG3atEmXLl1y1llnZcWKFZu8f4I9AAAAAPgrTz31VK6//vrsvvvuTfaPHj06d999d37wgx/k0Ucfze9+97scccQRleMrV67MsGHD0tDQkBkzZuSmm27K5MmTc/7552/yPgr2AAAAAOAd3n777QwfPjw33HBDtt5668r+RYsW5Vvf+lYuv/zyfPSjH83ee++dSZMmZcaMGfnpT3+aJLn//vvzm9/8Jt/73vey55575uMf/3guvvjiTJgwIQ0NDZu0n4I9AAAAAHiHkSNHZtiwYTnooIOa7J81a1aWL1/eZP8uu+ySXr16ZebMmUmSmTNnZuDAgenatWulzZAhQ7J48eI888wzm7SfFs8AAAAAaMa2KK3eqlHjuBYvXtxkf01NTWpqatb5mSlTpuQXv/hFnnrqqbWOvfbaa2nZsmU6dOjQZH/Xrl3z2muvVdq8M9RrPN54bFNSsQcAAABAVevZs2fat29f2err69fZ7uWXX85pp52Wm2++Oa1atfo793LjqdgDAAAAoKq9/PLLqaurq7xfX7XerFmzsnDhwuy1116VfStXrsxjjz2Wb37zm7nvvvvS0NCQN998s0nV3uuvv55u3bolSbp165Ynn3yyyXkbV81tbLOpqNgDAAAAoKrV1dU12dYX7H3sYx/LnDlzMnv27Mq2zz77ZPjw4ZWft9pqqzz00EOVz8ydOzcLFizI4MGDkySDBw/OnDlzsnDhwkqbBx54IHV1dRkwYMAmHZeKPQAAAIBmrLTmVY02dly1tbXZbbfdmuxr27ZtOnXqVNl/0kkn5YwzzkjHjh1TV1eXU089NYMHD86+++6bJDnkkEMyYMCAHHfccfna176W1157Leeee25Gjhy53kDxvRLsAQAAAMC7dMUVV2SLLbbIkUcemWXLlmXIkCG59tprK8dbtGiRe+65JyeffHIGDx6ctm3bZsSIERk7duwm74tgDwAAAADW45FHHmnyvlWrVpkwYUImTJiw3s/07t0799577/vcM3PsAQAAAEAhCfYAAAAAoIA8igsAAADQjJVKq7dqVK3jaqRiDwAAAAAKSLAHAAAAAAUk2AMAAACAAjLHHgAAAEAzVlqzVaNqHVcjFXsAAAAAUECCPQAAAAAoIMEeAAAAABSQYA8AAAAACsjiGQAAAADN2BYpZYtSdS4zsUWVL5+hYg8AAAAACkiwBwAAAAAFJNgDAAAAgAIyxx4AAABAM1Zas1Wjah1XIxV7AAAAAFBAgj0AAAAAKCDBHgAAAAAUkGAPAAAAAArI4hkAAAAAzZnVMwpLxR4AAAAAFJBgDwAAAAAKSLAHAAAAAAVkjj0AAACAZqy05lWNqnVcjVTsAQAAAEABCfYAAAAAoIAEewAAAABQQII9AAAAACggi2cAAAAANGelpFSta0xU67jWULEHAAAAAAUk2AMAAACAAhLsAQAAAEABmWMPAAAAoBkrpXqnoqvWcTVSsQcAAAAABSTYAwAAAIACEuwBAAAAQAEJ9gAAAACggCyeAQAAANCcWT2jsFTsAQAAAEABCfYAAAAAoIAEewAAAABQQObYAwAAAGjGSmte1ahax9VIxR4AAAAAFJBgDwAAAAAKSLAHAAAAAAUk2AMAAACAArJ4BgAAAEAzViqt3qpRtY6rkYo9AAAAACggwR4AAAAAFJBgDwAAAAAKyBx7AAAAAM1Yac1Wjap1XI1U7AEAAABAAQn2AAAAAKCABHsAAAAAUECCPQAAAAAoIItnAAAAADRnVs8oLBV7AAAAAFBAgj0AAAAAKCDBHgAAAAAUkDn2AAAAAJqx0ppXNarWcTVSsQcAAAAABSTYAwAAAIACEuwBAAAAQAEJ9gAAAACggCyeAQAAANCMlUqrt2pUreNqpGIPAAAAAApIsAcAAAAABSTYAwAAAIACMsceAAAAQDNWWrNVo2odVyMVewAAAABQQII9AAAAACggwR4AAAAAFJBgDwAAAAAKyOIZAAAAAM2Z1TMKS8UeAAAAABSQYA8AAAAACkiwBwAAAAAFZI49AAAAgGastOZVjap1XI1U7AEAAABAAQn2AAAAAKCABHsAAAAAUECCPQAAAAAoIItnAAAAADRjpdLqrRpV67gaqdgDAAAAgAIS7AEAAABAAQn2AAAAAKCAzLEHAAAA0IyV1mzVqFrH1UjFHgAAAAAUkGAPAAAAAApIsAcAAAAABSTYAwAAAIACsngGAAAAQHNm9YzCUrEHAAAAAAUk2AMAAACAAhLsAQAAAEABmWMPAAAAoBkrrXlVo2odVyMVewAAAABQQII9AAAAACggwR4AAAAAFJBgDwAAAAAKyOIZAAAAAM1YqbR6q0bVOq5GKvYAAAAAoIAEewAAAABQQII9AAAAACggc+wBAAAANGOlNVs1qtZxNVKxBwAAAAAFJNgDAAAAgAIS7AEAAABAAQn2AAAAAKCALJ4BAAAA0JxZPaOwVOwBAAAAQAEJ9gAAAACggAR7AAAAAFBAgj0AAAAAKCCLZwAAAAA0Y6U1r2pUreNqpGIPAAAAAApIsAcAAAAABSTYAwAAAIACMsceAAAAQDNWKq3eqlG1jquRij0AAAAAKCDBHgAAAAAUkGAPAAAAAApIsAcAAAAABWTxDAAAAIBmrLRmq0bVOq5GKvYAAAAAoIAEewAAAABQQII9AAAAACggc+wBAAAANGcm2SssFXsAAAAAUECCPQAAAAAoIMEeAAAAABSQYA8AAAAACsjiGQAAAADNWGnNqxpV67gaqdgDAAAAgAIS7AEAAABAAQn2AAAAAKCAzLEHAAAA0JyVklK1TkVXreNaQ8UeAAAAABSQYA8AAAAACkiwBwAAAAAFJNgDAAAAgAKyeAYAAABAM1ZK9a4xUa3jaqRiDwAAAAAKSLAHAAAAAAUk2AMAAACAAjLHHgAAAEBzZpK9wlKxBwAAAAAFJNgDAAAAgAIS7AEAAABAAQn2AAAAAKCALJ4BAAAA0IyV1ryqUbWOq5GKPQAAAAAoIMEeAAAAABSQYA8AAAAACsgcewAAAADNWKm0eqtG1TquRir2AAAAAKCABHsAAAAAUECCPQAAAAAoIMEeAAAAABSQxTMAAAAAmrHSmq0aVeu4GqnYAwAAAIACEuwBAAAAQAEJ9gAAAACggMyxBwAAANCcmWSvsFTsAQAAAEABCfYAAAAAoIAEewAAAABQQII9AAAAACggi2cAAAAANGOlNa9qVK3jaqRiDwAAAAAKSLAHAAAAAAUk2AMAAACAJPX19fnABz6Q2tradOnSJYcffnjmzp3bpM3SpUszcuTIdOrUKe3atcuRRx6Z119/vUmbBQsWZNiwYWnTpk26dOmSs846KytWrNjk/RXsAQAAADRjpSSlUpVuG/m7ePTRRzNy5Mj89Kc/zQMPPJDly5fnkEMOyZ/+9KdKm9GjR+fuu+/OD37wgzz66KP53e9+lyOOOKJyfOXKlRk2bFgaGhoyY8aM3HTTTZk8eXLOP//8TfMH9g6lcrlc3uRnhU1k8eLFad++fV7/w6LU1dVt7u4AQKFs/YFRm7sLAFA45ZUNWTbnhixaVP3fQxu/cz/94sLUVulY31q8OLtt3+U9/3m+8cYb6dKlSx599NF85CMfyaJFi9K5c+fccsst+bd/+7ckyXPPPZf+/ftn5syZ2XffffOjH/0on/jEJ/K73/0uXbt2TZJMnDgxX/7yl/PGG2+kZcuWm2x8KvYAAAAAYB0WLVqUJOnYsWOSZNasWVm+fHkOOuigSptddtklvXr1ysyZM5MkM2fOzMCBAyuhXpIMGTIkixcvzjPPPLNJ+7flJj0bAAAAAPyDWbx4cZP3NTU1qamp2eBnVq1aldNPPz377bdfdttttyTJa6+9lpYtW6ZDhw5N2nbt2jWvvfZapc07Q73G443HNiUVewAAAABUtZ49e6Z9+/aVrb6+/m9+ZuTIkXn66aczZcqUv0MP3xsVewAAAADNWCkbv8hEUTSO6+WXX24yx97fqtYbNWpU7rnnnjz22GPZbrvtKvu7deuWhoaGvPnmm02q9l5//fV069at0ubJJ59scr7GVXMb22wqKvYAAAAAqGp1dXVNtvUFe+VyOaNGjcqdd96Zn/zkJ9l+++2bHN97772z1VZb5aGHHqrsmzt3bhYsWJDBgwcnSQYPHpw5c+Zk4cKFlTYPPPBA6urqMmDAgE06LhV7AAAAAJDVj9/ecsst+e///u/U1tZW5sRr3759Wrdunfbt2+ekk07KGWeckY4dO6auri6nnnpqBg8enH333TdJcsghh2TAgAE57rjj8rWvfS2vvfZazj333IwcOfJvVgpuLMEeAAAAACS57rrrkiQHHHBAk/2TJk3KCSeckCS54oorssUWW+TII4/MsmXLMmTIkFx77bWVti1atMg999yTk08+OYMHD07btm0zYsSIjB07dpP3V7AHAAAA0IyVSqu3arSx4yqXy3+zTatWrTJhwoRMmDBhvW169+6de++9d+Mu/h6YYw8AAAAACkiwBwAAAAAFJNgDAAAAgAIS7AEAAABAAVk8AwAAAKBZK63ZqlG1jms1FXsAAAAAUECCPQAAAAAoIMEeAAAAABSQOfb4h1Yul5Mkby1evJl7AgDFU17ZsLm7AACF03j/bPw+2hyUSqu3alSt42ok2OMf2ltvvZUk6bt9z83cEwAAAJqTt956K+3bt9/c3YANEuzxD6179+55+eWXU1tbm1K1x+xQQIsXL07Pnj3z8ssvp66ubnN3BwAKwz0U/nGVy+W89dZb6d69++buCvxNgj3+oW2xxRbZbrvtNnc3gL+hrq7OlxIAeA/cQ+Efk0o9isLiGQAAAABQQCr2AAAAAJqx0pqtGlXruBqp2APgPaupqckFF1yQmpqazd0VACgU91AANoVSuTmt3wwAAABAktUL+bRv3z7PvfRGaqt0vs+3Fi/OLr07Z9GiRVU5p6mKPQAAAAAoIHPsAQAAADRjpdLqrRpV67gaqdgD4O9q8uTJ6dChwwbbnHDCCTn88MPf974ccMABOf300zfY5t30FwD+Fvc/AN4Pgj2A/6MTTjghpVIpl156aZP9d911V0pV/N9DF154Yfbcc8/N3Y1Nqk+fPrnyyis3dzcA2AiN9+EvfOELax0bOXJkSqVSTjjhhE12Pfc/AP6RCPYANoFWrVrlsssuy//7f/9vc3cFAJqdnj17ZsqUKfnzn/9c2bd06dLccsst6dWr12bsGQC8vwR7AJvAQQcdlG7duqW+vn69bW6//fbsuuuuqampSZ8+fTJ+/Pgmx/v06ZNx48blxBNPTG1tbXr16pX/+q//2uB1H3nkkZRKpdx3330ZNGhQWrdunY9+9KNZuHBhfvSjH6V///6pq6vLZz7zmSxZsqTyuVWrVqW+vj7bb799WrdunT322CO33XbbWud96KGHss8++6RNmzb5p3/6p8ydOzfJ6sdzLrroovzqV79KqVRKqVTK5MmTkySXX355Bg4cmLZt26Znz5455ZRT8vbbb2/srzRJ8o1vfCPbbrttOnXqlJEjR2b58uWVY8uWLcuYMWPSo0ePtG3bNh/60IfyyCOPVI7/4Q9/yKc//en06NEjbdq0ycCBA3Prrbeu91oHHHBAXnrppYwePboypne677770r9//7Rr1y5Dhw7Nq6+++p7GBMCmt9dee6Vnz5654447KvvuuOOO9OrVK4MGDarsc/9bm/sfQLEJ9gA2gRYtWmTcuHG55ppr8r//+79rHZ81a1aOOuqoHHPMMZkzZ04uvPDCnHfeeZUvA43Gjx+fffbZJ7/85S9zyimn5OSTT658mdiQCy+8MN/85jczY8aMvPzyyznqqKNy5ZVX5pZbbsm0adNy//3355prrqm0r6+vz3e+851MnDgxzzzzTEaPHp1jjz02jz76aJPzfvWrX8348ePz85//PFtuuWVOPPHEJMnRRx+dM888M7vuumteffXVvPrqqzn66KOTJFtssUWuvvrqPPPMM7npppvyk5/8JF/60pc29leahx9+OPPmzcvDDz+cm266KZMnT27y+xo1alRmzpyZKVOm5Ne//nU+9alPZejQofntb3+bZHWlxt57751p06bl6aefzuc///kcd9xxefLJJ9d5vTvuuCPbbbddxo4dWxlToyVLluQb3/hGvvvd7+axxx7LggULMmbMmI0eEwDvnxNPPDGTJk2qvP/2t7+df//3f2/Sxv1vbe5/QJKUqvxV1coA/J+MGDGifNhhh5XL5XJ53333LZ944onlcrlcvvPOO8uNf81+5jOfKR988MFNPnfWWWeVBwwYUHnfu3fv8rHHHlt5v2rVqnKXLl3K11133Xqv/fDDD5eTlB988MHKvvr6+nKS8rx58yr7/uM//qM8ZMiQcrlcLi9durTcpk2b8owZM5qc66STTip/+tOfXu95p02bVk5S/vOf/1wul8vlCy64oLzHHnts+JdTLpd/8IMflDt16lR5P2nSpHL79u03+JkRI0aUe/fuXV6xYkVl36c+9any0UcfXS6Xy+WXXnqp3KJFi/Irr7zS5HMf+9jHymefffZ6zzts2LDymWeeWXm///77l0877bTK+969e5evuOKKJp+ZNGlSOUn5hRdeqOybMGFCuWvXrhscAwB/H4334YULF5ZramrK8+fPL8+fP7/cqlWr8htvvFE+7LDDyiNGjHD/c/8D1mHRokXlJOXnF/y+/OqbDVW5Pb/g9+Uk5UWLFm3uX/f7YsvNlCcCVKXLLrssH/3oR9f63+xnn302hx12WJN9++23X6688sqsXLkyLVq0SJLsvvvuleOlUindunXLwoULkyQf//jH8/jjjydJevfunWeeeabS9p2f69q1a9q0aZMddtihyb7G/6l/4YUXsmTJkhx88MFN+tPQ0NDkcaW/Pu+2226bJFm4cOEG5yt68MEHU19fn+eeey6LFy/OihUrsnTp0ixZsiRt2rRp0nbBggUZMGBA5f0555yTc845J0my6667Vn4vjdefM2dOkmTOnDlZuXJl+vXr1+R8y5YtS6dOnZIkK1euzLhx4zJ16tS88soraWhoyLJly9bqw7vRpk2b7Ljjjk360vjnAsA/hs6dO2fYsGGZPHlyyuVyhg0blm222aZy3P3P/Q+gGgn2ADahj3zkIxkyZEjOPvvs97QC31ZbbdXkfalUyqpVq5IkN954Y2VS8L9u9873pVJpg+dpnO9n2rRp6dGjR5N2NTU1Gzxvksp51mX+/Pn5xCc+kZNPPjmXXHJJOnbsmCeeeCInnXRSGhoa1vpS0b1798yePbvyvmPHjuu89rrG0KJFi8yaNavJl58kadeuXZLk61//eq666qpceeWVlTmPTj/99DQ0NKy3/+uzrr6Uy+WNPg8A768TTzwxo0aNSpJMmDChyTH3P/c/gGok2APYxC699NLsueee2XnnnSv7+vfvn+nTpzdpN3369PTr12+tf5ivz19/CXmvBgwYkJqamixYsCD777//ez5Py5Yts3Llyib7Zs2alVWrVmX8+PHZYovV07hOnTp1vefYcsst07dv342+9qBBg7Jy5cosXLgw//zP/7zONtOnT89hhx2WY489NsnqL2TPP/98kwqJv7auMQFQHEOHDk1DQ0NKpVKGDBnS5Jj7n/sfsAGlNVs1qtZxrSHYA9jEBg4cmOHDh+fqq6+u7DvzzDPzgQ98IBdffHGOPvrozJw5M9/85jdz7bXX/t37V1tbmzFjxmT06NFZtWpVPvzhD2fRokWZPn166urqMmLEiHd1nj59+uTFF1/M7Nmzs91226W2tjZ9+/bN8uXLc8011+TQQw/N9OnTM3HixE0+hn79+mX48OE5/vjjM378+AwaNChvvPFGHnrooey+++4ZNmxYdtppp9x2222ZMWNGtt5661x++eV5/fXXN/jFpk+fPnnsscdyzDHHpKampskjXAD842vRokWeffbZys/v5P7n/gdQjayKC/A+GDt2bJNHdvbaa69MnTo1U6ZMyW677Zbzzz8/Y8eOfU+P624KF198cc4777zU19enf//+GTp0aKZNm5btt9/+XZ/jyCOPzNChQ3PggQemc+fOufXWW7PHHnvk8ssvz2WXXZbddtstN998c+rr69+XMUyaNCnHH398zjzzzOy88845/PDD89RTT1XmPzr33HOz1157ZciQITnggAPSrVu3HH744Rs859ixYzN//vzsuOOO6dy58/vSbwDeX3V1damrq1vnMfe/dXP/AyiuUtkkCQAAAADNzuLFi9O+ffs8//LvU7ue/xQpurcWL06/nttk0aJF6/2PnyJTsQcAAAAABWSOPQAAAIBmzNoZxaViDwAAAAAKSLAHAAAAAAUk2AMAAACAAjLHHgAAAEAzViqt3qpRtY6rkYo9AAAAACggwR4AAAAAFJBgDwBgMzrhhBNy+OGHV94fcMABOf300//u/XjkkUdSKpXy5ptvrrdNqVTKXXfd9a7PeeGFF2bPPff8P/Vr/vz5KZVKmT179v/pPAAA1UiwBwDwV0444YSUSqWUSqW0bNkyffv2zdixY7NixYr3/dp33HFHLr744nfV9t2EcQAAVC+LZwAArMPQoUMzadKkLFu2LPfee29GjhyZrbbaKmefffZabRsaGtKyZctNct2OHTtukvMAALxbpTWvalSt42qkYg8AYB1qamrSrVu39O7dOyeffHIOOuig/PCHP0zyl8dnL7nkknTv3j0777xzkuTll1/OUUcdlQ4dOqRjx4457LDDMn/+/Mo5V65cmTPOOCMdOnRIp06d8qUvfSnlcrnJdf/6Udxly5bly1/+cnr27Jmampr07ds33/rWtzJ//vwceOCBSZKtt946pVIpJ5xwQpJk1apVqa+vz/bbb5/WrVtnjz32yG233dbkOvfee2/69euX1q1b58ADD2zSz3fry1/+cvr165c2bdpkhx12yHnnnZfly5ev1e76669Pz54906ZNmxx11FFZtGhRk+M33nhj+vfvn1atWmWXXXbJtddeu9F9AQBojgR7AADvQuvWrdPQ0FB5/9BDD2Xu3Ll54IEHcs8992T58uUZMmRIamtr8/jjj2f69Olp165dhg4dWvnc+PHjM3ny5Hz729/OE088kT/+8Y+58847N3jd448/PrfeemuuvvrqPPvss7n++uvTrl279OzZM7fffnuSZO7cuXn11Vdz1VVXJUnq6+vzne98JxMnTswzzzyT0aNH59hjj82jjz6aZHUAecQRR+TQQw/N7Nmz89nPfjZf+cpXNvp3Ultbm8mTJ+c3v/lNrrrqqtxwww254oormrR54YUXMnXq1Nx999358Y9/nF/+8pc55ZRTKsdvvvnmnH/++bnkkkvy7LPPZty4cTnvvPNy0003bXR/AACaG4/iAgBsQLlczkMPPZT77rsvp556amV/27Ztc+ONN1Yewf3e976XVatW5cYbb0yptPqRj0mTJqVDhw555JFHcsghh+TKK6/M2WefnSOOOCJJMnHixNx3333rvfbzzz+fqVOn5oEHHshBBx2UJNlhhx0qxxsf2+3SpUs6dOiQZHWF37hx4/Lggw9m8ODBlc888cQTuf7667P//vvnuuuuy4477pjx48cnSXbeeefMmTMnl1122Ub9bs4999zKz3369MmYMWMyZcqUfOlLX6rsX7p0ab7zne+kR48eSZJrrrkmw4YNy/jx49OtW7dccMEFGT9+fOV3sv322+c3v/lNrr/++owYMWKj+gMA0NwI9gAA1uGee+5Ju3btsnz58qxatSqf+cxncuGFF1aODxw4sMm8er/61a/ywgsvpLa2tsl5li5dmnnz5mXRokV59dVX86EPfahybMstt8w+++yz1uO4jWbPnp0WLVpk//33f9f9fuGFF7JkyZIcfPDBTfY3NDRk0KBBSZJnn322ST+SVELAjfH9738/V199debNm5e33347K1asSF1dXZM2vXr1qoR6jddZtWpV5s6dm9ra2sybNy8nnXRSPve5z1XarFixIu3bt9/o/gAA71FpzVaNqnVcawj2AADW4cADD8x1112Xli1bpnv37tlyy6b/bGrbtm2T92+//Xb23nvv3HzzzWudq3Pnzu+pD61bt97oz7z99ttJkmnTpjUJ1JLV8wZuKjNnzszw4cNz0UUXZciQIWnfvn2mTJlSqQLcmL7ecMMNawWNLVq02GR9BQCoVoI9AIB1aNu2bfr27fuu2++11175/ve/ny5duqxVtdZo2223zc9+9rN85CMfSbK6Mm3WrFnZa6+91tl+4MCBWbVqVR599NHKo7jv1FgxuHLlysq+AQMGpKamJgsWLFhvpV///v0rC4E0+ulPf/q3B/kOM2bMSO/evfPVr361su+ll15aq92CBQvyu9/9Lt27d69cZ4sttsjOO++crl27pnv37vmf//mfDB8+fKOuDwCAxTMAADaJ4cOHZ5tttslhhx2Wxx9/PC+++GIeeeSRfPGLX8z//u//JklOO+20XHrppbnrrrvy3HPP5ZRTTsmbb7653nP26dMnI0aMyIknnpi77rqrcs6pU6cmSXr37p1SqZR77rknb7zxRt5+++3U1tZmzJgxGT16dG666abMmzcvv/jFL3LNNddUFqT4whe+kN/+9rc566yzMnfu3Nxyyy2ZPHnyRo13p512yoIFCzJlypTMmzcvV1999ToXAmnVqlVGjBiRX/3qV3n88cfzxS9+MUcddVS6deuWJLnoootSX1+fq6++Os8//3zmzJmTSZMm5fLLL9+o/gAANEeCPQCATaBNmzZ57LHH0qtXrxxxxBHp379/TjrppCxdurRSwXfmmWfmuOOOy4gRIzJ48ODU1tbmX//1Xzd43uuuuy7/9m//llNOOSW77LJLPve5z+VPf/pTkqRHjx656KKL8pWvfCVdu3bNqFGjkiQXX3xxzjvvvNTX16d///4ZOnRopk2blu233z7J6nnvbr/99tx1113ZY489MnHixIwbN26jxvvJT34yo0ePzqhRo7LnnntmxowZOe+889Zq17dv3xxxxBH5l3/5lxxyyCHZfffdc+2111aOf/azn82NN96YSZMmZeDAgdl///0zefLkSl8BAFi/Unl9szUDAAAAULUWL16c9u3b539e+UNq1zOVSNG9tXhxdujRKYsWLVrvdClFpmIPAAAAAApIsAcAAAAABSTYAwAAAIAC2nJzdwAAAACAzadUWr1Vo2odVyMVewAAAABQQII9AAAAACggwR4AAAAAFJBgDwAAAAAKyOIZAAAAAM1aKaVU6yoT1Tqu1VTsAQAAAEABCfYAAAAAoIAEewAAAABQQObYAwAAAGjGSqXVWzWq1nE1UrEHAAAAAAUk2AMAAACAAhLsAQAAAEABCfYAAAAAoIAEewAAAABQQII9AAAAACggwR4AAAAAFJBgDwAAAAAKaMvN3QEAAAAANp9SafVWjap1XI1U7AEAAABAAQn2AAAAAKCABHsAAAAAUECCPQAAAAAoIItnAAAAADRjpTWvalSt42qkYg8AAAAACkiwBwAAAAAFJNgDAAAAgAIyxx4AAABAM1Yqrd6qUbWOq5GKPQAAAAAoIMEeAAAAABSQYA8AAAAACkiwBwAAAAAFZPEMAAAAgGastGarRtU6rkYq9gAAAACggAR7AAAAAFBAgj0AAAAAKCBz7AEAAAA0ZybZKywVewAAAABQQII9AAAAACggwR4AAAAAFJBgDwAAAAAKyOIZAAAAAM1Yac2rGlXruBqp2AMAAACAAhLsAQAAAEABCfYAAAAAoIDMsQcAAADQjJVKq7dqVK3jaqRiDwAAAAAKSLAHAAAAAAUk2AMAAACAAhLsAQAAAEABWTwDAAAAoBkrrdmqUbWOq5GKPQAAAAAoIMEeAAAAABSQYA8AAAAACsgcewAAAADNmUn2CkvFHgAAAAAUkGAPAAAAAApIsAcAAAAABSTYAwAAAIACsngGAAAAQDNWWvOqRtU6rkYq9gAAAACggAR7AAAAAFBAgj0AAAAAKCBz7AEAAAA0Y6XS6q0aVeu4GqnYAwAAAIACEuwBAAAAQAEJ9gAAAACggMyxBwAAANCMLV68eHN34X1TzWNLBHsAAAAAzVLLli3TrVu37LR9z83dlfdVt27d0rJly83djfdFqVwulzd3JwAAAAD4+1u6dGkaGho2dzfeVy1btkyrVq02dzfeF4I9AAAAACggi2cAAAAAQAEJ9gAAAACggAR7AAAAAFBAgj0AAAAAKCDBHgAAAAAUkGAPAAAAAApIsAcAAAAABfT/Aa5qpbXaZjXuAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install colorama\n",
        "from colorama import Fore, Style\n",
        "import numpy as np\n",
        "\n",
        "def random_predictions(model, X_valid, y_valid, num_samples=5, class_names=None):\n",
        "    # Check if it's binary or multi-class classification\n",
        "    is_binary_classification = len(np.unique(y_valid)) == 2\n",
        "\n",
        "    # Getting indices of the random samples\n",
        "    random_indices = np.random.choice(np.arange(len(X_valid)), size=num_samples, replace=False)\n",
        "\n",
        "    # Selecting the random samples\n",
        "    random_X_samples = X_valid[random_indices]\n",
        "    random_y_samples = y_valid[random_indices]\n",
        "    # Making predictions on the random samples\n",
        "    y_pred_probs = model.predict(random_X_samples)\n",
        "\n",
        "    if is_binary_classification:\n",
        "        y_pred = np.squeeze(np.round(y_pred_probs).astype(int))\n",
        "    else:\n",
        "        y_pred = np.argmax(y_pred_probs, axis=1)\n",
        "\n",
        "    # Print the actual and predicted labels\n",
        "    for i in range(num_samples):\n",
        "        text = random_X_samples[i]\n",
        "        true_label = random_y_samples[i] if is_binary_classification else np.argmax(random_y_samples[i])\n",
        "        predicted_label = y_pred[i]\n",
        "        # If class names are provided, use them for printing\n",
        "        if class_names is not None:\n",
        "            true_label_name = class_names[true_label]\n",
        "            predicted_label_name = class_names[predicted_label]\n",
        "        else:\n",
        "            true_label_name = true_label\n",
        "            predicted_label_name = predicted_label\n",
        "\n",
        "        # Determine the color of the text (green for correct, red for incorrect)\n",
        "        text_color = Fore.GREEN if true_label == predicted_label else Fore.RED\n",
        "\n",
        "        print(f\"\\nSample {i + 1}:\")\n",
        "        print(f\"Text: {text}\")\n",
        "        print(text_color + f\"True: {true_label_name} \\n Predicted: {predicted_label_name}\" + Style.RESET_ALL)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E7l8SDBBdvDY",
        "outputId": "eff0a0f8-4ee0-4eb6-84f0-d219e2e9c12a"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting colorama\n",
            "  Downloading colorama-0.4.6-py2.py3-none-any.whl.metadata (17 kB)\n",
            "Downloading colorama-0.4.6-py2.py3-none-any.whl (25 kB)\n",
            "Installing collected packages: colorama\n",
            "Successfully installed colorama-0.4.6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "random_predictions(model_dense,\n",
        "                   X_valid,\n",
        "                   y_valid,\n",
        "                   num_samples=10,\n",
        "                   class_names=class_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V1kvAX8Ae78a",
        "outputId": "4ee62737-e278-4b00-e592-85a61a467e2e"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n",
            "\n",
            "Sample 1:\n",
            "Text: career chose process making rich also extremely depressed overworkedoverjudged point least one episode day want curl disappear job involves social medium fame expectation perfection sometimes sure much handle human break able make real friend family never begin relationship someone cared pursuit wellbeing people respect name almost daily disrespect person make sense people always watching extremely careful almost feel like redflag now people around want something also constantly comparing body next best thing type industry mentally draining wish fame anyonei wanted see anyone advice going therapy still super depressive episode lot regret pursuing loved much everything sacrifice success word advice successful depressed\n",
            "\u001b[32mTrue: Mental-health \n",
            " Predicted: Mental-health\u001b[0m\n",
            "\n",
            "Sample 2:\n",
            "Text: glad valentine day really like holiday couple love ive never able relate kind sad day im happy got spend someone specially case made sad im glad thats overwith\n",
            "\u001b[32mTrue: Non-mental-health \n",
            " Predicted: Non-mental-health\u001b[0m\n",
            "\n",
            "Sample 3:\n",
            "Text: white girl built like fucking refridgerator always got highest standard like nah bitch im sorry cant high standard whole as quadrilateral\n",
            "\u001b[32mTrue: Non-mental-health \n",
            " Predicted: Non-mental-health\u001b[0m\n",
            "\n",
            "Sample 4:\n",
            "Text: jimmy jimsonweed update seed pod opened seed look little premature im going see successfully grow anything datura stramonium toxicityhigh careful\n",
            "\u001b[32mTrue: Non-mental-health \n",
            " Predicted: Non-mental-health\u001b[0m\n",
            "\n",
            "Sample 5:\n",
            "Text: getting harderthe man love stupid stupid young man one point got exgirlfriend pregnant despite supposed use condom amp birth control stayed raised child two year ditched love raised unwanted child alone another year join army get woman take back child tell child mean nothing never wanted child kill child another woman helped raise child initially kill know stupid avoid easily avoidable mistake tell never loved never truly wanted matter convenience pain convenient ten thousand dollar spent unwanted child convenient love want loving hurt jealous upset child another woman albeit unwanted child part completely love respect man unintentionally hurt bad unintentional easily avoidable ive never real purpose live really one never believed true love met man loving hurt ive tried hard let child another woman bother cry every day lost ten pound matter week distraught became holding together better now still hard know much longer try keep either im miserable without miserable point\n",
            "\u001b[32mTrue: Mental-health \n",
            " Predicted: Mental-health\u001b[0m\n",
            "\n",
            "Sample 6:\n",
            "Text: please save meim much pain think losing mind feel depression eating brain want scream hurt even try articulate brain want confront anything hardly left bed week use minimal amount information processing possible get im losing everything im lonely im fucking lonely im scared im lost im detached wtf fuck becoming terrified world people im angry insidiously angry disgusting\n",
            "\u001b[32mTrue: Mental-health \n",
            " Predicted: Mental-health\u001b[0m\n",
            "\n",
            "Sample 7:\n",
            "Text: got skeleton floorboard got secret need keep\n",
            "\u001b[32mTrue: Non-mental-health \n",
            " Predicted: Non-mental-health\u001b[0m\n",
            "\n",
            "Sample 8:\n",
            "Text: mom found account kill isnt even anything bad hate melt\n",
            "\u001b[31mTrue: Non-mental-health \n",
            " Predicted: Mental-health\u001b[0m\n",
            "\n",
            "Sample 9:\n",
            "Text: one dayjust one day everything greateverything went wellbut still old thought keep awake wanting aau fuck end\n",
            "\u001b[32mTrue: Mental-health \n",
            " Predicted: Mental-health\u001b[0m\n",
            "\n",
            "Sample 10:\n",
            "Text: im alone useless world nobody need anymoreim ive lived enough tell world shitty place\n",
            "\u001b[32mTrue: Mental-health \n",
            " Predicted: Mental-health\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ***lstm***"
      ],
      "metadata": {
        "id": "oeUj7ITn19O5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tf.random.set_seed(42)\n",
        "from tensorflow.keras import layers\n",
        "model_1LSTM_embedding = layers.Embedding(input_dim=max_vocab_length,\n",
        "                                     output_dim=300,\n",
        "                                     embeddings_initializer=\"uniform\",\n",
        "                                     input_length=max_length,\n",
        "                                     name=\"embedding_2\")\n",
        "\n",
        "\n",
        "# Create LSTM model\n",
        "inputs = layers.Input(shape=(1,), dtype=\"string\")\n",
        "x = text_vectorizer(inputs)\n",
        "x = model_1LSTM_embedding(x)\n",
        "print(x.shape)\n",
        "x = layers.LSTM(64, return_sequences=True)(x) # return vector for each word in the text (you can stack RNN cells as long as return_sequences=True)\n",
        "x = layers.LSTM(64)(x) # return vector for whole sequence\n",
        "print(x.shape)\n",
        "x = layers.Dense(64, activation=\"relu\")(x) # optional dense layer on top of output of LSTM cell\n",
        "outputs = layers.Dense(1, activation=\"sigmoid\")(x)\n",
        "model_LSTM = tf.keras.Model(inputs, outputs, name=\"model_1LSTM\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N7tiQVd21-2r",
        "outputId": "aef4ae25-9b32-45c6-977d-c5c4443b338d"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(None, 100, 300)\n",
            "(None, 64)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_LSTM.compile(loss=\"binary_crossentropy\",\n",
        "                optimizer=tf.keras.optimizers.Adam(),\n",
        "                metrics=[\"accuracy\"])"
      ],
      "metadata": {
        "id": "E5EDM0Ua2Iw1"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_LSTM.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "id": "VQfAYsta2I0T",
        "outputId": "f8a44248-88f5-4a3c-cf4e-22c1c7999072"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"model_1LSTM\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"model_1LSTM\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer_5 (\u001b[38;5;33mInputLayer\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ text_vectorization_3                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
              "│ (\u001b[38;5;33mTextVectorization\u001b[0m)                  │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ embedding_2 (\u001b[38;5;33mEmbedding\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m300\u001b[0m)            │       \u001b[38;5;34m3,000,000\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ lstm_2 (\u001b[38;5;33mLSTM\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │          \u001b[38;5;34m93,440\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ lstm_3 (\u001b[38;5;33mLSTM\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │          \u001b[38;5;34m33,024\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_6 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │           \u001b[38;5;34m4,160\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_7 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │              \u001b[38;5;34m65\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ text_vectorization_3                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TextVectorization</span>)                  │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ embedding_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">300</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">3,000,000</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ lstm_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │          <span style=\"color: #00af00; text-decoration-color: #00af00\">93,440</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ lstm_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">33,024</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,160</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">65</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m3,130,689\u001b[0m (11.94 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3,130,689</span> (11.94 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m3,130,689\u001b[0m (11.94 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3,130,689</span> (11.94 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_LSTM_history = model_LSTM.fit(X_train, y_train,\n",
        "                              epochs=10,\n",
        "                              validation_data=(X_valid, y_valid),)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QZHEua5O2T4y",
        "outputId": "89a173cd-6250-4150-c5df-4268f3d9b341"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m316s\u001b[0m 261ms/step - accuracy: 0.7555 - loss: 0.4838 - val_accuracy: 0.9103 - val_loss: 0.2279\n",
            "Epoch 2/10\n",
            "\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m252s\u001b[0m 249ms/step - accuracy: 0.9317 - loss: 0.1900 - val_accuracy: 0.9099 - val_loss: 0.2288\n",
            "Epoch 3/10\n",
            "\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m191s\u001b[0m 243ms/step - accuracy: 0.9547 - loss: 0.1306 - val_accuracy: 0.9067 - val_loss: 0.2666\n",
            "Epoch 4/10\n",
            "\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m179s\u001b[0m 227ms/step - accuracy: 0.9697 - loss: 0.0902 - val_accuracy: 0.9135 - val_loss: 0.3012\n",
            "Epoch 5/10\n",
            "\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m203s\u001b[0m 229ms/step - accuracy: 0.9769 - loss: 0.0712 - val_accuracy: 0.9113 - val_loss: 0.3033\n",
            "Epoch 6/10\n",
            "\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m200s\u001b[0m 226ms/step - accuracy: 0.9815 - loss: 0.0549 - val_accuracy: 0.9067 - val_loss: 0.3472\n",
            "Epoch 7/10\n",
            "\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m204s\u001b[0m 230ms/step - accuracy: 0.9882 - loss: 0.0378 - val_accuracy: 0.9131 - val_loss: 0.3445\n",
            "Epoch 8/10\n",
            "\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m208s\u001b[0m 237ms/step - accuracy: 0.9907 - loss: 0.0293 - val_accuracy: 0.9067 - val_loss: 0.4244\n",
            "Epoch 9/10\n",
            "\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m195s\u001b[0m 228ms/step - accuracy: 0.9916 - loss: 0.0232 - val_accuracy: 0.9095 - val_loss: 0.4153\n",
            "Epoch 10/10\n",
            "\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m204s\u001b[0m 230ms/step - accuracy: 0.9951 - loss: 0.0184 - val_accuracy: 0.9088 - val_loss: 0.4444\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_LSTM.evaluate(X_valid, y_valid)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HF9_d4sZ2T8U",
        "outputId": "82d6d900-bab1-4c9d-96df-633a67cf7ebb"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 78ms/step - accuracy: 0.9147 - loss: 0.4260\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.44439882040023804, 0.9088308811187744]"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_LSTM_pred_probs = model_LSTM.predict(X_valid)\n",
        "model_LSTM_pred_probs.shape, model_LSTM_pred_probs[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sJAvA-DR2eGX",
        "outputId": "e9936588-0d03-4c98-a945-21868b3267af"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 83ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((2797, 1),\n",
              " array([[9.9698228e-01],\n",
              "        [2.0925628e-03],\n",
              "        [9.9688494e-01],\n",
              "        [9.9725360e-01],\n",
              "        [9.9652076e-01],\n",
              "        [7.5133961e-01],\n",
              "        [5.3763986e-03],\n",
              "        [9.4419885e-01],\n",
              "        [9.9305242e-01],\n",
              "        [8.7692359e-05]], dtype=float32))"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_LSTM_preds = tf.squeeze(tf.round(model_LSTM_pred_probs))\n",
        "model_LSTM_preds[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EDXlNUVl2eKN",
        "outputId": "c9fd7238-3ab9-4e31-9203-5994eb719fb5"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10,), dtype=float32, numpy=array([1., 0., 1., 1., 1., 1., 0., 1., 1., 0.], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_LSTM_results = calculate_results(y_true=y_valid,\n",
        "                                    y_pred=model_LSTM_preds)\n",
        "model_LSTM_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pfkFBaA-3YVa",
        "outputId": "3a3f84ad-6ab2-42e1-f929-940845c3ea67"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 90.88308902395423,\n",
              " 'precision': 0.9088304233041032,\n",
              " 'recall': 0.9088308902395423,\n",
              " 'f1': 0.9088305405891652}"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_true = y_valid.tolist()  # Convert labels to a list\n",
        "preds = model_LSTM.predict(X_valid)\n",
        "y_probs = preds.squeeze().tolist()  # Store the prediction probabilities as a list\n",
        "y_preds = tf.round(y_probs).numpy().tolist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hi9_F79P3g6L",
        "outputId": "1e964a6c-86c5-4a9d-a1e2-2a7ce807180f"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 53ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "make_confusion_matrix(y_true=y_true,\n",
        "                      y_pred=y_preds,\n",
        "                      classes=class_names,\n",
        "                      figsize=(15, 15),\n",
        "                      text_size=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 941
        },
        "id": "iu-895F03g9t",
        "outputId": "5a657922-146b-4fbe-c3bf-1942f63154ff"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1500 with 2 Axes>"
            ],
            "image/png": 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z2jr/0wEHHFDn5w022CAff/xx7Wc4J3bZZZc8+OCDGTNmTIYOHZoxY8bM8mu4SfW+fDWh3tSpU/Pxxx/Xfs34mWeemeP3bNy4cfbcc885mrv55ptn//33z2mnnZbtt98+TZo0ya9//es5fi8AgLIQ7AEApdeiRYskyWeffTZH899+++1UVVVl2WWXrXO9Xbt2adWqVd5+++061zt16jTTM1q3bp1PPvnkO654ZjvttFPWW2+97LPPPmnbtm369u2bm2666RtDvpp1rrDCCjPd69atWz766KN88cUXda5/vZbWrVsnyVzVstVWW6V58+b5wx/+kCFDhuQHP/jBTJ9ljWnTpuXCCy/Mcsstl8aNG2exxRbL4osvnueffz6ffvrpHL/nkksuOVcHZQwcODBt2rTJiBEjcvHFF2eJJZaY49cCAJSFYA8AKL0WLVqkQ4cOefHFF+fqdV8/vGJ2GjRoMMvr06dP/87vUbP/W42mTZtm2LBhue+++7Lbbrvl+eefz0477ZTNNttsprn/jf+mlhqNGzfO9ttvn8GDB+fWW2+dbbdekpx11lk54ogjsuGGG+b//u//cvfdd+fee+/NyiuvPMediUn15zM3nn322Xz44YdJkhdeeGGuXgsA9U5RVdmjglV2dQBAvbHNNtvk9ddfz/Dhw791bufOnTNt2rSMGjWqzvUPPvgg48aNqz3hdl5o3bp1nRNka3y9KzBJqqqqsummm+aCCy7IP/7xj5x55pkZOnRoHnjggVk+u2adr7zyykz3Xn755Sy22GJp1qzZf1fAbOyyyy559tln89lnn83ywJEaN998czbZZJNcc8016du3bzbffPP06tVrps9kTkPWOfHFF19kzz33zEorrZT99tsv5557bp588sl59nwAgO8LwR4AUBGOOeaYNGvWLPvss08++OCDme6//vrrueiii5JUf5U0yUwn115wwQVJkq233nqerWuZZZbJp59+mueff7722vvvv59bb721zryxY8fO9NoePXokSSZNmjTLZ7dv3z49evTI4MGD6wRlL774Yu65557aOueHTTbZJKeffnouvfTStGvXbrbzGjRoMFM34B//+Me8++67da7VBJCzCkHn1rHHHpvRo0dn8ODBueCCC9KlS5f0799/tp8jAEBZORUXAKgIyyyzTG644YbstNNO6datW3bfffesssoq+eqrr/LYY4/lj3/8Y/bYY48kyWqrrZb+/fvnqquuyrhx47LRRhvliSeeyODBg9OnT586J+L+t/r27Ztjjz02P/nJT3LooYdmwoQJueKKK7L88svXOTzitNNOy7Bhw7L11lunc+fO+fDDD3P55ZdnqaWWyvrrrz/b55933nnZcssts84662TvvffOl19+mUsuuSQtW7bMKaecMs/q+LqqqqqceOKJ3zpvm222yWmnnZY999wz6667bl544YUMGTIkSy+9dJ15yyyzTFq1apUrr7wyzZs3T7NmzbL22muna9euc7WuoUOH5vLLL8+vfvWrrL766kmS3/3ud9l4441z0kkn5dxzz52r5wEAfJ/p2AMAKsaPf/zjPP/88/npT3+aP//5zzn44INz3HHH5a233sr555+fiy++uHbu1VdfnVNPPTVPPvlkDjvssAwdOjTHH398brzxxnm6pkUXXTS33nprFl544RxzzDEZPHhwBgwYkG233XamtXfq1Cm//e1vc/DBB+eyyy7LhhtumKFDh6Zly5azfX6vXr1y1113ZdFFF83JJ5+cgQMH5oc//GEeffTRuQ7F5odf/vKXOfLII3P33XfnF7/4RZ555pnceeed6dixY515Cy20UAYPHpwGDRrkgAMOyM4775yHHnport7rs88+y1577ZWePXvmhBNOqL2+wQYb5Be/+EXOP//8/P3vf58ndQEAfB8U0+dmp2QAAAAAKsL48ePTsmXLNO51VoqGTRb0cuaL6VMmZtJ9v8ynn36aFi1aLOjlzHM69gAAAACghAR7AAAAAFBCgj0AAAAAKCGn4gIAAADUa1VJUam9X5VaV7XKrg4AAAAAKpRgDwAAAABKyFdx+V6bNm1a3nvvvTRv3jxFUSzo5QAAAFDhpk+fns8++ywdOnRIVZV+KL7fBHt8r7333nvp2LHjgl4GAAAA9cw777yTpZZaakEvA76RYI/vtebNmydJGq3UP0WDRgt4NQBQLqMfHLiglwAApfPZ+PFZtmvH2t9H64WiqB6VqFLrmkGwx/dazddviwaNBHsAMJdatGixoJcAAKVlOyjKwJfFAQAAAKCEBHsAAAAAUEK+igsAAABQnxVFUlRo71eFf6W6Qv/UAAAAAKCyCfYAAAAAoIQEewAAAABQQoI9AAAAACghh2cAAAAA1GdFVQUfnlGhdc1Q2dUBAAAAQIUS7AEAAABACQn2AAAAAKCE7LEHAAAAUJ8VRfWoRJVa1ww69gAAAACghAR7AAAAAFBCgj0AAAAAKCHBHgAAAACUkMMzAAAAAOqzoqp6VKJKrWuGyq4OAAAAACqUYA8AAAAASkiwBwAAAAAlZI89AAAAgPqsKKpHJarUumbQsQcAAAAAJSTYAwAAAIASEuwBAAAAQAkJ9gAAAACghByeAQAAAFCfFVXVoxJVal0zVHZ1AAAAAFChBHsAAAAAUEKCPQAAAAAoIXvsAQAAANRnRVE9KlGl1jWDjj0AAAAAKCHBHgAAAACUkGAPAAAAAEpIsAcAAAAAJeTwDAAAAIB6rCiKFJV6yESl1jWDjj0AAAAAKCHBHgAAAACUkGAPAAAAAErIHnsAAAAA9Zg99spLxx4AAAAAlJBgDwAAAABKSLAHAAAAACUk2AMAAACAEnJ4BgAAAEB9VswYlahS65pBxx4AAAAAlJBgDwAAAABKSLAHAAAAACVkjz0AAACAeqwoihRFhW5GV6l1zaBjDwAAAABKSLAHAAAAACUk2AMAAACAEhLsAQAAAEAJOTwDAAAAoB5zeEZ56dgDAAAAgBIS7AEAAABACQn2AAAAAKCE7LEHAAAAUI/ZY6+8dOwBAAAAQAkJ9gAAAACghAR7AAAAAFBCgj0AAAAAKCGHZwAAAADUYw7PKC8dewAAAABQQoI9AAAAACghwR4AAAAAlJA99gAAAADqs2LGqESVWtcMOvYAAAAAoIQEewAAAABQQoI9AAAAACghwR4AAAAAlJDDMwAAAADqsaIoUhQVespEpdY1g449AAAAACghwR4AAAAAlJBgDwAAAABKyB57AAAAAPVYUaSC99hb0AuYv3TsAQAAAEAJCfYAAAAAoIQEewAAAABQQoI9AAAAACghh2cAAAAA1GNFiso9PKPCT8/QsQcAAAAAJSTYAwAAAIASEuwBAAAAQAkJ9gAAAACghByeAQAAAFCPFUUFH55RqXXNoGMPAAAAAEpIsAcAAAAAJSTYAwAAAIASssceAAAAQH1WzBiVqFLrmkHHHgAAAACUkGAPAAAAAEpIsAcAAAAAJSTYAwAAAIAScngGAAAAQH1WFCmKyjxlYnqF1lVDxx4AAAAAlJBgDwAAAABKSLAHAAAAACVkjz0AAACAeqyo4D32KrWuGjr2AAAAAKCEBHsAAAAAUEKCPQAAAAAoIcEeAAAAAJSQwzMAAAAA6jGHZ5SXjj0AAAAAKCHBHgAAAACUkGAPAAAAAErIHnsAAAAA9VkxY1SiSq1rBh17AAAAAFBCgj0AAAAAKCHBHgAAAACUkGAPAAAAAErI4RkAAAAA9VhRFCmKyjxlolLrqqFjDwAAAABKSLAHAAAAACUk2AMAAACAErLHHgAAAEA9Zo+98tKxBwAAAAAlJNgDAAAAgBIS7AEAAABACQn2AAAAAKCEHJ4BAAAAUI85PKO8dOwBAAAAQAkJ9gAAAACghAR7AAAAAFBC9tgDAAAAqMfssVdeOvYAAAAAoIQEewAAAABQQoI9AAAAACghwR4AAAAAlJDDMwAAAADqs2LGqESVWtcMOvYAAAAAoIQEewAAAABQQoI9AAAAACghe+wBAAAA1GNFUaQoKnMzukqtq4aOPQAAAAAoIcEeAAAAAJSQYA8AAAAASkiwBwAAAAAl5PAMAAAAgHrM4RnlpWMPAAAAAEpIsAcAAAAAJSTYAwAAAIASssceAAAAQD1mj73y0rEHAAAAACUk2AMAAACAEhLsAQAAAEAJCfYAAAAAoIQcngEAAABQnxUzRiWq1Lpm0LEHAAAAACUk2AMAAACAEhLsAQAAAEAJ2WMPAAAAoB4riiJFUZmb0VVqXTV07AEAAABACQn2AAAAAKCEBHsAAAAAUEKCPQAAAAAoIYdnAAAAANRjDs8oLx17AAAAAFBCgj0AAAAAKCHBHgAAAACUkD32AAAAAOqxIhW8x14qs64aOvYAAAAAoIQEewAAAABQQoI9AAAAACghwR4AAAAAlJDDMwAAAADqsaKo4MMzKrSuGjr2AAAAAGCGYcOGZdttt02HDh1SFEVuu+22OvenT5+ek08+Oe3bt0/Tpk3Tq1evjBo1qs6csWPHpl+/fmnRokVatWqVvffeO59//nmdOc8//3w22GCDNGnSJB07dsy5554712sV7AEAAADADF988UVWW221XHbZZbO8f+655+biiy/OlVdemccffzzNmjVL7969M3HixNo5/fr1y0svvZR77703d9xxR4YNG5b99tuv9v748eOz+eabp3Pnznn66adz3nnn5ZRTTslVV101V2v1VVwAAAAAmGHLLbfMlltuOct706dPz6BBg3LiiSdmu+22S5Jcd911adu2bW677bb07ds3I0eOzF133ZUnn3wya665ZpLkkksuyVZbbZWBAwemQ4cOGTJkSL766qv89re/TaNGjbLyyitnxIgRueCCC+oEgN9Gxx4AAABAfVZU+Eh1h9x/jkmTJn2nj+rNN9/MmDFj0qtXr9prLVu2zNprr53hw4cnSYYPH55WrVrVhnpJ0qtXr1RVVeXxxx+vnbPhhhumUaNGtXN69+6dV155JZ988skcr0ewBwAAAEBF69ixY1q2bFk7BgwY8J2eM2bMmCRJ27Zt61xv27Zt7b0xY8ZkiSWWqHO/YcOGadOmTZ05s3rGf77HnPBVXAAAAAAq2jvvvJMWLVrU/ty4ceMFuJp5R8ceAAAAABWtRYsWdcZ3DfbatWuXJPnggw/qXP/ggw9q77Vr1y4ffvhhnftTpkzJ2LFj68yZ1TP+8z3mhGAPAAAAAOZA165d065du9x///2118aPH5/HH38866yzTpJknXXWybhx4/L000/Xzhk6dGimTZuWtddeu3bOsGHDMnny5No59957b1ZYYYW0bt16jtcj2AMAAACox4qiqOgxtz7//POMGDEiI0aMSFJ9YMaIESMyevToFEWRww47LGeccUZuv/32vPDCC9l9993ToUOH9OnTJ0nSrVu3bLHFFtl3333zxBNP5NFHH80hhxySvn37pkOHDkmSXXbZJY0aNcree++dl156KX/4wx9y0UUX5YgjjpirtdpjDwAAAABmeOqpp7LJJpvU/lwTtvXv3z/XXnttjjnmmHzxxRfZb7/9Mm7cuKy//vq566670qRJk9rXDBkyJIccckg23XTTVFVVZYcddsjFF19ce79ly5a55557cvDBB2eNNdbIYostlpNPPjn77bffXK21mD59+vT/sl6Yb8aPH5+WLVumcfd9UzRo9O0vAABqffLkpQt6CQBQOuPHj0/bRVvm008/rXPYQiWq+Z2780F/TFXjhRf0cuaLaZMm5O3Lf1axf56+igsAAAAAJeSruAAAAAD12Hfdi64MKrWuGjr2AAAAAKCEBHsAAAAAUEKCPQAAAAAoIcEeAAAAAJSQwzMAAAAA6rGiqB6VqFLrqqFjDwAAAABKSLAHAAAAACUk2AMAAACAErLHHgAAAEA9Vr3HXmVuRlehZdXSsQcAAAAAJSTYAwAAAIASEuwBAAAAQAkJ9gAAAACghByeAQAAAFCfFRV8yESl1jWDjj0AAAAAKCHBHgAAAACUkGAPAAAAAErIHnsAAAAA9VhRFCkqdJO9Sq2rho49AAAAACghwR4AAAAAlJBgDwAAAABKSLAHAAAAACXk8AwAAACAeqwoqkclqtS6aujYAwAAAIASEuwBAAAAQAkJ9gAAAACghOyxBwAAAFCPVVUVqaqqzM3opldoXTV07AEAAABACQn2AAAAAKCEBHsAAAAAUEKCPQAAAAAoIYdnAAAAANRjRVE9KlGl1lVDxx4AAAAAlJBgDwAAAABKSLAHAAAAACVkjz0AAACAeqwoihQVuhldpdZVQ8ceAAAAAJSQYA8AAAAASkiwBwAAAAAlJNgDAAAAgBJyeAYAAABAPVYU1aMSVWpdNXTsATDHNl5r+Tz7pxNTVVXOvx33+en6uXnQ/gt6GQDUQw8MvT89unfL1KlTF/RSZnLP3Xdl7TV6ZNq0aQt6KQDMJcEewPfEeqsvk5sH7Z837jkzXz57abbdeNU69xs2rMoZh26XJ2/6ZT567Py8cc+Zufr03dJ+8ZZ15i3baYncdOF+eWfo2fng4fNy/28Pz4ZrLjfT++267dp54g/H55O/X5i37x+QC4/b8VvXeOYv+uScq+/KtGnTa6/tv+OGefZPJ2bs8Avy3K0nZZdt1qrzmm5Lt8vvB+6Tl+88NV8+e2kO2WXjOfo8VlmuQ+675rB88vcLM+pvp+eI/r3q3P/R2ivm+dtOzgcPn5drTt89CzVsUHuvxSJN8vxtJ6dT+9Z1XjP4tuHp2a1j1uu5zBytAYDK8cjDw7JDn23TtVOHNF2oyO1/vq3O/cmTJ+eE44/Nmj26Z9GWzdK1U4fsvcfuee+992rnDHvowTRdqJjleOrJJ7/x/U847pgce/yJadDg339fXXn5ZenRvVtaN2+aVVdeIUOuv+5b6xg9enR+8uOt06bFwunUYYkcf+zRmTJlSu39Ec8+mx+u2TOLtVokO/TZNmPHjq29N2XKlKy71hp58okn6jxz895bpOFCC+XGG4Z86/sD8P0i2AP4nmjWtHFeePXdHDbgD7O8v3CTRunRrWPO/s3fss7O56Tvkb/J8p3b5o9f60C75eID0rBBVbbc/+Ks2+/cPP/qu7nl4gPSdtHmtXMO3fVHOfWQbXP+7+7N6j89M1sfcEnuGz7yG9e3bo+ls3THxXLr/SNqr+37s/Vz2s+3zZm//mtW/+mZOePKv2bQcTtmqw1XqbPuN//5UU66+Pa8/69P5+izaN6sSf5y+SEZ/f7YrLvLOfnloNtywv5bZa/t10uSFEWRawf0z9U3P5KN+5+f1VfqlL13WK/29acful2uvvmRjH7/kzrPnTxlav7wt6dy0M4bzdE6AKgcX3zxRbqvuloGXXzZLO9PmDAhI559JsedcFKGP/FMbrzplrz66iv52U9+XDvnh+usmzffeb/O2HOvfdKla9esseaas33vRx95JG+88Xp+sv0OtdeuuvKKnHzi8TnhpFPyzHMv5cSTT81hhx6cO+/4y2yfM3Xq1Gz/463z1Vdf5YFhj+U3vx2c/7vu2px2ysm1cw7af59stMmPMvyJZ/Lpp5/m3LPPqr036MLz88N118sP1lprpmfvtvseuezSi2f73gB8P9ljD+B74p5H/5F7Hv3HbO+P/3xitjnw0jrXDj/7pjwy5Jh0bNc674z5JIu2apblOi+RA08dkhdHVXcYnHTxn3PAThtmpWU75IOPX0mr5k3zq4O2yQ6HXZkHn3i19lk182fnZ73XyNC/v5xJX/27K2CXrdfKNX96NDff80yS5K13P84aK3fKkXtslr8OezFJ8vQ/Rufpf4xOkpx+6I9nfvAs9N1qzTRaqEH2P2VIJk+ZmpFvjMmqKyyZQ3fdJL+95dEs1qpZFm/dPL++aVgmfTUldz70Qlbo2i5J8sPVumaNlTvn8LNvmuWz7xz2Qu684pA0abxQJk6aPEfrAaD8em+xZXpvseVs77ds2TJ33nVvnWsXXnRpNlh3rYwePTqdOnVKo0aN0q5du9r7kydPzh1/+XMOPPjnKb5hE6c/3nRjftRrszRp0qT22g1Drs/e++6fn+24U5Kk69JL5+mnnsz5552TrbfZdpbPue/eezJy5D9y5933pW3btlktPXLyKafnxF8emxNPPiWNGjXKyy+PzO+uG5Llll8+O+60c/721zuSJG++8UYG/+6aPPb407N89lbbbJvDf3FI3nj99Sy9jM52qG+KovjGf46VWaXWVUPHHkCJtWjeNNOmTcu4z75Mknw87ou88uaY7LLNWlm4SaM0aFCVfXZYPx98PD7PzgjXNv3hiqmqKtJhiVZ59k8n5rW7Ts//nbNXlmrb6hvfa93Vl8kzM55Ro9FCDTPxq7rh2JcTJ2fNVTqnYcPv/lfM2qt2zaPPvJbJU/69D9G9j43MCl3bpVXzpvnXJ5/n/X99ml7rdEvTJgtlvdWXyYuj3k3DhlW56Jc75edn/L7O14X/0zP/GJ2GDRrkB6t0+c7rA6B+GD/+0xRFkVatWs3y/h1/uT0ff/xxduu/5zc+57FHHs7qa9Tt6Ptq0qQ6QV+SNG3aNE89+UQmT571v3h6/O/Ds8oq3dO2bdvaa5tt3jvjx4/PP156KUnSfdXVcv9992bKlCl54IH7s0r36q09fn7wATlzwLlp3rz5LJ/dqVOntG3bNo8+8vA31gLA94tgr0Suvfba2f6fihp77LFH+vTpM9/XsvHGG+ewww77xjlzsl7gu2vcqGHOOHS73HTX0/nsi4m117c+4NKstmLH/OvRgRn39wtz6G4/ynYHX14b/nVdarFUVRU5Zq/Nc/TAP2WXo69J65YL544rDqmzT93XdWrfJu997au09w0fmT36rJue3TomSVZfqVP2+Mm6abRQwyzWapHvXFvbRVvkg48/q3Ptw7HVP7ddrEWSZNdjrsnx+26RZ24+ISNe/mcG/3l4jtpz8wx7clQmTpqcob87PM/delIO2GnDOs/5cuLkfPr5l+nUoc13Xh8AlW/ixIk58fhjs+NOO6dFixaznDP4d9dks817Z6mllvrGZ40e/XY6tO9Q51qvzXvn2t9enWeefjrTp0/P0089lWt/e3UmT56cjz76aJbP+WDMmCzxH6FektqfP/hgTJLkil9fnVtvuTkrrbBMGjVqlKOPPT43/N/1abrwwlljzR9k2616Z+UVl80pJ5840/Pbt++Q0aPf/sZaAPh+WaDB3h577JGiKHL22WfXuX7bbbdVdKvkKaeckh49eizoZcxTXbp0yaBBgxb0MqDeaNiwKv937t4piiKHnlV3T74Lj98x/xr7WXrtNSgb7HZebn/gufzpov3TbkYgVhRFGi3UMEeee3PuGz4yT7zwVvoff22W7bRENvrB8rN9z6aNF8qkSVPqXBvwm7tyz6P/yEODj8pnT16UP164X4b85fEkmW3H3Lzy2Ig3sv6u56XbNqfk8LNvSpcOi6XfNmvllMv+kmvO6J9rbnksvfa6MMfvt2VWWa7uL1MTJ03Owk0Wmq/rA6C8Jk+enF133jHTp0/PxZddMcs5//znP3PvPXen/557f+vzvvzyyzT+Wnfe8SeclM17b5mN1v9hmjddKD/bYbv0261/kqSq6rv/mrbSyivn3qEP5dXX387g62/I5MmTc/ppv8qFF12aIw77eX64zrp54unn8ufbbplpP78mTZtmwoQJ3/m9AfjfW+Ade02aNMk555yTTz755NsnA5CGDasy5Jy906l962xz4KV1uvU2Xmv5bLXBKtn9uN9l+HNvZMTL/8xhA27Kl5MmZ9dt106SjPlofJLk5TfG1L7uo08+z0fjPk/HdnVPkf1PH4/7Iq1aLFzn2sRJk3PAqUPSZt3Ds+LWv8pyW56Ut9//OOM//zL/+uTz71zjBx+Pr3PYR5Is0ab65w9mrP/rLj2xb4674NZUVVWlZ7eOueXeZ/KvTz7PI0+PygZr1D0VuHWLhfPRf7E+ACrX5MmT02/nHTP67bdzx133zrZb7/rBv8uiiy6abbb99v1jF11ssYz72u87TZs2za+v/m3Gjp+Ql197K6PeGJ3OnbukefPmWXzxxWf5nLbt2uXDDz6oc63m57Zt283qJTn2qCNyyM8Py1JLLZWHH3ow2+/wszRr1ixbbLl1hj30YJ25n3wyNovN5r0B+H5a4MFer1690q5duwwYMGC2c/70pz9l5ZVXTuPGjdOlS5ecf/75de536dIlZ511Vvbaa680b948nTp1ylVXXfWN7/vggw+mKIrcfffd6dmzZ5o2bZof/ehH+fDDD/O3v/0t3bp1S4sWLbLLLrvU+bdW06ZNy4ABA9K1a9c0bdo0q622Wm6++eaZnnv//fdnzTXXzMILL5x11103r7zySpLqr6eeeuqpee6552o3p7z22muTJBdccEG6d++eZs2apWPHjjnooIPy+eff7RfPgQMHpn379ll00UVz8MEH19mnY9KkSTnqqKOy5JJLplmzZll77bXz4IMP1t7/+OOPs/POO2fJJZfMwgsvnO7du+f3v//9bN9r4403zttvv53DDz98lhtu3n333enWrVsWWWSRbLHFFnn//fe/U03Av0O9ZTotnq0PuDRjP/2izv2FmzRKUv3Pqv80bdr02v9tDh/xRpJkuS5L1N5v3WLhLNZqkYx+f+xs33vEy/9Mt6Vn/UvDlCnT8u6H4zJt2vT8rPca+dvDL2X69O/esff4829mvdWXrbNP36Y/XDGvvDmm9ivF/6l/n3XyyfgJufOhF9KgqrrOmq8VL9SwQe21pPqryE2bNMqIl//5ndcHQGWqCfVef21U7rz7viy66KKznDd9+vRcN/h32WXX3bPQQt/eAd6jR8+MHDnrA7IWWmihLLXUUmnQoEH+eNON2XLrbWbbsbf2D9fJiy++kA8//LD22v33VYeP3VZaaab5Dwy9Py+/PDIHHnxIkupTdWt+L5g8eXKmTv33XrYTJ07MG6+/nh49en5rPUDlqfldvlJHJVvgwV6DBg1y1lln5ZJLLsk//znzL1lPP/10dtxxx/Tt2zcvvPBCTjnllJx00km1YViN888/P2uuuWaeffbZHHTQQTnwwANrw7Rvcsopp+TSSy/NY489lnfeeSc77rhjBg0alBtuuCF33nln7rnnnlxyySW18wcMGJDrrrsuV155ZV566aUcfvjh2XXXXfPQQw/Vee4JJ5yQ888/P0899VQaNmyYvfbaK0my00475cgjj8zKK6+c999/P++//3522qn6JKyqqqpcfPHFeemllzJ48OAMHTo0xxxzzNx+pHnggQfy+uuv54EHHsjgwYNz7bXX1vm8DjnkkAwfPjw33nhjnn/++fzsZz/LFltskVGjRiWp/kt9jTXWyJ133pkXX3wx++23X3bbbbc88cQTs3y/W265JUsttVROO+202ppqTJgwIQMHDsz111+fYcOGZfTo0TnqqKNmu/ZJkyZl/PjxdQbUF82aNsqqyy+ZVZdfMknSZclFs+ryS9Z20TVsWJUbztsnq6/UKXueMDgNqoq0XbR52i7avDbEevz5N/PJ+Am5+vTd0335JbNspyVy1mF90mXJRXPXI9Wbar82+sP85YHnMvDon+aHq3XNSsu0z29O2y2vvPVBHnrq1VkvLtX76a3Ts+4pect2WiJ9t/pBlum0eNZcuXOuO3vPrLRMh5x8ye21cxZq2KC2rkYLNUyHJVpl1eWXzNIdF6udc8BOG+avV/689uc//O2pfDV5aq78Vb90W7pdfrr56jl4l41z8f89MNO6Fm+9SI7bp3eOOOePSZJxn32ZkW+8n0N22SRrr9o1G6+1Qm2YmSTr9Vwmb7zzr7z5z1nvXwRAZfr888/z3IgReW7EiCTJW2++medGjMjo0dUHQ02ePDm77PTTPPP0U/nd4CGZOnVqxowZkzFjxuSrr76q86wHHxiat958M3vutc8cvXevzXpn+KOP1Lk26tVX8/sh/5fXRo3Kk088kd369c0/Xnoxp51+Vu2cP992a1ZbZcX/eM7m6dZtpey9x255/rnncu89d+fUX52Y/Q88OI0bN67z/IkTJ+bwXxySy664qjYoXGfd9fLrKy7L8889l9tu/VPWWXe92vlPPP73NG7cOGv/cJ05qgmA74di+n/TUvFf2mOPPTJu3LjcdtttWWeddbLSSivlmmuuyW233Zaf/OQnmT59evr165d//etfueeee2pfd8wxx+TOO+/MSzNOfurSpUs22GCDXH/99Umq/w1au3btcuqpp+aAAw6Y5Xs/+OCD2WSTTXLfffdl0003TZKcffbZOf744/P6669n6aWXTpIccMABeeutt3LXXXdl0qRJadOmTe67776ss86//8LbZ599MmHChNxwww2zfO5f//rXbL311vnyyy/TpEmTnHLKKbntttsyYsb/qZidm2++OQcccEDt5rnXXnttDjvssIwbN+4bP9MHH3wwr7/+eho0qP5Ff8cdd0xVVVVuvPHGjB49OksvvXRGjx6dDh3+vedUr169stZaa+Wss86a5XO32WabrLjiihk4cGCS6i69Hj161O6r16VLlxx22GF1DtS49tprs+eee+a1117LMstUhwGXX355TjvttIwZM+brb5GkOmg99dRTZ7reuPu+KRo0mm3dUAk2WGO53HP1L2a6fv3tf89+v/q/dGrfJq/89bRZvnbzfS7Kw09Xh/Orr9Qppxy8bVZfqVMWaliVkW+MyVlX/S33PPrvToHmzZrk3KO2z3Y/6pFp06bnkadH5ajzbs4/Pxg32/W1brFwXr/7jKzd9+yMeru6U2CFrm1z7Vl7ZPnObTN5ytQMe+rVnHDRn2vvJ5ntuoc9NSq9970oSXLC/ltltx+vnRW3/lXt/VWW65BBx+2YNVbunI/HfZ4rbnwo519730zPGTxgjwwf8Uau/MOw2mtrrtw5vzlttyzepnku+/0DGXDVXbX3br/s4Ax76tUM/N29s60VKsUnT166oJcA3xvDHnowvXttMtP1XXfrn9/89tq8/dZbWXG5rrN87d33PZANN9q49uf+u+2S0W+/nQeGPTpH7z127Ngs03nJPP7UiCy/wgpJkpdHjsweu+2SV199JQsttFA23HiTnHnWObX3k+T6wddmv332zJeT//0r29tvv51fHHJghj30YJo1a5Z+u/XPGWednYYNG9Z5z5NOOD6TJ0/O2ecOrL32+muvpf9uu2TUq6+k7879cuHFl9aGfoccuH+Kosgll185RzVBJRs/fnzaLtoyn3766Wy/jl8pxo8fn5YtW2blY/+cBo2bLejlzBdTJ32Rl87ZrmL/PBt++5T/jXPOOSc/+tGPZurmGjlyZLbbbrs619Zbb70MGjQoU6dOrQ2vVl111dr7RVGkXbt2tS3qW265ZR5+uPrY9s6dO9cGgl9/Xdu2bbPwwgvXhno112o61V577bVMmDAhm222WZ31fPXVV+nZs27L+n8+t3379kmSDz/8MJ06dZrtZ3DfffdlwIABefnllzN+/PhMmTIlEydOzIQJE7LwwnX3tRo9enRW+o92+1/+8pf55S9/mSRZeeWVaz+Xmvd/4YUXkiQvvPBCpk6dmuWXr7tB/qRJk2q/ajB16tScddZZuemmm/Luu+/mq6++yqRJk2Zaw5xYeOGFa0O9mrX851cHvu7444/PEUccUfvz+PHj07Fjx7l+Xyijh58elaY9D5nt/dHvj/3G+zWe+cfo/Pjgy75xzmdfTMyBp96QA0+9YY7X98n4CbnyD8Ny6K4/ys/PvDFJ8sqbH2Sdnc/5xtfNybrP/PVfc+av/1rn2ouj3kuvvQd967r6H3/tTNeeeunt9NzhjJmud1u6XVZdYcnseuw13/pcACrLhhttXCcg+7rOXbp84/3/NPj6Of/7M0natGmTAw46JBcPuiCXXvHrJMmK3brl7089+42v263/Htmt/x5119m5c277y19n/YL/cPqZM291tMyyy+aR4TN/C+ejjz7KrbfcnEf//tS3PheA75fvTbC34YYbpnfv3jn++OOzxx57zPXrv763RVEUtXtMXX311fnyyy9nOe8/fy6K4hufU7Pf3Z133pkll1yyzryvt75//bnJzHte/ae33nor22yzTQ488MCceeaZadOmTR555JHsvffe+eqrr2YK1Tp06FCn469NmzazfO9Z1dCgQYM8/fTTdcK/JFlkkUWSJOedd14uuuiiDBo0qHbPv8MOO2ymryDMiVmt5ZuaRBs3bjzTZwl8f5xz9d3Zb8cNvvV/y99X7RZvmX1Ouj7jP5/47ZMBYB469vgTctWVl2fatGn/1am388Pbb72VQZdcni5dZ92xCFS+oqgelahS66rxvQn2kuqvwvbo0SMr/Ef7ebdu3fLoo3Vb3B999NEsv/zyMwVTs/P1EO67WmmlldK4ceOMHj06G2200Xd+TqNGjepsVJtU7yU4bdq0nH/++bV/0d90002zfUbDhg2z7LLLzvV79+zZM1OnTs2HH36YDTbYYJZzHn300Wy33XbZddddk1QHkq+++mqdDsGvm1VNQOX59PMvc95v7/n2id9TDzz+7XuvAsD80KpVqxxz3C8X9DJmaY0118waa665oJcBwHfwvQr2unfvnn79+uXiiy+uvXbkkUfmBz/4QU4//fTstNNOGT58eC699NJcfvnl//P1NW/ePEcddVQOP/zwTJs2Leuvv34+/fTTPProo2nRokX69+8/R8/p0qVL3nzzzYwYMSJLLbVUmjdvnmWXXTaTJ0/OJZdckm233TaPPvporrxy3u9vsfzyy6dfv37Zfffdc/7556dnz57517/+lfvvvz+rrrpqtt566yy33HK5+eab89hjj6V169a54IIL8sEHH3xjsNelS5cMGzYsffv2TePGjbPYYovNdi4AAAAA/73vVw94ktNOO63OV1ZXX3313HTTTbnxxhuzyiqr5OSTT85pp532nb6uOy+cfvrpOemkkzJgwIB069YtW2yxRe688850nYu29R122CFbbLFFNtlkkyy++OL5/e9/n9VWWy0XXHBBzjnnnKyyyioZMmRIBgyYeV+MeeF3v/tddt999xx55JFZYYUV0qdPnzz55JO1+/+deOKJWX311dO7d+9svPHGadeuXfr06fONzzzttNPy1ltvZZlllsniiy8+X9YNAAAAwL8t0FNx4dvUnNDjVFwAmHtOxQWAuVcfT8Vd5bjKPhX3xbOdigsAAABABSpS1B78WWmKVGZdNb53X8UFAAAAAL6dYA8AAAAASkiwBwAAAAAlZI89AAAAgHqsKKpHJarUumro2AMAAACAEhLsAQAAAEAJCfYAAAAAoIQEewAAAABQQg7PAAAAAKjHiqJIUaGnTFRqXTV07AEAAABACQn2AAAAAKCEBHsAAAAAUEL22AMAAACox4qielSiSq2rho49AAAAACghwR4AAAAAlJBgDwAAAABKSLAHAAAAACXk8AwAAACAeqwoihQVespEpdZVQ8ceAAAAAJSQYA8AAAAASkiwBwAAAAAlZI89AAAAgHqsKKpHJarUumro2AMAAACAEhLsAQAAAEAJCfYAAAAAoIQEewAAAABQQg7PAAAAAKjHiqJIUaGnTFRqXTV07AEAAABACQn2AAAAAKCEBHsAAAAAUEL22AMAAACoz4qkYreiq9S6ZtCxBwAAAAAlJNgDAAAAgBIS7AEAAABACQn2AAAAAKCEHJ4BAAAAUI8VRZGiQk/PqNS6aujYAwAAAIASEuwBAAAAQAkJ9gAAAACghAR7AAAAAFBCDs8AAAAAqMeKonpUokqtq4aOPQAAAAAoIcEeAAAAAJSQYA8AAAAASsgeewAAAAD1WFEUKSp0M7pKrauGjj0AAAAAKCHBHgAAAACUkGAPAAAAAEpIsAcAAAAAJeTwDAAAAIB6rCiqRyWq1Lpq6NgDAAAAgBIS7AEAAABACQn2AAAAAKCE7LEHAAAAUI8VRZGiQjejq9S6aujYAwAAAIASEuwBAAAAQAkJ9gAAAACghAR7AAAAAFBCDs8AAAAAqMccnlFeOvYAAAAAoIQEewAAAABQQoI9AAAAACghe+wBAAAA1GNFUT0qUaXWVUPHHgAAAACUkGAPAAAAAEpIsAcAAAAAJSTYAwAAAIAScngGAAAAQD1WFEWKCj1lolLrqqFjDwAAAABKSLAHAAAAACUk2AMAAACAErLHHgAAAEA9VhTVoxJVal01dOwBAAAAQAkJ9gAAAACghAR7AAAAAFBCgj0AAAAAKCGHZwAAAADUY0VRpKjQUyYqta4aOvYAAAAAoIQEewAAAABQQoI9AAAAACghe+wBAAAA1GNFkkrdiq5Cy6qlYw8AAAAASkiwBwAAAAAlJNgDAAAAgBIS7AEAAABACTk8AwAAAKAeqyqKVFXo6RmVWlcNHXsAAAAAUEKCPQAAAAAoIcEeAAAAAJSQPfYAAAAA6rGiqB6VqFLrqqFjDwAAAABKSLAHAAAAACUk2AMAAACAEhLsAQAAAEAJOTwDAAAAoB4riiJFhZ4yUal11dCxBwAAAAAlJNgDAAAAgBIS7AEAAABACdljDwAAAKAeqyqqRyWq1Lpq6NgDAAAAgBIS7AEAAABACQn2AAAAAKCEBHsAAAAAUEIOzwAAAACoz4qkKCr0lIkKLauGjj0AAAAAKCHBHgAAAACUkGAPAAAAAErIHnsAAAAA9VhRVI9KVKl11dCxBwAAAAAlJNgDAAAAgBIS7AEAAABACQn2AAAAAKCEHJ4BAAAAUI8VM/5TiSq1rho69gAAAACghAR7AAAAAFBCgj0AAAAAKCF77AEAAADUY1VF9ahElVpXDR17AAAAAFBCgj0AAAAAKCHBHgAAAACUkGAPAAAAAErI4RkAAAAA9VhRFCmKyjxlolLrqqFjDwAAAABKSLAHAAAAACUk2AMAAACAErLHHgAAAEA9VhTVoxJVal01dOwBAAAAQAkJ9gAAAAAgydSpU3PSSSela9euadq0aZZZZpmcfvrpmT59eu2c6dOn5+STT0779u3TtGnT9OrVK6NGjarznLFjx6Zfv35p0aJFWrVqlb333juff/75PF+vYA8AAAAAkpxzzjm54oorcumll2bkyJE555xzcu655+aSSy6pnXPuuefm4osvzpVXXpnHH388zZo1S+/evTNx4sTaOf369ctLL72Ue++9N3fccUeGDRuW/fbbb56v1x57AAAAAJDksccey3bbbZett946SdKlS5f8/ve/zxNPPJGkultv0KBBOfHEE7PddtslSa677rq0bds2t912W/r27ZuRI0fmrrvuypNPPpk111wzSXLJJZdkq622ysCBA9OhQ4d5tl4dewAAAAD1WFVRVPRIkvHjx9cZkyZNmuVnse666+b+++/Pq6++miR57rnn8sgjj2TLLbdMkrz55psZM2ZMevXqVfuali1bZu21187w4cOTJMOHD0+rVq1qQ70k6dWrV6qqqvL444/P0z87HXsAAAAAVLSOHTvW+flXv/pVTjnllJnmHXfccRk/fnxWXHHFNGjQIFOnTs2ZZ56Zfv36JUnGjBmTJGnbtm2d17Vt27b23pgxY7LEEkvUud+wYcO0adOmds68ItgDAAAAoKK98847adGiRe3PjRs3nuW8m266KUOGDMkNN9yQlVdeOSNGjMhhhx2WDh06pH///v+r5c4xwR4AAAAAFa1FixZ1gr3ZOfroo3Pcccelb9++SZLu3bvn7bffzoABA9K/f/+0a9cuSfLBBx+kffv2ta/74IMP0qNHjyRJu3bt8uGHH9Z57pQpUzJ27Nja188r9tgDAAAAqMeKorLH3JgwYUKqqurGZQ0aNMi0adOSJF27dk27du1y//33194fP358Hn/88ayzzjpJknXWWSfjxo3L008/XTtn6NChmTZtWtZee+3v+Kc0azr2AAAAACDJtttumzPPPDOdOnXKyiuvnGeffTYXXHBB9tprryRJURQ57LDDcsYZZ2S55ZZL165dc9JJJ6VDhw7p06dPkqRbt27ZYostsu++++bKK6/M5MmTc8ghh6Rv377z9ETcRLAHAAAAAEmSSy65JCeddFIOOuigfPjhh+nQoUP233//nHzyybVzjjnmmHzxxRfZb7/9Mm7cuKy//vq566670qRJk9o5Q4YMySGHHJJNN900VVVV2WGHHXLxxRfP8/UW06dPnz7PnwrzyPjx49OyZcs07r5vigaNFvRyAKBUPnny0gW9BAAonfHjx6ftoi3z6aefztGebGVW8zv3tpc+mIWaLrKglzNfTP7y8/zlkI0r9s/THnsAAAAAUEK+igsAAABQjxVFkWJuT5koiUqtq4aOPQAAAAAoIcEeAAAAAJSQYA8AAAAASsgeewAAAAD1WFFUj0pUqXXV0LEHAAAAACUk2AMAAACAEhLsAQAAAEAJCfYAAAAAoIQcngEAAABQj1UVRaoq9JSJSq2rho49AAAAACghwR4AAAAAlJBgDwAAAABKyB57AAAAAPVYMWNUokqtq4aOPQAAAAAoIcEeAAAAAJSQYA8AAAAASkiwBwAAAAAl5PAMAAAAgHqsKIoURWUeM1GpddXQsQcAAAAAJSTYAwAAAIASEuwBAAAAQAnZYw8AAACgHqsqqkclqtS6aujYAwAAAIASEuwBAAAAQAkJ9gAAAACghAR7AAAAAFBCDs8AAAAAqMeKokhRVOYpE5VaVw0dewAAAABQQoI9AAAAACghwR4AAAAAlJA99gAAAADquQrfiq5i6dgDAAAAgBIS7AEAAABACQn2AAAAAKCEBHsAAAAAUEIOzwAAAACox4qiSFGhp2dUal01dOwBAAAAQAkJ9gAAAACghAR7AAAAAFBC9tgDAAAAqMeqiupRiSq1rho69gAAAACghAR7AAAAAFBCgj0AAAAAKCHBHgAAAACUkMMzAAAAAOqxoihSFJV5ykSl1lVDxx4AAAAAlJBgDwAAAABKSLAHAAAAACVkjz0AAACAeqyYMSpRpdZVQ8ceAAAAAJSQYA8AAAAASkiwBwAAAAAlNEd77N1+++1z/MAf//jH33kxAAAAAMCcmaNgr0+fPnP0sKIoMnXq1P9mPQAAAAD8D1UVRaqKyjxmolLrqjFHwd60adPm9zoAAAAAgLnwX+2xN3HixHm1DgAAAABgLsx1sDd16tScfvrpWXLJJbPIIovkjTfeSJKcdNJJueaaa+b5AgEAAACAmc11sHfmmWfm2muvzbnnnptGjRrVXl9llVVy9dVXz9PFAQAAADB/FUVlj0o218Heddddl6uuuir9+vVLgwYNaq+vttpqefnll+fp4gAAAACAWZvrYO/dd9/NsssuO9P1adOmZfLkyfNkUQAAAADAN5vrYG+llVbKww8/PNP1m2++OT179pwniwIAAAAAvlnDuX3BySefnP79++fdd9/NtGnTcsstt+SVV17JddddlzvuuGN+rBEAAAAA+Jq57tjbbrvt8pe//CX33XdfmjVrlpNPPjkjR47MX/7yl2y22WbzY40AAAAAzCdFUVT0qGRz3bGXJBtssEHuvffeeb0WAAAAAGAOfadgL0meeuqpjBw5Mkn1vntrrLHGPFsUAAAAAPDN5jrY++c//5mdd945jz76aFq1apUkGTduXNZdd93ceOONWWqppeb1GgEAAACAr5nrPfb22WefTJ48OSNHjszYsWMzduzYjBw5MtOmTcs+++wzP9YIAAAAwHxSFJU9Ktlcd+w99NBDeeyxx7LCCivUXlthhRVyySWXZIMNNpiniwMAAAAAZm2uO/Y6duyYyZMnz3R96tSp6dChwzxZFAAAAADwzeY62DvvvPPy85//PE899VTttaeeeiq/+MUvMnDgwHm6OAAAAABg1uboq7itW7dO8R9fSv7iiy+y9tprp2HD6pdPmTIlDRs2zF577ZU+ffrMl4UCAAAAAP82R8HeoEGD5vMyAAAAAFgQqooiVRV6ykSl1lVjjoK9/v37z+91AAAAAABzYa5Pxf1PEydOzFdffVXnWosWLf6rBQEAAAAA326uD8/44osvcsghh2SJJZZIs2bN0rp16zoDAAAAAJj/5jrYO+aYYzJ06NBcccUVady4ca6++uqceuqp6dChQ6677rr5sUYAAAAA5pOiqOxRyeb6q7h/+ctfct1112XjjTfOnnvumQ022CDLLrtsOnfunCFDhqRfv37zY50AAAAAwH+Y6469sWPHZumll05SvZ/e2LFjkyTrr79+hg0bNm9XBwAAAADM0lwHe0svvXTefPPNJMmKK66Ym266KUl1J1+rVq3m6eIAAAAAgFmb62Bvzz33zHPPPZckOe6443LZZZelSZMmOfzww3P00UfP8wUCAAAAADOb6z32Dj/88Nr/3qtXr7z88st5+umns+yyy2bVVVedp4sDAAAAYP4qiiJFhZ4yUal11ZjrYO/rOnfunM6dO8+LtQAAAAAAc2iOgr2LL754jh946KGHfufFAAAAAABzZo6CvQsvvHCOHlYUhWAPAAAAAP4H5ijYqzkFFxaU0Q8OTIsWLRb0MgCgVFqv52AzAJhb06dOWtBLgDn2X++xBwAAAEB5Vc0YlahS66pR6fUBAAAAQEUS7AEAAABACQn2AAAAAKCE7LEHAAAAUI8VRZGiKBb0MuaLSq2rxnfq2Hv44Yez6667Zp111sm7776bJLn++uvzyCOPzNPFAQAAAACzNtfB3p/+9Kf07t07TZs2zbPPPptJk6qPgf70009z1llnzfMFAgAAAAAzm+tg74wzzsiVV16Z3/zmN1looYVqr6+33np55pln5uniAAAAAIBZm+tg75VXXsmGG2440/WWLVtm3Lhx82JNAAAAAMC3mOvDM9q1a5fXXnstXbp0qXP9kUceydJLLz2v1gUAAADA/0BRJFUVesZEhZ+dMfcde/vuu29+8Ytf5PHHH09RFHnvvfcyZMiQHHXUUTnwwAPnxxoBAAAAgK+Z64694447LtOmTcumm26aCRMmZMMNN0zjxo1z1FFH5ec///n8WCMAAAAA8DVzHewVRZETTjghRx99dF577bV8/vnnWWmllbLIIovMj/UBAAAAALMw18FejUaNGmWllVaal2sBAAAA4H+sqoL32KvUumrMdbC3ySabpPiGnQeHDh36Xy0IAAAAAPh2cx3s9ejRo87PkydPzogRI/Liiy+mf//+82pdAAAAAMA3mOtg78ILL5zl9VNOOSWff/75f70gAAAAAODbVc2rB+2666757W9/O68eBwAAAAB8g+98eMbXDR8+PE2aNJlXjwMAAADgf6Aoim88T6HMKrWuGnMd7G2//fZ1fp4+fXref//9PPXUUznppJPm2cIAAAAAgNmb62CvZcuWdX6uqqrKCiuskNNOOy2bb775PFsYAAAAADB7cxXsTZ06NXvuuWe6d++e1q1bz681AQAAAADfYq6CvQYNGmTzzTfPyJEjBXsAAAAAFaCqqB6VqFLrqjHXp+KussoqeeONN+bHWgAAAACAOTTXwd4ZZ5yRo446KnfccUfef//9jB8/vs4AAAAAAOa/Of4q7mmnnZYjjzwyW221VZLkxz/+cZ0jg6dPn56iKDJ16tR5v0oAAAAAoI45DvZOPfXUHHDAAXnggQfm53oAAAAAgDkwx8He9OnTkyQbbbTRfFsMAAAAAP9bRVE9KlGl1lVjrvbYKyr90wAAAACAkpjjjr0kWX755b813Bs7dux/tSAAAAAA4NvNVbB36qmnpmXLlvNrLQAAAADAHJqrYK9v375ZYokl5tdaAAAAAPgfqyqKVFXo9muVWleNOd5jz/56AAAAAPD9McfBXs2puAAAAADAgjfHX8WdNm3a/FwHAAAAADAX5rhjDwAAAAD4/pirwzMAAAAAqCxVqdzOr0qtq0al1wcAAAAAFUmwBwAAAAAlJNgDAAAAgBKyxx4AAABAPVYU1aMSVWpdNXTsAQAAAEAJCfYAAAAAoIQEewAAAABQQoI9AAAAACghh2cAAAAA1GNVKVJVoadMVKUy66qhYw8AAAAASkiwBwAAAAAlJNgDAAAAgBKyxx4AAABAPVYU1aMSVWpdNXTsAQAAAEAJCfYAAAAAoIQEewAAAABQQoI9AAAAACghh2cAAAAA1GNVRfWoRJVaVw0dewAAAABQQoI9AAAAACghwR4AAAAAlJA99gAAAADqsaJIqorK3IyuQsuqpWMPAAAAAEpIsAcAAAAAJSTYAwAAAIASEuwBAAAAQAk5PAMAAACgHiuKyj1kolLrqqFjDwAAAABKSLAHAAAAACUk2AMAAACAErLHHgAAAEA9VlVUj0pUqXXV0LEHAAAAACUk2AMAAACAEhLsAQAAAEAJCfYAAAAAoIQcngEAAABQjxUz/lOJKrWuGjr2AAAAAKCEBHsAAAAAUEKCPQAAAAAoIXvsAQAAANRjVUX1qESVWlcNHXsAAAAAUEKCPQAAAAAoIcEeAAAAAJSQYA8AAAAASsjhGQAAAAD1mMMzykvHHgAAAACUkGAPAAAAAEpIsAcAAAAAJWSPPQAAAIB6rCiKFEVlbkZXqXXV0LEHAAAAACUk2AMAAACAEhLsAQAAAEAJCfYAAAAAoIQcngEAAABQj1UV1aMSVWpdNXTsAQAAAEAJCfYAAAAAoIQEewAAAABQQvbYAwAAAKjHiqJ6VKJKrauGjj0AAAAAKCHBHgAAAACUkGAPAAAAAEpIsAcAAAAAJeTwDAAAAIB6rKooUlWhp0xUal01dOwBAAAAQAkJ9gAAAABghnfffTe77rprFl100TRt2jTdu3fPU089VXt/+vTpOfnkk9O+ffs0bdo0vXr1yqhRo+o8Y+zYsenXr19atGiRVq1aZe+9987nn38+z9cq2AMAAACAJJ988knWW2+9LLTQQvnb3/6Wf/zjHzn//PPTunXr2jnnnntuLr744lx55ZV5/PHH06xZs/Tu3TsTJ06sndOvX7+89NJLuffee3PHHXdk2LBh2W+//eb5eu2xBwAAAFCPVRXVoxLNbV3nnHNOOnbsmN/97ne117p27Vr736dPn55BgwblxBNPzHbbbZckue6669K2bdvcdttt6du3b0aOHJm77rorTz75ZNZcc80kySWXXJKtttoqAwcOTIcOHf77wmbQsQcAAAAASW6//fasueaa+dnPfpYlllgiPXv2zG9+85va+2+++WbGjBmTXr161V5r2bJl1l577QwfPjxJMnz48LRq1ao21EuSXr16paqqKo8//vg8Xa9gDwAAAICKNn78+Dpj0qRJs5z3xhtv5Iorrshyyy2Xu+++OwceeGAOPfTQDB48OEkyZsyYJEnbtm3rvK5t27a198aMGZMllliizv2GDRumTZs2tXPmFcEeAAAAABWtY8eOadmyZe0YMGDALOdNmzYtq6++es4666z07Nkz++23X/bdd99ceeWV/+MVzxl77AEAAABQ0d555520aNGi9ufGjRvPcl779u2z0kor1bnWrVu3/OlPf0qStGvXLknywQcfpH379rVzPvjgg/To0aN2zocffljnGVOmTMnYsWNrXz+v6NgDAAAAqM+KpKjQkRmHZ7Ro0aLOmF2wt9566+WVV16pc+3VV19N586dk1QfpNGuXbvcf//9tffHjx+fxx9/POuss06SZJ111sm4cePy9NNP184ZOnRopk2blrXXXnse/sHp2AMAAACAJMnhhx+eddddN2eddVZ23HHHPPHEE7nqqqty1VVXJUmKoshhhx2WM844I8stt1y6du2ak046KR06dEifPn2SVHf4bbHFFrVf4Z08eXIOOeSQ9O3bd56eiJsI9gAAAAAgSfKDH/wgt956a44//vicdtpp6dq1awYNGpR+/frVzjnmmGPyxRdfZL/99su4ceOy/vrr56677kqTJk1q5wwZMiSHHHJINt1001RVVWWHHXbIxRdfPM/XW0yfPn36PH8qzCPjx49Py5Yt88HHn9b5LjwA8O1ar3f0gl4CAJTO9KmTMunpS/Lpp5X/e2jN79zn3P1cmjZrvqCXM198+cVnObb3ahX756ljDwAAAKAeq0qRqprN6CpMpdZVw+EZAAAAAFBCgj0AAAAAKCHBHgAAAACUkGAPAAAAAErI4RkAAAAA9VhRVI9KVKl11dCxBwAAAAAlJNgDAAAAgBIS7AEAAABACdljDwAAAKAeqyqqRyWq1Lpq6NgDAAAAgBIS7AEAAABACQn2AAAAAKCEBHsAAAAAUEIOzwAAAACox6qKIlVFZZ4yUal11dCxBwAAAAAlJNgDAAAAgBIS7AEAAABACdljDwAAAKAeK4rqUYkqta4aOvYAAAAAoIQEewAAAABQQoI9AAAAACghwR4AAAAAlJDDMwAAAADqsaoUqarQUyaqUpl11dCxBwAAAAAlJNgDAAAAgBIS7AEAAABACdljDwAAAKAeK4rqUYkqta4aOvYAAAAAoIQEewAAAABQQoI9AAAAACghwR4AAAAAlJDDMwAAAADqsapUbudXpdZVo9LrAwAAAICKJNgDAAAAgBIS7AEAAABACdljDwAAAKAeK4oiRVEs6GXMF5VaVw0dewAAAABQQoI9AAAAACghwR4AAAAAlJBgDwAAAABKyOEZAAAAAPVYMWNUokqtq4aOPQAAAAAoIcEeAAAAAJSQYA8AAAAASsgeewAAAAD1WFVRpKqozN3oKrWuGjr2AAAAAKCEBHsAAAAAUEKCPQAAAAAoIcEeAAAAAJSQwzMAAAAA6rnKPmKicunYAwAAAIASEuwBAAAAQAkJ9gAAAACghOyxBwAAAFCPFUX1qESVWlcNHXsAAAAAUEKCPQAAAAAoIcEeAAAAAJSQYA8AAAAASsjhGQAAAAD1WFEUKSr0lIlKrauGjj0AAAAAKCHBHgAAAACUkGAPAAAAAErIHnsAAAAA9VhVKrfzq1LrqlHp9QEAAABARRLsAQAAAEAJCfYAAAAAoIQEewAAAABQQg7PAAAAAKjHiqJIURQLehnzRaXWVUPHHgAAAACUkGAPAAAAAEpIsAcAAAAAJWSPPQAAAIB6rJgxKlGl1lVDxx4AAAAAlJBgDwAAAABKSLAHAAAAACUk2AMAAACAEnJ4BgAAAEA9VhRFiqIyj5mo1Lpq6NgDAAAAgBIS7AEAAABACQn2AAAAAKCEBHsAAAAAUEIOzwAAAACox6pSuZ1flVpXjUqvDwAAAAAqkmAPAAAAAEpIsAcAAAAAJWSPPQAAAIB6rCiKFEWxoJcxX1RqXTV07AEAAABACQn2AAAAAKCEBHsAAAAAUEKCPQAAAAAoIYdnAAAAANRjxYxRiSq1rho69gAAAACghAR7AAAAAFBCgj0AAAAAKCF77AEAAADUY0VRPSpRpdZVQ8ceAAAAAJSQYA8AAAAASkiwBwAAAAAlJNgDAAAAgBJyeAYAAABAPVaVIlWpzFMmKrWuGjr2AAAAAKCEBHsAAAAAUEKCPQAAAAAoIXvsAQAAANRjRVE9KlGl1lVDxx4AAAAAlJBgDwAAAABKSLAHAAAAACUk2AMAAACAEnJ4BgAAAEA9Vsz4TyWq1Lpq6NgDYI68+sor6bJUu3z22WcLeikzGfmPf2SZLkvliy++WNBLAYBZ6r/tD/KXi/dd0Mv4zk4/aMtccOR2C3oZAHyNYA+gJB55eFh26LNtunbqkKYLFbn9z7fVuT958uSccPyxWbNH9yzaslm6duqQvffYPe+9916deaNefTU/2367LNVusSzRpkV+tNH6eejBB771/U868fgcePDP07x589prN//xpqy9Ro+0abFwll+mcy44/7xvfMbbb72VA/bdOysu1zWtmzfNSissk9NP/VW++uqrOnN6bbJhFm3ZLL022TBvv/VWnWdsv902ufWWP9W51m2llbLW2j/MxYMu+NY6AKg86/XompsH7pk37jgxXz5+XrbdcOU69xs2qMoZB2+VJ4cckY8ePDNv3HFirv5V37RfrEWdeS/feny+fPy8OuOo3TeZ6f0O67dRnv/jMRn38IC8/pcTc8weP/rG9TVu1DC/2r93zrr63jprOn7vXnnpT8flk2Fn5fH/Ozyb/XCFmV67/0/Xzcu3Hp9Php2VYdf8PGuu1PFbP49D+q6f5246OmMfOiujbj8h5x62bRo3+veXtfr27plRt5+Q9+49Nef8Yts6r+3UvnWe/+Mxad6scZ3rg4Y8lH5br5kuHdp86/sD8L8j2AMoiS+++CLdV10tgy6+bJb3J0yYkBHPPpPjTjgpw594JjfedEteffWV/OwnP64zb/s+22TKlCn52z1D89jjT2fVVVfL9tttkzFjxsz2vUePHp2/3XlHdt19j9prd9/1t+y5e7/ss98BeXrEi7nokstzyUUX5orLLp3tc1555eVMmzYtl17+6zzz3Es5d+CFufqqK3Pyib+snXPsMUemw5JL5vGnRqRd+/Y57tijau/98aY/pKqqKj/ZfoeZnr17/z1z1a+vyJQpU2b7/gBUpmZNG+WFUe/lsPNum+X9hZs0So8VlszZv70v6+w+KH2Puy7Ld1o8fxy4x0xzT/313emy5Wm14/KbHqlz//wjtsseP14rx198R1bb8dz89Ojf5al/vPON6/vJj1bN+C8mZfjzb9VeO+WALbJPnx/miPNvS8++A3P1LX/PH87pn9WW71A756e9Vss5v9g2Z15zb9bpPyjPv/Zebr9onyzeutls32unzXvk9IO2yllX35sefc/LAWf+MT/ttVpOO3DLJMmiLRfO5b/8WY6/+I5se+hv0neL1bPlet1qX3/RMT/JSZf9NZ99ManOcz/+dELu+/sr2W+Hdb6xVgD+t+yxB1ASvbfYMr232HK291u2bJk777q3zrULL7o0G6y7VkaPHp1OnTrlo48+ymujRuWKq65J91VXTZKcftbZ+fWVl+cfL72Ydu3azfLZf7r5pnRfdbUsueSStdduGHJ9tt2uT/bd/4AkSdell87Rxxyf8weekwMOOjhFMfNeFpv33iKb996i9ueuSy+dV199Jb/59RU5+9yBSZJXXh6Zc867IMsut1x2232PHD8j2Bs3blxO/dWJ+ds9Q2e5xk17bZZPxo7Nw8MeyiY/2nS2nxMAleee4a/knuGvzPb++C8mZptDf1Pn2uEDb80j1/4iHdu2yjsfjKu9/vmEiflg7Ky3nVihyxLZd4d1ssbO52fU6H8lSd5+/5NvXd/PNlstf33kH3Wu7bLl6jnn2qG5+7GXkyS/uWV4frTWcvnFLhtlr1N+nyQ5dOcN87s/P57r73gqSfLzs2/Jlut2S/9t18rA62bdbf/DVbtk+PNv5Q/3jEiSjH7/k9x0z4j8YJVOSZKuSy6aT7+YmJvvey5JMuzp17Ji1yXyt0dHZsfNe2TylGn584MvzvLZdz48MqceuEV+ecmd31ozUC5FUT0qUaXWVUPHHkAFGz/+0xRFkVatWiVJFl100Sy/wgq54frr8sUXX2TKlCm5+je/zhJLLJGeq68x2+c89sjDWWONNetcmzRpUpo0blLnWtOmTfPuP/+Z0W+/Pedr/PTTtGn976/1dF91tQy9/75MmzYt9917T1bpXh1A/vLYo7P/AQenY8dZfwWpUaNGWXW1Hnn0kYfn+L0BqL9aLNI006ZNy7jPv6xz/cjdf5R/3nNKhl93WA7fdaM0aPDvX5m2Xn+lvPnux9lq/W4ZeevxefnW43P5L3+a1i2afuN7rbta1zwz8p91rjVq1DATJ02uc+3LiZOz7mpdkiQLNWyQnisumaFPjKq9P3369Ax9clTW6t55tu/19+ffSs8Vl6r9ym6XDm3Se90Vc9ej1QHia+98lIWbLJTVlu+Q1i2aZo2VOuaFUe+nVfOmOXm/3jnivFtn++yn/jE6S7VtlU7tW39jvQD87wj2SuTaa6+t/eV8dvbYY4/06dNnvq9l4403zmGHHfaNc+ZkvcD8M3HixJx4/LHZcaed06JF9R5CRVHkzrvuy3Mjns3irZun1SJNcvGgC/LnO+5K69az/z/po99+O+07dKhzbbPNeufPt92SB4ben2nTpmXUq6/mokHnJ0nef//9OVrj66+9lisuuyR777t/7bUB5wzMq6+8nBWW7ZLXXxuVAecMzCMPD8tzz41Iv912T7+dd0y35ZfOzw86oM7efEnSvkOHuQoVAaifGjdqmDMO2So33TOizldOL7/p0ex+4v9li4N+nWtu/XuO7v+jnHXI1rX3uyzZJp3atc72m66afU69Mfue/of0XHGp3DBg99m+V8tFmqRV86Z5/6Pxda7f9/dXc+guG2aZjoulKIr8aK3lst0mq6TdjH3/FmvVLA0bNsiHYz+v87oPx36edm2aZ3b+cM+InH7V3bn/qoMy/tGzM/LW4zPsmTdy3uDqjvdxn32ZfU/9Q67+Vd88/NtDM+SvT+e+x1/NgEO3yZU3P5rOHdpk+HWH5akbjsxPftS9zrNraujUTrAH8H0h2Et1GFYURQ444ICZ7h18cPXXyfbYY4959n6nnHJKevToMc+e933QpUuXDBo0aEEvA5hh8uTJ2XXnHTN9+vRcfNkVtdenT5+eww89OIsvsUTue+DhPPzYE/nxj/tkh59s+41h3JcTv0zjJnW78/baZ98ccNAh2X67bdJi4UbZaP0f5mc79k2SVFV9+18v7777bn68zRbZfoefZa99/n1K4JJLLplb/nxHRr0xOrf8+Y4stthi+cXPD8oll12Zs886I80XaZ7nX3olr702Kldf9es6z2zapGkmfDlhjj4jAOqnhg2q8n9n7poiyaHn3lLn3sW/H5aHn3kjL772fq6+9e857uI7cuCO66XRQg2SJFVVRZo0Xih7n3JjHh3xZh5+5o0ceOYfs/Gay2a5TovP8v2aNl4oSWbqzjvqgj/n9Xc+ynN/ODrjHxmQC4/qk+vueCrTpk3/r+rbYPWlc/Qem+YX596adXYflJ2OGZwt11sxx+3Vq3bO7Q+9mB/0uyCr/PScnHn1vVm/59Lpvmz7XHPr47n+jH45+sLbs/Nx1+WKE35WZz+/LydW17Bwk4X+qzUCMO8I9mbo2LFjbrzxxnz55b9b8SdOnJgbbrghnTp1WoArA5g7kydPTr+dd8zot9/OHXfdW9utlyQPPjA0f73zjlw35Masu9566bn66rno0svTtGnT/N/1g2f7zEUXXSzjPqm7h1BRFDlzwDn5aNzneeX1t/PWP8dkzR+slaR677xv8t5772WLzTbJD3+4bi678qpvnHvu2Wdl016bZ/U11sjDDz2YPtvvkIUWWijb9dk+w4Y9WGfuJ5+MzWKLzfoXKwBo2KAqQ87aLZ3at842P//NTAdEfN2TL47OQg0bpHP76i0jxnz0WSZPmZrX3vmods7Lb32QJOnYrtUsn/HxpxMybdq0tGqxcJ3rH437IjseMziLbnxCVuhzVlbb8bx8MWFS3nzv49r7U6ZMzRJtFqnzuiXaLJIxs9kDMEl+tX/v/P5vT+fa25/IS6+Pye0PvZiTr7grR/ffZJb73zZaqEEuOuYnOeTsP2WZjoulQYMGeeTZNzJq9L/y2uiP8oOV//27UJuW1TV89MkXs31/AP63BHszrL766unYsWNuueXf/9bulltuSadOndKzZ8/aa9OmTcuAAQPStWvXNG3aNKuttlpuvvnm2vsPPvhgiqLI/fffnzXXXDMLL7xw1l133bzySvVmvtdee21OPfXUPPfccymKIkVR5Nprr02SXHDBBenevXuaNWuWjh075qCDDsrnn9dtvZ9TAwcOTPv27bPooovm4IMPzuTJ//43hJMmTcpRRx2VJZdcMs2aNcvaa6+dBx98sPb+xx9/nJ133jlLLrlkFl544XTv3j2///3vZ/teG2+8cd5+++0cfvjhtTX9p7vvvjvdunXLIosski222GKOv6IHzL2aUO/110blzrvvy6KLLlrn/oQJ1d1sX++oq6qqyvRp02b73NV69MzIkf+Y5b0GDRpkySWXTKNGjXLTjb/P2j9cJ4svPvtw7d13303vXhun5+pr5KprfveN3X0vjxyZP9x4Q3516ulJkqlTp9b+82zy5MmZOnVqnfkvvfTi/7d35/FWlfX+wD9bkMN0QFEBUQQUB3CeUrKbQyp4qas/zaEcMCxLoZs4zwNeOVpizmJqoKUSmXlvYpHgDHY1CuM6YJJ4zFDs3itoBIdh//6As/PIoHQxXPu83/u1XrLXevZaz3N4vVzsz/mu58kuu+y6wnkAoDHU26r7xhk49Hv5n3kfXuG98zbdsmTJ0rz9v8v+Tf7072Zl/ZYt0muzv91fGyv16lexiMaixUvy4qtz0qdX55UeX9iwOH96e15atlgvh+2/Yx584vnK53770hvZf8/elbalUin779k7z0xf9bQTbVq3WqHqb+nye/zKJpA/d/CBefjpGZk24420WK+Ulu+bU7BlyxZp8b77dN8tu6Zh0eK88Oqbq7w+UEyllLJelW6lVPfqGYK99xk8eHBGjx5def/9738/X/nKV5q0qaury1133ZVRo0bl+eefz7Bhw3Lcccfl8ccfb9LuggsuyMiRI/PrX/86LVu2zODBg5MkRx99dM4444xsv/32mT17dmbPnp2jjz46ybIv1tdff32ef/753HnnnXnkkUdy9tlnr/E4Hn300cycOTOPPvpo7rzzzowZM6YSHibJ0KFD8/TTT2fs2LH53e9+lyOPPDIDBgzI73+/bGLeBQsWZPfdd8/48ePzX//1Xzn55JNz/PHH55lnnlnp9e6///5svvnmGT58eGVMjebPn5+rr746P/jBD/LEE0+kvr4+Z5555ir7vnDhwsybN6/JBizz3nvv5blp0/LctGlJklmvvprnpk1LfX19kmVB15eP/mJ+M/XXGX3n3VmyZEnefPPNvPnmm5W56Pbau1823HDDfHXwoPzuuefy+5dfznnnnJVZr76aAYcMXNWlc9DB/fPMr55uEqT9+c9/zm23jsqMl17Kc9Om5Yxh38r9P/lxvjPy2kqbZ595JjvvsF3eeOONJH8L9bp33yJ1V12dt99+u9LHDyqXyxlyysn59tXfTbt2yx4D6vfpfTL6jtvy0osv5p4f3pV+n96n0v61WbPypzfeyP6fO3CFcwFQ3dq1aZWdtu6WnbZeNh9sz26dstPW3dK9ywZJloV691x5Qnbrs3m+csk9abHeeunSqTZdOtVm/ZbLHrPda4ceGXrMZ7Lj1pumZ7dOOab/rrnqtH/Jvb/4Td55d9lTPY888/v85qU/5tYLj8zO23TLrtttlhvPPSIT//PlJlV8HzTxVzPy6Z17Ndm35/bdc+h+O6Rnt07ZZ5de+Y/rvpr11ivlmh88Vmlz/b1P5CuH7pVj/3n3bNuzc64/5/C0bd0qdz34bKXN7Zcck+GnHlJ5/9CTL+RrR/TLkQftnB6bbpgDPrV1Lj65fx568oUVAr/tenXOFw/cOcO/NyFJMuO1OVlaLmfQF/bMgH22y7Y9NsnUF1+vtN9nl16ZPO3VLFi4+CP+zQDwcWu5rjvwSXLcccflvPPOy2vLJ16fPHlyxo4dW6lmW7hwYUaMGJGJEyemX79+SZItt9wyTz31VG699dbsu+++lXNdccUVlffnnntuBg4cmAULFqRNmzZp3759WrZsma5duza5/vsXo+jZs2f+7d/+Ld/4xjdy8803r9E4Ntxww9x4441p0aJFtttuuwwcODCTJk3K1772tdTX12f06NGpr69Pt+UT4Z955pn5xS9+kdGjR2fEiBHZbLPNmoRv3/zmNzNhwoSMGzcun/rUp1a4XqdOndKiRYvU1tauMKZFixZl1KhR2WqrrZIsCxWHDx++yr7X1dXlsssuW6PxQnPxm6m/Tv8D96+8P+es05Mkxx0/KLd9f0z+9MYbefBn/5Ek2WuPXZp8dsLER/PZfffLxhtvnH9/8Be59OILcsjBB2TRokXp03f7/Pj+f89OO++8ymv3H3BIWrRsmUcmTcxBB/ev7P/hD+7MeeecmXK5nL327pcJEx/Lnu/7/8Rf/zo/L8+YkcXLq+wemfhwZr7ySma+8kp699y8yTX+uqjpl407bvteOnfpkn8e+PnKvgsuvjQnHv/lfHafvXJQ/wH5xilDKsfG/ejeHHjQwenRY9UrBQJQnXbrs3l+ecsplfffHvYvSZIfPPjrnHz5j9Ktc8d84bPbJ0me+eHpTT578Cm35Mnf/CELFy3OkQftkgu+enBq1m+ZWbP/JzeMfSLX3/NEpW25XM4Xzxida848LA+POiV/WbAov3z6pZx73c9W278xP3smk8d8Kx3atc68vyxIktS0Wj+XfGNAenXrlPf+2pAJU17KSZeOzdz3FlQ+d9/E57LxBu1y8cn902Wj2vzu5T/l0NNub7KgRvcuGzQJ7K4cPSnlcnLJ1wek2yYd8+d33sv4p17Mpbf8fIV+3XTeF3POtT/L/OVz5y1YuDgnD/9Rrj3r/6VVq5YZdvUD+dPbf/tF+5EH7ZIrbv/lascKwD+WYO99NtlkkwwcODBjxoxJuVzOwIEDs/HGG1eOv/LKK5k/f34OOuigJp9raGho8rhukuy0006VP2+66aZJkjlz5qx2vr6JEyemrq4uL730UubNm5fFixdnwYIFmT9/ftq2bTonR319ffr27Vt5f/755+f8889Pkmy//fZp0aJFk+tPnz49STJ9+vQsWbIk22yzTZPzLVy4sPLI3pIlSzJixIiMGzcub7zxRhoaGrJw4cIV+vBRtG3bthLqNfZlzpw5q2x/3nnn5fTT//aPrXnz5qV79+5rfF2oRp/dd78Vwq/369Gz52qPN9p9jz3ys4cmrNG1W7ZsmbPPOT/XX3tNJdjbeOON8/hTT69Rn48fdGKOH3TiR7rmV0/+er568teb7OvcuXMemjBxhbYNDQ257XujMuauez7SuQGoLk/+5g9ps9dZqzxeP/t/V3s8SabNeCP7nnTjh15r9p/n5Uvn3rVG/Xvp1Tn5xeQXc/IX++XqOx9Nkjz12z9kt2Ou/tDPjrpvSkbdN2WVx/ufOqrJ+yVLlmbEHQ9nxB0Pf+i5P3fyigUEP5/8Yn4++cUV9h/cb9ssLZdz/yPTP/S8APzjCPY+YPDgwRk6dGiS5KabbmpyrHG+u/Hjx2ezzTZrcqympqbJ+/XX/9tKUY1zzi1dzfxVs2bNyuc///mccsopueKKK9KpU6c89dRTOemkk9LQ0LBCqNatW7dMW/44XrKsam5l1268fuO133vvvbRo0SJTp05tEv4lSfv2yybm/c53vpPrrrsu1157bWXOv9NOO63yKN+aWFlfyuVVBw81NTUr/CyBT4avnvz1zJ37Tt59993U1tau6+408Xp9fc4+5/x8ep99PrwxAKwD590wPgM/0/fDG35CtWvTKl+//EdZsmTV32mA4iqVVj4PZzWo1nE1Eux9wIABA9LQ0JBSqZT+/fs3Oda3b9/U1NSkvr6+yWO3a6pVq1YrTPg+derULF26NCNHjqxMJD9u3LhVnqNly5bp3bv3Ko+vyq677polS5Zkzpw5+ad/+qeVtpk8eXIOPfTQHHfccUmWBZIvv/xykwrBD1rZmIDq0rJly5xz3gXruhsrtVXv3tnq7/h/IgD8o9TP/t/c8uPJ67obf7efqtQD+EQS7H1AixYt8uKLL1b+/H61tbU588wzM2zYsCxdujSf+cxnMnfu3EyePDkdOnTIoEGDPtI1evbsmVdffTXTpk3L5ptvntra2vTu3TuLFi3KDTfckC984QuZPHlyRo0a9eEnW0PbbLNNjj322JxwwgkZOXJkdt1117z99tuZNGlSdtpppwwcODBbb7117rvvvkyZMiUbbrhhrrnmmrz11lurDfZ69uyZJ554Isccc0xqamqaPMIMAAAAwNpnVdyV6NChQzp06LDSY5dffnkuuuii1NXVpU+fPhkwYEDGjx+fXr16rbT9yhxxxBEZMGBA9t9//2yyySa59957s/POO+eaa67JVVddlR122CF333136urq1taQmhg9enROOOGEnHHGGdl2221z2GGH5dlnn63M/3fhhRdmt912S//+/bPffvula9euOeyww1Z7zuHDh2fWrFnZaqutsskmm3ws/QYAAADgb0rl1U14BuvYvHnz0rFjx7z133NXGbYCACu34T6rXywAAFhRecnCLJx6Q+bOrf7voY3fuX/ynzPTrv0nax7tteUv772bI/baqmr/Pj2KCwAAANCMWTyjuDyKCwAAAAAFJNgDAAAAgAIS7AEAAABAAZljDwAAAKAZKy1/VaNqHVcjFXsAAAAAUECCPQAAAAAoIMEeAAAAABSQYA8AAAAACsjiGQAAAADN2HqlZVs1qtZxNVKxBwAAAAAFJNgDAAAAgAIS7AEAAABAAZljDwAAAKAZKy1/VaNqHVcjFXsAAAAAUECCPQAAAAAoIMEeAAAAABSQYA8AAAAACsjiGQAAAADNWKm0bKtG1TquRir2AAAAAKCABHsAAAAAUECCPQAAAAAoIHPsAQAAADRjpSSlVOdkdNU5qr9RsQcAAAAABSTYAwAAAIACEuwBAAAAQAEJ9gAAAACggCyeAQAAANCMrVdatlWjah1XIxV7AAAAAFBAgj0AAAAAKCDBHgAAAAAUkDn2AAAAAJqx0vJXNarWcTVSsQcAAAAABSTYAwAAAIACEuwBAAAAQAEJ9gAAAACggCyeAQAAANCMlUrLtmpUreNqpGIPAAAAAApIsAcAAAAABSTYAwAAAIACMsceAAAAQDNWWr5Vo2odVyMVewAAAABQQII9AAAAACggwR4AAAAAFJBgDwAAAAAKyOIZAAAAAM3YeillvVJ1LjOxXpUvn6FiDwAAAAAKSLAHAAAAAAUk2AMAAACAAjLHHgAAAEAzVlq+VaNqHVcjFXsAAAAAUECCPQAAAAAoIMEeAAAAABSQYA8AAAAACsjiGQAAAADNmdUzCkvFHgAAAAAUkGAPAAAAAApIsAcAAAAABWSOPQAAAIBmrLT8VY2qdVyNVOwBAAAAQAEJ9gAAAACggAR7AAAAALASV155ZUqlUk477bTKvgULFmTIkCHZaKON0r59+xxxxBF56623mnyuvr4+AwcOTNu2bdO5c+ecddZZWbx48Vrvn2APAAAAAD7g2Wefza233pqddtqpyf5hw4blZz/7WX784x/n8ccfz5/+9KccfvjhleNLlizJwIED09DQkClTpuTOO+/MmDFjcvHFF6/1Pgr2AAAAAJqzUlKq0u3vXTvjvffey7HHHpvbbrstG264YWX/3Llzc8cdd+Saa67JAQcckN133z2jR4/OlClT8qtf/SpJ8stf/jIvvPBCfvjDH2aXXXbJIYcckssvvzw33XRTGhoa1sJf2N8I9gAAAADgfYYMGZKBAwfmwAMPbLJ/6tSpWbRoUZP92223XbbYYos8/fTTSZKnn346O+64Y7p06VJp079//8ybNy/PP//8Wu1ny7V6NgAAAAD4hJk3b16T9zU1NampqVlp27Fjx+Y3v/lNnn322RWOvfnmm2nVqlU22GCDJvu7dOmSN998s9Lm/aFe4/HGY2uTij0AAAAAqlr37t3TsWPHylZXV7fSdq+//nq+9a1v5e67707r1q3/wb1ccyr2AAAAAJqx/8NUdJ94jeN6/fXX06FDh8r+VVXrTZ06NXPmzMluu+1W2bdkyZI88cQTufHGGzNhwoQ0NDTknXfeaVK199Zbb6Vr165Jkq5du+aZZ55pct7GVXMb26wtKvYAAAAAqGodOnRosq0q2Pvc5z6X6dOnZ9q0aZVtjz32yLHHHlv58/rrr59JkyZVPjNjxozU19enX79+SZJ+/fpl+vTpmTNnTqXNww8/nA4dOqRv375rdVwq9gAAAAAgSW1tbXbYYYcm+9q1a5eNNtqosv+kk07K6aefnk6dOqVDhw755je/mX79+mXvvfdOkhx88MHp27dvjj/++Hz729/Om2++mQsvvDBDhgxZZaD49xLsAQAAAMBH9N3vfjfrrbdejjjiiCxcuDD9+/fPzTffXDneokWLPPjggznllFPSr1+/tGvXLoMGDcrw4cPXel8EewAAAACwCo899liT961bt85NN92Um266aZWf6dGjRx566KGPuWeCPQAAAIDmrTmsnlGlLJ4BAAAAAAUk2AMAAACAAhLsAQAAAEABmWMPAAAAoBkrLX9Vo2odVyMVewAAAABQQII9AAAAACggwR4AAAAAFJBgDwAAAAAKyOIZAAAAAM1YqbRsq0bVOq5GKvYAAAAAoIAEewAAAABQQII9AAAAACggc+wBAAAANGOl5Vs1qtZxNVKxBwAAAAAFJNgDAAAAgAIS7AEAAABAAQn2AAAAAKCALJ4BAAAA0JxZPaOwVOwBAAAAQAEJ9gAAAACggAR7AAAAAFBA5tgDAAAAaMZKy1/VqFrH1UjFHgAAAAAUkGAPAAAAAApIsAcAAAAABSTYAwAAAIACsngGAAAAQDNWKi3bqlG1jquRij0AAAAAKCDBHgAAAAAUkGAPAAAAAArIHHsAAAAAzVhp+VaNqnVcjVTsAQAAAEABCfYAAAAAoIAEewAAAABQQII9AAAAACggi2cAAAAANGdWzygsFXsAAAAAUECCPQAAAAAoIMEeAAAAABSQOfYAAAAAmrHS8lc1qtZxNVKxBwAAAAAFJNgDAAAAgAIS7AEAAABAAQn2AAAAAKCALJ4BAAAA0IyVSsu2alSt42qkYg8AAAAACkiwBwAAAAAFJNgDAAAAgAIyxx4AAABAM1ZavlWjah1XIxV7AAAAAFBAgj0AAAAAKCDBHgAAAAAUkGAPAAAAAArI4hkAAAAAzZnVMwpLxR4AAAAAFJBgDwAAAAAKSLAHAAAAAAVkjj0AAACAZqy0/FWNqnVcjVTsAQAAAEABCfYAAAAAoIAEewAAAABQQII9AAAAACggi2cAAAAANGOl0rKtGlXruBqp2AMAAACAAhLsAQAAAEABCfYAAAAAoIDMsQcAAADQjJWWb9WoWsfVSMUeAAAAABSQYA8AAAAACkiwBwAAAAAFJNgDAAAAgAKyeAYAAABAc2b1jMJSsQcAAAAABSTYAwAAAIACEuwBAAAAQAEJ9gAAAACggCyeAQAAANCMlZa/qlG1jquRij0AAAAAKCDBHgAAAAAUkGAPAAAAAArIHHsAAAAAzViptGyrRtU6rkYq9gAAAACggAR7AAAAAFBAgj0AAAAAKCDBHgAAAAAUkMUzAAAAAJqx0vKtGlXruBqp2AMAAACAAhLsAQAAAEABCfYAAAAAoIDMsQcAAADQnJlkr7BU7AEAAABAAQn2AAAAAKCABHsAAAAAUECCPQAAAAAoIItnAAAAADRjpeWvalSt42qkYg8AAAAACkiwBwAAAAAFJNgDAAAAgAIyxx4AAABAc1ZKStU6FV21jms5FXsAAAAAUECCPQAAAAAoIMEeAAAAABSQYA8AAAAACsjiGQAAAADNWCnVu8ZEtY6rkYo9AAAAACggwR4AAAAAFJBgDwAAAAAKyBx7AAAAAM2ZSfYKS8UeAAAAABSQYA8AAAAACkiwBwAAAAAFJNgDAAAAgAKyeAYAAABAM1Za/qpG1TquRir2AAAAAKCABHsAAAAAUECCPQAAAAAoIHPsAQAAADRjpdKyrRpV67gaqdgDAAAAgAIS7AEAAABAAQn2AAAAAKCABHsAAAAAUEAWzwAAAABoxkrLt2pUreNqpGIPAAAAAApIsAcAAAAABSTYAwAAAIACMsceAAAAQHNmkr3CUrEHAAAAAAUk2AMAAACAAhLsAQAAAEABCfYAAAAAoIAsngEAAADQjJWWv6pRtY6rkYo9AAAAACggwR4AAAAAFJBgDwAAAAAKyBx7AAAAAM1YKUmpSqeiq9JhVajYAwAAAIACEuwBAAAAQAEJ9gAAAACggAR7AAAAAFBAFs8AAAAAaMZKqd5FJqp1XI1U7AEAAABAAQn2AAAAAKCABHsAAAAAUEDm2AMAAABoxkqlZVs1qtZxNVKxBwAAAAAFJNgDAAAAgAIS7AEAAABAAQn2AAAAAKCALJ4BAAAA0KyVlm/VqFrHtYyKPQAAAAAoIMEeAAAAABSQYA8AAAAACsgce3yilcvlJMm78+at454AQPGUlyxc110AgMIpL2lY9t/l30ebg1Jp2VaNqnVcjQR7fKK9++67SZLevbqv454AAADQnLz77rvp2LHjuu4GrJZgj0+0bt265fXXX09tbW1K1R6zQwHNmzcv3bt3z+uvv54OHTqs6+4AQGG4h8InV7lczrvvvptu3bqt667AhxLs8Ym23nrrZfPNN1/X3QA+RIcOHXwpAYC/g3sofDKp1KMoLJ4BAAAAAAWkYg8AAACgGSst36pRtY6rkYo9AP5uNTU1ueSSS1JTU7OuuwIAheIeCsDaUCo3p/WbAQAAAEiybCGfjh075qXX3k5tlc73+e68edmuxyaZO3duVc5pqmIPAAAAAArIHHsAAAAAzViptGyrRtU6rkYq9gD4hxozZkw22GCD1bY58cQTc9hhh33sfdlvv/1y2mmnrbbNR+kvAHwY9z8APg6CPYD/oxNPPDGlUilXXnllk/0PPPBASlX866FLL700u+yyy7ruxlrVs2fPXHvtteu6GwCsgcb78De+8Y0Vjg0ZMiSlUiknnnjiWrue+x8AnySCPYC1oHXr1rnqqqvyv//7v+u6KwDQ7HTv3j1jx47NX//618q+BQsW5J577skWW2yxDnsGAB8vwR7AWnDggQema9euqaurW2Wbn/zkJ9l+++1TU1OTnj17ZuTIkU2O9+zZMyNGjMjgwYNTW1ubLbbYIt/73vdWe93HHnsspVIpEyZMyK677po2bdrkgAMOyJw5c/Lzn/88ffr0SYcOHfLlL3858+fPr3xu6dKlqaurS69evdKmTZvsvPPOue+++1Y476RJk7LHHnukbdu2+fSnP50ZM2YkWfZ4zmWXXZbnnnsupVIppVIpY8aMSZJcc8012XHHHdOuXbt07949p556at577701/ZEmSa6++upsuumm2WijjTJkyJAsWrSocmzhwoU588wzs9lmm6Vdu3bZa6+98thjj1WO//d//3e+9KUvZbPNNkvbtm2z44475t57713ltfbbb7+89tprGTZsWGVM7zdhwoT06dMn7du3z4ABAzJ79uy/a0wArH277bZbunfvnvvvv7+y7/77788WW2yRXXfdtbLP/W9F7n8AxSbYA1gLWrRokREjRuSGG27IH//4xxWOT506NUcddVSOOeaYTJ8+PZdeemkuuuiiypeBRiNHjswee+yR3/72tzn11FNzyimnVL5MrM6ll16aG2+8MVOmTMnrr7+eo446Ktdee23uueeejB8/Pr/85S9zww03VNrX1dXlrrvuyqhRo/L8889n2LBhOe644/L44483Oe8FF1yQkSNH5te//nVatmyZwYMHJ0mOPvronHHGGdl+++0ze/bszJ49O0cffXSSZL311sv111+f559/PnfeeWceeeSRnH322Wv6I82jjz6amTNn5tFHH82dd96ZMWPGNPl5DR06NE8//XTGjh2b3/3udznyyCMzYMCA/P73v0+yrFJj9913z/jx4/Nf//VfOfnkk3P88cfnmWeeWen17r///my++eYZPnx4ZUyN5s+fn6uvvjo/+MEP8sQTT6S+vj5nnnnmGo8JgI/P4MGDM3r06Mr773//+/nKV77SpI3734rc/4AkKVX5q6qVAfg/GTRoUPnQQw8tl8vl8t57710ePHhwuVwul3/605+WG/83++Uvf7l80EEHNfncWWedVe7bt2/lfY8ePcrHHXdc5f3SpUvLnTt3Lt9yyy2rvPajjz5aTlKeOHFiZV9dXV05SXnmzJmVfV//+tfL/fv3L5fL5fKCBQvKbdu2LU+ZMqXJuU466aTyl770pVWed/z48eUk5b/+9a/lcrlcvuSSS8o777zz6n845XL5xz/+cXmjjTaqvB89enS5Y8eOq/3MoEGDyj169CgvXry4su/II48sH3300eVyuVx+7bXXyi1atCi/8cYbTT73uc99rnzeeeet8rwDBw4sn3HGGZX3++67b/lb3/pW5X2PHj3K3/3ud5t8ZvTo0eUk5VdeeaWy76abbip36dJltWMA4B+j8T48Z86cck1NTXnWrFnlWbNmlVu3bl1+++23y4ceemh50KBB7n/uf8BKzJ07t5yk/HL9n8uz32moyu3l+j+Xk5Tnzp27rn/cH4uW6yhPBKhKV111VQ444IAVfpv94osv5tBDD22yb5999sm1116bJUuWpEWLFkmSnXbaqXK8VCqla9eumTNnTpLkkEMOyZNPPpkk6dGjR55//vlK2/d/rkuXLmnbtm223HLLJvsaf1P/yiuvZP78+TnooIOa9KehoaHJ40ofPO+mm26aJJkzZ85q5yuaOHFi6urq8tJLL2XevHlZvHhxFixYkPnz56dt27ZN2tbX16dv376V9+eff37OP//8JMn2229f+bk0Xn/69OlJkunTp2fJkiXZZpttmpxv4cKF2WijjZIkS5YsyYgRIzJu3Li88cYbaWhoyMKFC1fow0fRtm3bbLXVVk360vj3AsAnwyabbJKBAwdmzJgxKZfLGThwYDbeeOPKcfc/9z+AaiTYA1iLPvvZz6Z///4577zz/q4V+NZff/0m70ulUpYuXZokuf322yuTgn+w3fvfl0ql1Z6ncb6f8ePHZ7PNNmvSrqamZrXnTVI5z8rMmjUrn//853PKKafkiiuuSKdOnfLUU0/lpJNOSkNDwwpfKrp165Zp06ZV3nfq1Gml117ZGFq0aJGpU6c2+fKTJO3bt0+SfOc738l1112Xa6+9tjLn0WmnnZaGhoZV9n9VVtaXcrm8xucB4OM1ePDgDB06NEly0003NTnm/uf+B1CNBHsAa9mVV16ZXXbZJdtuu21lX58+fTJ58uQm7SZPnpxtttlmhX+Yr8oHv4T8vfr27ZuamprU19dn3333/bvP06pVqyxZsqTJvqlTp2bp0qUZOXJk1ltv2TSu48aNW+U5WrZsmd69e6/xtXfdddcsWbIkc+bMyT/90z+ttM3kyZNz6KGH5rjjjkuy7AvZyy+/3KRC4oNWNiYAimPAgAFpaGhIqVRK//79mxxz/3P/A1ajtHyrRtU6ruUEewBr2Y477phjjz02119/fWXfGWeckT333DOXX355jj766Dz99NO58cYbc/PNN//D+1dbW5szzzwzw4YNy9KlS/OZz3wmc+fOzeTJk9OhQ4cMGjToI52nZ8+eefXVVzNt2rRsvvnmqa2tTe/evbNo0aLccMMN+cIXvpDJkydn1KhRa30M22yzTY499ticcMIJGTlyZHbddde8/fbbmTRpUnbaaacMHDgwW2+9de67775MmTIlG264Ya655pq89dZbq/1i07NnzzzxxBM55phjUlNT0+QRLgA++Vq0aJEXX3yx8uf3c/9z/wOoRlbFBfgYDB8+vMkjO7vttlvGjRuXsWPHZocddsjFF1+c4cOH/12P664Nl19+eS666KLU1dWlT58+GTBgQMaPH59evXp95HMcccQRGTBgQPbff/9ssskmuffee7PzzjvnmmuuyVVXXZUddtghd999d+rq6j6WMYwePTonnHBCzjjjjGy77bY57LDD8uyzz1bmP7rwwguz2267pX///tlvv/3StWvXHHbYYas95/DhwzNr1qxstdVW2WSTTT6WfgPw8erQoUM6dOiw0mPufyvn/gdQXKWySRIAAAAAmp158+alY8eOefn1P6d2Fb8UKbp3583LNt03zty5c1f5i58iU7EHAAAAAAVkjj0AAACAZszaGcWlYg8AAAAACkiwBwAAAAAFJNgDAAAAgAIyxx4AAABAM1YqLduqUbWOq5GKPQAAAAAoIMEeAAAAABSQYA8AYB068cQTc9hhh1Xe77fffjnttNP+4f147LHHUiqV8s4776yyTalUygMPPPCRz3nppZdml112+T/1a9asWSmVSpk2bdr/6TwAANVIsAcA8AEnnnhiSqVSSqVSWrVqld69e2f48OFZvHjxx37t+++/P5dffvlHavtRwjgAAKqXxTMAAFZiwIABGT16dBYuXJiHHnooQ4YMyfrrr5/zzjtvhbYNDQ1p1arVWrlup06d1sp5AAA+qtLyVzWq1nE1UrEHALASNTU16dq1a3r06JFTTjklBx54YP7jP/4jyd8en73iiivSrVu3bLvttkmS119/PUcddVQ22GCDdOrUKYceemhmzZpVOeeSJUty+umnZ4MNNshGG22Us88+O+Vyucl1P/go7sKFC3POOeeke/fuqampSe/evXPHHXdk1qxZ2X///ZMkG264YUqlUk488cQkydKlS1NXV5devXqlTZs22XnnnXPfffc1uc5DDz2UbbbZJm3atMn+++/fpJ8f1TnnnJNtttkmbdu2zZZbbpmLLrooixYtWqHdrbfemu7du6dt27Y56qijMnfu3CbHb7/99vTp0yetW7fOdtttl5tvvnmN+wIA0BwJ9gAAPoI2bdqkoaGh8n7SpEmZMWNGHn744Tz44INZtGhR+vfvn9ra2jz55JOZPHly2rdvnwEDBlQ+N3LkyIwZMybf//7389RTT+V//ud/8tOf/nS11z3hhBNy77335vrrr8+LL76YW2+9Ne3bt0/37t3zk5/8JEkyY8aMzJ49O9ddd12SpK6uLnfddVdGjRqV559/PsOGDctxxx2Xxx9/PMmyAPLwww/PF77whUybNi1f/epXc+65567xz6S2tjZjxozJCy+8kOuuuy633XZbvvvd7zZp88orr2TcuHH52c9+ll/84hf57W9/m1NPPbVy/O67787FF1+cK664Ii+++GJGjBiRiy66KHfeeeca9wcAoLnxKC4AwGqUy+VMmjQpEyZMyDe/+c3K/nbt2uX222+vPIL7wx/+MEuXLs3tt9+eUmnZIx+jR4/OBhtskMceeywHH3xwrr322px33nk5/PDDkySjRo3KhAkTVnntl19+OePGjcvDDz+cAw88MEmy5ZZbVo43PrbbuXPnbLDBBkmWVfiNGDEiEydOTL9+/Sqfeeqpp3Lrrbdm3333zS233JKtttoqI0eOTJJsu+22mT59eq666qo1+tlceOGFlT/37NkzZ555ZsaOHZuzzz67sn/BggW56667stlmmyVJbrjhhgwcODAjR45M165dc8kll2TkyJGVn0mvXr3ywgsv5NZbb82gQYPWqD8AAM2Nij0AgJV48MEH0759+7Ru3TqHHHJIjj766Fx66aWV4zvuuGOTefWee+65vPLKK6mtrU379u3Tvn37dOrUKQsWLMjMmTMzd+7czJ49O3vttVflMy1btswee+yxyj5MmzYtLVq0yL777vuR+/3KK69k/vz5Oeiggyr9aN++fe66667MnDkzSfLiiy826UeSSgi4Jn70ox9ln332SdeuXdO+fftceOGFqa+vb9Jmiy22qIR6jddZunRpZsyYkb/85S+ZOXNmTjrppCZ9/bd/+7dKXwGAf4BSlW9roK6uLnvuuWdqa2vTuXPnHHbYYZkxY0aTNgsWLMiQIUOy0UYbpX379jniiCPy1ltvNWlTX1+fgQMHpm3btuncuXPOOuusj2UhNhV7AAArsf/+++eWW25Jq1at0q1bt7Rs2fSfTe3atWvy/r333svuu++eu+++e4VzbbLJJn9XH9q0abPGn3nvvfeSJOPHj28SqCXL5g1cW55++ukce+yxueyyy9K/f/907NgxY8eOrVQBrklfb7vtthWCxhYtWqy1vgIAfFSPP/54hgwZkj333DOLFy/O+eefn4MPPjgvvPBC5d9/w4YNy/jx4/PjH/84HTt2zNChQ3P44Ydn8uTJSZbNqzxw4MB07do1U6ZMyezZs3PCCSdk/fXXz4gRI9ZqfwV7AAAr0a5du/Tu3fsjt99tt93yox/9KJ07d06HDh1W2mbTTTfNf/7nf+azn/1skmTx4sWZOnVqdtttt5W233HHHbN06dI8/vjjlUdx36+xYnDJkiWVfX379k1NTU3q6+tXWenXp0+fykIgjX71q199+CDfZ8qUKenRo0cuuOCCyr7XXntthXb19fX505/+lG7dulWus95662XbbbdNly5d0q1bt/zhD3/Iscceu0bXBwD4OPziF79o8n7MmDHp3Llzpk6dms9+9rOZO3du7rjjjtxzzz054IADkiybfqVPnz751a9+lb333ju//OUv88ILL2TixInp0qVLdtlll1x++eU555xzcumllzZ56uP/yqO4AABrwbHHHpuNN944hx56aJ588sm8+uqreeyxx/Kv//qv+eMf/5gk+da3vpUrr7wyDzzwQF566aWceuqpeeedd1Z5zp49e2bQoEEZPHhwHnjggco5x40blyTp0aNHSqVSHnzwwbz99tt57733UltbmzPPPDPDhg3LnXfemZkzZ+Y3v/lNbrjhhsqCFN/4xjfy+9//PmeddVZmzJiRe+65J2PGjFmj8W699dapr6/P2LFjM3PmzFx//fUrXQikdevWGTRoUJ577rk8+eST+dd//dccddRR6dq1a5LksssuS11dXa6//vq8/PLLmT59ekaPHp1rrrlmjfoDALA68+bNa7ItXLjwI31u7ty5Sf42t/HUqVOzaNGiJr903W677bLFFlvk6aefTrLsyYYdd9wxXbp0qbTp379/5s2bl+eff35tDSmJYA8AYK1o27ZtnnjiiWyxxRY5/PDD06dPn5x00klZsGBBpYLvjDPOyPHHH59BgwalX79+qa2tzf/7f/9vtee95ZZb8sUvfjGnnnpqtttuu3zta1/LX/7ylyTJZpttlssuuyznnntuunTpkqFDhyZJLr/88lx00UWpq6tLnz59MmDAgIwfPz69evVKsmzeu5/85Cd54IEHsvPOO2fUqFFr/FjIv/zLv2TYsGEZOnRodtlll0yZMiUXXXTRCu169+6dww8/PP/8z/+cgw8+ODvttFNuvvnmyvGvfvWruf322zN69OjsuOOO2XfffTNmzJhKXwEA1obu3bunY8eOla2uru5DP7N06dKcdtpp2WeffbLDDjskSd588820atWqsnBZoy5duuTNN9+stHl/qNd4vPHY2lQql8vltXpGAAAAAD7x5s2bl44dO+YPb/x3alcxlUjRvTtvXrbcbKO8/vrrTaZLqamp+dD5h0855ZT8/Oc/z1NPPZXNN988SXLPPffkK1/5ygoVf5/61Key//7756qrrsrJJ5+c1157LRMmTKgcnz9/ftq1a5eHHnoohxxyyFobn4o9AAAAAKpahw4dmmwfFuoNHTo0Dz74YB599NFKqJckXbt2TUNDwwrTqbz11luVqUa6du26wiq5je8b26wtgj0AAAAASFIulzN06ND89Kc/zSOPPLLC9CC777571l9//UyaNKmyb8aMGamvr0+/fv2SJP369cv06dMzZ86cSpuHH344HTp0SN++fddqf62KCwAAAABJhgwZknvuuSf//u//ntra2sqceB07dkybNm3SsWPHnHTSSTn99NPTqVOndOjQId/85jfTr1+/7L333kmSgw8+OH379s3xxx+fb3/723nzzTdz4YUXZsiQIR9aKbimzLEHAAAA0Aw1zrH36p+qe469Xt02yty5c5vMsbcqpVJppftHjx6dE088MUmyYMGCnHHGGbn33nuzcOHC9O/fPzfffHOTx2xfe+21nHLKKXnsscfSrl27DBo0KFdeeWVatly7NXaCPQAAAIBmSLBXfObYAwAAAIACEuwBAAAAQAEJ9gAAAACggKyKCwAAANCslVLKyheNKL5qHdcyKvYAAAAAoIAEewAAAABQQII9AAAAACggc+wBAAAANGOl0rKtGlXruBqp2AMAAACAAhLsAQAAAEABCfYAAAAAoIAEewAAAABQQII9AAAAACggwR4AAAAAFJBgDwAAAAAKSLAHAAAAAAXUcl13AAAAAIB1p1RatlWjah1XIxV7AAAAAFBAgj0AAAAAKCDBHgAAAAAUkGAPAAAAAArI4hkAAAAAzVhp+asaVeu4GqnYAwAAAIACEuwBAAAAQAEJ9gAAAACggMyxBwAAANCMlUrLtmpUreNqpGIPAAAAAApIsAcAAAAABSTYAwAAAIACEuwBAAAAQAFZPAMAAACgGSst36pRtY6rkYo9AAAAACggwR4AAAAAFJBgDwAAAAAKyBx7AAAAAM2ZSfYKS8UeAAAAABSQYA8AAAAACkiwBwAAAAAFJNgDAAAAgAKyeAYAAABAM1Za/qpG1TquRir2AAAAAKCABHsAAAAAUECCPQAAAAAoIHPsAQAAADRjpdKyrRpV67gaqdgDAAAAgAIS7AEAAABAAQn2AAAAAKCABHsAAAAAUEAWzwAAAABoxkrLt2pUreNqpGIPAAAAAApIsAcAAAAABSTYAwAAAIACMsceAAAAQHNmkr3CUrEHAAAAAAUk2AMAAACAAhLsAQAAAEABCfYAAAAAoIAsngEAAADQjJWWv6pRtY6rkYo9AAAAACggwR4AAAAAFJBgDwAAAAAKyBx7AAAAAM1YqbRsq0bVOq5GKvYAAAAAoIAEewAAAABQQII9AAAAACggc+wBAAAANGPz5s1b11342FTz2BLBHgAAAECz1KpVq3Tt2jVb9+q+rrvyseratWtatWq1rrvxsSiVy+Xyuu4EAAAAAP94CxYsSENDw7ruxseqVatWad269bruxsdCsAcAAAAABWTxDAAAAAAoIMEeAAAAABSQYA8AAAAACkiwBwAAAAAFJNgDAAAAgAIS7AEAAABAAQn2AAAAAKCA/j94kHUJnbXMTwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "random_predictions(model_LSTM,\n",
        "                   X_valid,\n",
        "                   y_valid,\n",
        "                   num_samples=20,\n",
        "                   class_names=class_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ix6ubrKS3p3C",
        "outputId": "288e3c23-7106-4f25-cf2d-486ec2cce632"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
            "\n",
            "Sample 1:\n",
            "Text: im going kill needed relese iner pain somwhereim going kill tonight thought present like cancer spinning around mind end present im tired everything call life walk hell knowing thing get worse nothing get better know big piece shit deserves world made suffering im tired bad emotion come hard handle everything want turn nightmare keep asking wrong life say think death much used drug stopped already two year made open later prelude accepting mistake yah say get work earn money understand much take care person barely move pour salt meal worked year trying fix problem spend money saving someone life spend dam even started live buddhist monk who sleep floor empty room without material possession life moving anywhere cant anything really help heal dad feel like shit hard take care think gon na ower hate im week mind hurt nobady help fuck nothing others help feel like prison opportunity thought run save life people meet walk door worry responsibility cant person behave still hate thought think want leave end day get harder life spiritual awakening thing cant share nobody ill gon na end mental hospital fuck im sick care someone really sick get word somebody better place hows everyone better gratitude others slowly dying jealousy anger taking ower first one like kill become monster like hard think im worth think im egoist think piece shit say try best harm others trying withstand another day people see struggle wish thing different wish world love\n",
            "\u001b[32mTrue: Mental-health \n",
            " Predicted: Mental-health\u001b[0m\n",
            "\n",
            "Sample 2:\n",
            "Text: p forgot mention know put back go parent big house live room small apartment one brother lived nd house parent left last year fixing completely mess spend lot money make habitable planing move christmas put hold thesis focus said plenty time apartment big issue small moved parent nd house great improvement relationship life\n",
            "\u001b[32mTrue: Non-mental-health \n",
            " Predicted: Non-mental-health\u001b[0m\n",
            "\n",
            "Sample 3:\n",
            "Text: current meits since much happened really know start feel now turned day ago friend flew south korea yesterday join reason huge breakdown last year october sent psychiatry something filled many negative emotion towards guilt grief anger cut contact beloved friend actually planning holiday trip s korea able motivate get job earn money trip like like year now waiting til th month enough money buy new guitar nd collectioni classic one one buy either western electric guitar wan na future friend arrive back germany th feel kinda lonely now since used around spent time together make feel happy time forget actually depressed especially best friend somewhat life room drive fun together instead driving apartment work happened alot past cried screamed alot edge ending life many time destroyed many thing value ruined relationship walked path thought good enough make gave many thingswhich still struggle motivation right now cant even tell ok barely cried breakdown last month feel sad anymore feel guilty angry think reached point got numb think killing anymore want anymore moment feel relieved happy love anymore rejected fell love cry rejected sit front pc play video game listen music play guitar piano spend time friend someone emotionally unstable always edge really wanted someone love someone lacking emotion actually live happy life real pain anymore want end life think happened good thing apologise english good thank reading sincerely d vy\n",
            "\u001b[32mTrue: Mental-health \n",
            " Predicted: Mental-health\u001b[0m\n",
            "\n",
            "Sample 4:\n",
            "Text: yo fuck school im hella pissed right now given almost practice paper week ago solve even year considered submission shit pulled allnighters finish paper got approx hr sleep week fast forward today say reconsidered submission want artwork creative thing next week like wtf kidding fuck supposed paper huh shove as serve dinner next day write every last ounce insult know directed towards fuck ruining week\n",
            "\u001b[32mTrue: Non-mental-health \n",
            " Predicted: Non-mental-health\u001b[0m\n",
            "\n",
            "Sample 5:\n",
            "Text: im going commit soon way look genuinely wont happy ever looking look there point nothing happening gt unhappy think everyone agree thati cant live life face body people might able accept personally cant people think look others treat really dont care people want treat bad feel ill saying dramatic dont respect way look look extremely important human nature ill never like cant accept way look fucking pain come know good life like like way look know definitely missing whole lot good shit ill never good shit even continue living better stop dont think im making sense whatsoever point mind frustrated angry confused depressed feel like cant even explain im feeling\n",
            "\u001b[32mTrue: Mental-health \n",
            " Predicted: Mental-health\u001b[0m\n",
            "\n",
            "Sample 6:\n",
            "Text: im lonely feel like onei feel way though yes may broken fiance month agobut good reason began getting abusive stood first time promised got back together ever laid hand id leave good nine month happened drink first time went complete shit feel happy sticking feel happy admitting deserve better deserve better he right really person loved im going alone rest life person liked actually liked back ive always gotten rejected used sex justmaybe thats im good im fucking disgusting im weak im fucking stupid crazy fat ugly bitch who never going fucking go anywhere life im going die alone everyone say oh youre going place going change life gon na gon na feel know im though think world hate thats attempt worked maybe im even stupider think im stupid kill know dunno man wan na stay high wan na talk someone though like alone miss taking care someone made feel worth im attempt free since april th amp selfharm free since january th wan na mess againfuck\n",
            "\u001b[32mTrue: Mental-health \n",
            " Predicted: Mental-health\u001b[0m\n",
            "\n",
            "Sample 7:\n",
            "Text: flag big thing america like australia nearly common see government building souvenir shop hardly ever see private property even government building usually front flag pole thats one\n",
            "\u001b[32mTrue: Non-mental-health \n",
            " Predicted: Non-mental-health\u001b[0m\n",
            "\n",
            "Sample 8:\n",
            "Text: people say pain temporary pain isthis isnt there healing growing past pain head someone escape pain without dying thing cause pain answer there one way escape thats death way act way look talk think oh god way think one biggest source pain nah cant escape way thought jump one thought next like youre skipping tv channel fast complete nonsense try think get random clip song past event story quote noise static picture movement always moving completely still time there escaping toxic personality victimising narcissistic personality craves attention cant stand spot light pulling pushing away people ruining everyones life causing drama completely unnecessary unnecessary thats unnecessary weak god damn pathetic im dark see thing move around little shadow grow big move towards everything go black go cold cant see theyre everywhere blocking cant move make sound tbh scare longer look trapped feel lay eye closed dont look else get im probably tired hear feel heart beating im sorry\n",
            "\u001b[32mTrue: Mental-health \n",
            " Predicted: Mental-health\u001b[0m\n",
            "\n",
            "Sample 9:\n",
            "Text: hello rredflagwatchim going cut chase ask tell point serving society doomed fail point fighting life going end day poi thing vain point fight anything tell instead telling much live\n",
            "\u001b[32mTrue: Mental-health \n",
            " Predicted: Mental-health\u001b[0m\n",
            "\n",
            "Sample 10:\n",
            "Text: quite accomplishment three story filmed three different filmmaker simultaneously insightful gay bisexual relationship struggle br br pool day awkwardness adolescence mutual attraction older man younger one story experience vulnerabilitybr br friend dorothy portrays common dilemma many gay bisexual people experience point life intense attraction towards someone heterosexual sensitively examined story truly left feeling movedbr br disco year show another version nowin situation getting involved someone confused sexual orientation also terrified exposed anything straight empowering story u experienced betrayal hand sexually confused frightened person br br three story appeal anyone iota empathy towards others psychologically empower consider gay bisexual searching story uplifting unique way\n",
            "\u001b[32mTrue: Non-mental-health \n",
            " Predicted: Non-mental-health\u001b[0m\n",
            "\n",
            "Sample 11:\n",
            "Text: hey everyone keep checking tell whatcha anything interesting happened comment thats ok love\n",
            "\u001b[32mTrue: Non-mental-health \n",
            " Predicted: Non-mental-health\u001b[0m\n",
            "\n",
            "Sample 12:\n",
            "Text: anyone central ohio area willing assist redflag greatly appreciate iti cant continue living anymore\n",
            "\u001b[32mTrue: Mental-health \n",
            " Predicted: Mental-health\u001b[0m\n",
            "\n",
            "Sample 13:\n",
            "Text: im thinking jumping bridge nearby im pretty sure impact kill meive lost hope cant find enjoyment anything ive decided might think jumping tall place quickest way way need worry second thought like medication overdose wish make meaningful reality ill probably another number another static story matter\n",
            "\u001b[32mTrue: Mental-health \n",
            " Predicted: Mental-health\u001b[0m\n",
            "\n",
            "Sample 14:\n",
            "Text: absolute outrage year people still freeze chocolate bar beg take nearest chocolate bar freeze make much better psa\n",
            "\u001b[32mTrue: Non-mental-health \n",
            " Predicted: Non-mental-health\u001b[0m\n",
            "\n",
            "Sample 15:\n",
            "Text: bad lot people understand next episodebr br worry ill explain br br episode split partsbr br first part tonys dream coma second part happens real lifebr br now people understand tonys dream dream episode preparation death loses identity eventually even forgets thus disconnect binding world notice im saying doctor scene tony say lost briefcase contains life maker really superb job interpreting theyre thought happens die br br understand whole plot find next episode unique thing great spiritual meaningsbr br like every soprano episode acting filming superb br br thing understand role monk gon na watch till get thisbr br anyways episode really touched think anyone else make better view happens almost death experiencebr br doubt\n",
            "\u001b[32mTrue: Non-mental-health \n",
            " Predicted: Non-mental-health\u001b[0m\n",
            "\n",
            "Sample 16:\n",
            "Text: spiraling downhill since august im renting room basement crappy job im separated ex wifenot divorced im sending money constantly girl ive dating since split found im separated technically married still friend family actually care one near notice sign ive cheated ive lied ive grown spoiled people say leech ruin theyre life im head heel debt nothing going like tell im ending tonight\n",
            "\u001b[32mTrue: Mental-health \n",
            " Predicted: Mental-health\u001b[0m\n",
            "\n",
            "Sample 17:\n",
            "Text: know im feeling lonely m please chat need something ungodly hour news time invented clock company sell clock\n",
            "\u001b[32mTrue: Non-mental-health \n",
            " Predicted: Non-mental-health\u001b[0m\n",
            "\n",
            "Sample 18:\n",
            "Text: anybody else recently hear use tylenol acetomenaphin fight existential anxiety god ever description life written titleherehttpwwwchroncomlifehealthzonearticlecantylenolhelpeaseanxietyphp link tylenol frankly im glad hear existential anxiety till thought person felt like hear grass grow car rusting seems worry go wrong next\n",
            "\u001b[32mTrue: Mental-health \n",
            " Predicted: Mental-health\u001b[0m\n",
            "\n",
            "Sample 19:\n",
            "Text: know anymoreive always nightmare young age developed hallucination eventually id start get panic attack even happen day environment felt safe even around family friend counsellor say there nothing going grow well im august started nightmare sometimes think im still dreaming cant wake cause feel withdrawn family sometimes think love theyre real im worried life oncoming year know\n",
            "\u001b[32mTrue: Mental-health \n",
            " Predicted: Mental-health\u001b[0m\n",
            "\n",
            "Sample 20:\n",
            "Text: movie british tv last night wonderful strong woman great music time make think stereotype older people fantastic cameo sensible worried child getting near best movie ever\n",
            "\u001b[32mTrue: Non-mental-health \n",
            " Predicted: Non-mental-health\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ***comparing pipeline, dense, lstm models and ensembling***"
      ],
      "metadata": {
        "id": "JuQebXGYBFV9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "all_model_results = pd.DataFrame({\"baseline\": baseline_results,\n",
        "                                  \"Simple Dense\": model_dense_results,\n",
        "                                  \"LSTM\": model_LSTM_results,\n",
        "                                  })\n",
        "\n",
        "all_model_results = all_model_results.transpose()\n",
        "all_model_results[\"accuracy\"] = all_model_results[\"accuracy\"]/100\n",
        "all_model_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "863akZoi_Gz0",
        "outputId": "49f2b1ee-9ed1-4de8-e2d8-cd55d04320ca"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              accuracy  precision    recall        f1\n",
              "baseline      0.851269   0.877685  0.851269  0.848862\n",
              "Simple Dense  0.900250   0.905465  0.900250  0.899993\n",
              "LSTM          0.908831   0.908830  0.908831  0.908831"
            ],
            "text/html": [
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              "      <th></th>\n",
              "      <th>accuracy</th>\n",
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              "      <th>f1</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>baseline</th>\n",
              "      <td>0.851269</td>\n",
              "      <td>0.877685</td>\n",
              "      <td>0.851269</td>\n",
              "      <td>0.848862</td>\n",
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              "    <tr>\n",
              "      <th>Simple Dense</th>\n",
              "      <td>0.900250</td>\n",
              "      <td>0.905465</td>\n",
              "      <td>0.900250</td>\n",
              "      <td>0.899993</td>\n",
              "    </tr>\n",
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              "      <th>LSTM</th>\n",
              "      <td>0.908831</td>\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
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              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
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              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\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",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8c018020-916c-4d5c-ace0-0188cafd79b0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_e4984549-0fa4-4c09-b468-ac034248f5c3\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "\n",
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              "summary": "{\n  \"name\": \"all_model_results\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03105402998271378,\n        \"min\": 0.8512692170182338,\n        \"max\": 0.9088308902395422,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.8512692170182338,\n          0.9002502681444404,\n          0.9088308902395422\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.017093038521416345,\n        \"min\": 0.8776854575982233,\n        \"max\": 0.9088304233041032,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.8776854575982233,\n          0.905464874180341,\n          0.9088304233041032\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.031054029982713817,\n        \"min\": 0.8512692170182338,\n        \"max\": 0.9088308902395423,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.8512692170182338,\n          0.9002502681444404,\n          0.9088308902395423\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03237467789160122,\n        \"min\": 0.8488621374386252,\n        \"max\": 0.9088305405891652,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.8488621374386252,\n          0.8999934965873262,\n          0.9088305405891652\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_model_results_sorted = all_model_results.sort_values(\"f1\", ascending=False)\n",
        "all_model_results_sorted"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "mGHHR1l3_rhH",
        "outputId": "33cb3814-57ee-411d-848e-81bdae3807dc"
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              accuracy  precision    recall        f1\n",
              "LSTM          0.908831   0.908830  0.908831  0.908831\n",
              "Simple Dense  0.900250   0.905465  0.900250  0.899993\n",
              "baseline      0.851269   0.877685  0.851269  0.848862"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>accuracy</th>\n",
              "      <th>precision</th>\n",
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              "      <th>f1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>LSTM</th>\n",
              "      <td>0.908831</td>\n",
              "      <td>0.908830</td>\n",
              "      <td>0.908831</td>\n",
              "      <td>0.908831</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Simple Dense</th>\n",
              "      <td>0.900250</td>\n",
              "      <td>0.905465</td>\n",
              "      <td>0.900250</td>\n",
              "      <td>0.899993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>baseline</th>\n",
              "      <td>0.851269</td>\n",
              "      <td>0.877685</td>\n",
              "      <td>0.851269</td>\n",
              "      <td>0.848862</td>\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "all_model_results_sorted",
              "summary": "{\n  \"name\": \"all_model_results_sorted\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03105402998271378,\n        \"min\": 0.8512692170182338,\n        \"max\": 0.9088308902395422,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.9088308902395422,\n          0.9002502681444404,\n          0.8512692170182338\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.017093038521416345,\n        \"min\": 0.8776854575982233,\n        \"max\": 0.9088304233041032,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.9088304233041032,\n          0.905464874180341,\n          0.8776854575982233\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.031054029982713817,\n        \"min\": 0.8512692170182338,\n        \"max\": 0.9088308902395423,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.9088308902395423,\n          0.9002502681444404,\n          0.8512692170182338\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03237467789160123,\n        \"min\": 0.8488621374386252,\n        \"max\": 0.9088305405891652,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.9088305405891652,\n          0.8999934965873262,\n          0.8488621374386252\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "combined_pred_probs =  tf.squeeze(model_dense_pred_probs, axis=1) + tf.squeeze(model_LSTM_pred_probs)\n",
        "combined_preds = tf.round(combined_pred_probs/2) # average and round the prediction probabilities to get prediction classes\n",
        "combined_preds[:20]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XGKQ8p4EAB1w",
        "outputId": "5f3de73a-4bdc-4ca9-df9e-1dbd0da76806"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(20,), dtype=float32, numpy=\n",
              "array([1., 0., 1., 1., 1., 1., 0., 1., 1., 0., 1., 1., 1., 0., 0., 1., 0.,\n",
              "       0., 0., 0.], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ensemble_results = calculate_results(y_valid, combined_preds)\n",
        "ensemble_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HzKnawhCAB5Q",
        "outputId": "4c17d42d-1b46-49ad-8cb8-71aa0a59c4a0"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 91.63389345727565,\n",
              " 'precision': 0.9163503199451426,\n",
              " 'recall': 0.9163389345727565,\n",
              " 'f1': 0.9163404104387038}"
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_model_results.loc[\"ensemble_results\"] = ensemble_results"
      ],
      "metadata": {
        "id": "rDvT13JiA1u3"
      },
      "execution_count": 125,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_model_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "z4rAuPu2A1xY",
        "outputId": "3840cce9-4dd0-4d67-8734-eb497c8b3358"
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   accuracy  precision    recall        f1\n",
              "baseline           0.851269   0.877685  0.851269  0.848862\n",
              "Simple Dense       0.900250   0.905465  0.900250  0.899993\n",
              "LSTM               0.908831   0.908830  0.908831  0.908831\n",
              "ensemble_results  91.633893   0.916350  0.916339  0.916340"
            ],
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              "      <th>accuracy</th>\n",
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              "      <td>0.851269</td>\n",
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              "      <td>0.848862</td>\n",
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              "    <tr>\n",
              "      <th>Simple Dense</th>\n",
              "      <td>0.900250</td>\n",
              "      <td>0.905465</td>\n",
              "      <td>0.900250</td>\n",
              "      <td>0.899993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LSTM</th>\n",
              "      <td>0.908831</td>\n",
              "      <td>0.908830</td>\n",
              "      <td>0.908831</td>\n",
              "      <td>0.908831</td>\n",
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              "    <tr>\n",
              "      <th>ensemble_results</th>\n",
              "      <td>91.633893</td>\n",
              "      <td>0.916350</td>\n",
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              "    async function quickchart(key) {\n",
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              "        document.querySelector('#id_94ee550c-e67c-42b7-9f80-a019e78c325a button.colab-df-generate');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "all_model_results",
              "summary": "{\n  \"name\": \"all_model_results\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 45.37356208394711,\n        \"min\": 0.8512692170182338,\n        \"max\": 91.63389345727565,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.9002502681444404,\n          91.63389345727565,\n          0.8512692170182338\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.016889457142115587,\n        \"min\": 0.8776854575982233,\n        \"max\": 0.9163503199451426,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.905464874180341,\n          0.9163503199451426,\n          0.8776854575982233\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.029347630126894734,\n        \"min\": 0.8512692170182338,\n        \"max\": 0.9163389345727565,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.9002502681444404,\n          0.9163389345727565,\n          0.8512692170182338\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.030503627757208537,\n        \"min\": 0.8488621374386252,\n        \"max\": 0.9163404104387038,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.8999934965873262,\n          0.9163404104387038,\n          0.8488621374386252\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    }
  ]
}