02. LLM_analysis.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/rdemarqui/llm_complaint_management/blob/main/02.%20LLM_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Tagging Customer Feedback with LLM"
      ],
      "metadata": {
        "id": "JdXiJzMm7ewB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This study aims to explore the advanced LLM tool Mistral 7B to analyze a vast collection of complaints gathered from the website [reclameaqui.com.br](reclameaqui.com.br). Through this analysis, we intend to demonstrate the potential and effectiveness of LLMs in interpreting and handling large-scale user feedback.\n",
        "\n",
        "For more information, please visit github page: https://github.com/rdemarqui/llm_complaint_management"
      ],
      "metadata": {
        "id": "1Ex4zf24D1-m"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Complaint Dataset"
      ],
      "metadata": {
        "id": "e_s1QiOU98Rc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Load"
      ],
      "metadata": {
        "id": "kZWO80q3-PsC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
        "\n",
        "sns.set_theme()"
      ],
      "metadata": {
        "id": "tSvJsvLbHoQp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Loading data\n",
        "data_url = 'https://raw.githubusercontent.com/rdemarqui/llm_complaint_management/main/datasets/full_dataset_claro.xlsx'\n",
        "data = pd.read_excel(data_url, usecols=['problem', 'description'])\n",
        "print(data.shape)"
      ],
      "metadata": {
        "id": "cduSRhNh4VB5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "89761091-7577-42bc-cc76-421655128a9a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(7000, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Clean"
      ],
      "metadata": {
        "id": "pAx7QfsGBLfN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove extra space and uncase all words\n",
        "data['description'] = data['description'].astype(str)\n",
        "data['description'] = data['description'].str.replace(r'\\s{2,}', ' ', regex=True)\n",
        "data['description'] = data['description'].str.lower()"
      ],
      "metadata": {
        "id": "UmvxMuJuBOy7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Modify mask made by reclame aqui\n",
        "def remove_mask(text):\n",
        "  return text.replace(\"[editado pelo reclame aqui]\", \"[mask]\")\n",
        "\n",
        "data['description'] = data['description'].apply(remove_mask)"
      ],
      "metadata": {
        "id": "mSHPHLv0JdLR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove description less than 3 characters\n",
        "data = data[data['description'].str.len() > 3].copy()"
      ],
      "metadata": {
        "id": "n9DYRkEGdB5L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Stratify"
      ],
      "metadata": {
        "id": "m7LKh33Oe5eu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "As this work is intended for study purposes, we will only work with a fraction of the data. With the infrastructure provided by the Colab free tier, we noticed that on average, Mistral 7B analyzes about 2.000 complaints per hour (zero shot). Therefore, we will use this number of cases in our analysis. To do this, we will perform a stratified sample.\n",
        "\n",
        "We also will use validation data with 202 examples to test some prompt engineering."
      ],
      "metadata": {
        "id": "qPR19OFje_TM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get stratified data\n",
        "df, data_val = train_test_split(data, train_size=2002, stratify=data['problem'], random_state=42)\n",
        "df_val, _  = train_test_split(data_val, train_size=202, stratify=data_val['problem'], random_state=42)\n",
        "df.reset_index(inplace=True, drop=True)\n",
        "df_val.reset_index(inplace=True, drop=True)\n",
        "print(f\"Validation data: {df_val.shape} \\nTest data: {df.shape}\")"
      ],
      "metadata": {
        "id": "KMLxpZ5hl4tu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fabcaf65-e8a6-47bc-a9fc-45969248776a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation data: (202, 2) \n",
            "Test data: (2002, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Export dataset to tag mannualy\n",
        "df_val.to_excel('df_validation.xlsx', index=False)"
      ],
      "metadata": {
        "id": "E8CIQ22yqstY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Comparing word distributions\n",
        "data['word_count'] = data['description'].str.split().str.len()\n",
        "df['word_count'] = df['description'].str.split().str.len()\n",
        "df_val['word_count'] = df_val['description'].str.split().str.len()\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(8, 4))\n",
        "sns.kdeplot(data=data['word_count'], label='Full dataset 7000 samples', fill=True, ax=ax)\n",
        "sns.kdeplot(data=df_val['word_count'], label='Validation dataset 202 samples', fill=True, ax=ax)\n",
        "sns.kdeplot(data=df['word_count'], label='Test dataset 2002 samples', fill=True, ax=ax)\n",
        "ax.legend()\n",
        "plt.title('Words Quantity Comparison in Stratified Datasets')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "uuoynKmm5AcB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "outputId": "d126fd98-5c10-4d6b-dd8c-9feeb3c9618e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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xu/Ssuul0Oql69erG3/hMERERUrVq1Uy2Z/c7NWnSJKlq1apm3ydByCuiC5MgFADe3t44ODgYxzZcuXKFxMRE/P39gYw7ZqdPnwbg7Nmz6PV6492vgwcPAtC9e3eTMnv06GHyfqbixYtTv359k22HDh3C1dWVFi1aGLdZWVnx0UcfmaTLfHpw+PBhkpKScnx8CQkJANjY2Dw1nY2NjTFtbigUCmN/a4PBQGxsLOnp6VSsWJFLly6ZpW/VqhX29vbG15nTTt6/fz/X+34aOzs7mjRpwrZt24xdZ/R6PTt27KBJkyZYW1tnm1en0wHPPmeZDh48SOXKlY3Hkpm3Y8eOBAcHc+PGDZP0bdq0MRkIW7lyZSRJon379ibpKleuTGhoKOnp6Sbb/f39qVixovF1sWLFaNKkCYcPHzZ21Xm8z31aWhoxMTF4eXmh0WiybJePPvromU9bMp9y/fnnn6SlpWWZ5tChQygUCrPuMz169ECSJLPuSW+//TZeXl7G1+XKlcPW1vaFPg8ff/yxyetq1arlaMak7777jrFjx1K8eHH27NnDtGnTaNWqFV27diUsLCzH+8/qew6mbRIfH090dDQ1a9bk/v37z+yG+KJ27tyJt7c3pUuXJjo62vgnc4KIzG5Rmb9ZT7ZfXt45z/zuZf7eqNVq5PKMSyK9Xk9MTAzW1taUKlUqy89qVh4/t4mJiURHR+Pv748kScYyLC0tUalUnDx5kri4uCzLOXr0KFqtlnfffdfkPMnlct56660cTaut0WhISkriyJEjOaq7IOSW6MIkCAWATCbD39+fU6dOYTAYOH36NM7OzsYZTfz9/Y19XTMDicwAIjg4GLlcbnIBBODq6opGoyE4ONhke1YztgQHB1OiRAmzfvaZ3R4yeXp60r17d5YsWcKWLVuoXr06jRs35v3338+2+xI8ugh+VnCQkJCAh4fHU9NkZ8OGDSxevJjbt2+bXFhmdbxFixY1eZ0ZTLyMaVjbtm3L9u3bOXXqFDVq1ODo0aNERkaaDe58UmawltOAKiQkhLfeestse2b3lpCQEJNuVJndZTJltt+T58bOzg6DwUB8fLxJd57HZ9vJVLJkSZKSkoiOjsbV1ZXk5GSCgoL4448/CAsLMxl/kNXFak5mE6pZsybNmzdn9uzZLF26lJo1a9K0aVPee+89YxAZHByMm5ubWXe5zC4+T34nnjxmyPhMPO/nwcLCAicnJ7PysrtgfJxcLueTTz7hk08+ISYmhtOnT7N69WoOHTrEkCFDzMYrZCe7c/nPP/8QGBjI2bNnzW4CxMfHP/V7/KLu3r3LzZs3qVOnTpbvZw7Gzu43LfOznBcyu8hl/jYZDAZ+/fVXVq5cyYMHD0zGqzg4OOSozJCQEGbNmsX+/fvN2jrzhoBarWb48OFMmzaNunXr8tZbb9GoUSPatm2Lq6srkNHlE7IPmLLrBvq4zp07s2PHDnr37o27uzt169alZcuWNGjQIEfHIgjPIgIIQSggqlWrxoEDB7h27Zpx/EMmf39/Y//tf/75Bzc3Nzw9PU3yZzXINisvOhPLyJEj+eCDD9i3bx9Hjhxh0qRJBAUFsWbNGooUKZJlnjJlygBw9erVbMsNDg5Gp9OZHVdW9Hq9yZ3qTZs2MXLkSJo2bUrPnj1xdnZGoVAQFBSU5V3k7O5yS08MsM0L9erVw8XFhc2bN1OjRg02b96Mq6srb7/99lPzZV4sXb16laZNm+Z5vTLvtuZ0+/Ocm4kTJxpnkapSpQp2dnbIZDKGDBmSZXk5mdFIJpMxa9Yszp49y4EDB/jrr78YPXo0S5Ys4ffff8/xE5vH5fXnIa9mjXJ0dKRJkyY0adKELl26cPLkSYKDg3MUZGf1Pb937x7dunWjdOnSjBw5kqJFi6JSqTh48CBLly41majhZTAYDPj4+JgNeM+U3e/Hy3D9+nWcnZ2NF+Pz5s3j559/pn379gwePBh7e3vkcjlTpkzJ0edAr9fTvXt34uLi6NWrF6VLl8ba2pqwsDBGjhxpcm67detG48aN2bt3L4cPH+bnn39m/vz5LFu2jPLlyxv3N336dGNQ8bicfL6cnZ3ZuHEjhw8f5tChQxw6dIg//viDtm3bMm3atJyeJkHIlgggBKGAeHw9iCcHulWsWBG1Ws2JEyc4d+6cyV0kDw8PDAYDd+/eNRlEGRkZiVarzdHFhoeHB9euXUOSJJNA5Pbt21mmz5zto3///pw+fZqPP/6YVatWMWTIkCzTlyxZklKlSrFv3z50Ol2Wd9A2btwIYNKNKru7wCEhISaBxq5du/D09GT27Nkm9X984GJu5TQge1ZahUJB69at2bBhA8OHD2fv3r056qpTrVo17O3t2bZtG3379n1m+mLFimXZXrdu3TK+n5fu3r1rtu3OnTtYWVkZ777v2rWLtm3bmsyklZKSkiddZapUqUKVKlUYMmQIW7ZsYfjw4Wzfvp0PP/wQDw8Pjh07ZvZZyzwXz/uUKz9VrFiRkydPEhERgYeHR64+n5n2799Pamoqv/zyi8nnIasuMc9T/rN4eXlx5coV6tSp89TyM3/T7t27Z/LUIbP9XtSZM2e4d++eyQxpu3btolatWkyZMsUkrVarNXnyll29r127xp07d5g2bRpt27Y1bs+uC5GXlxc9evSgR48e3Llzh7Zt27J48WK+//5742+bs7PzM280PO08qtVqGjduTOPGjTEYDIwfP57ff/+d/v37Z/kEURByQ4yBEIQComLFilhYWLBlyxbCwsJMnkCo1WoqVKjAypUrSUxMNFn/IXO6yyen51uyZInJ+0/ToEEDwsPD2blzp3FbUlISa9asMUmn0+nM+sL7+Pggl8uznPL1cQMHDiQuLo5x48aZTWd54cIFFi5cSPny5U2CI09PT/7991+Tsg8cOEBoaKhJ/syL68fvFP77778mK/fmVuYMODnpxpKZNrsL4zZt2hAXF8fYsWNJTEzMdmrXJ8vs1asXN2/e5Pvvv8/yLuimTZuM0882bNiQc+fOcebMGeP7iYmJrFmzBg8PD+NToLxy5swZLl68aHwdGhrKvn37qFu3rrE9sgp6li9f/szpTJ8mLi7O7FxkzrKV+Tlp0KABer3e2O0v09KlS5HJZAW2G0dERITZWBXIOK5jx46ZdOt51mcuK1l9T+Lj41m/fr1ZWisrqzzv0teyZUvCwsLMflcgY9rizG5Fme2zfPlykzR5MQVpcHAwI0eORKVSmUyjrFAozD5XO3bsMBt3kt3vQuaTu8fLkCSJX3/91SRdUlISKSkpJtu8vLywsbExfn7r16+Pra0tQUFBWY7zeXw9kOzqExMTY1a/zNmxnvVbLQg5IZ5ACEIBoVarqVSpEqdOnUKtVpsMUIWMbkyLFy8GTBeQK1euHB988AG///47Wq2WGjVqcP78eTZs2EDTpk1NVrDOzkcffcRvv/3GiBEjuHjxIq6urmzatMmsG8Tx48f59ttvadGiBSVLlkSv17Np0yYUCgXNmzd/6j5at27NhQsXWLJkCTdv3uS9994zDqZdv349Tk5OzJo1y2RhtA8//JBdu3bRq1cvWrZsyb1799iyZYtZ3+hGjRqxe/duBgwYQKNGjXjw4AGrV6+mTJky2U4H+iwVKlQAMlYGrlevHgqFItuVazPT/vjjj7Rq1QqVSsU777xjHKhZvnx5fHx8jINIM9M/S69evbhx4waLFy/mxIkTNG/eHBcXFyIjI9m7dy/nzp0zzu/fp08ftm3bRu/evenSpQv29vZs3LiRBw8eEBgYmG3XpOfl4+NDz549TaZxBUxWxG3UqBGbNm3C1taWMmXKcPbsWY4ePZrjPuVZ2bBhA6tWraJp06Z4eXmRkJDAmjVrsLW1NV54Nm7cmFq1avHjjz8SHByMr68vR44cYd++fXTt2tXs81NQPHz4kA8//JDatWtTp04dXFxciIqKYtu2bVy5coWuXbsan+74+fmhUChYsGAB8fHxqNVqateubVwrIit169ZFpVLRt29fOnXqREJCAmvXrsXZ2ZmIiAiTtBUqVGDVqlXMnTuXEiVK4OTklO3YhZxq06YNO3bsYNy4cZw4cYKqVaui1+u5desWO3fuZOHChVSqVAk/Pz9at27NypUriY+Px9/fn+PHj2f51OtpLl26xKZNm5AkCa1Wy/nz59m9ezcymYzp06ebLCLXqFEj5syZw6hRo/D39+fatWts2bLFrEtl5iQAq1evxsbGBmtraypXrkzp0qXx8vJi2rRphIWFYWtry65du8wu7O/cuUO3bt1o0aIFZcqUQaFQsHfvXiIjI42/L7a2towfP56vvvqKdu3a0apVK5ycnAgJCeHgwYNUrVqVsWPHAtn/Tn399dfExcVRu3Zt3N3dCQkJYcWKFfj5+Zk8qRaE5yUCCEEoQKpVq8apU6eoUKGC2SquVatWZfHixdjY2Jitnjpp0iSKFy/Ohg0b2Lt3Ly4uLgQEBJjN3Z8dKysrli5dysSJE1mxYgWWlpa89957NGjQgF69ehnT+fr6Uq9ePQ4cOEBYWBhWVlb4+vqyYMECqlSp8sz9jBw5kpo1a7J8+XLmzZtn/M+1bNmyrF692qxrU/369Rk5ciRLlixhypQpVKxYkXnz5pn14W3Xrp1x3vbDhw9TpkwZZsyYwc6dOzl58mSOzsGT/ve//9GlSxe2bdvG5s2bkSQp2wCicuXKDB48mNWrV/PXX39hMBjYt2+fySxLbdq0YcaMGc8cPP04uVzO9OnTadKkCWvWrGHx4sXodDocHR2pUaMGX375pfFJlYuLC6tXr2bGjBmsWLGClJQUfH19mTdvHo0aNXquc/A0NWrUoEqVKsyZM4eQkBDKlCnDd999Z/LZHDNmDHK5nC1btpCSkkLVqlVZsmSJyWcqt2rWrMn58+fZvn07kZGR2NnZUblyZZOuH3K5nF9++YVZs2axfft241oWX331lXF2soKoVKlSjB49moMHD7Jy5UqioqJQq9X4+PgwadIk4yJmkDFJwoQJEwgKCmLMmDHo9Xp+/fXXpwYQpUuXZtasWfz0009MmzYNFxcXPv74Y5ycnMzWxhgwYAAhISEsXLiQhIQEatas+cIBhFwuZ86cOSxdupRNmzaxZ88erKysKF68OF26dDGZtGHKlCk4OjqyZcsW9u3bR61atZg/f36Onqhm2rp1K1u3bkWpVGJra0uJEiXo2rUrnTp1MuvS17dvX5KSktiyZQvbt2+nfPnyBAUFMXPmTJN0KpWKqVOn8sMPPzB+/HjS09P57rvvaNeuHfPmzTOOCbOwsKBZs2Z88sknJt/5IkWK8O6773Ls2DE2b96MQqGgdOnS/PTTTyY3Yd577z3c3NyYP38+ixYtIjU1FXd3d6pXr067du2M6bL7nXr//fdZs2YNK1euRKvV4urqSsuWLRk0aFCe30wQ3kwy6WWMGhQEQcihMWPGsG7dOiZNmsSHH36Y39V5aZYtW8Z3333H/v3783w8wqvm6+vLJ598YrwLKgiCILxZxBMIQRDy1bfffktUVBTjx4/Hzc0tV3cYCwtJkli3bh01atQo9MGDIAiCIIgAQhCEfKVQKJg3b15+V+OlSExMZP/+/Zw4cYJr164xd+7c/K6SIAiCILwwEUAIgiC8JNHR0QwbNgyNRkPfvn1p0qRJfldJEARBEF6YGAMhCIIgCIIgCEKOiaH4giAIgiAIgiDkmAggBEEQBEEQBEHIMRFACIIgCIIgCIKQY2IQ9SsiSRIGgxhukhW5XCbOTSEj2qzwEW1W+Ig2K3xEmxU+os0ekctlyGSyHKUVAcQrYjBIREcn5Hc1ChylUo6jow1abSLp6Yb8ro6QA6LNCh/RZoWPaLPCR7RZ4SPazJSTkw0KRc4CCNGFSRAEQRAEQRCEHBMBhCAIgiAIgiAIOSYCCEEQBEEQBEEQckwEEIIgCIIgCIIg5JgIIARBEARBEARByDExC5MgCIIgvCQGgwG9Pv058slITlaQmpqCXi+mmCwMRJsVPm9SmykUSuTyvHtuIAIIQRAEQchjkiSh1UaTlKR77jIiI+UYDGJqycJEtFnh8ya1mZWVLRqNU47XengaEUAIgiAIQh7LDB5sbR1Rqy2e6z9shUL22t8Vfd2INit83oQ2kySJ1NQUdLoYAOztnV+4TBFACIIgCEIeMhj0xuDB1lbz3OUolXKxuFUhI9qs8HlT2kyttgBAp4vBzs7xhbsziUHUgiAIgpCH9Ho98Og/bEEQhIIg8zfpecZlPUkEEIIgCILwEuRFP2NBEIS8kpe/SQWuC9PNmzeZNGkSZ86cwcbGhjZt2vDFF1+gVqufmk+SJBYsWMDKlSuJjo7Gz8+PUaNGUaVKFZN0YWFhTJo0icOHD6NSqWjWrBmjRo3C1tbWmMbX1zfb/fz111+4ubm90DEKBUe6IR0ZMhRyRX5XRRAEQRAEoVAoUAFEXFwcXbt2pWTJkgQGBhIWFsbUqVNJTk5m7NixT827YMECZs2axfDhw/H19eW3336jR48ebNq0CU9PTwDS0tLo1asXADNnziQ5OZlp06YxbNgwgoKCjGX9/vvvZuWPGDECKysrETy8JnRJaaw/cYYzhi2kkYJSpuIt1/J0Ld9JBBOCILw0UXHJ6JLScpQ2Lwd32lqpcLa3zHW+RYuCWLJkgdn2UqVKs3z5mhyVERoawocfvs/EiVN5552mAHTo8B5vv12PoUNH5Ko+169fpXv3T5g1ax5Vq1bPcb5Dh/4kMjKCdu0+zNX+8tL27VtQKlX8738tnpm2Xr3sj23jxp24uLgAGdc18+fPZdeu7SQmJlCxYmWGDv0KL6+SJnnu3r3Djz9O58KFc1hb29CiRSt69+6PSqUySbd160ZWrPiV8PCHeHqWoE+f/tStWz/3B1sALVoUxOrVK9iz56/8rsproUAFEKtXryYhIYHZs2fj4OAAZPQlnTBhAgEBAbi7u2eZLyUlhaCgIHr06EG3bt0AqFatGi1atGDRokWMHz8egF27dnH9+nW2b99O6dKlAdBoNPTs2ZNz585RuXJlALOnFg8ePODOnTt8+eWXeX7MwqsnSRKLdp3hquVWZJKS9OhipCtSOS2dQylX0sXvI9H1QBCEPBcVl8yYBcdJzYcBm2qlnMm9az9XEGFhYcHPP88z2WZpmfty8tNff/3JlSuX8j2AsLa2zlEAMW/eErNtkyaNw8rK0hg8APz00wz27dvNwIFDcHV149dfFzN4cH+WL19j7Fmh1Wr5/PO+eHp6MXnyDCIiwpk9+0eSk5NNAri9e3cxbdpkPvusB9Wq1WDfvt2MHj2cOXMWUrFipTw4A8LrpEAFEIcOHaJOnTrG4AGgZcuWjBs3jiNHjtCuXbss850+fRqdTkfLli2N29RqNc2aNWPPnj0m5fv6+hqDB4C6devi4ODAwYMHjQHEk7Zu3YpMJqN169YveIRCQXDscjBX5LuxUEMT92ZYlrDizI1IboZYcIJ/0KjtaFumVX5XUxCE14wuKY3UdAPv1imBs+bZF+AKuQy94cWfQERpk9l27C66pLTnCiDkcrm4gHzFnjzfoaEhPHhwj/79PzduCw8PY+vWTQwdOoLWrdsA4OdXnvbtW7Np03o++aQrAJs2rScxMYEpU2ag0dgDGTdnf/hhGp991gMXF1cg4w59kyb/o3fvfgBUrVqdmzdvsHTpAr7/ftZLP2ahcClQAcStW7do3769yTaNRoOrqyu3bt16aj7AJDAA8Pb2ZtmyZSQnJ2NpacmtW7fM0shkMkqVKvXU8rdt20aNGjUoUqRIbg9JKGDidCmsPLMHeZF46rk0x0phDcBb3i5Enk4iJdbAHv6kkkt5vB1K5m9lBUF4LTlrLCniZP3MdIVhfvqsuiIdOvQno0cPZ+3azRQtWuyFyl+6dCF//LGWpKREatSoTdu27c3SrFq1gn37dnP//l1UKjXly1dg4MAheHmVAGDy5PHs2LEVeNQ1qGXL1owZM54LF86xfPkSrly5TEKCjuLFvejU6RNatHjXWH56ejpBQXPYt283MTHRaDQafH3LM3bsRONd/vj4eIKC5vDXX3+i1cZRqpQ3ffsOpGbN2gAMHNiHs2dPm9She/fe9OwZkKPzsGfPTmQyGU2bNjduO3nyOAaDwdgtDECjsadGjdocO3bEGEAcP36U6tVrGoMHgMaNm/H9999x8uRxWrV6j+DgB9y/f49+/R4FKABNmvyPuXN/JjU1NduxqOHhYQQG/sjZs6dJSNDh7OxC/foN+fzzYUBG96nFi4M4f/4ccXGxFC1ajHffbUPHjp2NU4lmdnP7+usJnD//L/v27UapVPHpp13p1OlT9u7dxaJFQURFRVG9ek1GjRqLnZ0dAKdPn+Lzz/syY8ZPbNmyiZMnj2FjY0P79h357LMeTz2v8fHxLFgwl4MH96PVas3aDeDcubMEBc3hxo1rGAwSRYsW5eOPu9Cy5Zt9U7lABRBarRaNxnzObHt7e+Li4p6aT61WY2FhOmWeRqNBkiTi4uKwtLREq9UaP3A5Lf/KlStcu3aNb7/9NpdHY06pFJNePUmhkJv8/TJJksSy3VeQnO5RzNITJ8tHC6nI5TLqVirKjhOpWGmi2HZ7F0Nr9HvpdSqMXmWbCXlDtNmrZTC8eBfIzF6UMhlIBSCGSE83nfZRoVC89K6e69f/zsKF8/j44y5Ur16Tv/8+wdSpE83SRUSE0b79R7i7FyExMYGNG9fTr18PVq36A43Gnm7dehEbG8Pdu3cYO3YSAI6OjgA8fBhKpUpv0bZte9RqC86f/5epUyciSZLxAnH58iX/lTmIUqVKExcXy8mTx0lLSwUyxiEMGTKAmJgo+vbtj7OzK7t2befLLwezePFveHuXYdiwkUyc+A0WFpYMGPAFQK7GVO7du4u33vLHze1RV+579+7g6Ohkdt1UokRJtm3bbHx99+4d3n33fZM0dnZ2ODu7cPfuHWNZmXkfV7JkSdLS0ggNDTF7L9OkSeOIjIzgiy+G4+joRFjYQ65evWx8PyIiHC+vkjRr1hJra2tu3LjGokVBJCUl0qNHH5Oy5s+fS6NGjZk4cSqHDh1k9uyfiI2N5cyZf+jf/3MSEhL46acZzJ07ixEjxpjknT59Ck2bNmfy5BmcOnWS+fPnotFoaNu2Q5b1ftRu0fTp0x8XFzd27zZtt4QEHV999QWVK1dh/PjJqFRq7ty5RXx8fJZlFhYKheyFr0kLVABREG3ZsgWVSkXz5s2fnfgp5HIZjo42eVSr149GY/XS93H6ajjnQm5gUT6B8q71sbY2DTitrS2oV7k4h65Fc1V+g5C0YCq4+bz0ehVWr6LNhLwl2uzVSE5WEBkpN/tPWqHIuOBWyGXGfz+LXP7iF+mK/8p4nosGuVxGUlISjRrVNtk+btxEWrZ815gmy+NUyFEq5SYB7OPpnsz3OL1ez4oVS2nZ8l0GDx4CZHQ5jouLYceObSZlDR36pUm+2rXr0KpVUw4d2k/btu0pUcLLeGFbpcpbJvtp0eJR12dJkqhWrRqRkeFs3vwH772XcdF95cpFatWqzUcfdTSmbdq0mfHfO3fu5Pr1a6xYsZpSpTJ6Obz9dl0ePLjPr78uYvLkaZQtWwYbG1usra3M6vAs169f49atm4wcOcbkfOl0Ouzs7MzOoYODPVptnHF7fHw89vYas3QajQadTotSKSchQfdfXtN0mV3KExLis22ry5cv0q/fIJo3fzS2I/PcAdSuXZvatTM+P5IkUbVqVVJTU1i37nf69OkLPLq5UalSZWN71qxZi4MH97N+/e9s3LgNe/uMuty6dYMtWzYyZsw3JnmrV69h8lmJjY1m2bLFtGvXAblcbvwuZR5HVu1Wt65pu4WEPECn09G//yDKlClrPJ7CymCQIZfLsbe3fuFxTAUqgNBoNFlGdXFxcdjb22eR41G+1NRUUlJSTJ5CaLVaZDKZMW/Gl0WXZflFixY12y5JEtu3b6d+/fom4zKeh8EgodUmvlAZryOFQo5GY4VWm4Re/3IHFv556h7WxUKwVNiikZxITEwxS1PMyRJ7eRHS0sNYcWYDX9YYIAZUP+FVtpmQN0SbvVqpqSkYDAb0eslkhdvM7kh6g/TMrkkyWcYFtsEgvfATiMxxFE/WJycMBgkLCwvmzDGdialYMQ9jWQZDNsepN5CebjB+5jJfP152dvV5+PAhERER1KvXyCRNw4aN2bFjm0lZFy6cZ+HCX7h27Spa7aPeBHfu3DWmkSQJSTLfn1arZfHiIP766yCRkRHGRQDt7e2NacuWLcfKlcsJCvqFt9+uh6+vn8kqvseOHcPb25tixYqTnp6OXm9AkqB69Vrs3r3jiTqQ6zbYsWM7SqWSBg0am+TN7pgyz/+j7VKWbS9JEgZDRjqD4VEe07bMuu0e5+NTjt9++xWZTE6NGrUoXtzT5P2UlBRWrFjK7t07CAt7aPI0S6vVYW1tbdxP9eq1HtuPjGLFPJDJZNjYaIzbPTw8iY+PN8ub3WclJOQhRYoUMTlGeNRunp5epKSkGr9nj7ebu3sxbGxsmDZtCh06dKJq1erGp1eFkV4vYTAYiItLJClJb/a+RmOV4yfVBSqAKF26tNlYhPj4eCIiIszGLjyZD+D27duUK1fOuP3WrVsUK1bMGGWVLl2aa9eumeSVJInbt29Tt25ds3L/+ecfQkJC8mz2pTdhqfTn9bQfp7wgSRKnb4Rg8A2hhHUFJCljW1Z8izty8m4RbiqvcyHiKn5O4ilEVl52mwl5T7TZq5EX4xYyf54KQvcluVxOuXLlX+k+IyMjAcwu1hwdnU1eP3z4kKFDB1KunB9ffjkKFxdXVCoVX375Bamp5jeJnjRlSsY4iG7delGqlDc2NjZs2LCO/fsfTcDy2Wc9kMlk7Ny5jSVLFuDg4Ei7dh/SvXtvZDIZcXGxXLt2lYYNze9MKxQvNi24JEns27eb2rXfNhnDABndkDKfHDwuPl5rktbOTpNNunhj96fM7t2ZYxgeLwsw2/fjJkz4jvnz5zB//lxmzpyKl1cJAgIG0LBhYwB++SWQLVs20L17b3x9/bCzs+Ovvw6ybNkiUlNTsbZ+NB7o8TW5AJRKpcn7gHHq2SfzZvdZiYqKzHIMa2a71atX0+y9zHbTaDT8+OMcFi2az6RJY9Hr9VSuXIUhQ77C27tMtuekoHuemwlPKlABRIMGDZg3b57JWIidO3cil8uzvMDPVLVqVWxtbdmxY4cxgEhLS2P37t00aNDApPzNmzdz584dSpYsCWREoLGxsTRs2NCs3C1bMqZca9y4cR4epZAf7oXp0FncQy0z4GWdfTAK4OVmy9mbzqilMP56cEwEEIIgCNlQq9WkpZmOj8i86HwRmVOVxsTEmGyPiYkyeX3ixFGSkhKZPHmG8SI4PT3d5ElEdlJSUjh69DADBw6hQ4dOxu1P3lxSq9X07BlAz54BPHhwn23bNrN48XyKFfOgRYt30Wjs8fYuy6hR36BQyPP0Kd+5c2cJC3toMvtSJi+vkkRHR5uNH713745xADlkjGvIHOuQSafTERUVaRzXkLluxN27d0zWkLh79w4qlYpixTyyraOLiwujR4/DYDBw9eplli1bxNixo1i5cj0eHsU5cGAvbdq049NPuxnzHD16OOcnIYey+6w8HhA9LrPdvv563FPbrHz5isycOYuUlGROnz7FnDk/M2rUMNas2ZR3lS+ECtSIuk6dOmFjY8OAAQM4fPgw69evZ/r06XTq1MlkDYiuXbvSrNmj/ocWFhYEBASwePFili1bxrFjxxg2bBixsbH07NnTmK558+aULVuWQYMGceDAAbZv387o0aNp1KiR2RSu6enp7Nq1i6ZNmxa6+a4Fc//ejETl8hA3dRHjzEvZkStklPVwICnCkfNRl4lPNb9zIwiCIICrqzt379422Xby5PE8KNcNZ2cXDh06YLL9wIF9Jq9TUlKQyWQolY/uh+7fv9fYFSmTUqkiNTXVZFtaWhoGg8FkMbXExAQOHz6Ubb2KF/ckIGAAGo298aK8evWahIQE4+Liip9fecqVM/2TSaVSkZKSmk3JWduzZydWVtbUq9fA7L2aNWsjl8s5eHC/cZtWq+XkyRPUqfPopmvt2m9z6tRJky7iBw7sRS6XG2cb8vAojqenl9n53bdvD9Wq1TBbcC4rcrkcP78K9O7dH71eT3DwAyCjjZTKR/n1ej379u3O4RnIuaw+Ky4urtkOVn+83Z5ss6yeuFlYWFKnTj3atm1PaGgIKSnPfsL1OitQTyDs7e1ZtmwZEydOZMCAAdjY2NChQweGDBliki6jb6npj0Pv3r2RJInFixcTHR2Nn58fixYtMq5CDRlf3oULFzJp0iSGDh2KUqmkWbNmjB492qwuhw8fJiYmRqz98Jo4cyMMWfEY3C2r5Ci9t4eGS/edkNzu83fYGRp7vh4rcQqCIOSld95pzPffT2Xx4vlUqlSZY8eOcPHi+RcuV6FQ8Omn3fj55+9xcnKmRo1anDx5nDNn/jFJV61aDQCmTJlAmzbtuH37FqtXr8DW1nTGxZIlS7J9+2b27NmJp6cX9vYOFC1aDD+/8qxYsRQHBwcUCiUrVizFxsaW2NhoY95Ro4bh6+tH2bK+WFlZceTIIeLjtcaVsFu0eJdNm/5g4MAAPv20C8WKeaLT6bh+/SppaWn07TsQgBIlSrFz51YOHz6Ei4sLLi6uxjUYspKens6ff+6jQYOGWFiY38h0c3Ondes2zJnzM3K53LiQnK2tLW3aPJrutk2b9qxb9zujRg3js896EBERzpw5P9OmTTuT/ffo0Ydvv/0GD4/i+PtXY//+PVy6dMFs/MvjdDodQ4cOpHnzVnh5lSA9PY1169Zga2uHj09Gj5AaNWqxZctGSpUqjb29Axs2rCU1NWersedG5tOBGjVq8fffJ9i1aztDh44wGa/yuMx269+/N506fYqnp5dZux09epitWzfRoEEj3N2LEB0dxbp1a6hU6S2zmT/fNAUqgICMtRuWLl361DTLly832yaTyQgICCAg4OlzKru7uxMYGPjMejRq1IirV68+M51Q8MUlpHI//gEWMgMu6pxNm2epVuLl6kRogiNHg0/yTvF6YjC1IAh5IkqbnKN0ebmQ3MvSunVbgoOD2bhxPWvWrKRJk/8REDCACRO+fuGyO3ToiE4Xzx9/rGXDhrVUr16TESO+ZtiwQcY03t5lGD16HIsXz+err4ZQtqwPkyZN45tvRj5RzzZcunSRn36aQVxcnHEdiHHjJjNjxhQmTx6PRmNPhw6dSEpKZPXqFca8lSq9xf79e1m9egV6vR5PzxKMHTuRGjVqARldnGbN+oXFi+ezdOkiIiMjsbd3wMfHlw8+eLTy9SeffEZw8H0mTRqHThf/zHUgTp48TmxsLE2bZr9y9eDBw7GysmbevNkkJiZQqdJb/PTTXJOxBBqNhp9//oUff5zBqFHDsLa24b332tKnT3+Tspo1a0FKSjIrVixjxYqleHmVYMqU76lYMetFdjOP3du7DOvX/05Y2EMsLCwpV86PH3+cbZx8ZsiQL5kx4zt+/HEGlpaWtGzZmgYN3mHatEnZlvs8vvxyNJs3/8GGDWuxtrahV6++T115PLPdlixZwK+/LiYqyrzdihcvjlwuY/78ucTGxqDR2FOzZm0CAgbkad0LI5mU3UhSIU/p9QaioxPyuxoFjlIpx9HRhpiYhJc2uPOvcyEsP7sNK887tC7SHpksZz33YuNT2H3xHCqv64yo/jlemuIvpX6FzatoMyFviTZ7tdLSUomKCsXZuSgq1aPFt6Likhmz4Dip+dAGaqWcyb1rP9dK1ELOKZVy8R17xTIXklu48NfnGuz/JrVZdr9NmZycbArnLEyC8DL8eyMSK6c4XNSuj4IHfTrI5JDNo00ABzsLNHI3Ug33OBb6twggBEF4Ic72lkzuXRtdUs66b+TlStS2VioRPAiCkGdEACG81tLSDVy4HYmqSjTOFhXBYMAy/Dw2wSdAJiPVvhS6kg2QFOaROEAJNw2XYp34R/0vH/q0QZ7DpxeCIAhZcba3zPGF/Jt0Z1QQhMJFXA0Jr7Vr92NJU8dikKXjonbD5v5hbO8fJs2uOCkOpVHH3kJzbTsYzBdUAfB0tyVd60BCeiK34u6+4toLgiAIgvA0VatW5/DhU698rZI3nQgghNfarZA4LBxjUcqUuKakYhV+niTXSiQVeYsUl3IkeNRGpQvF9u7BLPPbWqlwVDmjMFjwb8SFV1x7QRAEQRCEgkcEEMJr7X64DrVjDE4qFzR3D6K3dCTV8dFCcnprZ5LcK2EZeRllQniWZXi5a0jV2nMm/EK2q1cLgiAIgiC8KUQAIbzW7obHo7eMwt2gQpEUTZJbJXhiOtZU+5Lo1XZY3z8KWQQIXm62GLQOxKTEEKwLfVVVFwRBEARBKJBEACG8tlJS9UQlRWKQpVM0Phq9hQN6KyfzhDIZya4VUMcHo9I+MHvb2lKJk9oNmUHJv5EXX0HNBUEQBEEQCi4RQAivrQcROmTW8QC4xYaSau+Vbdp0G3f0FvZYhf2b5fsl3DSkx9tzOuzcS6mrIAiCIAhCYSECCOG1dT9ch9xahxVKrAwSqU9bx0EmI9WxFOq4e8hT4s3e9nSzxaBz4GFiGFFJ0S+x1oIgCIIgCAWbCCCE19b9cB0Wdgk4pelJtS0K2az1kCnVrjiSXIlVuHk3JUu1EieFO0gyLkVfe1lVFgRBEARBKPBEACG8tu6FxyO3isM5OYl0W/dnZ5ArSdV4YhFxCQzmizd5ONkjJdlyMfLKS6itIAhvAoMuCn3knRz9SY/IWbqc/DHoonJd16++GkKnTh9k+/66daupV686wcHmY8eyUq9edVauXG58PXBgH7766otn5mvRohGLFgXlaB+Zrl+/yqJFQSQnJ5ts3759C/XqVSc2NjZX5eW1UaOGMXBgn1zliY+PZ9GiIG7fvvWSavVsoaEhLFoURGRkxDPTXr58kSlTJtCxY1uaNKlLp04fMG/ebJKSkszSnj//LwEB3WncuC7t27dmxYqlJrMeRkZGMnfuz3Tr1plmzRrwwQetGD9+DA8fvj4Tm0yePJ4uXT7K72rkmFiJWngtGSSJB5GxyD2ScErTk2bjlqN8afZeWMTeQhX/gLQnxkwUc7Hmwg0NV6JvoDfoUcgVL6PqgiC8pgy6KBLWjIL01Fe/c6Uam4++Q27rnOMszZo1Z8KEr7l8+SJ+fhXM3t+7dzcVKlTCw+Mp3UOfYtiwkSgUL+c+5vXr11iyZAHt23fE0vLRyt916tRj3rwl2NravpT9vkw6XTxLliygdGlvSpUq/ewML0FoaAhLliygbt36uLi4PjXtvn17ePDgPp07f4anpxe3b99i0aIgLl26wKxZ84zpHjy4z9Chg6hRoxa9e/fj5s3rzJs3G7lcQefOXQC4evUyBw8e4N1336dChUrExsaybNlCevfuyq+//o6jo+NLPW7BnAgghNdSZGwSaao4LAB7mRUoLZ+ZB0BvYY9ebYtF1HWzAEJjo8ZC70yaFMxt7T3KOJR6CTUXBOF1JSXHQ3oqqiqtkds9+0JeLpdjyOJpaG4Z4qNIO7s1Y/+5CCDq12+ElZU1e/bsNAsgQkNDuHDhHF98Mfy565UfF8GOjo7iYvMV+eSTribnumrV6tjZafj226+5cuUy5cr5AbBy5a/Y29szYcIUVCoV1avXJDY2ll9/XUyHDh1Rq9VUrlyF335bh1L56LK1UqXKtG/fmp07t/Hxx5++8uN704kAQngt3QvTIbeKRyZJ2Fq6kpbTjDIZaXYeWMTcRGdoBI89ZZDJZHjYuXFPr+JS1FURQAiC8Fzkds7I7Ys8M51CIQf9iwcQz8vS0pL69Ruyf/9eBg4cglz+6GnB3r27UCgUNGnyPyIjI5k/fw5nzpwmKioSNzc33nmnKd2790atzn7s2cCBfbC2tmb69J+M2/76609++SWQhw9D8fYuw9ChI8zyHT16mDVrVnLjxnVSU1MpUaIkPXsGULv220BGN6UpUyYA0Lp1UwCKFCnKunVbjO9t3boXBwcHALTaOGbP/okjRw6RlJSMj48vffsOpEqVqmZ1bdmyNfPnzyUyMgI/vwqMGPH1M5/A3Llzm++//45Lly7g4uJK9+69zdLcvXuHxYuDOH/+HHFxsRQtWox3321Dx46dkcvlhIaG8OGH7wPwzTcjjfnWrt1M0aLF+OWXQI4dO0xoaAg2Nra89ZY/gwYNxcXFxZj23LmzBAXN4caNaxgMEkWLFuXjj7vQsmVrk3O7ZMkCbt68gbW1FY0aNWHAgC+wsrLi9OlTfP55XwB69frMmOfw4VNZHndWgZqPjy/Af12gMgKI48eP0rBhY1QqlTFdkyb/Y/nyJVy4cO6/wMPOrCw3N3ccHByf2Z3q1q2bzJ37M5cuXSQlJRk3N3dat27DJ590BTK6Ty1dupgrVy6TkKCjeHEvOnX6hBYt3jWWkXnsM2cGsnXrJo4fP4KdnYa+fQfxv/+1YO3a1axatZykpCQaNnyHoUNHGD/7mZ+5efOWMH/+HC5ePI+joxPduvWides2T617eHgY8+bN5sSJoyQlJePnV55Bg4Yagy+Aw4cPsmTJQu7du4NCocDDw5NevQKoU6feU8t+USKAEF5L98N12GuisEzXY7DJwfiHx6TZFccy6irq2LukOpneISvuYsvtMDvOhl3ife8WeVllQRCEAqdZs+bs3r2DM2f+oVq1Gsbte/bspHr1Wjg6OnHz5g00GnsGDRqCnZ0d9+/fY/Hi+URFRTJ69Lgc7+v69at8/fUIatV6m0GDhhASEsLYsaNITTW9BRQaGkzdug34+OMuyOUyjh8/ypdfDubnn3+hatXq1KlTj65de7Js2SJmzgzExsYWtVqV5T71ej3Dhn1OaGgw/foNwtHRmXXrVjNkyAB++WWxyYXa9evXiIlZTt++gzAY9AQG/si3335DUNCSbI8pJSWFoUMHYmlpyddffwvAokXzSEhIoHhxT2O6iIhwvLxK0qxZS6ytrblx4xqLFgWRlJRIjx59cHZ2YfLkGYwZ8yUBAQPw968OgLNzRoAQExNNly7dcXFxJTY2htWrf2PgwD6sWLEGpVJJQoKOr776gsqVqzB+/GRUKjV37twiPv7RrIMHDuxl3LjRtGr1Hj17BhAVFcm8ebOJj9cyYcJ3+PqWY+jQEfzwwzRGjx6Hl1fJHLdtpnPnzgJQokRG3qSkJMLDwyhRooRJuhIlSiKTybh37w5Vq1bPsqx79+4SExNNyZJPv5k3YsRQnJycGDnyG2xtbXnw4D4REeHG9x8+fEilSm/Rtm171GoLzp//l6lTJyJJkklwBfD991Np1ao177/fls2bNzJp0lhu3LjG7ds3+fLLUYSEBBMY+CPFinnw2Wc9TPKOHz+aNm3a8cknXdm3bzdTp07ExcXVGPg+SavV0r9/L6ysrPjiiy+xtbVl3bo1DB7cl9WrN+Do6ERw8AO+/noETZs2p2/fARgMEjduXDNp15dFBBDCa+l+uA61jRbHNAN6h5w/sgcwWNihV9tjEXPTLIBwdbBCdseRsOSbxKfqsFMXvn60giAIOVWjRm0cHBzZu3eXMYC4desGt27dpHPnjLvQ3t5lGDjwC2OeSpXewtLSismTxzF06AiTMQhPs2LFUtzcivDdd9+jUGQ8/bWwsGDq1Ikm6dq372j8t8FgwN+/Ordv32Lz5g1UrVodR0dH41MBX18/45OGrBw7dpjLly8yc2YgtWrVAaBWrTp07NiW5csXM3nyDGNanS6exYt/M95ZT0pKYsqUCYSHh+HmlvWNqh07thAZGcFvv63D0zOjW6yPjy+dO7c3CSCqV69J9eo1AZAkicqVq5CcnMz69Wvo0aMParXaePe+eHFPKlasZLKfxwM1vV5PxYqV+eCDVpw+fYqaNWtz//49dDodAQED8fYuY9xnJkmSmDPnZxo3bsbIkd8Ytzs7u/Dll4Pp2rUXpUt7Gy/WS5f2ply58tme16zExsayePF86tdvaDwXOl3Gha6trekTBpVKhaWlJVqtNsuyJEnip5++x8XFlaZNmz91n6GhwQwePIx69RoAmAUkzZo1Jz3dYCz3rbf8CQ8PY9OmP8wCiHfeaWJ8guTnV5FDhw6wd+8u1qzZZOxedebMPxw4sNcsgGjR4l26dOkOZHzGQkKCWbJkQbYBxNq1q9Dp4lmwYBmOjhmL4FarVpOPP27HqlXL6d9/MNeuXSE9PZ2hQ7/C2trGWParIAII4bV0N1yL3icZ7yQ1yHI/SC/NrggWsbczZmN67LG9XC7DzcKdSG5yOfoaNYtUfUopgiAIhZtSqeSdd5qyd+8uhg4dgUqlYs+eXVhaWtKgwTtAxkXX2rWr2Lx5AyEhIaSmphjzh4Q8oHTpMjna16VLF6lbt4ExeICMC7YnA4jw8DDmz5/LqVMniYqKNM7W4+vrR279++9ZbGxsTC66lEolDRu+w549u0zSlinjY9ItJ/NiOjw8PNsA4tKli5Qq5W28YIaMAKBMmbIm6VJSUlixYim7d+8gLOwh6enpxvcSExOxtrZ+6nEcO3aEZcsWcfv2TRISEozb79+/S82atSlWrDg2NjZ8//13dOjQyRhoPZ7u4cNQPv98mMm+/f2rIpfLuXr1MqVLez+1Dk+Tnp7OuHGjARg2bNRzl5Np8eL5/PPPSWbODMTKyirbdPb29hQpUpSgoIwnKdWq1TBrK61Wy/z5v/DXXweJjIxAr9cb8z6pRo1axn/b2tri4OBIlSpVTcZmeHqW4MyZf8zyNmjQyOR1w4aNmTPnJ/R6vclnPtPJk8fx988YN5LZJnK5nCpVqnL58iUAvL3LolAoGD/+a95//wOqVKn6yiYIEAGE8NpJTE4jNjkOS7mEveL5vkjptkWxjLqKKj6ENHvT/q2ezs6EJ1pzPlwEEIIgvP6aNWvOhg1rOXHiKPXqNWTv3t3UrdvAeFG7Zs1K5sz5mc6dPzP2V798+RI//DCN1NSczzgVFRVp1m8+o/uRhfG1wWBg5Mih6HQ6evUKwMPDEysrKxYunEdY2MNcH1t8vNZ4d/dxjo7OaLVxJtue7Ief2Wf/8YDpSZGR5seUWX5KyqMpZn/5JZAtWzbQvXtvfH39sLOz46+/DrJs2SJSU1OfGkBcvnyRkSOHUr9+Qz79tCsODk7IZDICArqRkpJx/jUaDT/+OIdFi+YzadJY9Ho9lStXYciQr/D2LmOc1nb06KwHxT/Puc0kSRLffTeBy5cvMmfOApNxGZlPHnQ6nUmetLQ0kpOT0Wg0ZuVt3ryBJUsWMHLkNyZPUbIik8n44YfZzJ8/lx9+mEZSUhK+vn4MGjTEOMZl4sRxnD//L9269aJUKW9sbGzYsGEd+/fvMSsvq8/AkxfsSqUyy8/9k58zJycn0tPTiYuLxcnJvKdEXFwsFy+ep1Gj2mbvZT5h8/IqwbRpP7J8+RLGjPkSmUxGrVp1GDJkBEWKPHuc1YsQAYTw2rkfrsPKOhoJ0KjN/2PICb2FPQalFerY22YBRFEna6RIO65EX8+D2gqCIBRslSq9RdGixdizZxcODk7GLiGZDhzYR926Dejbd6Bx2507t3O9H2dnF2JiYky2JSToTC7QHzy4z7VrV/nuu++pX7+RcXtKSvYX8U+j0WiIiYk22x4TE4VGY34HOrdcXFy4etV87aCYmChjlxPIGH/Qpk07Pv20m3Hb0aOHc7SPQ4f+xNbWlm+/nWoc6J7V+gjly1dk5sxZpKQkc/r0KebM+ZlRo4axZs0m47EOGfIVFSpUzOI4nj5l69PMnv0T+/fvZcaMnylb1sfkPSsrK9zc3Ll3747J9nv37iJJktk4i4MHDzBz5lR69er7zAHImby8SjBp0jTS09M5f/5f5s+fw4gRQ9iwYQcKhYIjR/5i4MAhdOjQyZjn8TUo8kpMTDSuro+mlI+OjkapVGJv75Blejs7DbVqvU3v3n3N3lOpHk1OULv229Su/TYJCTqOHz9GYOAPfPfdBH7++Zc8P4bHiYXkhNdOcGQCTjbhqAwSasucrf9gRiYj3aYI6tjb8MQPiYVaga3kQqIhnsgk8/94BEEQXicymYymTZtz5MghtmzZgL29vUm/7ZSUZJMZdAB2796R6/34+VXgyJG/jF1IICM4eVxmoKBUPtrfw4ehnD//r0m6zPef9nQAoHLlKiQkJHDy5HHjtvT0dA4d+pPKld/K9TE8yc+vArdv3+TBg/vGbQ8e3OfGDdMbUCkpKSbHpNfr2bdvt0maR088Up/Im4xSqUQmkxm3Pe38W1hYUqdOPdq2bU9oaAgpKSmUKFESNzd3QkKCKVeuvNmfzAAisw6ZTzaeZfnypaxZs5LRo8dl+7Sgdu23+euvQyZdp/bt242trR2VKj1qg9OnTzFhwhjee68t3br1ytH+H6dUKvH3r8Ynn3QjISGByMgI0tLSMBgMJp/fxMQEDh8+lOvyn+XQoT9NXh88uB9fX78suy9BxhiVO3duUaJEKbP2yBzH8jgbG1uaNGlGkyb/e64APrfEEwjhtRMRm4S1TTwyvQSqp/cbfZpUuyLYxt1GkRSN3tr08WJRW3duShe5EnWdesVrZVOCIAjC66FZs+YsX76E7du30KZNO5M+3zVq1GLt2tWsX/87np4l2LVrOw8e5Gx16sd9+mlXevfuyqhRw/nggw6EhASzevUKky5MmRe68+bNxmAwkJSUyKJFQSZ3dgFKliwJwB9/rKV+/UZYWlpmedFVp049/Pwq8O2339C370CcnJxZt+53oqIi6dKlh1n63GrVqjXLli3iq6++oFevfkDGLExPdlmpUaMWW7ZspFSp0tjbO7Bhw1qz2aecnJyxtbVj795dFC1aDLVajbd3WWrUqMWaNav48cfpNGjwDhcunGPXru0meY8ePczWrZto0KAR7u5FiI6OYt26NVSq9BYWFhnnd+DAIUyYMIbk5CTq1KmHlZUVDx+GcuzYYfr0GYCXVwk8PUugUCjYtm0TCoUCpVKR7WDq3bt3EhQ0m+bNW1K0qAcXLpw3vufhUdzYtatz58/Ys2cn48eP5oMPPuTmzRusWrWc3r37Gy/s79y5zejRwyle3JPmzVuZlPX4oPkn3bhxndmzf6RJk//h4VEcnU7H8uVLKFq0GB4exVEoFJQvX4EVK5bi4OCAQqFkxYql2NjYEhubtzcId+7choWFBT4+5di3bzdnz55mxoyfsk3fqdMn7Nmzk4ED+/Dhh51wdy9CbGwMly5dxMXFhY4dP2HjxvVcvHieWrXq4OzsQmhoCLt376BmzZd/XSICCOG1ExGbhN4iCVtJCY/dkcktvbULyBSo4+6R9EQAUdzJgRuRNpwOvSICCEEQcsUQH5WzhHm4kNyLKl26DN7eZbl58zrNmplOYd2tW29iY2NZuDAIgEaNmvDFF8MZMWJIrvbh41OOb7+dyrx5gYwZ8yWlSnkzfvwUhg171DVKrVYzefJ0fvhhGt98MxI3N3e6du3B6dOnuHLlkklZPXr0YevWTaxc+Stubu6sW7fFbJ8KhYLvv/+ZOXN+Zu7cWSQnJ+HjU44ffphtMoXr87KwsOSHH2Yzc+ZUJk78BhcXN7p168nhwwdNptocMuRLZsz4jh9/nIGlpSUtW7amQYN3mDZtkjGNXC5n9OhxzJ8/hy++6E9qaipr126mTp169Os3iPXr17B9+xYqVXqL6dN/4uOP2xnzFi9eHLlcxvz5c4mNjUGjsadmzdoEBAwwpmncuCl2drYsW7bY+ASjSJGi1Kr1tjHgcXBwYMiQr1i58ld27dqOXq/Pdh2Iv//OeKqza9cOdu0yfSIyevQ4WrV677+6efLDD7MJDPyRL78cjIODIz16BJgsDnfp0gV0Oh06nY5+/XqalNWyZWvGjBmfZR2cnZ1xdnZm+fIlREZG/LdGRhXGjp1ovPM/YcJkpk6dzOTJ49Fo7OnQoRNJSYmsXr0iyzKf1/jxk5k3bzZLlizE0dGRr74a89S1GuztHQgKWsKCBb/wyy+BaLVxODo6Ub58ReOA7DJlynL06F8EBv6IVhuHk5MzTZs2z7LbU16TSS+jo5dgRq83EB2d8OyEbxilUo6jow0xMQnGadRe1NiFx0nzXIeXzI7yDlnPH51T1g+OgUxJnF9bk+2SQWLj5T9RO8bxwzvjTB4dv+5eRpsJL5dos1crLS2VqKhQnJ2LmvRVNuiiSFgzCtJzPrA4zyjV2Hz0HfJcrEQt5J5SKRffsULmZbdZVosX5pfsfpsyOTnZZCxgmQPiCYTwWpEkiTRdOFqVHBvMV67MrXQbd6zCLyDTpyIpHn3ZZHIZDkpXYgklPCkSd+vnH2AmCMKbQW7rjM1H3yEl52yRJ4VCjj6PVqKWWdqJ4EEQhDxT4AKImzdvMmnSJM6cOYONjQ1t2rThiy++MC4Jnh1JkliwYAErV64kOjoaPz8/Ro0aRZUqVUzShYWFMWnSJA4fPoxKpaJZs2aMGjXKbBqulJQU5s2bx6ZNmwgPD8fFxYWWLVsyYsSIvD5kIQ8lJKdjrwpFC9iozafOy600G3esOIcq7oH5qtR2xYiRznMu7CrNSokAQhCEZ5PbOkMOL+SVSjmIu9mCIBRABWoWpri4OLp27UpaWhqBgYEMGTKENWvWMHXq1GfmXbBgAbNmzaJbt24EBQXh6upKjx49uH//0cwHaWlp9OrVizt37jBz5kzGjx/P4cOHGTZsmElZBoOB/v37s23bNgYOHMjixYtzFMQI+S8iNgk7q4yBTzYK8/mjc0tS26BX26LW3jN7r7izBinJhjMh5lP0CYIgCIIgtGr1HocPn8r37kt5rUA9gVi9ejUJCQnMnj3beKL1ej0TJkwgICAAd/esV3pMSUkhKCiIHj160K1bNwCqVatGixYtWLRoEePHjwdg165dXL9+ne3bt1O6dMbdZI1GQ8+ePTl37hyVK1cGYP369fz7779s374dN7fnnAZUyBcRsUmoLONRSmApt3h2hhxIt3ZDFXcvYzrXx8Y6WFkoUac7EJx8D0mS3qhxEIIgCIIgvLkK1BOIQ4cOUadOHZMorWXLlhgMBo4cOZJtvtOnT6PT6WjZsqVxm1qtplmzZhw69Ggu30OHDuHr62sMHgDq1q2Lg4MDBw8eNG5bu3YtLVq0EMFDIRQRmwSWyWgMijy7oE+3cUORGo88Oc7sPWe1G+myZMISI/JkX4IgCIIgCAVdgQogbt26ZXJxDxlPCFxdXbl169ZT8wFmeb29vQkJCSE5OTnb8mUyGaVKlTKWkZaWxqVLlyhWrBhfffUVVapUwd/fn8GDBxMRIS4SC7roaC06tQHbPHr6AJBu7QLIUWvvm71X0r4okgR/3xfdmARBEARBeDMUqC5MWq0Wjca837q9vT1xceZ3fx/Pp1arjYuhZNJoNEiSRFxcHJaWlmi1WuzszGfmebz82NhY0tLSWLBgATVq1GD27NlER0czY8YMBg0axOrVq5/7+JTKAhWvFQiZ04XldNqwZzHEBhPloqCYwgZ5Xp1uuRK9tRNq7X1Si1Y2eauoswZu2XAh/DofVGyURzss2PK6zYSXT7TZq2UwvPjTz8wHqDJZRu9JoeATbVb4vKltplDIXviatEAFEAVB5qI9NjY2zJ492zhw2sXFhe7du3Ps2DHq1KmT63LlchmOjjZ5WtfXiUZjlSflqBKCiS0ix8/KCQsL1bMz5JSmCOqIq1hbKkFuuuy8NY48TH3wxrVvXrWZ8OqINns1kpMVREbK8+Q/aRH0FT6izQqfN6XNDAYZcrkce3trLC0tX6isAhVAaDQak5UZM8XFxWFvb//UfKmpqaSkpJg8hdBqtchkMmNejUaDTqfLsvyiRYsa08hkMqpWrWoy61LNmjVRKBTcuHHjuQIIg0FCq03Mdb7XnUIhR6OxQqtNeuH5ztP1BuSGcJDJsJIsSUlJy6NagtzSBRv9BVLD75GuKWbynrPKjQfyB1y5dw93u9d/nvW8bDPh1RBt9mqlpqZgMBjQ66XnXqBKJnu0DsSbdGe0MBNtVvi8aW2m10sYDAbi4hJJStKbva/RWBXOheRKly5tNtYhPj6eiIgIs7ELT+YDuH37NuXKlTNuv3XrFsWKFTNGWaVLl+batWsmeSVJ4vbt29StWxcAKysrPDw8st1XSkpK7g7qMWJ1yuzp9YYXPj/hsUmoLLUAWGOFIQ9Pt0Ftj6RQo4y9R6ptUZP3vDTFeJBwmr9uXqBtxfp5t9MCLi/aTHi1RJu9Gnr9i1+JZF7M5OdFTb161Z+ZZvTocbRq9d5z7+P69ascOvQnn3zS9bnviLZo0YgPP/yYnj0DXul+n5TbNjt9+hQXLpzjs896PDPt/v172b17O1evXiE+Xkvx4l506NCRd99932zCkK1bN7Jixa+Ehz/E07MEffr0p25d0/+bdDodgYE/cOjQn6Snp1OrVm2++OIrXFxcjGk2blzPwYP7uXnzBsnJyZQsWYouXbpRv36jnB1gAXf69Ck+/7wvS5asoGzZcs/O8Bp5kZsbmQpUANGgQQPmzZtnMhZi586dyOVy4wV+VqpWrYqtrS07duwwBhBpaWns3r2bBg0amJS/efNm7ty5Q8mSJQE4duwYsbGxNGzY0JjunXfeYefOnSZPNI4fP45er6dChQp5fdhCHsmYgSkJhWSBlSyP1+yQyUizckGlvQ/UMnnLTWMH0VZcjLhBW96cAEIQhNyLTo5Bl5aQo7RKhZz0PHpiZKuywckyd4trzpu3xOR1377d6dChI02btjBu8/Ao/kL1un79GkuWLKB9+455diFfkPf7uDNn/mH16hU5CiB+//03ihQpysCBX+Dg4Mjff59g+vTJhIeH0aNHH2O6vXt3MW3aZD77rAfVqtVg377djB49nDlzFlKxYiVjurFjR3Hnzi2GDx+FhYWa+fPnMnz45yxc+CtKZcal4a+/LqZWrTq0bdsBa2sr9u/fx6hRwxkzZjwtW7bO+xMiFCoFKoDo1KkTy5cvZ8CAAQQEBBAWFsb06dPp1KmTyRoQXbt2JSQkhD179gBgYWFBQEAAgYGBODk54ePjw6pVq4iNjaVnz57GfM2bNycoKIhBgwYxdOhQkpKSmD59Oo0aNTKuAQHQs2dPNm3aRP/+/fnss8+Ijo5m5syZVKtWjdq1a7+6EyLkSlRUHIlqPXaoXsqaDHobN6zCziFLT0FSPuoqJ5PJsJIcCUt9kOf7FATh9RGdHMO3x78nzZB33StzSiVXMbb28FwFEY9fcGZycyuS5Xbh5Zo27UeTKe6rVatBXFwcv//+G9269UL+36whixYF0aTJ/+jdux8AVatW5+bNGyxduoDvv58FwIUL5zh58hg//DCbmjUzrmm8vErwyScfcvDgAZo0aQbA4sW/meyzRo3aPHwYwqpVy0UAIRSsAMLe3p5ly5YxceJEBgwYgI2NDR06dGDIkCEm6TL6lpr23erduzeSJLF48WKio6Px8/Nj0aJFeHp6GtOoVCoWLlzIpEmTGDp0KEqlkmbNmjF69GiTsooWLcqvv/7KlClTGDRoEFZWVjRp0oSRI0eKxcIKsMSIYKLUCmxlL2egaJq1K1YYUMUHk+po2qXOxcKN+7IQwrWxuGkcXsr+BUEo3HRpCaQZ0mheojFOlg7PTC+XyzAYXrwPU3RyLLvu7keXlpDrpxDPsn37Fn7//Tfu37+HRmNPy5at6dWrLwpFxmQT8fHxzJ37M8eOHUGrjcPBwZFKlSozYcJ3bN++hSlTJgDQunVTAIoUKcq6dVuy3d9ff/3JL78E8vBhKN7eZRg6dIRZmqNHD7NmzUpu3LhOamoqJUqUpGfPAGrXfttY5+z2GxkZyfz5czhz5jRRUZG4ubnxzjtN6d69t8m4yK1bN7F69W+EhARjaWlJiRIl+fzzoVSqlBFcSZLEqlUr2Lx5A2Fhobi4uNGhw0d07PgJkHGhv2TJAuBRV7EqVaoye/b8LI87q1WMfXx82bJlA8nJSVhb2xAc/ID79+/Rr9/nJumaNPkfc+f+TGpqKmq1muPHj2Jra0eNGo+epnt5laRsWR+OHz9iDCCy2mfZsr5cvfr0acvT09MJCprDvn27iYmJRqPR4OtbnrFjJ2Jra0tSUhK//DKLv/8+QXh4GI6OTtSqVYd+/T7H1tbWWE6HDu/x9tv1KF7ck9WrfyM+Pp5GjRrz5ZejuXv3Nj/8MI3r169RqlRpRo4ci7d3GWPeevWqExAwkPj4OLZv30JKSiqNGjVmyJAvsbbOfsKTZ7UbQHh4GIGBP3L27GkSEnQ4O7tQv35DPv982FPPy+umQAUQkLF2w9KlS5+aZvny5WbbZDIZAQEBBAQ8vQ+ku7s7gYGBz6yHn59flvsRCi4pNpRoBwXOipczG5KktsGgskEV98AsgCjp4MH9uLMcuX2RD97KvrudIAiCk6UDbtauz0ynUMjyZDzFy7J69Qp++SWQjz7qzMCBX3Dnzh3mz5+LwWCgX79BAAQG/sCJE0fp23cQRYoUJSoqkuPHjwJQp049unbtybJli5g5MxAbG1vU6uxnz7t+/Spffz2CWrXeZtCgIYSEhDB27ChSU02f6ISGBlO3bgM+/rgLcrmM48eP8uWXg/n551+oWrX6U/cbFxeLRmPPoEFDsLOz4/79eyxePJ+oqEhGjx4HwNmzp5k6dSIff9yFOnXqkpyczOXLF9HpHk0C8/PP37Nly0Y++6wH5ctX5MKFc/zySyAWFha0bduB995rS0REOHv27OTnn+cBGbM/5sa5c2dxdXUzXhDfu3cHgBIlSpqkK1myJGlpaYSGhlCiREnu3r2Dl1cJsxuiJUqU4u7dO0/d5/nzZ83Kf9Ly5UvYuHE9/foNolSp0sTFxXLy5HHS0lIBSE5OxmAw0KdPfxwcHAkPD+PXXxczatQwAgODTMo6fPgQpUp58+WXowgJCSYw8EeUShUXL56jY8dPcHJy4pdfAvnmmxGsWLHW+CQGYP363/HxKceYMeMJCQlh3rzZpKamMGHCd9nW/VntBjBp0jgiIyP44ovhODo6ERb2kKtXLz/1nLyOClwAIQjPS657SJyLAk+F9UvbR7q1C2rtPZ7swexiYw+RFlyMuMkHiABCEITXW2JiAosWzadz588ICBgAZHRxUamUBAb+SOfOXbC3d+Dy5Ys0bdrCpMtL06bNAXB0dDSOofD19cvyjvfjVqxYiptbEb777nvjEw4LCwumTp1okq59+47GfxsMBvz9q3P79i02b95A1arVn7pfb+8yDBz4hfF1pUpvYWlpxeTJ4xg6dASWlpZcunQRjcaeAQMGG9O9/XY947+Dgx+wfv0ahg8fRZs27f47N7VITk5myZIFvP9+O9zc3HF1dUMulz9Xl7B//z3Lvn27TeqaOYvl43fxAezsMsaUarVx/6XTYmtrviaWnZ0dWq02233u3r2T8+fPMWXK90+t2+XLF6lZsxbt2n1o3NaoURPjvx0dHRk+fJTxdXp6OkWLFqN//17cu3cXL68SJuVNnToTlSojwDtz5h+2bNnA99/PMj5RMhgkRowYws2bNyhb1seYT6VSm31Wpk2bRI8eAVkGQTlpN7lczuXLFwkIGECTJv8z5n0Tu3S9GRPfCm8EhT6KdLkMG/nLGxCXZuOGIiUOeYr5dMNWkiPhqcEvbd+CIAgFxfnz50hKSuSdd5qQnp5u/FO9ei1SUlK4desmAD4+5dixYysrVy7n1q0bL7TPS5cuUrdufeMFIcA77zQxSxceHsakSeNo27YlDRvWolGj2pw8eZz79+89cx+SJLFmzUo+/fRDGjeuS6NGtfn226/R6/WEhGSMc/P1LYdWG8fkyeP5++/jJCcnm5Tx998nAGjUqPET56YmUVFRhIeHvchpIDw8jHHjRuHvX50OHTq9UFk5dePGdb7//jtatXqPBg0aPTWtj085jh07yqJFQVy+fNG4vtbjdu7cRvfunWnWrD6NGtWmf/9eAGZtVKVKVWPwAODpWQK5XE61ajUe2+YFYHZes/qsSJLEpUsXsqx3TtvNx6ccq1atYMOGdTx4cP+p5+J1Jp5ACK+FxOQ0VKo4QIW1zOKZ6Z9X+n/dDtTaYJJdTad9c7Fw5Z7hAuFx8bjZm9/dEQRBeF3ExcUC0KPHp1m+n3mxNWTIV2g0Qfz++wrmzv0ZNzd3unTpzgcfdMj1PqOiInF0NB3DkdH96NFvvsFgYOTIoeh0Onr1CsDDwxMrKysWLpxHWNjDZ+5jzZqVzJnzM507f0bVqtWxs7Pj8uVL/PDDNFJTM7rgVKtWg2+++Za1a1czdOgg1Go1jRo1YfDgYTg5ORIXF4skSbz7btMs9xEWFkaRIkWzfO9Z4uPjGT78c+zt7Zk8ebpJlx07u4z/dzL75T/Kk/FUQaOx/y+dJssgJj4+3jgD5uMePgxl+PDP8fOrwFdfjXlmHT/7rAcymYydO7exZMkCHBwcadfuQ7p3741MJuPgwQNMmjSO99//gD59+qPROPzXRWw4qammU+U/+TRFqVRiYWFhElRk/vvJvNl9VqKiIrOsd07bbcKE75g/fw7z589l5sypeHmVICBgAA0bNn7muXmdiABCeC1ExCQis0gCVFi/xCcQKNToLRxRae+ZBRAl7ItyP/Y8R25e4YOqNbIpQBAEofDL7BYzefIMk1kSMxUtmrHgpq2tLYMHD2Pw4GHcvHmDtWtXMXPmVEqX9uatt/xztU9nZxdiYmJMtiUk6EwuHB88uM+1a1f57rvvTdYryOkaTgcO7KNu3Qb07TvQuO3Ondtm6Zo3b0Xz5q2IjY3l8OE/mTXrR5RKJd98Mx6Nxh6ZTMbcuQtNLnQzPdlFJ6dSUpL56qsv0Ol0BAUtMbu49vIqCfDfGIeSxu13795BpVJRrFjGGlclSpTk1KmTSJJkMg7i7t07JgORAWJjYxk6dCCOjo5MmTLdOMXr06jVanr2DKBnzwAePLjPtm2bWbx4PsWKedCixbscOLCXsmV9TIKRM2f+ye3peKbsPiuPB1ePy2m7ubi4MHr0OAwGA1evXmbZskWMHTuKlSvXv/C0xoWJ6MIkvBZiw0KJV8lQSnLULzkuTrdxQRX3wGy1IBcrZzAouRTxYo/pBUEQCrqKFStjaWlJREQY5cqVN/tjb+9glsfbuwyffz4UeHRRrlRmffc4K35+FThy5C+TWRgPHNhnkiYzUMgsFzLuoJ8//69Juuz2m5KSbHbxuHv3jmzr5ODgQOvWbalRo6ZxAHJm95q4uLgsz03moGeVSmV8qvEs6enpfPPNKO7evcPMmYG4urqZpfHwKI6np5fZOdm3bw/VqtUwHlft2m8TH6/l1KmTxjT37t3l+vWr1K79aAxfYmIiw4d/TlpaOjNmzMLGxjRgyYnixT0JCBiARmNvPD8pKSkm7QMZ4yvyWlafFZlMhp9f1ut55bTdMsnlcvz8KtC7d3/0ej3BwW/WVO7iCYTwWkiMDCFWqcBGrn7pU+2mWbtiEX0dRVI0emtn43aZTIaVwYGHYhyEIAivOTs7O3r27MvcuYGEh4fj718NhUJBSMgD/vrrEJMnT8fS0pJ+/XpQv/47lC7tjUIhZ+fObahUKuPTh8xFXf/4Yy316zfC0tLS7C54pk8/7Urv3l0ZNWo4H3zQgZCQYFavXmHShalEiZK4ubkzb95sDAYDSUmJLFoUZHbBnd1+a9Soxdq1q1m//nc8PUuwa9d2HjwwvTBctCiIuLhY/P2r4ejoxM2bNzhx4phxqk8vrxK0a/chkyaN5eOPu1C+fEXS09O5f/8eZ86c4rvvZv5X11Lo9XrWrFlFpUqVsbGxMXly8LiZM6dx9OhfDBz4BQkJCVy4cN74no+Pr3GK2R49+vDtt9/g4VEcf/9q7N+/h0uXLjBnzgJj+ooVK1OzZh2+++5bBg4cglqtZsGCuXh7l6Vhw3eM6caM+ZLr168yatRYHj4M5eHD0MfKyH7g96hRw/D19aNsWV+srKw4cuQQ8fFaqlbNmK62Ro1a/PDDNJYuXUiFCpU4fvwI//xzMtvynldaWqrxsxIaGsIvvwTSqFETSpYslWX6nLSbTqdj6NCBNG/eCi+vEqSnp7Fu3Rpsbe3w8XmzVrMWAYTwWpC04UQrFVjJX84aEI/LCBrkqLX3SXosgABwUrvyQHaNiNhEXB1e3mxQgiAUXtHJsTlKl5frQLwMH3/8Ka6urvz++2+sX/87SqUSD4/ivP12fWNXl0qV3mLXrm2EhIQgl8soXboM06b9aLyI8/EpR48efdi6dRMrV/6Km5t7tutA+PiU49tvpzJvXiBjxnxJqVLejB8/hWHDHnU3UqvVTJ48nR9+mMY334zEzc2drl17cPr0Ka5cuWRSVlb77datN7GxsSxcmDGdaKNGTfjii+GMGPFoPapy5cqzZs0q9u/fS2JiAq6ubnz8cRe6dn20cO0XX3yJl1cJNm36g6VLF2JlZY2XVwmTQd9169bngw8+ZMWKpcTERPPWW/7ZrgPx99/HAZg9+yez99au3WzsMtasWQtSUpJZsWIZK1YsxcurBFOmfE/FipVN8nz77XcEBv7A9OmT0ev11KxZiyFDvjLpopQ5qHjSpHFm+zx8+FSW9YSMNt+/fy+rV69Ar9fj6VmCsWMnGtedaNOmHSEhwaxb9zsrVy6nZs3ajBs3mYCAbtmW+Tzat+9IbGwMEyeOJS0tjQYNGjF06FdPzfOsdlOr1Xh7l2H9+t8JC3uIhYUl5cr58eOPs585i9jrRiZJUsGdZPo1otcbiI5+cvJPQamU4+hoQ0xMAunp5jM15NSJ5YHsdLmLrY0H1S19np3hBdncP4pBZYnW5z2T7aEJoRyPO0BLx09p7V85m9yFW161mfDqiDZ7tdLSUomKCsXZuSgq1aPFxwrbStRC7imVcvEdKwDq1atO//6D6dy5yzPTvkltlt1vUyYnJxsUipyNbhBPIITXgmVKFHFKOe4vcwD1Y9JsXLGMvAoGPcgfTRPnZu0KsTIuhF2nNa9nACEIwvNxsnRkbO3h6NJydjNJqZCTrs+bCxtblY0IHgRByDMigBBeC2oplhSF+qVO4fo4vbUrMukiKt1D0jQexu0KmRILgz0hyW/WYCpBEHLGydIxxxfyb9KdUUEQChcRQAiFnt6gB2UCoH6pi8iZ7NPCHkmhRqV9YBJAADgqnQmxeEBkXBIu9i9/TIYgCIIgCI88bYyGkDfENK5CoZcQGYFOlTHzkrXs1QQQyGSkWbmi1pqvbFrMtghyixRO3372qqeCIAiCIAiFjQgghEIvPjyYGKUCmQSWMvNBQS+L3tYNZUIEsnTTecSLWGcsqvTvw+uvrC6CIAiCIAiviggghEIvOSqUGKUcK5kF8pe8BsTj0qxdAQmV1nTdBwuFJUqDNQ8S7r+yugiCUPCISQ4FQShI8vI3SQQQQqFniAsjQvnqxj9kklTW6NW2qLLoxuSgcCbVIpLI2KRXWidBEPKfQpExM1tOVlcWBEF4VTJ/kxSKFx8CLQZRC4WeXBdBtL0S61ccQADord1Qx93nyUkZi9oUIcJwn/N3w3jHoeQrr5cgCPlHLldgZWWLThcDgFptgew5no4aDDL0evEUozARbVb4vAltJkkSqakp6HQxWFnZIpe/+PMDEUAIhZ46JQqtSobzK5rC9XFpNq6oY28hT47DYGlv3O5u7Y5MB2dDrvPOWyVfeb0EQchfGo0TgDGIeB5yuRyDQUzjWpiINit83qQ2s7KyNf42vSgRQAiFmiRJqNPiSFI45MsTiHRrV0COOu4+yY8FELYKO+QGFXd1YiYmQXgTyWQy7O2dsbNzRK9Pz3V+hUKGvb01cXGJr/3d0deFaLPC501qM4VCmSdPHjKJAEIo3FISSFKmg4xXtoicCbmSdCtH1Np7JLtXNG6WyWTYyZ2JVkUQE5+Co10+1E0QhHwnl8uRy3M/O5xSKcfS0pKkJL1YTK6QEG1W+Ig2e35iELVQqBniI9EqMwYsWsvz5yI93cYNlfYBPPEItIi1G3KbWK7ej8qXegmCIAiCILwMIoAQCrXU2DDilBkfY6v8eAIBpFu7ITOkoUp4aLK9iHURZAoDZx7cypd6CYIgCIIgvAwigBAKtcSoh0QrVCglBSpZ/vTI01s6ICnUqOJM131wUDkik+TcjLudL/USBEEQBEF4GUQAIRRqaTFhRCrUr3QFajMyGWnWbqjj7ppslssU2Mgc0cnC0CWl5VPlBEEQBEEQ8pYIIIRCzRAfQYxSkW/jHzKl27iiTIxEnma6cJyrpRtyuxiu34/Nn4oJgiAIgiDkMRFACIWaMjEKrVJeAAIId0DKGEz9mCLW7shUafz74G7WGQVBEARBEAoZEUAIhZYkGbBIjSNBJcu3AdTGuigt0avtzboxOatdQYKr0WIgtSAIgiAIr4cCF0DcvHmT7t27U6VKFerWrcv06dNJTU19Zj5Jkpg/fz6NGjWicuXKdOzYkbNnz5qlCwsLY9CgQfj7+1OzZk3GjBmDTqczSTNy5Eh8fX3N/hw6dCivDlPIA1JiHKAnRWnAKp+fQACk27qhir0H0qPFaFRyFZZoiDGEkpKqz8faCYIgCIIg5I0CtZBcXFwcXbt2pWTJkgQGBhIWFsbUqVNJTk5m7NixT827YMECZs2axfDhw/H19eW3336jR48ebNq0CU9PTwDS0tLo1asXADNnziQ5OZlp06YxbNgwgoKCTMrz9PTk+++/N9nm7e2dh0crvChDfAQ6hRwpvxaRe0KajTsW0ddRJkaQbuNm3O5i4UqibQg3Q+IoXzJvlpAXBEEQBEHILwUqgFi9ejUJCQnMnj0bBwcHAPR6PRMmTCAgIAB3d/cs86WkpBAUFESPHj3o1q0bANWqVaNFixYsWrSI8ePHA7Br1y6uX7/O9u3bKV26NAAajYaePXty7tw5KleubCzT0tKSKlWqvKxDFfKApI14bA2IfJyF6T96KyckuQpV3D2TAKKodREepN7k/L1gEUAIgiAIglDoFaguTIcOHaJOnTrG4AGgZcuWGAwGjhw5km2+06dPo9PpaNmypXGbWq2mWbNmJt2ODh06hK+vrzF4AKhbty4ODg4cPHgwbw9GeOn08ZFEKDKePBSELkzI5KRbu6COu2ey2dnCFYBLkTfzo1aCIAiCIAh5qkAFELdu3TK5uIeMJwSurq7cupX9INTM957M6+3tTUhICMnJydmWL5PJKFWqlFn5d+/epVq1alSsWJF27dqxd+/e5z4u4eVIjY0gQqFGJsmwQJXf1QEg3dYdlS4MWXqKcZuVwhq1ZE14ajDpekM+1k4QBEEQBOHFFaguTFqtFo1GY7bd3t6euLi4p+ZTq9VYWJjehdZoNEiSRFxcHJaWlmi1Wuzs7J5Zvp+fH5UqVaJMmTLEx8ezatUqBgwYwM8//0yLFi2e+/iUygIVrxUICoXc5O/cSNdGEKVQYSWzQKGQ5XXVnovexh0wYKG9R6qLj3G7k8qNENtIQqISKF3MPv8qmAdepM2E/CHarPARbVb4iDYrfESbPb8CFUAUFF27djV53bhxYzp16sSsWbOeO4CQy2U4OtrkRfVeSxqNVa7zRCZGE2elxEZhgYVFwXgCgYUKycoBq4QHKL0qGTeXcvLkYfodrodHUa1CsXysYN55njYT8pdos8JHtFnhI9qs8BFtlnsFKoDQaDTEx8ebbY+Li8PePvu7thqNhtTUVFJSUkyeQmi1WmQymTGvRqMxm7I1s/yiRYtmW75cLud///sfM2bMIDk5GUtLy9wcFgAGg4RWm5jrfK87hUKORmOFVpuEPhfdeyTJgCwxmng7d2xkalJS0l5iLXPJyhVVxC0SvZJAlnFXw0HuDMDfty/SvErpp+Uu8J63zYT8I9qs8BFtVviINit8RJuZ0miscvw0pkAFEKVLlzYbixAfH09ERITZ2IUn8wHcvn2bcuXKGbffunWLYsWKGS/4S5cuzbVr10zySpLE7du3qVu3bl4dRrbS08WHMzt6vSFX58eQGItc0pOklnCVWWAoQKc21aYI6ujryOMjSLfNmI3JUm6N0mDFg6R7pKXpkckKRperF5HbNhPyn2izwke0WeEj2qzwEW2WewWq01eDBg04evQoWq3WuG3nzp3I5fKnXuBXrVoVW1tbduzYYdyWlpbG7t27adCggUn5V65c4c6dO8Ztx44dIzY2loYNG2ZbvsFgYOfOnZQtW/a5nj4IeU+Kj0SCjEXkCsAaEI/LnM5VHXfHZLuD0gW9VSThMUn5UzFBEARBEIQ8UKACiE6dOmFjY8OAAQM4fPgw69evZ/r06XTq1MlkDYiuXbvSrFkz42sLCwsCAgJYvHgxy5Yt49ixYwwbNozY2Fh69uxpTNe8eXPKli3LoEGDOHDgANu3b2f06NHG1asBgoOD6dKlC6tXr+bYsWPs3LmT7t27c+HCBQYPHvzqTobwVAZdFElyGQa5VDCmcH2cTEaajRvq2Dsmm4tZF0VmHc+Few/zp16CIAiCIAh5oEB1YbK3t2fZsmVMnDiRAQMGYGNjQ4cOHRgyZIhJOoPBgF6vN9nWu3dvJEli8eLFREdH4+fnx6JFi4yrUAOoVCoWLlzIpEmTGDp0KEqlkmbNmjF69GhjGhsbG2xtbfnll1+IiopCpVJRsWJFFixYQP369V/uCRByzBAfRZQyY/G4grAK9ZPSbYpg/fAf5KkJGNQZg+fdrd2R6eDfhzdoQuEeByEIgiAIwptLJkmSlN+VeBPo9QaioxPyuxoFjlIpx9HRhpiYhFz1P0w+/CvHbv/DOg8LWlvXwlpewLqWpadgf3MHupKNSXb1AzLG22wJ/gNFnCc/tu+TzxV8fs/bZkL+EW1W+Ig2K3xEmxU+os1MOTnZ5HgQdYHqwiQIOWWIjyRcrgYJLGXq/K6OOaUFeitnk25MMpkMe7kLyeowtAmp+Vc3QRAEQRCEFyACCKFQ0msjiFYoUaNCLiuYH+M0GzdU2vtgeNTdrphNMWTW8Zy/K8ZBCIIgCIJQOBXMKy9BeApJkpASotEqFQXz6cN/0myLIDOkoYoPNm7zsC2GTAanQy/nY80EQRAEQRCenwgghMInNRFZego6JVgV4ADCoNZgUFqbdGOyVtig0FtzN+F2/lVMEARBEAThBYgAQih0DLpoAJKUYF3QpnB9nExGum2RjADisbkK7OUuJCgfkpKqzz6vIAiCIAhCASUCCKHQkXRRAKSq9AU7gABSbYugSI1HkRRt3FbUuihyy0T+vXc/H2smCIIgCILwfEQAIRQ6Bl0UacjRK/VYFvAAQm/tgiRToo591GWphMYDSYJ/gsU4CEEQBEEQCh8RQAiFjqSLIlplA7KCPQYCAJmcdBtXLGIeBRAWCkuU6bbc1olxEIIgCIIgFD4igBAKHYMumghFxsJxBT6AANJsi6JMDEee+mghQXu5KzplKOl6MQ5CEARBEITCRQQQQqFj0EUSqVABYCUr2F2YANJs3AGZyWxMxayLIVOlcPrerXyrlyAIgiAIwvMQAYRQ6Ei6KCJlCpDAQqbK7+o8m9KCdCsn03EQ9h5IBjl/B1/Mx4oJgiAIgiDknggghEJFMuiREmOJlstRS2pkMll+VylH0myLoNY+QKZPA0CtVKJKdeBO4o18rpkgCIIgCELuiABCKFSkxFiQJHQKGZaF4enDf9Jti4KkRxX3aOpWB7k7iYoIEtOS8rFmgiAIgiAIuSMCCKFQMfy3BkSiCiwLwQDqTAa1LXq1xqQbU3EbD5BJ/H3/Uj7WTBAEQRAEIXdeKIDo1asXW7ZsITk5Oa/qIwhPlbmIXIpSj1UBXwPiSWm2RbCIvQ0GAwDFHVwwpFhyKlSMgxAEQRAEofB4oQDi/v37fPnll7z99tuMGDGCo0ePIklSXtVNEMwYdFHoFRboVenYFLIAIt22KDJ9CipdKAAqlRxVqhP3k26J740gCIIgCIWG8kUy79q1i3PnzrF582Z27tzJ5s2bcXFxoXXr1rz//vv4+fnlVT0FAQBJF02SwgaDSo+1onAFEHpLBwwKK9Qxt0jTeADgpHAnQh5CaEIYxWyL5HMNBUEQBEEQnu2Fx0BUrlyZr7/+mkOHDjF//nxq167N77//Trt27WjdujULFizg4cOHeVFXQcAQH0WUKnMRucIVQCCTkW5XFHXsLfjviYOnbVEkvZwTwefyuXKCIAiCIAg5k2eDqOVyOfXr12fGjBn8+eefNG/enBs3bjBz5kwaN25Mt27d+PPPP/Nqd8IbStJFESHPGDxdGFahflKqXREUqTqUiREAuDvaYkhw4EzY+XyumSAIgiAIQs68UBemJ506dYrNmzeza9cu4uLiKFu2LG3btkWpVLJ+/Xr69etH3759GTx4cF7uVniDGBKiiLQsDlDoBlED6K1ckBRq1DG3Sbdxw9JCiUWqC1Hp14hJjsXR0iG/qygIgiAIgvBULxxA3Lhxg82bN7N161ZCQ0Nxdnbmgw8+oE2bNiZjILp27co333zDypUrRQAhPBcpLRlSk4iykiGTZKjzNv59NWRy0myKYBFzk8TitQBwUxclRLrO+chLNCj+dj5XUBAEQRAE4ele6AqsTZs2XLt2DbVaTZMmTRg3bhz169dHLs+6Z1StWrVYu3bti+xSeIMZdNEAxMhAJakKzSrUT0qzK4o6+B6KpBj0Vo4UcdDwQGvHPw/PiwBCEARBEIQC74UCCI1Gw7fffkvLli2xtbV9ZvomTZqwb9++F9ml8AbLXAMiXmbAgsKzCvWT0m3ckGRK1DE3SbKqjquDFfoQR25qb5OYloS1yiq/qygIgiAIgpCtFxpEPW3aNN57771sg4fk5GRCQkKMr62srPDw8HiRXQpvMENCNBKQrDIUqlWozcgUpNm4YxF9EwBrSyVWaS5IGLgYdSWfKycIgiAIgvB0LxRANGnShD179mT7/v79+2nSpMmL7EIQjCRdFJLaFr0qvXAHEECaphjKpEjkyXEAuNk5IE+140y4mM5VEARBEISC7YUCiGetnpuWlpbteAhByC2DLpp0lQ16VVqhW4X6Sek27iBTGJ9CuDlYkRrjwIWoKySlJ+dz7QRBEARBELKX66t7nU5HSEiIsWtSbGys8fXjf65cucL27dtxdXXNVfk3b96ke/fuVKlShbp16zJ9+nRSU1OfmU+SJObPn0+jRo2oXLkyHTt25OzZs2bpwsLCGDRoEP7+/tSsWZMxY8ag0+myLffChQv4+fnh7++fq+MQ8p6kiyJRYY2k1GOjtMzv6rwYuTKjG1PMDQBcHa3Qax3RS3rORVzM58oJgiAIgiBkL9eDqJcuXcqcOXMAkMlkTJkyhSlTpmSZVpIkvvjiixyXHRcXR9euXSlZsiSBgYGEhYUxdepUkpOTGTt27FPzLliwgFmzZjF8+HB8fX357bff6NGjB5s2bcLT0xPIeCLSq1cvAGbOnElycjLTpk1j2LBhBAUFZVn/iRMn4uTkRGJiYo6PQ3g5DLooohROQEKhfwIBkGZXDOvQU8iTtdhY2mGttMVCcuJU2FlqFa2W39UTBEEQBEHIUq4DiLp162JtbY0kScyYMYN3332XChUqmKSRyWRYWVlRoUIFKlWqlOOyV69eTUJCArNnz8bBwQEAvV7PhAkTCAgIwN3dPct8KSkpBAUF0aNHD7p16wZAtWrVaNGiBYsWLWL8+PEA7Nq1i+vXr7N9+3ZKly4NZMwk1bNnT86dO0flypVNyl2/fj0xMTG0b9+e5cuX5/g4hLwnSRJSQgwR9hmfgcI+BgIgzbYIoMAi+gZJxari6mBJbLwTV+TX0aUlYKuyye8qCoIgCIIgmMl1AOHv72/szpOUlESzZs3w9fXNk8ocOnSIOnXqGIMHgJYtWzJu3DiOHDlCu3btssx3+vRpdDodLVu2NG5Tq9U0a9bMZJD3oUOH8PX1NQYPkBEQOTg4cPDgQZMAQqvVMnPmTKZMmcKFCxfy5PiE5yclx4M+jShZRq87S3nhDyCQK0mzdcci+jpJxari7mjNvWu2WGgk/g2/QF2PWvldQ0EQBEEQBDMvNMJ54MCBeRY8ANy6dcvk4h4ynhC4urpy69atp+YDzPJ6e3sTEhJCcnJytuXLZDJKlSplVv5PP/1EhQoVeOedd577eIS8I/23iFwUIJNkhXodiMelajwyZmNKiqWIozWSXoWTyo1TYWfzu2qCIAiCIAhZytUTiNmzZyOTyejXrx9yuZzZs2c/M49MJmPAgAE5Kl+r1aLRaMy229vbExcX99R8arUaCwvTfvEajQZJkoiLi8PS0hKtVoudnd0zy798+TLr1q1jw4YNOap3TimVYkaqJykUcpO/s2NIigEgFgNKgxKFonCuQv0kg607klyJVewN8KiBnY0adYob19POE5+uxdHSIb+raCanbSYUHKLNCh/RZoWPaLPCR7TZ83uuAKJ3796o1eo8DyAKAkmSmDBhAp07d8bb2zvPypXLZTg6ij7t2dFonr76cpxBR4JCiU6WjoVMjYXF6/EEAlQY7IthGX0dWZm6eLjaEh4mofRQci72Am39mud3BbP1rDYTCh7RZoWPaLPCR7RZ4SPaLPdyFUBcuXLlqa9flEajIT4+3mx7XFwc9vb2T82XmppKSkqKyVMIrVaLTCYz5tVoNFlO2RoXF0fRokUB2L59O7du3WLmzJlotVogY5B2ZnkWFhZmTzpywmCQ0GrFTE5PUijkaDRWaLVJ6PWGbNMlhocgs7QjRZ6KjaQiJSXtFdby5VLaFMMq5jgpkSG4aCy4ciea8r4l2HfjCA3c6yKTFaynLTltM6HgEG1W+Ig2K3xEmxU+os1MaTRWOX4ak+tB1C9T6dKlzcYixMfHExERYTZ24cl8ALdv36ZcuXLG7bdu3aJYsWJYWloa0127ds0kryRJ3L59m7p16xrzxMXF0bhxY7P91KhRg969ezN8+PDnOr70dPHhzI5eb3jq+dFr/1uFWpmGpcwKw2t0KlOt3bBUWKCOvIqrey1kgF16MW4m3uRG9F1K2XvldxWz9Kw2Ewoe0WaFj2izwke0WeEj2iz38jyASEpKYtu2baSmptKwYUM8PDxynLdBgwbMmzfPZCzEzp07kcvlxgv8rFStWhVbW1t27NhhDCDS0tLYvXs3DRo0MCl/8+bN3Llzh5IlSwJw7NgxYmNjadiwIQAffPABNWvWNCl/w4YNbN++nQULFlCsWLEcH4+Qdwy6aNJUNhhUcVi9DjMwPU4mJ82uGBZR17AoXgcHOwu0UVbYamw48fCfAhtACIIgCILwZnqhAGL06NGcO3eOrVu3ApCamspHH33E9evXAbCzs2PZsmWUL18+R+V16tSJ5cuXM2DAAAICAggLC2P69Ol06tTJZA2Irl27EhISYpyi1cLCgoCAAAIDA3FycsLHx4dVq1YRGxtLz549jfmaN29OUFAQgwYNYujQoSQlJTF9+nTj6tUAxYsXp3jx4ib1OnnyJAqFglq1xLSa+UXSRZFkXwqDKhpbRSFfhToLqXbFUcfeRhUfgpujNfce6nirRFlOhZ2lfdn3UMkL1MNCQRAEQRDeYC807PzEiRM0a9bM+Hrr1q1cv36d77//nq1bt+Li4pKjgdaZ7O3tWbZsGQqFggEDBjBz5kw6dOjAyJEjTdIZDAb0er3Jtt69ezNw4EAWL15Mnz59ePjwIYsWLTKuQg2gUqlYuHAhJUuWZOjQoYwbN463336bmTNnPucZEF4FyaBHSoolSp4xcNpGUfhXoX6S3soJg8oGi6grFHG0JiE5jWLqkiSlJ3Eu4mJ+V08QBEEQBMHohW5rRkZGmnRR2rt3LxUrVqR169YAfPTRRyxatChXZXp7e7N06dKnpslqVWiZTEZAQAABAQFPzevu7k5gYGCu6jRo0CAGDRqUqzxC3pESY0GSiPzv42ojf/0CCGQyUjWeWETfxNWjHnK5jNgYOcVsinA05CTV3N/K7xoKgiAIgiAAL/gEwsrKyjhrUnp6OidPnqRevXrG921sbLKcVUkQcsPw3yJykf+9tnodAwgg1d4LmSENm7jbONtbcuehlgrO5bgac52opOj8rp4gCIIgCALwggFEhQoVWLNmDZcuXWLevHkkJCSYzF507949nJ2dX7iSwptN0kUBEG2QQAIL2euyBoQpSWVNupULlpEZ3ZjuhekorSmJSq7mWOip/K6eIAiCIAgC8IIBxBdffEF0dDTt27dn9uzZ/O9//zMORgbYs2cPVatWfeFKCm82KSEalBbESakoDcoCty5CXkq190KlC8bLLp3UND3h0an4OHpzLPQkBklMMScIgiAIQv57oTEQlSpVYseOHZw+fRqNRmMy/alWq6Vz585mU6IKQm4ZdFHIrDQkSsmoDK/n04dMaXbFkMLP4550AyuLotwK0VLR148LUZe5HH2NCs7lnl2IIAiCIAjCS/TCc0M6OTnRtGlTs+0ajYauXbu+aPGCgEEXjczSjiRZCmpe7wACuZI0jSeWEVco6liKm8FxNKzih6uVM0eCT4gAQhAEQRCEfJcnk8vrdDpCQkLQarVIkmT2fo0aNfJiN8IbStJFIbN2JNUQiw02+V2dly7FoSTq2FuUs45m+0M18YlpVHAux6HgY8SlxGNvYZffVRQEQRAE4Q32QgFETEwMEydOZPfu3WbrMgBIkoRMJuPy5csvshvhDWfQRSNz9ESfFoGV7DVbhToLBgsNeisniidfQyaryK0QLeVKleVw8HFOhJ7if/9v787jo6rux/+/7r2zz2QmCyELCZAECLtsgogNbqhY69JqxfopKC7UD+pXtJ9WsbZaeVTlVz+2YmsB8VNbt6pVsRUQBRRZRNkVRSAJWyAhZJmZLLPe+/sjMGUMS4CQTMj76QNhzpx75n1zssw7Z+t5UXuHKIQQQohO7LQSiEceeYRly5bx05/+lBEjRuB2u1srLiEAMCJBCNbRoDrQTRHsnJ1buH5XMDkfx/61FHgKKdnnZUjvLvRKzmfFvjVc2mMsqnJa+x8IIYQQQpyy00ogVq5cyaRJk/jFL37RWvEIEcc4dAZEtWoG/Sw9RO4owknZ6AdsnGPezfwKK5GozsAu/Xhr+3tsqymmb2rv9g5RCCGEEJ3Uaf0a02azxZ1ELURrO3yIXKXRtHWry9Q5EggUlVBKHpmBnWjRAHsr68h2ZpJqS2HlvjXtHZ0QQgghOrHTSiCuvvpqPvroo9aKRYhmDh8idzDadAZCkmZrz3DaVNDTEwUYZi+jZJ8PRVEYkFrIpsot1IXq2zs8IYQQQnRSp5VAXH755Xi9Xm677TYWL17M5s2b2bJlS7M/Qpwqvb4arC5qogEwwNZJpjABYLISSu7OINMudpZVYRgGfVP7YGDwRcWG9o5OCCGEEJ3Uaa2B+MlPfhL796pVq5o9L7swidNlHDpEzhdpQNNMqGfxKdRHE0jtjbt2J1mBUg56+5CebCff3YOV+z7nwpwxZ/Wp3EIIIYRITKeVQDzxxBOtFYcQR6XXVaHYXNTrAUzKWX6I3FEYZgehpG4M13eyfVcV6ck59E/ry3slC9nt30sPd257hyiEEEKITua0EojrrruuteIQ4qj0uirU1FzqQ34sRquce9jhBNMKcfnLiO7ZBOfk0MOdg8vsZNX+LySBEEIIIUSba7XN5A8cOMDWrVtpaGhorSZFJ2cYBkZdNardTVAJYuHsP0TuaHRrEn5bFv30b6mprUNVVPql9mFt+QZC0VB7hyeEEEKITua0E4iPPvqIK664grFjx3LdddexadMmAKqrq7n22mv58MMPTztI0TkZwTqIhlHsbkJaCFsnTSAA9Mx+2JQwNV83rTXqn1ZIIBpkY+VX7RyZEEIIITqb00ogli5dyj333ENKSgpTp07FMIzYc6mpqWRkZPD222+fdpCiczp8iFzY4kQ3RXConTeBUKxJ7Ney6FK9GSNQR7LVQzdXFmv2r2vv0IQQQgjRyZxWAvGnP/2JESNG8Nprr3HzzTc3e37IkCGyA5M4ZfqhMyCqdBUUsHemLVyPoiGlD7oODV8tA6Bvah++rdlOTaC2fQMTQgghRKdyWgnE9u3bGT9+/DGf79KlC1VVVafzEqITM+qqQDVREY0C4OpEh8gdTUqym28i3VDKtxCt3kvv5Hw0RZMzIYQQQgjRpk4rgbDb7TQ2Nh7z+T179pCcnHw6LyE6Mf3QGRBVwaZTl5NMnXsEwmxSqbXn4MdNaPNCLIpKQXIen+1fGzd9UAghhBDiTDqtBGLUqFG8++67RCKRZs9VVlbyxhtvcMEFF5zOS4hOzPAfRLG7qQ7WgwFOc+cegQDISHHweWMuRl0NkW2r6Jvam4qGSnb797Z3aEIIIYToJE4rgbjvvvsoLy/n+uuv5x//+AeKorBixQqeeeYZfvCDH2AYBlOnTm2tWEUn0zQCkYQv2oAaMWHSWm3X4Q4r3WOnDgc1rgLCO1bTLWzgNDtZUy6LqYUQQgjRNk7rHVl+fj6vvvoqycnJ/PGPf8QwDObNm8fs2bPp06cPr776Kjk5Oa0Vq+hkDH/TFCZftBGT3jkPkfsuk0mli8fGxrouqEldiGx4nz7uHqyr2ERUj7Z3eEIIIYToBE77XVnv3r3561//itfrZdeuXRiGQW5uLqmpqa0Rn+ikjEgII+BDsXuoD1RhlgQiJiPVwZfFVTT0GY6teCkF5XvYYK5na80OBqQVtnd4QgghhDjLnfK7slAoxPz581m5ciW7d++mvr4ep9NJjx49+N73vsdVV12FxdJ59+0Xp8eobzoDQrG7aawJYqVzL6A+UprbhtmkUVodZXCf80n+5hNScrryRfl6SSCEEEIIccad0hSmb7/9lvHjx/PrX/+aRYsWsWfPHgKBAHv27GHhwoU8/PDDXHXVVRQXF59028XFxdx6660MGTKEMWPGMHPmTEKh0AmvMwyDOXPmcOGFFzJ48GBuvPFGNm7c2KxeRUUF99xzD0OHDmXkyJE8/PDD1NXVxdV54YUXuPbaaxkxYgRDhgzhBz/4AS+//LLsdNOG9Lr/JBBBLYi1E59C/V2qotA1xc6uch+KOwtT93PI93rZdGAzoeiJv1aEEEIIIU7HSY9A1NfXc9ddd1FdXc20adO45ppryMjIiD1fUVHBu+++y/PPP8/PfvYz5s+fj8PhaFHbXq+XSZMm0bNnT2bNmkVFRQVPPvkkgUCAX//618e9du7cuTz77LP8/Oc/p7CwkFdeeYXJkyczf/58cnNzAQiHw9x+++0APP300wQCAZ566ikeeOABZs+eHWvL7/dz5ZVX0rt3b6xWK6tXr2bGjBnU1dXxs5/97GQ/ZOIUGP6DAEStDqJaBJsiIxBHyky1U1ZZx4GaBrpm96VP0Mc6o4aNWxcycsA17R2eEEIIIc5iJ51AvP322+zfv5+//vWvjBo1qtnzGRkZTJkyhcGDBzN58mTeeeedo55SfTSvv/469fX1PPfcc7HzI6LRKI899hhTpkyJS1SOFAwGmT17NpMnT+aWW24BYPjw4VxxxRXMmzePRx99FIAPPviA7du3s2DBAvLz8wFwu93cdtttbN68mcGDBwMwbdq0uPbPP/989u3bxzvvvCMJRBvR66pQbC680TAADlVGII7kcViw20zsrPCTkeogtedIuu76kDUlyxiWUogpu297hyiEEEKIs9RJT2H6+OOPGTNmzFGThyONHj2a888/n6VLl7a47eXLlzN69Oi4w+fGjx+PruusXLnymNetX7+eurq6uFOxLRYL48aNY/ny5XHtFxYWxpIHgDFjxpCcnMwnn3xy3NhSUlIIh8MtvhdxevS6KrC7qWzwA+DUZAQijqKQmeJgd0UdkaiOoigUpvZmm8NC1eI/EK3Y0d4RCiGEEOIsddIJxLZt2xg5cmSL6p533nls27atxW2XlJTEvbmHphGC9PR0SkpKjnsd0OzagoIC9u3bRyAQOGb7iqKQl5d31PYjkQh1dXV8/PHHvPvuu0ycOLHF9yJOj1FXhWpzUxloWp+SJAlEM1mpDqJRnd0VTR+jXs5sDAW2pKXSsOD3RA+c/BokIYQQQogTOekpTF6vl/T09BbV7dKlC16vt8Vt+3w+3G53s3KPx3Pcdnw+HxaLBas1/k2m2+3GMAy8Xi82mw2fz0dSUlKL2t+1axeXXXZZ7PFdd90Vmx51qkwmOQjtu7RDh8Np3zkkzqg7iNY1n+pQHRiQZLOhyocvjsNuIs1jY0eZl165ySRpdnJsaXzpMDMqqNKw4Pck/eAXmDIKWvV1j9VnInFJn3U80mcdj/RZxyN9dupOOoEIhUKYTC27TNO0DjvtJysri7feeouGhgbWrl3L3LlzUVWVe++995TaU1WFlBRnK0d59nC77bF/G4ZOTX0NrpQ0/A11qBETSS478vXdXM8sD+u2VtAYipLmsTMgOZcPyjdhufAmop++Td2//z+yf/IbrNm9Wv21j+wz0TFIn3U80mcdj/RZxyN9dvJO6RyIsrIytmzZcsJ6e/fuPal23W43fr+/WbnX68Xj8Rz3ulAoRDAYjBuF8Pl8KIoSu9btdjfbsvVw+1lZWXFlFouFQYMGATBq1ChcLhdPPfUUN910U4tHYI6k6wY+X8NJX3e20zQVt9uOz9dINKoDoNfXQDRCSHVQFdiPFjERCYeJtHOsicjjMGMxa3xZfJCR/TLINXdBAVZVlvK9c68nuvof7Hv1MVzXPISpS49Wec2j9ZlIbNJnHY/0WccjfdbxSJ/Fc7vtLR6NOaUE4o9//CN//OMfT1jPMAwURWlxu/n5+c3WIvj9fiorK5utXfjudQClpaX07fuf3WdKSkrIzs7GZrPF6n13TYZhGJSWljJmzJjjxjZgwACi0ShlZWWnlEAARCLyyXks0age+/hEaw4AYFjd+PVGzLoZXT50x5Sd5mDnfh9DCrpg1Uzk2NJYW1vMGE9fLCOvJ/jZP/DPfwrnNb9CTc5stdc9ss9ExyB91vFIn3U80mcdj/TZyTvpBOKJJ544E3EAUFRUxF/+8pe4tRCLFi1CVdXjvsEfNmwYLpeLhQsXxhKIcDjM4sWLKSoqimv/vffeY+fOnfTs2ROA1atXU1tby9ixY48b2/r161EUhZycnNO8S3Eiel3TGRCKw02D0YjZkKHF48lKc7Kz3M/uCj/52W56O7JYWv0lteF6ks1OrKN+THDVqzS8PxPHtY+gOlPaO2QhhBBCdGAnnUBcd911ZyIOACZMmMDf//53pk6dypQpU6ioqGDmzJlMmDAh7gyISZMmsW/fPj788EMArFYrU6ZMYdasWaSmptKnTx9ee+01amtrue2222LXXX755cyePZt77rmH+++/n8bGRmbOnBk7vRqaRjzuuOMOrr76anr06EEkEmHNmjX87W9/48Ybb6RLly5n7P5FE91/ECx2FJOVgBokiWNPXxNgt5pIdTctps7PdpNv78rHqGzw7+Si1AEoFjvWUTcQXPUKjYv+F8fVD6OYbe0dthBCCCE6qFOawnSmeDweXnrpJR5//HGmTp2K0+nk+uuvb3awm67rRKPRuLI77rgDwzB48cUXqa6upl+/fsybNy92CjWA2WzmhRdeYMaMGdx///2YTCbGjRvH9OnTY3WsVit5eXn89a9/paKiApvNRvfu3Xnssce49tprz+j9iyaGvwrF7iEQDaGrOjZFDpE7kW5dnHxZUkW1P0hqkpXu9i5s8JVyUeoAABS7G8u51zclEUv+gv2ye1FkWyshhBBCnALFMAyjvYPoDKJRnerq+vYOI+GYTCopKU5qaupj8w8b3v//MKJhagZdxBOl79C/oQ8Du2adoKXOzTAMVm8pJzPNyXn9M/i2fh8fVm/mNwU3kGp2xepFDxQT+vyfWIZdjXXEqY0mHq3PRGKTPut4pM86Humzjkf6LF5qqrPFi6jlV5Ai4ej+g6gOD1XBpoRLTqE+MUVR6JbuYleFn2AoSp69KxoqG3074+ppXQswFV5AaP18Irs3tU+wQgghhOjQJIEQCcUwDIz6ahS7m8qADwCXSebrt0R2mhMF2LHPi0U10cOeznp/8xPWTb1Go2b0onHZXPSG2jaPUwghhBAdmyQQIqEYjV6IhlHsHqoC9SgRDZs5oZbqJCyzSSUj1cH2vbXohkEvRyZ7AlVUheLPVlEUBcvg8QAEls3FMGTYVgghhBAtJwmESCiG//AWrh6qQ02nUFvMWjtH1XHkprsIBKPsPVBHT1s6JkVlo39ns3qK1YHlnPFEy7YQ/ubjNo9TCCGEEB2XJBAioeiHEwi7B2+kAS2iYTHJp2lLuexmkpOsfLu7tmkaky2d9b7m05gAtK75aN3PIfjZP9Drqto4UiGEEEJ0VPLOTCQUva4KzHYUsxW/0YBJN5/UaeYCcru6qPIFqPIG6e3IYm+wmgMh71HrmvtdhGKyEPj0JWRDNiGEEEK0hCQQIqEY/koUR9Mp5A1GAIthbueIOp4ubht2m4lv91TTw5aOWdHY4Cs9al3FbMU88FKiezYT2bWhjSMVQgghREckCYRIKLqvEsXuIWJECalhrMghcidLURRy013sPlBHKGiQZ+/K+mMkEABqRm/U9HyCq17BiITaMFIhhBBCdESSQIiEovsrUR3J+CKNANgkgTglWWkOTJrKtj219HZkUR6qZX+w5qh1FUXBPOASjIZaQpsWtnGkQgghhOhoJIEQCcPQdYy6KhSHh9pw0yFyDlUOkTsVmqrSLc3Jjn1eskypWBXTMacxAaiuVEw9hxHa9L6cDSGEEEKI45IEQiQMo6EG9CiKIxlvpAEApyQQpyynqwvdMNi530+ePYP1vtLjLpQ29TofFJXQ2nfbLkghhBBCdDiSQIiEofsOAIfOgAjWga5gN8sUplNlNWtkptj5dnctvRyZVIZ97A0ee7tWxWLD3Pt8wt9+gl67vw0jFUIIIURHIgmESBhNh8gpKHYPlcE6tLAJqxwid1pyuybRGIyg11qxqxbWHeNMiMO0HkNRbEkE177TRhEKIYQQoqORBEIkDN13AMXmQtFM1ITqm06hNkkCcTpcdjNpbhtbd9XQy5HJOm8JuqEfs76imTD1Gk2k5HOiVXvaMFIhhBBCdBSSQIiEofsPojiSAaiNNKBGNSxm+RQ9XT0yk/DVh0gLp+KLNlLcUHHc+lruIBRnCsG1b7dRhEIIIYToSOTdmUgYuu8AisMDgF9vaBqB0ORT9HQlOy24nRbK94bxmBys9RUft76iaph6jSa6a4OMQgghhBCiGXl3JhKG4atEcXjQDf3QKdQWUJT2DqvjUxR6ZCRxsCZAjprORv9OInr0uJdo3fqjODyE1r/XRkEKIYQQoqOQBEIkBCMcxAj4UA4dImcoBlZDtnBtLV08Nuw2E9GDNgJ6mC31e49bX1E1TAXnESldS7RmXxtFKYQQQoiOQBIIkRB0fyUAiiOZmnAdAHY5hbrVKIpC965JHCyPkKYl8YV3xwmv0XIHodhchDYtaIMIhRBCCNFRSAIhEkLU27SwV3UkUxNpOoXaqdraM6SzTmaqA4tZw9ngYUvdHuoigePWV1QNLX8Eke2r0OuOfX6EEEIIIToXSSBEQtC9B0CzgNVJTbgeJarilEPkWpWmKuSmu6gvMwOccDE1gKn7OWCyENr8wZkOTwghhBAdhCQQIiHo3goUZzKKolAVqkMNm7DIIXKtrlu6E5NhIiXqYY13+wnrKyYrpp7DCG/9GCNY3wYRCiGEECLRSQIhEkLUWxE7A+Jg0I8aMWGTMyBanUlTyeniJHLAzr5gDWWB6hNf03MY6Dqhb5a1QYRCCCGESHTyDk0kBN1bgepMAaA6XIcWMWE1m9o5qrNTboYLS70Ds2Hi8xYsplasTrScAYS/XIwRDbdBhEIIIYRIZJJAiHZnRMPodVWxEQhvtAE1bMJqkSlMZ4LFpJGd5sLic/G5d8cJz4QAMOWfi9HoI7LjszaIUAghhBCJLOESiOLiYm699VaGDBnCmDFjmDlzJqFQ6ITXGYbBnDlzuPDCCxk8eDA33ngjGzdubFavoqKCe+65h6FDhzJy5Egefvhh6urqYs9Ho1Hmzp3LzTffzKhRoxg5ciQ//elPWbt2bWvepjhCxFsJhoHiTCEQDRMijClqxqzJIXJnSveuLsw1STToQTbX7TphfdWVhprRi9DmRRiG0QYRCiGEECJRJVQC4fV6mTRpEuFwmFmzZjFt2jTeeOMNnnzyyRNeO3fuXJ599lluueUWZs+eTXp6OpMnT2bPnj2xOuFwmNtvv52dO3fy9NNP8+ijj7JixQoeeOCBWJ1AIMCcOXMYMGAATz31FL///e/xeDxMnDiR1atXn5H77uzC1eUAKM4UaiJNyZwNOYX6TLJbTeQkJWMN2FlZ822LrjHljUCvKSOyd8sZjk4IIYQQiSyhJpm//vrr1NfX89xzz5GcnAw0jQg89thjTJkyhYyMjKNeFwwGmT17NpMnT+aWW24BYPjw4VxxxRXMmzePRx99FIAPPviA7du3s2DBAvLz8wFwu93cdtttbN68mcGDB2Oz2fjoo4/weDyx9seMGcNVV13FSy+9xOjRo8/Y/XdW4ZpyUE0otiRq6ssAsCNnQJxpPTLc7NyTxA5bOQdCXrpaPMetr6Z1R3F3JbBpEQwe1UZRCiGEECLRJNQIxPLlyxk9enQseQAYP348uq6zcuXKY163fv166urqGD9+fKzMYrEwbtw4li9fHtd+YWFhLHmApuQgOTmZTz75BABN0+KSh8NlhYWFHDhw4HRvURxFuGY/6qEtXGvCdWCAU7O2d1hnPafNRI6pC2pUY1XNthPWVxQFU/65RHZvJnRwbxtEKIQQQohElFAJRElJSdybe2gaIUhPT6ekpOS41wHNri0oKGDfvn0EAoFjtq8oCnl5ecdtPxKJsGnTpmbXitYRri6PLaCuidRjipqxyQLqNpGf4cHic7GqZluLFlNrWX1RrE58Xyxog+iEEEIIkYgSagqTz+fD7XY3K/d4PHi93uNeZ7FYsFrjf2vtdrsxDAOv14vNZsPn85GUlHTS7b/wwgtUVFTEpkedKpMpofK1hKBpKuGqMrS0HmiaSm2kHiWsYbWYUOXDdca5XRayK9MpYQeb6nYxMqXX8S/QLBj5w/BvXkbK8OswmR1tE6g4LZqmxv0tEp/0WccjfdbxSJ+duoRKIBLRypUrmTVrFv/93//NwIEDT7kdVVVISXG2YmRnByMa5qC3Enefc3Em2aiJ1KOGTSR5LFit5vYOr1MY1C2LPVVlLK7YzCXdT/w5Hh1wHge+XQU7VpBy/nVtEKFoLW63vb1DECdJ+qzjkT7reKTPTl5CJRButxu/39+s3Ov1NluX8N3rQqEQwWAwbhTC5/OhKErsWrfbHbdl65HtZ2VlNSvfsmUL99xzD1dddRV33333qdxSjK4b+HwNp9XG2cjwloOhE7F48PsDVDb6UCMWVMUgGJRDy9qC3aLSNZROWXQ3m8t3kec8+mYFh6mqGXveYGo/fx+9z8UoWkJ9GxFHoWkqbrcdn6+RaFRv73BEC0ifdTzSZx2P9Fk8t9ve4tGYhPrJn5+f32wtgt/vp7Ky8rjrDw4/V1paSt++fWPlJSUlZGdnY7PZYvW2bYtfLGoYBqWlpYwZMyaufNeuXdxxxx0MHTqUGTNmnNZ9HRaJyCfnd+nVTbsu4UghHIng0xtxhB1YNA1dPlxtZmBKFvtD+5m/ewP39LnsBLVVnH3Po7F4A4EdazEXjGyTGMXpi0Z1+T7UwUifdTzSZx2P9NnJS6hJX0VFRaxatQqfzxcrW7RoEaqqNnuDf6Rhw4bhcrlYuHBhrCwcDrN48WKKiori2t+6dSs7d+6Mla1evZra2lrGjh0bKztw4ACTJ08mKyuLZ599FrNZptKcKXptOYrZimJz4Ys0YmCgRUxYZBF1m/K4bKQF0tgRLaMmVH/C+uaUTLQuPQh9ubgNohNCCCFEIkmoBGLChAk4nU6mTp3KihUr+Oc//8nMmTOZMGFC3BkQkyZNYty4cbHHVquVKVOm8OKLL/LSSy+xevVqHnjgAWpra7ntttti9S6//HJ69+7NPffcw7Jly1iwYAHTp0+PnV4NTQfJ3XHHHdTU1DB16lS2b9/Oxo0b2bhxI19//XXbfTA6iWjNfkzuNBRFoTrcNL3MalhR5RC5NjfYkwuGwvydG1tU35w/Av3ADqIHjr2DmRBCCCHOPgk1hcnj8fDSSy/x+OOPM3XqVJxOJ9dffz3Tpk2Lq6frOtFo/JaTd9xxB4Zh8OKLL1JdXU2/fv2YN28eubm5sTpms5kXXniBGTNmcP/992MymRg3bhzTp0+P1Tl48CBbt24F4K677op7jW7durF06dLWvu1OLVq7H3NSGgAHw03rXxyqHCLXHtKcDlIOpLLJWkxj5FzsJstx62uZvVAcyYS+XIz9kp+1UZRCCCGEaG+KYRhGewfRGUSjOtXVJ54a0tnU/e0enH2GQ95o/l2+jo8OfEWvg4UMLujS3qF1SpX1fpbp6xmpDeDmPkdf26BpKklJNvz+AMHtnxP+ZhnOnzyN6kxp42hFS5lMKikpTmpq6mWebwchfdbxSJ91PNJn8VJTnS1eRJ1QU5hE52IE6zECfkzupmShKuzHFDFjNSfUwFinku5MwhNMZn1wO4FQ5IT1te6DQDMT3rKkDaITQgghRCKQBEK0G712P0AsgagM+VBCJqyygLpdDXH1JGIO8fb2TSesq5isaLmDCH2zDCMSbIPohBBCCNHeJIEQ7Ub3lgOgJaUCh9ZAhExYzfJp2Z4ybB6SwkmsD22jrvHEZ3GY8oZDsIHwtpVtEJ0QQggh2pu8UxPtRq/Zh+LwoJqthPQIddEAWsiM1SwjEO1tiDOPsD3A21s3n7Cu6khGzepD6MsPMAyZQyqEEEKc7SSBEO0mWlOG6mqavnQw1HT2hxY2YZEEot1lWVJwR11sNrZT5W08YX1T3ggMbwXR3SdOOIQQQgjRsUkCIdqNXl2G6j6cQDRt4aqFzdgkgUgIQxz5hB2NvPHVZk60V5ua0g0lJZvQ5kVtE5wQQggh2o0kEKJdGOEgRl1VbASiKuxHNVRMhgmTJofIJYJMcwrJRhLF9lK+3V1z3LqKomDOO5fo/q1ED+5smwCFEEII0S4kgRDtomkHJiM2AlEZ8mMzrFgtJpBTqBPGEHseYUcj87d9SSgcPW5dNbNP08Fym2QUQgghhDibSQIh2oVeUwaA6jp0CnXIhzkqC6gTTVdTCl0UDwe6lLHyq/3HrauoKqa84URKPkevq2qjCIUQQgjR1iSBEO1Cr92HYnejmK1A0xoIJWTCLmdAJJxzbPlE7AE+OfAtB2uPv6Bayx0MJguhzR+0UXRCCCGEaGuSQIh2Ea0uQzm0/kE3DKrDfvSAhs1qbufIxHelaW6ytTTqssv512c70fVjb9WqmCyYeg4jvPVjjEBdG0YphBBCiLYiCYRoF3pNGWpS0/Sl2lA9EUOHgCYjEAlqkDWPsDnETm0Pq78qP25dU89hoOuEvl7aRtEJIYQQoi1JAiHanBEOYvgPxkYgDgSbzoBQQ2bsVkkgEpFHddLTlEl9two++Xov+yqPPbqgWJ1ouYMIfbkYIxJswyiFEEII0RYkgRBtTq/ZS9MOTF0BOBDwAqBFTNitpnaMTBzPIEtPdFUnmHOQN5ZsJxI99lQmU8FICDUQ3rq8DSMUQgghRFuQBEK0uWjVHlBUlKRDW7gGfdiwYFI0LJp8SiYqu2qlryWXmpRyyhtq+Xh92THrqo5ktOx+hDYtwIhG2jBKIYQQQpxp8m5NtDm9ajeKKxVFaxpt2N9YgyVqxW4zyxkQCa7QnItNsaD2reKLrQf4dnftMeuaeo3CqK8hsn1V2wUohBBCiDNOEgjR5qJVu1GT0mOPyxqrMYXNsoC6AzApGoOseZSpFXhyoyxcs4sa39HXOahJ6aiZfQhu/DeGfvxD6IQQQgjRcUgCIdqUYejo1XtRDq1/iBo6FQEvNMoC6o6ip7krXc3JlGfsxGJReHdFCZHI0ddDmHuPxvAdIFK8po2jFEIIIcSZIgmEaFOG/yCEA7EF1FUhP1FDJ9qgYbfIAuqOQFEURrkKqdH9ePo1Uu0LsnDNLgyjeV3Vk4natYDg+vcwjnN+hBBCCCE6DkkgRJuKVu0BQHU3TWEqD9Y2PQ6YsUkC0WGkmpLoZc5mg76Nwf09fLOrlpVf7j9qXXPv8zG85URKPm/jKIUQQghxJkgCIdqUXrUbLA6wugCoCNZiVkyoUU22cO1gBtny0BSVrc5tnFOQxqqvyvmqpLpZPTUlG7VrPsF178oohBBCCHEWkARCtKnowV2o7q4oh3ZbKg95SVLsKChYZRF1h2JRTAy19mJHeB9aVj352W4Wfb6b3eX+ZnXNvcccGoWQtRBCCCFERycJhGhTemUpanJW7HF5oAarbsVm0dBU2cK1o8nRutBNS2NJ4wYG9kmma7Kdt5YXs+9gfVw9NSUbNaMXwbXvyI5MQgghRAcnCYRoM3p9DUajF9WTCYBhGJSHvGghi0xf6qAURWGotTdhI8LywCYuGJxJisvKm8t2UFHdGFfX3OcCDN8BwttWtFO0QgghhGgNkkCINhOtLAVATW5KIHyRRoJ6GKPBhE0SiA7LoVoZYu3FltAuSqP7KTonG5fdzD+Wbqey9j9JhOrJQMvqS2jduxjRcDtGLIQQQojTIQmEaDN6ZSlYnWBLAqA8VAtApE6VLVw7uJ6mDLppXVjcsI6gGmLsOd2wWU289tF29h9siNUzFV6A0eAl/PXSdoxWCCGEEKcj4RKI4uJibr31VoYMGcKYMWOYOXMmoVDohNcZhsGcOXO48MILGTx4MDfeeCMbN25sVq+iooJ77rmHoUOHMnLkSB5++GHq6uri6qxcuZIHHniASy+9lMLCQn7729+21u11atHKUlRPZmwBdUWoFg2VSIOKwyYLqDsyRVEYYesDwAf1X2A2q1w8tBsuu5nXlm5j16GF1aorDS13UNO5EKGG4zUphBBCiASVUAmE1+tl0qRJhMNhZs2axbRp03jjjTd48sknT3jt3LlzefbZZ7nllluYPXs26enpTJ48mT179sTqhMNhbr/9dnbu3MnTTz/No48+yooVK3jggQfi2vr000/ZunUr5557Lm63u9XvszMyDKMpgTg0fQmgIuglSWvagUlGIDo+q2JmpLWQnZEKPg9sxWLWuHBoNukeO29+XMy3u2uBph2ZCAcJbVrYvgELIYQQ4pQk1Lu2119/nfr6ep577jmSk5MBiEajPPbYY0yZMoWMjIyjXhcMBpk9ezaTJ0/mlltuAWD48OFcccUVzJs3j0cffRSADz74gO3bt7NgwQLy8/MBcLvd3HbbbWzevJnBgwcD8Itf/IIHH3wQgDVrZNvJ1mD4KyFYj+o5YgemUC0Ow04IZBH1WSLTlEp/c3dWBr4i05RKD3MG3xuUxZpvDjB/RSkXDM5i9IBMTPkjCG1ehLn/xajOlPYOWwghhBAnIaFGIJYvX87o0aNjyQPA+PHj0XWdlStXHvO69evXU1dXx/jx42NlFouFcePGsXz58rj2CwsLY8kDwJgxY0hOTuaTTz6JlalqQn1YzgrRih0AqClHJBDBWqy6Fc2kYjbJx/xs0d/Sk65aCu/Xf4ZPb0DVVEYPyGBQfhorNu/nXytLMXqOBM1M8It/tne4QgghhDhJCfWuraSkJO7NPTSNEKSnp1NSUnLc64Bm1xYUFLBv3z4CgcAx21cUhby8vOO2L05ftHw7iisNxeIAwBtuoC4awBy04rSaUOQIiLOGqiicZ+uHisrb/k8JGmFQFAbkpTJmUBY7yry8unQX4e6jiGxbSfTgrvYOWQghhBAnIaHmjfh8vqOuOfB4PHi93uNeZ7FYsFqtceVutxvDMPB6vdhsNnw+H0lJSSfdfmsxdeLfskcrtqOl5qBpTR+DsobqpicazNhtJhRFRVX1doxQtJSiqLG/j9VndswUOQbyUf1G/lW3iuvd30NTVHpmunA7c1mxeT9//dLMLSkpBFe/QtK1D8cW14vWd/jr7vDfIvFJn3U80mcdj/TZqUuoBOJspqoKKSnO9g6jXUQD9dRUl5FUOAJHkg2ACm8tDs1CvU8nt6sFi0UDZCemjuREfdaVZC4yDeYj7wYWB9Zxber5qIpKN4eV68Y6+WTjXt4/kMe1gXVo+zbiGXhB2wXfSbnd9vYOQZwk6bOOR/qs45E+O3kJlUC43W78fn+zcq/Xi8fjOe51oVCIYDAYNwrh8/lQFCV2rdvtbrZl6+H2s7KympW3Jl038Pk657aV4V2bAIOwIwO/v2k6WXFtBalaEoGgjtthIRSKYhgyAtERKIqKxaK1qM9SSeI8e19WN2wlGtG50jUS9dBIw5gBmWx1WSnZuxv//Lk4rHl06ZLcBnfQ+Wiaitttx+drJBqVr7OOQPqs45E+63ikz+K53fYWj8YkVAKRn5/fbC2C3++nsrKy2dqF714HUFpaSt++fWPlJSUlZGdnY7PZYvW2bdsWd61hGJSWljJmzJjWuo1jikQ65ydnaN82sDjQbR6MQ1+guwMHySSNAOB2WjAMHb1zfng6nKZpS1qL+yxH68ooK6wJbkX3wxXOc9EOTYPq0z0Fn30s9tK3Wf7yC+Re9lOG9kk/szfQiUWjeqf9PtRRSZ91PNJnHY/02clLqElfRUVFrFq1Cp/PFytbtGgRqqoe9w3+sGHDcLlcLFz4n33lw+EwixcvpqioKK79rVu3snPnzljZ6tWrqa2tZezYsa17MyImsn8bakq32Bx3X6QRb6QBS8iKpik4bOZ2jlCcad3NXTnP2pdt4T28XfcpAf0/h0O60zNoyBxKkeUr/vHuCl5fsp2I/CZICCGESFgJlUBMmDABp9PJ1KlTWbFiBf/85z+ZOXMmEyZMiDsDYtKkSYwbNy722Gq1MmXKFF588UVeeuklVq9ezQMPPEBtbS233XZbrN7ll19O7969ueeee1i2bBkLFixg+vTpsdOrDysrK2PRokUsWrSIxsZGdu/eHXssTo4RCaIfKEbr0j1WtjdQBYBeZ8Zlt8gOTJ1ErrkrRbbBlEeqec2/lJrof6YTBnNGgNXFnRkb+GjtHp54eR0HvY3tGK0QQgghjiWhpjB5PB5eeuklHn/8caZOnYrT6eT6669n2rRpcfV0XScajcaV3XHHHRiGwYsvvkh1dTX9+vVj3rx55ObmxuqYzWZeeOEFZsyYwf3334/JZGLcuHFMnz49rq01a9bw0EMPxR5/+umnfPrppwB8++23rX3bZ7Vo+XbQI6hdesbK9gaqsKpmGvwGboel/YITba6rKZmLHUNZ0fgVf/d9yDjHMPpZe4Bqwt/jQtK3vce9Q/y8uM3CY//3BXf8YACDC9LaO2whhBBCHEExDMNo7yA6g2hUp7q6vr3DaHPBNW8Q/nY51kv+OzaFad7epVSF/QS/SqFf9xTyc5IJBsOyBqKDUFWwWs2n1WdhI8L64A52RSroa87lIsdQHKqVpJKPsHh3s+vcn/OvDTUU7/Nx1fk9ufaCPFRVhqpOlcmkkpLipKamXub5dhDSZx2P9FnHI30WLzXV2eJF1Ak1hUmcfSJ7v0JN6xG3x/+ewEHchhMMcNll/UNnZFZMjLL1ZZS1LyXhcv7Pt5Avg6X4c84HRSFzx9v88Ht5FA3O4v3VO3n6Hxvx1odO3LAQQgghzjhJIMQZowf86FV7ULv0iJXVRwPUROqxhu2gSALR2fUwZ3CFcwQZWiqLG9byt8BKvuwxFHvlFhzl6zhvQCY/vqgXuyv8/ObFz/lmZ3V7hyyEEEJ0epJAiDMmWvYNYKAdkUDsbjzY9I8GMw6rGU2TaSmdnU2xMMrWl4vtQ9ANndfYzl96ZFFTMh+tsZoeGUlMuqIvKUlWfv/6Rt79tISozHcTQggh2o0kEOKMiezeiJLUFcXujpVtb9iPU7PS6AeXPaHW8It21kXzcLF9COfbBlBrsTE3y8k7u+ZSWrcNp83EDWMLOH9QJv9atZPf/X0dFdWd82BGIYQQor1JAiHOCEOPEtm9CS2jIK58e0M52dZUvHUhXHbZgUnEUxSFHFMXxjmG8z2lO416iH9X/JNX9szl67pNjOzfhZsv7UNtXYjfvPg5y9bvRZd9IIQQQog2JQmEOCOi5dshWI+W0StWFoiG2BuoIl1NJhLRSZL1D+IYFEUhy5nHZXTjqko/Dt1gWeVC/m/Xc5Qqn3PdJRn075nK3xdv48mX17G3su7EjQohhBCiVUgCIc6IyK4NKDYXSnJWrKy4sQIdA0fICYDLIQmEOL5QaiFdzF0YX7aXy90XkmPvwZfe9by2bw6BbqsZO1aj2h/g0f/7gjeX7aAxGGnvkIUQQoiznkxCF63OMAwiuzagdi2I2751e8N+XJqNUD1YzBpWk+Sv4gQUhYas4bh2L6dbyTKc/a+nf9I57G3cxc6GHewKv49zgIuegV58uDnAp5v3c9338igako2myueXEEIIcSZIAiFanV61C8N3AK3v2Ljy7fXldLOm4j0Ybtq+VZEdmEQLaGYauo3CtesT3NsX4O3zA3o6C+jpLKAmVM3Ohh3s1b/CPCiCJZTJq1/sYfHaHvyoqDfDC9PjklghhBBCnD75FZ1odeHtq8HqRO3SM1bWEA1SFqwm25pKZW2jrH8QJ0W3uKjvdh7m+grcxR9w+AjsFEsqQ5NHMj7jOoYmj8LhjGLpvRF/3kLmrn+TR175kC9LqjBkobUQQgjRamQEQrQqQ9eJ7PgMLasvyhFTSIobKjAwSIq4CIUrSfPY2jFK0RFFHWnUZ5+Ls+xz3MUf4Cu4DFQNAJNqpqejgJ6OAnxhL7saStillVDDLv68ZQ0pmwq44ZwihubntPNdCCGEEB2fJBCiVUX3fYPR6MXUrX9c+baGfSRpdny1UTRNxe2ULVzFyYu4Mqnvdi7Osi9w71iIv+ByDC1+NMtt9jDIM5QB7nMoD5SxTdlBjb6JuaWbcHybyYU9R3JJ7+HYTZLECiGEEKdCEgjRqsLbVqA4U+N2X9INg03+XfS0p7O/rIFUtxVV5qWLUxRxZVGXcx7Oss/xfPM2vj7fR7e4mtVTFZVsey7Z9lyC0QBfVe5gDztZuH8+i/b9i4KkXozKGcyAtL54rO6jvJIQQgghjkYSCNFq9EYfkZIvMBVeELdwtbTxAN5IAz0tGaz2V1OYm9KOUYqzQdTZlboe38O59zOSv/oH/oLLCHtyj1nfqtkYnjmQYfoAdlQc4JvqYrY17mOHfxso0M2ZRWFqL3ol55Pn6Y7bktSGdyOEEEJ0LJJAiFYT3voJAKbcwXHlG/2luDQb+M1gQBe3tT3CE2cZ3erB3+NCnPvX4dn2Ho3pA2nIHY2hHXt6nKIq9M7KoFdGV3ZW+Pl6zwHqlUqqujSyunEdS/d8CkCKNZnuSd3ITepGTlI23VxZpFiTZUcnIYQQAkkgRCsx9Cjhr5eideuPYrHHynVDZ4OvlAJ7BuXljbjsZqwW+bQTrcRkpT5nNJbaUuyVW7DWlNCQcx6BtEI4zjkQiqqQl+WmZ0YSew9m8s2uGqp9jThcOt27K1itjVQHavm2pphANACAw2Sne1IO3d059ErOI9/TU9ZRCCGE6JTknZxoFZGSzzHqazANuyauvLihAn80QC9HFp8drCYjxX6MFoQ4RYpCKCWfsCsTW+UWXDuXYt+3jsasoQTTCjG0Y3+bU1SF3K4ucru6qPEF2bHPy45v/USiTlz2ZPKyB5KaoqLaGgmpPqqDVawsW8PiXctQUOjp7s7g9P4M7jKATGdXAKK6TjiiE4o0bTVr1lRMmoJJU2UEQwghxFlBEghx2gxdJ7huPmrXAlRPRtxzG/yluDU7tpCNYChKqlsSCHFmGGYHjdnnEgr0xlr1La5dH+Pcu4pAah9CXQoJOzOOe3hhitvKue6uDOudTmVtI/uq6tldXsdXJSGajpHQMGmZWMzZ2B0hdIuX3Y01lNZ+wPzihdDgJlKVTbgyGyLNp1FZzCrpHjtdU+zkdnXRr0cKBd08mDQ5jkcIIUTHIgmEOG2RkjUY3nIsF0yMKw/pETb6d9LHkc3+qgY0VSFZtm8VZ1jUlkxDt1EooXqs3p1Yq4uxV36FbnYSTM4jlNyDSFK3Ztu/HqZpCplpDjLTHE3tRQ38DSG89SGC4SihSJRwWMcgFSWah96oEzRVUW/bT33Ot5hzt5GhFNDTNJBkNZOobhCNGtQHwnjrQ9TWBdm6u4b3Vu7EYlLpn5fKmIFZnNMrTZIJIYQQHYIkEOK0GNEwwbXvNI0+HLF1K8Aa73YaokEGunL5Ykc1KUk2VFWmcIi2YVicBNIHEOjSH62xGrO/DGttKfbKrwCVsCuLkCeHsDuXiCP9mGsmNE0hOclKctLxFv9nAP0JRgPsaixhZ/0OPgtvJ8OaxZCUc+nt6oumaP+JzTCoqGlkV7mfb/fU8qd3vsRlN3P+wEwuHp5D12QZqRNCCJG4JIEQpyW0aSGG/yCWoT+IK48aOkuqvqS3I4tog8ZBbyMDeqa2U5SiU1MUoo40oo40AoaBGqrD1HAAU0Mljn3rUMrWYGgWwq5uhNw5hD3dido8x53udCxWzUYfV396O/tRHtxHcd23fHDgPZxVSxnkGc5A9xDsmgNFUchMdZCZ6mBU/wwqaxv5sqSKTzfv48Mv9nBOry5cMiKH/j1SZN2EEEKIhKMYRtPsXnFmRaM61dX17R1Gq9L9ldS/MR1Tj6GY+18U99wX3h28vP9TJmSM4ZutdVT7A5zXL6PZmyFVBavVTDAYRtfbMnpxqs6qPjN0tEBtU0JRX4mpsQbQiVpchN3dCXlyCSflYJhPfbclb7iW4vpv2duwEwMocBYy0DOEbrbuzb4ewhGdb3bVsG5bJZW1jWSmOrhkeA7nD8zEbj313/eYTCopKU5qauqJRDp6p3UO0mcdj/RZxyN9Fi811YnWwqm0MgIhTomhR2lcNhfFYsfU5/y453TD4MOqzfS0pWMOWymrLKdvd/lNqkhAikrUnkrUnkowrS/oEUwNBzE1HMDs24vt4NeAQsSRTsjTnbA7h7ArE1TthE0f5jEnMyx5FAPcQ9jTUEppww627/sap5ZEYVJ/8p19yLBmoyoqZpPK4II0BuWnsqeyjg3bDvLaR9t4c9kOhvVJ5/yBmfTrmYJ2nC1qhRBCiDNNEghxSkLr30Mv347l/J+gmOLnhq/17aAi5OVHXUfxzY5qrBaNzFRHO0UqxElQTURcmURcmQAo4QbMDZVo9QewH9iMY/9aDMVE2JVBJCmbsCuLiCvjuIfXHWZVrfRy9aXAWUh1+CB7GnayxbeR9bVrsKl2cuw9yLbnkmXrRqo5ne5dk+jeNQl/Q4ivSqvZsrOaz76uIMlhZnB+GoMK0hiYl4rDdvTF4EIIIcSZIgmEOGnhks8JrX8PU58xaKk5cc/VhOt4q2INhY5skvQkdlYcpHc3jyyeFh2SYXYQ8vQATw8aDQM15MVUfxBT40Hs5Rtx6F8AClFbMhFHFyL2NKK2FKJ2D7rFc9QzKBRFIc2STpolnXOM4VSHqygP7KMqeICS+m3o6CgoJJtTSTGnkWxJISnHw8U9kgjUOygrj/Dt3hpWflWOAnRLd9I7J5mCbm56ZCSRmeaQEQohhBBnlCQQ4qREyr4msHQOWnY/TL2bT116Zf+nmBWNopR+fLmtGrNJI7uLq52iFaIVKQq6NZmQNZkQveDQgmwtUI0pUIupsRpL7U4UPRy7xNCs6GYnUbMdw2TD0KwYmhlDNWOoGqgm7IpGN8WEoeQSsXSnhgC1RoBaGqkLHaQisJcGvRGDQ8vVXKD0UUhR7Wi6jYaQhS8azHy60YQRtqJFbXRxJpPtTqN7ahdyunjISnfidMmp2UIIIVpHwiUQxcXFzJgxgw0bNuB0Ornmmmu47777sFiOP0XAMAzmzp3Lq6++SnV1Nf369eOhhx5iyJAhcfUqKiqYMWMGK1aswGw2M27cOB566CFcrvg3uUuXLuUPf/gDpaWlZGdnc+edd/KjH/2otW+3QwmXfEFg6WzUtFzMQ65stqZhSfWXbG8o55r0czlwMMiOMi+9unnQZPRBnI0UBd2ahG5NIuzp0VRmGCjRIGqoHjXcgBppQI0EIRJEC/pR9GrQIyh6FMWIgK6jGDrwn8V7KUd5KR1oVBXqzRbqzVbqzRYaTToNWpBGS5hGq0FjcpRGI4iOTg1QA2xpBKPEjPGtDWOpDbPuwKG5cFs8pFg9pNrdpNqTSXMm4bKbsVtNOGwmHFYTNqsJVdYtCSGEOIqESiC8Xi+TJk2iZ8+ezJo1i4qKCp588kkCgQC//vWvj3vt3LlzefbZZ/n5z39OYWEhr7zyCpMnT2b+/Pnk5uYCEA6Huf322wF4+umnCQQCPPXUUzzwwAPMnj071tbatWu5++67uf7665k+fTqfffYZDz/8ME6nkyuuuOLMfQASlKFHCK2bT2jDv9Gy+2I+50qU7ywi/bBqM/+uXMcIdz7WRgcfbykjI8VO964y+iA6EUXBMNmImmxESWv5dYYBGGDooEdR0JuSDD3S9FgPoUTDJEWDeKIhlEgQNRhAiTQlKooRaWoGCKkadbYk/FY7dWYrflXDb4N6e5h6/SCNyj7q1DD7AIJNf4xqFSNsgbAFI2zFiFgwwhZMhhWLYsNmsuMw2XBanLhtDpJtDlKdLpJddjwuCx5n0x+LueWLy4UQQnRcCZVAvP7669TX1/Pcc8+RnJwMQDQa5bHHHmPKlClkZGQc9bpgMMjs2bOZPHkyt9xyCwDDhw/niiuuYN68eTz66KMAfPDBB2zfvp0FCxaQn58PgNvt5rbbbmPz5s0MHjwYgOeff57Bgwfz29/+FoDzzjuPPXv28Oyzz3a6BCKy/1uCq15Br96LqfACTL1Gx408RA2d9yvXsaT6K851F9BHzWXpxjI8Tit9e6Se0l76QnQ6igIooKigmjiUTrSMYYAebhrtCDeihhpwRRpwhxtQG2pRIwGUaCDukihQbzJTbzZTr5loMEG9EqTRFKTRUkdQMQgqEFQNwiqEAf+RDUQAL6jVCpquokQ0jKgZohZUxYVFdeA0O3GZXbitSaTa3KTZk0l1uHHazdgsGjaLCatZw2JW0VRFdmkTQogOJKESiOXLlzN69OhY8gAwfvx4fvOb37By5Up++MMfHvW69evXU1dXx/jx42NlFouFcePG8eGHH8a1X1hYGEseAMaMGUNycjKffPIJgwcPJhQKsWbNGn7+85/HvcaVV17Jv//9b/bu3UtOTvzC4bONEY0Q3fMloa8+JLrva5TkLKzn34yakh1Xb3v9ft6sWM2BkJfzPYW4fSksK9mH1WJiUH6qTF0Soi0oCmgWdM2Cbk0+ahUVHasWJdzgh3AIJRrCHA2TYoRJ1aMoehT0KET1poTEMFAOTavSDZ2QohMGQkQJKQZhI0pYMQgRJYxBUIWgqhDSFIJqJY2qSrWmUK4qTdnH4QxEVzDCVvSgHSNka/oTtkLIhhq1oek2FN2KhhlNUWO3pygKCjTlWIqCGvtbwaQpaKqCpqlYTOqhpETDbm1KUuxWE06bCafdjNNmxmU343KYcdnM2K2aJC5CCHEKEiqBKCkpabbOwO12k56eTklJyXGvA+ISA4CCggJeeuklAoEANpuNkpKSZnUURSEvLy/Wxu7duwmHw0dt6/BrnW0JhKFH0Wv3ox8oIbLvGyK7N0GoASU5C/Owq9Gy+qIoCoZhUB2u48u63az1FbMnUEWGOZkLTOewZ0uIbQ2VZKY56NXNg6mFB5EIIdqAqoLFim6Y0a0nrn40GmA/9CdObAQk2LT+IxJAiTSiNTagh+sJRgME9QD1qkG9plKvNVJnrqfObqJeVWhUdfTvvIePGgpgRjVMKJjAUAEVUDAwiGAc+k/HIIquNP3d9NgA5YgxnCAYjQpUq6BrGLoG0cMjJmZMhgWzYsOiWLGqTdO1bKoNm2bFZrJhVi1YVStm1YxJM6EqNK0NUUBB4dA/m5Ic5Yi/AY5IfI74qyky48h/Nx9v0jQVp9NKY2MIQzdQVQVVPZQsqSqa1pQ8mVQVk0nFpCmYtaazRExaU9nhx6cywmMYBoYBUd0gGtUJRSNE9ChRQyeqR2ODZipN8ZhNGmZNQ1M1NEVFVeRngBBns4RKIHw+H263u1m5x+PB6/Ue9zqLxYLVGv+T0e12YxgGXq8Xm82Gz+cjKSnpuO0f/vu7cRx+fLw4jkdVFVJTnad07WmLRjDCjUfMszYwdL1pvvXhH1xdUwlnpBI551wMRQVVxUAhahz+kdy0B0w6cDHncAmg6ArqET/5NU1B4SR/m3foB+2RP1BFgpM+63gSos8MFA6Pbhj/+X50iK4Qm7plKErsGeOIq+MoHFGiHPH/776qGrv5w9/HiP3/LP0EjnV0FIge+rgq363wH8f5tq0d+mNBoWVvGZpes/3P9D3G54ShxNVp/jmkfOei7/5UO0q7sQcGNfV1YAGb1XLyPw8TiNHsH4cfNito8VfRkcn0yQRyZPvGEd/EjvX97MiiZi/33e5FwVsXRDWbsJzGkTrfvbdmff+dXyKcyOFfBLS1k9lyP6ESiLOZoihoWvt8M4k0+DEikePWMQC/6YgFkMbxv/0bgKEa6Op/vlSP/wot0HG/13Ze0mcdT3v3mXLkP1ormOO9hYl2vkT3qB/WzvZBODJVPIJiHKVWq7xUnGAkDJETHzApxLFkpDoSeoplQiUQbrcbv9/frNzr9eLxeI57XSgUIhgMxo1C+Hw+FEWJXet2u6mrqztq+1lZWQCxut+Nw+fzxT3fkZiSWrYbzNGXqAshhBBCCPEfCTVJMT8/v9laB7/fT2VlZbM1Cd+9DqC0tDSuvKSkhOzsbGw22zHbNwyD0tLSWBvdu3fHbDY3q3esdRZCCCGEEEJ0JgmVQBQVFbFq1arYb/sBFi1ahKqqjBkz5pjXDRs2DJfLxcKFC2Nl4XCYxYsXU1RUFNf+1q1b2blzZ6xs9erV1NbWMnbsWKBp96ZRo0bxwQcfxL3GggULKCgoOOsWUAshhBBCCHEyFMM4yvYP7cTr9fL973+fvLw8pkyZEjtI7gc/+EHcQXKTJk1i3759cVu0zpkzh1mzZvHzn/+cPn368Nprr7FixYpmB8kd3gr2/vvvp7GxkZkzZ1JYWNjsILmJEyfy4x//mPHjx7NmzRr+/Oc/88wzz8RtFSuEEEIIIURnk1AJBEBxcTGPP/44GzZswOl0cs011zBt2jQslv8sRvrpT39KWVkZS5cujZUZhsGcOXN49dVXqa6upl+/fjz00EMMHTo0rv2KigpmzJjBihUrMJlMjBs3junTp+NyxZ+YvGTJEv7whz9QWlpKdnY2d955J9dff/2ZvXkhhBBCCCESXMIlEEIIIYQQQojElVBrIIQQQgghhBCJTRIIIYQQQgghRItJAiGEEEIIIYRoMUkghBBCCCGEEC0mCYQQQgghhBCixSSBEEIIIYQQQrSYJBCiXRQXF3PrrbcyZMgQxowZw8yZMwmFQu0dVqezcOFC7rrrLoqKihgyZAjXXHMNb731Ft/d3fnNN9/k8ssvZ9CgQVx99dUsW7asWVt+v5/p06czcuRIhg4dyr333suBAwfa6lY6rfr6eoqKiigsLOTLL7+Me076LbG88847XHvttQwaNIhRo0Zx++23EwgEYs8vXbqUq6++mkGDBnH55Zfzz3/+s1kboVCIp556ijFjxjBkyBBuvfVWSkpK2vI2Oo0lS5Zwww03MHToUC644AL+3//7f+zZs6dZPfk6ax+7du3i17/+Nddccw39+/fnqquuOmq91uyf9evXc+ONNzJ48GAuuugi5syZ0+znZWchCYRoc16vl0mTJhEOh5k1axbTpk3jjTfe4Mknn2zv0Dqdv/71r9jtdh588EGef/55ioqKeOSRR/jTn/4Uq/P+++/zyCOPMH78eObOncuQIUO4++672bhxY1xb9913HytXruTRRx/l97//PaWlpdxxxx1EIpE2vqvO5c9//jPRaLRZufRbYnn++ed5/PHHufLKK5k3bx6//e1vycnJifXd2rVrufvuuxkyZAhz585l/PjxPPzwwyxatCiunRkzZvDmm28ybdo0Zs2aRSgU4pZbbsHv97fHbZ211qxZw913302vXr3405/+xPTp09m6dSuTJ0+OS/rk66z9bN++nU8++YQePXpQUFBw1Dqt2T+7du3itttuIz09ndmzZzNp0iSeffZZXnzxxTN5m4nLEKKN/eUvfzGGDBli1NTUxMpef/11o1+/fkZ5eXn7BdYJVVVVNSv71a9+ZQwbNsyIRqOGYRjGZZddZtx///1xdW688Ubj9ttvjz1ev3690adPH+PTTz+NlRUXFxuFhYXG+++/f4aiFzt27DCGDBlivPbaa0afPn2MzZs3x56TfkscxcXFRv/+/Y2PP/74mHUmT55s3HjjjXFl999/vzF+/PjY4/379xv9+vUzXn/99VhZTU2NMWTIEGPOnDmtH3gn9sgjjxgXX3yxoet6rGz16tVGnz59jC+++CJWJl9n7efwzyjDMIxf/vKXxve///1mdVqzfx555BHjoosuMoLBYKzs6aefNkaMGBFX1lnICIRoc8uXL2f06NEkJyfHysaPH4+u66xcubL9AuuEUlNTm5X169ePuro6Ghoa2LNnDzt37mT8+PFxda688kpWr14dm3a2fPly3G43Y8aMidXJz8+nX79+LF++/MzeRCc2Y8YMJkyYQF5eXly59Ftiefvtt8nJyWHs2LFHfT4UCrFmzRquuOKKuPIrr7yS4uJi9u7dC8CKFSvQdT2uXnJyMmPGjJH+amWRSASn04miKLGypKQkgNiUFfk6a1+qevy3sK3dP8uXL+eSSy7BYrHEteXz+diwYUNr3FKHIgmEaHMlJSXk5+fHlbndbtLT02UubwJYt24dGRkZuFyuWH989w1qQUEB4XA4Nh+4pKSEvLy8uB+20PRNWPr0zFi0aBHbtm1j6tSpzZ6TfkssmzZtok+fPvz5z39m9OjRDBw4kAkTJrBp0yYAdu/eTTgcbvZ98fC0jMN9UVJSQlpaGh6Pp1k96a/W9cMf/pDi4mJeeeUV/H4/e/bs4X//93/p378/w4YNA+TrLNG1Zv80NDSwf//+Zl+j+fn5KIrSKftREgjR5nw+H263u1m5x+PB6/W2Q0TisLVr17JgwQImT54MEOuP7/bX4ceHn/f5fLHfzh1J+vTMaGxs5Mknn2TatGm4XK5mz0u/JZbKykpWrFjB/Pnz+c1vfsOf/vQnFEVh8uTJVFVVnXZ/ud1u6a9WNmLECJ577jmefvppRowYwaWXXkpVVRVz585F0zRAvs4SXWv2z+E1Rt9ty2KxYLfbO2U/SgIhhACgvLycadOmMWrUKCZOnNje4YjjeP7550lLS+NHP/pRe4ciWsAwDBoaGvjjH//IFVdcwdixY3n++ecxDIOXX365vcMTR7F+/Xp+8Ytf8OMf/5iXXnqJP/7xj+i6zp133hm3iFqIzkoSCNHm3G73UXcM8Xq9zYbmRdvw+XzccccdJCcnM2vWrNjc0sP98d3+8vl8cc+73W7q6uqatSt92vrKysp48cUXuffee/H7/fh8PhoaGoCmYfb6+nrptwTjdrtJTk6mb9++sbLk5GT69+/Pjh07Tru/fD6f9FcrmzFjBueddx4PPvgg5513HldccQVz5szh66+/Zv78+YB8f0x0rdk/h0covttWKBSisbGxU/ajJBCizR1t3qff76eysrLZ/EJx5gUCAaZMmYLf7+eFF16IG8o93B/f7a+SkhLMZjO5ubmxeqWlpc32wy4tLZU+bWV79+4lHA5z5513cu6553Luuefys5/9DICJEydy6623Sr8lmF69eh3zuWAwSPfu3TGbzUftL/jP12F+fj4HDx5sNl3iaOvKxOkpLi6OS/gAMjMzSUlJYffu3YB8f0x0rdk/DoeDrKysZm0dvq4z9qMkEKLNFRUVsWrVqthvAaBpQaiqqnG7IIgzLxKJcN9991FSUsILL7xARkZG3PO5ubn07Nmz2V70CxYsYPTo0bHdKIqKivB6vaxevTpWp7S0lK+//pqioqIzfyOdSL9+/fjb3/4W9+ehhx4C4LHHHuM3v/mN9FuCueiii6itreWbb76JldXU1LBlyxYGDBiAxWJh1KhRfPDBB3HXLViwgIKCAnJycgC44IILUFWVxYsXx+p4vV5WrFgh/dXKsrOz+frrr+PKysrKqKmpoVu3boB8f0x0rd0/RUVFLFmyhHA4HNeW2+1m6NChZ/huEo+pvQMQnc+ECRP4+9//ztSpU5kyZQoVFRXMnDmTCRMmNHsDK86sxx57jGXLlvHggw9SV1cXd7hO//79sVgs3HPPPfz85z+ne/fujBo1igULFrB58+a4uduHT2qdPn06v/zlL7FarTzzzDMUFhZy2WWXtcOdnb3cbjejRo066nMDBgxgwIABANJvCeTSSy9l0KBB3HvvvUybNg2r1cqcOXOwWCz85Cc/AeCuu+5i4sSJPProo4wfP541a9bw73//m2eeeSbWTmZmJtdffz0zZ85EVVUyMjKYPXs2SUlJTJgwob1u76w0YcIEfve73zFjxgwuvvhiamtrY2uPjtwWVL7O2k9jYyOffPIJ0JTc1dXVxZKFkSNHkpqa2qr9c9ttt/Gvf/2LBx54gJtuuolt27Yxb948pk2bFre1a2ehGN8dsxGiDRQXF/P444+zYcMGnE4n11xzTaf9ImxPF198MWVlZUd9bsmSJbHffL755pvMnTuXffv2kZeXx/33389FF10UV9/v9/PEE0/w4YcfEolEuOCCC/jVr34lSWEbWLNmDRMnTuStt95i0KBBsXLpt8RRXV3NE088wbJlywiHw4wYMYKHHnoobnrTkiVL+MMf/kBpaSnZ2dnceeedXH/99XHthEIhnnnmGebPn099fT3Dhg3jV7/61TFP4hWnxjAMXn/9dV577TX27NmD0+lkyJAhTJs2rdnHWr7O2sfevXu55JJLjvrc3/72t9gvWlqzf9avX8+TTz7JN998Q2pqKjfffDN33HFHsy1gOwNJIIQQQgghhBAtJmsghBBCCCGEEC0mCYQQQgghhBCixSSBEEIIIYQQQrSYJBBCCCGEEEKIFpMEQgghhBBCCNFikkAIIYQQQgghWkwSCCGEEEIIIUSLSQIhhBBCCCGEaDFJIIQQQiSMvXv3UlhYyNtvv93eoQghhDgGSSCEEEKIM+wvf/kLH330UXuHIYQQrUISCCGEEOIMmz17tiQQQoizhiQQQggh2lRjY2N7hyCEEOI0SAIhhBCd2NatWyksLGTJkiWxsq+++orCwkKuu+66uLq33347N9xwQ+zxK6+8wve//30GDhzIBRdcwGOPPYbP54u75qc//SlXXXUVX331FTfffDPnnHMO//u//wuAz+fjwQcfZPjw4YwYMYJf/vKX+P3+U7qPYDDIrFmzuPzyyxk0aBAXXHABd999N7t3747VaWho4Mknn2Ts2LEMHDiQyy+/nHnz5mEYRqzO8dZgFBYWMmvWrNjjWbNmUVhYyK5du3jwwQcZMWIEw4cP56GHHopLkgoLC2loaOCdd96hsLCQwsJCHnzwwVO6TyGESASm9g5ACCFE++nTpw9ut5u1a9dyySWXALB27VpUVWXr1q3U1dXhcrnQdZ0NGzbw4x//GGh68/zcc89x/vnnc9NNN1FaWsprr73Gl19+yWuvvYbZbI69Rm1tLXfccQff//73ufrqq0lLS8MwDP77v/+bdevWMWHCBAoKCvjwww/55S9/edL3EI1GmTJlCqtXr+b73/8+EydOpL6+npUrV7Jt2za6d++OYRjcddddrFmzhuuvv55+/frx6aefMnPmTCoqKpg+ffopfwzvu+8+cnJyuP/++/n666958803SU1N5X/+538AmDlzJr/61a8YPHhw7OPXvXv3U349IYRob5JACCFEJ6aqKsOGDWPt2rWxsnXr1nHppZeyZMkS1q9fT1FRUSyZGDFiBNXV1cyePZsLLriAuXPnoqpNg9n5+fn89re/5b333uNHP/pRrL3Kykoee+wxJkyYECv76KOP+OKLL/if//kfbr/9dgBuuukmJk6ceNL38O6777J69Woeeughbrnlllj5nXfeGRtdWLJkCZ999hn33Xcfd911FwA333wz9957L3/729/4r//6r1N+U9+vXz9+97vfxR7X1tby1ltvxRKIa665hkcffZTc3FyuueaaU3oNIYRIJDKFSQghOrnhw4fz9ddf09DQADQlEEVFRfTt25d169YBTaMSiqIwfPhwVq1aRTgcZuLEibHkAeCGG27A5XLxySefxLVvsVj44Q9/GFe2fPlyTCYTN910U6xM0zT+67/+66TjX7x4MSkpKUe9VlGU2OtpmsZPf/rTuOcnT56MYRgsX778pF/3sCMTI4ARI0ZQW1tLXV3dKbcphBCJTEYghBCikxsxYgSRSISNGzeSmZlJVVUVI0aMYMeOHbGRibVr19KrVy+Sk5PZt28f0DTicCSLxUJubi5lZWVx5RkZGVgslriysrIy0tPTcTqdceV5eXknHf/u3bvJy8vDZDr2j7SysjK6du2Ky+WKKy8oKIg9f6qys7PjHrvdbgC8Xm+z1xNCiLOBjEAIIUQnN3DgQKxWK1988QXr1q0jLS2NvLw8RowYwebNmwmFQqxbt47hw4efUvs2m62VIz5zDo9YfFc0Gj3mNUeOwhzpyMXZQghxNpEEQgghOjmLxcLgwYNZu3Yta9euZcSIEUDT1KZQKMR7773HwYMHOffcc4H//Ma9pKQkrp1QKMTevXvp1q3bCV+zW7duVFZWUl9fH1deWlp60vF3796d0tJSwuHwcV/vwIEDzaYVHb6HwzF7PB6AZrtJHR51EUIIIQmEEEIImpKFzZs3s2bNmthIQ2pqKgUFBcydOxcgllicf/75mM1m/v73v8f9lv2tt97C7/czduzYE75eUVERkUiE1157LVYWjUZ5+eWXTzr2yy67jJqaGl555ZVmzx2Or6ioiGg02qzOX//6VxRFoaioCACXy0VKSkrconKAV1999aTjOpLD4WiWlAghREclayCEEEIwYsQI/vKXv7B///5YonC4/B//+AfdunUjMzMTaEospkyZwnPPPcftt9/OxRdfTGlpKa+++iqDBg3i6quvPuHrXXzxxQwbNoynn36asrIyevXqxeLFi0/pHIhrr72Wd999lyeeeILNmzczfPhwGhsbWb16NTfddBOXXnopF198MaNGjeKZZ56hrKyMwsJCVq5cyZIlS5g0aVLcDkw33HADc+bM4eGHH2bgwIGsXbv2lEZGjjRgwABWr17N//3f/9G1a1dycnI455xzTqtNIYRoL5JACCGEYOjQoWiahs1mo2/fvrHywwnEkUkFwD333ENqaiovv/wyTzzxBB6Phx//+Mfcf//9cWdAHIuqqjz//PP87ne/47333kNRFC6++GIefPBBrr322pOKXdM05s6dy/PPP8+///1vFi9eTHJyMsOGDaOwsDDu9Z599lkWLFjA22+/Tbdu3fjFL37B5MmT49qbOnUq1dXVfPDBByxcuJCioiJeeOEFRo8efVJxHenBBx/k17/+NX/4wx8IBAJcd911kkAIITosxZBVXkIIIYQQQogWkjUQQgghhBBCiBaTKUxCCCESUigUwuv1HrdOUlJSh9omVgghzgaSQAghhEhIGzZsYOLEicet88QTTzQ75VoIIcSZJWsghBBCJCSv18uWLVuOW6dXr1507dq1jSISQggBkkAIIYQQQgghToIsohZCCCGEEEK0mCQQQgghhBBCiBaTBEIIIYQQQgjRYpJACCGEEEIIIVpMEgghhBBCCCFEi0kCIYQQQgghhGgxSSCEEEIIIYQQLSYJhBBCCCGEEKLF/n9U/PvOAF4oLAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Analysis"
      ],
      "metadata": {
        "id": "RlSuXm0N-APD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "id": "kximFpZZpSed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "dc6308f9-2f9f-4c59-9103-3ed3a29a0667"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   problem                                        description  \\\n",
              "0     Problemas com o chip  boa noite!!!fiz uma portabilidade da vivo para...   \n",
              "1     Problemas com o chip  perdi meu celular e ao tentar solicitar um nov...   \n",
              "2  Corte indevido da linha  é a segunda reclamação que estou fazendo e a c...   \n",
              "3             Cancelamento  solicitei portabilidade da vivo para a claro p...   \n",
              "4     Problemas com o chip  contratei este prezao celular, paguei 20,00 re...   \n",
              "\n",
              "   word_count  \n",
              "0         203  \n",
              "1          66  \n",
              "2          32  \n",
              "3         136  \n",
              "4          13  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9cbf2ccb-4958-42b1-b715-2667e8b53b88\" 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",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>problem</th>\n",
              "      <th>description</th>\n",
              "      <th>word_count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Problemas com o chip</td>\n",
              "      <td>boa noite!!!fiz uma portabilidade da vivo para...</td>\n",
              "      <td>203</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Problemas com o chip</td>\n",
              "      <td>perdi meu celular e ao tentar solicitar um nov...</td>\n",
              "      <td>66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Corte indevido da linha</td>\n",
              "      <td>é a segunda reclamação que estou fazendo e a c...</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Cancelamento</td>\n",
              "      <td>solicitei portabilidade da vivo para a claro p...</td>\n",
              "      <td>136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Problemas com o chip</td>\n",
              "      <td>contratei este prezao celular, paguei 20,00 re...</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9cbf2ccb-4958-42b1-b715-2667e8b53b88')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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",
              "    }\n",
              "\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-9cbf2ccb-4958-42b1-b715-2667e8b53b88 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-9cbf2ccb-4958-42b1-b715-2667e8b53b88');\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",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-becf3ebd-34ef-4d19-b568-7f501077e4f4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-becf3ebd-34ef-4d19-b568-7f501077e4f4')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <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-becf3ebd-34ef-4d19-b568-7f501077e4f4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Show each problem category\n",
        "df['problem'].value_counts()"
      ],
      "metadata": {
        "id": "trUhFZEarsl5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "111ca3ce-09a6-41a9-c78a-686e2ffb6ade"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Problemas com o chip                       143\n",
              "Corte indevido da linha                    143\n",
              "Cancelamento                               143\n",
              "Problema com recarga                       143\n",
              "Planos e tarifas                           143\n",
              "Mudança de plano                           143\n",
              "Promoções                                  143\n",
              "Qualidade da internet                      143\n",
              "Consumo de crédito                         143\n",
              "Instabilidade do sinal                     143\n",
              "Demora na execução                         143\n",
              "Cobrança indevida                          143\n",
              "Portabilidade                              143\n",
              "Mau atendimento do prestador de serviço    143\n",
              "Name: problem, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Show words statistics\n",
        "df['word_count'] = df['description'].str.split().str.len()\n",
        "df['text_len'] = df['description'].str.len()\n",
        "df['word_count'].describe()"
      ],
      "metadata": {
        "id": "G0gvCGaxsuBp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e2220457-19e5-4022-dbc6-c6ebd3cc315f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    2002.000000\n",
              "mean      110.619381\n",
              "std        86.131142\n",
              "min         2.000000\n",
              "25%        53.000000\n",
              "50%        87.000000\n",
              "75%       143.000000\n",
              "max       747.000000\n",
              "Name: word_count, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check correlation between word count and text lenght\n",
        "chart_joint = sns.jointplot(data=df, x=\"text_len\", y=\"word_count\", height=5)\n",
        "chart_joint.fig.suptitle(\"Correlation between word count and text lenght\")\n",
        "chart_joint.fig.tight_layout()\n",
        "chart_joint.fig.subplots_adjust(top=0.92)"
      ],
      "metadata": {
        "id": "KMrNKNN4vdjj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 509
        },
        "outputId": "dcbf1b58-a76f-4c4f-cdfd-d5f2b0fbe4f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check word median per category\n",
        "df.groupby('problem')['word_count'].median()"
      ],
      "metadata": {
        "id": "GoZe2yuG9B4u",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "58916ef6-98fb-472e-a3f6-e245d1170222"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "problem\n",
              "Cancelamento                                95.0\n",
              "Cobrança indevida                          113.0\n",
              "Consumo de crédito                          83.0\n",
              "Corte indevido da linha                    106.0\n",
              "Demora na execução                          82.0\n",
              "Instabilidade do sinal                      67.0\n",
              "Mau atendimento do prestador de serviço     92.0\n",
              "Mudança de plano                           100.0\n",
              "Planos e tarifas                            98.0\n",
              "Portabilidade                               99.0\n",
              "Problema com recarga                        73.0\n",
              "Problemas com o chip                        90.0\n",
              "Promoções                                   63.0\n",
              "Qualidade da internet                       74.0\n",
              "Name: word_count, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Text Classification"
      ],
      "metadata": {
        "id": "0pWtuhOjN6UU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Load Model"
      ],
      "metadata": {
        "id": "ezJirXNkaeVC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are several 7B models available as open source, such as Mistral, Falcon, Zephyr, and Openchat. In this study we will use Mistral, but other models can also be tested, just paying attention to adapting the instruction structure."
      ],
      "metadata": {
        "id": "5d_Rtp_pCVpv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "from datetime import timedelta, datetime\n",
        "from google.colab import files\n",
        "from IPython.display import clear_output"
      ],
      "metadata": {
        "id": "Bi0-tycumPr4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Latest HF transformers version for Mistral-like models\n",
        "!pip install git+https://github.com/huggingface/transformers.git\n",
        "\n",
        "# GPTQ Dependencies\n",
        "!pip install optimum\n",
        "!pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/  # Use cu117 if on CUDA 11.7\n",
        "\n",
        "clear_output()"
      ],
      "metadata": {
        "id": "NLHr_hhG0DyG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline"
      ],
      "metadata": {
        "id": "gJCc0MNL0D1A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will load the pre-trained Mistral 7B model, quantized by the user TheBloke and available at https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ.\n",
        "\n",
        "For more information on quantization, there is an excellent article written by Maxime Labonne:\n",
        "* https://towardsdatascience.com/introduction-to-weight-quantization-2494701b9c0c"
      ],
      "metadata": {
        "id": "LNrC6Je7DLJ_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load LLM and Tokenizer\n",
        "model_id = \"TheBloke/Mistral-7B-Instruct-v0.2-GPTQ\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_id,\n",
        "    device_map=\"auto\",\n",
        "    trust_remote_code=False,\n",
        "    revision=\"main\"\n",
        ")\n",
        "clear_output()"
      ],
      "metadata": {
        "id": "4HxNvdf90D4I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a pipeline\n",
        "generator = pipeline(model=model,\n",
        "                tokenizer=tokenizer,\n",
        "                task='text-generation',\n",
        "                max_new_tokens=60,\n",
        "                temperature=0.1,\n",
        "                do_sample=True,\n",
        "                return_full_text = False\n",
        "                )\n",
        "clear_output()"
      ],
      "metadata": {
        "id": "aW5V7xEs3Vne"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Prompt Engineering"
      ],
      "metadata": {
        "id": "LE5bdD4xai9x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def llm_analysis(prompt, complain):\n",
        "  response = generator(prompt.format(user_complain=complain))\n",
        "  llm_classification = response[0]['generated_text'].replace('.', '').lower()\n",
        "\n",
        "  return llm_classification"
      ],
      "metadata": {
        "id": "rLWWtjhppGl0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def iterate_over_interval(df, column, prompt, limit_char, start_row, end_row, save):\n",
        "  start_time = time.time()\n",
        "\n",
        "  for nloop, index in enumerate(range(start_row, end_row)):\n",
        "    complaint_content = str(df.loc[index, column]).lower()\n",
        "\n",
        "    # Checkpoint\n",
        "    if (nloop + 1) % 100 == 0:\n",
        "      delta_time = str(timedelta(seconds=time.time() - start_time)).split('.')[0]\n",
        "      print(f\"{delta_time} Analysis number {nloop + 1}\")\n",
        "\n",
        "    # Limit text lengh\n",
        "    if len(complaint_content) > limit_char: complaint_content = complaint_content[:limit_char]+'\\n'\n",
        "\n",
        "    #print(prompt.format(user_complain=complaint_content))  # check prompt\n",
        "\n",
        "    # Text analysis\n",
        "    analysis = llm_analysis(prompt, complaint_content)\n",
        "    df.at[index, 'llm_class'] = analysis\n",
        "\n",
        "  # Save final result\n",
        "  delta_time = str(timedelta(seconds=time.time() - start_time)).split('.')[0]\n",
        "  print(f\"{nloop + 1} cases analyzed. Total execution time: {delta_time}\")\n",
        "\n",
        "  if save == True:\n",
        "    current_datetime = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n",
        "    file_name = 'analise_reclamacao_' + str(start_row) + '_' + str(end_row) + current_datetime + '.xlsx'\n",
        "    df.to_excel(file_name, index=False)\n",
        "\n",
        "  return df, file_name if save == True else df"
      ],
      "metadata": {
        "id": "Wr9kWm_8lU-j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prompt Guide:\n",
        "* https://www.promptingguide.ai/models/mistral-7b\n",
        "* https://huggingface.co/docs/transformers/main/tasks/prompting"
      ],
      "metadata": {
        "id": "xAqjCsmUSkzH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define tags\n",
        "tags = ['sinal/conexão de rede', 'cobrança indevida', 'consumo saldo/crédito',\n",
        "        'plano/benefício', 'cancelamento linha/plano', 'chip/sim card', 'spam',\n",
        "        'portabilidade', 'recarga/pagamento', 'dificuldade de contato']\n",
        "\n",
        "# Transform tag list in one string\n",
        "tags_string = \", \".join(f\"{tag}\" for tag in tags)\n",
        "\n",
        "tags_string"
      ],
      "metadata": {
        "id": "9-WCnO807zU_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "0bb3b7ad-06ac-44db-885f-df6b4b7fa664"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'sinal/conexão de rede, cobrança indevida, consumo saldo/crédito, plano/benefício, cancelamento linha/plano, chip/sim card, spam, portabilidade, recarga/pagamento, dificuldade de contato'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create tamplates\n",
        "complain_template = \"\"\"\n",
        "Reclamação: {user_complain}\"\"\"\n",
        "\n",
        "task_template = \"\"\"\n",
        "Tarefa: Classifique a reclamação. Atenção use apenas as categorias abaixo.\n",
        "{tags}\"\"\".format(tags=tags_string)\n",
        "\n",
        "warning = \"\"\"\n",
        "Importante, apenas classifique sem explicar!\"\"\""
      ],
      "metadata": {
        "id": "6wQGSoxCDQl1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Prompt user complain\n",
        "prompt_task_after = '[INST]' + complain_template + '\\n' + task_template + '\\n' + warning +'\\n\\nRótulos:[/INST]'\n",
        "prompt_task_before = '[INST]' + task_template + '\\n' + complain_template + '\\n' + warning + '\\n\\nRótulos:[/INST]'"
      ],
      "metadata": {
        "id": "nV9GLEVP8md_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Multi-turn conversation\n",
        "dialog_dict = {\n",
        "    \"conv1_1t\": [\"Esse plano é ruim!\", \"plano/benefício\"],\n",
        "    \"conv2_1t\": [\"Recebo muitas mensagens de propaganda\", \"spam\"]\n",
        "}\n",
        "\n",
        "# Conversation 1 with 1 tags, task after\n",
        "a_conv1_1t = prompt_task_after.format(user_complain=dialog_dict['conv1_1t'][0]) + \" \" + dialog_dict['conv1_1t'][1]\n",
        "\n",
        "# Conversation 2 with 2 tags, task after\n",
        "a_conv2_1t = prompt_task_after.format(user_complain=dialog_dict['conv2_1t'][0]) + \" \" + dialog_dict['conv2_1t'][1]\n",
        "\n",
        "# Conversation 1 with 1 tags, task before\n",
        "b_conv1_1t = prompt_task_before.format(user_complain=dialog_dict['conv1_1t'][0]) + \" \" + dialog_dict['conv1_1t'][1]\n",
        "\n",
        "# Conversation 2 with 2 tags, task before\n",
        "b_conv2_1t = prompt_task_before.format(user_complain=dialog_dict['conv2_1t'][0]) + \" \" + dialog_dict['conv2_1t'][1]"
      ],
      "metadata": {
        "id": "w8HkBsK6gW58"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Prompt: task after complain\n",
        "p_tsk_aft_0s = prompt_task_after  # zero shot\n",
        "p_tsk_aft_1s = '<s>' + a_conv1_1t + '\\n</s>\\n' + prompt_task_after  # one shot\n",
        "p_tsk_aft_2s = '<s>' + a_conv1_1t + '\\n</s>' + '\\n<s>' + a_conv2_1t + '\\n</s>\\n' + prompt_task_after  # two shot\n",
        "\n",
        "# Prompt: task before complain\n",
        "p_tsk_bfr_0s = prompt_task_before  # zero shot\n",
        "p_tsk_bfr_1s = '<s>' + b_conv1_1t + '\\n</s>\\n' + prompt_task_before  # one shot\n",
        "p_tsk_bfr_2s = '<s>' + b_conv1_1t + '\\n</s>' + '\\n<s>' + b_conv2_1t + '\\n</s>\\n' + prompt_task_before  # two shot\n",
        "\n",
        "prompt_dict = {'p_tsk_aft_0s': p_tsk_aft_0s, 'p_tsk_aft_1s': p_tsk_aft_1s, 'p_tsk_aft_2s': p_tsk_aft_2s,\n",
        "               'p_tsk_bfr_0s': p_tsk_bfr_0s, 'p_tsk_bfr_1s': p_tsk_bfr_1s, 'p_tsk_bfr_2s': p_tsk_bfr_2s}"
      ],
      "metadata": {
        "id": "xSVo4EWqgzL-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Print each prompt (check)\n",
        "for prompt in prompt_dict:\n",
        "  print(30*'-')\n",
        "  print('-', prompt)\n",
        "  print(prompt_dict[prompt])"
      ],
      "metadata": {
        "id": "cUgpc_mdl0Cr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9ef9ec38-db32-46e7-bea3-2a6c9a610651"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "------------------------------\n",
            "- p_tsk_aft_0s\n",
            "[INST]\n",
            "Reclamação: {user_complain}\n",
            "\n",
            "Tarefa: Classifique a reclamação. Atenção use apenas as categorias abaixo.\n",
            "sinal/conexão de rede, cobrança indevida, consumo saldo/crédito, plano/benefício, cancelamento linha/plano, chip/sim card, spam, portabilidade, recarga/pagamento, dificuldade de contato\n",
            "\n",
            "Importante, apenas classifique sem explicar!\n",
            "\n",
            "Rótulos:[/INST]\n",
            "------------------------------\n",
            "- p_tsk_aft_1s\n",
            "<s>[INST]\n",
            "Reclamação: Esse plano é ruim!\n",
            "\n",
            "Tarefa: Classifique a reclamação. Atenção use apenas as categorias abaixo.\n",
            "sinal/conexão de rede, cobrança indevida, consumo saldo/crédito, plano/benefício, cancelamento linha/plano, chip/sim card, spam, portabilidade, recarga/pagamento, dificuldade de contato\n",
            "\n",
            "Importante, apenas classifique sem explicar!\n",
            "\n",
            "Rótulos:[/INST] plano/benefício\n",
            "</s>\n",
            "[INST]\n",
            "Reclamação: {user_complain}\n",
            "\n",
            "Tarefa: Classifique a reclamação. Atenção use apenas as categorias abaixo.\n",
            "sinal/conexão de rede, cobrança indevida, consumo saldo/crédito, plano/benefício, cancelamento linha/plano, chip/sim card, spam, portabilidade, recarga/pagamento, dificuldade de contato\n",
            "\n",
            "Importante, apenas classifique sem explicar!\n",
            "\n",
            "Rótulos:[/INST]\n",
            "------------------------------\n",
            "- p_tsk_aft_2s\n",
            "<s>[INST]\n",
            "Reclamação: Esse plano é ruim!\n",
            "\n",
            "Tarefa: Classifique a reclamação. Atenção use apenas as categorias abaixo.\n",
            "sinal/conexão de rede, cobrança indevida, consumo saldo/crédito, plano/benefício, cancelamento linha/plano, chip/sim card, spam, portabilidade, recarga/pagamento, dificuldade de contato\n",
            "\n",
            "Importante, apenas classifique sem explicar!\n",
            "\n",
            "Rótulos:[/INST] plano/benefício\n",
            "</s>\n",
            "<s>[INST]\n",
            "Reclamação: Recebo muitas mensagens de propaganda\n",
            "\n",
            "Tarefa: Classifique a reclamação. Atenção use apenas as categorias abaixo.\n",
            "sinal/conexão de rede, cobrança indevida, consumo saldo/crédito, plano/benefício, cancelamento linha/plano, chip/sim card, spam, portabilidade, recarga/pagamento, dificuldade de contato\n",
            "\n",
            "Importante, apenas classifique sem explicar!\n",
            "\n",
            "Rótulos:[/INST] spam\n",
            "</s>\n",
            "[INST]\n",
            "Reclamação: {user_complain}\n",
            "\n",
            "Tarefa: Classifique a reclamação. Atenção use apenas as categorias abaixo.\n",
            "sinal/conexão de rede, cobrança indevida, consumo saldo/crédito, plano/benefício, cancelamento linha/plano, chip/sim card, spam, portabilidade, recarga/pagamento, dificuldade de contato\n",
            "\n",
            "Importante, apenas classifique sem explicar!\n",
            "\n",
            "Rótulos:[/INST]\n",
            "------------------------------\n",
            "- p_tsk_bfr_0s\n",
            "[INST]\n",
            "Tarefa: Classifique a reclamação. Atenção use apenas as categorias abaixo.\n",
            "sinal/conexão de rede, cobrança indevida, consumo saldo/crédito, plano/benefício, cancelamento linha/plano, chip/sim card, spam, portabilidade, recarga/pagamento, dificuldade de contato\n",
            "\n",
            "Reclamação: {user_complain}\n",
            "\n",
            "Importante, apenas classifique sem explicar!\n",
            "\n",
            "Rótulos:[/INST]\n",
            "------------------------------\n",
            "- p_tsk_bfr_1s\n",
            "<s>[INST]\n",
            "Tarefa: Classifique a reclamação. Atenção use apenas as categorias abaixo.\n",
            "sinal/conexão de rede, cobrança indevida, consumo saldo/crédito, plano/benefício, cancelamento linha/plano, chip/sim card, spam, portabilidade, recarga/pagamento, dificuldade de contato\n",
            "\n",
            "Reclamação: Esse plano é ruim!\n",
            "\n",
            "Importante, apenas classifique sem explicar!\n",
            "\n",
            "Rótulos:[/INST] plano/benefício\n",
            "</s>\n",
            "[INST]\n",
            "Tarefa: Classifique a reclamação. Atenção use apenas as categorias abaixo.\n",
            "sinal/conexão de rede, cobrança indevida, consumo saldo/crédito, plano/benefício, cancelamento linha/plano, chip/sim card, spam, portabilidade, recarga/pagamento, dificuldade de contato\n",
            "\n",
            "Reclamação: {user_complain}\n",
            "\n",
            "Importante, apenas classifique sem explicar!\n",
            "\n",
            "Rótulos:[/INST]\n",
            "------------------------------\n",
            "- p_tsk_bfr_2s\n",
            "<s>[INST]\n",
            "Tarefa: Classifique a reclamação. Atenção use apenas as categorias abaixo.\n",
            "sinal/conexão de rede, cobrança indevida, consumo saldo/crédito, plano/benefício, cancelamento linha/plano, chip/sim card, spam, portabilidade, recarga/pagamento, dificuldade de contato\n",
            "\n",
            "Reclamação: Esse plano é ruim!\n",
            "\n",
            "Importante, apenas classifique sem explicar!\n",
            "\n",
            "Rótulos:[/INST] plano/benefício\n",
            "</s>\n",
            "<s>[INST]\n",
            "Tarefa: Classifique a reclamação. Atenção use apenas as categorias abaixo.\n",
            "sinal/conexão de rede, cobrança indevida, consumo saldo/crédito, plano/benefício, cancelamento linha/plano, chip/sim card, spam, portabilidade, recarga/pagamento, dificuldade de contato\n",
            "\n",
            "Reclamação: Recebo muitas mensagens de propaganda\n",
            "\n",
            "Importante, apenas classifique sem explicar!\n",
            "\n",
            "Rótulos:[/INST] spam\n",
            "</s>\n",
            "[INST]\n",
            "Tarefa: Classifique a reclamação. Atenção use apenas as categorias abaixo.\n",
            "sinal/conexão de rede, cobrança indevida, consumo saldo/crédito, plano/benefício, cancelamento linha/plano, chip/sim card, spam, portabilidade, recarga/pagamento, dificuldade de contato\n",
            "\n",
            "Reclamação: {user_complain}\n",
            "\n",
            "Importante, apenas classifique sem explicar!\n",
            "\n",
            "Rótulos:[/INST]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We gonna try several prompt examples, as described in the table below:\n",
        "\n",
        "|Prompt name|Task|Shot|\n",
        "|--|--|--|\n",
        "|p_tsk_aft_0s|After complaint|zero|\n",
        "|p_tsk_aft_1s|After complaint|one|\n",
        "|p_tsk_aft_2s|After complaint|two|\n",
        "|p_tsk_bfr_0s|Before complaint|zero|\n",
        "|p_tsk_bfr_1s|Before complaint|one|\n",
        "|p_tsk_bfr_2s|Before complaint|two|"
      ],
      "metadata": {
        "id": "aOyGO5YEjMZi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Run the example few times to see model consistency\n",
        "example =\"\"\"\n",
        "como sempre, atendimento da claro vago e sem nenhuma solução. estou a uma semana sem serviço na minha linha,\n",
        "tentando falar na central e só fico presa na ura ou não consigo chegar a lugar algum. detalhe que tudo isso\n",
        "precisa ser feito de outro celular já que não tenho sinal!!!!!!!!!!!!!não é a primeira vez que fico sem sinal\n",
        "esse ano pelo chip virtual e que sou impactada em trabalho e já estou nisso a 1 semana. a resposta da central\n",
        "foi: vá até uma loja física. eu tenho um chip virtual, qual o sentido de ir a uma loja física olhar um chip\n",
        "virtual que o supervisor que consegui falar nem sabia do que se tratava???? fora que minha [ultima ida a loja\n",
        "fisica fui cobrada por um chip dependente indevido que colocaram na minha conta e que nunca recebi reembolso.segue\n",
        "protocolo:*******070quero saber qual o desconto que terei em fatura nesses dias sem serviço e não tenho\n",
        "disponibilidade para ir a loja física por isso adquiri um chip virtual pela facilidade (que não existe do lado\n",
        "de vocês) nesse processo.a qualidade do serviço é cada dia pior e eu ainda me pergunto pq sou cliente de vocës.\n",
        "\"\"\"[:2000].lower()\n",
        "\n",
        "for prompt in prompt_dict:\n",
        "  print('\\n - ', prompt)\n",
        "  for i in list(range(1, 4)):\n",
        "    analysis = llm_analysis(prompt_dict[prompt], example)\n",
        "    print(analysis)"
      ],
      "metadata": {
        "id": "YZjd_eAWdFVS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ba2a2cb4-a4c8-485a-b920-87272c7bfb7a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " -  p_tsk_aft_0s\n",
            " sinal/conexão de rede, dificuldade de contato, cobrança indevida\n",
            " sinal/conexão de rede, dificuldade de contato, cobrança indevida\n",
            " sinal/conexão de rede, dificuldade de contato, cobrança indevida\n",
            "\n",
            " -  p_tsk_aft_1s\n",
            " sinal/conexão de rede, dificuldade de contato, cobrança indevida\n",
            " sinal/conexão de rede, dificuldade de contato, cobrança indevida\n",
            " sinal/conexão de rede, dificuldade de contato, cobrança indevida\n",
            "\n",
            " -  p_tsk_aft_2s\n",
            " sinal/conexão de rede, dificuldade de contato, cobrança indevida\n",
            " sinal/conexão de rede, dificuldade de contato, cobrança indevida\n",
            " sinal/conexão de rede, dificuldade de contato, cobrança indevida\n",
            "\n",
            " -  p_tsk_bfr_0s\n",
            " sinal/conexão de rede, dificuldade de contato, chip/sim card, cancelamento linha/plano, cobrança indevida\n",
            " sinal/conexão de rede, dificuldade de contato, chip/sim card, cancelamento linha/plano, cobrança indevida\n",
            " sinal/conexão de rede, dificuldade de contato, chip/sim card, cancelamento linha/plano, cobrança indevida\n",
            "\n",
            " -  p_tsk_bfr_1s\n",
            " sinal/conexão de rede, dificuldade de contato, cancelamento linha/plano, chip/sim card, cobrança indevida\n",
            " sinal/conexão de rede, dificuldade de contato, cancelamento linha/plano, chip/sim card, cobrança indevida\n",
            " sinal/conexão de rede, dificuldade de contato, cancelamento linha/plano, chip/sim card, cobrança indevida\n",
            "\n",
            " -  p_tsk_bfr_2s\n",
            " sinal/conexão de rede, dificuldade de contato, cobrança indevida\n",
            " sinal/conexão de rede, dificuldade de contato, cobrança indevida\n",
            " sinal/conexão de rede, dificuldade de contato, cobrança indevida\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note:** As we can be seen below, model tends to use more labels wen we pass multi-turn conversations with 2 exemple labels."
      ],
      "metadata": {
        "id": "kTjb1jNadpv_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Multi-label Evaluation"
      ],
      "metadata": {
        "id": "JauKAKmspRHH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this section we will evaluate our prompt. To do that we labaled mannualy the **df_val** dataframe to use as comparision with model output.\n",
        "\n",
        "There are a few metrics to evaluate multi-label classification, in this work we gonna use `classification_report`from `sklearn`, that is a well known package wich give us manny metrics to analyse. If you you want to know more about it, there's a good article made by Aniruddha Karajgi, on https://towardsdatascience.com/evaluating-multi-label-classifiers-a31be83da6ea."
      ],
      "metadata": {
        "id": "p9brvCeTplPg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report"
      ],
      "metadata": {
        "id": "ar_auDgfT9G_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Creates binary classification columns and sum total classifications\n",
        "def binary_cols(dataframe, column, tags):\n",
        "  for tag in tags:\n",
        "      dataframe[tag] = dataframe[column].apply(lambda x: 1 if tag in x else 0)\n",
        "\n",
        "  dataframe[column + '_sum'] = dataframe[tags].sum(axis=1)"
      ],
      "metadata": {
        "id": "JNtkFEZ06aJl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note:** Depending on the amount of data, the classification process can take a few hours. Using the quantized model described above, with zero shot prompt and utilizing the free tier infrastructure of Colab, I noticed that Mistral 7B takes about an hour to classify 2.000 cases.\n",
        "\n",
        "Colab tends to disconnect due to inactivity. To prevent this, open your browser's inspector, go to the console tab, paste the code below, and press enter.\n",
        "\n",
        "`function ClickConnect(){\n",
        "    console.log(\"Working\");\n",
        "    document.querySelector(\"colab-connect-button\").click()\n",
        "}\n",
        "setInterval(ClickConnect,60000)`"
      ],
      "metadata": {
        "id": "ZERNC0giOUZJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Open validation dataset tagged manually\n",
        "df_val_url = 'https://raw.githubusercontent.com/rdemarqui/llm_complaint_management/main/datasets/df_validation.xlsx'\n",
        "df_val_labeled = pd.read_excel(df_val_url)"
      ],
      "metadata": {
        "id": "l8ToURtgqPRI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check each prompt performance\n",
        "y_expected = df_val_labeled[tags]\n",
        "score_dict = {}\n",
        "file_name_dict = {}\n",
        "\n",
        "for prompt in prompt_dict:\n",
        "  print(\"\\n- \", prompt)\n",
        "  temp_df, file_name = iterate_over_interval(df=df_val, column=\"description\", prompt=prompt_dict[prompt],\n",
        "                                             limit_char=2000, start_row=0, end_row=df_val.shape[0], save=True)\n",
        "\n",
        "  binary_cols(dataframe=temp_df, column='llm_class', tags=tags)\n",
        "\n",
        "  y_pred = temp_df[tags]\n",
        "\n",
        "  score_dict[prompt] = classification_report(y_expected, y_pred, output_dict=True, target_names=tags)\n",
        "  file_name_dict[prompt] = file_name"
      ],
      "metadata": {
        "id": "bCR1jNbTJTu4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6c995b8c-851f-4c3e-a197-702795bae725"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "-  p_tsk_aft_0s\n",
            "0:01:58 Analysis number 100\n",
            "0:03:48 Analysis number 200\n",
            "202 cases analyzed. Total execution time: 0:03:52\n",
            "\n",
            "-  p_tsk_aft_1s\n",
            "0:03:03 Analysis number 100\n",
            "0:06:04 Analysis number 200\n",
            "202 cases analyzed. Total execution time: 0:06:10\n",
            "\n",
            "-  p_tsk_aft_2s\n",
            "0:04:50 Analysis number 100\n",
            "0:09:37 Analysis number 200\n",
            "202 cases analyzed. Total execution time: 0:09:47\n",
            "\n",
            "-  p_tsk_bfr_0s\n",
            "0:01:53 Analysis number 100\n",
            "0:04:03 Analysis number 200\n",
            "202 cases analyzed. Total execution time: 0:04:07\n",
            "\n",
            "-  p_tsk_bfr_1s\n",
            "0:03:15 Analysis number 100\n",
            "0:06:26 Analysis number 200\n",
            "202 cases analyzed. Total execution time: 0:06:32\n",
            "\n",
            "-  p_tsk_bfr_2s\n",
            "0:05:08 Analysis number 100\n",
            "0:10:16 Analysis number 200\n",
            "202 cases analyzed. Total execution time: 0:10:26\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There's some ways to agregate metrics such as `micro average`, `macro avarage`, `weighted average` and `samples average`. In this study we gonna use `samples avg`, wich was specifically designed for multilabel scenarios. It calculates metrics like precision, recall, and F1-score for each instance individually and averages them, thus effectively evaluating the model's performance on each sample by considering all its labels, making it particularly useful for assessing how well the model predicts the label set for each individual sample."
      ],
      "metadata": {
        "id": "58JTqQxPrBlt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create score comparison table\n",
        "df_score_comparison = pd.DataFrame()\n",
        "\n",
        "for prompt in prompt_dict:\n",
        "  score_temp = score_dict[prompt]['samples avg']\n",
        "  df_temp = pd.DataFrame(score_temp, index=[prompt])\n",
        "  df_score_comparison = pd.concat([df_score_comparison, df_temp])\n",
        "\n",
        "round(df_score_comparison, 4)"
      ],
      "metadata": {
        "id": "oxLYxxWZv0ME",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "d193483a-c8f1-473a-d93d-bf1daf7414c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              precision  recall  f1-score  support\n",
              "p_tsk_aft_0s     0.6375  0.7140    0.6367      301\n",
              "p_tsk_aft_1s     0.6399  0.7800    0.6676      301\n",
              "p_tsk_aft_2s     0.6836  0.7388    0.6750      301\n",
              "p_tsk_bfr_0s     0.5785  0.7446    0.6190      301\n",
              "p_tsk_bfr_1s     0.5756  0.7822    0.6314      301\n",
              "p_tsk_bfr_2s     0.5792  0.7002    0.5978      301"
            ],
            "text/html": [
              "\n",
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              "\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>f1-score</th>\n",
              "      <th>support</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>p_tsk_aft_0s</th>\n",
              "      <td>0.6375</td>\n",
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              "      <td>301</td>\n",
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              "      <th>p_tsk_aft_1s</th>\n",
              "      <td>0.6399</td>\n",
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              "      <td>0.6676</td>\n",
              "      <td>301</td>\n",
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              "      <th>p_tsk_aft_2s</th>\n",
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              "      <td>301</td>\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "  .colab-df-spinner {\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "\n",
              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Remembering that:\n",
        "\n",
        "* **Precision:** It is the proportion of positive identifications that were actually correct. A low precision indicates that there were **too many False Positives (FP)**, meaning that the model predicted many instances as positive that were actually negative.\n",
        "\n",
        " $\\frac{\\text{True Positive}}{\\text{Total Predicted Positive}} = \\frac{\\text{True Positive (TP)}}{\\text{True Positive (TP)} + \\text{False Positive (FP)}}$\n",
        "\n",
        "* **Recall:** It is the proportion of actual positives that were identified correctly. A low recall indicates that there were **too many False Negatives (FN)**, meaning that the model failed to identify many actual positive instances.\n",
        "\n",
        " $\\frac{\\text{True Positive}}{\\text{Total Actual Positive}} = \\frac{\\text{True Positive (TP)}}{\\text{True Positive (TP)} + \\text{False Negative (FN)}}$\n",
        "\n",
        "* **F1 Score:** Is the harmonic mean of the precision and recall, bringing a good balance between both.\n",
        "\n",
        " ${\\text{F1}} = 2* \\frac{\\text{Precision * Recall}}{\\text{Precision + Recall}}$\n",
        "\n",
        "In this study we will use the prompt that achived the max F1 score, that represents a good balance between precision and recall."
      ],
      "metadata": {
        "id": "KBREo3Ex-QTW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "max_score_prompt = df_score_comparison['f1-score'].idxmax()\n",
        "print(f\"The max score obtained was achived by '{max_score_prompt}' prompt.\")"
      ],
      "metadata": {
        "id": "WXmGFR_OmOlW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0e8452da-d403-45cf-b18c-b78738d4b8ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The max score obtained was achived by 'p_tsk_aft_2s' prompt.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check detailed score from choosen prompt\n",
        "round(pd.DataFrame(score_dict[max_score_prompt]).transpose(), 4)"
      ],
      "metadata": {
        "id": "r_dxrDY6_EZ1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "a6a41c57-906f-4850-c6bf-0e2ec9973359"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          precision  recall  f1-score  support\n",
              "sinal/conexão de rede        0.9524  0.5882    0.7273     34.0\n",
              "cobrança indevida            0.6939  0.7556    0.7234     45.0\n",
              "consumo saldo/crédito        0.5556  0.6250    0.5882     16.0\n",
              "plano/benefício              0.4667  0.5250    0.4941     40.0\n",
              "cancelamento linha/plano     0.5370  0.6905    0.6042     42.0\n",
              "chip/sim card                0.8696  0.6452    0.7407     31.0\n",
              "spam                         0.7692  1.0000    0.8696     10.0\n",
              "portabilidade                0.6552  0.8636    0.7451     22.0\n",
              "recarga/pagamento            0.5091  0.8485    0.6364     33.0\n",
              "dificuldade de contato       0.4865  0.6429    0.5538     28.0\n",
              "micro avg                    0.6076  0.6944    0.6481    301.0\n",
              "macro avg                    0.6495  0.7184    0.6683    301.0\n",
              "weighted avg                 0.6419  0.6944    0.6525    301.0\n",
              "samples avg                  0.6836  0.7388    0.6750    301.0"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
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              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>f1-score</th>\n",
              "      <th>support</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>sinal/conexão de rede</th>\n",
              "      <td>0.9524</td>\n",
              "      <td>0.5882</td>\n",
              "      <td>0.7273</td>\n",
              "      <td>34.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cobrança indevida</th>\n",
              "      <td>0.6939</td>\n",
              "      <td>0.7556</td>\n",
              "      <td>0.7234</td>\n",
              "      <td>45.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>consumo saldo/crédito</th>\n",
              "      <td>0.5556</td>\n",
              "      <td>0.6250</td>\n",
              "      <td>0.5882</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>plano/benefício</th>\n",
              "      <td>0.4667</td>\n",
              "      <td>0.5250</td>\n",
              "      <td>0.4941</td>\n",
              "      <td>40.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cancelamento linha/plano</th>\n",
              "      <td>0.5370</td>\n",
              "      <td>0.6905</td>\n",
              "      <td>0.6042</td>\n",
              "      <td>42.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chip/sim card</th>\n",
              "      <td>0.8696</td>\n",
              "      <td>0.6452</td>\n",
              "      <td>0.7407</td>\n",
              "      <td>31.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spam</th>\n",
              "      <td>0.7692</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.8696</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>portabilidade</th>\n",
              "      <td>0.6552</td>\n",
              "      <td>0.8636</td>\n",
              "      <td>0.7451</td>\n",
              "      <td>22.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>recarga/pagamento</th>\n",
              "      <td>0.5091</td>\n",
              "      <td>0.8485</td>\n",
              "      <td>0.6364</td>\n",
              "      <td>33.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dificuldade de contato</th>\n",
              "      <td>0.4865</td>\n",
              "      <td>0.6429</td>\n",
              "      <td>0.5538</td>\n",
              "      <td>28.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>micro avg</th>\n",
              "      <td>0.6076</td>\n",
              "      <td>0.6944</td>\n",
              "      <td>0.6481</td>\n",
              "      <td>301.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>macro avg</th>\n",
              "      <td>0.6495</td>\n",
              "      <td>0.7184</td>\n",
              "      <td>0.6683</td>\n",
              "      <td>301.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weighted avg</th>\n",
              "      <td>0.6419</td>\n",
              "      <td>0.6944</td>\n",
              "      <td>0.6525</td>\n",
              "      <td>301.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>samples avg</th>\n",
              "      <td>0.6836</td>\n",
              "      <td>0.7388</td>\n",
              "      <td>0.6750</td>\n",
              "      <td>301.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\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",
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              "    .colab-df-buttons div {\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
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              "        document.querySelector('#df-625356e3-8817-4637-9b88-29a5e40499da 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-625356e3-8817-4637-9b88-29a5e40499da');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "  </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",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "  @keyframes spin {\n",
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              "    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",
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              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Classification"
      ],
      "metadata": {
        "id": "09uTIRku-kRi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "After choosing the best prompt in our validation dataset, we gonna apply it on test dataset with 2.000 samples.\n",
        "\n",
        "**Note:** As can be seen in the analysis above, the complaints have a median of 86 words and a text length with a median of 500 characters. Few texts exceed 2000 characters, for this reason, we will limit the text size to be passed to the model to this value on `limit_char` parameter."
      ],
      "metadata": {
        "id": "JKF6BRMcptsc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Run text classification\n",
        "df, file_name = iterate_over_interval(df=df, column=\"description\", prompt=prompt_dict[max_score_prompt],\n",
        "                                      limit_char=2000, start_row=0, end_row=df.shape[0], save=True)"
      ],
      "metadata": {
        "id": "LqfD3bzLl01G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "464efaae-b5f3-4d65-a4c3-004fd382011a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0:04:57 Analysis number 100\n",
            "0:09:53 Analysis number 200\n",
            "0:14:38 Analysis number 300\n",
            "0:19:19 Analysis number 400\n",
            "0:24:13 Analysis number 500\n",
            "0:28:48 Analysis number 600\n",
            "0:33:26 Analysis number 700\n",
            "0:38:08 Analysis number 800\n",
            "0:42:42 Analysis number 900\n",
            "0:47:20 Analysis number 1000\n",
            "0:51:49 Analysis number 1100\n",
            "0:56:23 Analysis number 1200\n",
            "1:01:03 Analysis number 1300\n",
            "1:05:31 Analysis number 1400\n",
            "1:10:15 Analysis number 1500\n",
            "1:14:50 Analysis number 1600\n",
            "1:19:49 Analysis number 1700\n",
            "1:24:24 Analysis number 1800\n",
            "1:29:03 Analysis number 1900\n",
            "1:33:39 Analysis number 2000\n",
            "2002 cases analyzed. Total execution time: 1:33:49\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Results"
      ],
      "metadata": {
        "id": "nhuJNcTl-sXZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create binary classification columns\n",
        "binary_cols(df, 'llm_class', tags)"
      ],
      "metadata": {
        "id": "lszAyhCcmR_p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get class quantity\n",
        "df_class = df[tags]\n",
        "class_qty = df_class.sum()\n",
        "sns.set_style(\"whitegrid\")\n",
        "\n",
        "chart_class = class_qty.sort_values(ascending=False)\n",
        "sns.barplot(x=chart_class.values, y=chart_class.index, orient='h')\n",
        "plt.title('Class quantity');"
      ],
      "metadata": {
        "id": "kw7sj0SqklwY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "outputId": "38a38691-dc1b-446a-9f12-90c03e139a84"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get frequency labels per category\n",
        "freq_matrix = round((df.groupby('problem')[tags].sum().T / df['problem'].value_counts().sort_index()), 2).T\n",
        "sns.heatmap(freq_matrix, annot=True, cmap='Blues')\n",
        "plt.title('Frequency Matrix of Tagged Problems');"
      ],
      "metadata": {
        "id": "l61F2lX_463l",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 640
        },
        "outputId": "14c38bf6-b4d5-4d58-c864-5bf05421b6b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "fUKGKT7JoNDS"
      }
    }
  ],
  "metadata": {
    "language_info": {
      "name": "python"
    },
    "orig_nbformat": 4,
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}