Gotcha_GPT_new.ipynb

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    {
      "cell_type": "code",
      "source": [
        "# Ensure the necessary packages are installed\n",
        "!pip install scikit-posthocs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ye3A_Er6mldU",
        "outputId": "610fd5b6-4149-44e9-b4ab-e4bc5649a328"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting scikit-posthocs\n",
            "  Downloading scikit_posthocs-0.10.0-py3-none-any.whl.metadata (5.8 kB)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from scikit-posthocs) (1.26.4)\n",
            "Requirement already satisfied: scipy>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from scikit-posthocs) (1.13.1)\n",
            "Requirement already satisfied: statsmodels in /usr/local/lib/python3.10/dist-packages (from scikit-posthocs) (0.14.4)\n",
            "Requirement already satisfied: pandas>=0.20.0 in /usr/local/lib/python3.10/dist-packages (from scikit-posthocs) (2.2.2)\n",
            "Requirement already satisfied: seaborn in /usr/local/lib/python3.10/dist-packages (from scikit-posthocs) (0.13.2)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from scikit-posthocs) (3.8.0)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.20.0->scikit-posthocs) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.20.0->scikit-posthocs) (2024.2)\n",
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            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->scikit-posthocs) (1.3.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->scikit-posthocs) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->scikit-posthocs) (4.54.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->scikit-posthocs) (1.4.7)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->scikit-posthocs) (24.2)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->scikit-posthocs) (11.0.0)\n",
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            "Requirement already satisfied: patsy>=0.5.6 in /usr/local/lib/python3.10/dist-packages (from statsmodels->scikit-posthocs) (1.0.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas>=0.20.0->scikit-posthocs) (1.16.0)\n",
            "Downloading scikit_posthocs-0.10.0-py3-none-any.whl (33 kB)\n",
            "Installing collected packages: scikit-posthocs\n",
            "Successfully installed scikit-posthocs-0.10.0\n"
          ]
        }
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    {
      "cell_type": "code",
      "source": [
        "# Import libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.utils import resample\n",
        "from sklearn.ensemble import ExtraTreesClassifier, RandomForestClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, matthews_corrcoef, cohen_kappa_score, ConfusionMatrixDisplay\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.cluster import KMeans\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "from scipy.cluster.hierarchy import dendrogram, linkage\n",
        "import io\n",
        "\n",
        "from scipy.stats import wilcoxon, friedmanchisquare\n",
        "import scikit_posthocs as sp"
      ],
      "metadata": {
        "id": "7UGr-ciGf-6p"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()\n",
        "file_name = next(iter(uploaded))\n",
        "df = pd.read_excel(io.BytesIO(uploaded[file_name]))\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "id": "Bc_nCn3ggDLz",
        "outputId": "0db22c2b-4aed-4230-a3db-cc96211b0a1e"
      },
      "execution_count": 3,
      "outputs": [
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              "      Upload widget is only available when the cell has been executed in the\n",
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              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
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              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
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              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
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              "  outputElement.steps = steps;\n",
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              "  return _uploadFilesContinue(outputId);\n",
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              "// This is roughly an async generator (not supported in the browser yet),\n",
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              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
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              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
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              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
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              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
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              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
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              "  outputElement.innerHTML = '';\n",
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              "  const pickedPromise = new Promise((resolve) => {\n",
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              "\n",
              "  const cancel = document.createElement('button');\n",
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              "  // Wait for the user to pick the files.\n",
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              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
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              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
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              "    li.append(span(\n",
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              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
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              "    const percent = span('0% done');\n",
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              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
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              "    do {\n",
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              "\n",
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              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
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          "text": [
            "Saving text_classification_port.xlsx to text_classification_port.xlsx\n"
          ]
        },
        {
          "output_type": "execute_result",
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              "     index                         text  Token  Perplexity  Burstiness  \\\n",
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              "887     55         conclusão_ia_56.docx    836   51.579498    0.413299   \n",
              "888    763  introdução_humano_76.2.docx    527  119.274704    0.905704   \n",
              "\n",
              "     AI Metric  \n",
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              "[889 rows x 6 columns]"
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare X and y\n",
        "X = df.drop(columns=['index', 'text', 'AI Metric'])\n",
        "y = df['AI Metric']\n",
        "\n",
        "# Split dataset into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)"
      ],
      "metadata": {
        "id": "iKjsXoRgh32r"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define classifiers\n",
        "classifiers = {\n",
        "    'ExtraTrees': ExtraTreesClassifier(random_state=42),\n",
        "    'RandomForest': RandomForestClassifier(random_state=42),\n",
        "    'GaussianNB': GaussianNB()\n",
        "}\n",
        "\n",
        "# Train and evaluate models\n",
        "metrics = ['accuracy', 'precision', 'recall', 'f1', 'mcc', 'kappa']\n",
        "results = {}\n",
        "for name, clf in classifiers.items():\n",
        "    clf.fit(X_train, y_train)\n",
        "    y_pred_train = clf.predict(X_train)\n",
        "    y_proba_train = clf.predict_proba(X_train)[:, 1] if hasattr(clf, \"predict_proba\") else None\n",
        "    y_pred_test = clf.predict(X_test)\n",
        "\n",
        "    # Calculate metrics\n",
        "    results[name] = {\n",
        "        'train': {\n",
        "            'accuracy': accuracy_score(y_train, y_pred_train),\n",
        "            'precision': precision_score(y_train, y_pred_train),\n",
        "            'recall': recall_score(y_train, y_pred_train),\n",
        "            'f1': f1_score(y_train, y_pred_train),\n",
        "            'mcc': matthews_corrcoef(y_train, y_pred_train),\n",
        "            'kappa': cohen_kappa_score(y_train, y_pred_train),\n",
        "            'confusion_matrix': confusion_matrix(y_train, y_pred_train)\n",
        "        },\n",
        "        'test': {\n",
        "            'accuracy': accuracy_score(y_test, y_pred_test),\n",
        "            'precision': precision_score(y_test, y_pred_test),\n",
        "            'recall': recall_score(y_test, y_pred_test),\n",
        "            'f1': f1_score(y_test, y_pred_test),\n",
        "            'mcc': matthews_corrcoef(y_test, y_pred_test),\n",
        "            'kappa': cohen_kappa_score(y_test, y_pred_test),\n",
        "            'confusion_matrix': confusion_matrix(y_test, y_pred_test)\n",
        "        }\n",
        "    }\n",
        "\n",
        "# Selecting the best model based on the average of metrics\n",
        "train_scores = {name: [results[name]['train'][metric] for metric in metrics] for name in classifiers.keys()}\n",
        "best_model = max(train_scores, key=lambda x: np.mean(train_scores[x]))\n",
        "\n",
        "# Predict on the test set with the best model\n",
        "best_clf = classifiers[best_model]\n",
        "y_pred_test = best_clf.predict(X_test)\n",
        "y_proba_test = best_clf.predict_proba(X_test)[:, 1] if hasattr(best_clf, \"predict_proba\") else None\n",
        "\n",
        "# Display test metrics and confusion matrix\n",
        "test_metrics = {\n",
        "    'accuracy': accuracy_score(y_test, y_pred_test),\n",
        "    'precision': precision_score(y_test, y_pred_test),\n",
        "    'recall': recall_score(y_test, y_pred_test),\n",
        "    'f1': f1_score(y_test, y_pred_test),\n",
        "    'mcc': matthews_corrcoef(y_test, y_pred_test),\n",
        "    'kappa': cohen_kappa_score(y_test, y_pred_test)\n",
        "}"
      ],
      "metadata": {
        "id": "rN2y_bRe0lFu"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Wilcoxon Rank Sum Test and Friedman Test\n",
        "friedman_stat, friedman_p = friedmanchisquare(*train_scores.values())\n",
        "\n",
        "# Restructure data for posthoc_conover\n",
        "data = []\n",
        "for classifier, scores in train_scores.items():\n",
        "    for metric_idx, score in enumerate(scores):\n",
        "        data.append([classifier, metrics[metric_idx], score])\n",
        "\n",
        "df_scores_melted = pd.DataFrame(data, columns=['classifier', 'metric', 'score'])\n",
        "\n",
        "# Pairwise Conover-Friedman Test\n",
        "posthoc_conover = sp.posthoc_conover(df_scores_melted, val_col='score', group_col='classifier', p_adjust='holm')\n",
        "\n",
        "# Create a DataFrame for Friedman test results\n",
        "friedman_results = pd.DataFrame({'Friedman Statistic': [friedman_stat], 'p-value': [friedman_p]})\n",
        "friedman_results = friedman_results.style.format({'Friedman Statistic': \"{:.2f}\", 'p-value': \"{:.2f}\"})\n",
        "\n",
        "# Format Conover-Friedman test results\n",
        "posthoc_conover = posthoc_conover.style.format(\"{:.2f}\")"
      ],
      "metadata": {
        "id": "yooXqdeOnug9"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "friedman_results"
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "id": "asmQ09aFob2T",
        "outputId": "43d02ce3-e94c-40d4-c2fe-3ada9c47a4d4"
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      "execution_count": 7,
      "outputs": [
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              "      <th class=\"blank level0\" >&nbsp;</th>\n",
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      "cell_type": "code",
      "source": [
        "posthoc_conover"
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          "base_uri": "https://localhost:8080/",
          "height": 163
        },
        "id": "mH-odZxdoW0J",
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      "outputs": [
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              "<pandas.io.formats.style.Styler at 0x7f35b361b3d0>"
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              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
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              "      <th id=\"T_dce2e_level0_col1\" class=\"col_heading level0 col1\" >GaussianNB</th>\n",
              "      <th id=\"T_dce2e_level0_col2\" class=\"col_heading level0 col2\" >RandomForest</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_dce2e_level0_row1\" class=\"row_heading level0 row1\" >GaussianNB</th>\n",
              "      <td id=\"T_dce2e_row1_col0\" class=\"data row1 col0\" >0.00</td>\n",
              "      <td id=\"T_dce2e_row1_col1\" class=\"data row1 col1\" >1.00</td>\n",
              "      <td id=\"T_dce2e_row1_col2\" class=\"data row1 col2\" >0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_dce2e_level0_row2\" class=\"row_heading level0 row2\" >RandomForest</th>\n",
              "      <td id=\"T_dce2e_row2_col0\" class=\"data row2 col0\" >1.00</td>\n",
              "      <td id=\"T_dce2e_row2_col1\" class=\"data row2 col1\" >0.00</td>\n",
              "      <td id=\"T_dce2e_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming 'results' dictionary from your code (containing train metrics)\n",
        "train_metrics = {model: results[model]['train'] for model in results}\n",
        "\n",
        "# Create a pandas DataFrame\n",
        "df_train_metrics = pd.DataFrame(train_metrics).T  # Transpose for better formatting\n",
        "df_train_metrics.index.name = 'Model'  # Set index name to \"Model\"\n",
        "print(f\"Training results:\")\n",
        "print(f\"Best Model: {best_model} based on training scores\")\n",
        "# Display the DataFrame\n",
        "df_train_metrics"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 229
        },
        "id": "3Y5WF9ajkn-X",
        "outputId": "6dc9e90f-2016-44cc-b6a7-fee3ecbd3cbb"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training results:\n",
            "Best Model: ExtraTrees based on training scores\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              accuracy precision    recall        f1      mcc     kappa  \\\n",
              "Model                                                                     \n",
              "ExtraTrees         1.0       1.0       1.0       1.0      1.0       1.0   \n",
              "RandomForest       1.0       1.0       1.0       1.0      1.0       1.0   \n",
              "GaussianNB    0.994374  0.997126  0.991429  0.994269  0.98876  0.988745   \n",
              "\n",
              "                  confusion_matrix  \n",
              "Model                               \n",
              "ExtraTrees    [[361, 0], [0, 350]]  \n",
              "RandomForest  [[361, 0], [0, 350]]  \n",
              "GaussianNB    [[360, 1], [3, 347]]  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>accuracy</th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>f1</th>\n",
              "      <th>mcc</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ExtraTrees</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>[[361, 0], [0, 350]]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RandomForest</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>[[361, 0], [0, 350]]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GaussianNB</th>\n",
              "      <td>0.994374</td>\n",
              "      <td>0.997126</td>\n",
              "      <td>0.991429</td>\n",
              "      <td>0.994269</td>\n",
              "      <td>0.98876</td>\n",
              "      <td>0.988745</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_train_metrics",
              "summary": "{\n  \"name\": \"df_train_metrics\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"ExtraTrees\",\n          \"RandomForest\",\n          \"GaussianNB\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"accuracy\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.9943741209563994,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.9943741209563994,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"precision\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.9971264367816092,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.9971264367816092,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"recall\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.9914285714285714,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.9914285714285714,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"f1\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.994269340974212,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.994269340974212,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mcc\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.988760225914248,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.988760225914248,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"kappa\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.9887445681855959,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.9887445681855959,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"confusion_matrix\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming 'results' dictionary from your code (containing train metrics)\n",
        "test_metrics = {model: results[model]['test'] for model in results}\n",
        "\n",
        "# Create a pandas DataFrame\n",
        "df_test_metrics = pd.DataFrame(test_metrics).T  # Transpose for better formatting\n",
        "df_test_metrics.index.name = 'Model'  # Set index name to \"Model\"\n",
        "print(f\"Testing results:\")\n",
        "# Display the DataFrame\n",
        "df_test_metrics"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        },
        "id": "XHOFdoj-p075",
        "outputId": "b295557c-6863-4ac0-f415-3b973a19dc1d"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Testing results:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              accuracy precision recall       f1       mcc     kappa  \\\n",
              "Model                                                                  \n",
              "ExtraTrees    0.994382  0.988764    1.0  0.99435  0.988826  0.988764   \n",
              "RandomForest  0.994382  0.988764    1.0  0.99435  0.988826  0.988764   \n",
              "GaussianNB    0.994382  0.988764    1.0  0.99435  0.988826  0.988764   \n",
              "\n",
              "                confusion_matrix  \n",
              "Model                             \n",
              "ExtraTrees    [[89, 1], [0, 88]]  \n",
              "RandomForest  [[89, 1], [0, 88]]  \n",
              "GaussianNB    [[89, 1], [0, 88]]  "
            ],
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              "      <th></th>\n",
              "      <th>accuracy</th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>f1</th>\n",
              "      <th>mcc</th>\n",
              "      <th>kappa</th>\n",
              "      <th>confusion_matrix</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ExtraTrees</th>\n",
              "      <td>0.994382</td>\n",
              "      <td>0.988764</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.99435</td>\n",
              "      <td>0.988826</td>\n",
              "      <td>0.988764</td>\n",
              "      <td>[[89, 1], [0, 88]]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RandomForest</th>\n",
              "      <td>0.994382</td>\n",
              "      <td>0.988764</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.99435</td>\n",
              "      <td>0.988826</td>\n",
              "      <td>0.988764</td>\n",
              "      <td>[[89, 1], [0, 88]]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GaussianNB</th>\n",
              "      <td>0.994382</td>\n",
              "      <td>0.988764</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.99435</td>\n",
              "      <td>0.988826</td>\n",
              "      <td>0.988764</td>\n",
              "      <td>[[89, 1], [0, 88]]</td>\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "\n",
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              "\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_test_metrics",
              "summary": "{\n  \"name\": \"df_test_metrics\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"ExtraTrees\",\n          \"RandomForest\",\n          \"GaussianNB\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"accuracy\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.9943820224719101,\n        \"max\": 0.9943820224719101,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.9943820224719101\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"precision\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.9887640449438202,\n        \"max\": 0.9887640449438202,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.9887640449438202\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"recall\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"f1\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.9943502824858758,\n        \"max\": 0.9943502824858758,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.9943502824858758\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mcc\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.9888264649460884,\n        \"max\": 0.9888264649460884,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.9888264649460884\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"kappa\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.9887640449438202,\n        \"max\": 0.9887640449438202,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.9887640449438202\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"confusion_matrix\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Show prediction and probability for each test sample\n",
        "train_results = pd.DataFrame({\"Prediction\": y_pred_train, \"Probability of being AI\": y_proba_train})\n",
        "# Format the 'p-value' column to display 2 decimal places\n",
        "train_results['Probability of being AI'] = train_results['Probability of being AI'].apply(lambda x: '{:.2f}'.format(x))\n",
        "print(\"Training Results:\")\n",
        "train_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "sWMaJS6T2P1l",
        "outputId": "047d1288-06ca-422e-bc51-bfda4f8ad42a"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Results:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Prediction Probability of being AI\n",
              "0             0                    0.00\n",
              "1             0                    0.00\n",
              "2             1                    1.00\n",
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              "4             0                    0.00\n",
              "..          ...                     ...\n",
              "706           1                    1.00\n",
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              "708           1                    1.00\n",
              "709           0                    0.00\n",
              "710           0                    0.00\n",
              "\n",
              "[711 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
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              "\n",
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              "      <th></th>\n",
              "      <th>Prediction</th>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>711 rows × 2 columns</p>\n",
              "</div>\n",
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              "\n",
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              "    <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-022e0eeb-25bb-47fb-ac5f-fa549129cc01 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-022e0eeb-25bb-47fb-ac5f-fa549129cc01');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
              "<div id=\"df-d7084baf-a830-4ef0-ac85-4ed25298f6c2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d7084baf-a830-4ef0-ac85-4ed25298f6c2')\"\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",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
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              "      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-d7084baf-a830-4ef0-ac85-4ed25298f6c2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_be3d3ee5-b98b-4cfa-81e7-b47661b2f40d\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('train_results')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_be3d3ee5-b98b-4cfa-81e7-b47661b2f40d button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('train_results');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train_results",
              "summary": "{\n  \"name\": \"train_results\",\n  \"rows\": 711,\n  \"fields\": [\n    {\n      \"column\": \"Prediction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Probability of being AI\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"0.00\",\n          \"1.00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the confusion matrix\n",
        "cm = confusion_matrix(y_train, y_pred_train)\n",
        "ConfusionMatrixDisplay(confusion_matrix=cm).plot()\n",
        "plt.title(\"Confusion Matrix for Training dataset\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "Cknod7pv2l_2",
        "outputId": "f8c4ff18-eac3-4263-a5f8-21e7d00a62ad"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Show prediction and probability for each test sample\n",
        "test_results = pd.DataFrame({\"Prediction\": y_pred_test, \"Probability of being AI\": y_proba_test})\n",
        "print(\"Test Results:\")\n",
        "test_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "iq-pwREggRAH",
        "outputId": "149ebb3b-9ea7-4869-9f90-f5b4bfd3dbec"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Results:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Prediction  Probability of being AI\n",
              "0             1                     0.95\n",
              "1             0                     0.07\n",
              "2             0                     0.02\n",
              "3             1                     1.00\n",
              "4             1                     0.99\n",
              "..          ...                      ...\n",
              "173           0                     0.00\n",
              "174           0                     0.03\n",
              "175           0                     0.03\n",
              "176           0                     0.07\n",
              "177           1                     0.97\n",
              "\n",
              "[178 rows x 2 columns]"
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            "application/vnd.google.colaboratory.intrinsic+json": {
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              "summary": "{\n  \"name\": \"test_results\",\n  \"rows\": 178,\n  \"fields\": [\n    {\n      \"column\": \"Prediction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Probability of being AI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4787397758653649,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          0.88,\n          0.93\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "# Create the confusion matrix\n",
        "cm = confusion_matrix(y_test, y_pred_test)\n",
        "ConfusionMatrixDisplay(confusion_matrix=cm).plot()\n",
        "plt.title(\"Confusion Matrix for Testing dataset\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "0ftxOtvK3ukt",
        "outputId": "4a6c82eb-2604-4516-97d6-e6848e43e00e"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Perform K-Means Clustering\n",
        "kmeans = KMeans(n_clusters=2, random_state=42)\n",
        "clusters = kmeans.fit_predict(X_test)\n",
        "\n",
        "# Access data using .iloc for numpy-style indexing\n",
        "plt.scatter(X_test.iloc[:, 0], X_test.iloc[:, 1], c=clusters, cmap='viridis')\n",
        "\n",
        "plt.title(\"K-Means Clustering (2 Clusters)\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "YvjnjRcD4zKq",
        "outputId": "8b402dc0-1c8a-49cd-cbc2-b599ae2319e1"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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qVYo1jCjOTDKEJE55l19+OZ9++im7du2iZ8+e5OTk0LVrV6cvd/MTIiIiePPNN3nsscfo06eP22sMGjQIh8PB448/XuSY3W4vuEnkf+Iv3IOSk5PDG2+8UeLnlz/HIl9QUBBNmjRBa01ubq7b8wYOHEjz5s158sknWbduXZHjqampHvOI1K1bF5vNVmQOysnPJSMjg6ysLKfHGjRoQGRkpNMy2vDw8CI3U7Be23Xr1vHtt98WOZaUlITdbnfbRn/o0aMHubm5vPvuuwWPmabpc7DRunVrgoKCimSGBli1ahWDBw+mY8eOfPLJJxiG72+p7du3p2fPnrz33nsuV93k5ORwzz33uD0/KiqKKlWqeP35ORyOIkObCQkJ1KxZs8jP7+RyYP38/vvvP6fXL19mZibp6elu25jv5N9xwzBo0aIFQJGl2Bs3bqR9+/Ze6xRCemBEpdCvXz/effddRowYwVVXXcXixYt9To7ly3LZTp06MWrUKKZOncrmzZvp3r07gYGB/Pnnn8yePZuXX36ZgQMHcvHFFxMbG8uwYcO4/fbbUUrx0UcfeZ0f4Un37t2pXr06HTp0oFq1auzYsYPXXnuN3r17e5zgGRgYyNy5c+natSsdO3Zk0KBBdOjQgcDAQLZt28bMmTOJjY11mwsmOjqaa665hldffRWlFA0aNGDRokVF5jT88ccfXH755QwaNIgmTZoQEBDAl19+yaFDhxg8eHBBudatW/Pmm2/yxBNP0LBhQxISEujSpQv33nsvCxYs4Morr2T48OG0bt2a9PR0tm7dypw5c9izZ0+ZDhf07duXtm3bcvfdd7Nr1y4aNWrEggULSExMBLzPtQgJCaF79+4sXbqUKVOmFDy+d+9errrqKpRSDBw4kNmzZzud16JFi4KbtDsffvgh3bt3p3///vTp04fLL7+c8PBw/vzzT2bNmsWBAwc85oK5+eabefrpp7n55pu58MILWbVqVZHtB1JTU6lVqxYDBw4s2G5i6dKlbNiwgeeff76gXOvWrfnss8+YMGECbdq0ISIigj59+jB06FA+//xzbrvtNpYvX06HDh1wOBz8/vvvfP7553z77bdceOGFHp/nzTffTGJiIl26dKFWrVrs3buXV199lfPPP5/GjRsXlDt8+DBbtmzxeYK1OMNV5BIoIVwpvBT6ZM8995wG9JVXXulymaqncws7eRl1vnfeeUe3bt1ah4aG6sjISN28eXN933336f379xeUWbNmjb7ooot0aGiorlmzpr7vvvv0t99+W2RJcqdOnVwujz55OfPbb7+tO3bsqOPj43VwcLBu0KCBvvfee3VycrLH55Dv+PHj+pFHHtHNmzfXYWFhOiQkRDdr1kxPmjRJHzhwwO11tdb6yJEjesCAATosLEzHxsbqUaNG6d9++81pee7Ro0f12LFjdaNGjXR4eLiOjo7W7dq1059//rlTXQcPHtS9e/fWkZGRGnBaUp2amqonTZqkGzZsqIOCgnSVKlX0xRdfrJ977jmdk5OjtT6xLNjVcm13y6jDw8OLlD15uXH+87zuuut0ZGSkjo6O1sOHD9dr1qzRgJ41a5bX13ju3LlaKVWw5FvrE8vQ3X0VXsbsSUZGhn7uued0mzZtdEREhA4KCtLnnHOOHj9+vN61a5fH55WRkaFHjhypo6OjdWRkpB40aJA+fPiw0/Wzs7P1vffeq1u2bKkjIyN1eHi4btmypX7jjTec6kpLS9PXXXedjomJKUhjkC8nJ0dPmzZNN23aVAcHB+vY2FjdunVrPXnyZKffU8Dlcu05c+bo7t2764SEBB0UFKTr1KmjR40a5fT7qbXWb775pg4LC9MpKSk+vXbizKa0LsVHRyGEqKTmzZtHv379+OGHH+jQoYPHsg6HgyZNmjBo0CCXw4zCP1q1asVll11WkExPCE8kgBFCnPYyMzOdJmc7HA66d+/Ozz//zMGDB31a+fTZZ58xevRo9u3bV5ClVvjP4sWLGThwIH///TcJCQkV3RxRCUgAI4Q47d18881kZmbSvn17srOzmTt3LmvXruWpp54q0QouIUTFkwBGCHHamzlzJs8//zy7du0iKyuLhg0bMnr0aMaNG1fRTRNClJAEMEIIIYSodCQPjBBCCCEqHQlghBBCCFHpVMpEdqZpsn//fiIjI2XDLyGEEKKS0FqTmppKzZo1i5W52pVKGcDs37+/RJvXCSGEEKLi/fPPP243R/VVpQxg8tOr//PPPwVb0gshhBDi1JaSkkLt2rU9bpPiq0oZwOQPG0VFRUkAI4QQQlQy/pj+IZN4hRBCCFHpSAAjhBBCiEpHAhghhBBCVDoSwAghhBCi0pEARgghhBCVjgQwQgghhKh0JIARQgghRKUjAYwQQgghKp1KmchOCE/Sk9PZuGQL2Rk51GtWm3MuOLuimySEEMLPJIARpw2H3cH0hz5l7itfk5uVW/D4ORfU594Z46jfrE4Ftk4IIYQ/yRCSOG28MvZdPn92vlPwAvDXr3u569KH2f/XwQpqmRBCCH+TAEacFvbu+Jev3/0erYseMx0mWelZzHxqbvk3TAghRJmQAEacFpZ+tApbgPtfZ4fd5PtPVpObk+u2jBBCiMqjWAHM1KlTadOmDZGRkSQkJNC3b1927tzpVCYrK4uxY8cSHx9PREQEAwYM4NChQ05l9u3bR+/evQkLCyMhIYF7770Xu91e+mcjzljHDyZ5LWPPsZORkln2jRFCCFHmihXArFy5krFjx/Ljjz+yZMkScnNz6d69O+np6QVl7rrrLhYuXMjs2bNZuXIl+/fvp3///gXHHQ4HvXv3Jicnh7Vr1/LBBx8wY8YMHnnkEf89K3HGqXJWnMvho8KCQgIJjw4rnwYJIYQoU0prb2/77h05coSEhARWrlxJx44dSU5OpmrVqsycOZOBAwcC8Pvvv9O4cWPWrVvHRRddxDfffMOVV17J/v37qVatGgBvvfUWEydO5MiRIwQFBXm9bkpKCtHR0SQnJxMVFVXS5ovTyL9/HuCm8253e9wWYNDzpi7c+faocmyVEEKIwvx5/y7VHJjk5GQA4uLiANi4cSO5ubl07dq1oEyjRo2oU6cO69atA2DdunU0b968IHgB6NGjBykpKWzbts3ldbKzs0lJSXH6EqKwWufUoN/tV7g8ZtgMwqPDGfJAf5fHhRBCVD4lDmBM0+TOO++kQ4cONGvWDICDBw8SFBRETEyMU9lq1apx8ODBgjKFg5f84/nHXJk6dSrR0dEFX7Vr1y5ps8Vp7LYXhjF8ymDCokKdHm92SSNeXvsk1epWraCWCSGE8LcSJ7IbO3Ysv/32Gz/88IM/2+PSpEmTmDBhQsH3KSkpEsSIIgzD4PqHBjDw7ivZsmoH2RnZ1G1Si9rnnVXRTRNCCOFnJQpgxo0bx6JFi1i1ahW1atUqeLx69erk5OSQlJTk1Atz6NAhqlevXlDmp59+cqovf5VSfpmTBQcHExwcXJKmijNQcGgwbXqcX9HNEEIIUYaKNYSktWbcuHF8+eWXLFu2jPr16zsdb926NYGBgXz//fcFj+3cuZN9+/bRvn17ANq3b8/WrVs5fPhwQZklS5YQFRVFkyZNSvNchBBCCHGGKFYPzNixY5k5cybz588nMjKyYM5KdHQ0oaGhREdHM3LkSCZMmEBcXBxRUVGMHz+e9u3bc9FFFwHQvXt3mjRpwtChQ3nmmWc4ePAgDz30EGPHjpVeFiGEEEL4pFjLqJVSLh+fPn06w4cPB6xEdnfffTeffvop2dnZ9OjRgzfeeMNpeGjv3r2MHj2aFStWEB4ezrBhw3j66acJCPAtnpJl1EIIIUTl48/7d6nywFQUCWCEEEKIyueUyQMjhBBCCFERJIARQgghRKUjAYwQQgghKh0JYIQQQghR6ZQ4E68QZ7K0pHSWfLiSLau2o7WmxaVN6DasE5GxERXdNCGEOCPIKiQhimnr6h081GcqmalZeY9oQBEcFsSU+RNp1aV5RTZPCCFOWbIKSYgKcvS/YzxwxZNkpWWhtc77srJUZ2fm8FCfpzm090hFN1MIIU57EsAIUQyL3l5CTlYuplm041KbGnuOnQVvfFsBLRNCiDOLBDBCFMPa+RswHabb46bDZO38n9weF0II4R8SwAhRDLnZdq9lcrJyy6ElQghxZpMARohiaNSuIbYA9382tgCDRu0almOLhBDizCQBjBDFcNWYnjjs7oeQHHaTq8f2KscWCSHEmUkCGCGKoXG7cxg+ZTAAhu3En0/+/69/cAAtOjapkLYJIcSZRBLZCVFM1z80gIat6jHnhUVsWbUdgKYdzmPghD5cfFWbCm6dEEKcGSSRnRClkP/no5Sq4JYIIcSpz5/3b+mBEaIUJHARQoiKIXNghBBCCFHpSAAjhBBCiEpHAhghhBBCVDoSwAghhBCi0pEARgghhBCVjgQwQgghhKh0JIARQgghRKUjAYwQQgghKh0JYIQQQghR6UgAI4QQQohKRwIYIYQQQlQ6EsAIIYQQotKRAEYIIYQQlY4EMEIIIYSodCSAEUIIIUSlIwGMEEIIISodCWCEEEIIUekUO4BZtWoVffr0oWbNmiilmDdvntNxpZTLr2effbagTL169Yocf/rpp0v9ZIQQQghxZih2AJOenk7Lli15/fXXXR4/cOCA09f777+PUooBAwY4lZsyZYpTufHjx5fsGQghhBDijBNQ3BN69epFr1693B6vXr260/fz58+nc+fOnH322U6PR0ZGFikrhBBCCOGLMp0Dc+jQIb766itGjhxZ5NjTTz9NfHw8rVq14tlnn8Vut7utJzs7m5SUFKcvIYQQQpy5it0DUxwffPABkZGR9O/f3+nx22+/nQsuuIC4uDjWrl3LpEmTOHDgAC+88ILLeqZOncrkyZPLsqlCCCGEqESU1lqX+GSl+PLLL+nbt6/L440aNaJbt268+uqrHut5//33GTVqFGlpaQQHBxc5np2dTXZ2dsH3KSkp1K5dm+TkZKKiokrafCGEEEKUo5SUFKKjo/1y/y6zHpjVq1ezc+dOPvvsM69l27Vrh91uZ8+ePZx33nlFjgcHB7sMbIQQQghxZiqzOTD/+9//aN26NS1btvRadvPmzRiGQUJCQlk1RwghhBCnkWL3wKSlpbFr166C73fv3s3mzZuJi4ujTp06gNVFNHv2bJ5//vki569bt47169fTuXNnIiMjWbduHXfddRc33HADsbGxpXgqQgghhDhTFDuA+fnnn+ncuXPB9xMmTABg2LBhzJgxA4BZs2ahtWbIkCFFzg8ODmbWrFk89thjZGdnU79+fe66666CeoQQQgghvCnVJN6K4s9JQEIIIYQoH/68f8teSEIIIYSodCSAEUIIIUSlIwGMEEIIISodCWCEEEIIUelIACOEEEKISkcCGCGEEEJUOhLACCGEEKLSkQBGCCGEEJWOBDBCCCGEqHQkgBFCCCFEpSMBjBBCCCEqHQlghBBCCFHpSAAjhBBCiEpHAhghhBBCVDoSwAghhBCi0gmo6AYIIfzrn53/8dfmPQQGB9LysqZExIRXdJOEEMLvJIAR4jRxYPchnhvxBltWbi94LDAkkKvH9GTk1OsICJQ/dyHE6UPe0YQ4DRw7cJw7OjxE8pEUp8dzs3L54sVFHDtwnAc+uaOCWieEEP4nc2CEOA3Mfm4ByUdSMB1mkWNaa5Z/+gM7N+yqgJYJIUTZkB4YrDf4Lau288173/PvH/uJio+k85BL6HRNe4JCgiq6eUJ4pLVm8fRlLoOXfLYAg+8+WMF5bRqWY8uEEKLsnPEBjMPh4IWb3+K7D1ZgCzBw2E0MQ7Fh8WZmPf0lz37/KHHVYyu6mRVi7/Z/WP7pGlKOpVKtXgJdh3YkvsaZ+Vqcyhx2B+lJGR7LmA6TxINJ5dMgIYQoB2d8ADP7uYV89+EKABx26xOsaWoA/v3zAFOueYGXVj9eUc2rELk5ubxw81ss/XgVtgADlMJ0mLz/4ExuemIIgyf2regmikJsATbCY8I8BjGGzSCuekz5NUoIIcrYGT0Hxp5r54sXF4F2fdy0m2xb8zt/bvq7fBtWwV6//X2+n7kasII6R64DbWpMh8n/Jn3C4veXVXALRWFKKXre1AXD5v7P2WE36T68czm2SgghytYZHcDs2/EfSYeTPZYxbAYbl2wppxZVvKP7E/nmf8vQppuoDvhoymxM0/18C1H+rrnnKqKrRmEEFP2TVkrRecglnHdhgwpomRBClI0zOoDxNOkxn1LWHIMzxY8LN3oNTg7vO8pfm/eUT4OET+JrxPLymidofkljp8cDQwIZcNeV3DdjbAW1TAghysYZPQem1nk1CY0MITM1y20Zh92k6cXnlWOrKlZmWhaGYXgN7jLT3L9momLUqF+N55Y9VpCJNygkiJaXNSE8WjLxCiFOP2d0ABMSFkzvW7rxxUuLXA6ZGDaDs86pTsvLmlZA6ypG3Sa1vAYvylDUOrdGObVIFFft886i9nlnVXQzhBCiTJ3RQ0gAwx+/lmaXNAKsG3M+w2YQGRvOo1/ci1LK3emnndbdW1ClVjyG4fo5GzaDi69uc8YuLRdCCHFqOOMDmODQYKZ99zB3vXMbDVvVJzwmjGr1qnLdA/15Z8vz1G1cq6KbWK5sNhv3fzgeW6CtyKoWI8AgpmoUY14cXjGNE0IIIfIorbX75SanqJSUFKKjo0lOTiYqKqqim3Na+nPT33z8+BzWLfwZbWqCQgLpNrQTNzwykCpnxVd084QQQlRC/rx/SwAjPMpIzSQ9OYPoKpGyrYIQQohS8ef9+4yexCu8C4sMJSwytKKbIYQQQjg54+fACCGEEKLyKXYAs2rVKvr06UPNmjVRSjFv3jyn48OHD0cp5fTVs2dPpzKJiYlcf/31REVFERMTw8iRI0lLSyvVExFCCCHEmaPYAUx6ejotW7bk9ddfd1umZ8+eHDhwoODr008/dTp+/fXXs23bNpYsWcKiRYtYtWoVt956a/FbL4QQQogzUrHnwPTq1YtevXp5LBMcHEz16tVdHtuxYweLFy9mw4YNXHjhhQC8+uqrXHHFFTz33HPUrFmzuE0SQgghxBmmTObArFixgoSEBM477zxGjx7NsWPHCo6tW7eOmJiYguAFoGvXrhiGwfr1613Wl52dTUpKitOXEEIIIc5cfg9gevbsyYcffsj333/PtGnTWLlyJb169cLhsDZEPHjwIAkJCU7nBAQEEBcXx8GDB13WOXXqVKKjowu+ateu7e9mCyGEEKIS8fsy6sGDBxf8v3nz5rRo0YIGDRqwYsUKLr/88hLVOWnSJCZMmFDwfUpKigQxQgghxBmszJdRn3322VSpUoVdu3YBUL16dQ4fPuxUxm63k5iY6HbeTHBwMFFRUU5fQgghhDhzlXkA8++//3Ls2DFq1LB2L27fvj1JSUls3LixoMyyZcswTZN27dqVdXOEEEIIcRoo9hBSWlpaQW8KwO7du9m8eTNxcXHExcUxefJkBgwYQPXq1fnrr7+47777aNiwIT169ACgcePG9OzZk1tuuYW33nqL3Nxcxo0bx+DBg2UFkhBCCCF8Uuy9kFasWEHnzp2LPD5s2DDefPNN+vbtyy+//EJSUhI1a9ake/fuPP7441SrVq2gbGJiIuPGjWPhwoUYhsGAAQN45ZVXiIiI8KkNsheSEEIIUfnIZo4SwAghhBCVjj/v37IXkhBCCCEqHQlghBBCCFHpSAAjhBBCiEpHAhghhBBCVDoSwAghhBCi0pEARgghhBCVjgQwQgghhKh0JIARQgghRKUjAYwQQgghKh0JYIQQQghR6UgAI4QQQohKRwIYIYQQQlQ6EsAIIYQQotKRAEYIIYQQlY4EMEIIIYSodAIqugFCAKQeT+PHRRtJT87grHNqcEHX5thstopulhBCiFOUBDCiQpmmyQePfMbs5xeQm21HKYXWmqq147nnf2O4oGuLim6iEEKIU5AMIYkK9d7Ej5n51Fxys+0AaK0BOPpfIg9c8RTb1+2syOYJIYQ4RUkPjCg1rTXb1u5k9ZwfyUzLonajs+h2Y0diqkZ7PO/of8eY8+Ii13WaGq000x+axbPfP1oWzRZCCFGJSQAjSiUtKZ3H+j/Dryu2Ywuw5qyYpsn7D3zC+Ndv4YqbL3d77orP1nqs23SYbF7+G4kHjxNXPdav7RZCCFG5yRCSKJUp1zzP1tW/A+CwO3DYHWhTY8918OKtb7H+q41uz00+moph8/4rmHw01W/tFUIIcXqQAEaU2M4Nu/jl+62YDtPlcWUoPnniC7fnJ9Spgml3fW7hOuJrSO+LEEIIZxLAiBL74cufCoaNXNGmZsf6P0k6kuzy+GXXXkxAkPvzDZtBh75tiYqPLHVbhRBCnF4kgBEllp2RjVK+lMtx+XhkbAS3PnOjy2OGzSA0IoSRT11XmiYKIYQ4TUkAI0qsXrM62O0Oj2XCokKJrR7j9njf8b24d/pYqtSKd3q8RccmvLz2SWqdW9MfTRVCCHGaUTo/8UYlkpKSQnR0NMnJyURFRVV0c85YmWmZXFvzVjLTs8DFb5FhM+h/R29GPee6l6Uwh8PBHz//TUZKBjUbVKfG2dXKoMVCCCEqkj/v39IDI0osNCKU+z4Yh2EYRVYTGTaDuk1qccMjA32qy2az0bjdObTu1lKCFyGEEF5JACNK5ZJ+7Xh+xWRad28JefNhImPDGXTv1by4+nHCo8IqtoFCCCFOSzKEJPwmMz2L7IxsIuMiZCNGIYQQRfjz/i2ZeIXfhIaHEBoeUtHNEEIIcQaQISQhhBBCVDoSwAghhBCi0pEARgghhBCVTrEDmFWrVtGnTx9q1qyJUop58+YVHMvNzWXixIk0b96c8PBwatasyY033sj+/fud6qhXrx5KKaevp59+utRPRghR9jLTszh+KAmHlySGQghRloo9iTc9PZ2WLVsyYsQI+vfv73QsIyODTZs28fDDD9OyZUuOHz/OHXfcwVVXXcXPP//sVHbKlCnccsstBd9HRsp+N0Kcynas/5NPnpjDT9/8gjY1YVGh9L6lK0Me6E9kbERFN08IcYYpdgDTq1cvevXq5fJYdHQ0S5YscXrstddeo23btuzbt486deoUPB4ZGUn16tWLe3khRAVY//UmHu07Da2tTToBMlIy+eKlr1i38GdeXvOkbLophChXZT4HJjk5GaUUMTExTo8//fTTxMfH06pVK5599lnsdrvbOrKzs0lJSXH6EkKUj5ysHJ4e+gqmQ2M6TKdjpsNk/1+HmPHwrApqnRDiTFWmAUxWVhYTJ05kyJAhTglrbr/9dmbNmsXy5csZNWoUTz31FPfdd5/beqZOnUp0dHTBV+3atcuy2UKIQlZ/sZ604+m4y3lpOky++2CFtSeWEEKUkzJLZJebm8ugQYPQWvPmm286HZswYULB/1u0aEFQUBCjRo1i6tSpBAcHF6lr0qRJTuekpKRIECNEOdmz7R9sgTYcue4n7WZn5nB431HqNq5Vji0TQpzJyiSAyQ9e9u7dy7Jly7ymC27Xrh12u509e/Zw3nnnFTkeHBzsMrARlVdKYirrF20iPTmDWufVpNXlzWT7gVNUSHhwwbwXj+XC5G9UCFF+/B7A5Acvf/75J8uXLyc+Pt7rOZs3b8YwDBISEvzdHHGKcTgcTH/wU7546SvsOXaUUmitqVo7nnveH8sFlzev6CaKk3To29bjHBelFPWb1yGhTpVybJUQ4kxX7AAmLS2NXbt2FXy/e/duNm/eTFxcHDVq1GDgwIFs2rSJRYsW4XA4OHjwIABxcXEEBQWxbt061q9fT+fOnYmMjGTdunXcdddd3HDDDcTGxvrvmYlT0tv3fMiXr3wNeR/o8+dVHP0vkQeveJLnV06hyUXnVmALz2z/7PyPf37fT0hECM0vbURgUCD1mtamfZ8LWf/1piKTeMH6Gd7w8ECUUhXQYiHEmarYu1GvWLGCzp07F3l82LBhPPbYY9SvX9/lecuXL+eyyy5j06ZNjBkzht9//53s7Gzq16/P0KFDmTBhgs/DRLIbdeV05N9jXF93tNvJoIbN4PzOTZn23SPl3DKxd8e/vHzbO2xdvaPgsci4CK5/cAD97+xNVnoWjw96gQ2LN2MLsIb6TNPEMBSjX7yJq8f2rKimCyEqEX/ev4sdwJwKJICpnGY/t4D37v8Y08t8is8PvkdsQnQ5tcq9xIPH+ea9ZaxbuIGcrFyaXHQufcb0oEHLehXdNL/a/9dBxrSZSGZqlsselusfGsDwKYMB2LlhFys+W0tGSgZnnVODbsMuOyV+VkKIysGf9+8yW4UkxMmSjqRg2AxM03MK+pRjqRV+U/xtze880OtJsjOyCwKufTv+5at3l3Lrszdyzd19KrR9/vThY5+7DV4AZj41lytHdaPKWfGc16Yh57VpWM4tFEKIomQzR1FuqtWtisPu+iaZz7AZxNeo2LlQaUnpPHTlVLIKBS9AQdvfufdDNi3dUlHN86vM9CxWfLbWbfAC1iTdpR+vLsdWCSGEdxLAiHJz2eCLsQW6Xypt2AwuHdCOiJjwcmxVUUs+XElGSqbbpcOGzWD2CwvLuVVlI+VoqtdNGQ1DcfTfY+XUIiGE8I0EMKLcRMVFcsvTN7g8ZtgMQiNDGP74kHJuVVGbl/9GwTIpF0yHyeZlv5Vfg8pQZFwEhuF59ZBpamKrxZRPg4QQwkcSwIhy1f/O3tzz/hiq1nbOD9Tysqa8svYpap1To4JadoI2Nd6mtlfCue8uhUWGcnHfthg2928FpmnS5fpLyqU9qcfT+GjKbK6rcxs9gwdz7Vm3MP2hT0k6klwu1xdCVB4yifcUlJOVw8rP17H+643kZttpeH59et3chSpneU8KWBn0GN6ZrkM78ufGv8lIyaRmw+pUr3fqJDFs2qERP3610eMQUtOLi2aMLk9aa376ehOL3lnCPzv+IyI2nC5DLqXHTZcRHl28IbgbHxvEhsWbyc3OLToXRkHfcb2oUb+a/xrvRuLB49x5ycMc2nO4YO5R4oEkZk2bx7czlvPSD0+cUr8nQoiKJcuoTzH//rGf+7pN4cg/xzAMhWlqDEOhDMWEd0fTfdhlFd3E017y0RSuqzua3Kxctz0tk7+8j4uvblPOLbM4HA6mDX2V5bPWWKu68oIOpRTxZ8XxworJ1Di7eAHHzg27eGb4a+zb8V/BY0EhgQyc0IcbJw8ql20eHuk7jZ++3uRyorctwKDJxefxwoopZd4OIUTZkTwwp2kAk5Ody/Bzx3Ns/3GXq0KUUrywcjLNLmlcAa07s/z0zS882u8ZTNPEzLuhGgEGpt1k8MS+jJx6fYW1bda0efzvgU9cTtOxBRjUbnQW7/z6fLEz42qt2bH+T/Zt/5fQyFAu7NGS8KgwP7Xas8P/HOWGemO8Ds2999sL1G1S8o1c7bl21szbwK8rtoHWNLu0MZf0b0dQcGCJ6xRC+E7ywJymVs1ex5F/3K/2MGyKz59bIAFMOWjbqxXvbn2BBa8vZs38DeRmW4nsrhrbs0L3a3LYHXzx4iK3c4wddpM9v/3DlpXbaXlZ02LVrZSiyUXnVshWDrs27fZpXtHODX+VOIDZvXUvD/SeytF/jxVkE1741nfEVovmiUWTOLd1gxLVK4SoGBLAnELWf73JaUjgZA67yU9fb0JrLfvOlINa59RgzEs3Mealmyq6KQX+23WQpMOeJ7TaAgx+XbGt2AFMRQoI8u2tyNdyJ0s5lsq9l08m9Xg6gNPS8eSjqdzXbQr/2/ZShecgEkL4TlYhnULsOXa3E0fzOewmpuk5GZw4ffk64lvZRoabdjiPoBDPwzi2AINWlzcn+WgK//6xn/SUDJ/r//q970lJTHP54cB0mGSmZPLV20uK3W4hRMWRAOYU0rBVffDQsaKUol6z2uUyoVKcms5qWJ3oKpEeyzjsJs07NimnFvlHeFQYV43p6bZnURmKNj1bMW3oKwxMGMlNje5gQJURPD30FQ7sPuS1/lVz1nn8cGCamhWfry1x+4UQ5U8CmFNIr5FdsHnIx6G1pt/tvcuxReJUExAYQL/be7u90Rs2axJvqy7NyrllpTfiqSFc0r8tYPW2FP73nAvOZsPizfxSKIGgw+5g+WdrGNd2Evv/Ouix7sy0LK/Xz/KhjBDi1CEBzCkkrnos984YhzJUwRs3WJ8+AToNak/PEZ0rqnniFDH4/r5c3Ndawl04AZ0yFDFVo5gyf2KlnCMVGBTIw5/fzQsrp9D1hk5c0LU5Xa67lKmLH+LY/kRrRdhJQ0Cm3SQtKZ3X75zuse4GLeo6/U2dzLAZ1G9Z1y/PQwhRPmQS7ymmy5BLqF4/gTnPL2Tdwp9x2B3Ub1aHfrdfQbdhnTAMiTnPdLYAG4/Mvpu18zew8K3v+Of3/UTEhHH59R3pdXMXouI8DzGdypRSNL+0Mc0vPbHSbv3Xmzi2/7jbc0yHNbn9yL/HqFrLdbLHPqN7sHL2Oo91XDW6R8kbLoQodxLAnIKaXHQuj8y+G0BWHAmXDMPgkn7tuKRfu4puShHpKRl8//Fqflm2FdNh0qT9efQc0ZnoKiXL+fDfHwc8rs4DQMP+XQfdBjAtOjWh7/hezHv1G5TixFYRyjq358gutO3VqkTtE0JUDAlgTnESvIjK5Pef/uSBXk+RmpSGQqHRrFv4Mx8+9hmPzL6bdr1bF7vO0MgQz8FLnrCoULfHlFKMeekmGrSsx+znFxRkHD6rYQ0G3HUlvW/tKn9rQlQykolXCOEXyUdTGHbOeDJTs4os9VcKbIEBvL35Oeo0OqtY9R4/nMyQWqOccrecrFrdqnz412s+DbFqrUlNTENrTVR8ZKkCF20mQe7voGwQ2Ayl3AdRQgj/3r9lQoUQwi+++d8yMlIzXeYp0hq0aTLv1W+KXW9sQjRXj3O/xBpg2ORrfZ4fppQiKj6S6CpRJQ5etJmGmfwg+nAH9PEb0YnXow9fjJn6PFrnlqhOIUTxSAAjhPCLdQt/9phrxWE3WbdgQ4nqvvWZofQe1RWUtWIoINCGMhQBgTZGvzicbjd2Kmmzi03rLHTiUMj8AigUrOh0SH8HnXRnpUskKERlJHNghBB+kZvtvefBlzKu2AJs3PHGrQy692pWfraWlGOpJNStSpchlxAVX86rrjLmgH07rjek0pC9BHLWQnCH8m2XEGcYCWCEEH7RqO05/P3rHhx21xNuDZvBeW0bluoaNepXY/D9/UpVR2npzM+8lLChM2ajJIARokzJEJIQwi/6jO7uNngBK9fK1WN7lWOLyohjP263A7cKgGNfebVGVFJa29G5W9E5G63J4KLYJIARQvgkOzObX5ZtZX1e0riT1W9Wh9EvDAecMwTn/3/AXVfSpuf55dHUsmV427HaAKNKuTRFVD5aa3T6dPSRS9HHBqATh1gTwJMmok33CRtFUTKEJITwyOFwMPPJucx5YSEZKZmAtZLnoj6tuf31m6ly1onkcf3v7E29ZrWZ/cJCfvl+K9rUNL7oHPrf0ZtLB1x0WuRaUaH90WmvAO56m0xUaN9ybJGoTHTqVMiYcdKjdshagM79FeJnowzP87q04wCYR8BIQNmql1lbT3WSB0YI4dGLo97i6/e+LzJqYtgM4mvG8sbP04ipGl3kvPy3ltMhaClMm8fRR68C8yhwcm4aGwQ0QsV/jlKBFdE8cQrTuX+gj13poYSBihiLihjv+vycX9Gpz0LuTyceDLoIFXkvKrC5fxtbRiQPjBCiXPz16x6+frdo8ALWnJZj+4/zxQuLXJ6rlCpR8HJg9yE+mjKbl0a9zYxHZvHvH/uLXYfWmpWz13FXx4e5Mvx6ro65kak3vMwfG/8qdl0nU0YsKu5TCMzf8bvQcwzuiIqbIcFLJaZ1JtpMQmvv2Z+LXXfmHMDmoYSJzpjl+tycDejE6yD3Z+cDOT+hjw1B5/zit3ZWFtIDI4Rw6407p7PgjW89ZsGNjItg7lHPu0GfTGvNpqVbWPDGt/y58W+Cw4K4pH870hLTWfTuEgzDQBkKbWpMh8mVt3Vn3KsjsNk8vfmfqPvlMe/y1dtLnPZQsgUYaA0PfHIHnQZdXKz2ur1W7jbI+QVUAAS1RwXIjtaVlc7ZgE57w1oCjwYjHhV2PYSP9FuGZfP4GMhe6rWcqrYDpU78rmut0Ud75E0OdxVYGRDQEBW/8JTv8fTn/VvmwAgh3Dr63zGv+xClJqaRm5NLYJBvvQ5aa968awZfvvK1U4Dx2bT5BcNOpsN0Gp356u3viIgJZ+RT13mtf+Xna/nq7SUn6snjsJugYOrQV2h6SSOq1Izzqb2eqMCmENi01PWIiqUzF6GT78YalMj7TG8eQ6e9BtkrIe5D/wQxRgxWD4z7DwSoMKfgBYDczeDY46FiE+x/gH1boZ7B058MIQlxhsvJzuXHRRtZPH05m5ZuweE48eYaUzXaaUWRK6GRIQQE+v5Z6PtPVvPlK18DzgGGp85grWHuS4tIT073Wv/cl7/CMNx8CtWgHSbfvPe9z+0tL1o70FnfYx4fjXn0KszEkdaNVedUdNNOa9pMRiffjxW4nBxYmJC7FdLf88u1VEgfF9cozAYhfYs+7OuyfMc/JWhV5SU9MEKcwb5+dynv3f8xqcdPBAZVzorj9jduoX2fC7n8ho4sfOs7t+cbAQY9hnUuVrf1nBcWFgwPFUdOVi4bl2yh48D2Hsv9sfFvTA91m6Zm54Zdxbr2v3/sZ9HbS9i9dR8h4cF06NuWy669mKCQoGLV447WOejjoyFnNSc+of+BzlkN6c0hbjrKkOHyMpE5D6ctIYow0RkzIXwsSpXyM3/QRdZXzk8UHQqygQpFhY8oep7y8Wfva7nThAQwp7jD/xzlq7eXsHHpFtCalp2acuVt3alxdrWKbpqo5Ba+9R2vjHm3yOPH9ifyaN9neGLRJNr0PJ+Lr27jcp8jw2YQHhnGNff08fmaOVk5/LV5T4nbnJmW5bWMYTNw5Lr/lKuUKlaP0eznFvDOxI+w2QwcdhNlKNbO38CHkz/n2aWP+uVvUac+Czlr8r7Lb3veDc6+HZ38ACr2tVJfRxSl7Tuxgka7+0LmMdDJoLzlAPJMKQUxb6CTJ0F2/gcDBZhgq4WKeRkVUKfoicEXg4oEneqh8lgIalOq9lU2xQ4nV61aRZ8+fahZsyZKKebNm+d0XGvNI488Qo0aNQgNDaVr1678+eefTmUSExO5/vrriYqKIiYmhpEjR5KWllaqJ3I6Wv/VRoafO55Z0+ax86dd7NzwF3NeXMRNjW5nxWdrvFcghBvZmdm8d/8nLo/lj+S8dfcHADz46Z30HNEFW4Dz28XZLery4g+Pk1Cnqu8XLuUEw3rNXLy5n6Rtz1ZF2lqYRtOm5/ns3fEvHz8+h7fv+ZCv3llCekpGkbJr52/gnfs+Ak1BluH8QO7ov8eY1OtJpyG3ktBmKmTMwn1eGQdkL0Hbz6zhgXLj89yWYP9czojAiH0VVWUJKvIhawl07AeoKt+hApu4PkcFoyJu91xv5J0o5Z8ewcqi2D0w6enptGzZkhEjRtC/f/8ix5955hleeeUVPvjgA+rXr8/DDz9Mjx492L59OyEhIQBcf/31HDhwgCVLlpCbm8tNN93ErbfeysyZM0v/jE4TB/ccZvLA57Dn2Ck8NSB/zsDUG16hfvM61G1Su4JaKCqzn77+hQwXN+x8Wmv++f0//tq8h4at6jPhndu46fHB/Pzdr+Rm5dKgVX3Ou7BBsa8bFBzIuRc2YNcmz8M8JzNsBvWa1ebc1md7LTvw7j6smf+Ty2OGzSAiNpyfv93MS7e9g2EzMAyF3e7gzbtmcOfbo+h6Q8eC8rOemYdhKJdtddhN/vvzAD99/Qvt+1zo83MpIncLkO2lkLaGHQK8/73r3C3orOVADiqgMYR0R6kgK/lZ7jbABkGtZUgqjwruhs74yEMJw8q1YoT597oBdSBgqO8nhN2I0jnotJexhrzyhxoDUZF3o8KG+LV9lUGxA5hevXrRq5fr/Uy01rz00ks89NBDXH311QB8+OGHVKtWjXnz5jF48GB27NjB4sWL2bBhAxdeaP3Rv/rqq1xxxRU899xz1KxZsxRP5/Sx6K3vcNhN3M1rVArmvfoNd7x5a/k2TJwWjh9KRinc/n6dKJdU8P/YajF0G9qp1NcedM9VPDH4RZ/LGwEGwaFB3Dt9rE9zbZp1aMTd747mxVvfAqUwHab1XIGImHDOueBs1s7fAFgfCMy8DpTszBymDXuV6CqRtOnZiozUTHas+8PjtWwBNjZ8U8oAxuOkTt/LaTMRfXw85G7AurkpNHZIiUYHNIDcXziR0CcIHTbE+vR/hn1qLyKoHQS0sFbwuHyNNSr8tvJuVRFKKYi4BcIGQda3BZl4CenpNXPv6cqvq5B2797NwYMH6dq1a8Fj0dHRtGvXjnXr1gGwbt06YmJiCoIXgK5du2IYBuvXr3dZb3Z2NikpKU5fp7ufv93scfmqw27y0+IzL3GR8I8qZ8V5DV4AqtSK916omDpe075gR+nCQz3KUIRFhXJRn9YEBFmfrWyBNjpf24HXf3qahufX9/kaPUd04YNdrzHonqs4v0szLuzZinGvjOSpbx5k43e/uu39UUrx4WOfA2DP9TAnopDcHN/KuRXYDJ8+SwZd4PaQ1g504s2QuynvEQcFczp0ct7jhZ9zDmR8iE663Wn1l9ba6sHJXITO/uGMWAGllELFvQMB+cvhA8gPACEQFT0NFXxRxTXwJMqIRoUNsjL2hl1zxgYv4OdJvAcPHgSgWjXnSW3VqlUrOHbw4EESEhKcGxEQQFxcXEGZk02dOpXJkyf7s6mnPIeX3BuA1/wcQgBsXb2DuS9/xa8rtqGUotXlzbhqTA8i4yJITXQ990wZirOb16G+D3NOikspxcinrqNd7wtY8Ma37Nr0N8FhwVzSvx1X3NKV2IRocrJySD2eTkRMGMGhJZt7UL1eAiOnXu/02CdPfuGUe+Zk2tT8/tMujv53jPiacSTUrcrhvUfcXsPhcHBem4Ylal8+ZcShQ66ErIW47gGwQeCFqAAP18lZDfbfinllDdnLIGcdBF9s7Yqc/Ag4Cs1ZVDEQeYeV0O00pow4iJ8NOT+is5eAzrJe79B+KK+bd4qKUilWIU2aNIkJEyYUfJ+SkkLt2qf33I8WHZuw7/f/MO2u32htAQYtO0kCLeHZFy8u4q27P8AWYBRMQv1h7npWfr6O7sMu47sPVhQ5RxkKw1CMfummMm1bsw6NaNahkctjQSFBxNfw/9BGZmqmNafFy6hNRmoWVZSi/+1X8PY9H7jsrVKGIiQsmMuvv6TU7VJRD6Ptf4B9O9Yn//wLKrDVQMU86/F8nfkNXhOkuWRDZ34BKhydeGPR83USOmWydUMPH1nMuisXpRQEt0cFe16mL04dfh1Cql7d2hXz0KFDTo8fOnSo4Fj16tU5fPiw03G73U5iYmJBmZMFBwcTFRXl9HW6u2pMD7TDfR+/w25y9TjXc5GEANi5YVfBSiJHoUA4///ffbiCW6bdQEKdKk7n1Wl0FtO+e+S0DJBrnVsTu4cl1gCBwQFUrWVl6e07vhcXXWkNd6tCyfFsAQa2ABsPz76b0IjSZ2hVRiQqfhYq6jEIaGz1fNgaoCLvQ8XP977jsE7H/SomTxzgOGAt48bhtg6d+qK1WkqIU4hfe2Dq169P9erV+f777zn//PMBq7dk/fr1jB49GoD27duTlJTExo0bad26NQDLli3DNE3atWvnz+ZUanWb1Oaud0bx4q1vY9hUwU0n/5P06BeG07jdORXcSnEqm/faN049Lyez2Qz27zrIR3+/zva1O0k6kkJCnSqcc8HZp/x+KiXVaVB7Xr/jfbIysl1uUGkEGHS9oWNBUGILsPHo3HtY8sFK5r++mL3b/yEoJIhLB1zEgLuupF7TEz3B2r4X7L+DCoGgtsVOPa9UCIRdhwrzvl1CEQH1INugJD0wqCjIWealXC5kLYawa4rfNiHKSLEDmLS0NHbtOpHFcvfu3WzevJm4uDjq1KnDnXfeyRNPPME555xTsIy6Zs2a9O3bF4DGjRvTs2dPbrnlFt566y1yc3MZN24cgwcPlhVIJ+k18nIanF+PL1/5mo3f/YrW0LJzU/qNv4KmF59X0c0Tp7itq3a4DV7A6onZsmo7hmHQ7JLG5diykvtz098sm/kDKYmpVKtTle7DL6N6vQTvJ+YJjQjl7vdG89R1L6MMhWmeeH2MAIOqZ8Vz0xPOy1FtNhs9R3Sh54guLuvU9n3olIcg58cTD6pwCB/hn+ytPlChg9DpRZMSeufIywzrLYCxgXnYSxkhylexd6NesWIFnTt3LvL4sGHDmDFjBlprHn30Ud555x2SkpK45JJLeOONNzj33HMLyiYmJjJu3DgWLlyIYRgMGDCAV155hYiICJ/aILtRC3/Iyc5l/VebOPrvMWISormoT2tCw0Mqull+c+M54zjw1yGPZWo2rMYHf5z6GV5zsnOZNvQVVs35EVuADdBoDaZpcv2DAxg2+dpi9RptWrqFDyd/zrY1OwEIDAmk+9BODJtyLbHVYnyuRzsOoo/1AzMJl70foUMwostnAYJOexOd9iLOc2g8sfKbEPUkHC36nu5MoaIeR4UNKn1DxRnNn/fvYgcwpwIJYERpLfloJW/cMZ20pPSCfXlCI0K46Ykh9Lv9iopunl+8fvv7zHv9G4/3ssDgAGbsfKV42XQrwPM3v8m3M5a73T9p/Gs3c9WYHsWuN/HgcTJSs4ivGVui4NVMmZyXRdfD1gVVvva8gsiPdOYCdNqb4Pgr7+IRENIHcneBfUOhkjYIuRoV/ShKhWIeux5yN+J+Hk0QKmGtJL8TpebP+3elWIUkhD+t+GwNzww70euQf1PMTMvijTunowxF39NggnS1elW9fhB32E2+ePErRr843OVxrTUH/j5EenIG1epVJSqu/HNOHP3vmMfgBazl0b1v7ZrXO+O7uOqxxLmYH/vPzv84uPswkXERnHthAwyj6DCQ1g7InIu33YV15peoyHu9tkWbSaAzwYgvcXI5FXqVFbCYB0Bng60mSlnL0LX9L2tnZQIgqB3KdiJoVZH3oRPz594UDWJU5J0SvIhTjgQw4oximibvTvzYY5npD82i18guJc4/UtEy0zJZM+8nZjwyy2tZ02Gy5KOVLgOYtfM3MOORWezeug+wJpB3vKY9Nz99Awm1qxQpX1bWf7UJbx3FiQeOs+uX3aXOyfLHxr94bfz/2PHjiVwo1esncPPU6+k06GLnwjrT+vJIg8PzMJ7OXoNOex1yf7YeUBHo0LxEZSVIUqaUAlvR+YQqoAEEuN7+QQW1hLgZ6OSHwfF3oQPR1h48YTcUux3eaPsudOZccBwAIw4VcpXVDiF8JAGMOKP8vv5PDu876rFMRkoGP3/7Kx36ti2nVvmH1po5zy/kw8mfk5XubW+dE9KTi+6JtHj6cp4f+YbTvBKH3WTl5+v4dcV2Xv9pKlXO8n+WXleyM3IwlML0EsRkZfj+nF3Z9ctu7ur4CPaTMuse3H2YJwa/SFZGNj2GF5orokKxNvjzdF0FhvtgT2fORyffZ5UreDANMmagc1ZD3Kxyy7SqgtpAlW+svZkc+8CIgqD2ft9qQGsTnToVMj7Ayl2jAYXO+Agd3B0V84JsbyB8UvbT44U4hSQd8W0bimQfy51KPn92Ae/c91GxgheAsKhQfvrml4IVOekpGbw27j2AIj0fpsMk6XAy7z/4qX8a7YN6zWp73fhRGYra55VuFeObE2Zgz7G7zdL7xp3Tyc488doqZYPQvlg3YXccqNC+Lo9oMwWd/BDWDfzka5pg/wud9obvT8APlFKooJao0D6o4E5lE0ikv5cXvMCJ3DP5G1ItQadM8f81xWlJAhhxRqlW17fJqtXqndqTWk+WnpLBh499VrJzkzN4sPdT3NhgHDt//osVs9aQneV+DxzTYbJ81hoyUr0Nn/jH+V2aUb1+gst5KGANbV3Srx1x1Uue8v3gnsNsWbnd4/YcGSmZrJ3/s9NjKnyUtWTaZRCjIKQvKtB1tmEy5wOe9hoyIfMztM711vxTgtam16E+rXO8LPfWkPkF2uF++wYh8kkAI84oZ7eoS4OWdZ2yqhamlLXR4fldmpVzy0pn7fwN5GSV7EaXPzn2yL/HuPfyx/jj578I8DIZ1p5j59j+xBJdr7gMw+CBmXcSGBzgtPkjgGEziK0ey5iXhpfqGkf/Pea9HTajyPCjCqiFip8FAU1OKh0EYTehop9yW592/IXn3hus4STTe9sqitZ2dMYszCO90YcaoQ81wzw+Dp3zq+sTcn+1Npf0yAHZq/3eVnH6kQBGnFGUUox7dSQ2m4FxUhBjzfdQjH/9Zmy24q1mqWjJR1IwbKX7czYdJtmZOez+bZ/XIRuA8OiwUl2vOBq3O4fXNzxNp0EXYwu0fjahESFcPbYnb2x4mvSUTJ6/+U2uihpKj4BB3NT4Dua9+g052b4FddFVva+wMU3TZTkV0BCjyheo+Hmo6GdQMa+gEtZiRN2PUh6mGapwfMnXonXp5vaUFa3t1m7WKY+CIz+5aS5kf49OvBad+bWLk7J8rP3UfM7i1CJ5YMQZadvanbx51wx2bjiRVbpes9rc+uyNtOlxfsU1rIRWzl7HE9e+4Je6QiNDyfQwPGQYiiYXn8eLqx73y/WKKzcnl8y0LMKjwrAF2Ph1xTYmXfEkpt1RkHnYCkY1LTo15amvHyAoxPtcjtsuuJe/t+x1u1w7MCSQz/e/S0RMuF+eh875FZ3oQ2p+ozoq/lOU7Sy/XNdfdMYnefNVXL1eCghAJay2dnrOP8dxEH2kk5tzCp0dNwsVdIE/mytOEf68f0sPjDgjNb34PF5bP5X/bX+Jad89zDu/Psc7vz5fKYMXgPZ9WhMe47lHJCQ8mLNb1vFaV3Z6Ft1u7OR6mE1Zt55hk68tYUtLLzAokKi4SGwBNnKycpg88DnsOXanbRO0tjL1blm1nVlPz/Op3lufGQpYw4iu3PDQQL8FLwAEtoCg9ngdRjKPoJPu8d91/USnf+DpKGCHjC+cHlW26hB8Oe6fsw1s50BgKz+1UpzOJIARZ7Q6jc7igq4tqN+8bqXewDAoJIhxr4z0WCY7K4fDe49iBHj+s0+oU5W73hlF92GXWR+kFSib9doooGqteP7ctJu0pHT/NL4UVs35kdTENLe9JtrULHhjMfZcu8vjhV3QtQWTv7yPmLytBPJ/HYLDghk59XqGTOrnr2bn1a9QMa9BoLeeBgfkbkTn7vTr9UtD6xxw7MFzT4pC27cVfTTqETASKBrE2ECFomKer9R/i+5ox3/ojE/R6R+ic372OuFZeCd5YIQ4TXS9oSOH9hxmxiOuVyNphyYtOcPjPUcZiitv605gUCD3/G8Mg+69moeunMqBvw+hlEJrzeF9R3l34kfMf+0bXlg5uUK3Ifhz498EBNqw57rPhpt8NJVj+4/7tAKtfZ8LadurFRuXbOHg7sNExUfQrvcFBbtT+5syIiFiFPr4Bu+Fc3+FQN83cdU6B7K+Reess64V2BpCe1u7XpeaDe97Limg6NCdslWH+Lno9Pcg8zNrojLBENoXFX4LKsB7L2Flos10a7PPrMJzgjTYGkLMS6jAc92eKzyTAEaICvTfrgN8/c5S/t66l5DwEDr0bUvHgRf5NGfDlYO7D2PYFKbDzY1FW3lfMlKKznExbAZnt6jLVWNP7Cm06K3vOLTH2oW48CdGbWqO/neMJ659kVfWuV9pU9YCggLw5YNsQJDvb3W2ABtte/l/CEPb96EzPoWc9aAMCLoEFTYYr0NI+efrDDDTUIb3TW917g708ZvBPIL1Nq/RmXMgdRrEvo0KKt3zU8qGDroUctbgfisFByrE9SaRyhaPipqIjrzPymasQspl1+7yprVGJ42xfuYnB3uO3db2DVUWoFxkThbenX6/MUJUEl+8uIjh593OnBcX8fO3v7Jm3k9Mu/FVRjS5kwO7Paefd+fQvqPug5c82RnZjHt1JFVqncikGxwaxJWjuvHc8scKNjXMSM3k63eXul2R5LCb7Fj/J39u+tvl8ZLIzcll+aw1vDb+f7x+x/usnb8Bh91970rbK1p5PK4MRf3mdYirHuO3NpaEzlyIPtodMmaA/Tcr2236W+gjXdFmKuBDr0jqU+jDF2AmDkfnuO+x0eZxdOIwMPOXudspCDJ0Cvr4TWjHgVI+I1Dht+B+80cb2GpBcFfPdSiFMsJQyrAm+GZ+jc78Cu04WOr2nRJy1llfLl8nB+h0dPr75d2q04b0wAhRAX5ctJG37rYmQeYnTyucj2VSzyf53/YXi72cO7ZaNIbN8JiQLbpKFFeP7cmVt3Vj347/sOfYqXVujSLDJH9v2Ut2pqdEa1aAsHX1Ds654OxitdOVnT//xcN9pnL8UHLBUul5r35D9foJPPnVA9Rp5LwK57c1v7Pore8ICAookv4/nzY1g+/vV6FzKnTuTnTyvbjMtksuJN8Dof2t4RQfllWTsx6d+CPEvIwKcbEDd8acvFwrruoyQWejMz5BRZZuYrAKbgdRT1nDI2jytwQAE2zVIfJBdOo0K1gyqqBCr4bAVkV+FlZG4och+1tOvEYKHdwDFf04yoguVTsrks5aiNXD5r6XiswvIeqhcmzV6UN6YISoALOmfVkkD00+027y358HWP/VpmLX2/WGjh6DF8Nm0OMmq1vfZrNRv1kdzrngbJdzPHy66Wsfy3lx5N9j3Nd1MslHUwFw5Dpw5M1rObzvKPd2eYz05BOThj949DPuuvRhVn/xo8vgJT8nzvDHB9NlyCWlbl9p6IyPcNrryPkoYAcjslBvRX7Q6u4cB6DRyRPRZtGJ1Dr7WzwHQg7IWuy94T5QYQNQVZejIsZa7Q+5AqKeg8B2kDQaMj6B7KWQ+Tk6cTA6aaxTXhuts9GJN0L2dzgHeNraViDxxlM2D45PzON43q0c0KkyobeEJIARopxlpmexbc1Oj8nibAE2fvr6l2LX3bp7S87v3MxlUjtbgEF01Sj63X6FT3U1OL8eoZGehza01n7JWrzgjW/JSs92GXyZDpPjh5L5dsYKANYu2MDHj88BcFo6nc+wGfQZ3Z33d7zE9Q8OKHXbSi17FZ5vYiZkr0HFvIaK/cAKAgJb4T6AAdCgMyDrKxeHfNjiweeEct4pW3VUxHiM2NcxYl6wVidlzc07agVbJ/Y6+h6d8uSJkzMXgX07rl8fB9h3QOZCv7W13Nlq4nWOk5FwWq66Kg8SwAhRzhweVsycoN0Oi3hiGAZTFkyk8+AORfK4nNO6AS+tfpzYvGXC3oSEBXPV6B5ut10wAgxadm5K/WalXzWy8vO1HnuOtNasmm2tppnzwkKPWYe1qal1Tk1qn3eqJH5z/7wKl1FKoYLbY8Q8j4p+1ofzAtD2XS4eboLnm6YNAtzsz1RKWmdCxnRPJSBzFubhzphH+6LTXvNSo7ImHxenDY6j6LQ3MRNHYR4fay1dNtOKVYe/qNCBeA5ejbyJ3KIkZA6MEOUsPDqMavWqcmiP+w3rHA6T89o0KFH9oeEh3P/R7dz89PVsWroVe46dc9s0oOH59Ytd17Ap17J3+7/8uGhjwdya/OXUZzWswYMz7yxRG0+Wle69RyAjNROtNdt++N3rVgdbVm2j7/hefmlbqQW1yRuycXcjs0FQW+eHlC/bNGiX5VTYEHTWfA/nOVBh1/tQfwnkbALtQ34g8z/ryysNDt8ntOusb9FJEzixy7VCZy+B1Bch7n1UYPF7C7VjP+TuBBUMQRcUaxm6CmyCDh0Cma52b7eBrTaE3VjsNgmLBDCiUjqw+xBfvb2E3374HSPA4MLu59Pr5suJTTj1J/wppeg3/grevudDl2PfSimCw4K4/IaOpbpOlbPirWR0pRAYFMjkeffx46KNfP3e9xzYdZDohCi6De1E5yGXEBIWXKr689VrVofko9vc9sLYAgzqN/exp+cU641XYTeiXQ31FNCosOucz7FVQQe2hNytuO+JcaBCuhe9XtAF6PBbIf0drE72ExNjQUPotRB8WXGfhm+050nfxaesCcG+XDr3d3TSnVjPN//vKu9fnYJOvAmqfo8yfEtfrx0HrX2eslecqEdFQPgICB/j87JvFfUo2Gqi0/8HOinv0QAIuQIV9YDP7RFFSQAjSk1rTeLBJOw5dqqcFYfNy07GpbVs5mqeGf4aWp9YwfPbD7/z6dS5PLFoEi07NS3T6/tD3/G9+HXFNtYt/BllqIIVSLYAA6UUD39+N2GRZZM8rbgMw+Diq9pw8VVtyuwafUb34Jfvt7o97rCbxFaL4Ye562nc/lx2/Pin+yEnDS06njq/AyqoFUTej059GucVKTbAREU9hQoouopLRYxFH7/VTa15eWQCT94FO//cuyGgkZUszr4979EQUIGQ9Q06ew06pCMq7AZUQMPSPcHCAhvhPcFdcWhU6In9orR9D2SvtAKlwMYQdHFBIHFiawN3q69SrBU/4cO8X9VxDH1sUF4enUL16TR02ivgOISK9m0vMKUMiBgF4TdB7ra8tp/jtEeUKBnZzFGUyrKZq5k59Uv2bvsHgOgqkVw1pieD7+9b4mRsnuz6ZTdj2kx0mTpeGYrg0CA+3PWaz/M8PHHYHaxd8DM/fb0Je66dc1s3oNuNnfy2H47D7uDbGSuY//o37Nv+L0EhQVw6oB0D7rqS+s3r+uUalYVpmky78VWWffqD9YCLd6X8oavg0CC3y7uVoQgJD+HTfW8SHu3555RyLJX5ry3m2xnLSTqSTHyNOK64pStX3taN8Cj/77Stczai0z+C3PVYAcilqPChqED3wZbOmGP1AmDHCnjyJsQGdUDFvOo1qZ12HLFW+Tj+cnHUCjRU1FOosIElfl4nM4+P8mHisi8MCGiCip8FOgedfH/eaiUDq+0OMGpA1GRUcEf04fagj3uuMqg9RpynPZzynkPqM5A+3eNzUPELUcXIjCws/rx/SwAjSuyTJ75gxiOzCm4s+ZShaNGxCVMXP0hgUKBfr/nsTa/z/SerXK4+yb/2sMnXlnr1yYG/D3F/j8fZ/9chbAE2K6OmqQkKDeSBmXeWaW/Emco0Tea/tpgvXlrkcX5QYbYAo+B3wbAZBAYH8tTXD9Cio+ueiXyH9x3hjkseJnF/ovN8GgWx1WJ4dukj1G1Su8TPxZ+0eRwy56Ptu8EIR4X0RAW28H6e1ujEwVbSPI/BhELFz3UbSGltQs4adOYiqxfDVhsVeg0q8BzX5R0H0ceuBfMQvk1gttrgHLUaeUMsk0FFoI/fCDkb3NenqublvvEyhBV4AUb8LI9FtNbow22s5+qWDcKGoiLvBscRMMK89qho8zg4DoMRi7IleG7naUwCGAlgKty+3/9jZJM73R5XCsa+MpKrx/b063UHVhtJ8hFPbyzQ7JJGvLjKt+5dV3KychjR5E6O/nusSKCkFBg2G6/9NLVEk2KFd9aQ5HHGtZ3E0f8S3ZYLjQihba9W/LHxb4JCAunQty1X3NrVGsb0kgBwQqdH2L5up9tAGKDXLV0Z/+oIvwfh5UXn/IpOvMZ7QQwI6YsR8zTavgudPgOyvgWdDQENrb2KHHs4MfyV92/YSFTkfS6XAGszEZ0+HTI+y5v34cOwUuh1oEIh4FwIqGdNfM5ZC2Y6mP8W56m7YYOwGzGiJnkspXUO+pAPk32NOmAeBvImoAe2QUWMRwVf5FyffTc69TnI/p6CACywLSryLlRQ62I9A23fg86YCTkbQdlQwZ0gdBDKVnH7kRWXP+/fsoxalMjX7y71uKuxBua//o3fr+tpqW0+Txv7+WLVnB85tOeIy5ubFe5r5jxfiXNTnOKUUhzcfcRj8AKQmZZFl+sv5cNdr/Hgp3dyaN8RbjrvDnoGDmZog7HMfn4hOVlFP5Hv2fYPW1fv8Bi8AHzz7lKeuPbFUj2XCpWzCt/e4k3ImoeZdA/66NWQ+UVeb0aWte2BY09eOYfzvxn/g4yPXdaojDiMyLsxqv2EqrYNIu7z3ozMmVadKQ9A4mDI+BDsO/0UvACYqLAhPpQLtAIpr9X9Q0HwAtaO4ceHo7NOvO9p+y70sYGQvQyn3qPcn9GJN6Czf/C18dZw4tGekPER2LdC7mZ02qvWdhTZ632u53QiAYwokX2//4fp6Qag4b8//b+fSdMO53kMnAybQbMOpctxsXbBBre5T8CaUPrDlz+V6hrCM2+9bIXLbVq6hTFt7mfFrDXkZucC1qaW7078iPu6TSE70zmT6+8/ucid4sba+RvYvXWv7w33kdba6iHJmIXOmIt2HPb/NXJ34PsQjglZC4BcijN3Rae/g9aeyysViAofDiF98h7xNsn/pOR3xZL/d1v4PcK6nop6DBVQz3sNSllbO3ht58k9StbqJ508CW1mWCVSplgJB4s8FxMwrbJeXj/I601LeTDvvMLlTSALnXQr2nHMaz2nGwlgRImERYa4TYWfz19LbAvrN/4KL4GT5srbupXqGtkZOS4nCReWf6MsD6ZpkpKYSlZGJU6pXkwJdar4VC62WjSPX/sCDrujSI+KNjU71v3Bp0996fS4zUMA7MrHT3xRrPLe6Nw/0ceuQideg055BJ1yP/pIR8zkB/yWNl9nLbVS+Jc18xDY//RaTCkbKvo5VMzbEHSptdGjEY//17xrUFEQ2BIrAAmE4E6ouI997H3Ja2/4zdaSaR93Cne6vs6ArK/R9n8h50fcB2Laev1y1nivNeMD3N+utTXclzm7mG2t/CSAESVy6YD2XlLhG3S69mK/X/eCri248dFBAE7ZWPOXH9/9vzGc1bBGqa5xdou6HjO9KkNRt0mtUl3DFzlZOXzy5BcMrjWKAVVG0CfiBu7tOplNHpYbnyq01vyybCuTBz7H0AZjubXF3Xw0ZTaJB72sEsnT4Px61GtW231PmIL4mrEkH00l7Xi624DTNDUL3vwWe669oF2//+T9hlvYvu3+GsIA7fgPnTgEimTQNSFzLjrpjlLvi6O1tuZclFtCnKLDdNq+GzPlKcyjfTCPXm2t6nH8iwrpjBH3DkbVZfkl/d8cIw4j/jNUte0Y1bdhxL6FOjlRoBfKdhYq/lMIOHmVkS8BTQDa8Tc4fOm5U2Df471Y9mq8bUehs1f7cL3TiwQwokQ69G1DncZnufw0axgKW4CNgRP6uDiz9IY+eg3TvnuYtr1aER4TRmRcBJ0GXcyr66eWOnEbwBW3XO7xJqJNTd/xvu0nVFI5WTnc3+MJPnj0M44fTCp4fMvK7UzsPoXF05eX6fVLQ2vNG3dO576uU1i3YAMHdx9m92/7+HjKbEY0uZM/Nrpa0utMKcW4V0diGEaRIEYphUJx++u38PevewkI9HxTSU1M49h+K3Ba+vEqFrz+bbGeT0i4/3oSdfq7eZlqXd2MTGuuRG7x98ByYv8THH9TJsFBEUFgc85hozMXoI/2ypursdPazyh9OvpoD3TWkhMFTc9znErGgJDeQOk3GVUBDTGqzLNWaEU9iYp+DuK+9H4iGqXCrQ06fSjrWzkfhgJzt6PNZB/qOn1IACNKJCAwgGeWPsrZLesB1uaDtrwbSXhsOE998yB1GpXdXjQXdG3B4wvuZ17iB8w9Op1JH9/BeReWLPX+yWrUr8bYl0cAzr08KOtN8eKr29Bj+GXk5uSy8vO1PDP8NZ4c8iKzps0j6Yh/3kDmvvw1v635vUjPgukwQcNLt73N8UNJfrmWvy39aBXzXrUmMhYe1jFNTWZqFg/2nupycu3JWnZqyjNLH6FB3u9Yvlrn1eTxhfdz8dVtCAwOwJcOC6uc5rNp84p9Y7vs2g7FKu+O1tpKpObxk7QNnelpGwBfLlReNzEbhPZzykWjc/9AJ99H0bkaDsBh9TDZ91kP+T2Rmw1UhMuhIm0motPewUy8EfPY9ZipL1hbBPhABTZDhV2DCr0KI6gRBLTA863TASE9IKCZlafGoyAI7lKonenWdggZc9A5P5/4IBXYGu+9PxnopLu9P6HTiCyjFqWitWbLqu389PUv1p47Fzbg0oEXERRcOZeeFrbh2818/sx8Ni//DYAaDarRb/wVXDWmB4f2HmFi98c5uPuwFeRojcYK5CZ+MK5UNz2tNdfVvY2j/7r/hKoMxYgnr2PwxL4lvk5ZubXl3ezZ9o/HeUQTPxxP12JslbD7t30c/fcYsdViaHB+vYIgZOvqHUzo9Ijb85ShOLt5Hd7c9CxJR1IYVP1m358IEBQaxNyj7xMcWvpeGN+W5yoI7o4R+2rJr+PYjz5yWYnP940BAQ1RcTOdUuGbyQ9D5hw87vsUNhwjaiJm6gt52x34OtEYQEHERGvDSPMQJ5LJ28Gohop9u0h2Yp2zAX38lrxduvN/J61keCr6OVRo72JcH3T2yrwMya5+vw0I7ooRa21SqTPn5QV0boSPwoi82wpU0t9Cp7/lvJu4rR4q+inQ2ejjN/nUPlXla/9mV/Yzf96/ZSsBUSpKKVp2alop0vcXV5se59Omx/nkZOfiyLUTEh6CUoqc7Fzu6zaFI/9Ys/4LL+2259p56vqXSahblSYXnVui62alZ3kMXsB63fds21ei+stSRmomu7d6bpctwGDLyu3FCmDqN6vjctfrZpc0olHbhvy56W/Xy95NzZAHBqCU8mkJfmEBgTae+voBvwQvlkBQMYX2w3HFAFvp5nApW0100MWQs56SZ8PNz9uSl/MloBU4/rCGv4zqVi9H2NCimYC9ztVwQM4PwERU+HB05ry8dP2uzimcOyZ/24XJqLDBmGE3QPrrkLnQSjhnxFubItrqOdWgHcfygpcsnAMO63dBJ98NAWejAhv79KoAVu6V6Kno5Eex5v8EUNDjFHw5KubZE2VD+4KZik6dhrXCy1ZwbcKGoyLustqR9iKkv1X0Yo596MThVjbi0MGQ6TkJHxjWVguncADjTxLACOFFUHAgFOpR+uGLH91nitVg2BSzn1vAo3PuKdH1AoMDnfZHckUpRUgJb6wbFv/CFy8uYsuq7aAULS5tzIAJfWjT4/wS1VeRlFJMmT+R+3s8wd9b9lo7ZpsmhmH9e8vTN9DpmvaAtWKpau34gsDTnZDwYLoPu4z+d/Yu9YTwk9uqwwZB+v9wf5N3oEJLl0UaQEU+aCWx01m47uGIANIokpwuqAsEd8zbwDATAhqjwq5FBVjDs1qbXjYx9KFD30zETH4UjAiIeszK95KztlCBIGsIhiDIWWfVGdS+YNsFrXMgeTxkLz/RbkcqpE5GZ3yAjn4Blfsz2kyxJks79bwUeaXQ6R+hYp7y3u7CZ4X2h+BukLXA2p9JReRlSC66tYAKHwqhV1srkxz7rYy9Ib1QtmqAtd2D1RPl8sUC7OjUF1CRd6K9BjAKdPmtkKxoEsAIUUzrFm20bpRuPtE77CY/LrTGr0sykTAgMIC2vVqxYfFmD9dwcHHf4q2sAPjkyS+Y8fAsp/b/suw3Ni7Zwognr2PIpH7FrrOwsMhQ6jev43EIyWE3ad7R90+83sRWi+GNjdNY/9UmVn/xI5lpWdQ+7yyuuPlyapxdraCcYRj0u7037973oct5M4bNIDIugpn73iqzIVAVPgKd+RWYB3EZxIRe55f9dVTgOejIKZDiLohOt+ZyBDSwssnaaqBC+0HghdbvbPh1Ls/yugNzUDvIWojHXhjzSN6SXw28ay2rjpuPcuy2NpsMaudxh2ad9kpegEWh6+T9QB17IbE/uvB+SR45IKdkE+KVEQlh1/u01ksZURA22HVZjzuVA+Rt5cBjQDDgaam9AwKb+9Ci04PfJ/HWq2eNT5/8NXbsWAAuu+yyIsduu+02fzdDiDKTm53rdTjCYXeUajns4Pv7Wee7eMezBRg0aFmXC3u0LFad29ftZMbD1ie4wu3P///7D85kx/riLTF2ZeCEPm6DF8NmEFM1qqBXxF9sNhsXX9WGiR+M57Ev7mXkU9c5BS/5+t9xBR36tbPaUmh1k2EzCA4NYsr8iWUWvGgzBTJm5X1CPunGqiJREXehotzP5ym2rAV4zB1i34IKvx4jbjpG9FOooDalX7kTPhTf5rTYKXgNctZA+ouo0CtQId08By86EzI+wX2PSv7jJ08i9sCHRHJlSZtH8X4r1iiyIXQA7ifz2sBWG4L8+7d1KvN7ALNhwwYOHDhQ8LVkibVs7pprTuzJccsttziVeeaZZ/zdDCHKTIMW9TAMD3lilKJOk1oey3jTrEMjHpx5J0HBgSilsAUY2AKsN656zerw1DcPFrv+ea8t9pjEzRZgsOCNxSVuc75uN3ai7/heBXXmMwxFaEQIT379QJnsVO4LW4CNhz67i0kf307ji84lPCaMKmfF0e/2K3hny/MlnrfkjXYcQR/rb/Ue6MJZd5U1LyZ+DipitPceDl+vZ6ZDzmq8BRM6cThm6rM+r8jxKuBcH1benMyE7BXo3N+9F83dmbcM3V9sEHSBH+srASMB78GWAlsVa/PIgHMpeuu2gQq1dij30+9QZeD3IaSqVZ03lXr66adp0KABnTp1KngsLCyM6tWr+/vSQpSLXjd34ZMn57i9N2g0fceVPk9Mp0EXc0G3Fiz5cCW7t+wlMCSIDn3b0Ory5iUKjnb8+IfH/X8cdpPta//wub70lAyWzfyBvdv+ISQ8mA792tGobUOUUox56SYuvroNC974lr827yE4LIiOA9vT+9auxFWPLXbb/clms9Hlukvpct2l5XZNnfIIOP6j6C+NBp0KyQ9C/Ew/XjADn+aj6DRIfx+d8QnEzkAFnV+8y2gTshahMz60ggvA8xCHOzZ01neoQG/bgPh70awDFTbU59Ja52XaVcEoVfrbp7bvgfQP8Py8bBDcGWXk/d3EfQoZn6AzPwXHflCREHo1Kmw4KqDsE2yeSsp0DkxOTg4ff/wxEyZMcOqa/OSTT/j444+pXr06ffr04eGHHyYsLMxtPdnZ2WRnn/ijSEnxbZ8UIcpClbPiufPt23j+5jesyaJ5QzBKWW9DF1/dhl43d/FciY8iYyPof0fxlnm6E+jD0EhgsG9vCStnr+PZm14nOzObgAAbWsNnz8zn/C7NeHTOPUTEhNOqS3NadTlzxuPd0Y4DeZv5ubtJOazN/ey7/Lf81YgBFe5jb4UDdBb6+ChIWIVSvk0O19pEJ9+bN+fFoHjLoU+m8oIuLwLOA0KBTG8lfRM+FoxYzNRnwUxG2WpbuW1sCU7FtJmGTv8fZH6al4DPhg7ugYq4tciybV9pMxWdeL2XhH6GFSzlrVYCUEYYRNyCirilRNc9nZRpX9O8efNISkpi+PDhBY9dd911fPzxxyxfvpxJkybx0UcfccMNN3isZ+rUqURHRxd81a5duyybLYRXPW/qzHPLHuPCHi0LMsWedW5Nbn/tZh6ZfTc2W3H3UCl77ftc6HGLBMNm0P6qNl7r2bJqO08OedHaJFFbu3877FYX+JaV23m03zOlTod/Wsndhk89B7lb/HjRgLy9hnxlgj4OhXZS9ipzTl7wknd+qdh9DN7soNx/2PWdslYR2Xehj/WF9Pch8wt02ovoIx3R6e8VlLQCjSGQ/mahYMMB2d+ij12Dmfkd2nHAWh1VHJlzwTyKx+Ejoyoq7lNU4DnFfYJnhDJNZNejRw+CgoJYuHCh2zLLli3j8ssvZ9euXTRo4DqTqqsemNq1a5drIrvszGzWfPkT//15kPCYMC4dcBFVaxXnDUKcrhwOB6bDJDDo1E7ed3DPYUY2uYvc7NwiAYYyFIHBgUzf8RIJdaq6qcFyf4/H+WXZbx4nMr/0wxM0vbj0q2m8yUzLZNnMH9i6egcoaNGxKV2uu6RMNhItKSvxmfdPyyr6BVTolf65Zs4v6MRri3lWAIQOxIie4lNp80hvcOyi9MM6ClQYquoaq3fB0zWPj8vbpLK0ARMQdBnkrMRd+1X006jQ/pgpT0DGx96vqSIgdBAqYozHicj5zGPXQO6vngvZzsaoWvp5aaeSSpHIbu/evSxdupS5c+d6LNeunbUiwFMAExwcTHBwxb0hrZy9jhdvfYv05AxsgTZMh8lbd3/AlaO6MealmwgIlNXoZzKbzXZK9ricrHq9BB5fMJFH+z5DdtaJHbeVoQpW4HgLXjLTMtm4xHNPgS3Axuo568o8gNm2dicP9ZlKWlJ6wZyg7z9ezXuTPubJRQ/QuN0p8qk1sDXel7/aIOgiv13S2o4gP8dLcfjWKa91DjhKv2LNaqO2ggUvwYt2/AfZS/BLwBTUBXK+93y91JfQwT3ylnz7EDDpNMj4AJ29CuJneQ9izDQf6vTnhGXfacdha3m9EYey1ayQNviizO6806dPJyEhgd69PY/fb968GYAaNfyXMMqffv7uV54c/CI674/GkXviDWHRW0vQWnPHG7dWVPOEKJYLurbgk71v8u305fy6chsALS9rRo/hlxEV731TuawM37rJM9OyStVOb478e4xJPZ8gK8MaxircG5R+PIP7ezzO9N9frvAJwwDKiECHXW+lv3eXfj6kL8pWxX8XNRMpfi+FHRXsaxBlwzlTro/nqGjQ2UC6dX5Qe6vHIuhCl2doMwky56Ptf+VNgvbhekZtMP9x34bQa8F2ltcABvOglUhPF2e+jQMcu9BHuqDDbkSFDUHZ3HwoCDgHHHtwH2QaVp6ecqRzf0enPmMtbc97rXXgBajIe9z+jCpSmQQwpmkyffp0hg0bRkDAiUv89ddfzJw5kyuuuIL4+Hi2bNnCXXfdRceOHWnRokVZNKXUpj/0qfV36uK9QGvNV28vZcj9/bx+chXiVBEVH8k191zFNfdcVfxz4yKIjA0n9bj7T4amaVKncdmuhlj45rdkZ+a4zDdjmiZZaVl89c5Shj5yjYuzy5+KnJDXg/AtRbPftkdFP+pTPVpryN2AzpgF9j/yMsBekbepYqEA1FYdqzfF1x4Ym7WcN/hy356PsqGD2hdjuwJlraSJfsqaXGwmggovuhVBITrzS3TywxRJwe+NEem6qIqBiAkY4YMxj9/hW11mkm/lTqZTIP11dMbHEPehy9VVKmwwOtvT8JCJCnOdULAs6Nzt6GNDsLZHKPR3lbsZnTgUYt9DBftnY1N/KZNJvEuXLmXfvn2MGDHC6fGgoCCWLl1K9+7dadSoEXfffTcDBgzwOEemIh3ed4Q/fv7LY0p3FKya82P5NUqICmQLsNH71m4eJwPbAmx0u7GT2+P+8MOXP3mcg2Oamh/mri/TNhSHUkGomFdQcZ9AaF8r2VhIb1TsdFTs/1AqxGsdWmt0yhR04g3WZFv7H5D7Czr1SfTRXtaS3PzrhfbH9+BFgRGDivsfSvk+j0uF3+LhGjYwqkDk46iop1FVlmLEvoEyYlAqEGWr5jl4yV6DTr6fEzdTOz4HMPYdbipNgtRHMNM/g9xffKvLqAa2BrjMKOmVtUReH78N7SpZXlB7CHUXYCsIvgKCu5bguiWjUx7DGuY8ua0mYKKTH7SWzZ9CyqQHpnv37i5XIdSuXZuVK1eWxSXLhKdPmfkMm0FaUsWMUwpREQZP6sdP3/zCnm3/OAUR+fsQ3fHmrT4NR5VGdob3XCPZmcVcFVLGlFIQ1AYV5H2ll0uZn0HmJ3nfnJRC3zyCPjYYHfMqKuhCVGAjdOgQa9lvEQYQZGVtNSJRIT0htL9PE0+dnk9wB4h8BJ36uHNbAIxYVNxHBXsoFZdOe53iD1Hll/dyTuqj+BoMqYCzIGIsOnlCMdpRmAPM/dYGiyHOqRWUUhD1OAQ0Qme8nzdEhrWrdvhwa7PHckpKp+27IHezpxLW88hZD8GnTqZfmX3qQdXa8dgCDM/Jv3Id1GwgSfnEqS0zPYvdW/ehlKJmgwQ2L9tG8tFUEupU4cIeLYs1ET08KowXVk1h5hNf8NW7S0lPtvJ3NGl/Ltc9OKBcNoU898IGHNuf6PZv0xZgcG7rs8u8HeVFa41OdbfhH1if9hPh+PVoW0OIeREV9SjYqln5S3RqXrm8oZyoxwo2EyydbOvaJ/dQmHa0mQNZ36EzPgX732BEoEL6QNgga0NDd8/ETILcn4vRBh8DlxON87FcONjqowLOBvOQNTek2NcCCEDn/owKKZobSikDwodC2PVgHgCtrX2pVDkvCrC7mzN0Esc+QAKYSiEqLpJL+rfjh7nr3b5RhkaEcOlA/60eEMKfcrJymPHwLBa+vYSskyfW5r0XR1eN4vbXb6bjQN/fmMKjwrjlmaEMf2IwSYdTCA4LIiqubHtdCrtqTA+PQ0QOu8lVY3q4PJZ6PI0Vs9ZwaO8RouIjuezai8t9Dpu2/wVZS9A608p/EtLdbQI5M3szpEwB/a9vlTt2oxOvQ1VZgIoYA+EjIecXIBsCzkPZ/POBS2evQKdOy//upKMpkHgNmhwK5vqYoNNegowPIO5j970z2pcJ4IY1IVgFWP86dlP8FVdeBJ1fkIBVhY+EkCvQGbOtDSNzfrI2pfQ5GPI8BKWUYU0srii+9r4Vs5eurJVpHpiy4s915N4c3HOYce0mkXY8zSmIUYZCa83ED8bT9YaOZdoGIUrCYXfwwBVP8suy3zzP4wJQ8Pj8+7noytbl07hS0lrzzr0fMeeFhRiGwsx7fvn/H3x/P0Y+VXQC5LzXvuGdez/EnuPAFmBgmhptaq4a24PRLw4v8+Xw2sxAJ98H2d9hDeUYWMnZolDRz6JCOjuVN9PehbRnS3AlG4RdjxH1kB9a7Zp57Ia8npLizouwge0sVJXvXA6RaJ2LPtzOWpbslkJFPYIKux7z2HWQu6kE7fBMxbyOCunm8pg209BJ4/NW6/hQV+z7qOBLvJbT2oSc1eiMeYV2CR8AQReVeqNNz9d1oI90sq7pVigqYS3KCC/Vtfx5/z5zdn0qoer1Enht/VQ69G3rNHHx7OZ1eGLB/RK8CJeO7k9kzgsLeXfix3z5ytckHUku9zasnL2OTUu3eg9eAIXinfs+rDQZdJVS3PrsUB6YeSe1zjuRp6Juk1o89NkEl8HL0o9X8frt75ObbUdrjT3XSkCotWbB64t57/5Pipzjbzr5zrxEbGDdcO15B1LRSWPQOZsLyprZv5QweAFwWJley4jWdsjdQMmCBoc1FJHzg8ujSgVaS5099loY6KBL0PZ/SxhEeWGrB8Gd3R5WRgQq/Fbc7wxdUBHY6kPQxV4vqc0M9PHhVtLD7MXW65v1Ffr4MHTS2OJn+i0GpWyoSM/zfFTE6FIHL/4mPTDFkHw0hcP7jhIeHSbzXoRLpmky/cFP+fy5BaA1hs3A4TCx2QyGTb6Wayf2LdNPUoXd3flRfvvhd4+rdU725qZnaHh+/TJslf8c3Z/IU0NesrLwFlK1djwPfTbBaWdp0zQZ2mAch/cecVtfQKCNWf+9Q3SVsnlP0blb0ccGeChhg6BLMOLeBcA8ehXYfdih2QNV7fcymQiqdS76UNNS1BAA4SMxIu92edTMWg5Jt+F+rokBRlWIfBiSx5WiHS6oKFT8PKeNEbXOBcc/WLtC17EeO3KZ92Ekoxoq7mNUQF2vlzWT7oOsBW7qUxA2HCNqUnGeSbHp9I/z5vpkYc0wyVvuH34bKmK8X967KkUm3tNRdJWoMntzE6eHT574glnT5hV8b5rWuLzddPC/B2YSFhXmdm6Gvx34+1CxgheA5COVY6PUjNRM7u70CAf3FA1IjvxzjDsufpBxr4zg6nG9APhr8x6PwQtYezqtW/AzPUf4ZyPOk+msb/CcHdcBOavQZjpgL3XwghHvNXjROhPMFDCifVrKnU+pQHTAedZS7hJlxnUx8TevPTpjLqQ+6aVe09pHKOOjElzbHQWEQKHgRetcSH8Hnf6htVcU5OXL6QTmIe9Vxr7vU/CiHYc9BC8AGjJmoiPGe1x+Xloq/AZrmX/WYmtSsVEFQnqe2An7FCNDSEL4SUZqJp89M99jmQ8nf449114u7YmKjyx2+oqqtf2YDbYMfTdjBQf+PuwxQHvt9vf57QerdyZ/pZQnhqHISPHTLseumKl4/4Foa1dm0w9DjmaitQLJ1VXsuzGT7kYfao0+cin60AWYSRPR9n0+V6/CbqLkaf0dYDvb2tE6cx7m0f6YBxuhD7WE1MkUDK15qyP3JwhojOdbmQEqDq/DPbazUPGfYRQELw500nh02isnghew5olkzvahfaDMoz6VI+dHvA+DZUPuRt/qKwVlRKDCBlo9LmFDTtngBSSAEcJvfv52s9f8JMlHUvjth1J+svZR9xsvQ/kYwRiGolHbhtRpVIErIYrhuw9X+DRfZ84LiwCo2bC619jBNDVnnVt2W5qogHp43xAwHIwYMOIo/duzRqdOQ2d87vxo7g70sf6Q9TUnAgU7ZC1AH+tv5QTxRWi/QonYCgcH+dsMeJG9Dp08yZrUbN9GyeaxaAjxvF0NQZ2sJeZue74UBJyLqrLUOWNu1reQvYxS7b3kc2+Jjx9qdPl8+KksJIARwk98/fRepp/yC+lx02Uk1KmCLcD7n7lhMxjz8giv5U4VSYd9G+rasNjKuJpQuwoXdj/fbQZhZSjia8ZyYY+WJW6T1ibaTMwbAnIhtB+e33JtEHqNNYlVBVmTP/1Ap71kTbrN/z75/rz9fU6+oTtAp6OTfVu5pJRCRT2BinkVAi8EFQlGfN4EXB8+tWfPh6wv81vl0zVdCmgGge5+bgpyVuO590XnDYU5T5LVGTMp1S3SqAEBPs4TCvRlKx0DAksz7+j0IwGMEH5Sy8dP72X5Kb+w8OhwXlg5mbrN6ngt2+ySRjRudw57tv3DR5Nn8/Y9H/LVO0tIT/E+9FIRapyd4FO5wkNMY18ZQXhUGMZJAZ1hMzAMxT3vjy3RMmqts9Bpr6OPdEAfvgh9uBXmsaHobOcltsqIQ0U+kP/dSbXkLS2OGG0NXRwfB46/i90Wl8yjBVlWde62vFT77no7HJC7yedeGKUUKqQHRvxHGNU2YiSsQ0XcBiT60jCfruG5ARFWLhi3WwNorEDNl92kT+rdcPzt23lum3aHzxOoVUBDCGyL+0DLBsGX+y2Hz+lCAhgh/KRph0acdW4NDMN197lhM2jc/lzqlvFGh4Ul1KlKp4Ht3bYp35ZVO3joqqe5pfkEPnlyDvNe/ZqXRr/DtTVuYdnM1eXUWt/1vsX7HjHKUJzXtmHB97XOqcFrP03l0gEXOfVKtejUhOdXTOHC7sXvfdE6C504HJ32KpjHThzI3YA+PgKdMce5TeE3WD0WAecUejQIQgei4j+35htkfwc5K/DYKxHci2JNcDLzMvHa//StfF4Ao7UdnfEZ5tErMQ82wjzYwpo7k7vd/bm6/IJeFT7SWnLscT2Kt8y5ytpWQZ20RFj5kpgxzDofw7kNKhadvRSdvdrn1AQq5hlrZVWR27LVPhU1xad6ziSyCkkIP1FKcc//xnBf18lgN4vsExQcGsSdb95a7u06fjApb58i95lKTYfJT19vAnBK2JidmcPUoa8QVSWqRDf4stLxmvYsePNbtq3Z6baMNjX9bneeH1GzQXUe+vQu0pLSObY/kaj4SGKrxZS8IenT83o3Tv6kbn2vUx6xUvfb4guOqJAeENwdHP9aQzm2mk4rS3TGLKybmLtP/zbQ6RB8mbXHji+9BPkrYVSoT08LFWYFL0njIft7TqTQz4Ksr9FZX0PMq6gQF4GkUR0I4uQhGf/Ja0voEAgfDckT8K2nxP3eSipsWJElwir0KmsCr8fgJwOingP7b5AxC6u3R1uTfrOXo7OXQshAiH7Ca2+MstWEKvMg4xMr8DUTwZaACh0EYdc57zYuAOmBEcKvmnVoxMtrnqR195YFH5CVobj4qgt5df1Uzm7hfUmlv8XXjC3IVOuJu4R3hlJ8NPlzl8cqSkBgANO+e5i2vVoVPZj3ul89ticd3WzzERETTt0mtUsVvGit0Rmf4PnmaULmF0WbqBQqoDYq8Nyiy2Lte73U6QDHHlTEeLwnUjMg8AJrPx+AoA6Al+XSKgKC2qLT388LXsD5Jm4Nyeiku9Bm0blIygiD0AGUbAdnD4y61g7OYcNR8YswoicXSsHv7VqheWUKl8v7f3AXCCua+JCwIeB1abkNshZC9hIgl6KvE5A1BzJmeqknr0VGHCpiPEbCSozqWzGqfo+KGOUUvGjHQXTWEnTWMrQ/VqtVYpLITogyknw0haQjKcRWiy7XfYJOduTfY1xfb7TbAEUp5VM39+cH3i1db0UZ+W3NDj55Yi7b1u5EmyaN2p5D3/G9uPjqNmWbft1MQx++wEspAwIaAMqaYxF0ISrsOlRgE7dnmEf75s1TcfczURDYHCN+Djp7PTrpdudlvoWvrUJQcZ+hAs87UX/qS5D+htvrq4gJ6NBr4MileF4do1CRD6DChxU5os1E9LFrrF6m0kzQzeuJUlFPocIGuiyh7X+hj/byUkcg1saThSkIvR4V9aDbzRPNY9d6mF+Tz1tvk7J62ap8X6qkgtpMRCc/nJfJOf81DbImfkfd73YvrVONJLITohI4VRIfVq0Vz+CJffl06pdFjhk2A8NmoE3T467rAOkpmadkANOsQ2OmfvNg+V9YBeFpWMJiOs87ydyLzvwcIh9Chd/outrQq9CpO1weKygTcrX1b3A7SFhrJVrLnGltNGgdgeAuqMgJ1gTRwudGjEebqZD5MdbNXVntREPYCAgfBcn3431pr4HO3eqy70MZcRA/G532GmTMwcrsWgIBTax8JCftEeV0rYAG6LDhkDHDZRutn0+u65MzZ0H4dXDSa3Si8jAfGmli3UrdvV4aHP/l7W10YhKutv9tPW7EQkBTj8G2NtPQx4bk7QZd+PctBzI/RTv+gdh3yiTr8qlMAhghzgA3PTGEyNgIPnnyC6ekbue1bciF3Vry0eOeE3MFhwZR5ay4sm5mpaJUEDqoU94yXV93QrbK6dQnILAxKqhN0SKh10D6h3mZXk+u12bdBEP7FWqHDRVxE0TchHYczZs7UcUKIly224aKfhgdPgydOQ/MoyhbNQi5GhVQywpushb58Fx0XhDnmjLiUFGPoCMnolOmWgGWL8InQkh763wfV92oyElgS0CnvQM6Ke9RA2wNwLEL10Ny1uRenf4RKnqy63qD2qFz1rk5H8BmzfkxD/rUTgCd86v188/9tVA1dSDyXmt+lCuZn4FjD66DZRNyVll7SwWfWXvzyRCSEGeQnKwcfl25nay0LGo3Oot6TWuTkZrJoBq3uE3CZ9gMrrj5cu6ogAnIpzqdsxGdeD3eV7qczFoWa8S+5rpe+7/opHFg386JqYomBDRHxb5qTfgsIzp3G/pYP+8FARXzGiqku9dyZsrj4HW+UF6dsR9ZPUsloHUO5G4BnQ0BDdFJd+Vt9uiBURUjwfWu0tpxzNrziBzc/nzDb4f0V7xcoyaq6jKw/4Y+dh1Wb03h18LqyVPRz6NC+xQ53TzSPS+AcccGwd0xYl/23I5TgAwhCSFKJCgkiDY9znd6LCwylAnv3sbUG17GUAam6bx6KqFOFYZNubacW1o5qKDWEPMiOuk+rJtc/lwKb8MvDshZ577egFoQ/6W1winnJ0BBUFsIbFn2m4H6OpdCxVgTYH0pGnAO2uecKiX/TK1UEARdWKgmH1ZCedjlWdniIfYN9PHRWL1h+T1i1p5WKvI+CBuOzpzjpscsr57wEShlYKY8SdHgxWopgE6ZAiE9rOdRmHnYy5NwWHsXnWHOrAEzIYRLXYZcwtOLH6Jx+xP5SYJDg+h9S1de/fEpYqpGV2DrTm0qpBcqYQ0q8kFraCdsCAQ08n6ilxu1UgoV1MpahRJxKyro/PLZydzWIG/HZS/XinkJpXz8DBxyJeBLYBQEHiY4F1tgSzyv1LJ5zYKrgi9FVVkM4SPA1hBsdSHkKlT8HFT4zSgVgIp9B1QUzrfUvOuG9IOwG9D2vXkTgj0EcjoZspcXfdzwtkeZDYxqXsqcfqQHRggBQOtuLWndrSXHDyWRkZpJfM04QsIqx8qGiqaMKAi/seCWr9NeRaf9gce5E0Fty6l1xaOUgoix6OSJ7kpAUFeM4It9r9OIgJhn0El3eChlWAn9DP9NC1BhQ9AZH3so4UCFD/VeT0AtVOS9EHmv6+OB50LVbyDjM3TWV2CmQ8A5qLAhEHyZtdLPsd+HFhvgKNqTokIHotNewlMGZRXa34f6Ty8SwAghnMRWizklVxtVKqGDIO1tiuYGyedAhQ0v3zYVgwrtB47D6LQXcM6f4oCgDlbW2EK0zgUCCnqIrKmV2mlVjArphY4xIOkeii5pBgLPt4Zk/Pk8AhpC5IPWpNm8YR9LXqLAsGFo29mQ9jZap6BstSGkd4mSxikjDiJGoyJGuy7gZlK1M9N1ubDrIPPzvODm5GEqA4LanHETeEEm8QohRJnQWcusibgF+/FAwdyJiHtREbdUXON8pB37IXMu2r4PjChUSG8r0FDK2rQy40Nr00PzEBCclyjPhJy1QA7Y6qDCbrAyyebN69BmmpUAMHO+NWRiOwsVOhhCryw698NfzyN7nZWYL2et1b7AlhA2FLLXQdZsTmwH4ACCUFEPocL8O+9La40+2hscf+F++DAElbC2aHJDQDsOWRtx5hSecGxYq8eiHrUSCFYC/rx/SwAjhBBlRNv3WDf47GWgHRDUGhV2Ayro/IpuWqloM81afWXfieeVRXk9N4FtUXH/K7MApTi01iilMJMfg8xPcbu9QMyr7pc1u6o39w8rx4/9L1ARqJCeENLN6Tnr7BXo46Pyvyt6TR8CW23fk7cE2wZB7VC2qj638VQgAYwEMEII4Vda51oroxxHwVYVgtq7naRrpjyRtyza1/w3ytqdOWKM39pbGtpxMG95tLvgS4GtPqrKN14nTmutrT2T0l/nxDBV3hCVrQEq7gOU7cTu6TrrW3TKY3mbf+YlQlSh1tYQYSPLZ6J2BZJl1EIIIfxGZy5Epz7lvKO2UQUiH0SFOm+IqXUmZM7G9+AFQFuTacNHuU3bX66yCqfjd0WD429ruMddlt6Cur7MC17gxGuSFxg59lhLsOPnFAQm1maeXSD7hxOZeIMvQxnhRaoWnkkAI4QQZzCduQidfHfRA+ZRdPJdoAxUSKG9hux5u2gXl5mfJfgUGPLQqZyY8+KBme65Gq3RaW/jfksJB9i3Qu5Gp/w0SgWCh+0RhG8kgBFCiDOU1nar58VTmZSpENz9RM9JaeaxnAJzYAAIqI/3HiQDbLU8FzEPgWO3l3ps6OyVqEIBjCta50LWEnTWN3mTm89GhQ3yuPHnmU4CGCGEOFPlrLd6RjwxD+b1IOTlrbHVAVttcPxTjAsZENgcZZwiCRGDu4CKzds3yVXPibXVg7LFe67HQxbfExRoN5tJ5lfjOIo+Phzsf1Awf4YN6MyZ6PCbrcm9p/ncmJKQTLxCCHEa0DoHnfk1ZvJjmMmPWvNavN1gC8958cRxpOC/SilU+Nhits5EhY/yXqycKBWEip7KieXTJxcIhojx3iuy1QDlLSizozxk+9Vao5PGWquXgBMTi/N6iNLfszZzFEVIACOEEJWczt2JPnI5OvlOK+FZ5mx08t3oI53QuVvdn+hr+nmbczkV1j9vewBvDEChIh9AhXT17VrlRIV0gZjXARfDWjoTksahCwVuLutQgRB2A+5vpQYY8eDpuef+mrfFgPshLZ32Nlr7upfUmUMCGFFh0pLSOfzPUXKyPXevCiHc0+ZxdOKNhYaC7BRsJmkeRycORzsOuT456EIwauB+3yNlzQMJvKDIESPmBXCbTTgQAttZq46qfI8Kd1eugmXOxcqWfDINjn/RyZO8VqEiboPANjhnLAZrSXWwtWO3h7k/OnsVXm/F5n/g+NdrW840MgdGlLutq3fw0ZTZ/PK99ckwJDyYniO6cMPDA4muInl9hCiWjDke5nKYoNPRGbNQkUX3IVLKBlEPW0MYRVbS5C37jXzYaUuAwoyoB9ChV1vJ+nK3ggpBBXeDsAFWav1TmHYcgGxPy6kdkLMKbd+HCqjjth6lgiHuf1avV/on4NgDKszKLBw2HBVQz0tLvO1cntde+06P7TgTSSI7Ua5++HI9U6553sqE6TjRJWrYDKrXq8rLa5+UnY+FKAbzaH+w/+a5kK0+RtVv3R7WWcvQqU86T8y11UFFPog6TZf76qxv0Une57mo6OdRoX3KsB1L8gJIL0IHY0RPKbN2lBd/3r/9PoT02GOPWZO8Cn01anRia/msrCzGjh1LfHw8ERERDBgwgEOH3HRvitNKVkY2z970Olprp+AFwHSYHNxzhBkPy2Q1IYpFZ/hQxnPeFhXSBVVlCSruMyuFftzn1venafBi8TWhXhkn3gv28TV2/F227aiEymQOTNOmTTlw4EDB1w8//FBw7K677mLhwoXMnj2blStXsn//fvr3P/O2AT8TrZ7zIxkpmW57bE2HyZIPV5CZnlW+DROiMgtohOebrA0CzvNajVIGKqgVKqQHKuj803/ZbtD/27v3oCjPsw3g17O77LIIy0HkpEBAE1ER66lkPeUgE2uYHIxNU4IWo+03JpiKpgkemtA0n8VJ+7WT/qFN2nw4TTzU9FOTGA+hHjCkaJSKSNNBVBpNAmJVWBBdDnt/fxA2rIBCsoDv7vWb2Rn3fR5e7nsexr129z1MxK2PotC33em5DyllANSt7n6tejDH+/TJMTAGgwERERGdttfV1eHNN9/Epk2bcP/99wMA8vLyMGrUKBw+fBh33313X5RDt4nPPj0Pg48eLc3dH23fdL0ZF89fQkzC0H6sjGhgSGsVpHEzYD/Qdq0QnwlQg9KhfMb0eB9q0JMQ+66bzGiF8nvy2xfrYZQuGGL+fttZW13eE0kH+D7cPzdLND8CNG7qpg4AkLabQ5KLPvkEpqKiAlFRUYiPj0d6ejrOnTsHACguLkZzczNSUr4+pSwhIQExMTEoKirqdn92ux02m83lQdpj9jfD4bj1IVdmf99+qIZoYIm9CHLxAeDqG213dW49C1zfDrk0B3L1f3u+I5/JgF/GV086fmry1b/NjwOme91UtWdRllWAsf2Nc/unWF+9LPpMhLLk3PTnpbUKYj8MaT75rU5zVn4LAJjQ9UuyHtDHAh1v50AA+iDAJCcnY8OGDdizZw/Wr1+PyspKTJ8+HfX19aiurobRaERQUJDLz4SHh6O6urrbfebm5iIwMND5iI6OdnfZ1A+mPfbdTse+dKR0CndOiMeQYbe4+iWRxonjMuTKYgBNcH3X3fbppNSvhdgP92hfSn11nRXLWkA//OsBfSyU5ZdQlv/2/K+DviGlfKGC34QKeh0wzQQMiYDpfqigdVAhf4bS+XX6GZFmOK7+GY6ae9uus3PlR5BLcyEX74Nc2/7N6jDEQIXkAbqgr7YY4PyCxDACKuStm56K7a3c/hXS7Nlfp8SkpCQkJycjNjYWW7duhdls/kb7XLlyJZYvX+58brPZGGI0KHZ0NKY++l0UvXcMDkfnICMOwfycxwegMqJ+1vh/AK6j+1N49ZDGPChTz75WV0oBfo8B5jlt99GBACqIwaUHlNIDvvf16IBlaf4UcmVR11cwdlRB6rIBhw1qUEbn8VvVYZwADDnUdj+k5lJAGaCM0wFjMtexG31+IbugoCDcddddOH36NCIiItDU1ITa2lqXORcuXOjymJl2JpMJFovF5UHalP3Ws0hObbsolt6gh8FHD6UUDEYDlr2xGNaHbn7DMyJPIE2H0X14AYBWwN791+rdUUpB6YKgdMF80XMzab341QUDL998Xv2rEEftN/odShmhzKnQWVZCF/A8lOluruNN9PmF7BoaGnDmzBnMnz8fEydOhI+PD/bt24e5c+cCAMrLy3Hu3DlYrda+LoVuA+ZBvvjlu9k4XVKJgq1FaLQ1YthdUZg5bzosITzKnryF5i6/5fWkcTMgDbj12rUA194HBs3vj7K8mtsDzM9+9jM89NBDiI2NxZdffomcnBzo9XqkpaUhMDAQixYtwvLlyxESEgKLxYJnn30WVquVZyB5mRHficOI78QNdBlEA0IZJ0Oa/o7uzzrp+9N3qZeuf4Du16sjPaT1y25vzkDu4/YA8/nnnyMtLQ2XLl3CkCFDMG3aNBw+fBhDhrSdiva73/0OOp0Oc+fOhd1ux6xZs7Bu3Tp3l0FEdPsyPw40rEPbQbxdvaNvhRr0VD8XRTfVkwsGAgAcULrgPi2F2vBWAkREA0DsBZArz6DtXX37tZH0AFqh/JdB+T89cMVRJ47Li4Cmv+Nmd41uo6CG7IfS81pWXXHn6zdv5khENACU6R4gdBekcSNg3w9IC2AcD+U3D8o4caDLoxsov3RI00e3nmh+kuGlnzDAEBENEGWIbbuYGlYNdCl0K6b7AN85wPXurvWiAPMCKMsL/VqWN2OAISIiugWlFBCYC/iMgTRuAFo//2ogBDDdAwS8AJ2eF+HsTwwwREREPaCUDhj0I8BvPuD4DwAF6AbzWi0DhAGGiIioF5RSQH/c5JFuqs+vxEtERETkbgwwREREpDkMMERERKQ5DDBERESkOQwwREREpDkMMERERKQ5DDBERESkOQwwREREpDkMMERERKQ5DDBERESkOQwwREREpDkMMERERKQ5DDBERESkOQwwREREpDkMMERERKQ5DDBERESkOQwwREREpDkMMERERKQ5DDBERESkOQwwREREpDkMMERERKQ5DDBERESkOQwwREREpDkMMERERKQ5DDBERESkOQwwREREpDkMMERERKQ5DDBERESkOW4PMLm5uZg8eTICAgIQFhaGRx99FOXl5S5z7r33XiilXB6LFy92dylERETkodweYAoKCpCZmYnDhw8jPz8fzc3NeOCBB3D16lWXeT/5yU9QVVXlfLz66qvuLoWIiIg8lMHdO9yzZ4/L8w0bNiAsLAzFxcWYMWOGc7ufnx8iIiLc/euJiIjIC/T5MTB1dXUAgJCQEJftGzduRGhoKBITE7Fy5Uo0NjZ2uw+73Q6bzebyICIiIu/l9k9gOnI4HMjKysLUqVORmJjo3P7kk08iNjYWUVFRKC0tRXZ2NsrLy7Ft27Yu95Obm4uXX365L0slIiIiDVEiIn2186effhq7d+9GYWEhhg0b1u28/fv3Y+bMmTh9+jSGDx/eadxut8Nutzuf22w2REdHo66uDhaLpU9qJyIiIvey2WwIDAx0y+t3n30Cs2TJEuzcuROHDh26aXgBgOTkZADoNsCYTCaYTKY+qZOIiIi0x+0BRkTw7LPPYvv27Th48CDi4uJu+TMlJSUAgMjISHeXQ0RERB7I7QEmMzMTmzZtwrvvvouAgABUV1cDAAIDA2E2m3HmzBls2rQJDz74IAYPHozS0lIsW7YMM2bMQFJSkrvLISIiIg/k9mNglFJdbs/Ly8OCBQtw/vx5zJs3D2VlZbh69Sqio6MxZ84c/PznP+/x92Hu/A6NiIiI+sdtfQzMrfJQdHQ0CgoK3P1riYiIyIvwXkhERESkOQwwREREpDkMMERERKQ5DDBERESkOQwwREREpDkMMERERKQ5DDBERESkOQwwREREpDkMMERERKQ5DDBERESkOQwwREREpDkMMERERKQ5DDBERESkOW6/GzUREZG3EGkC7B8DcgXQDQWMk6EUPxvoDwwwRERE34A0boHU/w8gdV9v1EUBgb+EMs0YuMK8BGMiERFRL8nVtyG2l1zDCwA4qiBX/gti//vAFOZFGGCIiIh6QRyNkIbfdDcKQCD1uRCR/izL6zDAEBER9Yb9ACCNN5kgQEs50HK630ryRgwwREREveH4D3r08um42OeleDMGGCIiot7QhwFw9GBeeJ+X4s0YYIiIiHrDdB+g/G8yQQcYxkIZhvdbSd6IAYaIiKgXlPKFCljVzagOgA7KsqI/S/JKDDBERES9pPy+DxX4G0AX5jqgj4cK2QBlnDwwhXkRXsiOiIjoG1DmhwHfVKC5GHBcAfRRgCERSqmBLs0rMMAQERF9Q0rpAeN3B7oMr8SvkIiIiEhzGGCIiIhIcxhgiIiISHMYYIiIiEhzGGCIiIhIcxhgiIiISHMYYIiIiEhzGGCIiIhIcxhgiIiISHM0eSVeEQEA2Gy2Aa6EiIiIeqr9dbv9dfzb0GSAqa+vBwBER0cPcCVERETUW/X19QgMDPxW+1DijhjUzxwOB7788ksEBAT0y02zbDYboqOjcf78eVgslj7/fQPFG/pkj57DG/pkj57BG3oEetaniKC+vh5RUVHQ6b7dUSya/ARGp9Nh2LBh/f57LRaLR//xtfOGPtmj5/CGPtmjZ/CGHoFb9/ltP3lpx4N4iYiISHMYYIiIiEhzGGB6wGQyIScnByaTaaBL6VPe0Cd79Bze0Cd79Aze0CPQ/31q8iBeIiIi8m78BIaIiIg0hwGGiIiINIcBhoiIiDSHAYaIiIg0hwGGiIiINMdrA0xubi4mT56MgIAAhIWF4dFHH0V5ebnLnOvXryMzMxODBw+Gv78/5s6diwsXLrjMOXfuHFJTU+Hn54ewsDA8//zzaGlp6c9Wbmr9+vVISkpyXhnRarVi9+7dznFP6PFGa9euhVIKWVlZzm1a7/MXv/gFlFIuj4SEBOe41vtr98UXX2DevHkYPHgwzGYzxo4di2PHjjnHRQQvvfQSIiMjYTabkZKSgoqKCpd9XL58Genp6bBYLAgKCsKiRYvQ0NDQ361064477ui0lkopZGZmAvCMtWxtbcWLL76IuLg4mM1mDB8+HK+88orLDfw8YS3r6+uRlZWF2NhYmM1mTJkyBUePHnWOa7HHQ4cO4aGHHkJUVBSUUtixY4fLuLt6Ki0txfTp0+Hr64vo6Gi8+uqrvS9WvNSsWbMkLy9PysrKpKSkRB588EGJiYmRhoYG55zFixdLdHS07Nu3T44dOyZ33323TJkyxTne0tIiiYmJkpKSIsePH5ddu3ZJaGiorFy5ciBa6tJ7770nH3zwgZw6dUrKy8tl1apV4uPjI2VlZSLiGT129Mknn8gdd9whSUlJsnTpUud2rfeZk5MjY8aMkaqqKufj4sWLznGt9ycicvnyZYmNjZUFCxbIkSNH5OzZs7J37145ffq0c87atWslMDBQduzYISdOnJCHH35Y4uLi5Nq1a8453/ve92TcuHFy+PBh+eijj2TEiBGSlpY2EC11qaamxmUd8/PzBYAcOHBARDxjLdesWSODBw+WnTt3SmVlpbzzzjvi7+8vr732mnOOJ6zlD37wAxk9erQUFBRIRUWF5OTkiMVikc8//1xEtNnjrl27ZPXq1bJt2zYBINu3b3cZd0dPdXV1Eh4eLunp6VJWViabN28Ws9ksr7/+eq9q9doAc6OamhoBIAUFBSIiUltbKz4+PvLOO+845/zrX/8SAFJUVCQibQut0+mkurraOWf9+vVisVjEbrf3bwO9EBwcLH/60588rsf6+nq58847JT8/X+655x5ngPGEPnNycmTcuHFdjnlCfyIi2dnZMm3atG7HHQ6HREREyK9//WvnttraWjGZTLJ582YREfn0008FgBw9etQ5Z/fu3aKUki+++KLviv8Wli5dKsOHDxeHw+Exa5mamioLFy502fbYY49Jenq6iHjGWjY2Noper5edO3e6bJ8wYYKsXr3aI3q8McC4q6d169ZJcHCwy99rdna2jBw5slf1ee1XSDeqq6sDAISEhAAAiouL0dzcjJSUFOechIQExMTEoKioCABQVFSEsWPHIjw83Dln1qxZsNls+Oc//9mP1fdMa2srtmzZgqtXr8JqtXpcj5mZmUhNTXXpB/CctayoqEBUVBTi4+ORnp6Oc+fOAfCc/t577z1MmjQJjz/+OMLCwjB+/Hj88Y9/dI5XVlaiurrapc/AwEAkJye79BkUFIRJkyY556SkpECn0+HIkSP910wPNTU14e2338bChQuhlPKYtZwyZQr27duHU6dOAQBOnDiBwsJCzJ49G4BnrGVLSwtaW1vh6+vrst1sNqOwsNAjeryRu3oqKirCjBkzYDQanXNmzZqF8vJyXLlypcf1aPJu1O7mcDiQlZWFqVOnIjExEQBQXV0No9GIoKAgl7nh4eGorq52zun4n0j7ePvY7eLkyZOwWq24fv06/P39sX37dowePRolJSUe0+OWLVvwj3/8w+X753aesJbJycnYsGEDRo4ciaqqKrz88suYPn06ysrKPKI/ADh79izWr1+P5cuXY9WqVTh69Ch++tOfwmg0IiMjw1lnV3107DMsLMxl3GAwICQk5Lbps6MdO3agtrYWCxYsAOAZf6sAsGLFCthsNiQkJECv16O1tRVr1qxBeno6AHjEWgYEBMBqteKVV17BqFGjEB4ejs2bN6OoqAgjRozwiB5v5K6eqqurERcX12kf7WPBwcE9qocBBm3v3MvKylBYWDjQpfSJkSNHoqSkBHV1dfjrX/+KjIwMFBQUDHRZbnP+/HksXboU+fn5nd4NeYr2d64AkJSUhOTkZMTGxmLr1q0wm80DWJn7OBwOTJo0Cb/61a8AAOPHj0dZWRn+8Ic/ICMjY4Cr6xtvvvkmZs+ejaioqIEuxa22bt2KjRs3YtOmTRgzZgxKSkqQlZWFqKgoj1rLt956CwsXLsTQoUOh1+sxYcIEpKWlobi4eKBL8wpe/xXSkiVLsHPnThw4cADDhg1zbo+IiEBTUxNqa2td5l+4cAERERHOOTeeHdD+vH3O7cBoNGLEiBGYOHEicnNzMW7cOLz22mse02NxcTFqamowYcIEGAwGGAwGFBQU4Pe//z0MBgPCw8M9os+OgoKCcNddd+H06dMes46RkZEYPXq0y7ZRo0Y5vyprr7OrPjr2WVNT4zLe0tKCy5cv3zZ9tvvss8/wt7/9DT/+8Y+d2zxlLZ9//nmsWLECP/zhDzF27FjMnz8fy5YtQ25uLgDPWcvhw4ejoKAADQ0NOH/+PD755BM0NzcjPj7eY3rsyF09uetv2GsDjIhgyZIl2L59O/bv39/p46yJEyfCx8cH+/btc24rLy/HuXPnYLVaAQBWqxUnT550Waz8/HxYLJZO/xHfThwOB+x2u8f0OHPmTJw8eRIlJSXOx6RJk5Cenu78tyf02VFDQwPOnDmDyMhIj1nHqVOndrqUwalTpxAbGwsAiIuLQ0REhEufNpsNR44ccemztrbW5R3w/v374XA4kJyc3A9d9FxeXh7CwsKQmprq3OYpa9nY2AidzvXlRa/Xw+FwAPC8tRw0aBAiIyNx5coV7N27F4888ojH9Qi4b92sVisOHTqE5uZm55z8/HyMHDmyx18fAfDe06iffvppCQwMlIMHD7qc0tjY2Oics3jxYomJiZH9+/fLsWPHxGq1itVqdY63n874wAMPSElJiezZs0eGDBlyW53OuGLFCikoKJDKykopLS2VFStWiFJKPvzwQxHxjB670vEsJBHt9/ncc8/JwYMHpbKyUj7++GNJSUmR0NBQqampERHt9yfSdgq8wWCQNWvWSEVFhWzcuFH8/Pzk7bffds5Zu3atBAUFybvvviulpaXyyCOPdHkK5/jx4+XIkSNSWFgod95552116q2ISGtrq8TExEh2dnanMU9Yy4yMDBk6dKjzNOpt27ZJaGiovPDCC845nrCWe/bskd27d8vZs2flww8/lHHjxklycrI0NTWJiDZ7rK+vl+PHj8vx48cFgPz2t7+V48ePy2effSYi7umptrZWwsPDZf78+VJWViZbtmwRPz8/nkbdUwC6fOTl5TnnXLt2TZ555hkJDg4WPz8/mTNnjlRVVbns59///rfMnj1bzGazhIaGynPPPSfNzc393E33Fi5cKLGxsWI0GmXIkCEyc+ZMZ3gR8Yweu3JjgNF6n0888YRERkaK0WiUoUOHyhNPPOFyfRSt99fu/fffl8TERDGZTJKQkCBvvPGGy7jD4ZAXX3xRwsPDxWQyycyZM6W8vNxlzqVLlyQtLU38/f3FYrHIU089JfX19f3Zxi3t3btXAHSqXcQz1tJms8nSpUslJiZGfH19JT4+XlavXu1y2qwnrOVf/vIXiY+PF6PRKBEREZKZmSm1tbXOcS32eODAgS5fGzMyMkTEfT2dOHFCpk2bJiaTSYYOHSpr167tda1KpMOlEYmIiIg0wGuPgSEiIiLtYoAhIiIizWGAISIiIs1hgCEiIiLNYYAhIiIizWGAISIiIs1hgCEiIiLNYYAhIiIizWGAISIiIs1hgCEiIiLNYYAhIiIizfl/zPT5SgIia+sAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# PCA for 2D visualization\n",
        "pca = PCA(n_components=2)\n",
        "X_test_pca = pca.fit_transform(X_test)\n",
        "plt.scatter(X_test_pca[:, 0], X_test_pca[:, 1], c=clusters, cmap='viridis')\n",
        "plt.title(\"2D PCA Plot of Test Set\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "HsCMb6W64NIt",
        "outputId": "084772a0-7148-4be2-af02-a872e66e4b69"
      },
      "execution_count": 17,
      "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": [
        "# Extract AI and human class labels\n",
        "df_Human = df.loc[df['AI Metric'] == 0, 'Perplexity']\n",
        "df_AI = df.loc[df['AI Metric'] == 1, 'Perplexity']\n",
        "\n",
        "plt.hist(df_AI, bins=20, stacked=True, color=['blue'], alpha=0.5, label=['AI'])\n",
        "plt.hist(df_Human, bins=20, stacked=True, color=['cyan'], alpha=0.5, label=['Human'])\n",
        "plt.legend(loc='upper right')\n",
        "plt.xlabel('Perplexity')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Frequency Distribution of Perplexity')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "2365E2Q5t8Gh",
        "outputId": "38b3a7ed-d54b-4835-86be-e6258bedc2f1"
      },
      "execution_count": 18,
      "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": [
        "# Extract AI and human class labels\n",
        "df_Human = df.loc[df['AI Metric'] == 0, 'Burstiness']\n",
        "df_AI = df.loc[df['AI Metric'] == 1, 'Burstiness']\n",
        "\n",
        "plt.hist(df_AI, bins=20, stacked=True, color=['blue'], alpha=0.5, label=['AI'])\n",
        "plt.hist(df_Human, bins=20, stacked=True, color=['cyan'], alpha=0.5, label=['Human'])\n",
        "plt.legend(loc='upper right')\n",
        "plt.xlabel('Burstiness')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Frequency Distribution of Burstiness')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "R2BZ4zNFrmiH",
        "outputId": "354f44fe-62a7-49e2-b0ff-c6538ba896ec"
      },
      "execution_count": 19,
      "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": [
        "# Install required libraries for classifying real texts\n",
        "!pip install --upgrade docx2txt\n",
        "!pip install transformers==4.23.0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P3iSbfGBwiE-",
        "outputId": "a4bc8011-2ae9-40db-9cae-30e8f7a18ba5"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting docx2txt\n",
            "  Downloading docx2txt-0.8.tar.gz (2.8 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: docx2txt\n",
            "  Building wheel for docx2txt (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for docx2txt: filename=docx2txt-0.8-py3-none-any.whl size=3960 sha256=a128509698783b1392773c65114f5a43a4b04c470c460edb8a8f63acb7330ceb\n",
            "  Stored in directory: /root/.cache/pip/wheels/22/58/cf/093d0a6c3ecfdfc5f6ddd5524043b88e59a9a199cb02352966\n",
            "Successfully built docx2txt\n",
            "Installing collected packages: docx2txt\n",
            "Successfully installed docx2txt-0.8\n",
            "Collecting transformers==4.23.0\n",
            "  Downloading transformers-4.23.0-py3-none-any.whl.metadata (88 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m88.7/88.7 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers==4.23.0) (3.16.1)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.10.0 in /usr/local/lib/python3.10/dist-packages (from transformers==4.23.0) (0.26.2)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers==4.23.0) (1.26.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers==4.23.0) (24.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.23.0) (6.0.2)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers==4.23.0) (2024.9.11)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers==4.23.0) (2.32.3)\n",
            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers==4.23.0)\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.7 kB)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers==4.23.0) (4.66.6)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.10.0->transformers==4.23.0) (2024.10.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.10.0->transformers==4.23.0) (4.12.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.23.0) (3.4.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.23.0) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.23.0) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.23.0) (2024.8.30)\n",
            "Downloading transformers-4.23.0-py3-none-any.whl (5.3 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.3/5.3 MB\u001b[0m \u001b[31m50.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m58.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: tokenizers, transformers\n",
            "  Attempting uninstall: tokenizers\n",
            "    Found existing installation: tokenizers 0.20.3\n",
            "    Uninstalling tokenizers-0.20.3:\n",
            "      Successfully uninstalled tokenizers-0.20.3\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.46.2\n",
            "    Uninstalling transformers-4.46.2:\n",
            "      Successfully uninstalled transformers-4.46.2\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "sentence-transformers 3.2.1 requires transformers<5.0.0,>=4.41.0, but you have transformers 4.23.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed tokenizers-0.13.3 transformers-4.23.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import docx2txt\n",
        "import numpy as np\n",
        "import torch\n",
        "import pandas as pd\n",
        "from transformers import GPT2LMHeadModel, GPT2Tokenizer"
      ],
      "metadata": {
        "id": "-JI0cw4jwgvd"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 4: Upload the DOCX file of real texts to be check for plagiarism using AI\n",
        "from google.colab import files\n",
        "uploaded = files.upload()\n",
        "filenames = uploaded.keys()\n",
        "lista_texts = list(filenames)\n",
        "lista_texts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 636
        },
        "id": "vqMgnrGtvalX",
        "outputId": "494c5a9b-59d2-4a18-b3f2-ecc06a1177de"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-dc499e4a-ab9e-4817-8c5c-e9ba89ca7ee7\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-dc499e4a-ab9e-4817-8c5c-e9ba89ca7ee7\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving conclusão_humano_1.docx to conclusão_humano_1.docx\n",
            "Saving conclusão_humano_2.docx to conclusão_humano_2.docx\n",
            "Saving conclusão_humano_3.docx to conclusão_humano_3.docx\n",
            "Saving conclusão_humano_4.docx to conclusão_humano_4.docx\n",
            "Saving conclusão_humano_5.docx to conclusão_humano_5.docx\n",
            "Saving conclusão_humano_6a.docx to conclusão_humano_6a.docx\n",
            "Saving conclusão_humano_6b.docx to conclusão_humano_6b.docx\n",
            "Saving conclusão_humano_7.docx to conclusão_humano_7.docx\n",
            "Saving conclusão_humano_8.docx to conclusão_humano_8.docx\n",
            "Saving conclusão_humano_9.docx to conclusão_humano_9.docx\n",
            "Saving conclusão_humano_10.docx to conclusão_humano_10.docx\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['conclusão_humano_1.docx',\n",
              " 'conclusão_humano_2.docx',\n",
              " 'conclusão_humano_3.docx',\n",
              " 'conclusão_humano_4.docx',\n",
              " 'conclusão_humano_5.docx',\n",
              " 'conclusão_humano_6a.docx',\n",
              " 'conclusão_humano_6b.docx',\n",
              " 'conclusão_humano_7.docx',\n",
              " 'conclusão_humano_8.docx',\n",
              " 'conclusão_humano_9.docx',\n",
              " 'conclusão_humano_10.docx']"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step #1: Function to upload and read DOCX file\n",
        "def upload_and_read_word(uploaded_file):\n",
        "  text = docx2txt.process(uploaded_file)\n",
        "  text = text.replace('\\n', '')\n",
        "  return text\n",
        "\n",
        "# Step 2: Calculate Perplexity\n",
        "def calculate_perplexity(text):\n",
        "    tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n",
        "    model = GPT2LMHeadModel.from_pretrained('gpt2')\n",
        "\n",
        "    tokens = tokenizer.encode(text, return_tensors='pt')\n",
        "    max_length = 1024\n",
        "    stride = 512\n",
        "    lls = []\n",
        "\n",
        "    for i in range(0, tokens.size(1), stride):\n",
        "        begin_loc = max(i + stride - max_length, 0)\n",
        "        end_loc = min(i + stride, tokens.size(1))\n",
        "        trg_len = end_loc - i  # may be different from stride on last loop\n",
        "        input_ids = tokens[:, begin_loc:end_loc]\n",
        "        target_ids = input_ids.clone()\n",
        "        target_ids[:, :-trg_len] = -100\n",
        "\n",
        "        with torch.no_grad():\n",
        "            outputs = model(input_ids, labels=target_ids)\n",
        "            log_likelihood = outputs[0] * trg_len\n",
        "\n",
        "        lls.append(log_likelihood)\n",
        "\n",
        "    perplexity = torch.exp(torch.stack(lls).sum() / end_loc)\n",
        "    return perplexity.item()\n",
        "\n",
        "# Step 3: Analyze Burstiness\n",
        "def calculate_burstiness(text):\n",
        "    sentences = text.split('.')\n",
        "    sentence_lengths = [len(sentence.split()) for sentence in sentences if sentence]\n",
        "\n",
        "    mean_length = np.mean(sentence_lengths)\n",
        "    std_dev = np.std(sentence_lengths)\n",
        "\n",
        "    burstiness = std_dev / mean_length if mean_length else 0\n",
        "    return burstiness"
      ],
      "metadata": {
        "id": "i8V79fc-wz3G"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Main execution\n",
        "if __name__ == \"__main__\":\n",
        "    lista_perplexity = []\n",
        "    lista_burstiness = []\n",
        "    lista_token_length = []\n",
        "    for files in lista_texts:\n",
        "        text = upload_and_read_word(files)\n",
        "        perplexity = calculate_perplexity(text)\n",
        "        lista_perplexity.append(perplexity)\n",
        "        burstiness = calculate_burstiness(text)\n",
        "        lista_burstiness.append(burstiness)\n",
        "        tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n",
        "        token_length = len(tokenizer.encode(text))\n",
        "        lista_token_length.append(token_length)\n",
        "\n",
        "\n",
        "df_texts = pd.DataFrame({'text': lista_texts, 'Token': lista_token_length,\n",
        "                         'Perplexity': lista_perplexity, 'Burstiness': lista_burstiness})\n",
        "df_texts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "FWaEV87jvtr1",
        "outputId": "9471b3ff-ec69-471b-87c5-7538368bbbda"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        text  Token  Perplexity  Burstiness\n",
              "0    conclusão_humano_1.docx    794   52.118839    0.541733\n",
              "1    conclusão_humano_2.docx    468  130.004425    0.954643\n",
              "2    conclusão_humano_3.docx    388  143.195389    1.237567\n",
              "3    conclusão_humano_4.docx    371  100.938171    1.142330\n",
              "4    conclusão_humano_5.docx    271  103.201263    1.522167\n",
              "5   conclusão_humano_6a.docx    696   97.457260    0.923772\n",
              "6   conclusão_humano_6b.docx    376  113.768066    1.473744\n",
              "7    conclusão_humano_7.docx    811  117.469994    0.707268\n",
              "8    conclusão_humano_8.docx    799   92.631905    0.891354\n",
              "9    conclusão_humano_9.docx    516   93.188660    0.986518\n",
              "10  conclusão_humano_10.docx    484  106.107079    1.004706"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1b5fbfa0-f723-4f05-a922-2873b14d04b3\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\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>text</th>\n",
              "      <th>Token</th>\n",
              "      <th>Perplexity</th>\n",
              "      <th>Burstiness</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>conclusão_humano_1.docx</td>\n",
              "      <td>794</td>\n",
              "      <td>52.118839</td>\n",
              "      <td>0.541733</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>conclusão_humano_2.docx</td>\n",
              "      <td>468</td>\n",
              "      <td>130.004425</td>\n",
              "      <td>0.954643</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>conclusão_humano_3.docx</td>\n",
              "      <td>388</td>\n",
              "      <td>143.195389</td>\n",
              "      <td>1.237567</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>conclusão_humano_4.docx</td>\n",
              "      <td>371</td>\n",
              "      <td>100.938171</td>\n",
              "      <td>1.142330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>conclusão_humano_5.docx</td>\n",
              "      <td>271</td>\n",
              "      <td>103.201263</td>\n",
              "      <td>1.522167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>conclusão_humano_6a.docx</td>\n",
              "      <td>696</td>\n",
              "      <td>97.457260</td>\n",
              "      <td>0.923772</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>conclusão_humano_6b.docx</td>\n",
              "      <td>376</td>\n",
              "      <td>113.768066</td>\n",
              "      <td>1.473744</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>conclusão_humano_7.docx</td>\n",
              "      <td>811</td>\n",
              "      <td>117.469994</td>\n",
              "      <td>0.707268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>conclusão_humano_8.docx</td>\n",
              "      <td>799</td>\n",
              "      <td>92.631905</td>\n",
              "      <td>0.891354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>conclusão_humano_9.docx</td>\n",
              "      <td>516</td>\n",
              "      <td>93.188660</td>\n",
              "      <td>0.986518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>conclusão_humano_10.docx</td>\n",
              "      <td>484</td>\n",
              "      <td>106.107079</td>\n",
              "      <td>1.004706</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-1b5fbfa0-f723-4f05-a922-2873b14d04b3')\"\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",
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              "  </svg>\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_texts",
              "summary": "{\n  \"name\": \"df_texts\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \"conclus\\u00e3o_humano_6a.docx\",\n          \"conclus\\u00e3o_humano_1.docx\",\n          \"conclus\\u00e3o_humano_9.docx\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Token\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 197,\n        \"min\": 271,\n        \"max\": 811,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          696,\n          794,\n          516\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Perplexity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23.411613019048495,\n        \"min\": 52.118839263916016,\n        \"max\": 143.1953887939453,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          97.45726013183594,\n          52.118839263916016,\n          93.18865966796875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Burstiness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.29594480049376026,\n        \"min\": 0.5417330132714347,\n        \"max\": 1.5221673574388226,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          0.9237721712314578,\n          0.5417330132714347,\n          0.986518096971213\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_texts = df_texts.drop(columns=['text'])\n",
        "scaler = StandardScaler()\n",
        "X_texts = scaler.fit_transform(X_texts)\n",
        "# Assuming df_texts contains your new data\n",
        "X_texts = df_texts[['Token', 'Perplexity', 'Burstiness']]  # Select relevant columns\n",
        "X_texts = pd.DataFrame(X_texts, columns=['Token', 'Perplexity', 'Burstiness'])  # Assign feature names\n",
        "y_pred_texts = best_clf.predict(X_texts)\n",
        "y_proba_texts = best_clf.predict_proba(X_texts)[:, 1] if hasattr(best_clf, \"predict_proba\") else None\n",
        "\n",
        "# Show prediction and probability for each test sample\n",
        "texts_results = pd.DataFrame({\"Prediction\": y_pred_texts, \"Probability of being AI\": y_proba_texts})\n",
        "print(\"Prediction results:\")\n",
        "texts_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "id": "aELCraQw00_j",
        "outputId": "32a974f9-e7b1-4eae-d569-bde34e9526df"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction results:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Prediction  Probability of being AI\n",
              "0            0                      0.0\n",
              "1            0                      0.0\n",
              "2            0                      0.0\n",
              "3            0                      0.0\n",
              "4            0                      0.0\n",
              "5            0                      0.0\n",
              "6            0                      0.0\n",
              "7            0                      0.0\n",
              "8            0                      0.0\n",
              "9            0                      0.0\n",
              "10           0                      0.0"
            ],
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
              "<div id=\"df-9349ec2e-b3f0-4f1d-9e29-fe96d4cbf3e6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9349ec2e-b3f0-4f1d-9e29-fe96d4cbf3e6')\"\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-9349ec2e-b3f0-4f1d-9e29-fe96d4cbf3e6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_81eb58f0-18b8-4f1c-b025-67028590377c\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('texts_results')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_81eb58f0-18b8-4f1c-b025-67028590377c button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('texts_results');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "texts_results",
              "summary": "{\n  \"name\": \"texts_results\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"Prediction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Probability of being AI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    }
  ]
}