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                  "Training results:\n",
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                  "text/plain": "              accuracy precision    recall        f1      mcc     kappa  \\\nModel                                                                     \nExtraTrees         1.0       1.0       1.0       1.0      1.0       1.0   \nRandomForest       1.0       1.0       1.0       1.0      1.0       1.0   \nGaussianNB    0.994374  0.997126  0.991429  0.994269  0.98876  0.988745   \n\n                  confusion_matrix  \nModel                               \nExtraTrees    [[361, 0], [0, 350]]  \nRandomForest  [[361, 0], [0, 350]]  \nGaussianNB    [[360, 1], [3, 347]]  ",
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\n"
                },
                "metadata": {}
              },
              {
                "output_type": "stream",
                "name": "stdout",
                "text": [
                  "Testing results:\n"
                ]
              },
              {
                "output_type": "display_data",
                "data": {
                  "text/plain": "              accuracy precision recall       f1       mcc     kappa  \\\nModel                                                                  \nExtraTrees    0.994382  0.988764    1.0  0.99435  0.988826  0.988764   \nRandomForest  0.994382  0.988764    1.0  0.99435  0.988826  0.988764   \nGaussianNB    0.994382  0.988764    1.0  0.99435  0.988826  0.988764   \n\n                confusion_matrix  \nModel                             \nExtraTrees    [[89, 1], [0, 88]]  \nRandomForest  [[89, 1], [0, 88]]  \nGaussianNB    [[89, 1], [0, 88]]  ",
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\n"
                },
                "metadata": {}
              },
              {
                "output_type": "stream",
                "name": "stderr",
                "text": [
                  "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1150: 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": "display_data",
                "data": {
                  "text/plain": "Processing DOCX Files:   0%|          | 0/9 [00:00<?, ?it/s]",
                  "application/vnd.jupyter.widget-view+json": {
                    "version_major": 2,
                    "version_minor": 0,
                    "model_id": "6aeb3788ecad48eaae6509a3b533201c"
                  }
                },
                "metadata": {}
              },
              {
                "output_type": "stream",
                "name": "stdout",
                "text": [
                  "Prediction results:\n"
                ]
              },
              {
                "output_type": "display_data",
                "data": {
                  "text/plain": "                               text  Token  Perplexity  Burstiness  \\\n0  A vitória de Donald Trump_1.docx    902   92.761719    0.396619   \n1  A vitória de Donald Trump_2.docx    766  127.694397    0.405892   \n2  A vitória de Donald Trump_3.docx    550  135.766022    0.473965   \n3                      Texto_1.docx    655  123.294540    0.273790   \n4                      Texto_2.docx    431  134.129929    0.227035   \n5                      Texto_3.docx    387  153.042831    0.275613   \n6                      Texto_4.docx    344  137.060455    0.289474   \n7                      Texto_5.docx    243  163.669144    0.324324   \n8                      Texto_6.docx    459  129.746124    0.420394   \n\n   Prediction  Probability of being AI  \n0           0                     0.06  \n1           0                     0.04  \n2           0                     0.00  \n3           1                     0.54  \n4           1                     0.54  \n5           1                     0.54  \n6           1                     0.54  \n7           1                     0.54  \n8           0                     0.00  ",
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  "cells": [
    {
      "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/"
        },
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      },
      "execution_count": 1,
      "outputs": [
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          "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=3959 sha256=27239cbca846571543f359bfe489b872da1db12216b5a3c2647154bd9ea301b8\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[31m1.7 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",
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            "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) (4.66.5)\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) (2024.6.1)\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) (3.3.2)\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[31m33.6 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[31m42.8 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 0.19.1\n",
            "    Uninstalling tokenizers-0.19.1:\n",
            "      Successfully uninstalled tokenizers-0.19.1\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.44.2\n",
            "    Uninstalling transformers-4.44.2:\n",
            "      Successfully uninstalled transformers-4.44.2\n",
            "Successfully installed tokenizers-0.13.3 transformers-4.23.0\n"
          ]
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        "outputId": "9eaf4e59-5344-4ab5-97af-5a2ecdc1968e"
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "FileUpload(value={}, accept='.xlsx', description='Upload Text Characterization File')"
            ],
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              "version_major": 2,
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              "FileUpload(value={}, accept='.docx', description='Upload DOCX Files', multiple=True)"
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            "text/plain": [
              "Output()"
            ],
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      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import io\n",
        "from IPython.display import display, clear_output\n",
        "import ipywidgets as widgets\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.ensemble import ExtraTreesClassifier, RandomForestClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.metrics import (\n",
        "    accuracy_score, precision_score, recall_score, f1_score,\n",
        "    matthews_corrcoef, cohen_kappa_score, confusion_matrix, ConfusionMatrixDisplay\n",
        ")\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "from scipy.cluster.hierarchy import dendrogram, linkage\n",
        "import seaborn as sns\n",
        "import docx2txt\n",
        "import torch\n",
        "from transformers import GPT2LMHeadModel, GPT2Tokenizer\n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "# Upload text characterization file\n",
        "upload_char_file = widgets.FileUpload(\n",
        "    accept='.xlsx', multiple=False, description=\"Upload Text Characterization File\"\n",
        ")\n",
        "upload_docx_files = widgets.FileUpload(\n",
        "    accept='.docx', multiple=True, description=\"Upload DOCX Files\"\n",
        ")\n",
        "\n",
        "# Display widgets\n",
        "display(upload_char_file, upload_docx_files)\n",
        "\n",
        "output = widgets.Output()\n",
        "\n",
        "@output.capture(clear_output=True)\n",
        "def process_files(change):\n",
        "    if upload_char_file.value and upload_docx_files.value:\n",
        "        # Process Text Characterization File\n",
        "        char_file = upload_char_file.value\n",
        "        char_file_name = next(iter(char_file))\n",
        "        df = pd.read_excel(io.BytesIO(char_file[char_file_name]['content']))\n",
        "\n",
        "        # Prepare features and labels\n",
        "        X = df.drop(columns=['index', 'text', 'AI Metric'])\n",
        "        y = df['AI Metric']\n",
        "\n",
        "        # Split dataset\n",
        "        X_train, X_test, y_train, y_test = train_test_split(\n",
        "            X, y, test_size=0.2, random_state=42, stratify=y\n",
        "        )\n",
        "\n",
        "        # Train classifiers\n",
        "        classifiers = {\n",
        "            'ExtraTrees': ExtraTreesClassifier(random_state=42),\n",
        "            'RandomForest': RandomForestClassifier(random_state=42),\n",
        "            'GaussianNB': GaussianNB()\n",
        "        }\n",
        "        metrics = ['accuracy', 'precision', 'recall', 'f1', 'mcc', 'kappa']\n",
        "        results = {}\n",
        "\n",
        "        for name, clf in classifiers.items():\n",
        "            clf.fit(X_train, y_train)\n",
        "            y_pred_train = clf.predict(X_train)\n",
        "            y_pred_test = clf.predict(X_test)\n",
        "\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",
        "        # Select best model\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",
        "        best_clf = classifiers[best_model]\n",
        "\n",
        "        # Display training metrics\n",
        "        df_train_metrics = pd.DataFrame({model: results[model]['train'] for model in results}).T\n",
        "        df_train_metrics.index.name = 'Model'\n",
        "        print(f\"Training results:\")\n",
        "        print(f\"Best Model: {best_model} based on training scores\")\n",
        "        display(df_train_metrics)\n",
        "\n",
        "        # Show confusion matrix for training\n",
        "        cm_train = results[best_model]['train']['confusion_matrix']\n",
        "        ConfusionMatrixDisplay(confusion_matrix=cm_train).plot()\n",
        "        plt.title(\"Training Confusion Matrix\")\n",
        "        plt.show()\n",
        "\n",
        "        # Display testing metrics\n",
        "        df_test_metrics = pd.DataFrame({model: results[model]['test'] for model in results}).T\n",
        "        df_test_metrics.index.name = 'Model'\n",
        "        print(f\"Testing results:\")\n",
        "        display(df_test_metrics)\n",
        "\n",
        "        # Show confusion matrix for testing\n",
        "        cm_test = results[best_model]['test']['confusion_matrix']\n",
        "        ConfusionMatrixDisplay(confusion_matrix=cm_train).plot()\n",
        "        plt.title(\"Testing Confusion Matrix\")\n",
        "        plt.show()\n",
        "\n",
        "        # Process DOCX Files\n",
        "        docx_files = upload_docx_files.value\n",
        "        lista_texts, lista_perplexity, lista_burstiness, lista_token_length = [], [], [], []\n",
        "\n",
        "        tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n",
        "        model = GPT2LMHeadModel.from_pretrained('gpt2')\n",
        "\n",
        "        for name, file in tqdm(docx_files.items(), desc=\"Processing DOCX Files\"): # Wrap with tqdm\n",
        "            text = docx2txt.process(io.BytesIO(file['content'])).replace('\\n', '')\n",
        "            lista_texts.append(name)\n",
        "\n",
        "            # Perplexity\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\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",
        "            lista_perplexity.append(perplexity.item())\n",
        "\n",
        "            # Burstiness\n",
        "            sentence_lengths = [len(sentence.split()) for sentence in text.split('.') if sentence]\n",
        "            burstiness = np.std(sentence_lengths) / np.mean(sentence_lengths) if sentence_lengths else 0\n",
        "            lista_burstiness.append(burstiness)\n",
        "\n",
        "            # Token Length\n",
        "            token_length = len(tokenizer.encode(text))\n",
        "            lista_token_length.append(token_length)\n",
        "\n",
        "        df_texts = pd.DataFrame({\n",
        "            'text': lista_texts,\n",
        "            'Token': lista_token_length,\n",
        "            'Perplexity': lista_perplexity,\n",
        "            'Burstiness': lista_burstiness\n",
        "        })\n",
        "\n",
        "        # Predict on DOCX data\n",
        "        X_texts = df_texts[['Token', 'Perplexity', 'Burstiness']]\n",
        "        X_texts_scaled = StandardScaler().fit_transform(X_texts)\n",
        "        X_texts_scaled = pd.DataFrame(X_texts_scaled, columns=['Token', 'Perplexity', 'Burstiness'])  # Assign feature names\n",
        "        y_pred_texts = best_clf.predict(X_texts_scaled)\n",
        "        y_proba_texts = best_clf.predict_proba(X_texts_scaled)[:, 1]\n",
        "\n",
        "        # Display results\n",
        "        texts_results = pd.DataFrame({\"Prediction\": y_pred_texts, \"Probability of being AI\": y_proba_texts})\n",
        "        print(\"Prediction results:\")\n",
        "        # Concatenate df_texts and df_texts\n",
        "        concatenated_df = pd.concat([df_texts, texts_results], ignore_index=False, axis=1)\n",
        "\n",
        "        # Display the concatenated dataframe\n",
        "        display(concatenated_df)\n",
        "\n",
        "upload_char_file.observe(process_files, names='value')\n",
        "upload_docx_files.observe(process_files, names='value')\n",
        "display(output)"
      ]
    }
  ]
}