diff --git a/AI Guardian/AlertSystemGMAI/AlertSystemScript.ipynb b/AI Guardian/AlertSystemGMAI/AlertSystemScript.ipynb
new file mode 100644
index 00000000..4e87bd2a
--- /dev/null
+++ b/AI Guardian/AlertSystemGMAI/AlertSystemScript.ipynb	
@@ -0,0 +1,1443 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Xho6wPnq3c9t",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "6548bbab-7c6b-4637-c5a9-ff4a2a228279"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n"
+          ]
+        }
+      ],
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ------------------------\n",
+        "import re, json, math, numpy as np, pandas as pd\n",
+        "from datetime import datetime\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.metrics import classification_report, confusion_matrix\n",
+        "import matplotlib.pyplot as plt\n",
+        "import tensorflow as tf\n",
+        "from tensorflow import keras\n",
+        "from tensorflow.keras import layers"
+      ],
+      "metadata": {
+        "id": "3fpoJJvLMem1"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!ls \"/content/drive/MyDrive/William\""
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "f2qjVIJRMpOk",
+        "outputId": "4a0d6bf3-8830-45e5-8cbb-b1c4df672826"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "'New AI spreadsheet - Sheet1.csv'\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# -------- CONFIG --------\n",
+        "CSV_PATH = \"/content/drive/MyDrive/William/New AI spreadsheet - Sheet1.csv\" # <-- change to your path\n",
+        "SAVE_DIR = \"/content/drive/MyDrive/William\"                             # <-- change to Drive folder if you like\n",
+        "\n",
+        "PATIENT_ID_COL = \"patientId\"\n",
+        "TIME_START_COL = \"observationStart\"\n",
+        "TIME_END_COL   = \"observationEnd\"\n",
+        "\n",
+        "# features: vitals + behaviours (includes mealsSkipped now)\n",
+        "FEATURE_COLUMNS_RAW = [\n",
+        "    \"heartRate\",\"spo2\",\"temperature\",\"bloodPressure\",\n",
+        "    \"stepsTaken\",\"calorieIntake\",\"sleepHours\",\"waterIntakeMl\",\n",
+        "    \"exerciseMinutes\",\"bathroomVisits\",\"mealsSkipped\"\n",
+        "]\n",
+        "\n",
+        "# sequence config\n",
+        "SEQ_LEN   = 12\n",
+        "SEQ_STRIDE= 1\n",
+        "\n",
+        "# training config\n",
+        "EPOCHS = 30\n",
+        "BATCH_SIZE = 64\n",
+        "LR = 1e-3\n",
+        "VAL_SPLIT = 0.2\n",
+        "PATIENCE = 6\n",
+        "ANOMALY_PERCENTILE = 95  # for AE fallback\n",
+        "\n",
+        "print(\"TF:\", tf.__version__)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Drvsf3Fq4JtD",
+        "outputId": "5be7d73b-f68a-4155-befe-428c33e21e3e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "TF: 2.19.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# --------- Load ----------\n",
+        "df = pd.read_csv(CSV_PATH)\n",
+        "\n",
+        "# sanity check\n",
+        "req_base = [PATIENT_ID_COL, TIME_START_COL] + FEATURE_COLUMNS_RAW\n",
+        "missing = [c for c in req_base if c not in df.columns]\n",
+        "if missing:\n",
+        "    raise ValueError(f\"Missing required columns: {missing}\")"
+      ],
+      "metadata": {
+        "id": "07Sxqosl4Jpm"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.head(5)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 551
+        },
+        "id": "NYrBj-JzNBce",
+        "outputId": "c72afb76-f2e7-428d-eb85-715c20a65e44"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "  patientId  age gender           observationStart             observationEnd  \\\n",
+              "0     P0001   80   Male  2025-06-01T00:00:00+00:00  2025-06-01T06:00:00+00:00   \n",
+              "1     P0001   80   Male  2025-06-01T06:00:00+00:00  2025-06-01T12:00:00+00:00   \n",
+              "2     P0001   80   Male  2025-06-01T12:00:00+00:00  2025-06-01T18:00:00+00:00   \n",
+              "3     P0001   80   Male  2025-06-01T18:00:00+00:00  2025-06-02T00:00:00+00:00   \n",
+              "4     P0001   80   Male  2025-06-02T00:00:00+00:00  2025-06-02T06:00:00+00:00   \n",
+              "\n",
+              "                                         nursingNote  \\\n",
+              "0  Patient remains stable with a heart rate of 65...   \n",
+              "1  Patient is currently stable and alert, reporti...   \n",
+              "2  Patient is currently stable and alert, reporti...   \n",
+              "3  Patient remains stable with a heart rate of 73...   \n",
+              "4  Patient remains uncomfortable, but vital signs...   \n",
+              "\n",
+              "                                         medications heartRate spo2  \\\n",
+              "0  Acetaminophen(Taken)(500mg), Furosemide(Taken)...    65 bpm  97%   \n",
+              "1   Amlodipine(Taken)(10mg), Lisinopril(Taken)(20mg)    76 bpm  97%   \n",
+              "2  Metoprolol(Taken)(25mg), Lisinopril(Delayed)(1...    67 bpm  99%   \n",
+              "3   Metoprolol(Taken)(50mg), Lisinopril(Taken)(10mg)    73 bpm  99%   \n",
+              "4  Oxygen(Delayed)(2L/min via nasal cannula), Pre...    85 bpm  92%   \n",
+              "\n",
+              "  temperature  ... waterIntakeMl  mealsSkipped  exerciseMinutes  \\\n",
+              "0     36.5 °C  ...           411             0                5   \n",
+              "1     36.8 °C  ...           616             1               16   \n",
+              "2     36.8 °C  ...           650             0                6   \n",
+              "3     36.7 °C  ...           356             0               10   \n",
+              "4     37.1 °C  ...           254             1                4   \n",
+              "\n",
+              "   bathroomVisits                                      behaviourTags  \\\n",
+              "0               3    cooperative, complaining, sleepy, inPain, tired   \n",
+              "1               3  cooperative, talkative, sleepy, skippedMeals, ...   \n",
+              "2               1                                      sleepy, tired   \n",
+              "3               3              sleepy, talkative, cooperative, tired   \n",
+              "4               3  sleepy, skippedMeals, refusedActivity, noncomp...   \n",
+              "\n",
+              "   emotionTags                                    clinicalSummary  \\\n",
+              "0          NaN  Patient remains stable with a heart rate of 65...   \n",
+              "1          NaN  Patient is stable with a heart rate of 76 bpm ...   \n",
+              "2          NaN  Patient remains stable with vital signs within...   \n",
+              "3          NaN  Patient remains stable with a heart rate of 73...   \n",
+              "4   frustrated  Patient remains uncomfortable with fatigue and...   \n",
+              "\n",
+              "                                 entitiesExtracted  \\\n",
+              "0  {symptoms=, vitals=, procedures=, medications=}   \n",
+              "1  {symptoms=, vitals=, procedures=, medications=}   \n",
+              "2  {symptoms=, vitals=, procedures=, medications=}   \n",
+              "3  {symptoms=, vitals=, procedures=, medications=}   \n",
+              "4  {symptoms=, vitals=, procedures=, medications=}   \n",
+              "\n",
+              "                                       baselineStats state  \n",
+              "0  {avgHeartRate=70.1, avgSpo2=97.4, avgSleepHour...   NaN  \n",
+              "1  {avgHeartRate=75.7, avgSpo2=96.4, avgSleepHour...   NaN  \n",
+              "2  {avgHeartRate=78.5, avgSpo2=97.2, avgSleepHour...   NaN  \n",
+              "3  {avgHeartRate=84.6, avgSpo2=97.6, avgSleepHour...   NaN  \n",
+              "4  {avgHeartRate=74.6, avgSpo2=95.2, avgSleepHour...   NaN  \n",
+              "\n",
+              "[5 rows x 24 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-addadc82-e144-4418-be30-24ec5e487444\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>patientId</th>\n",
+              "      <th>age</th>\n",
+              "      <th>gender</th>\n",
+              "      <th>observationStart</th>\n",
+              "      <th>observationEnd</th>\n",
+              "      <th>nursingNote</th>\n",
+              "      <th>medications</th>\n",
+              "      <th>heartRate</th>\n",
+              "      <th>spo2</th>\n",
+              "      <th>temperature</th>\n",
+              "      <th>...</th>\n",
+              "      <th>waterIntakeMl</th>\n",
+              "      <th>mealsSkipped</th>\n",
+              "      <th>exerciseMinutes</th>\n",
+              "      <th>bathroomVisits</th>\n",
+              "      <th>behaviourTags</th>\n",
+              "      <th>emotionTags</th>\n",
+              "      <th>clinicalSummary</th>\n",
+              "      <th>entitiesExtracted</th>\n",
+              "      <th>baselineStats</th>\n",
+              "      <th>state</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>P0001</td>\n",
+              "      <td>80</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>2025-06-01T00:00:00+00:00</td>\n",
+              "      <td>2025-06-01T06:00:00+00:00</td>\n",
+              "      <td>Patient remains stable with a heart rate of 65...</td>\n",
+              "      <td>Acetaminophen(Taken)(500mg), Furosemide(Taken)...</td>\n",
+              "      <td>65 bpm</td>\n",
+              "      <td>97%</td>\n",
+              "      <td>36.5 °C</td>\n",
+              "      <td>...</td>\n",
+              "      <td>411</td>\n",
+              "      <td>0</td>\n",
+              "      <td>5</td>\n",
+              "      <td>3</td>\n",
+              "      <td>cooperative, complaining, sleepy, inPain, tired</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Patient remains stable with a heart rate of 65...</td>\n",
+              "      <td>{symptoms=, vitals=, procedures=, medications=}</td>\n",
+              "      <td>{avgHeartRate=70.1, avgSpo2=97.4, avgSleepHour...</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>P0001</td>\n",
+              "      <td>80</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>2025-06-01T06:00:00+00:00</td>\n",
+              "      <td>2025-06-01T12:00:00+00:00</td>\n",
+              "      <td>Patient is currently stable and alert, reporti...</td>\n",
+              "      <td>Amlodipine(Taken)(10mg), Lisinopril(Taken)(20mg)</td>\n",
+              "      <td>76 bpm</td>\n",
+              "      <td>97%</td>\n",
+              "      <td>36.8 °C</td>\n",
+              "      <td>...</td>\n",
+              "      <td>616</td>\n",
+              "      <td>1</td>\n",
+              "      <td>16</td>\n",
+              "      <td>3</td>\n",
+              "      <td>cooperative, talkative, sleepy, skippedMeals, ...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Patient is stable with a heart rate of 76 bpm ...</td>\n",
+              "      <td>{symptoms=, vitals=, procedures=, medications=}</td>\n",
+              "      <td>{avgHeartRate=75.7, avgSpo2=96.4, avgSleepHour...</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>P0001</td>\n",
+              "      <td>80</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>2025-06-01T12:00:00+00:00</td>\n",
+              "      <td>2025-06-01T18:00:00+00:00</td>\n",
+              "      <td>Patient is currently stable and alert, reporti...</td>\n",
+              "      <td>Metoprolol(Taken)(25mg), Lisinopril(Delayed)(1...</td>\n",
+              "      <td>67 bpm</td>\n",
+              "      <td>99%</td>\n",
+              "      <td>36.8 °C</td>\n",
+              "      <td>...</td>\n",
+              "      <td>650</td>\n",
+              "      <td>0</td>\n",
+              "      <td>6</td>\n",
+              "      <td>1</td>\n",
+              "      <td>sleepy, tired</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Patient remains stable with vital signs within...</td>\n",
+              "      <td>{symptoms=, vitals=, procedures=, medications=}</td>\n",
+              "      <td>{avgHeartRate=78.5, avgSpo2=97.2, avgSleepHour...</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>P0001</td>\n",
+              "      <td>80</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>2025-06-01T18:00:00+00:00</td>\n",
+              "      <td>2025-06-02T00:00:00+00:00</td>\n",
+              "      <td>Patient remains stable with a heart rate of 73...</td>\n",
+              "      <td>Metoprolol(Taken)(50mg), Lisinopril(Taken)(10mg)</td>\n",
+              "      <td>73 bpm</td>\n",
+              "      <td>99%</td>\n",
+              "      <td>36.7 °C</td>\n",
+              "      <td>...</td>\n",
+              "      <td>356</td>\n",
+              "      <td>0</td>\n",
+              "      <td>10</td>\n",
+              "      <td>3</td>\n",
+              "      <td>sleepy, talkative, cooperative, tired</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Patient remains stable with a heart rate of 73...</td>\n",
+              "      <td>{symptoms=, vitals=, procedures=, medications=}</td>\n",
+              "      <td>{avgHeartRate=84.6, avgSpo2=97.6, avgSleepHour...</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>P0001</td>\n",
+              "      <td>80</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>2025-06-02T00:00:00+00:00</td>\n",
+              "      <td>2025-06-02T06:00:00+00:00</td>\n",
+              "      <td>Patient remains uncomfortable, but vital signs...</td>\n",
+              "      <td>Oxygen(Delayed)(2L/min via nasal cannula), Pre...</td>\n",
+              "      <td>85 bpm</td>\n",
+              "      <td>92%</td>\n",
+              "      <td>37.1 °C</td>\n",
+              "      <td>...</td>\n",
+              "      <td>254</td>\n",
+              "      <td>1</td>\n",
+              "      <td>4</td>\n",
+              "      <td>3</td>\n",
+              "      <td>sleepy, skippedMeals, refusedActivity, noncomp...</td>\n",
+              "      <td>frustrated</td>\n",
+              "      <td>Patient remains uncomfortable with fatigue and...</td>\n",
+              "      <td>{symptoms=, vitals=, procedures=, medications=}</td>\n",
+              "      <td>{avgHeartRate=74.6, avgSpo2=95.2, avgSleepHour...</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 24 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-addadc82-e144-4418-be30-24ec5e487444')\"\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",
+              "    </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-addadc82-e144-4418-be30-24ec5e487444 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-addadc82-e144-4418-be30-24ec5e487444');\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-5c1019ec-8e4b-4610-b95c-d821eabd126b\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5c1019ec-8e4b-4610-b95c-d821eabd126b')\"\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-5c1019ec-8e4b-4610-b95c-d821eabd126b button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df"
+            }
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# --------- Parsers ----------\n",
+        "def parse_num(x):\n",
+        "    if pd.isna(x): return np.nan\n",
+        "    if isinstance(x,(int,float)): return float(x)\n",
+        "    m = re.search(r\"([\\d\\.]+)\", str(x))\n",
+        "    return float(m.group(1)) if m else np.nan\n",
+        "\n",
+        "def parse_heart_rate(x): return parse_num(x)          # \"65 bpm\" -> 65\n",
+        "def parse_spo2(x):       return parse_num(x)          # \"97%\"   -> 97\n",
+        "def parse_temperature(x):return parse_num(x)          # \"36.5 °C\" -> 36.5\n",
+        "def parse_bp(x):\n",
+        "    if pd.isna(x): return (np.nan, np.nan)\n",
+        "    if isinstance(x,(int,float)): return (float(x), np.nan)\n",
+        "    m = re.search(r\"(\\d+)\\s*/\\s*(\\d+)\", str(x))\n",
+        "    return (float(m.group(1)), float(m.group(2))) if m else (np.nan,np.nan)\n"
+      ],
+      "metadata": {
+        "id": "8cuVMvN94Jnh"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# --------- Timestamp & sort ----------\n",
+        "df[\"_ts\"] = pd.to_datetime(df[TIME_START_COL], errors=\"coerce\")\n",
+        "df = df.dropna(subset=[\"_ts\", PATIENT_ID_COL]).copy()\n",
+        "df = df.sort_values([PATIENT_ID_COL, \"_ts\"]).reset_index(drop=True)"
+      ],
+      "metadata": {
+        "id": "JBhKwLDT4JlN"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# --------- Parse features ----------\n",
+        "df[\"_hr\"]      = df[\"heartRate\"].apply(parse_heart_rate)\n",
+        "df[\"_spo2\"]    = df[\"spo2\"].apply(parse_spo2)\n",
+        "df[\"_temp\"]    = df[\"temperature\"].apply(parse_temperature)\n",
+        "bp = df[\"bloodPressure\"].apply(parse_bp)\n",
+        "df[\"_bp_sys\"]  = [b[0] for b in bp]\n",
+        "df[\"_bp_dia\"]  = [b[1] for b in bp]\n",
+        "df[\"_steps\"]   = df[\"stepsTaken\"].apply(parse_num) if \"stepsTaken\" in df.columns else np.nan\n",
+        "df[\"_cal\"]     = df[\"calorieIntake\"].apply(parse_num) if \"calorieIntake\" in df.columns else np.nan\n",
+        "df[\"_sleep\"]   = df[\"sleepHours\"].apply(parse_num) if \"sleepHours\" in df.columns else np.nan\n",
+        "df[\"_water\"]   = df[\"waterIntakeMl\"].apply(parse_num) if \"waterIntakeMl\" in df.columns else np.nan\n",
+        "df[\"_exercise\"]= df[\"exerciseMinutes\"].apply(parse_num) if \"exerciseMinutes\" in df.columns else np.nan\n",
+        "df[\"_bathroom\"]= df[\"bathroomVisits\"].apply(parse_num) if \"bathroomVisits\" in df.columns else np.nan\n",
+        "df[\"_meals\"]   = df[\"mealsSkipped\"].apply(parse_num) if \"mealsSkipped\" in df.columns else np.nan\n",
+        "\n",
+        "FEATURE_COLS = [\"_hr\",\"_spo2\",\"_temp\",\"_bp_sys\",\"_bp_dia\",\"_steps\",\"_cal\",\"_sleep\",\"_water\",\"_exercise\",\"_bathroom\",\"_meals\"]"
+      ],
+      "metadata": {
+        "id": "vXJONJA74Jix"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# drop rows where all features NaN\n",
+        "df = df.dropna(subset=FEATURE_COLS, how=\"all\").copy()"
+      ],
+      "metadata": {
+        "id": "qHZAIDzO4JgJ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "for col in FEATURE_COLS:\n",
+        "    df[col] = df.groupby(PATIENT_ID_COL)[col].ffill().bfill()\n",
+        "\n",
+        "df[FEATURE_COLS] = df[FEATURE_COLS].fillna(df[FEATURE_COLS].median())"
+      ],
+      "metadata": {
+        "id": "mcE8cwjn4Jdr"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# --------- Optional labels (state) ----------\n",
+        "def clean_state(s):\n",
+        "    if pd.isna(s): return None\n",
+        "    s = str(s).strip().lower()\n",
+        "    if s in (\"\", \"nan\", \"none\"): return None\n",
+        "    return s\n",
+        "\n",
+        "if \"state\" in df.columns:\n",
+        "    df[\"_state_clean\"] = df[\"state\"].apply(clean_state)\n",
+        "else:\n",
+        "    df[\"_state_clean\"] = None"
+      ],
+      "metadata": {
+        "id": "D8hU3PiZ4JbL"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Map common labels to 3-class risk {Low, Medium, High}\n",
+        "state_map = {\n",
+        "    \"low\": \"low\", \"normal\": \"low\", \"ok\": \"low\", \"stable\": \"low\",\n",
+        "    \"medium\": \"medium\", \"moderate\": \"medium\",\n",
+        "    \"high\": \"high\", \"critical\": \"high\", \"alert\": \"high\", \"abnormal\": \"high\"\n",
+        "}\n",
+        "def map_to_risk(s):\n",
+        "    if s is None: return None\n",
+        "    # accept phrases like \"high risk\", \"medium alert\"\n",
+        "    if \"high\" in s: return \"high\"\n",
+        "    if \"medium\" in s or \"moderate\" in s: return \"medium\"\n",
+        "    if \"low\" in s or \"normal\" in s or \"stable\" in s or \"ok\" in s: return \"low\"\n",
+        "    # fallback via dict exacts\n",
+        "    return state_map.get(s, None)\n",
+        "\n",
+        "df[\"_risk3\"] = df[\"_state_clean\"].apply(map_to_risk)\n",
+        "has_labels = df[\"_risk3\"].notna().any()\n",
+        "\n",
+        "print(\"Labels detected for supervised training?\" , has_labels)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "oPx2jQIY4JYs",
+        "outputId": "33c427b1-c26b-445b-9e91-cf3e78f21ef8"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Labels detected for supervised training? True\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# --------- Scale ----------\n",
+        "scaler = StandardScaler()\n",
+        "df_scaled = df.copy()\n",
+        "df_scaled[FEATURE_COLS] = scaler.fit_transform(df[FEATURE_COLS])"
+      ],
+      "metadata": {
+        "id": "dh623n0Z4JWO"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# --------- Build sequences per patient ----------\n",
+        "def build_sequences(group, seq_len=SEQ_LEN, stride=SEQ_STRIDE, include_labels=False):\n",
+        "    X, y, meta = [], [], []\n",
+        "    feats = group[FEATURE_COLS].values\n",
+        "    times = group[\"_ts\"].values\n",
+        "    risks = group[\"_risk3\"].values if include_labels else None\n",
+        "\n",
+        "    for i in range(0, len(feats) - seq_len + 1, stride):\n",
+        "        X.append(feats[i:i+seq_len])\n",
+        "        if include_labels:\n",
+        "            # label at sequence end (you can choose majority vote instead)\n",
+        "            lbl = risks[i+seq_len-1]\n",
+        "            y.append(lbl)\n",
+        "        meta.append((i, i+seq_len-1, times[i+seq_len-1]))\n",
+        "    X = np.array(X, dtype=np.float32)\n",
+        "    y = np.array(y) if include_labels else None\n",
+        "    return X, y, meta\n",
+        "\n",
+        "all_X, all_y, all_meta = [], [], []\n",
+        "for pid, g in df_scaled.groupby(PATIENT_ID_COL):\n",
+        "    Xp, yp, mp = build_sequences(g, include_labels=has_labels)\n",
+        "    if len(Xp) > 0:\n",
+        "        all_X.append(Xp)\n",
+        "        all_meta += [(pid, *m) for m in mp]\n",
+        "        if has_labels:\n",
+        "            all_y.append(yp)\n",
+        "\n",
+        "X = np.concatenate(all_X, axis=0)\n",
+        "y = np.concatenate(all_y, axis=0) if has_labels else None\n",
+        "\n",
+        "print(\"X shape:\", X.shape, \"| y shape:\", (None if y is None else y.shape))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "kyeSLjYy50CW",
+        "outputId": "166d53f6-ac18-4770-940a-f40064221eb1"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "X shape: (1755, 12, 12) | y shape: (1755,)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# SUPERVISED: LSTM Classifier (if labels exist)\n",
+        "if has_labels:\n",
+        "    # Drop sequences with unknown labels first (built earlier when constructing y)\n",
+        "    unique_labels = pd.Series(y).dropna().unique().tolist()\n",
+        "    if len(unique_labels) < 2:\n",
+        "        print(\"Only one (or zero) label present after windowing -> switching to Autoencoder path.\")\n",
+        "        has_labels = False  # force AE path below\n",
+        "\n",
+        "if has_labels:\n",
+        "    # Determine final label set and index mapping\n",
+        "    # Keep canonical order but only include present classes\n",
+        "    canonical = [\"low\", \"medium\", \"high\"]\n",
+        "    present = [c for c in canonical if c in unique_labels]\n",
+        "    cls_to_idx = {c: i for i, c in enumerate(present)}\n",
+        "\n",
+        "    # Filter to present classes only\n",
+        "    keep = np.array([lbl in cls_to_idx for lbl in y])\n",
+        "    X = X[keep]\n",
+        "    y = y[keep]\n",
+        "    filtered_all_meta = [all_meta[i] for i, v in enumerate(keep) if v]\n",
+        "    y_idx = np.array([cls_to_idx[lbl] for lbl in y], dtype=np.int64)\n",
+        "\n",
+        "    timesteps, n_features = X.shape[1], X.shape[2]\n",
+        "\n",
+        "    # Choose head: binary vs softmax\n",
+        "    num_classes = len(present)\n",
+        "    binary_mode = (num_classes == 2)\n",
+        "\n",
+        "    # Safe split: only stratify when >1 class and class counts okay\n",
+        "    try:\n",
+        "        X_train, X_val, y_train, y_val = train_test_split(\n",
+        "            X, y_idx, test_size=VAL_SPLIT, random_state=42, stratify=y_idx\n",
+        "        )\n",
+        "    except Exception as e:\n",
+        "        print(\"Stratified split failed (often due to tiny class counts). Falling back to non-stratified split.\", e)\n",
+        "        X_train, X_val, y_train, y_val = train_test_split(\n",
+        "            X, y_idx, test_size=VAL_SPLIT, random_state=42, shuffle=True\n",
+        "        )\n",
+        "\n",
+        "    # Model\n",
+        "    inputs = keras.Input(shape=(timesteps, n_features))\n",
+        "    x = layers.Masking()(inputs)\n",
+        "    x = layers.LSTM(128, return_sequences=True, dropout=0.2)(x)\n",
+        "    x = layers.LSTM(64, return_sequences=False, dropout=0.2)(x)\n",
+        "    x = layers.Dense(64, activation=\"relu\")(x)\n",
+        "    x = layers.Dropout(0.3)(x)\n",
+        "\n",
+        "    if binary_mode:\n",
+        "        outputs = layers.Dense(1, activation=\"sigmoid\")(x)\n",
+        "        loss_fn = \"binary_crossentropy\"\n",
+        "        metrics = [\"accuracy\"]\n",
+        "    else:\n",
+        "        outputs = layers.Dense(num_classes, activation=\"softmax\")(x)\n",
+        "        loss_fn = \"sparse_categorical_crossentropy\"\n",
+        "        metrics = [\"accuracy\"]\n",
+        "\n",
+        "    clf = keras.Model(inputs, outputs)\n",
+        "    clf.compile(optimizer=keras.optimizers.Adam(LR), loss=loss_fn, metrics=metrics)\n",
+        "    clf.summary()\n",
+        "\n",
+        "    cb = [keras.callbacks.EarlyStopping(monitor=\"val_loss\", patience=PATIENCE, restore_best_weights=True)]\n",
+        "    hist = clf.fit(X_train, y_train, validation_data=(X_val, y_val),\n",
+        "                   epochs=EPOCHS, batch_size=BATCH_SIZE, callbacks=cb, verbose=1)\n",
+        "\n",
+        "    # Plots\n",
+        "    plt.figure(figsize=(6,4))\n",
+        "    plt.plot(hist.history[\"loss\"], label=\"train_loss\")\n",
+        "    plt.plot(hist.history[\"val_loss\"], label=\"val_loss\")\n",
+        "    plt.xlabel(\"Epoch\"); plt.ylabel(\"Loss\"); plt.title(\"Classifier Training\"); plt.legend(); plt.show()\n",
+        "\n",
+        "    # Evaluation\n",
+        "    if binary_mode:\n",
+        "        y_prob = clf.predict(X_val, batch_size=256, verbose=0).ravel()\n",
+        "        y_pred_val = (y_prob >= 0.5).astype(int)\n",
+        "        target_names = [present[i] for i in sorted(range(num_classes))]\n",
+        "    else:\n",
+        "        y_pred_val = np.argmax(clf.predict(X_val, batch_size=256, verbose=0), axis=1)\n",
+        "        target_names = [present[i] for i in sorted(np.unique(y_val))]\n",
+        "\n",
+        "    from sklearn.metrics import classification_report, confusion_matrix\n",
+        "    print(classification_report(y_val, y_pred_val, target_names=target_names))\n",
+        "    print(\"Confusion matrix:\\n\", confusion_matrix(y_val, y_pred_val))\n",
+        "\n",
+        "    # Save model + scaler\n",
+        "    MODEL_PATH = f\"{SAVE_DIR}/guardian_lstm_classifier.keras\"\n",
+        "    SCALER_PATH = f\"{SAVE_DIR}/scaler.npy\"\n",
+        "    np.save(SCALER_PATH, {\"mean_\": scaler.mean_, \"scale_\": scaler.scale_, \"feature_names\": FEATURE_COLS}, allow_pickle=True)\n",
+        "    clf.save(MODEL_PATH)\n",
+        "    print(\"Saved:\", MODEL_PATH, \"and\", SCALER_PATH)\n",
+        "\n",
+        "    # Predictions for ALL sequences (filtered)\n",
+        "    if binary_mode:\n",
+        "        y_prob_all = clf.predict(X, batch_size=256, verbose=0).ravel()\n",
+        "        y_pred_all = (y_prob_all >= 0.5).astype(int)\n",
+        "        inv_idx = {i: c for c, i in cls_to_idx.items()}\n",
+        "        risk_pred = [inv_idx[i] for i in y_pred_all]\n",
+        "    else:\n",
+        "        y_pred_all = np.argmax(clf.predict(X, batch_size=256, verbose=0), axis=1)\n",
+        "        inv_idx = {i: c for c, i in cls_to_idx.items()}\n",
+        "        risk_pred = [inv_idx[i] for i in y_pred_all]\n",
+        "\n",
+        "    res = pd.DataFrame({\n",
+        "        \"patientId_seqEnd\": [m[0] for m in filtered_all_meta],\n",
+        "        \"seq_start_idx\": [m[1] for m in filtered_all_meta],\n",
+        "        \"seq_end_idx\":   [m[2] for m in filtered_all_meta],\n",
+        "        \"seq_end_time\":  [m[3] for m in filtered_all_meta],\n",
+        "        \"risk_pred\": risk_pred\n",
+        "    })\n",
+        "    res[\"risk_true\"] = [inv_idx[i] for i in y_idx]\n",
+        "    OUT_PATH = f\"{SAVE_DIR}/guardian_classifier_predictions.csv\"\n",
+        "    res.to_csv(OUT_PATH, index=False)\n",
+        "    print(\"Saved predictions:\", OUT_PATH)\n",
+        "\n",
+        "\n",
+        "# =========================\n",
+        "# UNSUPERVISED: LSTM Autoencoder (fallback)\n",
+        "# =========================\n",
+        "else:\n",
+        "    # Debug: check sequence/meta sizes before split\n",
+        "    print(f\"[AE] Total sequences: {len(X)} | meta entries: {len(all_meta)}\")\n",
+        "\n",
+        "    X_train, X_val = train_test_split(X, test_size=VAL_SPLIT, random_state=42, shuffle=True)\n",
+        "    timesteps, n_features = X.shape[1], X.shape[2]\n",
+        "\n",
+        "    inputs = keras.Input(shape=(timesteps, n_features))\n",
+        "    # Encoder\n",
+        "    x = layers.LSTM(128, return_sequences=True)(inputs)\n",
+        "    x = layers.LSTM(64, return_sequences=False)(x)\n",
+        "    encoded = layers.Dense(32, activation=\"relu\")(x)\n",
+        "    # Decoder\n",
+        "    x = layers.RepeatVector(timesteps)(encoded)\n",
+        "    x = layers.LSTM(64, return_sequences=True)(x)\n",
+        "    x = layers.LSTM(128, return_sequences=True)(x)\n",
+        "    outputs = layers.TimeDistributed(layers.Dense(n_features))(x)\n",
+        "\n",
+        "    ae = keras.Model(inputs, outputs)\n",
+        "    ae.compile(optimizer=keras.optimizers.Adam(LR), loss=\"mse\")\n",
+        "    ae.summary()\n",
+        "\n",
+        "    cb = [keras.callbacks.EarlyStopping(monitor=\"val_loss\", patience=PATIENCE, restore_best_weights=True)]\n",
+        "    hist = ae.fit(X_train, X_train, validation_data=(X_val, X_val),\n",
+        "                  epochs=EPOCHS, batch_size=BATCH_SIZE, callbacks=cb, verbose=1)\n",
+        "\n",
+        "    # Plot\n",
+        "    plt.figure(figsize=(6,4))\n",
+        "    plt.plot(hist.history[\"loss\"], label=\"train_loss\")\n",
+        "    plt.plot(hist.history[\"val_loss\"], label=\"val_loss\")\n",
+        "    plt.xlabel(\"Epoch\"); plt.ylabel(\"MSE\"); plt.title(\"Autoencoder Training\"); plt.legend(); plt.show()\n",
+        "\n",
+        "    # Errors\n",
+        "    def recon_err(m, Xb):\n",
+        "        Xh = m.predict(Xb, batch_size=256, verbose=0)\n",
+        "        return np.mean((Xh - Xb)**2, axis=(1,2))\n",
+        "\n",
+        "    train_err = recon_err(ae, X_train)\n",
+        "    thr = np.percentile(train_err, ANOMALY_PERCENTILE)\n",
+        "    print(f\"[AE] Threshold p{ANOMALY_PERCENTILE}: {thr:.6f}\")\n",
+        "\n",
+        "    all_err = recon_err(ae, X)\n",
+        "    is_anom = (all_err >= thr).astype(int)\n",
+        "\n",
+        "    def err_to_risk(e, t):\n",
+        "        if e < t: return \"low\"\n",
+        "        elif e < 1.5*t: return \"medium\"\n",
+        "        else: return \"high\"\n",
+        "\n",
+        "    risk = [err_to_risk(e, thr) for e in all_err]\n",
+        "\n",
+        "    # --- Align meta and error arrays if lengths differ (common after partial re-runs)\n",
+        "    len_meta = len(all_meta)\n",
+        "    len_err  = len(all_err)\n",
+        "    if len_meta != len_err:\n",
+        "        print(f\"[AE] Warning: meta ({len_meta}) != errors ({len_err}). Aligning to min length.\")\n",
+        "        k = min(len_meta, len_err)\n",
+        "        all_meta = all_meta[:k]\n",
+        "        all_err  = all_err[:k]\n",
+        "        is_anom  = is_anom[:k]\n",
+        "        risk     = risk[:k]\n",
+        "\n",
+        "    # Build results\n",
+        "    res = pd.DataFrame({\n",
+        "        \"patientId_seqEnd\": [m[0] for m in all_meta],\n",
+        "        \"seq_start_idx\":    [m[1] for m in all_meta],\n",
+        "        \"seq_end_idx\":      [m[2] for m in all_meta],\n",
+        "        \"seq_end_time\":     [m[3] for m in all_meta],\n",
+        "        \"recon_error\":      all_err,\n",
+        "        \"is_anomaly\":       is_anom,\n",
+        "        \"risk_pred\":        risk\n",
+        "    })\n",
+        "\n",
+        "    # Save artifacts\n",
+        "    MODEL_PATH = f\"{SAVE_DIR}/guardian_lstm_autoencoder.keras\"\n",
+        "    SCALER_PATH = f\"{SAVE_DIR}/scaler.npy\"\n",
+        "    np.save(SCALER_PATH, {\"mean_\": scaler.mean_, \"scale_\": scaler.scale_, \"feature_names\": FEATURE_COLS}, allow_pickle=True)\n",
+        "    ae.save(MODEL_PATH)\n",
+        "\n",
+        "    OUT_PATH = f\"{SAVE_DIR}/guardian_ae_predictions.csv\"\n",
+        "    res.to_csv(OUT_PATH, index=False)\n",
+        "    print(\"Saved:\", MODEL_PATH, SCALER_PATH, OUT_PATH)\n",
+        "\n",
+        "# =========================\n",
+        "# Inference helper (later for API)\n",
+        "# =========================\n",
+        "def make_sequence_from_rows(rows_df, scaler_obj=None, feature_cols=FEATURE_COLS, seq_len=SEQ_LEN):\n",
+        "    tmp = rows_df.copy()\n",
+        "    # parse again\n",
+        "    tmp[\"_hr\"]      = tmp[\"heartRate\"].apply(parse_heart_rate)\n",
+        "    tmp[\"_spo2\"]    = tmp[\"spo2\"].apply(parse_spo2)\n",
+        "    tmp[\"_temp\"]    = tmp[\"temperature\"].apply(parse_temperature)\n",
+        "    bp = tmp[\"bloodPressure\"].apply(parse_bp)\n",
+        "    tmp[\"_bp_sys\"]  = [b[0] for b in bp]\n",
+        "    tmp[\"_bp_dia\"]  = [b[1] for b in bp]\n",
+        "    tmp[\"_steps\"]   = tmp[\"stepsTaken\"].apply(parse_num) if \"stepsTaken\" in tmp.columns else np.nan\n",
+        "    tmp[\"_cal\"]     = tmp[\"calorieIntake\"].apply(parse_num) if \"calorieIntake\" in tmp.columns else np.nan\n",
+        "    tmp[\"_sleep\"]   = tmp[\"sleepHours\"].apply(parse_num) if \"sleepHours\" in tmp.columns else np.nan\n",
+        "    tmp[\"_water\"]   = tmp[\"waterIntakeMl\"].apply(parse_num) if \"waterIntakeMl\" in tmp.columns else np.nan\n",
+        "    tmp[\"_exercise\"]= tmp[\"exerciseMinutes\"].apply(parse_num) if \"exerciseMinutes\" in tmp.columns else np.nan\n",
+        "    tmp[\"_bathroom\"]= tmp[\"bathroomVisits\"].apply(parse_num) if \"bathroomVisits\" in tmp.columns else np.nan\n",
+        "    tmp[\"_meals\"]   = tmp[\"mealsSkipped\"].apply(parse_num) if \"mealsSkipped\" in tmp.columns else np.nan\n",
+        "\n",
+        "    tmp = tmp.sort_values(TIME_START_COL)\n",
+        "    feats = tmp[feature_cols].ffill().bfill()\n",
+        "    feats = feats.fillna(feats.median())\n",
+        "\n",
+        "    if scaler_obj is not None:\n",
+        "        # scaler_obj should be dict with mean_ and scale_\n",
+        "        mean_ = np.array(scaler_obj.item().get(\"mean_\")) if hasattr(scaler_obj, \"item\") else scaler_obj[\"mean_\"]\n",
+        "        scale_= np.array(scaler_obj.item().get(\"scale_\")) if hasattr(scaler_obj, \"item\") else scaler_obj[\"scale_\"]\n",
+        "        feats = (feats.values - mean_) / scale_\n",
+        "    else:\n",
+        "        # If scaler_obj is None, return the raw (filled) features\n",
+        "        feats = feats.values\n",
+        "\n",
+        "    if len(feats) != seq_len:\n",
+        "        raise ValueError(f\"Need exactly {seq_len} rows to form one sequence, got {len(feats)}.\")\n",
+        "    return np.expand_dims(feats.astype(np.float32), axis=0)\n",
+        "\n",
+        "print(\"Notebook ready: trains classifier if labels exist; otherwise trains AE fallback.\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "M64KyHbi5z-2",
+        "outputId": "6743c4d2-ec16-4e7e-9569-f63af4a2073d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[AE] Total sequences: 1755 | meta entries: 1755\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1mModel: \"functional_2\"\u001b[0m\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_2\"</span>\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ input_layer_3 (\u001b[38;5;33mInputLayer\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m)         │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ lstm_10 (\u001b[38;5;33mLSTM\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m128\u001b[0m)        │        \u001b[38;5;34m72,192\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ lstm_11 (\u001b[38;5;33mLSTM\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │        \u001b[38;5;34m49,408\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)             │         \u001b[38;5;34m2,080\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ repeat_vector_2 (\u001b[38;5;33mRepeatVector\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m32\u001b[0m)         │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ lstm_12 (\u001b[38;5;33mLSTM\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m64\u001b[0m)         │        \u001b[38;5;34m24,832\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ lstm_13 (\u001b[38;5;33mLSTM\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m128\u001b[0m)        │        \u001b[38;5;34m98,816\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ time_distributed_2              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m)         │         \u001b[38;5;34m1,548\u001b[0m │\n",
+              "│ (\u001b[38;5;33mTimeDistributed\u001b[0m)               │                        │               │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ input_layer_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ lstm_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)        │        <span style=\"color: #00af00; text-decoration-color: #00af00\">72,192</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ lstm_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">49,408</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,080</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ repeat_vector_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">RepeatVector</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ lstm_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)         │        <span style=\"color: #00af00; text-decoration-color: #00af00\">24,832</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ lstm_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)        │        <span style=\"color: #00af00; text-decoration-color: #00af00\">98,816</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ time_distributed_2              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,548</span> │\n",
+              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TimeDistributed</span>)               │                        │               │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m248,876\u001b[0m (972.17 KB)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">248,876</span> (972.17 KB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m248,876\u001b[0m (972.17 KB)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">248,876</span> (972.17 KB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/30\n",
+            "\u001b[1m22/22\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 127ms/step - loss: 0.9809 - val_loss: 0.9338\n",
+            "Epoch 2/30\n",
+            "\u001b[1m22/22\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 82ms/step - loss: 0.9426 - val_loss: 0.9103\n",
+            "Epoch 3/30\n",
+            "\u001b[1m22/22\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 152ms/step - loss: 0.9209 - val_loss: 0.8864\n",
+            "Epoch 4/30\n",
+            "\u001b[1m22/22\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 89ms/step - loss: 0.8949 - val_loss: 0.8715\n",
+            "Epoch 5/30\n",
+            "\u001b[1m22/22\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 88ms/step - loss: 0.8771 - val_loss: 0.8613\n",
+            "Epoch 6/30\n",
+            "\u001b[1m22/22\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 82ms/step - loss: 0.8703 - val_loss: 0.8357\n",
+            "Epoch 7/30\n",
+            "\u001b[1m22/22\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 89ms/step - loss: 0.8432 - val_loss: 0.8241\n",
+            "Epoch 8/30\n",
+            "\u001b[1m22/22\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 104ms/step - loss: 0.8154 - val_loss: 0.8056\n",
+            "Epoch 9/30\n",
+            "\u001b[1m22/22\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 141ms/step - loss: 0.7992 - val_loss: 0.7943\n",
+            "Epoch 10/30\n",
+            "\u001b[1m22/22\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 80ms/step - loss: 0.7899 - val_loss: 0.7907\n",
+            "Epoch 11/30\n",
+            "\u001b[1m22/22\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 90ms/step - loss: 0.7741 - val_loss: 0.7762\n",
+            "Epoch 12/30\n",
+            "\u001b[1m22/22\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 84ms/step - loss: 0.7747 - val_loss: 0.7610\n",
+            "Epoch 13/30\n",
+            "\u001b[1m22/22\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 90ms/step - loss: 0.7580 - val_loss: 0.7598\n",
+            "Epoch 14/30\n",
+            "\u001b[1m22/22\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 149ms/step - loss: 0.7455 - val_loss: 0.7462\n",
+            "Epoch 15/30\n",
+            "\u001b[1m22/22\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 78ms/step - loss: 0.7334 - val_loss: 0.7340\n",
+            "Epoch 16/30\n",
+            "\u001b[1m22/22\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 82ms/step - loss: 0.7161 - val_loss: 0.7279\n",
+            "Epoch 17/30\n",
+            "\u001b[1m22/22\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 82ms/step - loss: 0.7175 - val_loss: 0.7129\n",
+            "Epoch 18/30\n",
+            "\u001b[1m22/22\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 79ms/step - loss: 0.6995 - val_loss: 0.6936\n",
+            "Epoch 19/30\n",
+            "\u001b[1m22/22\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 117ms/step - loss: 0.6809 - val_loss: 0.6837\n",
+            "Epoch 20/30\n",
+            "\u001b[1m22/22\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 138ms/step - loss: 0.6705 - val_loss: 0.6753\n",
+            "Epoch 21/30\n",
+            "\u001b[1m22/22\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 82ms/step - loss: 0.6577 - val_loss: 0.6577\n",
+            "Epoch 22/30\n",
+            "\u001b[1m22/22\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 83ms/step - loss: 0.6466 - val_loss: 0.6445\n",
+            "Epoch 23/30\n",
+            "\u001b[1m22/22\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 82ms/step - loss: 0.6416 - val_loss: 0.6392\n",
+            "Epoch 24/30\n",
+            "\u001b[1m22/22\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 124ms/step - loss: 0.6281 - val_loss: 0.6499\n",
+            "Epoch 25/30\n",
+            "\u001b[1m22/22\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 89ms/step - loss: 0.6217 - val_loss: 0.6296\n",
+            "Epoch 26/30\n",
+            "\u001b[1m22/22\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 83ms/step - loss: 0.6175 - val_loss: 0.6208\n",
+            "Epoch 27/30\n",
+            "\u001b[1m22/22\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 81ms/step - loss: 0.6060 - val_loss: 0.6169\n",
+            "Epoch 28/30\n",
+            "\u001b[1m22/22\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 88ms/step - loss: 0.6014 - val_loss: 0.6394\n",
+            "Epoch 29/30\n",
+            "\u001b[1m22/22\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 123ms/step - loss: 0.6097 - val_loss: 0.6238\n",
+            "Epoch 30/30\n",
+            "\u001b[1m22/22\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 81ms/step - loss: 0.6037 - val_loss: 0.5985\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 600x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[AE] Threshold p95: 0.952465\n",
+            "Saved: /content/drive/MyDrive/William/guardian_lstm_autoencoder.keras /content/drive/MyDrive/William/scaler.npy /content/drive/MyDrive/William/guardian_ae_predictions.csv\n",
+            "Notebook ready: trains classifier if labels exist; otherwise trains AE fallback.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ==== Simple classifier from behaviour features (RF + MLP) ====\n",
+        "import numpy as np, pandas as pd\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.metrics import classification_report\n",
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "from sklearn.neural_network import MLPClassifier\n",
+        "import joblib\n",
+        "\n",
+        "# Reuse the AE dataframe from earlier cell:\n",
+        "# Check the column names in the 'res' DataFrame from the previous cell\n",
+        "# The column names should be 'seq_end_time'\n",
+        "ae_df = pd.read_csv(f\"{SAVE_DIR}/guardian_ae_predictions.csv\", parse_dates=[\"seq_end_time\"])\n",
+        "\n",
+        "# Build compact tabular features per sequence from X (already built earlier); if X isn’t in memory, re-run the sequence-build cell.\n",
+        "def seq_vector(seq):\n",
+        "    B = seq  # (T,F)\n",
+        "    mu = B.mean(axis=0); sd = B.std(axis=0)\n",
+        "    last = B[-1]; delta = B[-1] - B[0]\n",
+        "    t = np.arange(B.shape[0])[:, None]; t_c = t - t.mean()\n",
+        "    slope = (t_c*(B - B.mean(axis=0))).sum(axis=0) / (t_c**2).sum()\n",
+        "    return np.concatenate([mu, sd, last, delta, slope], axis=0)\n",
+        "\n",
+        "Z = np.vstack([seq_vector(x) for x in X])  # features from your sequences\n",
+        "y_bin = (ae_df[\"risk_pred\"].isin([\"medium\",\"high\"])).astype(int).to_numpy()  # weak labels\n",
+        "\n",
+        "Z_tr, Z_te, y_tr, y_te = train_test_split(Z, y_bin, test_size=0.2, stratify=y_bin, random_state=42)\n",
+        "\n",
+        "print(f\"Shape of Z_tr: {Z_tr.shape}\")\n",
+        "print(f\"Shape of y_tr: {y_tr.shape}\")\n",
+        "print(f\"Shape of Z_te: {Z_te.shape}\")\n",
+        "print(f\"Shape of y_te: {y_te.shape}\")\n",
+        "\n",
+        "\n",
+        "rf = RandomForestClassifier(n_estimators=400, class_weight=\"balanced\", random_state=42, n_jobs=-1).fit(Z_tr, y_tr)\n",
+        "print(\"[RF]\\n\", classification_report(y_te, rf.predict(Z_te), zero_division=0))\n",
+        "\n",
+        "mlp = MLPClassifier(hidden_layer_sizes=(64,32), activation=\"relu\", max_iter=200, early_stopping=True, random_state=42).fit(Z_tr, y_tr)\n",
+        "print(\"[MLP]\\n\", classification_report(y_te, mlp.predict(Z_te), zero_division=0))\n",
+        "\n",
+        "\n",
+        "joblib.dump(rf, f\"{SAVE_DIR}/guardian_behavior_rf.joblib\")\n",
+        "joblib.dump(mlp, f\"{SAVE_DIR}/guardian_behavior_mlp.joblib\")\n",
+        "\n",
+        "def prob_to_risk(p):  # map prob → Low/Med/High\n",
+        "    return \"low\" if p < 0.30 else (\"medium\" if p < 0.60 else \"high\")\n",
+        "\n",
+        "rf_prob  = rf.predict_proba(Z)[:,1]\n",
+        "mlp_prob = mlp.predict_proba(Z)[:,1]\n",
+        "\n",
+        "\n",
+        "out = pd.DataFrame({\n",
+        "    \"patientId_seqEnd\": ae_df[\"patientId_seqEnd\"],\n",
+        "    \"seq_end_time\":     ae_df[\"seq_end_time\"],\n",
+        "    \"rf_prob\":          rf_prob,\n",
+        "    \"rf_risk\":          [prob_to_risk(p) for p in rf_prob],\n",
+        "    \"mlp_prob\":         mlp_prob,\n",
+        "    \"mlp_risk\":         [prob_to_risk(p) for p in mlp_prob],\n",
+        "})\n",
+        "out.to_csv(f\"{SAVE_DIR}/guardian_classifier_predictions.csv\", index=False)\n",
+        "print(f\"Saved {SAVE_DIR}/guardian_classifier_predictions.csv\")"
+      ],
+      "metadata": {
+        "id": "hRF4MaHg5z5p",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "9228a08b-0a18-4695-eabe-dbfd0bf62caa"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Shape of Z_tr: (1404, 60)\n",
+            "Shape of y_tr: (1404,)\n",
+            "Shape of Z_te: (351, 60)\n",
+            "Shape of y_te: (351,)\n",
+            "[RF]\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.96      1.00      0.98       333\n",
+            "           1       0.80      0.22      0.35        18\n",
+            "\n",
+            "    accuracy                           0.96       351\n",
+            "   macro avg       0.88      0.61      0.66       351\n",
+            "weighted avg       0.95      0.96      0.95       351\n",
+            "\n",
+            "[MLP]\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.95      1.00      0.97       333\n",
+            "           1       0.00      0.00      0.00        18\n",
+            "\n",
+            "    accuracy                           0.95       351\n",
+            "   macro avg       0.47      0.50      0.49       351\n",
+            "weighted avg       0.90      0.95      0.92       351\n",
+            "\n",
+            "Saved /content/drive/MyDrive/William/guardian_classifier_predictions.csv\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "ae  = pd.read_csv(f\"{SAVE_DIR}/guardian_ae_predictions.csv\", parse_dates=[\"seq_end_time\"])\n",
+        "clf = pd.read_csv(f\"{SAVE_DIR}/guardian_classifier_predictions.csv\", parse_dates=[\"seq_end_time\"])\n",
+        "\n",
+        "alerts = ae[[\"patientId_seqEnd\",\"seq_end_time\",\"recon_error\",\"risk_pred\"]].merge(\n",
+        "    clf[[\"patientId_seqEnd\",\"seq_end_time\",\"rf_prob\",\"rf_risk\",\"mlp_prob\",\"mlp_risk\"]],\n",
+        "    on=[\"patientId_seqEnd\",\"seq_end_time\"], how=\"outer\"\n",
+        ").rename(columns={\"patientId_seqEnd\":\"user_id\",\"seq_end_time\":\"timestamp\"})\n",
+        "\n",
+        "# choose one risk for dashboard (prefer RF if present, else AE)\n",
+        "alerts[\"risk\"] = alerts[\"rf_risk\"].fillna(alerts[\"risk_pred\"])\n",
+        "alerts = alerts.sort_values([\"risk\",\"timestamp\"], key=lambda s: s.map({\"high\":0,\"medium\":1,\"low\":2}))\n",
+        "alerts.to_csv(f\"{SAVE_DIR}/alerts_dashboard.csv\", index=False)\n",
+        "\n",
+        "top = alerts[alerts[\"risk\"].isin([\"high\",\"medium\"])].sort_values([\"risk\",\"timestamp\"], ascending=[True, False]).head(30)\n",
+        "top.to_csv(f\"{SAVE_DIR}/top_alerts.csv\", index=False)\n",
+        "\n",
+        "print(\"Saved /content/alerts_dashboard.csv and /content/top_alerts.csv\")"
+      ],
+      "metadata": {
+        "id": "084wc5Ks4JTn",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "6d140dba-4247-44f1-ca60-32665ecd0ca3"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saved /content/alerts_dashboard.csv and /content/top_alerts.csv\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "uu1z7dKc4JRG"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/AI Guardian/AlertSystemGMAI/README.md b/AI Guardian/AlertSystemGMAI/README.md
new file mode 100644
index 00000000..94aebd12
--- /dev/null
+++ b/AI Guardian/AlertSystemGMAI/README.md	
@@ -0,0 +1,43 @@
+# Alert System Task – William Jackson
+
+## Overview
+
+Behavioural anomaly detection pipeline using an LSTM autoencoder and simple classifiers.  
+The autoencoder is trained on supporter behaviour sequences and reconstruction error is used to map anomalies into Low / Medium / High risk.  
+Random Forest and MLP classifiers are also trained to detect anomalies from the same features.
+
+## Features
+
+- Steps taken
+- Calorie intake
+- Sleep hours
+- Water intake (ml)
+- Bathroom visits
+- Exercise minutes (if available)
+- Meals skipped (if available)
+
+## Contents
+
+- `AlertSystemScript.ipynb` – main notebook (training, scoring, exporting results)
+- `artifacts/` – models and outputs
+  - `guardian_lstm_autoencoder.keras`
+  - `guardian_behavior_rf.joblib`
+  - `guardian_behavior_mlp.joblib`
+  - `scaler.npy`
+  - `guardian_ae_predictions.csv`
+  - `guardian_classifier_predictions.csv`
+  - `top_alerts.csv` – shortlist of medium/high alerts top 30
+
+## How to run (Colab)
+
+1. Upload the dataset with original headers (`patientId, observationEnd, stepsTaken, calorieIntake, sleepHours, waterIntakeMl, bathroomVisits, exerciseMinutes, mealsSkipped`) to `/content/`.
+2. Set the dataset path at the top of the notebook:
+   ```python
+   CSV_PATH = "/content/New AI spreadsheet - Sheet1.csv"
+   ```
+3. Run the notebook end-to-end.
+4. Outputs will be saved to /content/:
+
+- guardian_ae_predictions.csv
+- guardian_classifier_predictions.csv
+- top_alerts.csv
diff --git a/AI Guardian/AlertSystemGMAI/RUNBOOK_WJ_Alerts.md b/AI Guardian/AlertSystemGMAI/RUNBOOK_WJ_Alerts.md
new file mode 100644
index 00000000..4844ce80
--- /dev/null
+++ b/AI Guardian/AlertSystemGMAI/RUNBOOK_WJ_Alerts.md	
@@ -0,0 +1,44 @@
+# Runbook – William Jackson
+
+## Requirements
+
+- Python 3.9+
+- Recommended environment: Google Colab
+- Packages needed: numpy, pandas, scikit-learn, tensorflow, matplotlib, joblib
+
+In Colab install with:
+
+```bash
+!pip install numpy pandas scikit-learn tensorflow matplotlib joblib
+```
+
+## Dataset
+
+File: New AI spreadsheet - Sheet1.csv  
+Headers required:  
+patientId, observationEnd, stepsTaken, calorieIntake, sleepHours, waterIntakeMl, bathroomVisits, exerciseMinutes, mealsSkipped
+
+Upload the CSV to /content/ when running in Colab.
+
+## Steps
+
+1. Upload dataset to /content/.
+2. Open AlertSystemScript.ipynb.
+3. At the top, set the dataset path:
+   ```python
+   CSV_PATH = "/content/New AI spreadsheet - Sheet1.csv"
+   ```
+4. Run the notebook in its entirety
+5. Outputs will be saved to /content/:
+   - guardian_ae_predictions.csv
+   - guardian_classifier_predictions.csv
+   - top_alerts.csv (top 30 medium/high alerts)
+
+## Artifacts
+
+The ipynb file Training also creates:
+
+- guardian_lstm_autoencoder.keras
+- guardian_behavior_rf.joblib
+- guardian_behavior_mlp.joblib
+- scaler.npy
diff --git a/AI Guardian/AlertSystemGMAI/artifacts/guardian_ae_predictions.csv b/AI Guardian/AlertSystemGMAI/artifacts/guardian_ae_predictions.csv
new file mode 100644
index 00000000..646eab91
--- /dev/null
+++ b/AI Guardian/AlertSystemGMAI/artifacts/guardian_ae_predictions.csv	
@@ -0,0 +1,1756 @@
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diff --git a/AI Guardian/AlertSystemGMAI/artifacts/guardian_behavior_mlp.joblib b/AI Guardian/AlertSystemGMAI/artifacts/guardian_behavior_mlp.joblib
new file mode 100644
index 00000000..4c56334e
Binary files /dev/null and b/AI Guardian/AlertSystemGMAI/artifacts/guardian_behavior_mlp.joblib differ
diff --git a/AI Guardian/AlertSystemGMAI/artifacts/guardian_behavior_rf.joblib b/AI Guardian/AlertSystemGMAI/artifacts/guardian_behavior_rf.joblib
new file mode 100644
index 00000000..c028bd24
Binary files /dev/null and b/AI Guardian/AlertSystemGMAI/artifacts/guardian_behavior_rf.joblib differ
diff --git a/AI Guardian/AlertSystemGMAI/artifacts/guardian_classifier_predictions.csv b/AI Guardian/AlertSystemGMAI/artifacts/guardian_classifier_predictions.csv
new file mode 100644
index 00000000..b47cb83c
--- /dev/null
+++ b/AI Guardian/AlertSystemGMAI/artifacts/guardian_classifier_predictions.csv	
@@ -0,0 +1,1756 @@
+patientId_seqEnd,seq_end_time,rf_prob,rf_risk,mlp_prob,mlp_risk
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diff --git a/AI Guardian/AlertSystemGMAI/artifacts/guardian_lstm_autoencoder.keras b/AI Guardian/AlertSystemGMAI/artifacts/guardian_lstm_autoencoder.keras
new file mode 100644
index 00000000..99e5e3a0
Binary files /dev/null and b/AI Guardian/AlertSystemGMAI/artifacts/guardian_lstm_autoencoder.keras differ
diff --git a/AI Guardian/AlertSystemGMAI/artifacts/scaler.npy b/AI Guardian/AlertSystemGMAI/artifacts/scaler.npy
new file mode 100644
index 00000000..9ace11f8
Binary files /dev/null and b/AI Guardian/AlertSystemGMAI/artifacts/scaler.npy differ
diff --git a/AI Guardian/AlertSystemGMAI/artifacts/top_alerts.csv b/AI Guardian/AlertSystemGMAI/artifacts/top_alerts.csv
new file mode 100644
index 00000000..30e2138c
--- /dev/null
+++ b/AI Guardian/AlertSystemGMAI/artifacts/top_alerts.csv	
@@ -0,0 +1,31 @@
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