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@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Image/360_F_1375669005_ebg3mldxps5ZYrQFlY6EX3eCINwVDeoF.jpg filter=lfs diff=lfs merge=lfs -text
+Image/stress-main.png filter=lfs diff=lfs merge=lfs -text

Final_stress_detection.ipynb

@@ -0,0 +1,909 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hykGuW-gdEZM"
+      },
+      "source": [
+        "### 1. Import Libraries\n",
+        "\n",
+        "We start by importing the required Python libraries for data processing, modeling, visualization, and creating a user interface.\n",
+        "\n",
+        "- `pandas`, `numpy`: For data handling and numerical operations.\n",
+        "- `sklearn`: For machine learning modeling and evaluation.\n",
+        "- `matplotlib`, `seaborn`: For plotting.\n",
+        "- `joblib`: For saving and loading the trained model and scaler.\n",
+        "- `gradio`: To build an interactive web interface for predictions.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "c3o9yJlOyqZq"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "c:\\Users\\sourikta.nag\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+            "  from .autonotebook import tqdm as notebook_tqdm\n"
+          ]
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "from sklearn.svm import OneClassSVM\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "from sklearn.metrics import confusion_matrix, classification_report\n",
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt\n",
+        "import joblib\n",
+        "import gradio as gr"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SkxcqtAibqBS"
+      },
+      "source": [
+        "### 2. Load Dataset\n",
+        "\n",
+        "We load the simulated stress data from a CSV file into a pandas DataFrame.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "XuxMzEo2y_Rq"
+      },
+      "outputs": [],
+      "source": [
+        "df = pd.read_csv('simulated_stress_data.csv')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iDNDdAVudNOk"
+      },
+      "source": [
+        "### 3. Feature Selection and Standardization\n",
+        "\n",
+        "We select the important physiological features:  \n",
+        "- Heart Rate (`HR`)  \n",
+        "- Heart Rate Variability (`HRV`)  \n",
+        "- Electrodermal Activity (`EDA`)\n",
+        "\n",
+        "These features are standardized using `StandardScaler` to ensure they are on the same scale. This improves the performance of distance-based models like One-Class SVM.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "IccdG-6kzB1h"
+      },
+      "outputs": [],
+      "source": [
+        "features = ['HR', 'HRV', 'EDA']\n",
+        "X = df[features]\n",
+        "scaler = StandardScaler()\n",
+        "X_scaled = scaler.fit_transform(X)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gERCwbz3dXPs"
+      },
+      "source": [
+        "### 4. Train the One-Class SVM\n",
+        "\n",
+        "One-Class SVM is trained only on \"normal\" (non-stress) data (`label == 0`) to learn the typical behavior.  \n",
+        "It will later classify any data point that deviates significantly as an anomaly (stress).\n",
+        "\n",
+        "- `kernel='rbf'`: Allows for non-linear separation.\n",
+        "- `nu=0.05`: Indicates the expected proportion of outliers.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 80
+        },
+        "id": "kP_vOKCKzEv5",
+        "outputId": "37070512-d25e-4287-ac89-f3d2b798a2bd"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-1 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: #000;\n",
+              "  --sklearn-color-text-muted: #666;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-1 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: flex;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "  align-items: start;\n",
+              "  justify-content: space-between;\n",
+              "  gap: 0.5em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+              "  font-size: 0.6rem;\n",
+              "  font-weight: lighter;\n",
+              "  color: var(--sklearn-color-text-muted);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-1 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-1 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 0.5em;\n",
+              "  text-align: center;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneClassSVM(gamma=&#x27;auto&#x27;, nu=0.05)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>OneClassSVM</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.svm.OneClassSVM.html\">?<span>Documentation for OneClassSVM</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>OneClassSVM(gamma=&#x27;auto&#x27;, nu=0.05)</pre></div> </div></div></div></div>"
+            ],
+            "text/plain": [
+              "OneClassSVM(gamma='auto', nu=0.05)"
+            ]
+          },
+          "execution_count": 7,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "X_train = X_scaled[df['label'] == 0]\n",
+        "svm = OneClassSVM(kernel='rbf', gamma='auto', nu=0.05)  # nu is similar to contamination (regularization parameter)\n",
+        "svm.fit(X_train)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-hk_VnreeH4O"
+      },
+      "source": [
+        "### 5. Make Predictions and Add Column\n",
+        "\n",
+        "We use the trained SVM to predict the entire dataset and convert predictions to:\n",
+        "- `1` = Stress (anomaly)\n",
+        "- `0` = No Stress (normal)\n",
+        "\n",
+        "These are saved as a new column in the DataFrame.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "YdjO3Tz7zIDq"
+      },
+      "outputs": [],
+      "source": [
+        "pred = svm.predict(X_scaled)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "CNqr0aPszYbx"
+      },
+      "outputs": [],
+      "source": [
+        "df['svm_anomaly'] = np.where(pred == -1, 1, 0)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PchatUMqfH_f"
+      },
+      "source": [
+        "### 6. Evaluate the Model\n",
+        "\n",
+        "We compare the predicted labels (`svm_anomaly`) against the true labels (`label`) using:\n",
+        "- Confusion Matrix\n",
+        "- Classification Report (Precision, Recall, F1-score)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "tnkKPa6oza_R"
+      },
+      "outputs": [],
+      "source": [
+        "y_true = df['label']\n",
+        "y_pred = df['svm_anomaly']"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "MMm8CfDDzeDK",
+        "outputId": "b2507be4-9e68-41be-ca5b-7e3d4c414646"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Confusion Matrix:\n",
+            " [[4537  240]\n",
+            " [   0 1943]]\n",
+            "\n",
+            "Classification Report:\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "           0       1.00      0.95      0.97      4777\n",
+            "           1       0.89      1.00      0.94      1943\n",
+            "\n",
+            "    accuracy                           0.96      6720\n",
+            "   macro avg       0.95      0.97      0.96      6720\n",
+            "weighted avg       0.97      0.96      0.96      6720\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "cm = confusion_matrix(y_true, y_pred)\n",
+        "print(\"\\nConfusion Matrix:\\n\", cm)\n",
+        "print(\"\\nClassification Report:\\n\", classification_report(y_true, y_pred))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uQEOr85mcbZz"
+      },
+      "source": [
+        "### 7. Plot the Confusion Matrix\n",
+        "\n",
+        "We use Seaborn to plot a heatmap of the confusion matrix for better visualization.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 507
+        },
+        "id": "0duQF6pQzd8p",
+        "outputId": "202b31b9-bbdf-403d-de7f-8e088bc8f2a1"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 600x500 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.figure(figsize=(6, 5))\n",
+        "sns.heatmap(cm, annot=True, fmt='d', cmap='Purples',\n",
+        "            xticklabels=['No Stress', 'Stress'],\n",
+        "            yticklabels=['No Stress', 'Stress'])\n",
+        "plt.xlabel('Predicted Label')\n",
+        "plt.ylabel('True Label')\n",
+        "plt.title('Confusion Matrix - One-Class SVM Stress Detection')\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lpmFNKeSfRI3"
+      },
+      "source": [
+        "### 8. Save the Model and Scaler\n",
+        "\n",
+        "We save the trained One-Class SVM model and the scaler using `joblib` so they can be reused later for new data.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "RhL9Aqbqzk9x",
+        "outputId": "45a2425b-1c47-4211-af42-5b7617252372"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "['scaler.pkl']"
+            ]
+          },
+          "execution_count": 13,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Save the trained model to a file\n",
+        "joblib.dump(svm, 'one_class_svm_stress_model.pkl')\n",
+        "\n",
+        "# Save the scaler as well, to use the same scaling on new data\n",
+        "joblib.dump(scaler, 'scaler.pkl')\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JWvEAt3CfjZu"
+      },
+      "source": [
+        "### 9. Load Saved Model and Scaler\n",
+        "\n",
+        "We load the previously saved model and scaler for use in prediction.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "id": "KaTPWMO2fnYH"
+      },
+      "outputs": [],
+      "source": [
+        "try:\n",
+        "    model = joblib.load(\"one_class_svm_stress_model.pkl\")\n",
+        "    scaler = joblib.load(\"scaler.pkl\")\n",
+        "except Exception as e:\n",
+        "    print(f\"Error loading model or scaler: {e}\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4xYXYvpSfoJe"
+      },
+      "source": [
+        "### 10. Define Prediction Function\n",
+        "\n",
+        "This function generates a random sample of physiological signals and uses the model to classify whether the person is under stress.\n",
+        "- Values are scaled using the saved scaler.\n",
+        "- One-Class SVM makes the prediction.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "id": "rZ-ALqfNfqf-"
+      },
+      "outputs": [],
+      "source": [
+        "def predict_stress():\n",
+        "    try:\n",
+        "        hr = np.random.randint(60, 120)\n",
+        "        hrv = np.random.uniform(20, 80)\n",
+        "        eda = np.random.uniform(0.1, 5.0)\n",
+        "\n",
+        "        sample = np.array([[hr, hrv, eda]])\n",
+        "        sample_scaled = scaler.transform(sample)\n",
+        "\n",
+        "        pred = model.predict(sample_scaled)\n",
+        "        label = \"Stress\" if pred[0] == -1 else \"No Stress\"\n",
+        "\n",
+        "        return str(hr), f\"{hrv:.2f}\", f\"{eda:.2f}\", label\n",
+        "\n",
+        "    except Exception as e:\n",
+        "        return \"Error\", \"Error\", \"Error\", f\"Error: {str(e)}\"\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_wPFxQ9LfwaX"
+      },
+      "source": [
+        "### 11. Create Gradio Interface\n",
+        "\n",
+        "We use Gradio to build a simple web app with a button that:\n",
+        "- Simulates a reading\n",
+        "- Displays HR, HRV, EDA\n",
+        "- Shows the prediction (\"Stress\" or \"No Stress\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 646
+        },
+        "id": "BPta2ttifs3_",
+        "outputId": "34740214-7181-4de5-8d3a-404be7841da9"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "* Running on local URL:  http://127.0.0.1:7860\n",
+            "* To create a public link, set `share=True` in `launch()`.\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div><iframe src=\"http://127.0.0.1:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": []
+          },
+          "execution_count": 19,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "gr.Interface(\n",
+        "    fn=predict_stress,\n",
+        "    inputs=[],\n",
+        "    outputs=[\n",
+        "        gr.Textbox(label=\"Heart Rate (HR)\"),\n",
+        "        gr.Textbox(label=\"Heart Rate Variability (HRV)\"),\n",
+        "        gr.Textbox(label=\"Electrodermal Activity (EDA)\"),\n",
+        "        gr.Textbox(label=\"Prediction\")\n",
+        "    ],\n",
+        "    title=\"Stress Detection using One-Class SVM\",\n",
+        "    description=\"Click to generate random sensor data and detect if the person is under stress.\"\n",
+        ").launch()\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.13.2"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}