diff --git a/.gitignore b/.gitignore
index 320570ab92d943809ad97acee5d0a215efe64b23..bcd1b7e8b7b15844212bb0fd26623f26b4c497bb 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,3 @@
-# Python
 __pycache__/
 *.py[cod]
 *$py.class
@@ -12,25 +11,10 @@ env/
 .eggs/
 dist/
 build/
-
-# IDE
 .idea/
 .vscode/
 *.swp
 *.swo
-
-# Data and outputs (optional: uncomment if you don’t want to track large files)
-# data_preparation/raw/
-# data_preparation/processed/*.npy
-# evaluation/logs/
-# evaluation/results/
-
-# Model checkpoints (uncomment to ignore .pt files)
-# *.pt
-
-# Project
 docs/
-
-# OS
 .DS_Store
 Thumbs.db
diff --git a/MLP/models/meta_20260224_024200.npz b/MLP/models/meta_20260224_024200.npz
new file mode 100644
index 0000000000000000000000000000000000000000..7acc8f329a3d87e566889f58ac165361ffc8f63f
--- /dev/null
+++ b/MLP/models/meta_20260224_024200.npz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:769bb62c7bf04aafd808e9b2623e795c2d92bcb933313ebf553d6fce5ebe7143
+size 1616
diff --git a/MLP/models/mlp_20260224_024200.joblib b/MLP/models/mlp_20260224_024200.joblib
new file mode 100644
index 0000000000000000000000000000000000000000..85d2730c4f13778cef8f6f4bb35f47d0a16256ec
--- /dev/null
+++ b/MLP/models/mlp_20260224_024200.joblib
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a72933fcf2d0aed998c6303ea4298c04618d937c7f17bf492e76efcf3b4b54d7
+size 50484
diff --git a/MLP/models/scaler_20260224_024200.joblib b/MLP/models/scaler_20260224_024200.joblib
new file mode 100644
index 0000000000000000000000000000000000000000..91842d63baf45befe58910b03e0f9b6ef5e16e2a
--- /dev/null
+++ b/MLP/models/scaler_20260224_024200.joblib
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3f9ef3721cee28f1472886556e001d0f6ed0abe09011d979a70ca9bf447d453e
+size 823
diff --git a/README.md b/README.md
index 1352f993782a39732ebe68ae1046d17eacd0863a..c9c67ea8478e657d9656f067401efab6f5b7c9d4 100644
--- a/README.md
+++ b/README.md
@@ -1,10 +1,10 @@
-# GAP — FocusGuard
+# FocusGuard
 
-Real-time focus estimation from webcam (head pose + eye behaviour). 
+Webcam-based focus detection: face mesh, head pose, eye (geometry or YOLO), plus an MLP trained on collected features.
 
-## Layout
+- **data_preparation/** — collect data, notebooks, processed/collected files
+- **models/** — face mesh, head pose, eye scorer, YOLO classifier, MLP training, attention feature collection
+- **evaluation/** — metrics and run logs
+- **ui/** — live demo (geometry+YOLO or MLP-only)
 
-- **data_preparation/** — Dataset team (raw data, processed, scripts)
-- **models/** — Face orientation, eye behaviour, fusion, landmarks. Training entry: `models/train.py`
-- **evaluation/** — Metrics, runs, results
-- **ui/** — Live demo + session view
+Run from here: `pip install -r requirements.txt` then `python ui/live_demo.py` or `python ui/live_demo.py --mlp`.
diff --git a/data_preparation/CNN/eye_crops/val/open/.gitkeep b/data_preparation/CNN/eye_crops/val/open/.gitkeep
new file mode 100644
index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc
--- /dev/null
+++ b/data_preparation/CNN/eye_crops/val/open/.gitkeep
@@ -0,0 +1 @@
+
diff --git a/data_preparation/MLP/explore_collected_data.ipynb b/data_preparation/MLP/explore_collected_data.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..9a08be76286ae2d68fcd0d7658e84747004596b7
--- /dev/null
+++ b/data_preparation/MLP/explore_collected_data.ipynb
@@ -0,0 +1,790 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# FocusGuard — Collected Data Explorer\n",
+        "Load `.npz` files from `collect_features.py` and inspect the data before training."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Using: /Users/mohammedalketbi22/GAP/shared/data_preparation/collected/session_20260224_010131.npz\n",
+            "Loaded: /Users/mohammedalketbi22/GAP/shared/data_preparation/collected/session_20260224_010131.npz\n",
+            "Samples: 13218\n",
+            "Features: 17 -> [np.str_('ear_left'), np.str_('ear_right'), np.str_('ear_avg'), np.str_('h_gaze'), np.str_('v_gaze'), np.str_('mar'), np.str_('yaw'), np.str_('pitch'), np.str_('roll'), np.str_('s_face'), np.str_('s_eye'), np.str_('gaze_offset'), np.str_('head_deviation'), np.str_('perclos'), np.str_('blink_rate'), np.str_('closure_duration'), np.str_('yawn_duration')]\n",
+            "Labels: 0=6227, 1=6991\n"
+          ]
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import os\n",
+        "import glob\n",
+        "\n",
+        "# ── PATH CONFIG ──────────────────────────────────────────────\n",
+        "BASE_DIR      = \"/Users/mohammedalketbi22/GAP/shared/data_preparation\"\n",
+        "COLLECTED_DIR = os.path.join(BASE_DIR, \"collected\")\n",
+        "# ─────────────────────────────────────────────────────────────\n",
+        "\n",
+        "npz_files = sorted(glob.glob(os.path.join(COLLECTED_DIR, \"*.npz\")))\n",
+        "\n",
+        "if not npz_files:\n",
+        "    raise FileNotFoundError(f\"No .npz files in {COLLECTED_DIR} — run collect_features.py first\")\n",
+        "\n",
+        "NPZ_PATH = npz_files[-1]  # latest file\n",
+        "print(f\"Using: {NPZ_PATH}\")\n",
+        "\n",
+        "data = np.load(NPZ_PATH, allow_pickle=True)\n",
+        "features = data['features']\n",
+        "labels   = data['labels']\n",
+        "names    = list(data['feature_names'])\n",
+        "\n",
+        "print(f\"Loaded: {NPZ_PATH}\")\n",
+        "print(f\"Samples: {len(labels)}\")\n",
+        "print(f\"Features: {features.shape[1]} -> {names}\")\n",
+        "print(f\"Labels: 0={int((labels==0).sum())}, 1={int((labels==1).sum())}\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 1. Basic Stats"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "============================================================\n",
+            "FEATURE STATISTICS\n",
+            "============================================================\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<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>ear_left</th>\n",
+              "      <th>ear_right</th>\n",
+              "      <th>ear_avg</th>\n",
+              "      <th>h_gaze</th>\n",
+              "      <th>v_gaze</th>\n",
+              "      <th>mar</th>\n",
+              "      <th>yaw</th>\n",
+              "      <th>pitch</th>\n",
+              "      <th>roll</th>\n",
+              "      <th>s_face</th>\n",
+              "      <th>s_eye</th>\n",
+              "      <th>gaze_offset</th>\n",
+              "      <th>head_deviation</th>\n",
+              "      <th>perclos</th>\n",
+              "      <th>blink_rate</th>\n",
+              "      <th>closure_duration</th>\n",
+              "      <th>yawn_duration</th>\n",
+              "      <th>label</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>13218.0000</td>\n",
+              "      <td>13218.0000</td>\n",
+              "      <td>13218.0000</td>\n",
+              "      <td>13218.0000</td>\n",
+              "      <td>13218.0000</td>\n",
+              "      <td>13218.0000</td>\n",
+              "      <td>13218.0000</td>\n",
+              "      <td>13218.0000</td>\n",
+              "      <td>13218.0000</td>\n",
+              "      <td>13218.0000</td>\n",
+              "      <td>13218.0000</td>\n",
+              "      <td>13218.0000</td>\n",
+              "      <td>13218.0000</td>\n",
+              "      <td>13218.0000</td>\n",
+              "      <td>13218.0000</td>\n",
+              "      <td>13218.0000</td>\n",
+              "      <td>13218.0000</td>\n",
+              "      <td>13218.0000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.4875</td>\n",
+              "      <td>0.4402</td>\n",
+              "      <td>0.4639</td>\n",
+              "      <td>0.4701</td>\n",
+              "      <td>0.4416</td>\n",
+              "      <td>0.0421</td>\n",
+              "      <td>-1.2722</td>\n",
+              "      <td>-0.4010</td>\n",
+              "      <td>-10.7348</td>\n",
+              "      <td>0.2794</td>\n",
+              "      <td>0.6792</td>\n",
+              "      <td>0.1006</td>\n",
+              "      <td>26.9677</td>\n",
+              "      <td>0.0662</td>\n",
+              "      <td>17.8149</td>\n",
+              "      <td>0.0298</td>\n",
+              "      <td>0.0762</td>\n",
+              "      <td>0.5289</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.1371</td>\n",
+              "      <td>0.1961</td>\n",
+              "      <td>0.1467</td>\n",
+              "      <td>0.0405</td>\n",
+              "      <td>0.0969</td>\n",
+              "      <td>0.1516</td>\n",
+              "      <td>25.3797</td>\n",
+              "      <td>19.3968</td>\n",
+              "      <td>77.1641</td>\n",
+              "      <td>0.2975</td>\n",
+              "      <td>0.2910</td>\n",
+              "      <td>0.0722</td>\n",
+              "      <td>17.1708</td>\n",
+              "      <td>0.1531</td>\n",
+              "      <td>13.6274</td>\n",
+              "      <td>0.2308</td>\n",
+              "      <td>0.4204</td>\n",
+              "      <td>0.4992</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.1031</td>\n",
+              "      <td>0.0262</td>\n",
+              "      <td>0.0652</td>\n",
+              "      <td>0.3407</td>\n",
+              "      <td>0.0914</td>\n",
+              "      <td>0.0021</td>\n",
+              "      <td>-170.3972</td>\n",
+              "      <td>-35.0815</td>\n",
+              "      <td>-179.9991</td>\n",
+              "      <td>0.0000</td>\n",
+              "      <td>0.0000</td>\n",
+              "      <td>0.0007</td>\n",
+              "      <td>0.7361</td>\n",
+              "      <td>0.0000</td>\n",
+              "      <td>0.0000</td>\n",
+              "      <td>0.0000</td>\n",
+              "      <td>0.0000</td>\n",
+              "      <td>0.0000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.3861</td>\n",
+              "      <td>0.3065</td>\n",
+              "      <td>0.3608</td>\n",
+              "      <td>0.4465</td>\n",
+              "      <td>0.4020</td>\n",
+              "      <td>0.0097</td>\n",
+              "      <td>-13.0227</td>\n",
+              "      <td>-12.6651</td>\n",
+              "      <td>-3.8250</td>\n",
+              "      <td>0.0000</td>\n",
+              "      <td>0.5504</td>\n",
+              "      <td>0.0587</td>\n",
+              "      <td>14.1011</td>\n",
+              "      <td>0.0000</td>\n",
+              "      <td>8.0000</td>\n",
+              "      <td>0.0000</td>\n",
+              "      <td>0.0000</td>\n",
+              "      <td>0.0000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.5219</td>\n",
+              "      <td>0.4679</td>\n",
+              "      <td>0.4954</td>\n",
+              "      <td>0.4601</td>\n",
+              "      <td>0.4452</td>\n",
+              "      <td>0.0153</td>\n",
+              "      <td>4.1706</td>\n",
+              "      <td>-7.7802</td>\n",
+              "      <td>1.8995</td>\n",
+              "      <td>0.1760</td>\n",
+              "      <td>0.8006</td>\n",
+              "      <td>0.0804</td>\n",
+              "      <td>21.4356</td>\n",
+              "      <td>0.0000</td>\n",
+              "      <td>16.0000</td>\n",
+              "      <td>0.0000</td>\n",
+              "      <td>0.0000</td>\n",
+              "      <td>1.0000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.5875</td>\n",
+              "      <td>0.5473</td>\n",
+              "      <td>0.5671</td>\n",
+              "      <td>0.4852</td>\n",
+              "      <td>0.4745</td>\n",
+              "      <td>0.0253</td>\n",
+              "      <td>11.7079</td>\n",
+              "      <td>17.7648</td>\n",
+              "      <td>5.5040</td>\n",
+              "      <td>0.5363</td>\n",
+              "      <td>0.8900</td>\n",
+              "      <td>0.1182</td>\n",
+              "      <td>36.2545</td>\n",
+              "      <td>0.0667</td>\n",
+              "      <td>26.0000</td>\n",
+              "      <td>0.0000</td>\n",
+              "      <td>0.0000</td>\n",
+              "      <td>1.0000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1.3822</td>\n",
+              "      <td>2.5730</td>\n",
+              "      <td>1.5932</td>\n",
+              "      <td>1.0000</td>\n",
+              "      <td>1.0000</td>\n",
+              "      <td>2.4219</td>\n",
+              "      <td>176.9489</td>\n",
+              "      <td>74.3983</td>\n",
+              "      <td>179.9923</td>\n",
+              "      <td>0.9969</td>\n",
+              "      <td>1.0000</td>\n",
+              "      <td>0.7071</td>\n",
+              "      <td>176.9821</td>\n",
+              "      <td>1.0000</td>\n",
+              "      <td>62.0000</td>\n",
+              "      <td>3.9318</td>\n",
+              "      <td>7.8311</td>\n",
+              "      <td>1.0000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "         ear_left   ear_right     ear_avg      h_gaze      v_gaze         mar  \\\n",
+              "count  13218.0000  13218.0000  13218.0000  13218.0000  13218.0000  13218.0000   \n",
+              "mean       0.4875      0.4402      0.4639      0.4701      0.4416      0.0421   \n",
+              "std        0.1371      0.1961      0.1467      0.0405      0.0969      0.1516   \n",
+              "min        0.1031      0.0262      0.0652      0.3407      0.0914      0.0021   \n",
+              "25%        0.3861      0.3065      0.3608      0.4465      0.4020      0.0097   \n",
+              "50%        0.5219      0.4679      0.4954      0.4601      0.4452      0.0153   \n",
+              "75%        0.5875      0.5473      0.5671      0.4852      0.4745      0.0253   \n",
+              "max        1.3822      2.5730      1.5932      1.0000      1.0000      2.4219   \n",
+              "\n",
+              "              yaw       pitch        roll      s_face       s_eye  \\\n",
+              "count  13218.0000  13218.0000  13218.0000  13218.0000  13218.0000   \n",
+              "mean      -1.2722     -0.4010    -10.7348      0.2794      0.6792   \n",
+              "std       25.3797     19.3968     77.1641      0.2975      0.2910   \n",
+              "min     -170.3972    -35.0815   -179.9991      0.0000      0.0000   \n",
+              "25%      -13.0227    -12.6651     -3.8250      0.0000      0.5504   \n",
+              "50%        4.1706     -7.7802      1.8995      0.1760      0.8006   \n",
+              "75%       11.7079     17.7648      5.5040      0.5363      0.8900   \n",
+              "max      176.9489     74.3983    179.9923      0.9969      1.0000   \n",
+              "\n",
+              "       gaze_offset  head_deviation     perclos  blink_rate  closure_duration  \\\n",
+              "count   13218.0000      13218.0000  13218.0000  13218.0000        13218.0000   \n",
+              "mean        0.1006         26.9677      0.0662     17.8149            0.0298   \n",
+              "std         0.0722         17.1708      0.1531     13.6274            0.2308   \n",
+              "min         0.0007          0.7361      0.0000      0.0000            0.0000   \n",
+              "25%         0.0587         14.1011      0.0000      8.0000            0.0000   \n",
+              "50%         0.0804         21.4356      0.0000     16.0000            0.0000   \n",
+              "75%         0.1182         36.2545      0.0667     26.0000            0.0000   \n",
+              "max         0.7071        176.9821      1.0000     62.0000            3.9318   \n",
+              "\n",
+              "       yawn_duration       label  \n",
+              "count     13218.0000  13218.0000  \n",
+              "mean          0.0762      0.5289  \n",
+              "std           0.4204      0.4992  \n",
+              "min           0.0000      0.0000  \n",
+              "25%           0.0000      0.0000  \n",
+              "50%           0.0000      1.0000  \n",
+              "75%           0.0000      1.0000  \n",
+              "max           7.8311      1.0000  "
+            ]
+          },
+          "execution_count": 2,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "df = pd.DataFrame(features, columns=names)\n",
+        "df['label'] = labels\n",
+        "\n",
+        "print(\"=\" * 60)\n",
+        "print(\"FEATURE STATISTICS\")\n",
+        "print(\"=\" * 60)\n",
+        "df.describe().round(4)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No NaN values found\n"
+          ]
+        }
+      ],
+      "source": [
+        "# NaN check\n",
+        "nan_counts = df.isna().sum()\n",
+        "if nan_counts.sum() == 0:\n",
+        "    print(\"No NaN values found\")\n",
+        "else:\n",
+        "    print(\"NaN counts:\")\n",
+        "    print(nan_counts[nan_counts > 0])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 2. Label Distribution"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x400 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Transitions: 8\n",
+            "Avg segment: 1652 frames (55.1s)\n",
+            "⚠️  Too few transitions — switch every 10-30s when re-recording\n"
+          ]
+        }
+      ],
+      "source": [
+        "n0 = int((labels == 0).sum())\n",
+        "n1 = int((labels == 1).sum())\n",
+        "total = len(labels)\n",
+        "\n",
+        "fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n",
+        "\n",
+        "# bar chart\n",
+        "axes[0].bar(['Unfocused (0)', 'Focused (1)'], [n0, n1], color=['#EF476F', '#06D6A0'])\n",
+        "axes[0].set_ylabel('Samples')\n",
+        "axes[0].set_title('Label Distribution')\n",
+        "for i, v in enumerate([n0, n1]):\n",
+        "    axes[0].text(i, v + total*0.01, f'{v} ({v/total*100:.1f}%)', ha='center', fontsize=10)\n",
+        "\n",
+        "# label over time\n",
+        "axes[1].plot(labels, color='#00B4D8', linewidth=0.5)\n",
+        "axes[1].fill_between(range(len(labels)), labels, alpha=0.3, color='#06D6A0')\n",
+        "axes[1].set_xlabel('Frame')\n",
+        "axes[1].set_ylabel('Label')\n",
+        "axes[1].set_title('Label Over Time')\n",
+        "axes[1].set_yticks([0, 1])\n",
+        "axes[1].set_yticklabels(['Unfocused', 'Focused'])\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n",
+        "\n",
+        "# transitions\n",
+        "transitions = int(np.sum(np.diff(labels) != 0))\n",
+        "print(f\"Transitions: {transitions}\")\n",
+        "print(f\"Avg segment: {total/max(transitions,1):.0f} frames ({total/max(transitions,1)/30:.1f}s)\")\n",
+        "if transitions < 10:\n",
+        "    print(\"⚠️  Too few transitions — switch every 10-30s when re-recording\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 3. Feature Distributions (Focused vs Unfocused)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1400x1500 with 18 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "n_features = features.shape[1]\n",
+        "cols = 3\n",
+        "rows = (n_features + cols - 1) // cols\n",
+        "\n",
+        "fig, axes = plt.subplots(rows, cols, figsize=(14, rows * 2.5))\n",
+        "axes = axes.flatten()\n",
+        "\n",
+        "for i in range(n_features):\n",
+        "    ax = axes[i]\n",
+        "    f0 = features[labels == 0, i]\n",
+        "    f1 = features[labels == 1, i]\n",
+        "    ax.hist(f0, bins=40, alpha=0.6, color='#EF476F', label='Unfocused', density=True)\n",
+        "    ax.hist(f1, bins=40, alpha=0.6, color='#06D6A0', label='Focused', density=True)\n",
+        "    ax.set_title(names[i], fontsize=10, fontweight='bold')\n",
+        "    ax.tick_params(labelsize=8)\n",
+        "    if i == 0:\n",
+        "        ax.legend(fontsize=8)\n",
+        "\n",
+        "# hide empty axes\n",
+        "for i in range(n_features, len(axes)):\n",
+        "    axes[i].set_visible(False)\n",
+        "\n",
+        "plt.suptitle('Feature Distributions by Label', fontsize=14, fontweight='bold', y=1.01)\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 4. Feature-Label Correlations"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Top predictive features:\n",
+            "  head_deviation       r = -0.6339\n",
+            "  s_face               r = +0.6230\n",
+            "  s_eye                r = +0.4048\n",
+            "  h_gaze               r = -0.4012\n",
+            "  pitch                r = -0.4006\n"
+          ]
+        }
+      ],
+      "source": [
+        "correlations = [np.corrcoef(features[:, i], labels)[0, 1] for i in range(n_features)]\n",
+        "sort_idx = np.argsort(np.abs(correlations))[::-1]\n",
+        "\n",
+        "fig, ax = plt.subplots(figsize=(10, 5))\n",
+        "colors = ['#06D6A0' if c > 0 else '#EF476F' for c in [correlations[i] for i in sort_idx]]\n",
+        "bars = ax.barh([names[i] for i in sort_idx],\n",
+        "               [correlations[i] for i in sort_idx],\n",
+        "               color=colors)\n",
+        "ax.set_xlabel('Correlation with Label (focused=1)')\n",
+        "ax.set_title('Feature-Label Correlations (sorted by |r|)')\n",
+        "ax.axvline(0, color='gray', linewidth=0.5)\n",
+        "\n",
+        "for bar, idx in zip(bars, sort_idx):\n",
+        "    r = correlations[idx]\n",
+        "    ax.text(r + (0.01 if r >= 0 else -0.01), bar.get_y() + bar.get_height()/2,\n",
+        "            f'{r:.3f}', va='center', ha='left' if r >= 0 else 'right', fontsize=9)\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n",
+        "\n",
+        "print(\"\\nTop predictive features:\")\n",
+        "for i in sort_idx[:5]:\n",
+        "    print(f\"  {names[i]:<20} r = {correlations[i]:+.4f}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 5. Feature Correlation Matrix"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x800 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "corr_matrix = np.corrcoef(features.T)\n",
+        "\n",
+        "fig, ax = plt.subplots(figsize=(10, 8))\n",
+        "im = ax.imshow(corr_matrix, cmap='RdBu_r', vmin=-1, vmax=1)\n",
+        "ax.set_xticks(range(n_features))\n",
+        "ax.set_yticks(range(n_features))\n",
+        "ax.set_xticklabels(names, rotation=45, ha='right', fontsize=9)\n",
+        "ax.set_yticklabels(names, fontsize=9)\n",
+        "ax.set_title('Feature Correlation Matrix')\n",
+        "plt.colorbar(im, ax=ax, shrink=0.8)\n",
+        "\n",
+        "# annotate\n",
+        "for i in range(n_features):\n",
+        "    for j in range(n_features):\n",
+        "        val = corr_matrix[i, j]\n",
+        "        if abs(val) > 0.5 and i != j:\n",
+        "            ax.text(j, i, f'{val:.2f}', ha='center', va='center', fontsize=7,\n",
+        "                    color='white' if abs(val) > 0.7 else 'black')\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 6. Features Over Time"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1400x1080 with 6 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Plot key features over time with label shading\n",
+        "key_features = ['s_face', 's_eye', 'ear_avg', 'yaw', 'pitch']\n",
+        "# filter to only features that exist in this file\n",
+        "key_features = [f for f in key_features if f in names]\n",
+        "\n",
+        "fig, axes = plt.subplots(len(key_features) + 1, 1, figsize=(14, (len(key_features)+1) * 1.8),\n",
+        "                         sharex=True)\n",
+        "\n",
+        "# label timeline\n",
+        "axes[0].fill_between(range(len(labels)), labels, alpha=0.4, color='#06D6A0', step='mid')\n",
+        "axes[0].set_ylabel('Label')\n",
+        "axes[0].set_yticks([0, 1])\n",
+        "axes[0].set_yticklabels(['Unfocused', 'Focused'], fontsize=9)\n",
+        "axes[0].set_title('Label + Key Features Over Time', fontsize=12, fontweight='bold')\n",
+        "\n",
+        "for i, feat in enumerate(key_features):\n",
+        "    idx = names.index(feat)\n",
+        "    ax = axes[i + 1]\n",
+        "    ax.plot(features[:, idx], linewidth=0.8, color='#00B4D8')\n",
+        "    # shade focused regions\n",
+        "    ax.fill_between(range(len(labels)), ax.get_ylim()[0], ax.get_ylim()[1],\n",
+        "                    where=labels == 1, alpha=0.1, color='green')\n",
+        "    ax.set_ylabel(feat, fontsize=9)\n",
+        "\n",
+        "axes[-1].set_xlabel('Frame')\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 7. Quality Summary"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "DATA QUALITY CHECKLIST\n",
+            "========================================\n",
+            "  ✅  Duration >= 2 min\n",
+            "  ✅  Samples >= 3000\n",
+            "  ✅  Balance 30-70%\n",
+            "  ❌  Transitions >= 10\n",
+            "  ✅  No NaN values\n",
+            "  ✅  No constant features\n",
+            "\n",
+            "  5/6 checks passed\n",
+            "  Re-record or collect more data.\n"
+          ]
+        }
+      ],
+      "source": [
+        "duration_sec = len(labels) / 30.0\n",
+        "balance = n1 / max(total, 1)\n",
+        "\n",
+        "checks = {\n",
+        "    'Duration >= 2 min': duration_sec >= 120,\n",
+        "    'Samples >= 3000': total >= 3000,\n",
+        "    'Balance 30-70%': 0.3 <= balance <= 0.7,\n",
+        "    'Transitions >= 10': transitions >= 10,\n",
+        "    'No NaN values': int(np.isnan(features).sum()) == 0,\n",
+        "    'No constant features': all(features[:, i].std() > 0.001 for i in range(n_features)),\n",
+        "}\n",
+        "\n",
+        "print(\"DATA QUALITY CHECKLIST\")\n",
+        "print(\"=\" * 40)\n",
+        "for check, passed in checks.items():\n",
+        "    icon = '✅' if passed else '❌'\n",
+        "    print(f\"  {icon}  {check}\")\n",
+        "\n",
+        "passed = sum(checks.values())\n",
+        "print(f\"\\n  {passed}/{len(checks)} checks passed\")\n",
+        "if passed == len(checks):\n",
+        "    print(\"  Ready for training!\")\n",
+        "else:\n",
+        "    print(\"  Re-record or collect more data.\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 8. Merge Multiple Sessions (Optional)\n",
+        "Run this if you have multiple `.npz` files from different team members."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# ── PATH CONFIG (auto-inherited from Cell 1) ─────────────────\n",
+        "# COLLECTED_DIR is already set above; no need to redefine it.\n",
+        "\n",
+        "all_features    = []\n",
+        "all_labels      = []\n",
+        "all_participants = []  # for participant-aware splitting\n",
+        "\n",
+        "npz_files = sorted([f for f in os.listdir(COLLECTED_DIR) if f.endswith('.npz')])\n",
+        "print(f\"Found {len(npz_files)} .npz files:\\n\")\n",
+        "\n",
+        "for i, fname in enumerate(npz_files):\n",
+        "    d = np.load(os.path.join(COLLECTED_DIR, fname), allow_pickle=True)\n",
+        "    f, l = d['features'], d['labels']\n",
+        "    n    = len(l)\n",
+        "    n1   = int((l == 1).sum())\n",
+        "    trans = int(np.sum(np.diff(l) != 0))\n",
+        "    print(f\"  [{i}] {fname}\")\n",
+        "    print(f\"      {n} samples, {n1/n*100:.0f}% focused, {trans} transitions, {n/30:.0f}s\")\n",
+        "\n",
+        "    all_features.append(f)\n",
+        "    all_labels.append(l)\n",
+        "    all_participants.append(np.full(n, i, dtype=np.int32))\n",
+        "\n",
+        "if len(all_features) > 0:\n",
+        "    merged_features     = np.concatenate(all_features)\n",
+        "    merged_labels       = np.concatenate(all_labels)\n",
+        "    merged_participants = np.concatenate(all_participants)\n",
+        "\n",
+        "    print(f\"\\nMerged: {len(merged_labels)} total samples\")\n",
+        "    print(f\"  Focused:   {int((merged_labels==1).sum())} ({(merged_labels==1).mean()*100:.1f}%)\")\n",
+        "    print(f\"  Unfocused: {int((merged_labels==0).sum())} ({(merged_labels==0).mean()*100:.1f}%)\")\n",
+        "\n",
+        "    out_path = os.path.join(COLLECTED_DIR, \"merged_all.npz\")\n",
+        "    np.savez(out_path,\n",
+        "             features=merged_features,\n",
+        "             labels=merged_labels,\n",
+        "             participants=merged_participants,\n",
+        "             feature_names=d['feature_names'])\n",
+        "    print(f\"  Saved -> {out_path}\")\n",
+        "else:\n",
+        "    print(\"No .npz files found\")\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "venv",
+      "language": "python",
+      "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": "3.13.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4
+}
diff --git a/data_preparation/MLP/train_mlp.ipynb b/data_preparation/MLP/train_mlp.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..6c3c491f2bc52a25cbd9b51e18512f35d84d6e4e
--- /dev/null
+++ b/data_preparation/MLP/train_mlp.ipynb
@@ -0,0 +1,674 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# MLP Training Notebook\n",
+        "Train a Multi-Layer Perceptron to classify focused vs unfocused states.\n",
+        "\n",
+        "**Pipeline:**\n",
+        "1. Load & clean data\n",
+        "2. Feature selection\n",
+        "3. Preprocessing (StandardScaler)\n",
+        "4. Train/Val/Test split\n",
+        "5. MLP architecture + training\n",
+        "6. Evaluation (accuracy, F1, confusion matrix)\n",
+        "7. Save model + scaler"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 0. Imports & Config"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Selected 10 features: ['head_deviation', 's_face', 's_eye', 'h_gaze', 'pitch', 'ear_left', 'ear_avg', 'ear_right', 'gaze_offset', 'perclos']\n",
+            "Model output dir: /Users/mohammedalketbi22/GAP/shared/data_preparation/../MLP/models\n"
+          ]
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import os\n",
+        "import glob\n",
+        "import joblib\n",
+        "from sklearn.neural_network import MLPClassifier\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.metrics import (\n",
+        "    accuracy_score, f1_score, classification_report,\n",
+        "    confusion_matrix, ConfusionMatrixDisplay, roc_auc_score,\n",
+        "    RocCurveDisplay\n",
+        ")\n",
+        "from sklearn.pipeline import Pipeline\n",
+        "import warnings\n",
+        "warnings.filterwarnings('ignore')\n",
+        "\n",
+        "# ── PATH CONFIG ───────────────────────────────────────────────\n",
+        "BASE_DIR      = \"/Users/mohammedalketbi22/GAP/shared/data_preparation\"\n",
+        "COLLECTED_DIR = os.path.join(BASE_DIR, \"collected\")\n",
+        "MODEL_DIR     = os.path.join(BASE_DIR, \"..\", \"MLP\", \"models\")\n",
+        "os.makedirs(MODEL_DIR, exist_ok=True)\n",
+        "\n",
+        "# ── FEATURE SELECTION ────────────────────────────────────────\n",
+        "SELECTED_FEATURES = [\n",
+        "    'head_deviation',  # r = -0.634  strongest\n",
+        "    's_face',          # r = +0.623\n",
+        "    's_eye',           # r = +0.405\n",
+        "    'h_gaze',          # r = -0.401\n",
+        "    'pitch',           # r = -0.393\n",
+        "    'ear_left',        # r = +0.368\n",
+        "    'ear_avg',         # r = +0.338\n",
+        "    'ear_right',       # r = +0.294\n",
+        "    'gaze_offset',     # r = -0.287\n",
+        "    'perclos',         # r = -0.269\n",
+        "]\n",
+        "\n",
+        "RANDOM_SEED = 42\n",
+        "print(f\"Selected {len(SELECTED_FEATURES)} features: {SELECTED_FEATURES}\")\n",
+        "print(f\"Model output dir: {MODEL_DIR}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 1. Load & Clean Data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Loading: /Users/mohammedalketbi22/GAP/shared/data_preparation/collected/session_20260224_010131.npz\n",
+            "Raw samples : 13218\n",
+            "Features    : 17\n",
+            "Labels      : 0=6227  1=6991\n",
+            "\n",
+            "Cleaning applied ✅\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Auto-load latest .npz \n",
+        "npz_files = sorted(glob.glob(os.path.join(COLLECTED_DIR, \"*.npz\")))\n",
+        "\n",
+        "\n",
+        "merged = [f for f in npz_files if 'merged' in f]\n",
+        "NPZ_PATH = merged[-1] if merged else npz_files[-1]\n",
+        "print(f\"Loading: {NPZ_PATH}\")\n",
+        "\n",
+        "data     = np.load(NPZ_PATH, allow_pickle=True)\n",
+        "features = data['features'].astype(np.float32)\n",
+        "labels   = data['labels'].astype(np.int32)\n",
+        "names    = list(data['feature_names'])\n",
+        "\n",
+        "print(f\"Raw samples : {len(labels)}\")\n",
+        "print(f\"Features    : {features.shape[1]}\")\n",
+        "print(f\"Labels      : 0={int((labels==0).sum())}  1={int((labels==1).sum())}\")\n",
+        "\n",
+        "# ── CLEANING  ──────────────────────\n",
+        "# Clip head pose angles to realistic ranges\n",
+        "features[:, names.index('yaw')]   = np.clip(features[:, names.index('yaw')],   -45,  45)\n",
+        "features[:, names.index('pitch')] = np.clip(features[:, names.index('pitch')], -30,  30)\n",
+        "features[:, names.index('roll')]  = np.clip(features[:, names.index('roll')],  -30,  30)\n",
+        "# Clip EAR to valid range\n",
+        "for feat in ['ear_left', 'ear_right', 'ear_avg']:\n",
+        "    features[:, names.index(feat)] = np.clip(features[:, names.index(feat)], 0, 0.85)\n",
+        "\n",
+        "print(\"\\nCleaning applied ✅\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 2. Feature Selection"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Feature matrix shape : (13218, 10)\n",
+            "Label vector shape   : (13218,)\n",
+            "\n",
+            "Features used:\n",
+            "  [ 0] head_deviation\n",
+            "  [ 1] s_face\n",
+            "  [ 2] s_eye\n",
+            "  [ 3] h_gaze\n",
+            "  [ 4] pitch\n",
+            "  [ 5] ear_left\n",
+            "  [ 6] ear_avg\n",
+            "  [ 7] ear_right\n",
+            "  [ 8] gaze_offset\n",
+            "  [ 9] perclos\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Select only the 10 chosen features\n",
+        "feature_indices = [names.index(f) for f in SELECTED_FEATURES]\n",
+        "X = features[:, feature_indices]\n",
+        "y = labels\n",
+        "\n",
+        "print(f\"Feature matrix shape : {X.shape}\")\n",
+        "print(f\"Label vector shape   : {y.shape}\")\n",
+        "print(f\"\\nFeatures used:\")\n",
+        "for i, name in enumerate(SELECTED_FEATURES):\n",
+        "    print(f\"  [{i:2d}] {name}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 3. Train / Validation / Test Split"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Train :   9252 samples  (70.0%)\n",
+            "Val   :   1983 samples  (15.0%)\n",
+            "Test  :   1983 samples  (15.0%)\n",
+            "\n",
+            "Train balance: 4893 focused / 4359 unfocused\n",
+            "Val   balance: 1049 focused / 934 unfocused\n",
+            "Test  balance: 1049 focused / 934 unfocused\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 70% train | 15% val | 15% test\n",
+        "# When all data is added, we will switch to subject-aware splitting.\n",
+        "\n",
+        "X_train_val, X_test, y_train_val, y_test = train_test_split(\n",
+        "    X, y, test_size=0.15, random_state=RANDOM_SEED, stratify=y\n",
+        ")\n",
+        "X_train, X_val, y_train, y_val = train_test_split(\n",
+        "    X_train_val, y_train_val,\n",
+        "    test_size=0.15 / 0.85,   # ~15% of total\n",
+        "    random_state=RANDOM_SEED, stratify=y_train_val\n",
+        ")\n",
+        "\n",
+        "print(f\"Train : {len(y_train):>6} samples  ({len(y_train)/len(y)*100:.1f}%)\")\n",
+        "print(f\"Val   : {len(y_val):>6} samples  ({len(y_val)/len(y)*100:.1f}%)\")\n",
+        "print(f\"Test  : {len(y_test):>6} samples  ({len(y_test)/len(y)*100:.1f}%)\")\n",
+        "print(f\"\\nTrain balance: {int((y_train==1).sum())} focused / {int((y_train==0).sum())} unfocused\")\n",
+        "print(f\"Val   balance: {int((y_val==1).sum())} focused / {int((y_val==0).sum())} unfocused\")\n",
+        "print(f\"Test  balance: {int((y_test==1).sum())} focused / {int((y_test==0).sum())} unfocused\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 4. Preprocessing — StandardScaler"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "StandardScaler fitted on training data ✅\n",
+            "\n",
+            "Feature means (train): [27.0059  0.2774  0.6795  0.47   -1.2007  0.4882  0.4643  0.4359  0.1007\n",
+            "  0.0663]\n",
+            "Feature stds  (train): [17.107   0.2967  0.2903  0.0416 17.8555  0.1358  0.1423  0.1726  0.0723\n",
+            "  0.1533]\n"
+          ]
+        }
+      ],
+      "source": [
+        "scaler = StandardScaler()\n",
+        "X_train_sc = scaler.fit_transform(X_train)\n",
+        "X_val_sc   = scaler.transform(X_val)\n",
+        "X_test_sc  = scaler.transform(X_test)\n",
+        "\n",
+        "print(\"StandardScaler fitted on training data ✅\")\n",
+        "print(f\"\\nFeature means (train): {scaler.mean_.round(4)}\")\n",
+        "print(f\"Feature stds  (train): {scaler.scale_.round(4)}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 5. MLP Architecture & Training\n",
+        "Architecture: **10 → 64 → 32 → 16 → 1**  \n",
+        "- ReLU activations, Adam optimizer\n",
+        "- Early stopping on validation loss (patience=15)\n",
+        "- L2 regularization (alpha=0.001)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Training complete ✅\n",
+            "Iterations run : 84\n",
+            "Final loss     : 0.0726\n",
+            "\n",
+            "Validation Accuracy : 96.07%\n",
+            "Validation F1       : 0.9636\n"
+          ]
+        }
+      ],
+      "source": [
+        "mlp = MLPClassifier(\n",
+        "    hidden_layer_sizes=(64, 32, 16),\n",
+        "    activation='relu',\n",
+        "    solver='adam',\n",
+        "    alpha=0.001,              # L2 regularization\n",
+        "    batch_size=64,\n",
+        "    learning_rate_init=0.001,\n",
+        "    max_iter=500,\n",
+        "    early_stopping=True,\n",
+        "    validation_fraction=0.1,\n",
+        "    n_iter_no_change=15,      # patience\n",
+        "    random_state=RANDOM_SEED,\n",
+        "    verbose=False\n",
+        ")\n",
+        "\n",
+        "# Train — sklearn MLPClassifier uses internal val from training data\n",
+        "# We'll track val metrics manually \n",
+        "\n",
+        "train_accs, val_accs = [], []\n",
+        "train_losses = []\n",
+        "\n",
+        "# Full training run\n",
+        "mlp.fit(X_train_sc, y_train)\n",
+        "\n",
+        "print(f\"Training complete ✅\")\n",
+        "print(f\"Iterations run : {mlp.n_iter_}\")\n",
+        "print(f\"Final loss     : {mlp.loss_:.4f}\")\n",
+        "\n",
+        "val_pred = mlp.predict(X_val_sc)\n",
+        "val_acc  = accuracy_score(y_val, val_pred)\n",
+        "val_f1   = f1_score(y_val, val_pred)\n",
+        "print(f\"\\nValidation Accuracy : {val_acc*100:.2f}%\")\n",
+        "print(f\"Validation F1       : {val_f1:.4f}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 6. Training Loss Curve"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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uzTeGW1u4gZ3x/TvZk7H/hG4AAAAA3U53a7VF8P6dELoBAAAAAAgQQncXdcVPhTL8+3J5flN5sHcFAAAAQBeVm5srDz30ULsfv3DhQtP6S+X39iN0d1HlLo/kNXglv84V7F0BAAAAEGAadHe03HLLLbu13a+++kouvvjidj/+oIMOks2bN0tCQoIE0sIwCvdUL++iUp2NlfOK6z3B3hUAAAAAAaZB12fu3Lkybdo0WbFihf+22NjYZhXHtUq7VhzfmbS0tF3aD6fTaeatRvvR0t3lQ7c72LsCAAAAIMA06PoWbWXWVmDf9eXLl0tcXJy89957Mnz4cImIiJBPP/1UVq9eLSeeeKJkZGSYUD5y5EiZN2/eDruX63affPJJOemkk0zl7r322kveeuutNlugn3nmGUlMTJQPPvhA9tlnH/M8xxxzTLOTBC6XS6688krzOJ2m7frrr5fzzjtPJkyYsNvvx9atW2XixImSlJRk9nP8+PGycuVK//3r1q2T448/3twfExMjgwYNknfffdf/t+ecc4454aDTgg0YMECefvppCRRCdxeV4tgWuhsI3QAAAMCe0JbhKpcnKIs+d0e54YYb5O6775Zly5bJkCFDpLKyUv7whz/I/Pnz5ZtvvjFhWIPo+vXrd7idW2+9VU4//XT5/vvvTZjVgLxly5Y2H19dXS3333+/PP/88/LJJ5+Y7V977bX++++55x558cUXTbD97LPPpLy8XF5//fU9eq3nn3++fP311/Lmm2/KokWLzPuor9U3tdfll18udXV1Zn9++OEHsw++3gBTp06Vn3/+2Zyk0Pdq9uzZkpqaKoFC9/Iu3tJdQks3AAAAsEeq3V6J/eC3VtLOVDlugMTYO2b6sttuu02OOuoo//Xk5GQZOnSo//rtt98ur732mgmqkydP3mGgPeuss8z6XXfdJbNmzZLFixebAN4aDbpz5syRfv36meu6bd0Xn1mzZsmNN95oWs/VI4884m913h3aoq2vQQO8jjFXGupzcnJMmD/ttNNM8D/llFNk8ODB5v6+ffv6/17v23///WXEiBH+1v5AoqW7i6J7OQAAAICmfCHSR1u6tcVZu31r125t6dWW3Z21dGsruY92zY6Pj5fCwsI2H6/du32BW2VlZfkfX1ZWJgUFBTJq1Cj//TabzXSD3136GnS8+ujRo/23abf1vffe29yntDv7HXfcIQcffLBMnz7dtNr7XHrppfLyyy/LsGHD5LrrrpPPP/9cAomW7i4q1dF4voTu5QAAAMCeibZZTItzsJ67o2hAbkoD90cffWS6fvfv39+MXz711FOlvr5+h9txOBzNrusYbo/Hs0uP78hu87vjT3/6k4wbN07eeecd+fDDD2XGjBnywAMPyBVXXGFa7HXMt7a26/tz5JFHmu7o+j4FAi3dXVTatu7lWxs84vIE9x80AAAA0JVpSIyxW4Oy6HMHina/1q7i2q1bu1lr0bW1a9dKZ0pISDCF3HRqMh+trL506dLd3qa23Gtxti+//NJ/W0lJianmvu+++/pv0+7ml1xyibz66qtyzTXXyBNPPOG/T4uo6Vj1F154wRSSe/zxxyVQaOnuopIcNtHDU+P2lga3pEfwUQIAAAD4jVbl1sCpxdM03GsBsR21WAfKFVdcYVqatbV94MCBZoy3VhBvzwkHLYKmldl99G90nLpWZb/ooovkscceM/drEbkePXqY29WUKVNMi7ZWX9fnWrBggQnrSqdb0+7tWtFci629/fbb/vsCgaTWRdmtFkm0WWSr22umDSN0AwAAAGhq5syZcsEFF5hiY1qdW6fq0srhne3666+X/Px8M8WXjue++OKLTddvXd+Zww47rNl1/Rtt5dZK6FdddZUcd9xxpru8Pk67i/u6umtrunYZ37hxoxmTrpXbH3zwQf9c41rYTVv9tcv9oYceasZ4B4rFG+zO9iFI/yFqNwgd9K8fUCjSM1R7/Xe1rK7zyMe/y5HDUqKDvUtASB0fWrwjPT1drFZG0QAcGwDfG/hNbW2trFmzRvr06SORkZG8NTuhcVFDrhYu66iu8B6Px7Qs67RkWlG9q/57aW9upKW7C0u2W2R1nZiWbgAAAAAIRVq0TIuZjRkzxnTn1inDNMieffbZ0h3QBNTFQ7eigjkAAACAUGW1WuWZZ56RkSNHmim8dJz2vHnzAjqOOpTQ0h0OoZuWbgAAAAAhKicnx1RS765o6e7CCN0AAAAAENoI3V1Ysr3x46OlGwAAAABCE6G7C6OlGwAAANg9wZivGt3z30lIjOl+9NFH5b777jNzt+lE5zpZ+qhRo1p9rE7uftddd8mqVaukoaHBTPh+zTXXyLnnnut/zPnnny/PPvtss7/TeeDef/99CSeEbgAAAGDX6BzNWtgrLy9P0tLSzPWOmgorHAViyrCu8rp1/u+ioiLz70X/nXTZ0D137ly5+uqrZc6cOTJ69Gh56KGHTEBesWKFmWO3peTkZLn55ptl4MCB5oW//fbbMmnSJPNY/TsfnfxcJ0z3iYiIkHBD6AYAAAB2jQYonXN58+bNJnhj5+FTW3v1fetOodsnOjpaevXqZV5/lw3dM2fOlIsuusgEZ6Xh+5133pGnnnpKbrjhhu0ef/jhhze7ftVVV5lW7U8//bRZ6NaQnZmZKeGMKcMAAACAXaeNdxqktAXX7XbzFu6ABu6SkhJJSUnZo+DZFdlstg5p4Q9q6Nbm+iVLlsiNN97ov00/yLFjx8qiRYvaddblv//9r2kVv+eee5rdt3DhQtP6nZSUJEcccYTccccd5h9KOBZSq3B5pM7tkQhb9zoIAAAAgN2lQcrhcJgFOw7d+h5FRkZ2u9DdUYIauouLi82ZpYyMjGa36/Xly5e3+XdlZWXSo0cPqaurM2cf/vGPf8hRRx3VrGv5ySefbLqNrF69Wm666SYZP368CfL6+JZ0O7r4lJeX+/+BhWqBBd2vOKtXbBYRt1ekqM4l2ZFB77gAhMzx4esKBYBjA+B7A+B3VSC097dml0xpcXFx8u2330plZaXMnz/fjAnv27evv+v5mWee6X/s4MGDZciQIdKvXz/T+n3kkUdut70ZM2bIrbfeut3tOmi+trZWQvUDLi8rkySbTYpdXvklv0js0dufUAC6Iz0+9OScBm/OyAIcGwDfGwC/qwKhoqIi9EN3amqqaXkuKChodrte39F4bP0R3b9/f7M+bNgwWbZsmQnOLcd7+2gg1+fSiuethW7t3q7BvWlLd05OjqlmGB8fL6EaKrRLTHpBjRS7GsQTlyDpKdHB3i0gpI4PPYYJ3QDHBsD3BsDvqkDQLvchH7q1gMHw4cNNa/WECRP8P5b1+uTJk9u9Hf2bpt3DW9q4caMZ/J+VldXq/Vp0rbXq5vpjPZR/sGuoSHXaRaoaZEtDY0VBAL8dH6F+DAPBwLEBcGwAfHd0jPb+zgx693JtYT7vvPNkxIgRZm5unTKsqqrKX8184sSJZvy2tmQrvdTHandxDdrvvvuuPP/88zJ79mxzv3Y5167ip5xyimkt1zHd1113nWkZb1rdPFykOBs/6OJ6qi4CAAAAQKgJeug+44wzzNjpadOmSX5+vuku/v777/uLq61fv77ZGQQN5JdddplpvY6KijLzdb/wwgtmO0q7q3///fdmGrHS0lLJzs6Wo48+Wm6//fawnKs71dk4jru4gdANAAAAAKHG4tVKQ2hGx3QnJCSYQkyhPKa7sLBQHi61yozVW+WK3ER5eFDzKvBAd+U7PnTaQLqXAxwbAN8bAL+rgpkbGezYxaU6trV0070cAAAAAEIOobuLS/F1Lyd0AwAAAEDIIXSHy5huQjcAAAAAhBxCdxdH93IAAAAACF2E7i6Olm4AAAAACF2E7jAJ3TUer1S7PcHeHQAAAABAE4TuLi7WZhGn1WLWGdcNAAAAAKGF0N3FWSwWxnUDAAAAQIgidIcBxnUDAAAAQGgidIdR6C5i2jAAAAAACCmE7jCQxlzdAAAAABCSCN1hgO7lAAAAABCaCN1hFbpdwd4VAAAAAEAThO4wQEs3AAAAAIQmQncYIHQDAAAAQGgidIdT6G5wB3tXAAAAAABNELrDQKrDN6ab0A0AAAAAoYTQHWbdy71eb7B3BwAAAACwDaE7DKRsC90ur0i5yxPs3QEAAAAAbEPoDgNRNqvE2CxmnS7mAAAAABA6CN1hggrmAAAAABB6CN1hgtANAAAAAKGH0B0mmDYMAAAAAEIPoTtMMG0YAAAAAIQeQneYSHXazSWF1AAAAAAgdBC6wwRjugEAAAAg9BC6wwShGwAAAABCD6E7TBC6AQAAACD0ELrDBKEbAAAAAEIPoTtMMGUYAAAAAIQeQneYTRm2pd4tbq832LsDAAAAACB0h48UZ2Po9ohIaYP+FwAAAAAQbLR0hwmH1SIJ9saPk7m6AQAAACA0ELrDspiaK9i7AgAAAAAgdIcXKpgDAAAAQGgJiZbuRx99VHJzcyUyMlJGjx4tixcvbvOxr776qowYMUISExMlJiZGhg0bJs8//3yzx3i9Xpk2bZpkZWVJVFSUjB07VlauXCnhjtANAAAAAKEl6KF77ty5cvXVV8v06dNl6dKlMnToUBk3bpwUFha2+vjk5GS5+eabZdGiRfL999/LpEmTzPLBBx/4H3PvvffKww8/LHPmzJEvv/zShHPdZm1trYQzpg0DAAAAgNAS9NA9c+ZMueiii0xw3nfffU1Qjo6OlqeeeqrVxx9++OFy0kknyT777CP9+vWTq666SoYMGSKffvqpv5X7oYcekr/97W9y4oknmvuee+45ycvLk9dff126w7RhFFIDAAAAgNAQ1NBdX18vS5YsMd2//TtktZrr2pK9Mxqw58+fLytWrJDDDjvM3LZmzRrJz89vts2EhATTbb092+zK6F4OAAAAAKHFHswnLy4uFrfbLRkZGc1u1+vLly9v8+/KysqkR48eUldXJzabTf7xj3/IUUcdZe7TwO3bRstt+u5rSbeji095ebm59Hg8ZglFul960qHp/iU7Gs+hFNW5Q3a/gWAdHwA4NgC+NwB+V3Wk9v7WDGro3l1xcXHy7bffSmVlpWnp1jHhffv2NV3Pd8eMGTPk1ltv3e72oqKikB0Hrh+wnnzQYKG9A5SjpsFc5lfXtjkmHugOWjs+AHBsAHxvAPyu6kgVFRWhH7pTU1NNS3VBQUGz2/V6ZmZmm3+nP6L79+9v1rV6+bJly0xw1tDt+zvdhlYvb7pNfWxrbrzxRhPcm7Z05+TkSFpamsTHx0uohgqLxWL20Rcq+jtqRFZXS5nXKunp6cHeRSCkjg8AHBsA3xsAv6s6ks6+FfKh2+l0yvDhw01r9YQJE/w/lvX65MmT270d/Rtf9/A+ffqY4K3b8IVsDdFaxfzSSy9t9e8jIiLM0pL+WA/lH+waKpruY3qEw19ILZT3GwjG8QGAYwPgewPgd1VHau/vzKB3L9cW5vPOO8/MvT1q1ChTebyqqspUM1cTJ04047e1JVvppT5WK5dr0H733XfNPN2zZ8/2/9CeMmWK3HHHHTJgwAATwqdOnSrZ2dn+YB/uhdRKXR5p8HjFYbUEe5cAAAAAoFsLeug+44wzzNjpadOmmUJn2jr9/vvv+wuhrV+/vtkZBA3kl112mWzcuFGioqJk4MCB8sILL5jt+Fx33XXmcRdffLGUlpbKIYccYrbZ3ub/rirRYTXl6HU4/5YGt2REBP3jBQAAAIBuzeLVSkNoRruj6zRjWogplMd0a7E0Hbvd9KRE2kerTPfyHw/LlUFx23eZB7qDto4PoLvj2AA4NgC+Ozo/N/JrNMykOhq7mGvwBgAAAAAEF6E7TMd1FxG6AQAAACDoCN1hGrpp6QYAAACA4CN0hxlCNwAAAACEDkJ3mCF0AwAAAEDoIHSHbeh2BXtXAAAAAKDbI3SHa+huoHo5AAAAAAQboTvMMGUYAAAAAIQOQneYYUw3AAAAAIQOQneYIXQDAAAAQOggdIdp6K5ye6XG7Qn27gAAAABAt0boDjPxdqvYLY3rJfUUUwMAAACAYCJ0hxmLxUIXcwAAAAAIEYTuMMS0YQAAAAAQGgjdYYhpwwAAAAAgNBC6wxAVzAEAAAAgNBC6wxChGwAAAABCA6E7DKU67eaymOrlAAAAABBUhO4wREs3AAAAAIQGQncYInQDAAAAQGggdIchpgwDAAAAgNBA6A5DtHQDAAAAQGggdIf5PN1erzfYuwMAAAAA3RahO4xbuus8XqlyE7oBAAAAIFgI3WEo2maRSKvFrDNtGAAAAAAED6E7DFksFsZ1AwAAAEAIIHSHKYqpAQAAAEDwEbrDVNq2cd3F9a5g7woAAAAAdFuE7jDFXN0AAAAAEHyE7jBF93IAAAAACD5Cd5gidAMAAABA8BG6w1Sqwzem2x3sXQEAAACAbovQHaZo6QYAAACA4CN0hylCNwAAAAAEH6E7TFG9HAAAAACCLyRC96OPPiq5ubkSGRkpo0ePlsWLF7f52CeeeEIOPfRQSUpKMsvYsWO3e/z5558vFoul2XLMMcdId5LqtPvHdHu93mDvDgAAAAB0S0EP3XPnzpWrr75apk+fLkuXLpWhQ4fKuHHjpLCwsNXHL1y4UM466yxZsGCBLFq0SHJycuToo4+WTZs2NXuchuzNmzf7l5deekm6kxRH40fr9oqUuTzB3h0AAAAA6JaCHrpnzpwpF110kUyaNEn23XdfmTNnjkRHR8tTTz3V6uNffPFFueyyy2TYsGEycOBAefLJJ8Xj8cj8+fObPS4iIkIyMzP9i7aKdycRNqvE2Rs/XiqYAwAAAEA3DN319fWyZMkS00Xcv0NWq7murdjtUV1dLQ0NDZKcnLxdi3h6errsvffecumll0pJSYl0N0wbBgAAAADB1TjwN0iKi4vF7XZLRkZGs9v1+vLly9u1jeuvv16ys7ObBXftWn7yySdLnz59ZPXq1XLTTTfJ+PHjTZC32Rrnr26qrq7OLD7l5eXmUlvQdQlFul86VntH+5fqtMqaGpGC2gbxeCI6df+AUD8+gO6IYwPg2AD47ug47f2tGdTQvafuvvtuefnll02rthZh8znzzDP964MHD5YhQ4ZIv379zOOOPPLI7bYzY8YMufXWW7e7vaioSGprayVUP+CysjITLLR3QGvivG5zuaakVAot1Z28h0BoHx9Ad8SxAXBsAHx3dJyKiorQD92pqamm5bmgoKDZ7Xpdx2HvyP33329C97x580yo3pG+ffua51q1alWrofvGG280xdyatnRrgba0tDSJj4+XUP3hpFXZdR/bChXZm/NFyiukPjJG0tO715h2dG/tOT6A7ohjA+DYAPju6DhNG35DNnQ7nU4ZPny4KYI2YcIEc5uvKNrkyZPb/Lt7771X7rzzTvnggw9kxIgRO32ejRs3mjHdWVlZrd6vRdd0aUl/rIfyD3YNFTvax7SIxo+3pMET0q8DCMbxAXRXHBsAxwbAd0fHaO/vzKD/GtUWZp17+9lnn5Vly5aZomdVVVWmmrmaOHGiaYn2ueeee2Tq1KmmurnO7Z2fn2+WyspKc79e/vWvf5UvvvhC1q5dawL8iSeeKP379zdTkXUnqc7G8etULwcAAACA4Aj6mO4zzjjDjJ2eNm2aCc86Fdj777/vL662fv36ZmcQZs+ebaqen3rqqc22o/N833LLLaa7+vfff29CfGlpqSmypvN433777a22ZneL0N3QOLYbAAAAANDNQrfSruRtdSfX4mdNaev1jkRFRZlu52g6ZZiLtwMAAAAAgiDo3csROHQvBwAAAIDgInSHMUI3AAAAAAQXobsbhO6tDR5xebzB3h0AAAAA6HYI3WEseduYbo3bWymmBgAAAACdjtAdxuxWiyQ5Gj9ipg0DAAAAgM5H6A5zTBsGAAAAAMFD6O4204YxVzcAAAAAdInQvWHDBtm4caP/+uLFi2XKlCny+OOPd+S+oQNkRDROxb60rI73EwAAAAC6Qug+++yzZcGCBWY9Pz9fjjrqKBO8b775Zrnttts6eh+xB87KjjOXD6/dKiW0dgMAAABA6IfuH3/8UUaNGmXWX3nlFdlvv/3k888/lxdffFGeeeaZjt5H7IFTs+JkaHyElLs8cu/qLbyXAAAAABDqobuhoUEiIiLM+rx58+SEE04w6wMHDpTNmzd37B5ij1gtFrlzr1SzPmvtVtlc6+IdBQAAAIBQDt2DBg2SOXPmyP/+9z/56KOP5JhjjjG35+XlSUpKSkfvI/bQH9Jj5MDESKnxeOXOVSW8nwAAAAAQyqH7nnvukccee0wOP/xwOeuss2To0KHm9jfffNPf7Ryhw2KxyF0D08z64+tLZW11Q7B3CQAAAAC6hcbS1rtIw3ZxcbGUl5dLUlKS//aLL75YoqOjO3L/0EEOT4mWsanRMq+4Wm5dWSxPD83ivQUAAACAUGzprqmpkbq6On/gXrdunTz00EOyYsUKSU9P7+h9RAe5c+/Gsd3PbSyXZRVMIQYAAAAAIRm6TzzxRHnuuefMemlpqYwePVoeeOABmTBhgsyePbuj9xEdZFRilJyYESseEZm+krHdAAAAABCSoXvp0qVy6KGHmvV///vfkpGRYVq7NYg//PDDHb2P6EC375UqFhH51+YK+aaslvcWAAAAAEItdFdXV0tcXJxZ//DDD+Xkk08Wq9Uqv/vd70z4RugaHB8hZ2U3fnZ/W1Ec7N0BAAAAgLC2W6G7f//+8vrrr8uGDRvkgw8+kKOPPtrcXlhYKPHx8R29j+hgt+6VKjaLyLtFVfLZlmreXwAAAAAIpdA9bdo0ufbaayU3N9dMEXbggQf6W73333//jt5HdLD+MU65oGeCWb9pRbF4vV7eYwAAAAAIldB96qmnyvr16+Xrr782Ld0+Rx55pDz44IMduX8IkKkDUiTCapFPttTIR8W0dgMAAABAyIRulZmZaVq18/LyZOPGjeY2bfUeOHBgR+4fAiQnyiGX9k406zfT2g0AAAAAoRO6PR6P3HbbbZKQkCC9e/c2S2Jiotx+++3mPnQNN/ZLlhibRb4uq5XXCyqDvTsAAAAAEHZ2K3TffPPN8sgjj8jdd98t33zzjVnuuusumTVrlkydOrXj9xIBkR5hlyl9ksz61BXF4mZsNwAAAAB0KPvu/NGzzz4rTz75pJxwwgn+24YMGSI9evSQyy67TO68886O3EcE0LV9k+XRtaXyU2W9vLSpXP64rcAaAAAAACBILd1btmxpdey23qb3oetIdNjkun7JZn36yhJp8FDJHAAAAACCGrqHDh1qupe3pLdpize6litzkyTdaZNfqxtk2i/Fwd4dAAAAAOje3cvvvfdeOfbYY2XevHn+OboXLVokGzZskHfffbej9xEBFmO3yt8HpctZ32yWu1dvkcFxEXJ2j3jedwAAAAAIRkv3mDFj5JdffpGTTjpJSktLzXLyySfLTz/9JM8///ye7hOC4MzseLl+WzfzC7/Pl69Ka/gcAAAAAGAPWbzejitZ/d1338kBBxwgbrdburLy8nIzHVpZWZnEx4dmi69OzVZYWCjp6elite72dOvNaPXyCV9vkrcLqyQrwiZfHdJbekQ6OmTbQFc/PoBwwLEBcGwAfHd0fm7k1yj8bBaLvDgsSwbFOmVznVsmfJ0nNW7mXQcAAACA3UXoRjPxDpu8ObKHpDhs8nVZrelq3oGdIQAAAACgWyF0Yzt9o53y7+HZYreIvJRXYYqrAQAAAAACXL1ci6XtiBZUQ3g4PCVaZg3KkEt/LJCbVxTLoLgIOSEjNti7BQAAAADhG7p1kPjO7p84ceKe7hNCxCW9E+WHijr5x7pSOeebPPn8oN4yOD4i2LsFAAAAAOEZup9++umA7MSjjz4q9913n+Tn58vQoUNl1qxZMmrUqFYf+8QTT8hzzz0nP/74o7k+fPhwueuuu5o9XscgT58+3TxWW98PPvhgmT17tgwYMCAg+x/OHto3XZZV1suCkmo54etN8tUhvSTVuVvTuwMAAABAtxP0Md1z586Vq6++2oTkpUuXmtA9btw4M91PaxYuXChnnXWWLFiwQBYtWiQ5OTly9NFHy6ZNm/yPuffee+Xhhx+WOXPmyJdffikxMTFmm7W1tZ34ysKDw2qRfx2QLf2iHbK2pkFOXZIn9R4KqwEAAABAp8/TvTtGjx4tI0eOlEceecQ/h6gG6SuuuEJuuOGGnf69zgmelJRk/l67tuvLyc7OlmuuuUauvfZa8xidNy0jI0OeeeYZOfPMM3e6ze46T/eO/FxRJ7/7fL1UuDzy514JMnu/DLFYLAF/XmB3MBcxwLEB8L0BdAx+V3Xxebrr6+tlyZIlMnbs2N92yGo117UVuz2qq6uloaFBkpOTzfU1a9aYbupNt6lvhIb79m4T29s3LkJe3j9LNGY/tr5Mrl1WJB6mEgMAAACAHQrq4Nzi4mLTUq2t0E3p9eXLl7drG9dff71p2faFbA3cvm203Kbvvpbq6urM0vSMhe+sji6hSPdLW/U7c/+OSY2Wv++bJlf+XCQz12yVvNoGeWpwhkTYgj5KAQj68QF0BRwbAMcGwHdHx2nvb80uXRHr7rvvlpdfftmM846MjNzt7cyYMUNuvfXW7W4vKioK2XHg+gFrNwYNFp3RvdzntCgRS26U/GVtjby8uVI2VtbIU/1iJM5GV3OEjmAdH0Co49gAODYAvjs6TkVFReiH7tTUVLHZbFJQUNDsdr2emZm5w7+9//77TeieN2+eDBkyxH+77+90G1lZWc22OWzYsFa3deONN5pibk1bunVceVpaWkiP6dYx1bqPnR0qLksX6Z9aJad9s1k+rXDLaatr5Z0RPSQrskufw0EYCebxAYQyjg2AYwPgu6PjtLfhN6gpyel0mim/5s+fLxMmTPD/INDrkydPbvPvtDr5nXfeKR988IGMGDGi2X19+vQxwVu34QvZGqK1ivmll17a6vYiIiLM0pL+WA/lH+waKoK1j8dkxMnHBzpk/OKN8l1FvRz8xQb5YFSO7B3r7PR9AULt+ABCGccGwLEB8N3RMdr7OzPov0a1hVnn03722Wdl2bJlJhhXVVXJpEmTzP1akVxbon3uuecemTp1qjz11FOSm5trxmnrUllZ6f8xMWXKFLnjjjvkzTfflB9++MFsQ8d9+4I9OsYBCZGy6OBe0j/aIetqXHLQ5+tk0dYa3l4AAAAA2Cbo/YHPOOMMM3Z62rRpJjxr6/T777/vL4S2fv36ZmcQZs+ebaqen3rqqc22o/N833LLLWb9uuuuM8H94osvltLSUjnkkEPMNvdk3Dda1zfaKZ8f1EuO/WqTfFVWK0d+sUHmHpAtx2fE8pYBAAAA6PaCPk93KGKe7l1X5fLI6Uvz5N2iKtN94rHBGfKnXokB+HSAnWM+SYBjA9gVfG8AHB9hO083wkeM3SpvjOghF/RMEC2cf9EPBXLLL8Xi5pwOAAAAgG6M0I0OY7da5MkhGfK3/inm+q0rS2TEp+tkfnEV7zIAAACAbonQjQ6lhexu3ztVnhycIQl2q3xbXidjv9wox321UZZV1PFuAwAAAOhWCN0IiAt7Jcqq3/eRK3ITxW4ReaewSgb/b61c9kOBFNa5eNcBAAAAdAuEbgRMqtMuDw/KkJ8O6yMTMmLF7RWZvb5U+i9cIzNWlUiNW0d/AwAAAED4InQj4PaKdcprI3rIwt/lyPCECKlweeSmFcWy98I18sLGMvFQbA0AAABAmCJ0o9OMSYmWxQf3lheGZUlOpF021Lrk3O/yZfRn6+W78lo+CQAAAABhh9CNzv0HZ7HIOT3iZcXhfeSuvVMlzm6Vr8tqTZXz21YWS4OHaeMBAAAAhA9CN4IiymaVG/unyC9j+shJGbHi8opM/6VERn+2Tn4op8o5AAAAgPBA6EZQZUba5T/Ds+XFYVmS7LDKN+V1MvzTtXLnyhJx0eoNAAAAoIsjdCMk5vY+u0e8qXJ+YkasNHhF/vZLsfzu83XyI3N7AwAAAOjCCN0IqVbv14Zny/NDMyXJYZUlZdrqvc5ML0arNwAAAICuiNCNkGv1/mPPBNPqfXx6jNR7vGZ6sYM+Xy8/0+oNAAAAoIshdCMkZUXa5Y0RPeTZoZmSaLfKV2W1Mux/a+Xi7/NlTXV9sHcPAAAAANqF0I2QbvWe2DNBfhyTa1q9daz3ExvKZK+Fa+TC7/JldRXhGwAAAEBoI3Qj5PWIdMibI3vKpwfmyNGp0WZ6sac2lsneH6+R87/bLCsJ3wAAAABCFKEbXcbBydHywegc+fygXjI+LUbcXpFnN5bLwIVr5NxvN8uKSlq+AQAAAIQWQje6nAOTouTdUT3ly4N7ybHpMeIRkRc2lcs+H6+Rs7/Jo+AaAAAAgJBB6EaXNSoxSt4e2VO+PqS3nJARK14ReSmvQgZ9slbGfblB3iqoFLdXbwUAAACA4CB0o8sbnhBpKp0vPaS3nJwZKxYR+bC4Wk74epMMWLBG7l+9RbbUu4O9mwAAAAC6IUI3wsb+CZHyn+E9ZPXv+8i1fZMkyWGVNTUN8tflRdJz/mq56Pt8+b68Nti7CQAAAKAbIXQj7PSJdsp9+6TLxiP7yRODM2RIXITUeLzy5IYyGfq/dXLYovXyr80V0uCh6zkAAACAwCJ0I2xF26zyp16J8u2hveWTA3PktKw4sVlE/relRk5fmid9FvwqM1aVSAldzwEAAAAECKEbYc9iscihydHyygHZsu6IvvK3/imS7rTJplqX3LSiWHLmr5ZLfsiXZRV1wd5VAAAAAGGG0I1upUekQ27fO1XWH9FXnh2aKcPiG7ueP7a+TPb9ZK0cs3iDvF9YJV6qngMAAADoAIRudEsRNqtM7JlgKp4v/F2OTMhorHr+QVG1jP9qo5l27LF1pVLt1lnAAQAAAGD3ELoh3b3r+ZiUaHltRA9Z9fs+MqVPksTZrbKssl4u+bHAdD2/5udC+d+WanFReA0AAADALiJ0A9v0jXbKg/umy8Yj+sqD+6ZJnyiHbGnwyMw1W+WwRRskfd4qOeebPHlpU7lsbWDebwAAAAA7Z2/HY4BuJd5hkyl9kuWK3CR5u6DSTC/2blGVbG3wyD/zKsyiVdAPToqS49Jj5bj0GBkY6zSt5gAAAADQFKEbaIPNYpETM+PMol3LvyitkbcLq0wQ/6myXj7ZUmOW65YXSd9ohxybHiPj02Lk8JRoibLRiQQAAAAAoRtoF7vVIockR5vl7oFpsqa6Xt7RAF5YKQtKauTX6gaZtbbULJFWiwnef0iLkfHpMdI/xsm7DAAAAHRTtHQDu6FPtFMm5+qSJJUuj8wrrpJ3C6vkvaIq2VjrkveLqswiP4v0j3aY8N2yFVxbz4vr3VJU79526TLrvuv7xDrlz70STeAHAAAA0DURuoE9FGu3yoTMOLPo/N7a9fy9bQFcq56vatEKnhNlN6Fax4jvzHObyuW5oVmydyyt5QAAAEBXROgGOpAWU9svLsIsf+2XLBUuj8xv0Qq+sqrht8eLSIrTJqkOm6RF2CTN2bjE2qzyfxvKZHFprez/v7Vyz8A0uTw3UawUawMAAAC6FEI3EEBxLVrBdf5v7T7uC9fJTpsp2NYanTN80vf5Mq+4Wq78uVDeLKyUp4ZkSk6Ug88MAAAA6CKCXmL50UcfldzcXImMjJTRo0fL4sWL23zsTz/9JKeccop5vLYoPvTQQ9s95pZbbjH3NV0GDhwY4FcB7Jz+W9w3LkLGpESby7QIe5uBW/WMcsgHo3rKI4PSJcpqMeF78Cdr5fmNZSbAAwAAAAh9QQ3dc+fOlauvvlqmT58uS5culaFDh8q4ceOksLCw1cdXV1dL37595e6775bMzMw2tzto0CDZvHmzf/n0008D+CqAwNHu5JfnJsm3h+bK6MRIKXN5ZOJ3+XLq0jwpqnPx1gMAAAAhLqihe+bMmXLRRRfJpEmTZN9995U5c+ZIdHS0PPXUU60+fuTIkXLffffJmWeeKREREW1u1263m1DuW1JTUwP4KoDA2yvWKZ8e2Evu2CtV7BaRV/MrZb9P1sqbBZW8/QAAAEAIC1rorq+vlyVLlsjYsWN/2xmr1VxftGjRHm175cqVkp2dbVrFzznnHFm/fn0H7DEQXDp12M0DUmTxwb1lUKxTCuvdcuLXm+QPizfKnStL5L3CSimk9RsAAAAIKUErpFZcXCxut1syMjKa3a7Xly9fvtvb1XHhzzzzjOy9996ma/mtt94qhx56qPz4448SFxfX6t/U1dWZxae8vNxcejwes4Qi3S8d1xuq+4fAGRrnlMUH5ci0lSUyc02pqYqui0+PCLvsnxAhB8RHyP7xETI8IVKyI2xmTHl3wfEBcGwAfG8A/K4KtPZmsbCrXj5+/Hj/+pAhQ0wI7927t7zyyity4YUXtvo3M2bMMOG8paKiIqmtrZVQ/YDLyhoLamkPAXQ/1yaLjI+Mlf9VuOT7arf8UO2W1bUe2VTnkk2FLnm78Lcgnmq3yF6RVukVYZUcXZzb1p1WyXRYwm4qMo4PgGMD4HsD4HdVoFVUVIR26NZx1jabTQoKCprdrtd3VCRtVyUmJspee+0lq1atavMxN954oyno1rSlOycnR9LS0iQ+Pl5CNVRoy6XuI6G7+0oXkd83uV7p8si35XXyTXmdLC2vNZc/V9ZLscsrxZVu+bzSvd02nBaL9IqyS26UQ3Kj7TIyIVJOyog184d3VRwfAMcGwPcGwO+qQNMZuEI6dDudThk+fLjMnz9fJkyY4P+hrNcnT57cYc9TWVkpq1evlnPPPbfNx2hRttYKs2mYDeVAq6E71PcRnSveaZXDUu1yWGqM/7Yat0d+qKiTXyrrZW2NS9ZUN8iamgZZW90g62sbpN7rlVXVDWaREpEnN5TL5T8VytjUGDk9S+cYj5UkR9cL4BwfAMcGwPcGwO+qQGpvDgtq93JtXT7vvPNkxIgRMmrUKDPvdlVVlalmriZOnCg9evQw3b99xdd+/vln//qmTZvk22+/ldjYWOnfv7+5/dprr5Xjjz/edCnPy8sz05Fpi/pZZ50VxFcKBE+UzSqjEqPM0pLL45VNtS5/CF9VXS/vFFaZ1vL3i6rM8ucfRMalxcgZWXFyQkasxHfBAA4AAAAES1BD9xlnnGHGTU+bNk3y8/Nl2LBh8v777/uLq2nV8aZnDzRE77///v7r999/v1nGjBkjCxcuNLdt3LjRBOySkhLT9fqQQw6RL774wqwD2L4ieu9oh1kkpfG2O/ZOM63iczeXyyubK+THinozPlyXCKtFxmsAz46TUYmRkmC3SbzdKg5reI0JBwAAADqKxauVuNCMjulOSEgwhcpCeUx3YWGhpKen070cAfVTRZ0J33PzKmRFVX2rj4m0Wkz4brY4GgP50LgIOTkrVvpGOzvtk+L4ADg2AL43AH5XhUpuDLvq5QA61qC4CLk1LkJuGZAi32sAz6uQV/MrzXjwanfjObtaj1dq691m7vCWXhCRvy4vkqHxEXJyZqyclBEn+8U5u9UUZgAAAOi+CN0A2kVD8tD4SLPcOTDNPya8wu2R8gaPlLt0cUuZuWxcSurdMr+4WhZuqZbvyuvMMv2XEukf7ZCTM+PkpMxY00093KYsAwAAAHwI3QD2aEx4ktW2w+rmN/ZPMeH7rYJKeTW/Qj4srjaV0u/9dYtZsiPsJnxrkbbDkqMk0kY1fgAAAIQPQjeAgNM5v8/PSTBLhctjqqJrAH+7oFLy6lzy6LpSs0TbLHJkSrT8IT3WFGwzBd4AAACALozQDaBTxdmtclpWnFlq3R7T/fy1gkp5t7BSNte55a3CKrOoQbFO+UN6jAnhBydFUSUdAAAAXQ6hG0DQaFfyYzNizaITKeiY73eLquTdwipZtLVGfqqsN8t9v241Yf2o1GjZPz5SsiPt0iPSbrqm62WSw0phNgAAAIQkQjeAkCnUNiwh0iw39U+RLfVu+ai4MYC/V1QlRfVuUzVdl9amLMtuEsKzImyS7KqT/byV0jfGKblRDknYwbhzAAAAIFAI3QBCUrLTJmdkx5vF4/XKkrJa+bCoWtbUNMimWpfk1brMZUmD20xZ9mt1g1ma2bjZv5pot0putMMEcP9llF0OSoqStAj+VwgAAIDA4JcmgJCnU4qNTIwyS0s6Lnxz3W8hXAuzbahpkJWlVZLvscqaGpcU17ul1OWRb8vrzNJs2yJySHKUqaCuc4hTvA0AAAAdidANoMuPC+8T7TSLj8fjkcJCr6Snp4vVapVKl0fW1TTI2uoGWauX29aXV9XLjxX18smWGrP85eci2T8+ojGAZ8aZQm7a7R0AAADYXYRuAGEv1m6VQXERZmlJw/frBRXyWn6lfLqlRr4przPLtF9KpH+0wx/A94l1SoJ99wu2aaE4bW0vqnObLvE2i4jTYhGn1SIR1sZL/2JpvE0fQ+gHAADo2gjdALo1Hd89pU+yWYrqXPJmQaW8XlApHxVXy6rqBlM5XRelITjJYZNkh1VS9NJpa3apobxcg3W9WwrrXebSrNc1dnFv8O7avjksIkenxciknglyfEasCeQAAADoWgjdALCNFlS7sFeiWSpcHnm/qEpey68wl1sbPOL2ignPuoi0KNrWTjr1mQZ0VefxSL1HpN7rlTqPV+o9zVO5hvR3CqvMon9zTo84E8C1wjsAAAC6BkI3ALQRjk/LijOLr2DblgaPlNS7ZUtD4+JbL6nX+9xS2uCWeLtN0iJsku60SZrTJukRdnPpW3QM+o66oLu84g/gWhTuxU3l8tzGcrP+8NpSswyLjzDh++wecZLq5H/jAAAAoYxfawDQDhqWs3WJDNz/NnX8tnYpd2zrRq7d1mcMTJPb90o1c5Y/vbFc3iioNBXYr/q5UK5dVignZMSaAD4uLUbsdD8HAAAIOYRuAAhxGqbHp8eaRVvXX8orl6c3lMnS8jr5T36lWbIj7HJ+z3i5ICdB+sX8VskdAAAAwdV2P0cAQMhJcdpkcm6SLDk0V747tLdM6ZNkxntr9/O7Vm+R/gvXyBFfbDDd0mvcnmDvLgAAQLdH6AaALmpIfKQ8uG+65I3tJ/86IFvGpUWLdkxfUFItf/x2s2TPWy2TfyyQb8pqg72rAAAA3RbdywGgi9OpxE7NijPL+poG0/X86Y1lsq7GJY+uKzXLAfERcn7PBNkr1imJdquZ+izRYZVEh42pyAAAAAKI0A0AYaRXlEOm75UqUwekyPzianlyQ5mZd1zHfy/9ubDVv4m2WRpDuL0xhGuV9dGJkXJEarQcEB9JgTYAAIA9QOgGgDBktVjkqLQYsxTXu+SFTeXydkGVlDS4ZatZPFLuahzzXe32SrXbJZua/L0GdVnROHXaYclRckRKtPw+JVqGxkeYbQMAAKB9CN0AEOZ0Lu8pfZLN0pTb65WyBo+UuhpDuM4zrpcbahrkky01srCkWkpdHnmnsMosKslhlTHJ0SaEH5YSJX2iHCaY63RnAAAA2B6hGwC6KZvFYuYC16Wlv/RtDOXfldeZwmz/La6WT7ZUm1CureCmJbxJ93Sdsiwr0u6/zIqw+dd7RNolN8ph5joHAADobgjdAIA2Q/kBCZFmuaZvsrg8XllSViv/Lak2QXxxaa2UuTyme/qq6gaztEXbwXtF2aV/tFMGxDikf4xeOqV/tEP6RhPIAQBA+CJ0AwDa94VhtcjopCiz3Ng/xdxW5fLI5jqX5NW6zOVv627/bdpdvdLtNdXUdZlfsn0gz4myS79op2khz4iwS6b/0i4ZzsZ1LfCm+wAAANCVELoBALstxm6V/nanablui9frlcJ6t6yqqpeVVdoivu1Sr1c3SIXLI+trXGbZEY3bqU6bZEXYJTfaYcaT99HLJuuxdrqwAwCA0ELoBgAElBZZ05ZqXQ5O3j6QF9W7ZWVVvaypbpCCerfk17mkoM4tBXUuydfLepcU1blFa63rY3X5vqKu1efSUO4L4HvFOGVMSpQcnBQlUYwnBwAAQULoBgAENZCnR9jN0jKQN6VF3YrrG4P4plqXrK1pMCHdLNvWtzR4zGN0+aqs1vzdHatEIqwWE7zHpkabRceo63h1AACAzkDoBgCEPFuT1vIh8a0/pqzB3SyMf1teJ/NLqk1I1+Jvuty0QiTRbpUjUqPlyBQN4TGmsBtTngEAgEAhdAMAwkKCwyZDdYmPbNZ9fUVVvcwrrpb5xY1V13Xu8VfzK82itECbFm5LcdhM9/QUZ/N1c+mwSfq2adAi6KoOAAB2AaEbABC2tAV7YGyEWSbnJvmnPTMhvKRaPtta4x8n3l4ZETbJiXSYKdD0Uiuv50TaJSfKYS51bnK6rwMAAB9CNwCgW057dvOAFKl2e2R5Zb2UbBsLXtKw7bLFenGDjid3S53Hu63Im1u+Lmv7eRLsVkly2CTJ8dtlYovrja3ndkl3NraiJztsYmWsOQAAYYfQDQDotqJtVlNYrT20q7qG8A21jXOPN166ZENtg/9Sx4+7vCJlLo9Z1ta0f19slsbq6yaEO7W4XONldqRN+kY7pW+0Q/pFO0w3egAA0HUQugEAaGdX9bQIu1naCupaZV1bxrc2eGRrg1tKt102Lp5ml9p6rlOhFda7TOV1t1f8regi9W3uh7aQ94tpDOCNQdxp1rXInAZ3XaxiEatebls3t2nBdo9XajxePm8AADoRoRsAgA5i80+Btmt/1+BpbEXXAF5ogrhbCutc5lJb1VdXN8iv1Q3munZ7Lyl1y+LSxmnRdkeMrcK0qvuWNKe9cd1hk7SIxst945xmLDwAAOjiofvRRx+V++67T/Lz82Xo0KEya9YsGTVqVKuP/emnn2TatGmyZMkSWbdunTz44IMyZcqUPdomAADB5rBaTAE2XXakwuWRX6vrZXWVL4jXm0tdtIXdYxqzvdqgbdbdTdabqnJ7parGJetqXDt8vpEJkTIpJ17Oyo43Y9IBAEAXC91z586Vq6++WubMmSOjR4+Whx56SMaNGycrVqyQ9PT07R5fXV0tffv2ldNOO03+8pe/dMg2AQDoKuLsVjMlWtNp0dpLx6S7PB5Zk18olvgk2eJqbF3Xyu1NLxuLxrlMlfevti1/+blITsqMlUk9E+TI1GiqswMAsAssXv0WDhINxSNHjpRHHnnEXPd4PJKTkyNXXHGF3HDDDTv829zcXNPK3bKle0+26VNeXi4JCQlSVlYm8fHxEor0dRUWFpoTCVarNdi7A4QUjg9gz48N7d7+4qZyeXpjmfxQ8dsYc50WbWLPeDm/Z4L0j3HyViMs8L0BcHzsjvbmxqC1dNfX15tu4jfeeKP/Nv0BMHbsWFm0aFHIbBMAgO5Ix6b/pW+yTOmTJEvL6+TpDWXyz7xyU7X9zlVbzHJocpT8PiVatEab8p3F19P5/vVtaw6LxVSLj7Ftu7RbJdpqMZf+22yNJwJ0arY6j0dqzeW2xe1tdl2ne6vUxeXddum77lv3SpVbC9R5mxWT03H3+izmclvhOV0/NClK/tovmW70AIAOF7TQXVxcLG63WzIyMprdrteXL1/eqdusq6szS9MzFr6znrqEIt0v7aQQqvsHBBPHB9Cxx8b+cU7Zf980uXfvFHmzsEqe3lguHxVXy/+21JglHOjrmLO+VP7WL1ku7Z0oTlPuPTytr2mQnpF25oVvgu8NoG0cH21r7/dp0AuphYIZM2bIrbfeut3tRUVFUlu7+9VhA/0BazcG/fFE93KA4wPorO+Ow20ih/d2SF5WnLy2pUHW13nEYhF/a7deNl33rTR4xExXVm0W33qT29yN6yrCKuK0WMRpFYmwWH67bhGJ3LYeZdUq7BaJsWoruV5KY8v5tnXf7Vr+rbHAnIhbfiss13i98bLM7ZXZBXWystYjVy8vlr//ukVu6hEhxyc5zFRx4WJZjVtu31grC8pdMjzGJo/1jZYe+iaD31XADpA72lZRUSEhHbpTU1PFZrNJQUFBs9v1emZmZqduU7uja/G1pi3dOg48LS0tpMd0mzlj09II3QDHB9Dp3x1amnRYz/B54y8f6JWnN5XLLStLZF2dW/68pkb+b4tH7h2YZrrRd2V5tS6ZvrJEntlY6a9kv6TKLeOWV8lzQzPlmLQY6e74XQVwfOyOyMjI0A7dTqdThg8fLvPnz5cJEyb4/4en1ydPntyp24yIiDBLS/qDJJRbkfWHU6jvIxAsHB8Ax8au0AbfP/dOknN6JMgDv26R+37dIovL6uTwLzfKiRmxcs/ANNk7tvXCcbVuj6ytaZA11Y2LTsWmleZzo+ySG+2Q3lEOyY60d3rVdx3bfv+211LtbuxFcGpmrFzWO0n+urxQlpTVyXFf58nN/VPklr1Sdmv/tNdEQZ3bzO/e2a+vo/G9AXB87Kr25rCgdi/X1uXzzjtPRowYYebR1um9qqqqZNKkSeb+iRMnSo8ePUz3b1+htJ9//tm/vmnTJvn2228lNjZW+vfv365tAgAAtCXWbpXpe6XKn3slyi0ri+XJDWXyRkGlvF1YKRfnJMqoxEj5VcN1k5CdV7fj+c6V3SLSK8ohub4l2m7CuM5/Hmm1NC62bZdWq389ymY1lzqXe3tp8TgtfDf1l2LJr9NO9CK/S4yUB/ZJl4O2tdp/dmAvuXpZkfxjXancsapEPttaI/8cliWZO5kr3sfl8cq/8yvkvtVbTKG93lF2855dmJNgivABAEJkyjClU3vdd999kp+fL8OGDZOHH37YTPulDj/8cDM12DPPPGOur127Vvr06bPdNsaMGSMLFy5s1zbbgynDgK6NqV8Ajo2OsqyiTm5YUSxvFlTu8HGxNov0iXZIn2inCaDayry2xiVrqxtkQ22DuPbw11aq0yb9oh3SN9oh/aKdv63HOCQrorEomv6k+6Co2rRi/7htmjd9zN0D00wLd2vj01/aVC4X/ZAvVW6vZEbY5OX9s2VMSnSb+1Hl8php5B74datp3W/JYRE5NSvOFKM7JCmqy4yJ53sD4PjYHe3NjUEP3aGI0A10bfx4Ajg2OtrHJdWmm3a9x9sYrqM0YDcuGmxTHLY2A6a2POu4ag2pGsIbL12yvrZBKlyNU6PVminRmq43To3WHtoSrvsRZbWYVmeV5LDK1P4pclnvRInYNhVbW5ZX1smpS/Lkp8p6M53anXunynX9kptVNy+qc8mj60rlkbWlUtLg9p8ImNw7US7ISZD/llTL7HWl8mXpbwVo94tzyqW9EuWPPeIl3qEl7UIX3xsAx8fuIHTvAUI30LXx4wng2AgHHq/XhHwdj63TfGm39tVmqfevr6tpkG3Dtf0tzVfkJsnfBqRI0i4EXW3BvuzHAnluU+O0qcelx8izQ7Nka4NbZq7ZKk9tKDMnApSeZLimT5Kcn5Ng5ldvamlZrQnfOqe7bxy59gLQ4K2t30Pi21d0qLPxvQFwfOwOQvceIHQDXRs/ngCOje6iwfNbINex5YclR5ku7rtDOz/+34YymfxToWll19Z7Dd2+iucjEiJNC/jJmbE7LZpW2uCW5zaWmwC+vKqxq7svsB+REm2W36dEt3sMeaDxvQFwfAQyN4bG/+kAAACwy7TAWr8Yp1n2lHaP/1OvRBOuT12aZ1rS1fi0GBO2xyS3f4y2Foi7sk+SXJGbKB9vqTEF217LrzAnB36tLjMF6tQ+sU5/CD88JVqSnbZWW+F9hetMEbtthexK6t2mQNxxGbFycFLULhWbA4DOROgGAACA37CESFlySG/TRVzD7J50CdeQrmFaFx2//umWajP++7/F1fJNeZ0sq6w3i44X18g8LD5CfpcYJVtdbn/ILqpvHEPeGq26/sCarZJgt8q4tBjTLX58eoykOrvfT1ytHfByXoUsr6yXEzJizMmTrlLIDgh33e//SAAAANihBIdNLu2d1KHvks5dPj491ixqS71bPtYQXtwYxH+urDdBXJeWEu1W0zW9afG6WJtV5pdUy7uFVSaYv7K5wiwaM30t4Memx8iQuIiQDZ8/VdSZkwtHpEZvNz5+V3xYVCXXLS+S77a9dzoNnL5uncJNx9O31oMAQOehenkrGNMNdG2MzQM4NtD15Ne6ZEGJtoDXSrrT7g/Xeqnd1XfUwvtVaa28U1hl5lP/tkVoz46wy6A4ZyvTrTnNiYDO/t7YUNMgL+WVy4ubKuT7ijr/SQUNyJfnJu7SmPzvymvlumVF8mFxtbmuLf7aq+CDoip/4bsIq8WMw9ft6zj6plXpgfbgd1XbKKS2BwjdQNfGlwPAsYHua2NNg7xbVCVvF1TKvOJqqdnB1Gtp2+Y/1yngMqVe9klJkNxop/SKckhOlH2PWp+b0oJ0/95cIS9uKpdPttSIt0m1+TSn3RTBUxqHtYu8VqAfmxrdZgu9BvepvxSbYnXebdu5XKvW90+RFGdjAbx/bio3Y+ebnoTQ16nh+/yceOkR6eiQ14bwx++qthG69wChG+ja+HIAODYAVeP2yNdltbKqyjflWn3jtGtVDf75xndE5yLvFWk3IbxXVONlj0i7xNutppU8zmaVWN+63WrmSvcF5Vq3R94urDJBW08C6PRvPlpl/pwe8XJqZpwkOKzyXmGVzFq71d9irfaOccrk3EQ5r2eCv0W+rMEtd6/eIg+t2epvyT4jK07uGpgqfdtoIddp3J5cXyYv5pVLuauxFr1u7ei0GDl+2xj43a1475va7seKOql0eWV4QsRO54VH18PvqrYRuvcAoRvo2vhyADg2gJ3RANtYTb1BVlXVyU8lFVJkscuGGpeZ/7yy6QTo7aRx0xfCdftNtzE4zmmC9lnZ8Sa8t2Z5ZZ08urZUntlY5v9b3db5PeOld5RDZqza4j9ZcGhylNy/T5qMSoxq175Vuz3yn80VpvVbW9ub0oD/Bw3gaTHmhMCOgrNOU6dBXrfxyZZq+XRLjZRuC/N60uGQ5CjTjV0r0g9PiBT7LlaV1+EC+v5HWa2SFSJTyoWr4nqXJNptO/2M+F3VNkL3HiB0A10bXw4AxwawJ98bOmd5mctj5kBfX+NqvKxtvMyrdZlK7BVuj1TqpcvTZkDPibTL2T3i5ZzseBkcH9Hu/SlvcMuzG8vlkXVb5ZeqxqnbfAbGOOWefdJMK/XuFoj7pbJeXs2vkPeKqkwF+Ka7H22zmMCsAVxbwTMj7LK4VEN2tQnan2+tkeoWrzfWZjFd8QtbVJrXEwY61ZyZFi41WgbHRZgx5fr+bq5zyy9V9bKyqt5c6uvU/fq1psHfK+CgpEhzkuK0rDjJiCCAd6TH1pXK5T8VmPoGL++fLQcktD1LAb+r2kbo3gOEbqBr48sB4NgAOvN7Q7tYaxBtDOCNQVy3MiS+MWTuLt2ujkt/ZO1W2VDrkkt6JZox2bvaerwjpQ1u8xzaxf29okoThpuyW0RcLc4pJDuscmhytGkVPyw52kz1ZrOImf7NNyWcFsXztYD7pDhs0jPKLquq6qVqBz0JtPibBm/fI/S9PDI12gTwkzJjd1hYL1j0M19TXf/bfPI1DbK2usH0UNDPTKfiCwV6wmPaLyWmwr2P1gSYMTBN/tInqdV/r/yuahuhew8QuoGujS8HgGMD4Htj9wKZVlTXadi0FVxbtTUbZ0bYZIwvZKdEy76xzp2eTNBu4jqFmW9KOG0pbxq0NUhrZfq9YpxmGRDz27oWscuv02ngyuWlvArT0u7jtFrkD2kxclZ2nJkWrqOK3e2oLkBJvVuK692ma79vfWOtyx+u9VJv25FRiZFycU6CnJkdLzHbxuh3Nh0a8Ocf8uXpjeXm+k39kmVFVb38J7/SXD86NVqeHZolmS269fO7qm2E7j1A6Aa6Nr4cAI4NgO+NjmkFL2vwmCJyezrXuQY+ndptS4NbBsQ4TeDWAN0eq6vq5eW8CjPV2k+V9f7bY2wWf7doDfma6bVnulnf1lPAd5tyWLXl3rJtEdNjwL++7VJ7LDQN1zuqft+S9gDQgna+6e50eIF2yX8tv0Iatm1Gi/Dp3OkX90qQofGd1/qtQyFOW5on7xdVmV4Jc/bLkD/1SjQnWnSc/1U/FZrXqhX9nxmaKX9Ij/X/Lb+r2kbo3gOEbqBr48sB4NgA+N4ITz+U15nwrS3ga2uaj3cPFA3jOhWbdo/Xy1SHzRR56+ubSz6qcT75hDa6vRfWuUxxvMfXl5nq+U1bv//cK9FUoNfWbw3ARfVu87rW1bhM9/TG9cau6tq6rnPO/7VvspyQEdvuoQsFdS459quNsqSszhS7e+WAbNNLoKllFXVy1rebTe8ENaVPkty9d6opqsfvqrYRuvcAoRvo2vhyADg2AL43wpsG1K/Kas0YapvFYlpv9VI7bvvX9VIa79OGZpfXa8anuzze39a3XWpLfIPXa0KpCdYasrcFbW2d3tOWftnW8q5j3R9bX2Zav31j5XX72RF2E67b27K+T6xTru+XLGdnx4tjBz0GtFDdMYs3mi7w+preHtFDRie1XvFep7m7YXmx/H3tVnN9aHyEvDQsS/aOcexRzYNwRujuhDcvmAgVAMcHwHcHwO8qdE0F21q/n2jR+q3xOTvSLr2j7JIb5TCF2HKjHWY93WmTVzZXyKPrSv1zrmsX9mv6JsufchK2Gyu+uLRGjv1qk+kmr63x74/qKXvF7nxO9ncLK+X87/JNq7uehHhw3zQ5wVkrGRkZhO4WCN17gNANdG2clAI4NgC+N9AVaOv3oq01UuvxmmCdE7Xzse46B/yc9aXy4JqtUrCt2ry2yF/ZJ1Em906SZKdN3imolNO/yTNj1IcnRMg7I3vu0rRrm2tdct53m+Wj4mpzPcthkQGxkdI3prErfd8o7VrvNN3rMyJsHdIToCsidHfCmxdMhAqA4wPguwPgdxW6L+0OrvO53/vrFtN93Fdc7viMWPnX5gpTRG5cWrT8+4AeErsbFdP1hMDMX7fKzSuKpd7bdrd3bQ03Y9ujHZLssJm53mNsVtPy7luP1uv+9cYCdtrt3+ofEtA4HKDput6vc733iHRIV8+NzDIPAAAAAF1MpM0qf+7dOHf7v/Mr5O7VW0whNK30rs7rGS9PDM7c4ZjvHdFAfG2/ZLmgZ5x8ubFAtkbEydptU6U1LvWyocZlxqFrVfmmleU7ykkZsfLqiB7S1RG6AQAAAKCL0qnPdP5vrYL+QVG16Xr+u8RIU2itI7p9Jzpssn+MXdLT47Yb013v8cr6bXOVa1G7MpfbdGmvcnukyuWVao9een67ze2VarfHFLDTmnE6Mt03zVvjeuNl41Rv3t1qoQ9FhG4AAAAA6OI0YB+THmOWzqLjz/vHOM2CtoXHqQMAAAAAAEIQoRsAAAAAgAAhdAMAAAAAECCEbgAAAAAAAoTQDQAAAABAgBC6AQAAAAAIEEI3AAAAAAABQugGAAAAACBACN0AAAAAAAQIoRsAAAAAgAAhdAMAAAAAECD2QG24K/N6veayvLxcQpXH45GKigqJjIwUq5VzJwDHB8B3B8DvKoDc0Zl8edGXH9tC6G6FhlmVk5MTiM8GAAAAABBG+TEhIaHN+y3encXybkhbkfPy8iQuLk4sFouE6lkVPSmwYcMGiY+PD/buACGF4wPg2AD43gD4XRVoGqU1cGdnZ++w9zEt3a3QN6xnz57SFWjgJnQDHB8A3x0Av6sAckfn21ELtw+DgQEAAAAACBBCNwAAAAAAAULo7qIiIiJk+vTp5hIAxwfAdwfA7yqA3BGaKKQGAAAAAECA0NINAAAAAECAELoBAAAAAAgQQjcAAAAAAAFC6O6iHn30UcnNzZXIyEgZPXq0LF68ONi7BHSqGTNmyMiRIyUuLk7S09NlwoQJsmLFimaPqa2tlcsvv1xSUlIkNjZWTjnlFCkoKOCTQrdy9913i8VikSlTpvhv49hAd7Zp0yb54x//aL4boqKiZPDgwfL111/77/d6vTJt2jTJysoy948dO1ZWrlwZ1H0GAs3tdsvUqVOlT58+5t99v3795PbbbzfHgw/Hxu4jdHdBc+fOlauvvtpUL1+6dKkMHTpUxo0bJ4WFhcHeNaDTfPzxxyZQf/HFF/LRRx9JQ0ODHH300VJVVeV/zF/+8hd566235F//+pd5fF5enpx88sl8Sug2vvrqK3nsscdkyJAhzW7n2EB3tXXrVjn44IPF4XDIe++9Jz///LM88MADkpSU5H/MvffeKw8//LDMmTNHvvzyS4mJiTG/s/RkFRCu7rnnHpk9e7Y88sgjsmzZMnNdj4VZs2b5H8OxsQe86HJGjRrlvfzyy/3X3W63Nzs72ztjxoyg7hcQTIWFhXoq1vvxxx+b66WlpV6Hw+H917/+5X/MsmXLzGMWLVoUxD0FOkdFRYV3wIAB3o8++sg7ZswY71VXXWVu59hAd3b99dd7DznkkDbv93g83szMTO99993nv02PmYiICO9LL73USXsJdL5jjz3We8EFFzS77eSTT/aec845Zp1jY8/Q0t3F1NfXy5IlS0xXJx+r1WquL1q0KKj7BgRTWVmZuUxOTjaXepxo63fTY2XgwIHSq1cvjhV0C9oT5Nhjj212DCiODXRnb775powYMUJOO+00MzRp//33lyeeeMJ//5o1ayQ/P7/ZcZOQkGCG8vE7C+HsoIMOkvnz58svv/xirn/33Xfy6aefyvjx4811jo09Y9/Dv0cnKy4uNmMuMjIymt2u15cvX87ngW7J4/GY8araZXC//fYzt+mPJqfTKYmJidsdK3ofEM5efvllM/xIu5e3xLGB7uzXX381XWh1mN5NN91kjpErr7zSfF+cd955/u+H1n5n8d2BcHbDDTdIeXm5aaCw2Wwmb9x5551yzjnnmPs5NvYMoRtAWLTo/fjjj+aMLNDdbdiwQa666ipT60CLbQJofpJWW7rvuusuc11buvX7Q8dva+gGuqtXXnlFXnzxRfnnP/8pgwYNkm+//dY0aGRnZ3NsdAC6l3cxqamp5uxTywrMej0zMzNo+wUEy+TJk+Xtt9+WBQsWSM+ePf236/GgwzFKS0ubPZ5jBeFOu49rYc0DDjhA7Ha7WbSQoBaG0nVtsePYQHelFcn33XffZrfts88+sn79erPu+y3F7yx0N3/9619Na/eZZ55pKvqfe+65puimzhajODb2DKG7i9HuT8OHDzdjLpqetdXrBx54YFD3DehMOm2FBu7XXntN/vvf/5opLprS40Sr0zY9VnRKMf1hxbGCcHbkkUfKDz/8YFopfIu27GkXQd86xwa6Kx2G1HJ6SR3D2rt3b7Ou3yUaLpp+d2iXW61izncHwll1dbWpE9WUNvRpzlAcG3uG7uVdkI5D0i5Q+sNp1KhR8tBDD5lpkiZNmhTsXQM6tUu5doF64403zFzdvrFGWvBG55fUywsvvNAcL1pcLT4+Xq644grzo+l3v/sdnxTClh4PvtoGPjrlkc5J7LudYwPdlbbcacEo7V5++umny+LFi+Xxxx83i/LNaX/HHXfIgAEDTNDQuYu1i+2ECROCvftAwBx//PFmDLcWnNXu5d98843MnDlTLrjgAnM/x8Ye2sPq5wiSWbNmeXv16uV1Op1mCrEvvviCzwLdiv7vq7Xl6aef9j+mpqbGe9lll3mTkpK80dHR3pNOOsm7efPmoO43EAxNpwxTHBvozt566y3vfvvtZ6YBGzhwoPfxxx9vdr9OjTR16lRvRkaGecyRRx7pXbFiRdD2F+gM5eXl5ntC80VkZKS3b9++3ptvvtlbV1fnfwzHxu6z6H/2NLgDAAAAAIDtMaYbAAAAAIAAIXQDAAAAABAghG4AAAAAAAKE0A0AAAAAQIAQugEAAAAACBBCNwAAAAAAAULoBgAAAAAgQAjdAAAAAAAECKEbAAB0mNzcXHnooYd4RwEA2IbQDQBAF3X++efLhAkTzPrhhx8uU6ZM6bTnfuaZZyQxMXG727/66iu5+OKLO20/AAAIdfZg7wAAAAgd9fX14nQ6d/vv09LSOnR/AADo6mjpBgAgDFq8P/74Y/n73/8uFovFLGvXrjX3/fjjjzJ+/HiJjY2VjIwMOffcc6W4uNj/t9pCPnnyZNNKnpqaKuPGjTO3z5w5UwYPHiwxMTGSk5Mjl112mVRWVpr7Fi5cKJMmTZKysjL/891yyy2tdi9fv369nHjiieb54+Pj5fTTT5eCggL//fp3w4YNk+eff978bUJCgpx55plSUVHRae8fAACBROgGAKCL07B94IEHykUXXSSbN282iwbl0tJSOeKII2T//feXr7/+Wt5//30TeDX4NvXss8+a1u3PPvtM5syZY26zWq3y8MMPy08//WTu/+9//yvXXXedue+ggw4ywVpDtO/5rr322u32y+PxmMC9ZcsWc1Lgo48+kl9//VXOOOOMZo9bvXq1vP766/L222+bRR979913B/Q9AwCgs9C9HACALk5bhzU0R0dHS2Zmpv/2Rx55xATuu+66y3/bU089ZQL5L7/8InvttZe5bcCAAXLvvfc222bT8eHaAn3HHXfIJZdcIv/4xz/Mc+lzagt30+draf78+fLDDz/ImjVrzHOq5557TgYNGmTGfo8cOdIfznWMeFxcnLmurfH6t3feeWeHvUcAAAQLLd0AAISp7777ThYsWGC6dvuWgQMH+luXfYYPH77d386bN0+OPPJI6dGjhwnDGoRLSkqkurq63c+/bNkyE7Z9gVvtu+++pgCb3tc01PsCt8rKypLCwsLdes0AAIQaWroBAAhTOgb7+OOPl3vuuWe7+zTY+ui47aZ0PPhxxx0nl156qWltTk5Olk8//VQuvPBCU2hNW9Q7ksPhaHZdW9C19RsAgHBA6AYAIAxol2+3293stgMOOED+85//mJZku739X/lLliwxofeBBx4wY7vVK6+8stPna2mfffaRDRs2mMXX2v3zzz+bseba4g0AQHdA93IAAMKABusvv/zStFJrdXINzZdffrkpYnbWWWeZMdTapfyDDz4wlcd3FJj79+8vDQ0NMmvWLFP4TCuL+wqsNX0+bUnXsdf6fK11Ox87dqypgH7OOefI0qVLZfHixTJx4kQZM2aMjBgxIiDvAwAAoYbQDQBAGNDq4TabzbQg61zZOlVXdna2qUiuAfvoo482AVgLpOmYal8LdmuGDh1qpgzTbun77befvPjiizJjxoxmj9EK5lpYTSuR6/O1LMTm6yb+xhtvSFJSkhx22GEmhPft21fmzp0bkPcAAIBQZPF6vd5g7wQAAAAAAOGIlm4AAAAAAAKE0A0AAAAAQIAQugEAAAAACBBCNwAAAAAAAULoBgAAAAAgQAjdAAAAAAAECKEbAAAAAIAAIXQDAAAAABAghG4AAAAAAAKE0A0AAAAAQIAQugEAAAAACBBCNwAAAAAAEhj/DyI8cmzdvDJYAAAAAElFTkSuQmCC",
+            "text/plain": [
+              "<Figure size 1000x400 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Converged at iteration 84 | Best loss: 0.0726\n"
+          ]
+        }
+      ],
+      "source": [
+        "fig, ax = plt.subplots(figsize=(10, 4))\n",
+        "ax.plot(mlp.loss_curve_, color='#00B4D8', linewidth=1.5, label='Training Loss')\n",
+        "ax.set_xlabel('Iteration')\n",
+        "ax.set_ylabel('Loss')\n",
+        "ax.set_title('MLP Training Loss Curve', fontweight='bold')\n",
+        "ax.legend()\n",
+        "ax.grid(True, alpha=0.3)\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n",
+        "print(f\"Converged at iteration {mlp.n_iter_} | Best loss: {min(mlp.loss_curve_):.4f}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 7. Full Evaluation on Test Set"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "==================================================\n",
+            "  TEST SET RESULTS\n",
+            "==================================================\n",
+            "  Accuracy  : 95.06%\n",
+            "  F1 Score  : 0.9541\n",
+            "  ROC-AUC   : 0.9853\n",
+            "==================================================\n",
+            "\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "Unfocused (0)       0.97      0.93      0.95       934\n",
+            "  Focused (1)       0.94      0.97      0.95      1049\n",
+            "\n",
+            "     accuracy                           0.95      1983\n",
+            "    macro avg       0.95      0.95      0.95      1983\n",
+            " weighted avg       0.95      0.95      0.95      1983\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "y_pred      = mlp.predict(X_test_sc)\n",
+        "y_pred_prob = mlp.predict_proba(X_test_sc)[:, 1]\n",
+        "\n",
+        "test_acc = accuracy_score(y_test, y_pred)\n",
+        "test_f1  = f1_score(y_test, y_pred)\n",
+        "test_auc = roc_auc_score(y_test, y_pred_prob)\n",
+        "\n",
+        "print(\"=\" * 50)\n",
+        "print(\"  TEST SET RESULTS\")\n",
+        "print(\"=\" * 50)\n",
+        "print(f\"  Accuracy  : {test_acc*100:.2f}%\")\n",
+        "print(f\"  F1 Score  : {test_f1:.4f}\")\n",
+        "print(f\"  ROC-AUC   : {test_auc:.4f}\")\n",
+        "print(\"=\" * 50)\n",
+        "print()\n",
+        "print(classification_report(y_test, y_pred,\n",
+        "      target_names=['Unfocused (0)', 'Focused (1)']))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 8. Confusion Matrix & ROC Curve"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1200x500 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
+        "\n",
+        "# Confusion Matrix\n",
+        "cm = confusion_matrix(y_test, y_pred)\n",
+        "disp = ConfusionMatrixDisplay(cm, display_labels=['Unfocused', 'Focused'])\n",
+        "disp.plot(ax=axes[0], colorbar=False, cmap='Blues')\n",
+        "axes[0].set_title('Confusion Matrix', fontweight='bold')\n",
+        "\n",
+        "# ROC Curve\n",
+        "RocCurveDisplay.from_predictions(y_test, y_pred_prob, ax=axes[1],\n",
+        "                                  color='#06D6A0', name=f'MLP (AUC={test_auc:.3f})')\n",
+        "axes[1].plot([0,1],[0,1],'--', color='gray', linewidth=0.8)\n",
+        "axes[1].set_title('ROC Curve', fontweight='bold')\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 9. Feature Importance (Permutation)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Feature Importance Ranking:\n",
+            "  pitch                +0.2188 ± 0.0076\n",
+            "  ear_left             +0.2166 ± 0.0081\n",
+            "  s_face               +0.1939 ± 0.0071\n",
+            "  ear_right            +0.0886 ± 0.0064\n",
+            "  s_eye                +0.0838 ± 0.0056\n",
+            "  head_deviation       +0.0809 ± 0.0045\n",
+            "  h_gaze               +0.0786 ± 0.0037\n",
+            "  ear_avg              +0.0654 ± 0.0048\n",
+            "  perclos              +0.0491 ± 0.0046\n",
+            "  gaze_offset          +0.0210 ± 0.0023\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Permutation \n",
+        "from sklearn.inspection import permutation_importance\n",
+        "\n",
+        "result = permutation_importance(\n",
+        "    mlp, X_test_sc, y_test,\n",
+        "    n_repeats=10, random_state=RANDOM_SEED, scoring='f1'\n",
+        ")\n",
+        "\n",
+        "sorted_idx = result.importances_mean.argsort()[::-1]\n",
+        "\n",
+        "fig, ax = plt.subplots(figsize=(10, 5))\n",
+        "colors = ['#06D6A0' if result.importances_mean[i] > 0 else '#EF476F'\n",
+        "          for i in sorted_idx]\n",
+        "ax.barh(\n",
+        "    [SELECTED_FEATURES[i] for i in sorted_idx],\n",
+        "    result.importances_mean[sorted_idx],\n",
+        "    xerr=result.importances_std[sorted_idx],\n",
+        "    color=colors, capsize=4\n",
+        ")\n",
+        "ax.set_xlabel('Mean F1 Drop (higher = more important)')\n",
+        "ax.set_title('Permutation Feature Importance on Test Set', fontweight='bold')\n",
+        "ax.axvline(0, color='gray', linewidth=0.5)\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n",
+        "\n",
+        "print(\"\\nFeature Importance Ranking:\")\n",
+        "for i in sorted_idx:\n",
+        "    print(f\"  {SELECTED_FEATURES[i]:<20} {result.importances_mean[i]:+.4f} ± {result.importances_std[i]:.4f}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 10. Save Model & Scaler"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "==================================================\n",
+            "  MODEL SAVED\n",
+            "==================================================\n",
+            "  Model  -> /Users/mohammedalketbi22/GAP/shared/data_preparation/../MLP/models/mlp_20260224_024200.joblib\n",
+            "  Scaler -> /Users/mohammedalketbi22/GAP/shared/data_preparation/../MLP/models/scaler_20260224_024200.joblib\n",
+            "  Meta   -> /Users/mohammedalketbi22/GAP/shared/data_preparation/../MLP/models/meta_20260224_024200.npz\n",
+            "\n",
+            "  Accuracy : 95.06%\n",
+            "  F1       : 0.9541\n",
+            "  AUC      : 0.9853\n",
+            "==================================================\n"
+          ]
+        }
+      ],
+      "source": [
+        "from datetime import datetime\n",
+        "\n",
+        "timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')\n",
+        "model_path  = os.path.join(MODEL_DIR, f\"mlp_{timestamp}.joblib\")\n",
+        "scaler_path = os.path.join(MODEL_DIR, f\"scaler_{timestamp}.joblib\")\n",
+        "meta_path   = os.path.join(MODEL_DIR, f\"meta_{timestamp}.npz\")\n",
+        "\n",
+        "joblib.dump(mlp,    model_path)\n",
+        "joblib.dump(scaler, scaler_path)\n",
+        "np.savez(meta_path,\n",
+        "         feature_names=SELECTED_FEATURES,\n",
+        "         test_accuracy=test_acc,\n",
+        "         test_f1=test_f1,\n",
+        "         test_auc=test_auc)\n",
+        "\n",
+        "print(\"=\" * 50)\n",
+        "print(\"  MODEL SAVED\")\n",
+        "print(\"=\" * 50)\n",
+        "print(f\"  Model  -> {model_path}\")\n",
+        "print(f\"  Scaler -> {scaler_path}\")\n",
+        "print(f\"  Meta   -> {meta_path}\")\n",
+        "print()\n",
+        "print(f\"  Accuracy : {test_acc*100:.2f}%\")\n",
+        "print(f\"  F1       : {test_f1:.4f}\")\n",
+        "print(f\"  AUC      : {test_auc:.4f}\")\n",
+        "print(\"=\" * 50)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 11. Quick Sanity Check\n",
+        "Manually inspect a few predictions to make sure the model makes sense."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "#    True         Predicted    Confidence   Match\n",
+            "--------------------------------------------------\n",
+            "0    Unfocused    Unfocused    100.0      % ✅\n",
+            "1    Focused      Focused      85.4       % ✅\n",
+            "2    Focused      Focused      99.9       % ✅\n",
+            "3    Focused      Focused      99.9       % ✅\n",
+            "4    Focused      Focused      98.3       % ✅\n",
+            "5    Focused      Focused      93.4       % ✅\n",
+            "6    Focused      Focused      96.9       % ✅\n",
+            "7    Focused      Focused      99.9       % ✅\n",
+            "8    Focused      Focused      100.0      % ✅\n",
+            "9    Unfocused    Unfocused    99.1       % ✅\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Show 10 random test samples with true vs predicted label\n",
+        "np.random.seed(RANDOM_SEED)\n",
+        "sample_idx = np.random.choice(len(y_test), 10, replace=False)\n",
+        "\n",
+        "print(f\"{'#':<4} {'True':<12} {'Predicted':<12} {'Confidence':<12} {'Match'}\")\n",
+        "print(\"-\" * 50)\n",
+        "for i, idx in enumerate(sample_idx):\n",
+        "    true_label = 'Focused' if y_test[idx] == 1 else 'Unfocused'\n",
+        "    pred_label = 'Focused' if y_pred[idx] == 1 else 'Unfocused'\n",
+        "    confidence = y_pred_prob[idx] if y_pred[idx] == 1 else 1 - y_pred_prob[idx]\n",
+        "    match = '✅' if y_test[idx] == y_pred[idx] else '❌'\n",
+        "    print(f\"{i:<4} {true_label:<12} {pred_label:<12} {confidence*100:<11.1f}% {match}\")"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "venv",
+      "language": "python",
+      "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": "3.13.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4
+}
diff --git a/data_preparation/README.md b/data_preparation/README.md
index 9bc38c6e2a977224944df2ebf88c3b450ae2aa55..9131e390874f5b92f9943719e0811685d06d60e4 100644
--- a/data_preparation/README.md
+++ b/data_preparation/README.md
@@ -1,3 +1,41 @@
-# data_preparation
+# Data Preparation
 
-Dataset team owns layout and scripts here.
+## Folder Structure
+
+### collected/
+Contains raw session files in `.npz` format.  
+Generated using:
+
+python -m models.attention.collect_features
+
+Each session includes:
+- 17-dimensional feature vectors
+- Corresponding labels
+
+---
+
+
+### MLP/
+Contains notebooks for:
+- Exploring collected data
+- Training the sklearn MLP model (10 features)
+
+Trained models are saved to:
+../MLP/models/
+
+---
+
+### CNN/
+Eye crop directory structure for CNN training (YOLO).
+
+---
+
+## Collecting Data
+
+**Step-by-step**
+
+1. From repo root Install deps: `pip install -r requirements.txt`.
+3. Run: `python -m models.attention.collect_features --name yourname`.
+4. Webcam opens. Look at the camera; press **1** when focused, **0** when unfocused. Switch every 10–30 sec so you get both labels.
+5. Press **p** to pause/resume.
+6. Press **q** when done. One `.npz` is saved to `data_preparation/collected/` (17 features + labels).
diff --git a/data_preparation/collected_Abdelrahman/abdelrahman_20260306_023035.npz b/data_preparation/collected_Abdelrahman/abdelrahman_20260306_023035.npz
new file mode 100644
index 0000000000000000000000000000000000000000..2c5f7584acb0dd1f81cae7f7a7bc9f7de169ad09
--- /dev/null
+++ b/data_preparation/collected_Abdelrahman/abdelrahman_20260306_023035.npz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e2c48532150182c8933d4595e0a0711365645b699647e99976575b7c2adffaf8
+size 1207980
diff --git a/data_preparation/collected_Ayten/ayten_session_1.npz b/data_preparation/collected_Ayten/ayten_session_1.npz
new file mode 100644
index 0000000000000000000000000000000000000000..2b56cb4af8943f0d95cd8d0422039bf914487ea8
--- /dev/null
+++ b/data_preparation/collected_Ayten/ayten_session_1.npz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fbecdbffa1c1b03b3b0fb5f715dcb4ff885ecc67da4aff78e6952b8847a96014
+size 1341056
diff --git a/data_preparation/collected_Jarek/Jarek_20260225_012931.npz b/data_preparation/collected_Jarek/Jarek_20260225_012931.npz
new file mode 100644
index 0000000000000000000000000000000000000000..580c911af207e7046d5ad6bcb3f110a8b9023cf0
--- /dev/null
+++ b/data_preparation/collected_Jarek/Jarek_20260225_012931.npz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0fa68f4d587eee8d645b23b463a9f1c848b9bacc2adb68603d5fa9cd8cb744c7
+size 1128864
diff --git a/data_preparation/collected_Junhao/Junhao_20260303_113554.npz b/data_preparation/collected_Junhao/Junhao_20260303_113554.npz
new file mode 100644
index 0000000000000000000000000000000000000000..4397d6c92e54acffaff7783949a7855d52c36c97
--- /dev/null
+++ b/data_preparation/collected_Junhao/Junhao_20260303_113554.npz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ec321ee79800c04fdc0f999690d07970445aeca61f977bf6537880bbc996b5e5
+size 678336
diff --git a/data_preparation/collected_Kexin/kexin2_20260305_180229.npz b/data_preparation/collected_Kexin/kexin2_20260305_180229.npz
new file mode 100644
index 0000000000000000000000000000000000000000..434319e64fecad34ca84b00ce6e002cb80a13b0c
--- /dev/null
+++ b/data_preparation/collected_Kexin/kexin2_20260305_180229.npz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0e96fe17571fa1fcccc1b4bd0c8838270498883e4db6a608c4d4d4c3a8ac1d0d
+size 1129700
diff --git a/data_preparation/collected_Kexin/kexin_20260224_151043.npz b/data_preparation/collected_Kexin/kexin_20260224_151043.npz
new file mode 100644
index 0000000000000000000000000000000000000000..564daaf5b74f065b188f15a73634d503890fc79c
--- /dev/null
+++ b/data_preparation/collected_Kexin/kexin_20260224_151043.npz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8d402ca4e66910a2e174c4f4beec5d7b3db6a04213d29673b227ce6ef04b39c4
+size 1329732
diff --git a/data_preparation/collected_Langyuan/Langyuan_20260303_153145.npz b/data_preparation/collected_Langyuan/Langyuan_20260303_153145.npz
new file mode 100644
index 0000000000000000000000000000000000000000..80f3246385201815941ac2a56383739c53569465
--- /dev/null
+++ b/data_preparation/collected_Langyuan/Langyuan_20260303_153145.npz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5c679cdba334b2f3f0953b7e44f7209056277c826e2b7b5cfcf2b8b750898400
+size 1198784
diff --git a/data_preparation/collected_Mohamed/session_20260224_010131.npz b/data_preparation/collected_Mohamed/session_20260224_010131.npz
new file mode 100644
index 0000000000000000000000000000000000000000..c3b1aa0712b0f228962b786932f3535ba4ce84f5
--- /dev/null
+++ b/data_preparation/collected_Mohamed/session_20260224_010131.npz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0a784f703c13b83911f47ec507d32c25942a07572314b8a77cbf40ca8cdff16f
+size 1006428
diff --git a/data_preparation/collected_Saba/saba_20260306_230710.npz b/data_preparation/collected_Saba/saba_20260306_230710.npz
new file mode 100644
index 0000000000000000000000000000000000000000..be83434e108b1761e482a4ad7e5834fac23078af
--- /dev/null
+++ b/data_preparation/collected_Saba/saba_20260306_230710.npz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:db1cab5ddcf9988856c5bdca1183c8eba4647365e675a1d8a200d12f6b5d2097
+size 663212
diff --git a/data_preparation/collected_Yingtao/Yingtao_20260306_023937.npz b/data_preparation/collected_Yingtao/Yingtao_20260306_023937.npz
new file mode 100644
index 0000000000000000000000000000000000000000..6d5e1b3cca9fc32a866dfe993a9808fd4bbd6ae6
--- /dev/null
+++ b/data_preparation/collected_Yingtao/Yingtao_20260306_023937.npz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7a75af17e25dca5f06ea9e7443ea5fee9db638f68a5910e014ee7cb8b7ae80fd
+size 1338776
diff --git a/evaluation/README.md b/evaluation/README.md
index 013f6d4f7473338c03fc778c810c281a74f72b0b..bb5316f5a6ee78d18c9cb56f86a9f95b4610f409 100644
--- a/evaluation/README.md
+++ b/evaluation/README.md
@@ -1,3 +1,3 @@
 # evaluation
 
-Metrics, experiment configs, and results live here.
+Place metrics scripts, run configs, and results here. Logs dir is used by `models.mlp.train` for training logs.
diff --git a/models/attention_score_fusion/.gitkeep b/evaluation/logs/.gitkeep
similarity index 100%
rename from models/attention_score_fusion/.gitkeep
rename to evaluation/logs/.gitkeep
diff --git a/models/README.md b/models/README.md
index f23832f74884ec4e7036dde8bf82d2c224dde41d..54a51a0a507fd5a44dcefed05b1a561e8133af88 100644
--- a/models/README.md
+++ b/models/README.md
@@ -1,8 +1,10 @@
 # models
 
-- `face_orientation_model/` — S_face
-- `eye_behaviour_model/` — S_eye
-- `attention_score_fusion/` — fusion + smoothing
-- `face_landmarks_pretrained/` — MediaPipe FaceMesh (no training)
+- **cnn/eye_attention/** — YOLO open/closed eye classifier, crop helper, train stub
+- **mlp/** — PyTorch MLP on feature vectors (face_orientation / eye_behaviour); checkpoints under `mlp/face_orientation_model/`, `mlp/eye_behaviour_model/`
+- **geometric/face_orientation/** — head pose (solvePnP). **geometric/eye_behaviour/** — EAR, gaze, MAR
+- **pretrained/face_mesh/** — MediaPipe face landmarks (no training)
+- **attention/** — webcam feature collection (17-d), stubs for train/classifier/fusion
+- **prepare_dataset.py** — loads from `data_preparation/processed/` or synthetic; used by `mlp/train.py`
 
-`train.py` trains the MLP on feature vectors; `prepare_dataset.py` loads from `data_preparation/processed/` or synthetic.
+Run legacy MLP training: `python -m models.mlp.train`. The sklearn MLP used in the live demo is trained in `data_preparation/MLP/train_mlp.ipynb` and saved under `../MLP/models/`.
diff --git a/models/attention/__init__.py b/models/attention/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc
--- /dev/null
+++ b/models/attention/__init__.py
@@ -0,0 +1 @@
+
diff --git a/models/eye_behaviour_model/.gitkeep b/models/attention/classifier.py
similarity index 100%
rename from models/eye_behaviour_model/.gitkeep
rename to models/attention/classifier.py
diff --git a/models/attention/collect_features.py b/models/attention/collect_features.py
new file mode 100644
index 0000000000000000000000000000000000000000..c4c4ccf7aaa81f4d3534bb44c9c5c7570e406d7b
--- /dev/null
+++ b/models/attention/collect_features.py
@@ -0,0 +1,349 @@
+# Usage: python -m models.attention.collect_features [--name alice] [--duration 600]
+
+import argparse
+import collections
+import math
+import os
+import sys
+import time
+
+import cv2
+import numpy as np
+
+_PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+if _PROJECT_ROOT not in sys.path:
+    sys.path.insert(0, _PROJECT_ROOT)
+
+from models.pretrained.face_mesh.face_mesh import FaceMeshDetector
+from models.geometric.face_orientation.head_pose import HeadPoseEstimator
+from models.geometric.eye_behaviour.eye_scorer import EyeBehaviourScorer, compute_gaze_ratio, compute_mar
+
+FONT = cv2.FONT_HERSHEY_SIMPLEX
+GREEN = (0, 255, 0)
+RED = (0, 0, 255)
+WHITE = (255, 255, 255)
+YELLOW = (0, 255, 255)
+ORANGE = (0, 165, 255)
+GRAY = (120, 120, 120)
+
+FEATURE_NAMES = [
+    "ear_left", "ear_right", "ear_avg", "h_gaze", "v_gaze", "mar",
+    "yaw", "pitch", "roll", "s_face", "s_eye", "gaze_offset", "head_deviation",
+    "perclos", "blink_rate", "closure_duration", "yawn_duration",
+]
+
+NUM_FEATURES = len(FEATURE_NAMES)
+assert NUM_FEATURES == 17
+
+
+class TemporalTracker:
+    EAR_BLINK_THRESH = 0.21
+    MAR_YAWN_THRESH = 0.04
+    PERCLOS_WINDOW = 60
+    BLINK_WINDOW_SEC = 30.0
+
+    def __init__(self):
+        self.ear_history = collections.deque(maxlen=self.PERCLOS_WINDOW)
+        self.blink_timestamps = collections.deque()
+        self._eyes_closed = False
+        self._closure_start = None
+        self._yawn_start = None
+
+    def update(self, ear_avg, mar, now=None):
+        if now is None:
+            now = time.time()
+
+        closed = ear_avg < self.EAR_BLINK_THRESH
+        self.ear_history.append(1.0 if closed else 0.0)
+        perclos = sum(self.ear_history) / len(self.ear_history) if self.ear_history else 0.0
+
+        if self._eyes_closed and not closed:
+            self.blink_timestamps.append(now)
+        self._eyes_closed = closed
+
+        cutoff = now - self.BLINK_WINDOW_SEC
+        while self.blink_timestamps and self.blink_timestamps[0] < cutoff:
+            self.blink_timestamps.popleft()
+        blink_rate = len(self.blink_timestamps) * (60.0 / self.BLINK_WINDOW_SEC)
+
+        if closed:
+            if self._closure_start is None:
+                self._closure_start = now
+            closure_dur = now - self._closure_start
+        else:
+            self._closure_start = None
+            closure_dur = 0.0
+
+        yawning = mar > self.MAR_YAWN_THRESH
+        if yawning:
+            if self._yawn_start is None:
+                self._yawn_start = now
+            yawn_dur = now - self._yawn_start
+        else:
+            self._yawn_start = None
+            yawn_dur = 0.0
+
+        return perclos, blink_rate, closure_dur, yawn_dur
+
+
+def extract_features(landmarks, w, h, head_pose, eye_scorer, temporal):
+    from models.geometric.eye_behaviour.eye_scorer import _LEFT_EYE_EAR, _RIGHT_EYE_EAR, compute_ear
+
+    ear_left = compute_ear(landmarks, _LEFT_EYE_EAR)
+    ear_right = compute_ear(landmarks, _RIGHT_EYE_EAR)
+    ear_avg = (ear_left + ear_right) / 2.0
+    h_gaze, v_gaze = compute_gaze_ratio(landmarks)
+    mar = compute_mar(landmarks)
+
+    angles = head_pose.estimate(landmarks, w, h)
+    yaw = angles[0] if angles else 0.0
+    pitch = angles[1] if angles else 0.0
+    roll = angles[2] if angles else 0.0
+
+    s_face = head_pose.score(landmarks, w, h)
+    s_eye = eye_scorer.score(landmarks)
+
+    gaze_offset = math.sqrt((h_gaze - 0.5) ** 2 + (v_gaze - 0.5) ** 2)
+    head_deviation = math.sqrt(yaw ** 2 + pitch ** 2)
+
+    perclos, blink_rate, closure_dur, yawn_dur = temporal.update(ear_avg, mar)
+
+    return np.array([
+        ear_left, ear_right, ear_avg,
+        h_gaze, v_gaze,
+        mar,
+        yaw, pitch, roll,
+        s_face, s_eye,
+        gaze_offset,
+        head_deviation,
+        perclos, blink_rate, closure_dur, yawn_dur,
+    ], dtype=np.float32)
+
+
+def quality_report(labels):
+    n = len(labels)
+    n1 = int((labels == 1).sum())
+    n0 = n - n1
+    transitions = int(np.sum(np.diff(labels) != 0))
+    duration_sec = n / 30.0  # approximate at 30fps
+
+    warnings = []
+
+    print(f"\n{'='*50}")
+    print(f"  DATA QUALITY REPORT")
+    print(f"{'='*50}")
+    print(f"  Total samples : {n}")
+    print(f"  Focused       : {n1} ({n1/max(n,1)*100:.1f}%)")
+    print(f"  Unfocused     : {n0} ({n0/max(n,1)*100:.1f}%)")
+    print(f"  Duration      : {duration_sec:.0f}s ({duration_sec/60:.1f} min)")
+    print(f"  Transitions   : {transitions}")
+    if transitions > 0:
+        print(f"  Avg segment   : {n/transitions:.0f} frames ({n/transitions/30:.1f}s)")
+
+    # checks
+    if duration_sec < 120:
+        warnings.append(f"TOO SHORT: {duration_sec:.0f}s — aim for 5-10 minutes (300-600s)")
+
+    if n < 3000:
+        warnings.append(f"LOW SAMPLE COUNT: {n} frames — aim for 9000+ (5 min at 30fps)")
+
+    balance = n1 / max(n, 1)
+    if balance < 0.3 or balance > 0.7:
+        warnings.append(f"IMBALANCED: {balance:.0%} focused — aim for 35-65% focused")
+
+    if transitions < 10:
+        warnings.append(f"TOO FEW TRANSITIONS: {transitions} — switch every 10-30s, aim for 20+")
+
+    if transitions == 1:
+        warnings.append("SINGLE BLOCK: you recorded one unfocused + one focused block — "
+                         "model will learn temporal position, not focus patterns")
+
+    if warnings:
+        print(f"\n  ⚠️  WARNINGS ({len(warnings)}):")
+        for w in warnings:
+            print(f"    • {w}")
+        print(f"\n  Consider re-recording this session.")
+    else:
+        print(f"\n  ✅ All checks passed!")
+
+    print(f"{'='*50}\n")
+    return len(warnings) == 0
+
+
+# ---------------------------------------------------------------------------
+# Main
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--name", type=str, default="session",
+                        help="Your name or session ID")
+    parser.add_argument("--camera", type=int, default=0,
+                        help="Camera index")
+    parser.add_argument("--duration", type=int, default=600,
+                        help="Max recording time (seconds, default 10 min)")
+    parser.add_argument("--output-dir", type=str,
+                        default=os.path.join(_PROJECT_ROOT, "data_preparation", "collected"),
+                        help="Where to save .npz files")
+    args = parser.parse_args()
+
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    detector = FaceMeshDetector()
+    head_pose = HeadPoseEstimator()
+    eye_scorer = EyeBehaviourScorer()
+    temporal = TemporalTracker()
+
+    cap = cv2.VideoCapture(args.camera)
+    if not cap.isOpened():
+        print("[COLLECT] ERROR: can't open camera")
+        return
+
+    print("[COLLECT] Data Collection Tool")
+    print(f"[COLLECT] Session: {args.name}, max {args.duration}s")
+    print(f"[COLLECT] Features per frame: {NUM_FEATURES}")
+    print("[COLLECT] Controls:")
+    print("  1 = FOCUSED       (looking at screen normally)")
+    print("  0 = NOT FOCUSED   (phone, away, eyes closed, yawning)")
+    print("  p = pause")
+    print("  q = save & quit")
+    print()
+    print("[COLLECT] TIPS for good data:")
+    print("  • Switch between 1 and 0 every 10-30 seconds")
+    print("  • Aim for 20+ transitions total")
+    print("  • Act out varied scenarios: reading, phone, talking, drowsy")
+    print("  • Record at least 5 minutes")
+    print()
+
+    features_list = []
+    labels_list = []
+    label = None        # None = paused
+    transitions = 0     # count label switches
+    prev_label = None
+    status = "PAUSED -- press 1 (focused) or 0 (not focused)"
+    t_start = time.time()
+    prev_time = time.time()
+    fps = 0.0
+
+    try:
+        while True:
+            elapsed = time.time() - t_start
+            if elapsed > args.duration:
+                print(f"[COLLECT] Time limit ({args.duration}s)")
+                break
+
+            ret, frame = cap.read()
+            if not ret:
+                break
+
+            h, w = frame.shape[:2]
+            landmarks = detector.process(frame)
+            face_ok = landmarks is not None
+
+            # record if labeling + face visible
+            if face_ok and label is not None:
+                vec = extract_features(landmarks, w, h, head_pose, eye_scorer, temporal)
+                features_list.append(vec)
+                labels_list.append(label)
+
+                # count transitions
+                if prev_label is not None and label != prev_label:
+                    transitions += 1
+                prev_label = label
+
+            now = time.time()
+            fps = 0.9 * fps + 0.1 * (1.0 / max(now - prev_time, 1e-6))
+            prev_time = now
+
+            # --- draw UI ---
+            n = len(labels_list)
+            n1 = sum(1 for x in labels_list if x == 1)
+            n0 = n - n1
+            remaining = max(0, args.duration - elapsed)
+
+            bar_color = GREEN if label == 1 else (RED if label == 0 else (80, 80, 80))
+            cv2.rectangle(frame, (0, 0), (w, 70), (0, 0, 0), -1)
+            cv2.putText(frame, status, (10, 22), FONT, 0.55, bar_color, 2, cv2.LINE_AA)
+            cv2.putText(frame, f"Samples: {n}  (F:{n1}  U:{n0})  Switches: {transitions}",
+                        (10, 48), FONT, 0.42, WHITE, 1, cv2.LINE_AA)
+            cv2.putText(frame, f"FPS:{fps:.0f}", (w - 80, 22), FONT, 0.45, WHITE, 1, cv2.LINE_AA)
+            cv2.putText(frame, f"{int(remaining)}s left", (w - 80, 48), FONT, 0.42, YELLOW, 1, cv2.LINE_AA)
+
+            if n > 0:
+                bar_w = min(w - 20, 300)
+                bar_x = w - bar_w - 10
+                bar_y = 58
+                frac = n1 / n
+                cv2.rectangle(frame, (bar_x, bar_y), (bar_x + bar_w, bar_y + 8), (40, 40, 40), -1)
+                cv2.rectangle(frame, (bar_x, bar_y), (bar_x + int(bar_w * frac), bar_y + 8), GREEN, -1)
+                cv2.putText(frame, f"{frac:.0%}F", (bar_x + bar_w + 4, bar_y + 8),
+                            FONT, 0.3, GRAY, 1, cv2.LINE_AA)
+
+            if not face_ok:
+                cv2.putText(frame, "NO FACE", (w // 2 - 60, h // 2), FONT, 0.7, RED, 2, cv2.LINE_AA)
+
+            # red dot = recording
+            if label is not None and face_ok:
+                cv2.circle(frame, (w - 20, 80), 8, RED, -1)
+
+            # live warnings
+            warn_y = h - 35
+            if n > 100 and transitions < 3:
+                cv2.putText(frame, "! Switch more often (aim for 20+ transitions)",
+                            (10, warn_y), FONT, 0.38, ORANGE, 1, cv2.LINE_AA)
+                warn_y -= 18
+            if elapsed > 30 and n > 0:
+                bal = n1 / n
+                if bal < 0.25 or bal > 0.75:
+                    cv2.putText(frame, f"! Imbalanced ({bal:.0%} focused) - record more of the other",
+                                (10, warn_y), FONT, 0.38, ORANGE, 1, cv2.LINE_AA)
+                    warn_y -= 18
+
+            cv2.putText(frame, "1:focused  0:unfocused  p:pause  q:save+quit",
+                        (10, h - 10), FONT, 0.38, GRAY, 1, cv2.LINE_AA)
+
+            cv2.imshow("FocusGuard -- Data Collection", frame)
+
+            key = cv2.waitKey(1) & 0xFF
+            if key == ord("1"):
+                label = 1
+                status = "Recording: FOCUSED"
+                print(f"[COLLECT] -> FOCUSED (n={n}, transitions={transitions})")
+            elif key == ord("0"):
+                label = 0
+                status = "Recording: NOT FOCUSED"
+                print(f"[COLLECT] -> NOT FOCUSED (n={n}, transitions={transitions})")
+            elif key == ord("p"):
+                label = None
+                status = "PAUSED"
+                print(f"[COLLECT] paused (n={n})")
+            elif key == ord("q"):
+                break
+
+    finally:
+        cap.release()
+        cv2.destroyAllWindows()
+        detector.close()
+
+        if len(features_list) > 0:
+            feats = np.stack(features_list)
+            labs = np.array(labels_list, dtype=np.int64)
+
+            ts = time.strftime("%Y%m%d_%H%M%S")
+            fname = f"{args.name}_{ts}.npz"
+            fpath = os.path.join(args.output_dir, fname)
+            np.savez(fpath,
+                     features=feats,
+                     labels=labs,
+                     feature_names=np.array(FEATURE_NAMES))
+
+            print(f"\n[COLLECT] Saved {len(labs)} samples -> {fpath}")
+            print(f"  Shape: {feats.shape}  ({NUM_FEATURES} features)")
+
+            quality_report(labs)
+        else:
+            print("\n[COLLECT] No data collected")
+
+        print("[COLLECT] Done")
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/models/face_landmarks_pretrained/.gitkeep b/models/attention/fusion.py
similarity index 100%
rename from models/face_landmarks_pretrained/.gitkeep
rename to models/attention/fusion.py
diff --git a/models/face_orientation_model/.gitkeep b/models/attention/train.py
similarity index 100%
rename from models/face_orientation_model/.gitkeep
rename to models/attention/train.py
diff --git a/models/cnn/CNN_MODEL/.claude/settings.local.json b/models/cnn/CNN_MODEL/.claude/settings.local.json
new file mode 100644
index 0000000000000000000000000000000000000000..bb88014459865994fe1347826ae622ab6c31d8c3
--- /dev/null
+++ b/models/cnn/CNN_MODEL/.claude/settings.local.json
@@ -0,0 +1,7 @@
+{
+  "permissions": {
+    "allow": [
+      "Bash(# Check Dataset_subset counts echo \"\"=== Dataset_subset/train/open ===\"\" && ls /Users/mohammedalketbi22/Downloads/GAP_Large_project-feature-dataset-model-test-92_30-clean/Dataset_subset/train/open/ | wc -l && echo \"\"=== Dataset_subset/train/closed ===\"\" && ls /Users/mohammedalketbi22/Downloads/GAP_Large_project-feature-dataset-model-test-92_30-clean/Dataset_subset/train/closed/ | wc -l && echo \"\"=== Dataset_subset/val/open ===\"\" && ls /Users/mohammedalketbi22/Downloads/GAP_Large_project-feature-dataset-model-test-92_30-clean/Dataset_subset/val/open/ | wc -l && echo \"\"=== Dataset_subset/val/closed ===\"\" && ls /Users/mohammedalketbi22/Downloads/GAP_Large_project-feature-dataset-model-test-92_30-clean/Dataset_subset/val/closed/)"
+    ]
+  }
+}
diff --git a/models/cnn/CNN_MODEL/.gitattributes b/models/cnn/CNN_MODEL/.gitattributes
new file mode 100644
index 0000000000000000000000000000000000000000..7657d2812b393ae9bcd7d6c8e3ef726ccf91723e
--- /dev/null
+++ b/models/cnn/CNN_MODEL/.gitattributes
@@ -0,0 +1 @@
+DATA/** filter=lfs diff=lfs merge=lfs -text
diff --git a/models/cnn/CNN_MODEL/.gitignore b/models/cnn/CNN_MODEL/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..c948d0afb4a58db413e7dc6dbe0d54d4ed90462c
--- /dev/null
+++ b/models/cnn/CNN_MODEL/.gitignore
@@ -0,0 +1,4 @@
+Dataset/train/
+Dataset/val/
+Dataset/test/
+.DS_Store
diff --git a/models/cnn/CNN_MODEL/README.md b/models/cnn/CNN_MODEL/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..3f677e77988c4d8feb52f36289344e13fa77c1b2
--- /dev/null
+++ b/models/cnn/CNN_MODEL/README.md
@@ -0,0 +1,74 @@
+# Eye Open / Closed Classifier (YOLOv11-CLS)
+
+
+Binary classifier: **open** vs **closed** eyes.  
+Used as a baseline for eye-tracking, drowsiness, or focus detection.
+
+---
+
+## Model team task
+
+- **Train** the YOLOv11s-cls eye classifier in a **separate notebook** (data split, epochs, GPU, export `best.pt`).
+- Provide **trained weights** (`best.pt`) for this repo’s evaluation and inference scripts.
+
+
+
+---
+
+## Repo contents
+
+- **notebooks/eye_classifier_colab.ipynb** — Data download (Kaggle), clean, split, undersample, **evaluate** (needs `best.pt` from model team), export.
+- **scripts/predict_image.py** — Run classifier on single images (needs `best.pt`).
+- **scripts/webcam_live.py** — Live webcam open/closed (needs `best.pt` + optional `weights/face_landmarker.task`).
+- **scripts/video_infer.py** — Run on video files.
+- **scripts/focus_infer.py** — Focus/attention inference.
+- **weights/** — Put `best.pt` here; `face_landmarker.task` is downloaded on first webcam run if missing.
+- **docs/** — Extra docs (e.g. UNNECESSARY_FILES.md if present).
+
+---
+
+## Dataset
+
+- **Source:** [Kaggle — open/closed eyes](https://www.kaggle.com/datasets/sehriyarmemmedli/open-closed-eyes-dataset)
+- The Colab notebook downloads it via `kagglehub`; no local copy in repo.
+
+---
+
+## Weights
+
+- Put **best.pt** from the model team in **weights/best.pt** (or `runs/classify/runs_cls/eye_open_closed_cpu/weights/best.pt`).
+- For webcam: **face_landmarker.task** is downloaded into **weights/** on first run if missing.
+
+---
+
+## Local setup
+
+```bash
+pip install ultralytics opencv-python mediapipe "numpy<2"
+```
+
+Optional: use a venv. From repo root:
+- `python scripts/predict_image.py <image.png>`
+- `python scripts/webcam_live.py`
+- `python scripts/video_infer.py` (expects 1.mp4 / 2.mp4 in repo root or set `VIDEOS` env)
+- `python scripts/focus_infer.py`
+
+---
+
+## Project structure
+
+```
+├── notebooks/
+│   └── eye_classifier_colab.ipynb   # Data + eval (no training)
+├── scripts/
+│   ├── predict_image.py
+│   ├── webcam_live.py
+│   ├── video_infer.py
+│   └── focus_infer.py
+├── weights/                     # best.pt, face_landmarker.task
+├── docs/                        # extra docs
+├── README.md
+└── venv/                        # optional
+```
+
+Training and weight generation: **model team, separate notebook.**
diff --git a/models/cnn/CNN_MODEL/notebooks/eye_classifier_colab.ipynb b/models/cnn/CNN_MODEL/notebooks/eye_classifier_colab.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..f1a8ca5f5850181b23c64379f0f50b3aacfbba4b
--- /dev/null
+++ b/models/cnn/CNN_MODEL/notebooks/eye_classifier_colab.ipynb
@@ -0,0 +1,682 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Eye Open/Closed Classifier \u2014 Data Prep & Evaluation\n",
+    "\n",
+    "Downloads the open/closed eyes dataset from Kaggle, cleans out bad images, does an 80/10/10 stratified split, and undersamples the majority class (open) for training. Eval uses weights from the model team.\n",
+    "\n",
+    "**Training is done by the model team in a separate notebook.**  \n",
+    "Put `best.pt` from the model team in `runs/eye_cls/weights/` before running eval.\n",
+    "\n",
+    "**Runtime \u2192 Change runtime type \u2192 GPU (T4)** for faster eval."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "source": [
+    "!pip install -q ultralytics scikit-learn seaborn kagglehub\n",
+    "\n",
+    "import torch, os, random, shutil, pathlib, warnings\n",
+    "import numpy as np\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "# seed everything\n",
+    "SEED = 42\n",
+    "random.seed(SEED)\n",
+    "np.random.seed(SEED)\n",
+    "torch.manual_seed(SEED)\n",
+    "\n",
+    "if torch.cuda.is_available():\n",
+    "    DEVICE = 0\n",
+    "    print(f\"GPU detected: {torch.cuda.get_device_name(0)}\")\n",
+    "    print(f\"VRAM: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB\")\n",
+    "else:\n",
+    "    DEVICE = \"cpu\"\n",
+    "    print(\"No GPU detected \u2014 training will use CPU (much slower)\")\n",
+    "\n",
+    "print(f\"PyTorch {torch.__version__}  |  Device: {DEVICE}\")"
+   ],
+   "execution_count": 2,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "text": [
+      "GPU detected: Tesla T4\n",
+      "VRAM: 15.6 GB\n",
+      "PyTorch 2.9.0+cu128  |  Device: 0\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "source": [
+    "import kagglehub\n",
+    "\n",
+    "dataset_path = kagglehub.dataset_download(\"sehriyarmemmedli/open-closed-eyes-dataset\")\n",
+    "dataset_path = pathlib.Path(dataset_path)\n",
+    "print(f\"Dataset downloaded to: {dataset_path}\\n\")\n",
+    "\n",
+    "# print folder structure\n",
+    "for root, dirs, files in os.walk(dataset_path):\n",
+    "    depth = str(root).replace(str(dataset_path), \"\").count(os.sep)\n",
+    "    if depth < 3:\n",
+    "        indent = \"  \" * depth\n",
+    "        print(f\"{indent}{os.path.basename(root)}/  ({len(files)} files)\")\n",
+    "\n",
+    "# find the DATA folder (sometimes nested differently)\n",
+    "data_candidates = list(dataset_path.rglob(\"DATA\"))\n",
+    "if not data_candidates:\n",
+    "    data_candidates = list(dataset_path.rglob(\"data\"))\n",
+    "if data_candidates:\n",
+    "    DATA_ROOT = data_candidates[0]\n",
+    "else:\n",
+    "    # fallback\n",
+    "    DATA_ROOT = dataset_path\n",
+    "print(f\"\\nUsing DATA_ROOT: {DATA_ROOT}\")"
+   ],
+   "execution_count": 3,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "text": [
+      "Warning: Looks like you're using an outdated `kagglehub` version (installed: 0.3.13), please consider upgrading to the latest version (1.0.0).\n",
+      "Downloading from https://www.kaggle.com/api/v1/datasets/download/sehriyarmemmedli/open-closed-eyes-dataset?dataset_version_number=3...\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "text": [
+      "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1.44G/1.44G [00:20<00:00, 76.1MB/s]"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "text": [
+      "Extracting files...\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "text": [
+      "Dataset downloaded to: /root/.cache/kagglehub/datasets/sehriyarmemmedli/open-closed-eyes-dataset/versions/3\n",
+      "\n",
+      "3/  (0 files)\n",
+      "  train/  (0 files)\n",
+      "    open/  (106482 files)\n",
+      "    closed/  (33322 files)\n",
+      "  val/  (0 files)\n",
+      "    open/  (21296 files)\n",
+      "    closed/  (6665 files)\n",
+      "  test/  (0 files)\n",
+      "    open/  (5325 files)\n",
+      "    closed/  (1666 files)\n",
+      "\n",
+      "Using DATA_ROOT: /root/.cache/kagglehub/datasets/sehriyarmemmedli/open-closed-eyes-dataset/versions/3\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from PIL import Image\n",
+    "from collections import defaultdict\n",
+    "\n",
+    "IMG_EXTS = {\".jpg\", \".jpeg\", \".png\", \".bmp\", \".webp\"}\n",
+    "\n",
+    "def collect_images(root_dir):\n",
+    "    \"\"\"Return {class_name: [path, ...]} from a folder of class subdirs.\"\"\"\n",
+    "    result = defaultdict(list)\n",
+    "    root_dir = pathlib.Path(root_dir)\n",
+    "    if not root_dir.exists():\n",
+    "        return result\n",
+    "    for cls_dir in sorted(root_dir.iterdir()):\n",
+    "        if cls_dir.is_dir():\n",
+    "            for f in cls_dir.iterdir():\n",
+    "                if f.suffix.lower() in IMG_EXTS:\n",
+    "                    result[cls_dir.name.lower()].append(f)\n",
+    "    return result\n",
+    "\n",
+    "splits_raw = {}\n",
+    "for split_name in [\"train\", \"test\"]:\n",
+    "    split_dir = DATA_ROOT / split_name\n",
+    "    if not split_dir.exists():\n",
+    "        # try uppercase variant\n",
+    "        split_dir = DATA_ROOT / split_name.capitalize()\n",
+    "    splits_raw[split_name] = collect_images(split_dir)\n",
+    "\n",
+    "print(f\"{'Split':<8} {'Open':>8} {'Closed':>8} {'Total':>8} {'Ratio (O:C)':>12}\")\n",
+    "print(\"-\" * 48)\n",
+    "total_open, total_closed = 0, 0\n",
+    "for split_name, class_imgs in splits_raw.items():\n",
+    "    n_open = len(class_imgs.get(\"open\", []))\n",
+    "    n_closed = len(class_imgs.get(\"closed\", []))\n",
+    "    ratio = f\"{n_open / max(n_closed, 1):.1f}:1\"\n",
+    "    print(f\"{split_name:<8} {n_open:>8,} {n_closed:>8,} {n_open + n_closed:>8,} {ratio:>12}\")\n",
+    "    total_open += n_open\n",
+    "    total_closed += n_closed\n",
+    "\n",
+    "print(\"-\" * 48)\n",
+    "print(f\"{'TOTAL':<8} {total_open:>8,} {total_closed:>8,} {total_open + total_closed:>8,} {total_open / max(total_closed, 1):.1f}:1\")\n",
+    "print(f\"\\nImbalance: {total_open / max(total_closed, 1):.1f}x more open than closed\")\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
+    "\n",
+    "classes = [\"open\", \"closed\"]\n",
+    "counts = [total_open, total_closed]\n",
+    "colors = [\"#4CAF50\", \"#F44336\"]\n",
+    "axes[0].bar(classes, counts, color=colors)\n",
+    "axes[0].set_title(\"Total Class Distribution\")\n",
+    "axes[0].set_ylabel(\"Number of Images\")\n",
+    "for i, c in enumerate(counts):\n",
+    "    axes[0].text(i, c + 500, f\"{c:,}\", ha=\"center\", fontweight=\"bold\")\n",
+    "\n",
+    "# grab a few samples to look at\n",
+    "all_open = [p for s in splits_raw.values() for p in s.get(\"open\", [])]\n",
+    "all_closed = [p for s in splits_raw.values() for p in s.get(\"closed\", [])]\n",
+    "sample_open = random.sample(all_open, min(4, len(all_open)))\n",
+    "sample_closed = random.sample(all_closed, min(4, len(all_closed)))\n",
+    "\n",
+    "axes[1].axis(\"off\")\n",
+    "axes[1].set_title(\"Samples\")\n",
+    "fig2, axes2 = plt.subplots(2, 4, figsize=(12, 6))\n",
+    "for i, p in enumerate(sample_open):\n",
+    "    img = Image.open(p).convert(\"RGB\")\n",
+    "    axes2[0, i].imshow(img)\n",
+    "    axes2[0, i].set_title(\"open\", fontsize=10)\n",
+    "    axes2[0, i].axis(\"off\")\n",
+    "for i, p in enumerate(sample_closed):\n",
+    "    img = Image.open(p).convert(\"RGB\")\n",
+    "    axes2[1, i].imshow(img)\n",
+    "    axes2[1, i].set_title(\"closed\", fontsize=10)\n",
+    "    axes2[1, i].axis(\"off\")\n",
+    "fig2.suptitle(\"Sample Images (top: open, bottom: closed)\", fontsize=13)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ],
+   "execution_count": 4,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "text": [
+      "Split        Open   Closed    Total  Ratio (O:C)\n",
+      "------------------------------------------------\n",
+      "train     106,482   33,322  139,804        3.2:1\n",
+      "test        5,325    1,666    6,991        3.2:1\n",
+      "------------------------------------------------\n",
+      "TOTAL     111,807   34,988  146,795 3.2:1\n",
+      "\n",
+      "Imbalance: 3.2x more open than closed\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x400 with 2 Axes>"
+      ]
+     }
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 8 Axes>"
+      ]
+     }
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "source": [
+    "# clean out broken/corrupt/tiny images\n",
+    "from PIL import Image\n",
+    "from collections import Counter\n",
+    "\n",
+    "# pool all images together\n",
+    "all_images = {\"open\": [], \"closed\": []}\n",
+    "for split_imgs in splits_raw.values():\n",
+    "    for cls_name, paths in split_imgs.items():\n",
+    "        if cls_name in all_images:\n",
+    "            all_images[cls_name].extend(paths)\n",
+    "\n",
+    "issues = Counter()\n",
+    "clean_images = {\"open\": [], \"closed\": []}\n",
+    "\n",
+    "for cls_name, paths in all_images.items():\n",
+    "    for p in paths:\n",
+    "        try:\n",
+    "            if p.stat().st_size == 0:\n",
+    "                issues[\"zero_byte\"] += 1\n",
+    "                continue\n",
+    "\n",
+    "            img = Image.open(p)\n",
+    "            img.verify()\n",
+    "\n",
+    "            # reopen after verify() since it messes up the handle\n",
+    "            img = Image.open(p)\n",
+    "            img.load()\n",
+    "\n",
+    "            w, h = img.size\n",
+    "            if w < 10 or h < 10:\n",
+    "                issues[\"tiny\"] += 1\n",
+    "                continue\n",
+    "\n",
+    "            clean_images[cls_name].append(p)\n",
+    "\n",
+    "        except Exception as e:\n",
+    "            issues[\"corrupt\"] += 1\n",
+    "\n",
+    "total_before = sum(len(v) for v in all_images.values())\n",
+    "total_after = sum(len(v) for v in clean_images.values())\n",
+    "total_removed = total_before - total_after\n",
+    "\n",
+    "print(\"=== Cleaning Report ===\")\n",
+    "print(f\"Images before cleaning : {total_before:,}\")\n",
+    "print(f\"Zero-byte files removed: {issues['zero_byte']}\")\n",
+    "print(f\"Corrupt files removed  : {issues['corrupt']}\")\n",
+    "print(f\"Tiny images removed    : {issues['tiny']}\")\n",
+    "print(f\"Total removed          : {total_removed}\")\n",
+    "print(f\"Images after cleaning  : {total_after:,}\")\n",
+    "print(f\"  open  : {len(clean_images['open']):,}\")\n",
+    "print(f\"  closed: {len(clean_images['closed']):,}\")"
+   ],
+   "execution_count": 5,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "text": [
+      "=== Cleaning Report ===\n",
+      "Images before cleaning : 146,795\n",
+      "Zero-byte files removed: 0\n",
+      "Corrupt files removed  : 0\n",
+      "Tiny images removed    : 0\n",
+      "Total removed          : 0\n",
+      "Images after cleaning  : 146,795\n",
+      "  open  : 111,807\n",
+      "  closed: 34,988\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "source": [
+    "# 80/10/10 split\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "CLEAN_DIR = pathlib.Path(\"Dataset_clean\")\n",
+    "\n",
+    "if CLEAN_DIR.exists():\n",
+    "    shutil.rmtree(CLEAN_DIR)\n",
+    "\n",
+    "def split_and_copy(class_name, image_paths):\n",
+    "    \"\"\"80/10/10 split for one class, copies into CLEAN_DIR.\"\"\"\n",
+    "    paths = sorted(image_paths, key=lambda p: p.name)  # deterministic order\n",
+    "\n",
+    "    # 80/20, then halve the 20\n",
+    "    train_paths, temp_paths = train_test_split(\n",
+    "        paths, test_size=0.20, random_state=SEED\n",
+    "    )\n",
+    "    val_paths, test_paths = train_test_split(\n",
+    "        temp_paths, test_size=0.50, random_state=SEED\n",
+    "    )\n",
+    "\n",
+    "    counts = {}\n",
+    "    for split_name, split_paths in [(\"train\", train_paths), (\"val\", val_paths), (\"test\", test_paths)]:\n",
+    "        dest = CLEAN_DIR / split_name / class_name\n",
+    "        dest.mkdir(parents=True, exist_ok=True)\n",
+    "        for src in split_paths:\n",
+    "            shutil.copy2(src, dest / src.name)\n",
+    "        counts[split_name] = len(split_paths)\n",
+    "\n",
+    "    return counts\n",
+    "\n",
+    "print(\"Splitting and copying images...\")\n",
+    "split_counts = {}\n",
+    "for cls_name, paths in clean_images.items():\n",
+    "    split_counts[cls_name] = split_and_copy(cls_name, paths)\n",
+    "    print(f\"  {cls_name}: {split_counts[cls_name]}\")\n",
+    "\n",
+    "print(f\"\\n{'Split':<8} {'Open':>8} {'Closed':>8} {'Total':>8}\")\n",
+    "print(\"-\" * 36)\n",
+    "for split_name in [\"train\", \"val\", \"test\"]:\n",
+    "    n_open = split_counts[\"open\"][split_name]\n",
+    "    n_closed = split_counts[\"closed\"][split_name]\n",
+    "    print(f\"{split_name:<8} {n_open:>8,} {n_closed:>8,} {n_open + n_closed:>8,}\")\n",
+    "\n",
+    "print(f\"\\nDataset written to: {CLEAN_DIR.resolve()}\")"
+   ],
+   "execution_count": 6,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "text": [
+      "Splitting and copying images...\n",
+      "  open: {'train': 89445, 'val': 11181, 'test': 11181}\n",
+      "  closed: {'train': 27990, 'val': 3499, 'test': 3499}\n",
+      "\n",
+      "Split        Open   Closed    Total\n",
+      "------------------------------------\n",
+      "train      89,445   27,990  117,435\n",
+      "val        11,181    3,499   14,680\n",
+      "test       11,181    3,499   14,680\n",
+      "\n",
+      "Dataset written to: /content/Dataset_clean\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "source": [
+    "# undersample open class in train to match closed count\n",
+    "\n",
+    "train_open_dir = CLEAN_DIR / \"train\" / \"open\"\n",
+    "train_closed_dir = CLEAN_DIR / \"train\" / \"closed\"\n",
+    "\n",
+    "open_files = sorted(train_open_dir.glob(\"*\"))\n",
+    "closed_files = sorted(train_closed_dir.glob(\"*\"))\n",
+    "\n",
+    "n_open_before = len(open_files)\n",
+    "n_closed = len(closed_files)\n",
+    "n_target = n_closed  # match minority class count\n",
+    "\n",
+    "print(f\"Train BEFORE undersampling:\")\n",
+    "print(f\"  open  : {n_open_before:,}\")\n",
+    "print(f\"  closed: {n_closed:,}\")\n",
+    "\n",
+    "if n_open_before > n_target:\n",
+    "    # pick which ones to keep\n",
+    "    random.seed(SEED)\n",
+    "    keep = set(random.sample(open_files, n_target))\n",
+    "    removed = 0\n",
+    "    for f in open_files:\n",
+    "        if f not in keep:\n",
+    "            f.unlink()\n",
+    "            removed += 1\n",
+    "    print(f\"\\nRemoved {removed:,} open images to balance training set.\")\n",
+    "\n",
+    "n_open_after = len(list(train_open_dir.glob(\"*\")))\n",
+    "n_closed_after = len(list(train_closed_dir.glob(\"*\")))\n",
+    "print(f\"\\nTrain AFTER undersampling:\")\n",
+    "print(f\"  open  : {n_open_after:,}\")\n",
+    "print(f\"  closed: {n_closed_after:,}\")\n",
+    "print(f\"  ratio : {n_open_after / max(n_closed_after, 1):.2f}:1\")\n",
+    "\n",
+    "# val/test stay as-is\n",
+    "for split in [\"val\", \"test\"]:\n",
+    "    n_o = len(list((CLEAN_DIR / split / \"open\").glob(\"*\")))\n",
+    "    n_c = len(list((CLEAN_DIR / split / \"closed\").glob(\"*\")))\n",
+    "    print(f\"\\n{split} (unchanged): open={n_o:,}  closed={n_c:,}  total={n_o + n_c:,}\")"
+   ],
+   "execution_count": 7,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "text": [
+      "Train BEFORE undersampling:\n",
+      "  open  : 89,445\n",
+      "  closed: 27,990\n",
+      "\n",
+      "Removed 61,455 open images to balance training set.\n",
+      "\n",
+      "Train AFTER undersampling:\n",
+      "  open  : 27,990\n",
+      "  closed: 27,990\n",
+      "  ratio : 1.00:1\n",
+      "\n",
+      "val (unchanged): open=11,181  closed=3,499  total=14,680\n",
+      "\n",
+      "test (unchanged): open=11,181  closed=3,499  total=14,680\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "source": [
+    "# run eval on test set\n",
+    "results_dir = pathlib.Path(\"runs/eye_cls\")\n",
+    "import seaborn as sns\n",
+    "from sklearn.metrics import classification_report, confusion_matrix\n",
+    "\n",
+    "best_pt = results_dir / \"weights\" / \"best.pt\"\n",
+    "if not best_pt.exists():\n",
+    "    # try to find it\n",
+    "    candidates = list(pathlib.Path(\"runs\").rglob(\"best.pt\"))\n",
+    "    best_pt = candidates[0] if candidates else None\n",
+    "    print(f\"Using weights: {best_pt}\")\n",
+    "\n",
+    "eval_model = YOLO(str(best_pt))\n",
+    "\n",
+    "test_dir = CLEAN_DIR / \"test\"\n",
+    "class_names = sorted([d.name for d in test_dir.iterdir() if d.is_dir()])\n",
+    "print(f\"Classes: {class_names}\")\n",
+    "\n",
+    "y_true, y_pred, image_paths = [], [], []\n",
+    "\n",
+    "for cls_name in class_names:\n",
+    "    cls_dir = test_dir / cls_name\n",
+    "    for img_path in sorted(cls_dir.glob(\"*\")):\n",
+    "        if img_path.suffix.lower() in IMG_EXTS:\n",
+    "            result = eval_model.predict(str(img_path), verbose=False)[0]\n",
+    "            pred_idx = result.probs.top1\n",
+    "            pred_name = result.names[pred_idx]\n",
+    "            y_true.append(cls_name)\n",
+    "            y_pred.append(pred_name)\n",
+    "            image_paths.append(img_path)\n",
+    "\n",
+    "print(\"\\n\" + \"=\" * 50)\n",
+    "print(\"CLASSIFICATION REPORT\")\n",
+    "print(\"=\" * 50)\n",
+    "print(classification_report(y_true, y_pred, target_names=class_names, digits=4))\n",
+    "\n",
+    "# confusion matrix\n",
+    "cm = confusion_matrix(y_true, y_pred, labels=class_names)\n",
+    "fig, ax = plt.subplots(figsize=(6, 5))\n",
+    "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\",\n",
+    "            xticklabels=class_names, yticklabels=class_names, ax=ax)\n",
+    "ax.set_xlabel(\"Predicted\")\n",
+    "ax.set_ylabel(\"True\")\n",
+    "ax.set_title(\"Confusion Matrix \u2014 Test Set\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# show some correct + incorrect predictions\n",
+    "correct_idxs = [i for i in range(len(y_true)) if y_true[i] == y_pred[i]]\n",
+    "incorrect_idxs = [i for i in range(len(y_true)) if y_true[i] != y_pred[i]]\n",
+    "\n",
+    "n_correct_show = min(4, len(correct_idxs))\n",
+    "n_incorrect_show = min(4, len(incorrect_idxs))\n",
+    "n_cols = max(n_correct_show, n_incorrect_show, 1)\n",
+    "\n",
+    "fig, axes = plt.subplots(2, n_cols, figsize=(3 * n_cols, 7))\n",
+    "if n_cols == 1:\n",
+    "    axes = axes.reshape(2, 1)\n",
+    "\n",
+    "fig.suptitle(\"Sample Predictions (top: correct, bottom: incorrect)\", fontsize=13)\n",
+    "\n",
+    "sample_correct = random.sample(correct_idxs, n_correct_show) if correct_idxs else []\n",
+    "sample_incorrect = random.sample(incorrect_idxs, n_incorrect_show) if incorrect_idxs else []\n",
+    "\n",
+    "for col in range(n_cols):\n",
+    "    if col < len(sample_correct):\n",
+    "        idx = sample_correct[col]\n",
+    "        img = Image.open(image_paths[idx]).convert(\"RGB\")\n",
+    "        axes[0, col].imshow(img)\n",
+    "        axes[0, col].set_title(f\"T:{y_true[idx]} P:{y_pred[idx]}\", fontsize=9, color=\"green\")\n",
+    "    axes[0, col].axis(\"off\")\n",
+    "\n",
+    "    if col < len(sample_incorrect):\n",
+    "        idx = sample_incorrect[col]\n",
+    "        img = Image.open(image_paths[idx]).convert(\"RGB\")\n",
+    "        axes[1, col].imshow(img)\n",
+    "        axes[1, col].set_title(f\"T:{y_true[idx]} P:{y_pred[idx]}\", fontsize=9, color=\"red\")\n",
+    "    axes[1, col].axis(\"off\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "if not incorrect_idxs:\n",
+    "    print(\"\\nNo incorrect predictions on the test set!\")"
+   ],
+   "execution_count": 11,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "text": [
+      "Using weights: runs/classify/runs/eye_cls/weights/best.pt\n",
+      "Classes: ['closed', 'open']\n",
+      "\n",
+      "==================================================\n",
+      "CLASSIFICATION REPORT\n",
+      "==================================================\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "      closed     0.9630    0.9817    0.9723      3499\n",
+      "        open     0.9942    0.9882    0.9912     11181\n",
+      "\n",
+      "    accuracy                         0.9866     14680\n",
+      "   macro avg     0.9786    0.9850    0.9817     14680\n",
+      "weighted avg     0.9868    0.9866    0.9867     14680\n",
+      "\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x500 with 2 Axes>"
+      ]
+     }
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x700 with 8 Axes>"
+      ]
+     }
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "source": [
+    "# summary + export\n",
+    "from sklearn.metrics import accuracy_score, f1_score\n",
+    "\n",
+    "acc = accuracy_score(y_true, y_pred)\n",
+    "f1 = f1_score(y_true, y_pred, pos_label=\"closed\", average=\"binary\") if len(class_names) == 2 else f1_score(y_true, y_pred, average=\"macro\")\n",
+    "\n",
+    "n_train = len(list((CLEAN_DIR / \"train\" / \"open\").glob(\"*\"))) + len(list((CLEAN_DIR / \"train\" / \"closed\").glob(\"*\")))\n",
+    "n_val = len(list((CLEAN_DIR / \"val\" / \"open\").glob(\"*\"))) + len(list((CLEAN_DIR / \"val\" / \"closed\").glob(\"*\")))\n",
+    "n_test = len(y_true)\n",
+    "\n",
+    "print(\"=\" * 50)\n",
+    "print(\"FINAL SUMMARY\")\n",
+    "print(\"=\" * 50)\n",
+    "print(f\"{'Metric':<20} {'Value':>15}\")\n",
+    "print(\"-\" * 37)\n",
+    "print(f\"{'Train images':<20} {n_train:>15,}\")\n",
+    "print(f\"{'Val images':<20} {n_val:>15,}\")\n",
+    "print(f\"{'Test images':<20} {n_test:>15,}\")\n",
+    "print(f\"{'Test accuracy':<20} {acc:>15.4f}\")\n",
+    "print(f\"{'Test F1 (closed)':<20} {f1:>15.4f}\")\n",
+    "print(f\"{'Model':<20} {'YOLOv11s-cls':>15}\")\n",
+    "print(f\"{'Weights':<20} {str(best_pt):>15}\")\n",
+    "\n",
+    "try:\n",
+    "    from google.colab import files\n",
+    "    files.download(str(best_pt))\n",
+    "    print(f\"\\nDownloading {best_pt.name}...\")\n",
+    "except ImportError:\n",
+    "    print(f\"\\nNot running in Colab. Best weights at: {best_pt.resolve()}\")"
+   ],
+   "execution_count": 12,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "text": [
+      "==================================================\n",
+      "FINAL SUMMARY\n",
+      "==================================================\n",
+      "Metric                         Value\n",
+      "-------------------------------------\n",
+      "Train images                  55,980\n",
+      "Val images                    14,680\n",
+      "Test images                   14,680\n",
+      "Test accuracy                 0.9866\n",
+      "Test F1 (closed)              0.9723\n",
+      "Model                   YOLOv11s-cls\n",
+      "Weights              runs/classify/runs/eye_cls/weights/best.pt\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "application/javascript": "\n    async function download(id, filename, size) {\n      if (!google.colab.kernel.accessAllowed) {\n        return;\n      }\n      const div = document.createElement('div');\n      const label = document.createElement('label');\n      label.textContent = `Downloading \"${filename}\": `;\n      div.appendChild(label);\n      const progress = document.createElement('progress');\n      progress.max = size;\n      div.appendChild(progress);\n      document.body.appendChild(div);\n\n      const buffers = [];\n      let downloaded = 0;\n\n      const channel = await google.colab.kernel.comms.open(id);\n      // Send a message to notify the kernel that we're ready.\n      channel.send({})\n\n      for await (const message of channel.messages) {\n        // Send a message to notify the kernel that we're ready.\n        channel.send({})\n        if (message.buffers) {\n          for (const buffer of message.buffers) {\n            buffers.push(buffer);\n            downloaded += buffer.byteLength;\n            progress.value = downloaded;\n          }\n        }\n      }\n      const blob = new Blob(buffers, {type: 'application/binary'});\n      const a = document.createElement('a');\n      a.href = window.URL.createObjectURL(blob);\n      a.download = filename;\n      div.appendChild(a);\n      a.click();\n      div.remove();\n    }\n  ",
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     }
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "application/javascript": "download(\"download_907b33a9-973a-424b-9003-2e0856671bc3\", \"best.pt\", 11023490)",
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     }
+    },
+    {
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Downloading best.pt...\n"
+     ]
+    }
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "T4",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/models/cnn/CNN_MODEL/scripts/focus_infer.py b/models/cnn/CNN_MODEL/scripts/focus_infer.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a727a0cd39f8e393a78bed2966653e4a2530657
--- /dev/null
+++ b/models/cnn/CNN_MODEL/scripts/focus_infer.py
@@ -0,0 +1,199 @@
+from __future__ import annotations
+
+from pathlib import Path
+import os
+
+import cv2
+import numpy as np
+from ultralytics import YOLO
+
+
+def list_images(folder: Path):
+    exts = {".png", ".jpg", ".jpeg", ".bmp", ".webp"}
+    return sorted([p for p in folder.iterdir() if p.suffix.lower() in exts])
+
+
+def find_weights(project_root: Path) -> Path | None:
+    candidates = [
+        project_root / "weights" / "best.pt",
+        project_root / "runs" / "classify" / "runs_cls" / "eye_open_closed_cpu" / "weights" / "best.pt",
+        project_root / "runs" / "classify" / "runs_cls" / "eye_open_closed_cpu" / "weights" / "last.pt",
+        project_root / "runs_cls" / "eye_open_closed_cpu" / "weights" / "best.pt",
+        project_root / "runs_cls" / "eye_open_closed_cpu" / "weights" / "last.pt",
+    ]
+    return next((p for p in candidates if p.is_file()), None)
+
+
+def detect_eyelid_boundary(gray: np.ndarray) -> np.ndarray | None:
+    """
+    Returns an ellipse fit to the largest contour near the eye boundary.
+    Output format: (center(x,y), (axis1, axis2), angle) or None.
+    """
+    blur = cv2.GaussianBlur(gray, (5, 5), 0)
+    edges = cv2.Canny(blur, 40, 120)
+    edges = cv2.dilate(edges, np.ones((3, 3), np.uint8), iterations=1)
+    contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    if not contours:
+        return None
+    contours = sorted(contours, key=cv2.contourArea, reverse=True)
+    for c in contours:
+        if len(c) >= 5 and cv2.contourArea(c) > 50:
+            return cv2.fitEllipse(c)
+    return None
+
+
+def detect_pupil_center(gray: np.ndarray) -> tuple[int, int] | None:
+    """
+    More robust pupil detection:
+    - enhance contrast (CLAHE)
+    - find dark blobs
+    - score by circularity and proximity to center
+    """
+    h, w = gray.shape
+    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
+    eq = clahe.apply(gray)
+    blur = cv2.GaussianBlur(eq, (7, 7), 0)
+
+    # Focus on the central region to avoid eyelashes/edges
+    cx, cy = w // 2, h // 2
+    rx, ry = int(w * 0.3), int(h * 0.3)
+    x0, x1 = max(cx - rx, 0), min(cx + rx, w)
+    y0, y1 = max(cy - ry, 0), min(cy + ry, h)
+    roi = blur[y0:y1, x0:x1]
+
+    # Inverted threshold to capture dark pupil
+    _, thresh = cv2.threshold(roi, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
+    thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, np.ones((3, 3), np.uint8), iterations=2)
+    thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, np.ones((5, 5), np.uint8), iterations=1)
+
+    contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    if not contours:
+        return None
+
+    best = None
+    best_score = -1.0
+    for c in contours:
+        area = cv2.contourArea(c)
+        if area < 15:
+            continue
+        perimeter = cv2.arcLength(c, True)
+        if perimeter <= 0:
+            continue
+        circularity = 4 * np.pi * (area / (perimeter * perimeter))
+        if circularity < 0.3:
+            continue
+        m = cv2.moments(c)
+        if m["m00"] == 0:
+            continue
+        px = int(m["m10"] / m["m00"]) + x0
+        py = int(m["m01"] / m["m00"]) + y0
+
+        # Score by circularity and distance to center
+        dist = np.hypot(px - cx, py - cy) / max(w, h)
+        score = circularity - dist
+        if score > best_score:
+            best_score = score
+            best = (px, py)
+
+    return best
+
+
+def is_focused(pupil_center: tuple[int, int], img_shape: tuple[int, int]) -> bool:
+    """
+    Decide focus based on pupil offset from image center.
+    """
+    h, w = img_shape
+    cx, cy = w // 2, h // 2
+    px, py = pupil_center
+    dx = abs(px - cx) / max(w, 1)
+    dy = abs(py - cy) / max(h, 1)
+    return (dx < 0.12) and (dy < 0.12)
+
+
+def annotate(img_bgr: np.ndarray, ellipse, pupil_center, focused: bool, cls_label: str, conf: float):
+    out = img_bgr.copy()
+    if ellipse is not None:
+        cv2.ellipse(out, ellipse, (0, 255, 255), 2)
+    if pupil_center is not None:
+        cv2.circle(out, pupil_center, 4, (0, 0, 255), -1)
+    label = f"{cls_label} ({conf:.2f}) | focused={int(focused)}"
+    cv2.putText(out, label, (8, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
+    return out
+
+
+def main():
+    project_root = Path(__file__).resolve().parent.parent
+    data_dir = project_root / "Dataset"
+    alt_data_dir = project_root / "DATA"
+    out_dir = project_root / "runs_focus"
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    weights = find_weights(project_root)
+    if weights is None:
+        print("Weights not found. Train first.")
+        return
+
+    # Support both Dataset/test/{open,closed} and Dataset/{open,closed}
+    def resolve_test_dirs(root: Path):
+        test_open = root / "test" / "open"
+        test_closed = root / "test" / "closed"
+        if test_open.exists() and test_closed.exists():
+            return test_open, test_closed
+        test_open = root / "open"
+        test_closed = root / "closed"
+        if test_open.exists() and test_closed.exists():
+            return test_open, test_closed
+        alt_closed = root / "close"
+        if test_open.exists() and alt_closed.exists():
+            return test_open, alt_closed
+        return None, None
+
+    test_open, test_closed = resolve_test_dirs(data_dir)
+    if (test_open is None or test_closed is None) and alt_data_dir.exists():
+        test_open, test_closed = resolve_test_dirs(alt_data_dir)
+
+    if not test_open.exists() or not test_closed.exists():
+        print("Test folders missing. Expected:")
+        print(test_open)
+        print(test_closed)
+        return
+
+    test_files = list_images(test_open) + list_images(test_closed)
+    print("Total test images:", len(test_files))
+    max_images = int(os.getenv("MAX_IMAGES", "0"))
+    if max_images > 0:
+        test_files = test_files[:max_images]
+        print("Limiting to MAX_IMAGES:", max_images)
+
+    model = YOLO(str(weights))
+    results = model.predict(test_files, imgsz=224, device="cpu", verbose=False)
+
+    names = model.names
+    for r in results:
+        probs = r.probs
+        top_idx = int(probs.top1)
+        top_conf = float(probs.top1conf)
+        pred_label = names[top_idx]
+
+        img = cv2.imread(r.path)
+        if img is None:
+            continue
+        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+        ellipse = detect_eyelid_boundary(gray)
+        pupil_center = detect_pupil_center(gray)
+        focused = False
+        if pred_label.lower() == "open" and pupil_center is not None:
+            focused = is_focused(pupil_center, gray.shape)
+
+        annotated = annotate(img, ellipse, pupil_center, focused, pred_label, top_conf)
+        out_path = out_dir / (Path(r.path).stem + "_annotated.jpg")
+        cv2.imwrite(str(out_path), annotated)
+
+        print(f"{Path(r.path).name}: pred={pred_label} conf={top_conf:.3f} focused={focused}")
+
+    print(f"\nAnnotated outputs saved to: {out_dir}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/models/cnn/CNN_MODEL/scripts/predict_image.py b/models/cnn/CNN_MODEL/scripts/predict_image.py
new file mode 100644
index 0000000000000000000000000000000000000000..378a4641eb5e332d684e26b491af274249074f13
--- /dev/null
+++ b/models/cnn/CNN_MODEL/scripts/predict_image.py
@@ -0,0 +1,49 @@
+"""Run the eye open/closed model on one or more images."""
+import sys
+from pathlib import Path
+
+from ultralytics import YOLO
+
+
+def main():
+    project_root = Path(__file__).resolve().parent.parent
+    weight_candidates = [
+        project_root / "weights" / "best.pt",
+        project_root / "runs" / "classify" / "runs_cls" / "eye_open_closed_cpu" / "weights" / "best.pt",
+        project_root / "runs" / "classify" / "runs_cls" / "eye_open_closed_cpu" / "weights" / "last.pt",
+    ]
+    weights = next((p for p in weight_candidates if p.is_file()), None)
+    if weights is None:
+        print("Weights not found. Put best.pt in weights/ or runs/.../weights/ (from model team).")
+        sys.exit(1)
+
+    if len(sys.argv) < 2:
+        print("Usage: python scripts/predict_image.py <image1> [image2 ...]")
+        print("Example: python scripts/predict_image.py path/to/image.png")
+        sys.exit(0)
+
+    model = YOLO(str(weights))
+    names = model.names
+
+    for path in sys.argv[1:]:
+        p = Path(path)
+        if not p.is_file():
+            print(p, "- file not found")
+            continue
+        try:
+            results = model.predict(str(p), imgsz=224, device="cpu", verbose=False)
+        except Exception as e:
+            print(p, "- error:", e)
+            continue
+        if not results:
+            print(p, "- no result")
+            continue
+        r = results[0]
+        top_idx = int(r.probs.top1)
+        conf = float(r.probs.top1conf)
+        label = names[top_idx]
+        print(f"{p.name}: {label} ({conf:.2%})")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/models/cnn/CNN_MODEL/scripts/video_infer.py b/models/cnn/CNN_MODEL/scripts/video_infer.py
new file mode 100644
index 0000000000000000000000000000000000000000..d16958584089ad5f58d80706ad2130fe2acde4aa
--- /dev/null
+++ b/models/cnn/CNN_MODEL/scripts/video_infer.py
@@ -0,0 +1,281 @@
+from __future__ import annotations
+
+import os
+from pathlib import Path
+
+import cv2
+import numpy as np
+from ultralytics import YOLO
+
+try:
+    import mediapipe as mp
+except Exception:  # pragma: no cover
+    mp = None
+
+
+def find_weights(project_root: Path) -> Path | None:
+    candidates = [
+        project_root / "weights" / "best.pt",
+        project_root / "runs" / "classify" / "runs_cls" / "eye_open_closed_cpu" / "weights" / "best.pt",
+        project_root / "runs" / "classify" / "runs_cls" / "eye_open_closed_cpu" / "weights" / "last.pt",
+        project_root / "runs_cls" / "eye_open_closed_cpu" / "weights" / "best.pt",
+        project_root / "runs_cls" / "eye_open_closed_cpu" / "weights" / "last.pt",
+    ]
+    return next((p for p in candidates if p.is_file()), None)
+
+
+def detect_pupil_center(gray: np.ndarray) -> tuple[int, int] | None:
+    h, w = gray.shape
+    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
+    eq = clahe.apply(gray)
+    blur = cv2.GaussianBlur(eq, (7, 7), 0)
+
+    cx, cy = w // 2, h // 2
+    rx, ry = int(w * 0.3), int(h * 0.3)
+    x0, x1 = max(cx - rx, 0), min(cx + rx, w)
+    y0, y1 = max(cy - ry, 0), min(cy + ry, h)
+    roi = blur[y0:y1, x0:x1]
+
+    _, thresh = cv2.threshold(roi, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
+    thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, np.ones((3, 3), np.uint8), iterations=2)
+    thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, np.ones((5, 5), np.uint8), iterations=1)
+
+    contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    if not contours:
+        return None
+
+    best = None
+    best_score = -1.0
+    for c in contours:
+        area = cv2.contourArea(c)
+        if area < 15:
+            continue
+        perimeter = cv2.arcLength(c, True)
+        if perimeter <= 0:
+            continue
+        circularity = 4 * np.pi * (area / (perimeter * perimeter))
+        if circularity < 0.3:
+            continue
+        m = cv2.moments(c)
+        if m["m00"] == 0:
+            continue
+        px = int(m["m10"] / m["m00"]) + x0
+        py = int(m["m01"] / m["m00"]) + y0
+
+        dist = np.hypot(px - cx, py - cy) / max(w, h)
+        score = circularity - dist
+        if score > best_score:
+            best_score = score
+            best = (px, py)
+
+    return best
+
+
+def is_focused(pupil_center: tuple[int, int], img_shape: tuple[int, int]) -> bool:
+    h, w = img_shape
+    cx = w // 2
+    px, _ = pupil_center
+    dx = abs(px - cx) / max(w, 1)
+    return dx < 0.12
+
+
+def classify_frame(model: YOLO, frame: np.ndarray) -> tuple[str, float]:
+    # Use classifier directly on frame (assumes frame is eye crop)
+    results = model.predict(frame, imgsz=224, device="cpu", verbose=False)
+    r = results[0]
+    probs = r.probs
+    top_idx = int(probs.top1)
+    top_conf = float(probs.top1conf)
+    pred_label = model.names[top_idx]
+    return pred_label, top_conf
+
+
+def annotate_frame(frame: np.ndarray, label: str, focused: bool, conf: float, time_sec: float):
+    out = frame.copy()
+    text = f"{label} | focused={int(focused)} | conf={conf:.2f} | t={time_sec:.2f}s"
+    cv2.putText(out, text, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
+    return out
+
+
+def write_segments(path: Path, segments: list[tuple[float, float, str]]):
+    with path.open("w") as f:
+        for start, end, label in segments:
+            f.write(f"{start:.2f},{end:.2f},{label}\n")
+
+
+def process_video(video_path: Path, model: YOLO | None):
+    cap = cv2.VideoCapture(str(video_path))
+    if not cap.isOpened():
+        print(f"Failed to open {video_path}")
+        return
+
+    fps = cap.get(cv2.CAP_PROP_FPS) or 30.0
+    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+
+    out_path = video_path.with_name(video_path.stem + "_pred.mp4")
+    fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+    writer = cv2.VideoWriter(str(out_path), fourcc, fps, (width, height))
+
+    csv_path = video_path.with_name(video_path.stem + "_predictions.csv")
+    seg_path = video_path.with_name(video_path.stem + "_segments.txt")
+
+    frame_idx = 0
+    last_label = None
+    seg_start = 0.0
+    segments: list[tuple[float, float, str]] = []
+
+    with csv_path.open("w") as fcsv:
+        fcsv.write("time_sec,label,focused,conf\n")
+        if mp is None:
+            print("mediapipe is not installed. Falling back to classifier-only mode.")
+        use_mp = mp is not None
+        if use_mp:
+            mp_face_mesh = mp.solutions.face_mesh
+            face_mesh = mp_face_mesh.FaceMesh(
+                static_image_mode=False,
+                max_num_faces=1,
+                refine_landmarks=True,
+                min_detection_confidence=0.5,
+                min_tracking_confidence=0.5,
+            )
+
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                break
+            time_sec = frame_idx / fps
+            conf = 0.0
+            pred_label = "open"
+            focused = False
+
+            if use_mp:
+                rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+                res = face_mesh.process(rgb)
+                if res.multi_face_landmarks:
+                    lm = res.multi_face_landmarks[0].landmark
+                    h, w = frame.shape[:2]
+
+                    # Eye landmarks (MediaPipe FaceMesh)
+                    left_eye = [33, 160, 158, 133, 153, 144]
+                    right_eye = [362, 385, 387, 263, 373, 380]
+                    left_iris = [468, 469, 470, 471]
+                    right_iris = [473, 474, 475, 476]
+
+                    def pts(idxs):
+                        return np.array([(int(lm[i].x * w), int(lm[i].y * h)) for i in idxs])
+
+                    def ear(eye_pts):
+                        # EAR using 6 points
+                        p1, p2, p3, p4, p5, p6 = eye_pts
+                        v1 = np.linalg.norm(p2 - p6)
+                        v2 = np.linalg.norm(p3 - p5)
+                        h1 = np.linalg.norm(p1 - p4)
+                        return (v1 + v2) / (2.0 * h1 + 1e-6)
+
+                    le = pts(left_eye)
+                    re = pts(right_eye)
+                    le_ear = ear(le)
+                    re_ear = ear(re)
+                    ear_avg = (le_ear + re_ear) / 2.0
+
+                    # openness threshold
+                    pred_label = "open" if ear_avg > 0.22 else "closed"
+
+                    # iris centers
+                    li = pts(left_iris)
+                    ri = pts(right_iris)
+                    li_c = li.mean(axis=0).astype(int)
+                    ri_c = ri.mean(axis=0).astype(int)
+
+                    # eye centers (midpoint of corners)
+                    le_c = ((le[0] + le[3]) / 2).astype(int)
+                    re_c = ((re[0] + re[3]) / 2).astype(int)
+
+                    # focus = iris close to eye center horizontally for both eyes
+                    le_dx = abs(li_c[0] - le_c[0]) / max(np.linalg.norm(le[0] - le[3]), 1)
+                    re_dx = abs(ri_c[0] - re_c[0]) / max(np.linalg.norm(re[0] - re[3]), 1)
+                    focused = (pred_label == "open") and (le_dx < 0.18) and (re_dx < 0.18)
+
+                    # draw eye boundaries
+                    cv2.polylines(frame, [le], True, (0, 255, 255), 1)
+                    cv2.polylines(frame, [re], True, (0, 255, 255), 1)
+                    # draw iris centers
+                    cv2.circle(frame, tuple(li_c), 3, (0, 0, 255), -1)
+                    cv2.circle(frame, tuple(ri_c), 3, (0, 0, 255), -1)
+                else:
+                    pred_label = "closed"
+                    focused = False
+            else:
+                if model is not None:
+                    pred_label, conf = classify_frame(model, frame)
+                gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+                pupil_center = detect_pupil_center(gray) if pred_label.lower() == "open" else None
+                focused = False
+                if pred_label.lower() == "open" and pupil_center is not None:
+                    focused = is_focused(pupil_center, gray.shape)
+
+            if pred_label.lower() != "open":
+                focused = False
+
+            label = "open_focused" if (pred_label.lower() == "open" and focused) else "open_not_focused"
+            if pred_label.lower() != "open":
+                label = "closed_not_focused"
+
+            fcsv.write(f"{time_sec:.2f},{label},{int(focused)},{conf:.4f}\n")
+
+            if last_label is None:
+                last_label = label
+                seg_start = time_sec
+            elif label != last_label:
+                segments.append((seg_start, time_sec, last_label))
+                seg_start = time_sec
+                last_label = label
+
+            annotated = annotate_frame(frame, label, focused, conf, time_sec)
+            writer.write(annotated)
+            frame_idx += 1
+
+    if last_label is not None:
+        end_time = frame_idx / fps
+        segments.append((seg_start, end_time, last_label))
+    write_segments(seg_path, segments)
+
+    cap.release()
+    writer.release()
+    print(f"Saved: {out_path}")
+    print(f"CSV: {csv_path}")
+    print(f"Segments: {seg_path}")
+
+
+def main():
+    project_root = Path(__file__).resolve().parent.parent
+    weights = find_weights(project_root)
+    model = YOLO(str(weights)) if weights is not None else None
+
+    # Default to 1.mp4 and 2.mp4 in project root
+    videos = []
+    for name in ["1.mp4", "2.mp4"]:
+        p = project_root / name
+        if p.exists():
+            videos.append(p)
+
+    # Also allow passing paths via env var
+    extra = os.getenv("VIDEOS", "")
+    for v in [x.strip() for x in extra.split(",") if x.strip()]:
+        vp = Path(v)
+        if not vp.is_absolute():
+            vp = project_root / vp
+        if vp.exists():
+            videos.append(vp)
+
+    if not videos:
+        print("No videos found. Expected 1.mp4 / 2.mp4 in project root.")
+        return
+
+    for v in videos:
+        process_video(v, model)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/models/cnn/CNN_MODEL/scripts/webcam_live.py b/models/cnn/CNN_MODEL/scripts/webcam_live.py
new file mode 100644
index 0000000000000000000000000000000000000000..c9a3f78b5855193a238a79629ac0f9cba448b926
--- /dev/null
+++ b/models/cnn/CNN_MODEL/scripts/webcam_live.py
@@ -0,0 +1,184 @@
+"""
+Live webcam: detect face, crop each eye, run open/closed classifier, show on screen.
+Requires: opencv-python, ultralytics, mediapipe (pip install mediapipe).
+Press 'q' to quit.
+"""
+import urllib.request
+from pathlib import Path
+
+import cv2
+import numpy as np
+from ultralytics import YOLO
+
+try:
+    import mediapipe as mp
+    _mp_has_solutions = hasattr(mp, "solutions")
+except ImportError:
+    mp = None
+    _mp_has_solutions = False
+
+# New MediaPipe Tasks API (Face Landmarker) eye indices
+LEFT_EYE_INDICES_NEW = [263, 249, 390, 373, 374, 380, 381, 382, 362, 466, 388, 387, 386, 385, 384, 398]
+RIGHT_EYE_INDICES_NEW = [33, 7, 163, 144, 145, 153, 154, 155, 133, 246, 161, 160, 159, 158, 157, 173]
+# Old Face Mesh (solutions) indices
+LEFT_EYE_INDICES_OLD = [33, 160, 158, 133, 153, 144]
+RIGHT_EYE_INDICES_OLD = [362, 385, 387, 263, 373, 380]
+EYE_PADDING = 0.35
+
+
+def find_weights(project_root: Path) -> Path | None:
+    candidates = [
+        project_root / "weights" / "best.pt",
+        project_root / "runs" / "classify" / "runs_cls" / "eye_open_closed_cpu" / "weights" / "best.pt",
+        project_root / "runs" / "classify" / "runs_cls" / "eye_open_closed_cpu" / "weights" / "last.pt",
+    ]
+    return next((p for p in candidates if p.is_file()), None)
+
+
+def get_eye_roi(frame: np.ndarray, landmarks, indices: list[int]) -> np.ndarray | None:
+    h, w = frame.shape[:2]
+    pts = np.array([(int(landmarks[i].x * w), int(landmarks[i].y * h)) for i in indices])
+    x_min, y_min = pts.min(axis=0)
+    x_max, y_max = pts.max(axis=0)
+    dx = max(int((x_max - x_min) * EYE_PADDING), 8)
+    dy = max(int((y_max - y_min) * EYE_PADDING), 8)
+    x0 = max(0, x_min - dx)
+    y0 = max(0, y_min - dy)
+    x1 = min(w, x_max + dx)
+    y1 = min(h, y_max + dy)
+    if x1 <= x0 or y1 <= y0:
+        return None
+    return frame[y0:y1, x0:x1].copy()
+
+
+def _run_with_solutions(mp, model, cap):
+    face_mesh = mp.solutions.face_mesh.FaceMesh(
+        static_image_mode=False,
+        max_num_faces=1,
+        refine_landmarks=True,
+        min_detection_confidence=0.5,
+        min_tracking_confidence=0.5,
+    )
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+        rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        results = face_mesh.process(rgb)
+        left_label, left_conf = "—", 0.0
+        right_label, right_conf = "—", 0.0
+        if results.multi_face_landmarks:
+            lm = results.multi_face_landmarks[0].landmark
+            for roi, indices, side in [
+                (get_eye_roi(frame, lm, LEFT_EYE_INDICES_OLD), LEFT_EYE_INDICES_OLD, "left"),
+                (get_eye_roi(frame, lm, RIGHT_EYE_INDICES_OLD), RIGHT_EYE_INDICES_OLD, "right"),
+            ]:
+                if roi is not None and roi.size > 0:
+                    try:
+                        pred = model.predict(roi, imgsz=224, device="cpu", verbose=False)
+                        if pred:
+                            r = pred[0]
+                            label = model.names[int(r.probs.top1)]
+                            conf = float(r.probs.top1conf)
+                            if side == "left":
+                                left_label, left_conf = label, conf
+                            else:
+                                right_label, right_conf = label, conf
+                    except Exception:
+                        pass
+        cv2.putText(frame, f"L: {left_label} ({left_conf:.0%})", (20, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
+        cv2.putText(frame, f"R: {right_label} ({right_conf:.0%})", (20, 80), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
+        cv2.imshow("Eye open/closed (q to quit)", frame)
+        if cv2.waitKey(1) & 0xFF == ord("q"):
+            break
+
+
+def _run_with_tasks(project_root: Path, model, cap):
+    from mediapipe.tasks.python import BaseOptions
+    from mediapipe.tasks.python.vision import FaceLandmarker, FaceLandmarkerOptions
+    from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode
+    from mediapipe.tasks.python.vision.core import image as image_lib
+
+    model_path = project_root / "weights" / "face_landmarker.task"
+    if not model_path.is_file():
+        print("Downloading face_landmarker.task ...")
+        url = "https://storage.googleapis.com/mediapipe-models/face_landmarker/face_landmarker/float16/1/face_landmarker.task"
+        urllib.request.urlretrieve(url, model_path)
+        print("Done.")
+
+    options = FaceLandmarkerOptions(
+        base_options=BaseOptions(model_asset_path=str(model_path)),
+        running_mode=running_mode.VisionTaskRunningMode.IMAGE,
+        num_faces=1,
+    )
+    face_landmarker = FaceLandmarker.create_from_options(options)
+    ImageFormat = image_lib.ImageFormat
+
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+        left_label, left_conf = "—", 0.0
+        right_label, right_conf = "—", 0.0
+
+        rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        rgb_contiguous = np.ascontiguousarray(rgb)
+        mp_image = image_lib.Image(ImageFormat.SRGB, rgb_contiguous)
+        result = face_landmarker.detect(mp_image)
+
+        if result.face_landmarks:
+            lm = result.face_landmarks[0]
+            for roi, side in [
+                (get_eye_roi(frame, lm, LEFT_EYE_INDICES_NEW), "left"),
+                (get_eye_roi(frame, lm, RIGHT_EYE_INDICES_NEW), "right"),
+            ]:
+                if roi is not None and roi.size > 0:
+                    try:
+                        pred = model.predict(roi, imgsz=224, device="cpu", verbose=False)
+                        if pred:
+                            r = pred[0]
+                            label = model.names[int(r.probs.top1)]
+                            conf = float(r.probs.top1conf)
+                            if side == "left":
+                                left_label, left_conf = label, conf
+                            else:
+                                right_label, right_conf = label, conf
+                    except Exception:
+                        pass
+
+        cv2.putText(frame, f"L: {left_label} ({left_conf:.0%})", (20, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
+        cv2.putText(frame, f"R: {right_label} ({right_conf:.0%})", (20, 80), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
+        cv2.imshow("Eye open/closed (q to quit)", frame)
+        if cv2.waitKey(1) & 0xFF == ord("q"):
+            break
+
+
+def main():
+    project_root = Path(__file__).resolve().parent.parent
+    weights = find_weights(project_root)
+    if weights is None:
+        print("Weights not found. Put best.pt in weights/ or runs/.../weights/ (from model team).")
+        return
+    if mp is None:
+        print("MediaPipe required. Install: pip install mediapipe")
+        return
+
+    model = YOLO(str(weights))
+    cap = cv2.VideoCapture(0)
+    if not cap.isOpened():
+        print("Could not open webcam.")
+        return
+
+    print("Live eye open/closed on your face. Press 'q' to quit.")
+    try:
+        if _mp_has_solutions:
+            _run_with_solutions(mp, model, cap)
+        else:
+            _run_with_tasks(project_root, model, cap)
+    finally:
+        cap.release()
+        cv2.destroyAllWindows()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/models/cnn/CNN_MODEL/weights/yolo11s-cls.pt b/models/cnn/CNN_MODEL/weights/yolo11s-cls.pt
new file mode 100644
index 0000000000000000000000000000000000000000..d517fa51cabcb93329dc99f67d9d78453c7ae6b1
--- /dev/null
+++ b/models/cnn/CNN_MODEL/weights/yolo11s-cls.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e2b605d1c8c212b434a75a32759a6f7adf1d2b29c35f76bdccd4c794cb653cf2
+size 13630112
diff --git a/models/cnn/__init__.py b/models/cnn/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/models/cnn/eye_attention/__init__.py b/models/cnn/eye_attention/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc
--- /dev/null
+++ b/models/cnn/eye_attention/__init__.py
@@ -0,0 +1 @@
+
diff --git a/models/cnn/eye_attention/classifier.py b/models/cnn/eye_attention/classifier.py
new file mode 100644
index 0000000000000000000000000000000000000000..aaa43477f89887f50b8afe8364a2029885368a7e
--- /dev/null
+++ b/models/cnn/eye_attention/classifier.py
@@ -0,0 +1,69 @@
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+
+import numpy as np
+
+
+class EyeClassifier(ABC):
+    @property
+    @abstractmethod
+    def name(self) -> str:
+        pass
+
+    @abstractmethod
+    def predict_score(self, crops_bgr: list[np.ndarray]) -> float:
+        pass
+
+
+class GeometricOnlyClassifier(EyeClassifier):
+    @property
+    def name(self) -> str:
+        return "geometric"
+
+    def predict_score(self, crops_bgr: list[np.ndarray]) -> float:
+        return 1.0
+
+
+class YOLOv11Classifier(EyeClassifier):
+    def __init__(self, checkpoint_path: str, device: str = "cpu"):
+        from ultralytics import YOLO
+
+        self._model = YOLO(checkpoint_path)
+        self._device = device
+
+        names = self._model.names
+        self._attentive_idx = None
+        for idx, cls_name in names.items():
+            if cls_name in ("open", "attentive"):
+                self._attentive_idx = idx
+                break
+        if self._attentive_idx is None:
+            self._attentive_idx = max(names.keys())
+        print(f"[YOLO] Classes: {names}, attentive_idx={self._attentive_idx}")
+
+    @property
+    def name(self) -> str:
+        return "yolo"
+
+    def predict_score(self, crops_bgr: list[np.ndarray]) -> float:
+        if not crops_bgr:
+            return 1.0
+        results = self._model.predict(crops_bgr, device=self._device, verbose=False)
+        scores = [float(r.probs.data[self._attentive_idx]) for r in results]
+        return sum(scores) / len(scores) if scores else 1.0
+
+
+def load_eye_classifier(
+    path: str | None = None,
+    backend: str = "yolo",
+    device: str = "cpu",
+) -> EyeClassifier:
+    if path is None or backend == "geometric":
+        return GeometricOnlyClassifier()
+
+    try:
+        return YOLOv11Classifier(path, device=device)
+    except ImportError:
+        print("[CLASSIFIER] ultralytics required for YOLO. pip install ultralytics")
+        raise
diff --git a/models/cnn/eye_attention/crop.py b/models/cnn/eye_attention/crop.py
new file mode 100644
index 0000000000000000000000000000000000000000..bdb9816ffa085e8a7e87ba37e3da82494d0b57ef
--- /dev/null
+++ b/models/cnn/eye_attention/crop.py
@@ -0,0 +1,70 @@
+import cv2
+import numpy as np
+
+from models.pretrained.face_mesh.face_mesh import FaceMeshDetector
+
+LEFT_EYE_CONTOUR = FaceMeshDetector.LEFT_EYE_INDICES
+RIGHT_EYE_CONTOUR = FaceMeshDetector.RIGHT_EYE_INDICES
+
+IMAGENET_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_STD = (0.229, 0.224, 0.225)
+
+CROP_SIZE = 96
+
+
+def _bbox_from_landmarks(
+    landmarks: np.ndarray,
+    indices: list[int],
+    frame_w: int,
+    frame_h: int,
+    expand: float = 0.4,
+) -> tuple[int, int, int, int]:
+    pts = landmarks[indices, :2]
+    px = pts[:, 0] * frame_w
+    py = pts[:, 1] * frame_h
+
+    x_min, x_max = px.min(), px.max()
+    y_min, y_max = py.min(), py.max()
+    w = x_max - x_min
+    h = y_max - y_min
+    cx = (x_min + x_max) / 2
+    cy = (y_min + y_max) / 2
+
+    size = max(w, h) * (1 + expand)
+    half = size / 2
+
+    x1 = int(max(cx - half, 0))
+    y1 = int(max(cy - half, 0))
+    x2 = int(min(cx + half, frame_w))
+    y2 = int(min(cy + half, frame_h))
+
+    return x1, y1, x2, y2
+
+
+def extract_eye_crops(
+    frame: np.ndarray,
+    landmarks: np.ndarray,
+    expand: float = 0.4,
+    crop_size: int = CROP_SIZE,
+) -> tuple[np.ndarray, np.ndarray, tuple, tuple]:
+    h, w = frame.shape[:2]
+
+    left_bbox = _bbox_from_landmarks(landmarks, LEFT_EYE_CONTOUR, w, h, expand)
+    right_bbox = _bbox_from_landmarks(landmarks, RIGHT_EYE_CONTOUR, w, h, expand)
+
+    left_crop = frame[left_bbox[1] : left_bbox[3], left_bbox[0] : left_bbox[2]]
+    right_crop = frame[right_bbox[1] : right_bbox[3], right_bbox[0] : right_bbox[2]]
+
+    left_crop = cv2.resize(left_crop, (crop_size, crop_size), interpolation=cv2.INTER_AREA)
+    right_crop = cv2.resize(right_crop, (crop_size, crop_size), interpolation=cv2.INTER_AREA)
+
+    return left_crop, right_crop, left_bbox, right_bbox
+
+
+def crop_to_tensor(crop_bgr: np.ndarray):
+    import torch
+
+    rgb = cv2.cvtColor(crop_bgr, cv2.COLOR_BGR2RGB).astype(np.float32) / 255.0
+    for c in range(3):
+        rgb[:, :, c] = (rgb[:, :, c] - IMAGENET_MEAN[c]) / IMAGENET_STD[c]
+    return torch.from_numpy(rgb.transpose(2, 0, 1))
diff --git a/models/cnn/eye_attention/train.py b/models/cnn/eye_attention/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/models/face_orientation_model/best_model.pt b/models/face_orientation_model/best_model.pt
deleted file mode 100644
index cbbeaaea726544813e251c7cc5daa9532fab14e1..0000000000000000000000000000000000000000
--- a/models/face_orientation_model/best_model.pt
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:18c1f2750c7274e72538b94afcc9f0243287a5b2eb8fcce6be6e4ae18ec59cb0
-size 15033
diff --git a/models/geometric/__init__.py b/models/geometric/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/models/geometric/eye_behaviour/__init__.py b/models/geometric/eye_behaviour/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/models/geometric/eye_behaviour/eye_scorer.py b/models/geometric/eye_behaviour/eye_scorer.py
new file mode 100644
index 0000000000000000000000000000000000000000..1c6a7ebf30a3c4f8a5bae4d1e19af103b9bf2d6f
--- /dev/null
+++ b/models/geometric/eye_behaviour/eye_scorer.py
@@ -0,0 +1,164 @@
+import math
+
+import numpy as np
+
+_LEFT_EYE_EAR = [33, 160, 158, 133, 153, 145]
+_RIGHT_EYE_EAR = [362, 385, 387, 263, 373, 380]
+
+_LEFT_IRIS_CENTER = 468
+_RIGHT_IRIS_CENTER = 473
+
+_LEFT_EYE_INNER = 133
+_LEFT_EYE_OUTER = 33
+_RIGHT_EYE_INNER = 362
+_RIGHT_EYE_OUTER = 263
+
+_LEFT_EYE_TOP = 159
+_LEFT_EYE_BOTTOM = 145
+_RIGHT_EYE_TOP = 386
+_RIGHT_EYE_BOTTOM = 374
+
+_MOUTH_TOP = 13
+_MOUTH_BOTTOM = 14
+_MOUTH_LEFT = 78
+_MOUTH_RIGHT = 308
+_MOUTH_UPPER_1 = 82
+_MOUTH_UPPER_2 = 312
+_MOUTH_LOWER_1 = 87
+_MOUTH_LOWER_2 = 317
+
+MAR_YAWN_THRESHOLD = 0.55
+
+
+def _distance(p1: np.ndarray, p2: np.ndarray) -> float:
+    return float(np.linalg.norm(p1 - p2))
+
+
+def compute_ear(landmarks: np.ndarray, eye_indices: list[int]) -> float:
+    p1 = landmarks[eye_indices[0], :2]
+    p2 = landmarks[eye_indices[1], :2]
+    p3 = landmarks[eye_indices[2], :2]
+    p4 = landmarks[eye_indices[3], :2]
+    p5 = landmarks[eye_indices[4], :2]
+    p6 = landmarks[eye_indices[5], :2]
+
+    vertical1 = _distance(p2, p6)
+    vertical2 = _distance(p3, p5)
+    horizontal = _distance(p1, p4)
+
+    if horizontal < 1e-6:
+        return 0.0
+
+    return (vertical1 + vertical2) / (2.0 * horizontal)
+
+
+def compute_avg_ear(landmarks: np.ndarray) -> float:
+    left_ear = compute_ear(landmarks, _LEFT_EYE_EAR)
+    right_ear = compute_ear(landmarks, _RIGHT_EYE_EAR)
+    return (left_ear + right_ear) / 2.0
+
+
+def compute_gaze_ratio(landmarks: np.ndarray) -> tuple[float, float]:
+    left_iris = landmarks[_LEFT_IRIS_CENTER, :2]
+    left_inner = landmarks[_LEFT_EYE_INNER, :2]
+    left_outer = landmarks[_LEFT_EYE_OUTER, :2]
+    left_top = landmarks[_LEFT_EYE_TOP, :2]
+    left_bottom = landmarks[_LEFT_EYE_BOTTOM, :2]
+
+    right_iris = landmarks[_RIGHT_IRIS_CENTER, :2]
+    right_inner = landmarks[_RIGHT_EYE_INNER, :2]
+    right_outer = landmarks[_RIGHT_EYE_OUTER, :2]
+    right_top = landmarks[_RIGHT_EYE_TOP, :2]
+    right_bottom = landmarks[_RIGHT_EYE_BOTTOM, :2]
+
+    left_h_total = _distance(left_inner, left_outer)
+    right_h_total = _distance(right_inner, right_outer)
+
+    if left_h_total < 1e-6 or right_h_total < 1e-6:
+        return 0.5, 0.5
+
+    left_h_ratio = _distance(left_outer, left_iris) / left_h_total
+    right_h_ratio = _distance(right_outer, right_iris) / right_h_total
+    h_ratio = (left_h_ratio + right_h_ratio) / 2.0
+
+    left_v_total = _distance(left_top, left_bottom)
+    right_v_total = _distance(right_top, right_bottom)
+
+    if left_v_total < 1e-6 or right_v_total < 1e-6:
+        return h_ratio, 0.5
+
+    left_v_ratio = _distance(left_top, left_iris) / left_v_total
+    right_v_ratio = _distance(right_top, right_iris) / right_v_total
+    v_ratio = (left_v_ratio + right_v_ratio) / 2.0
+
+    return float(np.clip(h_ratio, 0, 1)), float(np.clip(v_ratio, 0, 1))
+
+
+def compute_mar(landmarks: np.ndarray) -> float:
+    # Mouth aspect ratio: high = mouth open (yawning / sleepy)
+    top = landmarks[_MOUTH_TOP, :2]
+    bottom = landmarks[_MOUTH_BOTTOM, :2]
+    left = landmarks[_MOUTH_LEFT, :2]
+    right = landmarks[_MOUTH_RIGHT, :2]
+    upper1 = landmarks[_MOUTH_UPPER_1, :2]
+    lower1 = landmarks[_MOUTH_LOWER_1, :2]
+    upper2 = landmarks[_MOUTH_UPPER_2, :2]
+    lower2 = landmarks[_MOUTH_LOWER_2, :2]
+
+    horizontal = _distance(left, right)
+    if horizontal < 1e-6:
+        return 0.0
+    v1 = _distance(upper1, lower1)
+    v2 = _distance(top, bottom)
+    v3 = _distance(upper2, lower2)
+    return (v1 + v2 + v3) / (2.0 * horizontal)
+
+
+class EyeBehaviourScorer:
+    def __init__(
+        self,
+        ear_open: float = 0.30,
+        ear_closed: float = 0.16,
+        gaze_max_offset: float = 0.28,
+    ):
+        self.ear_open = ear_open
+        self.ear_closed = ear_closed
+        self.gaze_max_offset = gaze_max_offset
+
+    def _ear_score(self, ear: float) -> float:
+        if ear >= self.ear_open:
+            return 1.0
+        if ear <= self.ear_closed:
+            return 0.0
+        return (ear - self.ear_closed) / (self.ear_open - self.ear_closed)
+
+    def _gaze_score(self, h_ratio: float, v_ratio: float) -> float:
+        h_offset = abs(h_ratio - 0.5)
+        v_offset = abs(v_ratio - 0.5)
+        offset = math.sqrt(h_offset**2 + v_offset**2)
+        t = min(offset / self.gaze_max_offset, 1.0)
+        return 0.5 * (1.0 + math.cos(math.pi * t))
+
+    def score(self, landmarks: np.ndarray) -> float:
+        ear = compute_avg_ear(landmarks)
+        ear_s = self._ear_score(ear)
+        if ear_s < 0.3:
+            return ear_s
+        h_ratio, v_ratio = compute_gaze_ratio(landmarks)
+        gaze_s = self._gaze_score(h_ratio, v_ratio)
+        return ear_s * gaze_s
+
+    def detailed_score(self, landmarks: np.ndarray) -> dict:
+        ear = compute_avg_ear(landmarks)
+        ear_s = self._ear_score(ear)
+        h_ratio, v_ratio = compute_gaze_ratio(landmarks)
+        gaze_s = self._gaze_score(h_ratio, v_ratio)
+        s_eye = ear_s if ear_s < 0.3 else ear_s * gaze_s
+        return {
+            "ear": round(ear, 4),
+            "ear_score": round(ear_s, 4),
+            "h_gaze": round(h_ratio, 4),
+            "v_gaze": round(v_ratio, 4),
+            "gaze_score": round(gaze_s, 4),
+            "s_eye": round(s_eye, 4),
+        }
diff --git a/models/geometric/face_orientation/__init__.py b/models/geometric/face_orientation/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc
--- /dev/null
+++ b/models/geometric/face_orientation/__init__.py
@@ -0,0 +1 @@
+
diff --git a/models/geometric/face_orientation/head_pose.py b/models/geometric/face_orientation/head_pose.py
new file mode 100644
index 0000000000000000000000000000000000000000..60ca39d09a2ab2404e2cc4c9a6693e997ddb0705
--- /dev/null
+++ b/models/geometric/face_orientation/head_pose.py
@@ -0,0 +1,112 @@
+import math
+
+import cv2
+import numpy as np
+
+_LANDMARK_INDICES = [1, 152, 33, 263, 61, 291]
+
+_MODEL_POINTS = np.array(
+    [
+        [0.0, 0.0, 0.0],
+        [0.0, -330.0, -65.0],
+        [-225.0, 170.0, -135.0],
+        [225.0, 170.0, -135.0],
+        [-150.0, -150.0, -125.0],
+        [150.0, -150.0, -125.0],
+    ],
+    dtype=np.float64,
+)
+
+
+class HeadPoseEstimator:
+    def __init__(self, max_angle: float = 30.0, roll_weight: float = 0.5):
+        self.max_angle = max_angle
+        self.roll_weight = roll_weight
+        self._camera_matrix = None
+        self._frame_size = None
+        self._dist_coeffs = np.zeros((4, 1), dtype=np.float64)
+
+    def _get_camera_matrix(self, frame_w: int, frame_h: int) -> np.ndarray:
+        if self._camera_matrix is not None and self._frame_size == (frame_w, frame_h):
+            return self._camera_matrix
+        focal_length = float(frame_w)
+        cx, cy = frame_w / 2.0, frame_h / 2.0
+        self._camera_matrix = np.array(
+            [[focal_length, 0, cx], [0, focal_length, cy], [0, 0, 1]],
+            dtype=np.float64,
+        )
+        self._frame_size = (frame_w, frame_h)
+        return self._camera_matrix
+
+    def _solve(self, landmarks: np.ndarray, frame_w: int, frame_h: int):
+        image_points = np.array(
+            [
+                [landmarks[i, 0] * frame_w, landmarks[i, 1] * frame_h]
+                for i in _LANDMARK_INDICES
+            ],
+            dtype=np.float64,
+        )
+        camera_matrix = self._get_camera_matrix(frame_w, frame_h)
+        success, rvec, tvec = cv2.solvePnP(
+            _MODEL_POINTS,
+            image_points,
+            camera_matrix,
+            self._dist_coeffs,
+            flags=cv2.SOLVEPNP_ITERATIVE,
+        )
+        return success, rvec, tvec, image_points
+
+    def estimate(
+        self, landmarks: np.ndarray, frame_w: int, frame_h: int
+    ) -> tuple[float, float, float] | None:
+        success, rvec, tvec, _ = self._solve(landmarks, frame_w, frame_h)
+        if not success:
+            return None
+
+        rmat, _ = cv2.Rodrigues(rvec)
+        nose_dir = rmat @ np.array([0.0, 0.0, 1.0])
+        face_up = rmat @ np.array([0.0, 1.0, 0.0])
+
+        yaw = math.degrees(math.atan2(nose_dir[0], -nose_dir[2]))
+        pitch = math.degrees(math.asin(np.clip(-nose_dir[1], -1.0, 1.0)))
+        roll = math.degrees(math.atan2(face_up[0], -face_up[1]))
+
+        return (yaw, pitch, roll)
+
+    def score(self, landmarks: np.ndarray, frame_w: int, frame_h: int) -> float:
+        angles = self.estimate(landmarks, frame_w, frame_h)
+        if angles is None:
+            return 0.0
+
+        yaw, pitch, roll = angles
+        deviation = math.sqrt(yaw**2 + pitch**2 + (self.roll_weight * roll) ** 2)
+        t = min(deviation / self.max_angle, 1.0)
+        return 0.5 * (1.0 + math.cos(math.pi * t))
+
+    def draw_axes(
+        self,
+        frame: np.ndarray,
+        landmarks: np.ndarray,
+        axis_length: float = 50.0,
+    ) -> np.ndarray:
+        h, w = frame.shape[:2]
+        success, rvec, tvec, image_points = self._solve(landmarks, w, h)
+        if not success:
+            return frame
+
+        camera_matrix = self._get_camera_matrix(w, h)
+        nose = tuple(image_points[0].astype(int))
+
+        axes_3d = np.float64(
+            [[axis_length, 0, 0], [0, axis_length, 0], [0, 0, axis_length]]
+        )
+        projected, _ = cv2.projectPoints(
+            axes_3d, rvec, tvec, camera_matrix, self._dist_coeffs
+        )
+
+        colors = [(0, 0, 255), (0, 255, 0), (255, 0, 0)]
+        for i, color in enumerate(colors):
+            pt = tuple(projected[i].ravel().astype(int))
+            cv2.line(frame, nose, pt, color, 2)
+
+        return frame
diff --git a/models/mlp/__init__.py b/models/mlp/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/models/train.py b/models/mlp/train.py
similarity index 82%
rename from models/train.py
rename to models/mlp/train.py
index 39cbce49caff34d5bc038d7a53ea06e9ae9a4151..2e606fc3638b70572a5d9ccffe3a8306076ff7cd 100644
--- a/models/train.py
+++ b/models/mlp/train.py
@@ -1,18 +1,18 @@
-# Run from repo root: python -m models.train (or cd models && python train.py)
-
 import json
-import os
+import os, sys
 import random
 
-import numpy as np as np
+import numpy as np
 import torch
 import torch.nn as nn
 import torch.optim as optim
 
-from prepare_dataset import get_dataloaders
+from clearml import Task
+
+from models.prepare_dataset import get_dataloaders
 
 CFG = {
-    "model_name": "face_orientation",  # "face_orientation" or "eye_behaviour"
+    "model_name": "face_orientation",
     "epochs": 30,
     "batch_size": 32,
     "lr": 1e-3,
@@ -22,10 +22,25 @@ CFG = {
         "face_orientation": os.path.join(os.path.dirname(__file__), "face_orientation_model"),
         "eye_behaviour": os.path.join(os.path.dirname(__file__), "eye_behaviour_model"),
     },
-    "logs_dir": os.path.join(os.path.dirname(__file__), "..", "evaluation", "logs"),
+    "logs_dir": os.path.join(os.path.dirname(__file__), "..", "..", "evaluation", "logs"),
 }
 
 
+# ==== ClearML Initialisation =============================================
+task = Task.init(
+    project_name="Focus Guard",
+    task_name=f"MLP Model Training",
+    tags=["training", "mlp_model"]
+)
+
+prefix = 'checkpoints/'+task.name+'_'+task.id+'/'
+os.makedirs(prefix, exist_ok=True)
+
+task.connect(CFG)   
+
+
+
+# ==== Model =============================================
 def set_seed(seed: int):
     random.seed(seed)
     np.random.seed(seed)
@@ -154,6 +169,16 @@ def main():
         history["val_loss"].append(round(val_loss, 4))
         history["val_acc"].append(round(val_acc, 4))
 
+
+        # Log scalars to ClearML
+        current_lr = optimizer.param_groups[0]['lr']
+        task.logger.report_scalar("Loss", "Train", float(train_loss), iteration=epoch)
+        task.logger.report_scalar("Accuracy", "Train", float(train_acc), iteration=epoch)    
+        task.logger.report_scalar("Loss", "Val", float(val_loss), iteration=epoch)
+        task.logger.report_scalar("Accuracy", "Val", float(val_acc), iteration=epoch)
+        task.logger.report_scalar("Learning Rate", "LR", float(current_lr), iteration=epoch)
+        task.logger.flush()
+
         marker = ""
         if val_acc > best_val_acc:
             best_val_acc = val_acc
diff --git a/models/pretrained/__init__.py b/models/pretrained/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/models/pretrained/face_mesh/.gitkeep b/models/pretrained/face_mesh/.gitkeep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/models/pretrained/face_mesh/__init__.py b/models/pretrained/face_mesh/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/models/pretrained/face_mesh/face_mesh.py b/models/pretrained/face_mesh/face_mesh.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9ede36d29cf4d0266506570c52890934ddae82f
--- /dev/null
+++ b/models/pretrained/face_mesh/face_mesh.py
@@ -0,0 +1,91 @@
+import os
+from pathlib import Path
+from urllib.request import urlretrieve
+
+import cv2
+import numpy as np
+import mediapipe as mp
+from mediapipe.tasks.python.vision import FaceLandmarkerOptions, FaceLandmarker, RunningMode
+from mediapipe.tasks import python as mp_tasks
+
+_MODEL_URL = (
+    "https://storage.googleapis.com/mediapipe-models/face_landmarker/"
+    "face_landmarker/float16/latest/face_landmarker.task"
+)
+
+
+def _ensure_model() -> str:
+    cache_dir = Path(os.environ.get(
+        "FOCUSGUARD_CACHE_DIR",
+        Path.home() / ".cache" / "focusguard",
+    ))
+    model_path = cache_dir / "face_landmarker.task"
+    if model_path.exists():
+        return str(model_path)
+    cache_dir.mkdir(parents=True, exist_ok=True)
+    print(f"[FACE_MESH] Downloading model to {model_path}...")
+    urlretrieve(_MODEL_URL, model_path)
+    print("[FACE_MESH] Download complete.")
+    return str(model_path)
+
+
+class FaceMeshDetector:
+    LEFT_EYE_INDICES = [33, 7, 163, 144, 145, 153, 154, 155, 133, 173, 157, 158, 159, 160, 161, 246]
+    RIGHT_EYE_INDICES = [362, 382, 381, 380, 374, 373, 390, 249, 263, 466, 388, 387, 386, 385, 384, 398]
+    LEFT_IRIS_INDICES = [468, 469, 470, 471, 472]
+    RIGHT_IRIS_INDICES = [473, 474, 475, 476, 477]
+
+    def __init__(
+        self,
+        max_num_faces: int = 1,
+        min_detection_confidence: float = 0.5,
+        min_tracking_confidence: float = 0.5,
+    ):
+        model_path = _ensure_model()
+        options = FaceLandmarkerOptions(
+            base_options=mp_tasks.BaseOptions(model_asset_path=model_path),
+            num_faces=max_num_faces,
+            min_face_detection_confidence=min_detection_confidence,
+            min_face_presence_confidence=min_detection_confidence,
+            min_tracking_confidence=min_tracking_confidence,
+            running_mode=RunningMode.VIDEO,
+        )
+        self._landmarker = FaceLandmarker.create_from_options(options)
+        self._frame_ts = 0  # ms, for video API
+
+    def process(self, bgr_frame: np.ndarray) -> np.ndarray | None:
+        # BGR in -> (478,3) norm x,y,z or None
+        rgb = cv2.cvtColor(bgr_frame, cv2.COLOR_BGR2RGB)
+        mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=rgb)
+        self._frame_ts += 33  # ~30fps
+        result = self._landmarker.detect_for_video(mp_image, self._frame_ts)
+
+        if not result.face_landmarks:
+            return None
+
+        face = result.face_landmarks[0]
+        return np.array([(lm.x, lm.y, lm.z) for lm in face], dtype=np.float32)
+
+    def get_pixel_landmarks(self, landmarks: np.ndarray, frame_w: int, frame_h: int) -> np.ndarray:
+        # norm -> pixel (x,y)
+        pixel = np.zeros((landmarks.shape[0], 2), dtype=np.int32)
+        pixel[:, 0] = (landmarks[:, 0] * frame_w).astype(np.int32)
+        pixel[:, 1] = (landmarks[:, 1] * frame_h).astype(np.int32)
+        return pixel
+
+    def get_3d_landmarks(self, landmarks: np.ndarray, frame_w: int, frame_h: int) -> np.ndarray:
+        # norm -> pixel-scale x,y,z (z scaled by width)
+        pts = np.zeros_like(landmarks)
+        pts[:, 0] = landmarks[:, 0] * frame_w
+        pts[:, 1] = landmarks[:, 1] * frame_h
+        pts[:, 2] = landmarks[:, 2] * frame_w
+        return pts
+
+    def close(self):
+        self._landmarker.close()
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, *args):
+        self.close()
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8c16169d1f391f38ee6788339272e2d0beb06262
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,8 @@
+mediapipe>=0.10.14
+opencv-python>=4.8.0
+numpy>=1.24.0
+torch>=2.0.0
+torchvision>=0.15.0
+scikit-learn>=1.2.0
+joblib>=1.2.0
+clearml>=2.0.2
\ No newline at end of file
diff --git a/ui/README.md b/ui/README.md
index ea78af8b5c89f80ddf6c4fde370b70fc089c5362..00b8f1539df8d3cc863b8e9cee4f1157f6e86257 100644
--- a/ui/README.md
+++ b/ui/README.md
@@ -1,3 +1,20 @@
 # ui
 
-Live demo and session view — structure up to the team.
+- **pipeline.py** — `FaceMeshPipeline` (head + eye geo ± YOLO → focus) and `MLPPipeline` (loads latest MLP from `MLP/models/`, 10 features → focus)
+- **live_demo.py** — webcam window, mesh overlay, FOCUSED / NOT FOCUSED
+
+From repo root:
+
+```bash
+python ui/live_demo.py
+```
+
+MLP only (no head/eye fusion, just your trained MLP):
+
+```bash
+python ui/live_demo.py --mlp
+```
+
+With YOLO eye model: `python ui/live_demo.py --eye-model path/to/yolo.pt`
+
+`q` quit, `m` cycle mesh (full / contours / off).
diff --git a/ui/live_demo.py b/ui/live_demo.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ad4f3e38a2a32f1dc165ccf63a6e724d66888e6
--- /dev/null
+++ b/ui/live_demo.py
@@ -0,0 +1,224 @@
+import argparse
+import os
+import sys
+import time
+
+import cv2
+import numpy as np
+from mediapipe.tasks.python.vision import FaceLandmarksConnections
+
+_PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+if _PROJECT_ROOT not in sys.path:
+    sys.path.insert(0, _PROJECT_ROOT)
+
+from ui.pipeline import FaceMeshPipeline, MLPPipeline
+from models.pretrained.face_mesh.face_mesh import FaceMeshDetector
+
+FONT = cv2.FONT_HERSHEY_SIMPLEX
+CYAN = (255, 255, 0)
+GREEN = (0, 255, 0)
+MAGENTA = (255, 0, 255)
+ORANGE = (0, 165, 255)
+RED = (0, 0, 255)
+WHITE = (255, 255, 255)
+YELLOW = (0, 255, 255)
+LIGHT_GREEN = (144, 238, 144)
+
+_TESSELATION = [(c.start, c.end) for c in FaceLandmarksConnections.FACE_LANDMARKS_TESSELATION]
+_CONTOURS = [(c.start, c.end) for c in FaceLandmarksConnections.FACE_LANDMARKS_CONTOURS]
+_LEFT_EYEBROW = [70, 63, 105, 66, 107, 55, 65, 52, 53, 46]
+_RIGHT_EYEBROW = [300, 293, 334, 296, 336, 285, 295, 282, 283, 276]
+_NOSE_BRIDGE = [6, 197, 195, 5, 4, 1, 19, 94, 2]
+_LIPS_OUTER = [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291, 409, 270, 269, 267, 0, 37, 39, 40, 185, 61]
+_LIPS_INNER = [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308, 415, 310, 311, 312, 13, 82, 81, 80, 191, 78]
+_LEFT_EAR_POINTS = [33, 160, 158, 133, 153, 145]
+_RIGHT_EAR_POINTS = [362, 385, 387, 263, 373, 380]
+
+MESH_FULL = 0
+MESH_CONTOURS = 1
+MESH_OFF = 2
+_MESH_NAMES = ["FULL MESH", "CONTOURS", "MESH OFF"]
+
+
+def _lm_to_px(landmarks, idx, w, h):
+    return (int(landmarks[idx, 0] * w), int(landmarks[idx, 1] * h))
+
+
+def draw_tessellation(frame, landmarks, w, h):
+    overlay = frame.copy()
+    for conn in _TESSELATION:
+        pt1 = _lm_to_px(landmarks, conn[0], w, h)
+        pt2 = _lm_to_px(landmarks, conn[1], w, h)
+        cv2.line(overlay, pt1, pt2, (200, 200, 200), 1, cv2.LINE_AA)
+    cv2.addWeighted(overlay, 0.3, frame, 0.7, 0, frame)
+
+
+def draw_contours(frame, landmarks, w, h):
+    for conn in _CONTOURS:
+        pt1 = _lm_to_px(landmarks, conn[0], w, h)
+        pt2 = _lm_to_px(landmarks, conn[1], w, h)
+        cv2.line(frame, pt1, pt2, CYAN, 1, cv2.LINE_AA)
+    for indices in [_LEFT_EYEBROW, _RIGHT_EYEBROW]:
+        for i in range(len(indices) - 1):
+            pt1 = _lm_to_px(landmarks, indices[i], w, h)
+            pt2 = _lm_to_px(landmarks, indices[i + 1], w, h)
+            cv2.line(frame, pt1, pt2, LIGHT_GREEN, 2, cv2.LINE_AA)
+    for i in range(len(_NOSE_BRIDGE) - 1):
+        pt1 = _lm_to_px(landmarks, _NOSE_BRIDGE[i], w, h)
+        pt2 = _lm_to_px(landmarks, _NOSE_BRIDGE[i + 1], w, h)
+        cv2.line(frame, pt1, pt2, ORANGE, 1, cv2.LINE_AA)
+    for i in range(len(_LIPS_OUTER) - 1):
+        pt1 = _lm_to_px(landmarks, _LIPS_OUTER[i], w, h)
+        pt2 = _lm_to_px(landmarks, _LIPS_OUTER[i + 1], w, h)
+        cv2.line(frame, pt1, pt2, MAGENTA, 1, cv2.LINE_AA)
+    for i in range(len(_LIPS_INNER) - 1):
+        pt1 = _lm_to_px(landmarks, _LIPS_INNER[i], w, h)
+        pt2 = _lm_to_px(landmarks, _LIPS_INNER[i + 1], w, h)
+        cv2.line(frame, pt1, pt2, (200, 0, 200), 1, cv2.LINE_AA)
+
+
+def draw_eyes_and_irises(frame, landmarks, w, h):
+    left_pts = np.array(
+        [_lm_to_px(landmarks, i, w, h) for i in FaceMeshDetector.LEFT_EYE_INDICES],
+        dtype=np.int32,
+    )
+    cv2.polylines(frame, [left_pts], True, GREEN, 2, cv2.LINE_AA)
+    right_pts = np.array(
+        [_lm_to_px(landmarks, i, w, h) for i in FaceMeshDetector.RIGHT_EYE_INDICES],
+        dtype=np.int32,
+    )
+    cv2.polylines(frame, [right_pts], True, GREEN, 2, cv2.LINE_AA)
+    for indices in [_LEFT_EAR_POINTS, _RIGHT_EAR_POINTS]:
+        for idx in indices:
+            pt = _lm_to_px(landmarks, idx, w, h)
+            cv2.circle(frame, pt, 3, YELLOW, -1, cv2.LINE_AA)
+    for iris_indices, eye_inner, eye_outer in [
+        (FaceMeshDetector.LEFT_IRIS_INDICES, 133, 33),
+        (FaceMeshDetector.RIGHT_IRIS_INDICES, 362, 263),
+    ]:
+        iris_pts = np.array(
+            [_lm_to_px(landmarks, i, w, h) for i in iris_indices],
+            dtype=np.int32,
+        )
+        center = iris_pts[0]
+        if len(iris_pts) >= 5:
+            radii = [np.linalg.norm(iris_pts[j] - center) for j in range(1, 5)]
+            radius = max(int(np.mean(radii)), 2)
+            cv2.circle(frame, tuple(center), radius, MAGENTA, 2, cv2.LINE_AA)
+            cv2.circle(frame, tuple(center), 2, WHITE, -1, cv2.LINE_AA)
+        eye_center_x = (landmarks[eye_inner, 0] + landmarks[eye_outer, 0]) / 2.0
+        eye_center_y = (landmarks[eye_inner, 1] + landmarks[eye_outer, 1]) / 2.0
+        eye_center = (int(eye_center_x * w), int(eye_center_y * h))
+        dx = center[0] - eye_center[0]
+        dy = center[1] - eye_center[1]
+        gaze_end = (int(center[0] + dx * 3), int(center[1] + dy * 3))
+        cv2.line(frame, tuple(center), gaze_end, RED, 1, cv2.LINE_AA)
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--camera", type=int, default=0)
+    parser.add_argument("--mlp", action="store_true", help="Use MLP model only (load latest from MLP/models/)")
+    parser.add_argument("--mlp-dir", type=str, default=None, help="MLP models dir (default: shared/MLP/models)")
+    parser.add_argument("--max-angle", type=float, default=22.0)
+    parser.add_argument("--alpha", type=float, default=0.4)
+    parser.add_argument("--beta", type=float, default=0.6)
+    parser.add_argument("--threshold", type=float, default=0.55)
+    parser.add_argument("--eye-model", type=str, default=None)
+    parser.add_argument("--eye-backend", type=str, default="yolo", choices=["yolo", "geometric"])
+    parser.add_argument("--eye-blend", type=float, default=0.5)
+    args = parser.parse_args()
+
+    use_mlp_only = args.mlp
+
+    if use_mlp_only:
+        print("[DEMO] MLP only — loading latest from MLP/models/")
+        pipeline = MLPPipeline(model_dir=args.mlp_dir)
+    else:
+        eye_mode = " + model" if args.eye_model else " only"
+        print("[DEMO] Face mesh + head pose + eye (geometry" + eye_mode + ")")
+        pipeline = FaceMeshPipeline(
+            max_angle=args.max_angle,
+            alpha=args.alpha,
+            beta=args.beta,
+            threshold=args.threshold,
+            eye_model_path=args.eye_model,
+            eye_backend=args.eye_backend,
+            eye_blend=args.eye_blend,
+        )
+
+    cap = cv2.VideoCapture(args.camera)
+    if not cap.isOpened():
+        print("[DEMO] ERROR: Cannot open camera")
+        return
+
+    print("[DEMO] q = quit, m = cycle mesh (full/contours/off)" if not use_mlp_only else "[DEMO] q = quit, m = mesh")
+    prev_time = time.time()
+    fps = 0.0
+    mesh_mode = MESH_FULL
+
+    try:
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                break
+
+            result = pipeline.process_frame(frame)
+            now = time.time()
+            fps = 0.9 * fps + 0.1 * (1.0 / max(now - prev_time, 1e-6))
+            prev_time = now
+
+            h, w = frame.shape[:2]
+            if result["landmarks"] is not None:
+                lm = result["landmarks"]
+                if mesh_mode == MESH_FULL:
+                    draw_tessellation(frame, lm, w, h)
+                    draw_contours(frame, lm, w, h)
+                elif mesh_mode == MESH_CONTOURS:
+                    draw_contours(frame, lm, w, h)
+                draw_eyes_and_irises(frame, lm, w, h)
+                if not use_mlp_only:
+                    pipeline.head_pose.draw_axes(frame, lm)
+                if result.get("left_bbox") and result.get("right_bbox"):
+                    lx1, ly1, lx2, ly2 = result["left_bbox"]
+                    rx1, ry1, rx2, ry2 = result["right_bbox"]
+                    cv2.rectangle(frame, (lx1, ly1), (lx2, ly2), YELLOW, 1)
+                    cv2.rectangle(frame, (rx1, ry1), (rx2, ry2), YELLOW, 1)
+
+            status = "FOCUSED" if result["is_focused"] else "NOT FOCUSED"
+            status_color = GREEN if result["is_focused"] else RED
+            cv2.rectangle(frame, (0, 0), (w, 55), (0, 0, 0), -1)
+            cv2.putText(frame, status, (10, 28), FONT, 0.8, status_color, 2, cv2.LINE_AA)
+            if use_mlp_only:
+                cv2.putText(frame, "MLP", (10, 48), FONT, 0.45, WHITE, 1, cv2.LINE_AA)
+                cv2.putText(frame, f"FPS: {fps:.0f}", (w - 80, 28), FONT, 0.45, WHITE, 1, cv2.LINE_AA)
+                cv2.putText(frame, "q:quit  m:mesh", (w - 120, 48), FONT, 0.4, (180, 180, 180), 1, cv2.LINE_AA)
+            else:
+                mar_str = f" MAR:{result['mar']:.2f}" if result.get("mar") is not None else ""
+                cv2.putText(frame, f"S_face:{result['s_face']:.2f} S_eye:{result['s_eye']:.2f}{mar_str} score:{result['raw_score']:.2f}", (10, 48), FONT, 0.45, WHITE, 1, cv2.LINE_AA)
+                if result.get("is_yawning"):
+                    cv2.putText(frame, "YAWN", (10, 75), FONT, 0.7, ORANGE, 2, cv2.LINE_AA)
+                if result["yaw"] is not None:
+                    cv2.putText(frame, f"yaw:{result['yaw']:+.0f} pitch:{result['pitch']:+.0f} roll:{result['roll']:+.0f}", (w - 280, 48), FONT, 0.4, (180, 180, 180), 1, cv2.LINE_AA)
+                eye_label = f"eye:{pipeline.eye_classifier.name}" if pipeline.has_eye_model else "eye:geo"
+                cv2.putText(frame, f"{_MESH_NAMES[mesh_mode]}  {eye_label}  FPS: {fps:.0f}", (w - 320, 28), FONT, 0.45, WHITE, 1, cv2.LINE_AA)
+                cv2.putText(frame, "q:quit  m:mesh", (w - 140, 48), FONT, 0.4, (180, 180, 180), 1, cv2.LINE_AA)
+
+            cv2.imshow("FocusGuard", frame)
+
+            key = cv2.waitKey(1) & 0xFF
+            if key == ord("q"):
+                break
+            elif key == ord("m"):
+                mesh_mode = (mesh_mode + 1) % 3
+                print(f"[DEMO] Mesh: {_MESH_NAMES[mesh_mode]}")
+
+    finally:
+        cap.release()
+        cv2.destroyAllWindows()
+        pipeline.close()
+        print("[DEMO] Done")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/ui/pipeline.py b/ui/pipeline.py
new file mode 100644
index 0000000000000000000000000000000000000000..a96d1ff2dbd3522233e777b2a35c51628a11fcae
--- /dev/null
+++ b/ui/pipeline.py
@@ -0,0 +1,167 @@
+import glob
+import os
+import sys
+
+import numpy as np
+import joblib
+
+_PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+if _PROJECT_ROOT not in sys.path:
+    sys.path.insert(0, _PROJECT_ROOT)
+
+from models.pretrained.face_mesh.face_mesh import FaceMeshDetector
+from models.geometric.face_orientation.head_pose import HeadPoseEstimator
+from models.geometric.eye_behaviour.eye_scorer import EyeBehaviourScorer, compute_mar, MAR_YAWN_THRESHOLD
+from models.cnn.eye_attention.crop import extract_eye_crops
+from models.cnn.eye_attention.classifier import load_eye_classifier, GeometricOnlyClassifier
+from models.attention.collect_features import FEATURE_NAMES, TemporalTracker, extract_features
+
+
+class FaceMeshPipeline:
+    def __init__(
+        self,
+        max_angle: float = 22.0,
+        alpha: float = 0.4,
+        beta: float = 0.6,
+        threshold: float = 0.55,
+        eye_model_path: str | None = None,
+        eye_backend: str = "yolo",
+        eye_blend: float = 0.5,
+    ):
+        self.detector = FaceMeshDetector()
+        self.head_pose = HeadPoseEstimator(max_angle=max_angle)
+        self.eye_scorer = EyeBehaviourScorer()
+        self.alpha = alpha
+        self.beta = beta
+        self.threshold = threshold
+        self.eye_blend = eye_blend
+
+        self.eye_classifier = load_eye_classifier(
+            path=eye_model_path if eye_model_path and os.path.exists(eye_model_path) else None,
+            backend=eye_backend,
+            device="cpu",
+        )
+        self._has_eye_model = not isinstance(self.eye_classifier, GeometricOnlyClassifier)
+        if self._has_eye_model:
+            print(f"[PIPELINE] Eye model: {self.eye_classifier.name}")
+
+    def process_frame(self, bgr_frame: np.ndarray) -> dict:
+        landmarks = self.detector.process(bgr_frame)
+        h, w = bgr_frame.shape[:2]
+
+        out = {
+            "landmarks": landmarks,
+            "s_face": 0.0,
+            "s_eye": 0.0,
+            "raw_score": 0.0,
+            "is_focused": False,
+            "yaw": None,
+            "pitch": None,
+            "roll": None,
+            "mar": None,
+            "is_yawning": False,
+            "left_bbox": None,
+            "right_bbox": None,
+        }
+
+        if landmarks is None:
+            return out
+
+        angles = self.head_pose.estimate(landmarks, w, h)
+        if angles is not None:
+            out["yaw"], out["pitch"], out["roll"] = angles
+        out["s_face"] = self.head_pose.score(landmarks, w, h)
+
+        s_eye_geo = self.eye_scorer.score(landmarks)
+        if self._has_eye_model:
+            left_crop, right_crop, left_bbox, right_bbox = extract_eye_crops(bgr_frame, landmarks)
+            out["left_bbox"] = left_bbox
+            out["right_bbox"] = right_bbox
+            s_eye_model = self.eye_classifier.predict_score([left_crop, right_crop])
+            out["s_eye"] = (1.0 - self.eye_blend) * s_eye_geo + self.eye_blend * s_eye_model
+        else:
+            out["s_eye"] = s_eye_geo
+
+        out["mar"] = compute_mar(landmarks)
+        out["is_yawning"] = out["mar"] > MAR_YAWN_THRESHOLD
+
+        out["raw_score"] = self.alpha * out["s_face"] + self.beta * out["s_eye"]
+        out["is_focused"] = out["raw_score"] >= self.threshold and not out["is_yawning"]
+
+        return out
+
+    @property
+    def has_eye_model(self) -> bool:
+        return self._has_eye_model
+
+    def close(self):
+        self.detector.close()
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, *args):
+        self.close()
+
+
+def _latest_mlp_artifacts(model_dir):
+    mlp_files = sorted(glob.glob(os.path.join(model_dir, "mlp_*.joblib")))
+    if not mlp_files:
+        return None, None, None
+    base = os.path.basename(mlp_files[-1]).replace("mlp_", "").replace(".joblib", "")
+    scaler_path = os.path.join(model_dir, f"scaler_{base}.joblib")
+    meta_path = os.path.join(model_dir, f"meta_{base}.npz")
+    if not os.path.isfile(scaler_path) or not os.path.isfile(meta_path):
+        return None, None, None
+    return mlp_files[-1], scaler_path, meta_path
+
+
+class MLPPipeline:
+    def __init__(self, model_dir=None):
+        if model_dir is None:
+            model_dir = os.path.join(_PROJECT_ROOT, "MLP", "models")
+        mlp_path, scaler_path, meta_path = _latest_mlp_artifacts(model_dir)
+        if mlp_path is None:
+            raise FileNotFoundError(f"No MLP artifacts in {model_dir}")
+        self._mlp = joblib.load(mlp_path)
+        self._scaler = joblib.load(scaler_path)
+        meta = np.load(meta_path, allow_pickle=True)
+        self._feature_names = list(meta["feature_names"])
+        self._detector = FaceMeshDetector()
+        self._head_pose = HeadPoseEstimator()
+        self._eye_scorer = EyeBehaviourScorer()
+        self._temporal = TemporalTracker()
+        self._indices = [FEATURE_NAMES.index(n) for n in self._feature_names]
+        print(f"[MLP] Loaded {mlp_path} | {len(self._feature_names)} features")
+
+    def process_frame(self, bgr_frame):
+        landmarks = self._detector.process(bgr_frame)
+        h, w = bgr_frame.shape[:2]
+        out = {
+            "landmarks": landmarks,
+            "is_focused": False,
+            "s_face": 0.0,
+            "s_eye": 0.0,
+            "raw_score": 0.0,
+            "mar": None,
+            "yaw": None,
+            "pitch": None,
+            "roll": None,
+        }
+        if landmarks is None:
+            return out
+        vec = extract_features(landmarks, w, h, self._head_pose, self._eye_scorer, self._temporal)
+        X = vec[self._indices].reshape(1, -1).astype(np.float64)
+        X_sc = self._scaler.transform(X)
+        pred = self._mlp.predict(X_sc)
+        out["is_focused"] = bool(pred[0] == 1)
+        return out
+
+    def close(self):
+        self._detector.close()
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, *args):
+        self.close()