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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run ML model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "import numpy as np\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# Load the data\n",
    "data = np.load('/home/shanin/Desktop/SHANIN/MAIN/ALL_CODE/Face_Recognition/Face_Embedding.npz')\n",
    "EMBEDDED_X = data['embeddings']\n",
    "Y = data['labels']\n",
    "\n",
    "# Encode the labels\n",
    "encoder = LabelEncoder()\n",
    "encoder.fit(Y)\n",
    "Y = encoder.transform(Y)\n",
    "\n",
    "# Train the SVM model on the entire dataset\n",
    "model = SVC(kernel='rbf', probability=True)   # kernal='linear'\n",
    "model.fit(EMBEDDED_X, Y)\n",
    "\n",
    "# Save the trained model\n",
    "import pickle\n",
    "with open('/home/shanin/Desktop/SHANIN/MAIN/ALL_CODE/Face_Recognition/Face_Model.pkl', 'wb') as f:\n",
    "    pickle.dump(model, f)\n"
   ]
  }
 ],
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