model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Load your dataset\n",
    "df = pd.read_csv('Data\\cleaned_Phishing.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assuming 'Result' is your target column\n",
    "X = df.drop(columns=['Result'])  # Features\n",
    "y = df['Result']  # Target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X_train and X_test: (8844, 36) (2211, 36)\n",
      "Shape of y_train and y_test: (8844,) (2211,)\n"
     ]
    }
   ],
   "source": [
    "print('Shape of X_train and X_test:', X_train.shape, X_test.shape)\n",
    "print('Shape of y_train and y_test:', y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Initialize the scaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Fit and transform the training data, transform the test data\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the training data into training and validation sets\n",
    "X_train_split, X_val_split, y_train_split, y_val_split = train_test_split(\n",
    "    X_train_scaled, \n",
    "    y_train, \n",
    "    test_size=0.2,  # 20% of the training data will be used for validation\n",
    "    random_state=42  # For reproducibility\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "222/222 [==============================] - 2s 4ms/step - loss: 1.0890 - accuracy: 0.8249 - auc: 0.9048 - val_loss: 0.7211 - val_accuracy: 0.9344 - val_auc: 0.9837\n",
      "Epoch 2/30\n",
      " 43/222 [====>.........................] - ETA: 0s - loss: 0.7282 - accuracy: 0.9164 - auc: 0.9730"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\anany\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\engine\\training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "222/222 [==============================] - 1s 3ms/step - loss: 0.6429 - accuracy: 0.9134 - auc: 0.9706 - val_loss: 0.4778 - val_accuracy: 0.9418 - val_auc: 0.9872\n",
      "Epoch 3/30\n",
      "222/222 [==============================] - 1s 3ms/step - loss: 0.4429 - accuracy: 0.9244 - auc: 0.9782 - val_loss: 0.3434 - val_accuracy: 0.9435 - val_auc: 0.9891\n",
      "Epoch 4/30\n",
      "222/222 [==============================] - 1s 3ms/step - loss: 0.3439 - accuracy: 0.9249 - auc: 0.9802 - val_loss: 0.2677 - val_accuracy: 0.9508 - val_auc: 0.9898\n",
      "Epoch 5/30\n",
      "222/222 [==============================] - 1s 3ms/step - loss: 0.2730 - accuracy: 0.9353 - auc: 0.9843 - val_loss: 0.2288 - val_accuracy: 0.9457 - val_auc: 0.9906\n",
      "Epoch 6/30\n",
      "222/222 [==============================] - 1s 3ms/step - loss: 0.2477 - accuracy: 0.9313 - auc: 0.9834 - val_loss: 0.1999 - val_accuracy: 0.9542 - val_auc: 0.9904\n",
      "Epoch 7/30\n",
      "222/222 [==============================] - 1s 4ms/step - loss: 0.2204 - accuracy: 0.9391 - auc: 0.9852 - val_loss: 0.1809 - val_accuracy: 0.9491 - val_auc: 0.9912\n",
      "Epoch 8/30\n",
      "222/222 [==============================] - 1s 3ms/step - loss: 0.2092 - accuracy: 0.9380 - auc: 0.9855 - val_loss: 0.1725 - val_accuracy: 0.9497 - val_auc: 0.9911\n",
      "Epoch 9/30\n",
      "222/222 [==============================] - 1s 3ms/step - loss: 0.2037 - accuracy: 0.9358 - auc: 0.9858 - val_loss: 0.1773 - val_accuracy: 0.9486 - val_auc: 0.9904\n",
      "Epoch 10/30\n",
      "222/222 [==============================] - 1s 3ms/step - loss: 0.2001 - accuracy: 0.9341 - auc: 0.9855 - val_loss: 0.1654 - val_accuracy: 0.9508 - val_auc: 0.9911\n",
      "Epoch 11/30\n",
      "222/222 [==============================] - 1s 3ms/step - loss: 0.1857 - accuracy: 0.9398 - auc: 0.9873 - val_loss: 0.1680 - val_accuracy: 0.9497 - val_auc: 0.9894\n",
      "Epoch 12/30\n",
      "222/222 [==============================] - 1s 3ms/step - loss: 0.1850 - accuracy: 0.9428 - auc: 0.9869 - val_loss: 0.1634 - val_accuracy: 0.9520 - val_auc: 0.9904\n",
      "Epoch 13/30\n",
      "222/222 [==============================] - 1s 3ms/step - loss: 0.1771 - accuracy: 0.9411 - auc: 0.9878 - val_loss: 0.1565 - val_accuracy: 0.9514 - val_auc: 0.9910\n",
      "Epoch 14/30\n",
      "222/222 [==============================] - 1s 3ms/step - loss: 0.1703 - accuracy: 0.9412 - auc: 0.9888 - val_loss: 0.1460 - val_accuracy: 0.9576 - val_auc: 0.9921\n",
      "Epoch 15/30\n",
      "222/222 [==============================] - 1s 3ms/step - loss: 0.1772 - accuracy: 0.9399 - auc: 0.9870 - val_loss: 0.1480 - val_accuracy: 0.9542 - val_auc: 0.9919\n",
      "Epoch 16/30\n",
      "222/222 [==============================] - 1s 3ms/step - loss: 0.1715 - accuracy: 0.9433 - auc: 0.9880 - val_loss: 0.1468 - val_accuracy: 0.9553 - val_auc: 0.9914\n",
      "Epoch 17/30\n",
      "222/222 [==============================] - 1s 3ms/step - loss: 0.1656 - accuracy: 0.9447 - auc: 0.9886 - val_loss: 0.1463 - val_accuracy: 0.9536 - val_auc: 0.9911\n",
      "Epoch 18/30\n",
      "222/222 [==============================] - 1s 3ms/step - loss: 0.1623 - accuracy: 0.9471 - auc: 0.9891 - val_loss: 0.1416 - val_accuracy: 0.9565 - val_auc: 0.9922\n",
      "Epoch 19/30\n",
      "222/222 [==============================] - 1s 2ms/step - loss: 0.1615 - accuracy: 0.9474 - auc: 0.9892 - val_loss: 0.1364 - val_accuracy: 0.9570 - val_auc: 0.9926\n",
      "Epoch 20/30\n",
      "222/222 [==============================] - 1s 3ms/step - loss: 0.1685 - accuracy: 0.9423 - auc: 0.9882 - val_loss: 0.1368 - val_accuracy: 0.9582 - val_auc: 0.9923\n",
      "Epoch 21/30\n",
      "222/222 [==============================] - 1s 3ms/step - loss: 0.1641 - accuracy: 0.9470 - auc: 0.9888 - val_loss: 0.1412 - val_accuracy: 0.9582 - val_auc: 0.9921\n",
      "Epoch 22/30\n",
      "222/222 [==============================] - 1s 3ms/step - loss: 0.1576 - accuracy: 0.9467 - auc: 0.9899 - val_loss: 0.1352 - val_accuracy: 0.9599 - val_auc: 0.9925\n",
      "Epoch 23/30\n",
      "222/222 [==============================] - 1s 3ms/step - loss: 0.1699 - accuracy: 0.9416 - auc: 0.9878 - val_loss: 0.1356 - val_accuracy: 0.9582 - val_auc: 0.9928\n",
      "Epoch 24/30\n",
      "222/222 [==============================] - 1s 3ms/step - loss: 0.1615 - accuracy: 0.9469 - auc: 0.9890 - val_loss: 0.1430 - val_accuracy: 0.9525 - val_auc: 0.9920\n",
      "Epoch 25/30\n",
      "222/222 [==============================] - 1s 3ms/step - loss: 0.1628 - accuracy: 0.9466 - auc: 0.9885 - val_loss: 0.1335 - val_accuracy: 0.9565 - val_auc: 0.9926\n",
      "Epoch 26/30\n",
      "222/222 [==============================] - 1s 3ms/step - loss: 0.1523 - accuracy: 0.9511 - auc: 0.9903 - val_loss: 0.1363 - val_accuracy: 0.9565 - val_auc: 0.9921\n",
      "Epoch 27/30\n",
      "222/222 [==============================] - 1s 3ms/step - loss: 0.1470 - accuracy: 0.9511 - auc: 0.9910 - val_loss: 0.1302 - val_accuracy: 0.9587 - val_auc: 0.9922\n",
      "Epoch 28/30\n",
      "222/222 [==============================] - 1s 3ms/step - loss: 0.1498 - accuracy: 0.9471 - auc: 0.9907 - val_loss: 0.1349 - val_accuracy: 0.9587 - val_auc: 0.9919\n",
      "Epoch 29/30\n",
      "222/222 [==============================] - 1s 3ms/step - loss: 0.1611 - accuracy: 0.9454 - auc: 0.9887 - val_loss: 0.1358 - val_accuracy: 0.9593 - val_auc: 0.9918\n",
      "Epoch 30/30\n",
      "222/222 [==============================] - 1s 3ms/step - loss: 0.1422 - accuracy: 0.9536 - auc: 0.9915 - val_loss: 0.1320 - val_accuracy: 0.9627 - val_auc: 0.9927\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "\n",
    "# Set parameters\n",
    "INPUT_SHAPE = X_train_scaled.shape[1]\n",
    "DENSE_LAYERS = [64, 32]\n",
    "OUTPUT_UNITS = 1\n",
    "BATCH_SIZE = 32\n",
    "EPOCHS = 30\n",
    "VALIDATION_SPLIT = 0.2\n",
    "\n",
    "# Initialize callbacks\n",
    "early_stopping = EarlyStopping(\n",
    "   monitor='val_loss',\n",
    "   patience=5,\n",
    "   restore_best_weights=True,\n",
    "   mode='min'\n",
    ")\n",
    "\n",
    "# Add model checkpoint to save best model \n",
    "model_checkpoint = ModelCheckpoint(\n",
    "   'best_model.h5',\n",
    "   monitor='val_loss', \n",
    "   save_best_only=True,\n",
    "   mode='min'\n",
    ")\n",
    "\n",
    "# Build the model with regularization and dropout\n",
    "model2 = keras.Sequential([\n",
    "   layers.Input(shape=(INPUT_SHAPE,)),\n",
    "   \n",
    "   # Add batch normalization at the start\n",
    "   layers.BatchNormalization(),\n",
    "   \n",
    "   # First dense layer\n",
    "   layers.Dense(DENSE_LAYERS[0], activation='relu',\n",
    "               kernel_regularizer=keras.regularizers.l2(0.01)),\n",
    "   layers.Dropout(0.3),\n",
    "   layers.BatchNormalization(),\n",
    "   \n",
    "   # Second dense layer\n",
    "   layers.Dense(DENSE_LAYERS[1], activation='relu', \n",
    "               kernel_regularizer=keras.regularizers.l2(0.01)),\n",
    "   layers.Dropout(0.2),\n",
    "   layers.BatchNormalization(),\n",
    "   \n",
    "   # Output layer\n",
    "   layers.Dense(OUTPUT_UNITS, activation='sigmoid')\n",
    "])\n",
    "\n",
    "# Compile with learning rate scheduling\n",
    "initial_learning_rate = 0.001\n",
    "lr_schedule = keras.optimizers.schedules.ExponentialDecay(\n",
    "   initial_learning_rate,\n",
    "   decay_steps=1000,\n",
    "   decay_rate=0.9\n",
    ")\n",
    "\n",
    "model2.compile(\n",
    "   optimizer=keras.optimizers.Adam(learning_rate=lr_schedule),\n",
    "   loss='binary_crossentropy',\n",
    "   metrics=['accuracy', keras.metrics.AUC()]\n",
    ")\n",
    "\n",
    "# Training\n",
    "history2 = model2.fit(\n",
    "   X_train_scaled,\n",
    "   y_train,\n",
    "   batch_size=BATCH_SIZE, \n",
    "   epochs=EPOCHS,\n",
    "   validation_split=VALIDATION_SPLIT,\n",
    "   callbacks=[early_stopping, model_checkpoint],\n",
    "   verbose=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_5\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " batch_normalization (Batch  (None, 36)                144       \n",
      " Normalization)                                                  \n",
      "                                                                 \n",
      " dense_15 (Dense)            (None, 64)                2368      \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 64)                0         \n",
      "                                                                 \n",
      " batch_normalization_1 (Bat  (None, 64)                256       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " dense_16 (Dense)            (None, 32)                2080      \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 32)                0         \n",
      "                                                                 \n",
      " batch_normalization_2 (Bat  (None, 32)                128       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " dense_17 (Dense)            (None, 1)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 5009 (19.57 KB)\n",
      "Trainable params: 4745 (18.54 KB)\n",
      "Non-trainable params: 264 (1.03 KB)\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(model2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy: 0.9627\n",
      "Test Accuracy: 0.9593\n"
     ]
    }
   ],
   "source": [
    "# Get validation accuracy from history\n",
    "val_accuracy = history2.history['val_accuracy'][-1]  # Last validation accuracy\n",
    "\n",
    "# Get test accuracy\n",
    "test_metrics = model2.evaluate(X_test_scaled, y_test, verbose=0)\n",
    "test_accuracy = test_metrics[1]  # Index 1 contains accuracy (0 is loss, 2 is AUC)\n",
    "\n",
    "# Print both accuracies\n",
    "print(f'Validation Accuracy: {val_accuracy:.4f}')\n",
    "print(f'Test Accuracy: {test_accuracy:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create subplots for loss and accuracy\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))\n",
    "\n",
    "# Plot Loss\n",
    "ax1.plot(history2.history['loss'], label='Training Loss')\n",
    "ax1.plot(history2.history['val_loss'], label='Validation Loss')\n",
    "ax1.set_title('Model Loss')\n",
    "ax1.set_xlabel('Epoch')\n",
    "ax1.set_ylabel('Loss')\n",
    "ax1.legend()\n",
    "ax1.grid(True)\n",
    "\n",
    "# Plot Accuracy\n",
    "ax2.plot(history2.history['accuracy'], label='Training Accuracy')\n",
    "ax2.plot(history2.history['val_accuracy'], label='Validation Accuracy')\n",
    "ax2.axhline(y=test_accuracy, color='r', linestyle='--', label='Test Accuracy')\n",
    "ax2.set_title('Model Accuracy')\n",
    "ax2.set_xlabel('Epoch')\n",
    "ax2.set_ylabel('Accuracy')\n",
    "ax2.legend()\n",
    "ax2.grid(True)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70/70 [==============================] - 0s 1ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.94      0.95       956\n",
      "           1       0.95      0.98      0.96      1255\n",
      "\n",
      "    accuracy                           0.96      2211\n",
      "   macro avg       0.96      0.96      0.96      2211\n",
      "weighted avg       0.96      0.96      0.96      2211\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Confusion Matrix and Classification Report\n",
    "\n",
    "from sklearn.metrics import confusion_matrix, classification_report, roc_curve, auc, precision_recall_curve\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "# Get predictions\n",
    "y_pred_proba = model2.predict(X_test_scaled)\n",
    "y_pred = (y_pred_proba > 0.5).astype(int)\n",
    "\n",
    "# Confusion Matrix with Seaborn\n",
    "plt.figure(figsize=(8, 6))\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
    "            xticklabels=['Predicted 0', 'Predicted 1'],\n",
    "            yticklabels=['Actual 0', 'Actual 1'])\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()\n",
    "\n",
    "# Print Classification Report\n",
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC Curve\n",
    "plt.figure(figsize=(4, 3))\n",
    "fpr, tpr, _ = roc_curve(y_test, y_pred_proba)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "plt.plot(fpr, tpr, color='darkorange', lw=2, \n",
    "         label=f'ROC curve (AUC = {roc_auc:.3f})')\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic (ROC)')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yEi+99JJITEysUr9ICIkQlfydTURETxWO6RMRmREmfSIiM8KkT0RkRpj0iYjMCJM+EZEZYdInIjIjZvflLIVCgRs3bsDe3r7SOz0SEdUWQgjcvXsXnp6eavfKepzZJf0bN27A29vb1GEQEend1atX1e6K+zizS/rl9wq/evUqHBwcTBwNEVH15eXlwdvbu0rPQjC7pF8+pOPg4MCkT0RPlaoMWfNCLhGRGWHSJyIyI0z6RERmxKRJ/9dff0Xfvn3h6ekJiUSCzZs3P3Gf/fv3o127dpDL5fD398fatWsNHicR0dPCpEk/Pz8fbdu2xfLly6tUPy0tDX369EHXrl2RmpqKd955ByNHjtT4VCIiIlJn0tk7vXv3Ru/evatcPz4+Ho0aNcJnn30GAHjmmWeQnJyMxYsXV/iwZH0QAjh92mCHJxNo2hSwsjJ1FETGV6umbB45ckTtochhYWF45513KtynqKhI5ZmpeXl5WrdbUgK0aqX1blTD7dwJ2NiUfagrFJUvj9fx9AQ6dgSe9i91C6Hed03vl0QCODmZOlqqilqV9DMzM+Hm5qZS5ubmhry8PNy/fx82NjZq+8TGxmp82DRRdf84tLYG3n4bKC2tfFEo1NcrSp5V/dAxVn1tn6sXHa16rKrG8Xi9oqKyE63XXitbB4CAAEAur97PjGpZ0tfFtGnTEB0drVwv/+aaNiQSYMQIfUdGprB+PVBYqJ9jFRYC8+fr51hPi0WL9HesrVuBTz9VLYuLAxo21PzhWlKiui4E0L070KSJ/mJ6GtSqpO/u7o6srCyVsqysLDg4OGg8ywcAuVwOeTVPDywtgf/+t1qHoBoiLg5Yuxb455+yD3MLi8qXx+sAwPvvmy5+beM1Rn1jzqMYP163/fz8yvYdORJwdNRvTLVNrUr6wcHB2L59u0rZrl27EBwcbKKIqLaRyYDRo6t3jLffBn77rexM0sICkEqfvDxaT5sk+2iircnXD9LSgGvXdPtAebze3btlH8z5+WXryclAamr14rt0CZg8uWxJSAAiIvTR69rJpEn/3r17uHDhgnI9LS0NqampqFevHho2bIhp06bh+vXr+N///gcAGDt2LL744gtMmTIFb731Fvbu3Yvvv/8e27ZtM1UXyAxZWwNdupg6ipqlUaOyRV8eHzY7dgzYsqXyD1lLy4evs7KAJUuAW7fUj/3GG2XbAaC4GPD3Bzp3Bh48KFsvX9zdgXr19NenGkOY0L59+wQAtSUyMlIIIURkZKQICQlR2ycgIEDIZDLh5+cn1qxZo1Wbubm5AoDIzc3VTyeIqMYqKRHit9+EGDasfB6SdkvdukLExgrx77+m7knltMlrEiG0vT5fu+Xl5cHR0RG5ubm8yyaRGeneHdi7V/f9nZzK/sLz9QUGDACef77mDLlpk9eY9InIbBw7Bpw4UTb1UyoFFi4E6tcvW5fJyha5HLh588kXqC0tgVOngGbNjBN7ZZj0K8GkT0RVkZYG/PwzMG8ecONGxfUuXwZ8fIwWlkba5DXeZZOISINGjcpmal2/DqSnA7//DsycqV7P17dsmOfoUaOHqBOe6RMRaaG0FPDyejgD6HHlw0NVeHKh3vBMn4jIQKTSsrP/tWsBZ2f17UVFwLhxZd8QromY9ImItCSVApGRFV/wXb++7C6u//+GwDUKkz4RUTX07Fk2qz87W33b5MnAxIllX/aqKZj0iYj0wMUFWL4ceOEF1fLPPy8b51+82DRxPY4XcomI9OzPP4G2bdXL/f2B8+f13x4v5BIRmVCbNmXz98eOVS2/cKFsemdpqUnCAsCkT0RkED4+wIoVwNWr6ttsbctm+ZgCkz4RkQE1aADcvq1aVlwMxMebJh4mfSIiA6tbV30Gj6m+wcukT0RkBFZWQErKw/X1600TB5M+EZGRmPrGbACTPhGR0dStq7r+55/Gj4FJn4jIiGSyh6+vXTN++0z6RERGNGPGw9eabtVsaEz6RERGZG398HVKCpCRYdz2mfSJiIxo5EjV9RdfNG77TPpEREZUrx7QrdvD9QsXjHtBl0mfiMjIvvlGdV3TzdkMhUmfiMjIPDzKnsFrCkz6REQmcOnSw9dSqfHaZdInIjKR9u2N3yaTPhGRiRnz/vpM+kREJmJj8/B1erpx2mTSJyIykcaNH77OzDROm0z6REQmYm//8PXcucZpk0mfiMhELB7JwD/9ZKQ2jdMMERE97sMPH75u0MA4bTLpExGZiKPjw9cSiXHaZNInIjIhD4+yf69eNU57TPpERCYkxMPXn31m+PaY9ImITOjRqZqTJ6t+CBgCkz4RkQnt3q26/uCBYdtj0iciMqHu3VXXDX1vfSZ9IiITa9Pm4eviYsO2ZfKkv3z5cvj6+sLa2hpBQUH4448/Kqz74MEDzJkzB40bN4a1tTXatm2LpKQkI0ZLRKR/oaEPX585Y9i2TJr0N2zYgOjoaMTExODEiRNo27YtwsLCkJ2drbH+zJkz8eWXX2LZsmU4ffo0xo4di1dffRUpKSlGjpyISH+uXHn4+vp1w7Zl0qS/aNEijBo1CsOHD0eLFi0QHx8PW1tbrF69WmP9r7/+GtOnT0d4eDj8/Pwwbtw4hIeH4zNjzHMiIjKQnj0fvra1NWxbJkv6xcXFOH78OEIf+bvGwsICoaGhOHLkiMZ9ioqKYG1trVJmY2OD5OTkCtspKipCXl6eykJEVJPUr2+8tkyW9G/duoXS0lK4ubmplLu5uSGzgnuMhoWFYdGiRTh//jwUCgV27dqFTZs2ISMjo8J2YmNj4ejoqFy8vb312g8iIn2aMcOwxzf5hVxtLF26FE2aNEHz5s0hk8kQFRWF4cOHw8Ki4m5MmzYNubm5yuWqsb7rTERURY/eYlmhMGxbJkv6zs7OkEqlyMrKUinPysqCu7u7xn1cXFywefNm5Ofn48qVK/jnn39gZ2cHPz+/CtuRy+VwcHBQWYiIapIuXR6+dnY2bFsmS/oymQyBgYHYs2ePskyhUGDPnj0IDg6udF9ra2t4eXmhpKQEP/zwA1555RVDh0tEZDAyGeDjY5y2LI3TjGbR0dGIjIxE+/bt0aFDByxZsgT5+fkYPnw4AGDo0KHw8vJCbGwsAOD333/H9evXERAQgOvXr2P27NlQKBSYMmWKKbtBRFRrmDTpR0RE4ObNm/jggw+QmZmJgIAAJCUlKS/upqenq4zXFxYWYubMmbh06RLs7OwQHh6Or7/+Gk5OTibqARFR7SIRwtD3dKtZ8vLy4OjoiNzcXI7vE1GN4etb9iUtd3egkgmJGmmT12rV7B0iIqoeJn0iIjPCpE9EVINU8N1UvWHSJyKqAe7ff/g6N9dw7TDpExHVAI/eXPjOHcO1w6RPRFQDDBhgnHaY9ImIaoBKbiGm33aM0wwREdUETPpERGaESZ+IqIa5eNFwx2bSJyKqAVJTH7425HNymfSJiGqAgQMfvrY04K0wmfSJiGqAunWN0w6TPhGRGWHSJyIyI0z6RERmhEmfiMiMMOkTEZkRJn0iIjPCpE9EZEaY9ImIzAiTPhGRGWHSJyIyIzrd4aG0tBRr167Fnj17kJ2dDYVCobJ97969egmOiIj0S6ekP3HiRKxduxZ9+vRBq1atIJFI9B0XEREZgE5JPyEhAd9//z3Cw8P1HQ8RERmQTmP6MpkM/v7++o6FiIgMTKek/+6772Lp0qUQQug7HiIiMiCdhneSk5Oxb98+7NixAy1btoSVlZXK9k2bNuklOCIi0i+dkr6TkxNeffVVfcdCREQGplPSX7Nmjb7jICIiI6jWkxhv3ryJs2fPAgCaNWsGFxcXvQRFRESGodOF3Pz8fLz11lvw8PDAiy++iBdffBGenp4YMWIECgoK9B0jERHpiU5JPzo6GgcOHMDPP/+MnJwc5OTk4KeffsKBAwfw7rvv6jtGIiLSE52Gd3744QckJiaiS5cuyrLw8HDY2NhgwIABWLFihb7iIyIiPdLpTL+goABubm5q5a6urhzeISKqwXRK+sHBwYiJiUFhYaGy7P79+/jwww8RHByst+CIiEi/dEr6S5cuxaFDh9CgQQN0794d3bt3h7e3Nw4fPoylS5dqdazly5fD19cX1tbWCAoKwh9//FFp/SVLlqBZs2awsbGBt7c3Jk2apPLhQ0REFdNpTL9Vq1Y4f/481q9fj3/++QcAMHDgQAwePBg2NjZVPs6GDRsQHR2N+Ph4BAUFYcmSJQgLC8PZs2fh6uqqVv/bb7/F1KlTsXr1anTs2BHnzp3DsGHDIJFIsGjRIl26QkRkVnSep29ra4tRo0ZVq/FFixZh1KhRGD58OAAgPj4e27Ztw+rVqzF16lS1+ocPH0anTp0waNAgAICvry8GDhyI33//vVpxEBGZiyon/S1btqB3796wsrLCli1bKq378ssvP/F4xcXFOH78OKZNm6Yss7CwQGhoKI4cOaJxn44dO+Kbb77BH3/8gQ4dOuDSpUvYvn07hgwZUmE7RUVFKCoqUq7n5eU9MTYioqdVlZN+v379kJmZCVdXV/Tr16/CehKJBKWlpU883q1bt1BaWqo2C8jNzU05ZPS4QYMG4datW3jhhRcghEBJSQnGjh2L6dOnV9hObGwsPvzwwyfGQ0RkDqp8IVehUCjH2RUKRYVLVRK+rvbv34+5c+ciLi4OJ06cwKZNm7Bt2zZ89NFHFe4zbdo05ObmKperV68aLD4iIn148MBwx67WvXcelZOTAycnpyrXd3Z2hlQqRVZWlkp5VlYW3N3dNe4za9YsDBkyBCNHjgQAtG7dGvn5+Rg9ejRmzJgBCwv1zzC5XA65XF71jhARmcCj58tffw1ERhqmHZ2mbM6bNw8bNmxQrvfv3x/16tWDl5cXTp48WaVjyGQyBAYGYs+ePcoyhUKBPXv2VDjXv6CgQC2xS6VSAOADXYioVnv0UePPPGO4dnRK+vHx8fD29gYA7Nq1C7t370ZSUhJ69+6N9957r8rHiY6OxsqVK7Fu3TqcOXMG48aNQ35+vnI2z9ChQ1Uu9Pbt2xcrVqxAQkIC0tLSsGvXLsyaNQt9+/ZVJn8iotqoUyfjtKPT8E5mZqYy6W/duhUDBgxAz5494evri6CgoCofJyIiAjdv3sQHH3yAzMxMBAQEICkpSXlxNz09XeXMfubMmZBIJJg5cyauX78OFxcX9O3bF5988oku3SAiMjs6Jf26devi6tWr8Pb2RlJSEj7++GMAZUMs2l7IjYqKQlRUlMZt+/fvVw3W0hIxMTGIiYnRJWwiIrOnU9J/7bXXMGjQIDRp0gT//vsvevfuDQBISUmBv7+/XgMkIiL90SnpL168GL6+vrh69Srmz58POzs7AEBGRgbGjx+v1wCJiEh/dEr6VlZWmDx5slr5pEmTqh0QEREZjsluw0BERMZnstswEBGR8VU56SsUCo2viYio9tDpy1lERFQ76ZT03377bXz++edq5V988QXeeeed6sZEREQGolPS/+GHH9BJw3eGO3bsiMTExGoHRUREhqFT0v/333/h6OioVu7g4IBbt25VOygiIjIMnZK+v78/kpKS1Mp37NgBPz+/agdFRESGodOXs6KjoxEVFYWbN2+iW7duAIA9e/bgs88+w5IlS/QZHxER6ZFOSf+tt95CUVERPvnkE+VTq3x9fbFixQoMHTpUrwESEZH+6PzkrHHjxmHcuHG4efMmbGxslPffISKimkvnefolJSXYvXs3Nm3apHxq1Y0bN3Dv3j29BUdERPql05n+lStX0KtXL6Snp6OoqAg9evSAvb095s2bh6KiIsTHx+s7TiIi0gOdzvQnTpyI9u3b486dO7CxsVGWv/rqqyrPvCUioppFpzP9gwcP4vDhw5DJZCrlvr6+uH79ul4CIyIi/dPpTF+hUGi8k+a1a9dgb29f7aCIiMgwdEr6PXv2VJmPL5FIcO/ePcTExCA8PFxfsRERkZ7pNLyzcOFC9OrVCy1atEBhYSEGDRqE8+fPw9nZGd99952+YyQiIj3RKel7e3vj5MmT2LBhA06ePIl79+5hxIgRGDx4sMqFXSIiqlm0TvoPHjxA8+bNsXXrVgwePBiDBw82RFxERGQAWo/pW1lZobCw0BCxEBGRgel0IXfChAmYN28eSkpK9B0PEREZkE5j+kePHsWePXvwyy+/oHXr1qhTp47K9k2bNuklOCIi0i+dkr6TkxNef/11fcdCREQGplXSVygUWLBgAc6dO4fi4mJ069YNs2fP5owdIqJaQqsx/U8++QTTp0+HnZ0dvLy88Pnnn2PChAmGio2IiPRMq6T/v//9D3Fxcdi5cyc2b96Mn3/+GevXr4dCoTBUfEREpEdaJf309HSV2yyEhoZCIpHgxo0beg+MiIj0T6ukX1JSAmtra5UyKysrPHjwQK9BERGRYWh1IVcIgWHDhkEulyvLCgsLMXbsWJVpm5yySURUM2mV9CMjI9XK3nzzTb0FQ0REhqVV0l+zZo2h4iAiIiPQ+cHoRERU+zDpExGZESZ9IiIzUiOS/vLly+Hr6wtra2sEBQXhjz/+qLBuly5dIJFI1JY+ffoYMWIiotrJ5El/w4YNiI6ORkxMDE6cOIG2bdsiLCwM2dnZGutv2rQJGRkZyuXUqVOQSqXo37+/kSMnIqp9TJ70Fy1ahFGjRmH48OFo0aIF4uPjYWtri9WrV2usX69ePbi7uyuXXbt2wdbWlkmfiKgKTJr0i4uLcfz4cYSGhirLLCwsEBoaiiNHjlTpGKtWrcIbb7yhdk//ckVFRcjLy1NZiIjMlUmT/q1bt1BaWgo3NzeVcjc3N2RmZj5x/z/++AOnTp3CyJEjK6wTGxsLR0dH5eLt7V3tuImIaiuTD+9Ux6pVq9C6dWt06NChwjrTpk1Dbm6ucrl69aoRIyQiqll0enKWvjg7O0MqlSIrK0ulPCsrC+7u7pXum5+fj4SEBMyZM6fSenK5XOVeQURE5sykZ/oymQyBgYHYs2ePskyhUGDPnj0IDg6udN+NGzeiqKiI9/4hItKCSc/0ASA6OhqRkZFo3749OnTogCVLliA/Px/Dhw8HAAwdOhReXl6IjY1V2W/VqlXo168f6tevb4qwiYhqJZMn/YiICNy8eRMffPABMjMzERAQgKSkJOXF3fT0dFhYqP5BcvbsWSQnJ+OXX34xRchERLWWyZM+AERFRSEqKkrjtv3796uVNWvWDEIIA0dFRPT0qdWzd4iISDtM+kREZoRJn4jIjDDpExGZESZ9IiIzwqRPRGRGmPSJiMwIkz4RkRlh0iciMiNM+kREZoRJn4jIjDDpExGZESZ9IiIzwqRPRGRGmPSJiMwIkz4RkRlh0iciMiNM+kREZoRJn4jIjDDpExGZESZ9IiIzwqRPRGRGmPSJiMwIkz4RkRlh0iciMiNM+kREZoRJn4jIjDDpExGZESZ9IiIzwqRPRGRGmPSJiMwIkz4RkRlh0iciMiNM+kREZoRJn4jIjFiaOoCaSAiBkpISlJaWmjoUoqeCVCqFpaUlJBKJqUMxe0z6jykuLkZGRgYKCgpMHQrRU8XW1hYeHh6QyWSmDsWsMek/QqFQIC0tDVKpFJ6enpDJZDwzIaomIQSKi4tx8+ZNpKWloUmTJrCw4MiyqZg86S9fvhwLFixAZmYm2rZti2XLlqFDhw4V1s/JycGMGTOwadMm3L59Gz4+PliyZAnCw8OrHUtxcTEUCgW8vb1ha2tb7eMRURkbGxtYWVnhypUrKC4uhrW1talDMlsmTfobNmxAdHQ04uPjERQUhCVLliAsLAxnz56Fq6urWv3i4mL06NEDrq6uSExMhJeXF65cuQInJye9xsWzECL94+9VzWDSpL9o0SKMGjUKw4cPBwDEx8dj27ZtWL16NaZOnapWf/Xq1bh9+zYOHz4MKysrAICvr2+lbRQVFaGoqEi5npeXp78OEBHVMib76C0uLsbx48cRGhr6MBgLC4SGhuLIkSMa99myZQuCg4MxYcIEuLm5oVWrVpg7d26ls2xiY2Ph6OioXLy9vfXeFyKi2sJkSf/WrVsoLS2Fm5ubSrmbmxsyMzM17nPp0iUkJiaitLQU27dvx6xZs/DZZ5/h448/rrCdadOmITc3V7lcvXpVr/2gmmfVqlXo2bOnqcMwK6dPn0aDBg2Qn59v6lDoCWrVIJtCoYCrqyu++uorBAYGIiIiAjNmzEB8fHyF+8jlcjg4OKgsT6Nhw4ZBIpFAIpFAJpPB398fc+bMQUlJCQBg//79yu0SiQQuLi4IDw/HX3/9ZeLI9auwsBCzZs1CTEyM2rZr165BJpOhVatWatsuX74MiUSC1NRUtW1dunTBO++8o1KWkpKC/v37w83NDdbW1mjSpAlGjRqFc+fO6asraoQQ+OCDD+Dh4QEbGxuEhobi/Pnzle5z9+5dvPPOO/Dx8YGNjQ06duyIo0ePqtTJysrCsGHD4OnpCVtbW/Tq1avC4woh0Lt3b0gkEmzevFlZ3qJFCzz//PNYtGhRtftJhmWypO/s7AypVIqsrCyV8qysLLi7u2vcx8PDA02bNoVUKlWWPfPMM8jMzERxcbFB460NevXqhYyMDJw/fx7vvvsuZs+ejQULFqjUOXv2LDIyMrBz504UFRWhT58+Rn/vHjx4YLBjJyYmwsHBAZ06dVLbtnbtWgwYMAB5eXn4/fffdW5j69ateP7551FUVIT169fjzJkz+Oabb+Do6IhZs2ZVJ/xKzZ8/H59//jni4+Px+++/o06dOggLC0NhYWGF+4wcORK7du3C119/jb/++gs9e/ZEaGgorl+/DqAsiffr1w+XLl3CTz/9hJSUFPj4+CA0NFTjWfuSJUsqnMY8fPhwrFixQnmiQTWUMKEOHTqIqKgo5Xppaanw8vISsbGxGutPmzZN+Pj4iNLSUmXZkiVLhIeHR5XbzM3NFQBEbm6u2rb79++L06dPi/v372vRi5ohMjJSvPLKKyplPXr0EM8//7wQQoh9+/YJAOLOnTvK7Vu2bBEAxMmTJys9dnJysggJCRE2NjbCyclJ9OzZU9y+fVsIIYSPj49YvHixSv22bduKmJgY5ToAERcXJ/r27StsbW3FrFmzhJeXl4iLi1PZ78SJE0IikYjLly8LIYS4c+eOGDFihHB2dhb29vaia9euIjU1tdJY+/TpIyZPnqxWrlAohJ+fn0hKShLvv/++GDVqlMr2tLQ0AUCkpKSo7RsSEiImTpwohBAiPz9fODs7i379+mls/9H3V58UCoVwd3cXCxYsUJbl5OQIuVwuvvvuO437FBQUCKlUKrZu3apS3q5dOzFjxgwhhBBnz54VAMSpU6eU20tLS4WLi4tYuXKlyn4pKSnCy8tLZGRkCADixx9/VNleVFQk5HK52L17t8Z4avPvlzEcPSoEULY8kharpLK89jiTDu9ER0dj5cqVWLduHc6cOYNx48YhPz9fOZtn6NChmDZtmrL+uHHjcPv2bUycOBHnzp3Dtm3bMHfuXEyYMMGgcbZvDzRoYPylffvqxW1jY1PhWXxubi4SEhIAoNJvSKampqJ79+5o0aIFjhw5guTkZPTt21frW1TMnj0br776Kv766y+MHDkSAwcOxLfffqtSZ/369ejUqRN8fHwAAP3790d2djZ27NiB48ePo127dujevTtu375dYTvJyclor+GN27dvHwoKChAaGoo333wTCQkJOo0/79y5E7du3cKUKVM0bq9s+vDYsWNhZ2dX6VKRtLQ0ZGZmqkx8cHR0RFBQUIUTH8pvJfL4nHgbGxskJycDgHJm26N1LCwsIJfLlXUAoKCgAIMGDcLy5csr/EtcJpMhICAABw8erLAfZHomnbIZERGBmzdv4oMPPkBmZiYCAgKQlJSkvLibnp6uMrfX29sbO3fuxKRJk9CmTRt4eXlh4sSJeP/99w0aZ2Ym8P//Gq4VhBDYs2cPdu7cif/7v/9T2dagQQMAUCa8l19+Gc2bN6/wWPPnz0f79u0RFxenLGvZsqXWMQ0aNEj5YQ4AgwcPxmeffYb09HQ0bNgQCoUCCQkJmDlzJoCy5P3HH38gOzsbcrkcALBw4UJs3rwZiYmJGD16tFobOTk5yM3Nhaenp9q2VatW4Y033oBUKkWrVq3g5+eHjRs3YtiwYVr1o3ysu7L3rCJz5szB5MmTtd4PgHJygzYTH+zt7REcHIyPPvoIzzzzDNzc3PDdd9/hyJEj8Pf3B1DWj4YNG2LatGn48ssvUadOHSxevBjXrl1DRkaG8liTJk1Cx44d8corr1Qap6enJ65cuaJTH8k4TP6N3KioKERFRWnctn//frWy4OBg/PbbbwaOSlUFJzY1rt2tW7fCzs4ODx48gEKhwKBBgzB79myVOgcPHoStrS1+++03zJ07t9KL4EDZmX7//v21jFzd42ffAQEBeOaZZ/Dtt99i6tSpOHDgALKzs5VtnTx5Evfu3UP9+vVV9rt//z4uXryosY379+8DgNqZbU5ODjZt2qRy5vrmm29i1apVWid9IYRW9R/l6uqq8UuHhvT111/jrbfegpeXF6RSKdq1a4eBAwfi+PHjAAArKyts2rQJI0aMQL169SCVShEaGorevXsr+7plyxbs3bsXKSkpT2zPxsaG962q4Uye9GuDY8dMHUHVdO3aFStWrIBMJoOnpycsLdV/vI0aNYKTkxOaNWuG7OxsRERE4Ndff63wmDY2NpW2aWFhoZYINV2orVOnjlrZ4MGDlUn/22+/Ra9evZRJ/t69e/Dw8ND4wV/REEr9+vUhkUhw584dlfJvv/0WhYWFCAoKUpYJIaBQKHDu3Dk0bdpUOasrNzdX7bg5OTlwdHQEADRt2hQA8M8//yA4OFhjHBUZO3Ysvvnmm0rr3Lt3T2N5+ZBKVlYWPDw8lOVZWVkICAio8HiNGzfGgQMHkJ+fj7y8PHh4eCAiIgJ+fn7KOoGBgUhNTUVubi6Ki4vh4uKCoKAg5Qf13r17cfHiRbX3/fXXX0fnzp1Vfka3b99G48aNK+0jmVatmrJJlatTpw78/f3RsGFDjQn/cRMmTMCpU6fw448/VlinTZs22LNnT4XbXVxcVIYB8vLykJaWVqV4Bw0ahFOnTuH48eNITEzE4MGDldvatWuHzMxMWFpawt/fX2VxdnbWeDyZTIYWLVrg9OnTKuWrVq3Cu+++i9TUVOVy8uRJdO7cGatXrwYA1KtXD87Ozsoz4Ef7c+HCBWWy79mzJ5ydnTF//nyNMeTk5FTY3zlz5qjEoGmpSKNGjeDu7q7ysyifhVSVD586derAw8MDd+7cwc6dOzUO0zg6OsLFxQXnz5/HsWPHlHWmTp2KP//8Uy3OxYsXY82aNSrHOHXqFJ599tknxkMmpP015trNnGbvPErT7B0hhJgyZYpo3bq1UCgUGvc7e/askMlkYty4ceLkyZPizJkzIi4uTty8eVMIIcTUqVOFu7u7+PXXX8Wff/4p+vXrJ+zs7NRm7zw+06Ncp06dRNu2bYW9vb0oKChQlisUCvHCCy+Itm3bip07d4q0tDRx6NAhMX36dHH06NEK+xkdHS1ef/115XpKSooAIM6cOaNWNy4uTri7u4sHDx4IIYSYO3euqF+/vvjmm2/EhQsXxO+//y5eeukl4evrqxLb5s2bhZWVlejbt6/YtWuXSEtLE0ePHhXvvfeeiIiIqDC26vr000+Fk5OT+Omnn8Sff/4pXnnlFdGoUSOV/6/dunUTy5YtU64nJSWJHTt2iEuXLolffvlFtG3bVgQFBYni4mJlne+//17s27dPXLx4UWzevFn4+PiI1157rdJYNP1M09LSVGZfPa42/34Zg7Fm7zDpP6I2/6fUNemnp6cLS0tLsWHDhgr33b9/v+jYsaOQy+XCyclJhIWFKY+Tm5srIiIihIODg/D29hZr167VOGWzoqQfFxcnAIihQ4eqbcvLyxP/93//Jzw9PYWVlZXw9vYWgwcPFunp6RXG+vfffwsbGxuRk5MjhBAiKipKtGjRQmPdjIwMYWFhIX766SchhBAlJSXi888/F61btxa2traiQYMGIiIiQqSlpante/ToUfHaa68JFxcXIZfLhb+/vxg9erQ4f/58hbFVl0KhELNmzRJubm5CLpeL7t27i7Nnz6rU8fHxUXnvN2zYIPz8/IRMJhPu7u5iwoQJyvem3NKlS0WDBg2ElZWVaNiwoZg5c6YoKiqqNBZNP9O5c+eKsLCwCvepzb9fxmCspC8RohpXpmqhvLw8ODo6Ijc3V+3buYWFhUhLS0OjRo1469darH///mjXrp3KdF8yrOLiYjRp0gTffvutxi/GAfz9epJjx4Dnnit7HRUFLFtW9X0ry2uP45g+PXUWLFhQ6Zx30r/09HRMnz69woRPNQdn79BTx9fXV+37CWRY5RfZqebjmT4RkRlh0iciMiMc3tHAzK5tExkFf68q5+wMjBhR9lrL7/1phUn/EeWPYCwoKHjiN1GJSDvlt2co/z0jVb6+wH//a/h2mPQfIZVK4eTkhOzsbACAra1thfcOJ6KqEUKgoKAA2dnZcHJyUnkeBhkfk/5jyu9xUp74iUg/nJycKrwtMxkPk/5jJBIJPDw84OrqatAnPBGZEysrK57h1xBM+hWQSqX8T0pETx1O2SQiMiNM+kREZoRJn4jIjJjdmH75F0Ty8vJMHAkRkX6U57OqfAHO7JL+3bt3AZQ9ZJ2I6Gly9+5d5aM9K2J299NXKBS4ceMG7O3ttfriVV5eHry9vXH16tUn3q+6tnra+8j+1X5Pex917Z8QAnfv3oWnpycsLCoftTe7M30LCws0aNBA5/0dHByeyv9sj3ra+8j+1X5Pex916d+TzvDL8UIuEZEZYdInIjIjTPpVJJfLERMTA7lcbupQDOZp7yP7V/s97X00Rv/M7kIuEZE545k+EZEZYdInIjIjTPpERGaESZ+IyIww6T9i+fLl8PX1hbW1NYKCgvDHH39UWn/jxo1o3rw5rK2t0bp1a2zfvt1IkepOmz6uXLkSnTt3Rt26dVG3bl2EhoY+8T0xNW1/huUSEhIgkUjQr18/wwZYTdr2LycnBxMmTICHhwfkcjmaNm1a4/+fatvHJUuWoFmzZrCxsYG3tzcmTZqEwsJCI0WrnV9//RV9+/aFp6cnJBIJNm/e/MR99u/fj3bt2kEul8Pf3x9r166tXhCChBBCJCQkCJlMJlavXi3+/vtvMWrUKOHk5CSysrI01j906JCQSqVi/vz54vTp02LmzJnCyspK/PXXX0aOvOq07eOgQYPE8uXLRUpKijhz5owYNmyYcHR0FNeuXTNy5FWjbf/KpaWlCS8vL9G5c2fxyiuvGCdYHWjbv6KiItG+fXsRHh4ukpOTRVpamti/f79ITU01cuRVp20f169fL+RyuVi/fr1IS0sTO3fuFB4eHmLSpElGjrxqtm/fLmbMmCE2bdokAIgff/yx0vqXLl0Stra2Ijo6Wpw+fVosW7ZMSKVSkZSUpHMMTPr/X4cOHcSECROU66WlpcLT01PExsZqrD9gwADRp08flbKgoCAxZswYg8ZZHdr28XElJSXC3t5erFu3zlAhVosu/SspKREdO3YU//3vf0VkZGSNTvra9m/FihXCz89PFBcXGyvEatO2jxMmTBDdunVTKYuOjhadOnUyaJz6UJWkP2XKFNGyZUuVsoiICBEWFqZzuxzeAVBcXIzjx48jNDRUWWZhYYHQ0FAcOXJE4z5HjhxRqQ8AYWFhFdY3NV36+LiCggI8ePAA9erVM1SYOtO1f3PmzIGrqytGjBhhjDB1pkv/tmzZguDgYEyYMAFubm5o1aoV5s6di9LSUmOFrRVd+tixY0ccP35cOQR06dIlbN++HeHh4UaJ2dAMkWfM7oZrmty6dQulpaVwc3NTKXdzc8M///yjcZ/MzEyN9TMzMw0WZ3Xo0sfHvf/++/D09FT7T1gT6NK/5ORkrFq1CqmpqUaIsHp06d+lS5ewd+9eDB48GNu3b8eFCxcwfvx4PHjwADExMcYIWyu69HHQoEG4desWXnjhBQghUFJSgrFjx2L69OnGCNngKsozeXl5uH//PmxsbLQ+Js/0qUo+/fRTJCQk4Mcff4S1tbWpw6m2u3fvYsiQIVi5ciWcnZ1NHY5BKBQKuLq64quvvkJgYCAiIiIwY8YMxMfHmzo0vdm/fz/mzp2LuLg4nDhxAps2bcK2bdvw0UcfmTq0Gotn+gCcnZ0hlUqRlZWlUp6VlQV3d3eN+7i7u2tV39R06WO5hQsX4tNPP8Xu3bvRpk0bQ4apM237d/HiRVy+fBl9+/ZVlikUCgCApaUlzp49i8aNGxs2aC3o8vPz8PCAlZUVpFKpsuyZZ55BZmYmiouLIZPJDBqztnTp46xZszBkyBCMHDkSANC6dWvk5+dj9OjRmDFjxhPvLV/TVZRnHBwcdDrLB3imDwCQyWQIDAzEnj17lGUKhQJ79uxBcHCwxn2Cg4NV6gPArl27Kqxvarr0EQDmz5+Pjz76CElJSWjfvr0xQtWJtv1r3rw5/vrrL6SmpiqXl19+GV27dkVqamqNe7KaLj+/Tp064cKFC8oPMwA4d+4cPDw8alzCB3TrY0FBgVpiL/+QE0/BbcUMkmd0vgT8lElISBByuVysXbtWnD59WowePVo4OTmJzMxMIYQQQ4YMEVOnTlXWP3TokLC0tBQLFy4UZ86cETExMbViyqY2ffz000+FTCYTiYmJIiMjQ7ncvXvXVF2olLb9e1xNn72jbf/S09OFvb29iIqKEmfPnhVbt24Vrq6u4uOPPzZVF55I2z7GxMQIe3t78d1334lLly6JX375RTRu3FgMGDDAVF2o1N27d0VKSopISUkRAMSiRYtESkqKuHLlihBCiKlTp4ohQ4Yo65dP2XzvvffEmTNnxPLlyzllU5+WLVsmGjZsKGQymejQoYP47bfflNtCQkJEZGSkSv3vv/9eNG3aVMhkMtGyZUuxbds2I0esPW366OPjIwCoLTExMcYPvIq0/Rk+qqYnfSG079/hw4dFUFCQkMvlws/PT3zyySeipKTEyFFrR5s+PnjwQMyePVs0btxYWFtbC29vbzF+/Hhx584d4wdeBfv27dP4O1Xep8jISBESEqK2T0BAgJDJZMLPz0+sWbOmWjHw1spERGaEY/pERGaESZ+IyIww6RMRmREmfSIiM8KkT0RkRpj0iYjMCJM+EZEZYdInIjIjTPpENdCjj9K7fPkyJBJJrbgFNNV8TPpEjxk2bBgkEgkkEgmsrKzQqFEjTJkypcY+d5VIG7y1MpEGvXr1wpo1a/DgwQMcP34ckZGRkEgkmDdvnqlDI6oWnukTaSCXy+Hu7g5vb2/069cPoaGh2LVrF4Cy2/3GxsaiUaNGsLGxQdu2bZGYmKiy/99//42XXnoJDg4OsLe3R+fOnXHx4kUAwNGjR9GjRw84OzvD0dERISEhOHHihNH7SOaJSZ/oCU6dOoXDhw8r70EfGxuL//3vf4iPj8fff/+NSZMm4c0338SBAwcAANevX8eLL74IuVyOvXv34vjx43jrrbdQUlICoOypXZGRkUhOTsZvv/2GJk2aIDw8HHfv3jVZH8l8cHiHSIOtW7fCzs4OJSUlKCoqgoWFBb744gsUFRVh7ty52L17t/JBFn5+fkhOTsaXX36JkJAQLF++HI6OjkhISICVlRUAoGnTpspjd+vWTaWtr776Ck5OTjhw4ABeeukl43WSzBKTPpEGXbt2xYoVK5Cfn4/FixfD0tISr7/+Ov7++28UFBSgR48eKvWLi4vx7LPPAgBSU1PRuXNnZcJ/XFZWFmbOnIn9+/cjOzsbpaWlKCgoQHp6usH7RcSkT6RBnTp14O/vDwBYvXo12rZti1WrVqFVq1YAgG3btsHLy0tlH7lcDgBPfHZpZGQk/v33XyxduhQ+Pj6Qy+UIDg5GcXGxAXpCpIpJn+gJLCwsMH36dERHR+PcuXOQy+VIT09HSEiIxvpt2rTBunXr8ODBA41n+4cOHUJcXBzCw8MBAFevXsWtW7cM2geicryQS1QF/fv3h1QqxZdffonJkydj0qRJWLduHS5evIgTJ05g2bJlWLduHQAgKioKeXl5eOONN3Ds2DGcP38eX3/9Nc6ePQsAaNKkCb7++mucOXMGv//+OwYPHvzEvw6I9IVn+kRVYGlpiaioKMyfPx9paWlwcXFBbGwsLl26BCcnJ7Rr1w7Tp08HANSvXx979+7Fe++9h5CQEEilUgQEBKBTp04AgFWrVmH06NFo164dvL29MXfuXEyePNmU3SMzwmfkEhGZEQ7vEBGZESZ9IiIzwqRPRGRGmPSJiMwIkz4RkRlh0iciMiNM+kREZoRJn4jIjDDpExGZESZ9IiIzwqRPRGRG/h/7mTXJKiCxAAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Precision-Recall Curve\n",
    "plt.figure(figsize=(4,3))\n",
    "precision, recall, _ = precision_recall_curve(y_test, y_pred_proba)\n",
    "pr_auc = auc(recall, precision)\n",
    "\n",
    "plt.plot(recall, precision, color='blue', lw=2,\n",
    "         label=f'PR curve (AUC = {pr_auc:.3f})')\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.title('Precision-Recall Curve')\n",
    "plt.legend(loc=\"lower left\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\anany\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\engine\\training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    }
   ],
   "source": [
    "model2.save('PhishingWebsites.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<generator object representative_dataset at 0x0000018450D7DA80>\n"
     ]
    }
   ],
   "source": [
    "def representative_dataset():\n",
    "    for _ in range(100):\n",
    "      data =  X_test\n",
    "      yield [data.astype(np.float32)]\n",
    "        \n",
    "print(representative_dataset())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<generator object representative_dataset at 0x0000018450CDFCA0>\n"
     ]
    }
   ],
   "source": [
    "def representative_dataset():\n",
    "    for _ in range(100):\n",
    "      data =  X_test\n",
    "      yield [data.astype(np.float32)]\n",
    "        \n",
    "print(representative_dataset())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: saved_website_seq_model_keras_dir\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: saved_website_seq_model_keras_dir\\assets\n"
     ]
    }
   ],
   "source": [
    "# Converting a tf.Keras model to a TensorFlow Lite model.\n",
    "# It is preferred to use TFLiteConverter from saved model and then\n",
    "# Also provide representative dataset to train the converted TFLite model\n",
    "# Avoid calling the TFLite converter directly from model\n",
    "#converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
    "\n",
    "tf.saved_model.save(model2, \"saved_website_seq_model_keras_dir\")\n",
    "converter = tf.lite.TFLiteConverter.from_saved_model(\"saved_website_seq_model_keras_dir\")\n",
    "\n",
    "# Though its size is not much, optimizer is used here to check whether it works on ESP32\n",
    "# if this is chosen, tf.lite.Optimize.OPTIMIZE_FOR_SIZE, the TFLite does not work on ESP32\n",
    "# Observed that even with Optimize.DEFAULT the TFLite model does not work on ESP32\n",
    "converter.optimizations = [tf.lite.Optimize.DEFAULT]\n",
    "converter.representative_dataset = representative_dataset\n",
    "\n",
    "tflite_model = converter.convert()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the model in TFlite format whose size is just 5 KB\n",
    "# It brings down the size from 49.52 KB to 3.836 KB, 13 times reduction\n",
    "with open('WebsiteClassifyModel.tflite', 'wb') as f:\n",
    "  f.write(tflite_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run the inference on TFLITE model on Python ... here itself first\n",
    "# Let us now first try to run this tflinte model on Python itself\n",
    "# Ref: https://www.tensorflow.org/lite/guide/inference\n",
    "# Load the TFLite model and allocate tensors.\n",
    "interpreter = tf.lite.Interpreter(model_path=\"WebsiteClassifyModel.tflite\")\n",
    "interpreter.allocate_tensors()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input_details:\n",
      " [{'name': 'serving_default_input_6:0', 'index': 0, 'shape': array([ 1, 36]), 'shape_signature': array([-1, 36]), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}]\n",
      "output_details:\n",
      " [{'name': 'StatefulPartitionedCall:0', 'index': 12, 'shape': array([1, 1]), 'shape_signature': array([-1,  1]), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}]\n"
     ]
    }
   ],
   "source": [
    "# Get input and output tensors.\n",
    "input_details = interpreter.get_input_details()\n",
    "output_details = interpreter.get_output_details()\n",
    "print('input_details:\\n', input_details)\n",
    "print('output_details:\\n', output_details)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TFLite model output for random input:\n",
      "[[0.95703125]]\n",
      "1/1 [==============================] - 0s 27ms/step\n",
      "Original Keras model output for random input:\n",
      "[[0.95730674]]\n",
      "Shape of input_data for input 1: (1, 36)\n",
      "TFLite model output for input 1:\n",
      "[[0.98046875]]\n",
      "1/1 [==============================] - 0s 26ms/step\n",
      "Original Keras model output for input 1:\n",
      "[[0.9817738]]\n",
      "Shape of input_data for input 2: (1, 36)\n",
      "TFLite model output for input 2:\n",
      "[[0.98828125]]\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "Original Keras model output for input 2:\n",
      "[[0.9884219]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "\n",
    "# Load the TFLite model and allocate tensors\n",
    "interpreter = tf.lite.Interpreter(model_path=\"WebsiteClassifyModel.tflite\")\n",
    "interpreter.allocate_tensors()\n",
    "\n",
    "# Get input and output tensors\n",
    "input_details = interpreter.get_input_details()\n",
    "output_details = interpreter.get_output_details()\n",
    "\n",
    "# Test the model on random input data\n",
    "input_shape = input_details[0]['shape']\n",
    "input0_data = np.random.random_sample(input_shape)\n",
    "input0_data = np.array(input0_data, dtype=np.float32)\n",
    "\n",
    "# Set the input tensor to the random data\n",
    "interpreter.set_tensor(input_details[0]['index'], input0_data)\n",
    "\n",
    "# Invoke the model\n",
    "interpreter.invoke()\n",
    "\n",
    "# Get the output from the TFLite model\n",
    "output0_data = interpreter.get_tensor(output_details[0]['index'])\n",
    "print(\"TFLite model output for random input:\")\n",
    "print(output0_data)\n",
    "\n",
    "# Verify the output with the original Keras model\n",
    "output0_data_keras = model2.predict(input0_data)\n",
    "print(\"Original Keras model output for random input:\")\n",
    "print(output0_data_keras)\n",
    "\n",
    "input1_data = [[0.0, 0.1, 0.2, 0.3, 0.0, 1.0, 0.0, 0.5, 0.7, 0.9, \n",
    "                 0.4, 0.8, 0.1, 0.6, 0.2, 0.3, 0.0, 1.0, 0.0, \n",
    "                 0.5, 0.7, 0.9, 0.4, 0.8, 0.1, 0.6, 0.2, \n",
    "                 0.3, 0.0, 1.0, 0.0, 0.5, 0.7, 0.9, 0.4,  # 35 features\n",
    "                 0.5]]  # Add one more feature to make it 36\n",
    "\n",
    "input2_data = [[1.0, 0.1, 0.2, 0.4, 0.3, 0.0, 0.2, 0.5, 0.8, 0.6, \n",
    "                 0.1, 0.3, 0.9, 0.0, 0.5, 0.8, 0.1, 0.0, \n",
    "                 0.3, 0.9, 0.6, 0.4, 0.7, 0.1, 0.3, 0.2, \n",
    "                 0.0, 1.0, 0.3, 0.4, 0.5, 0.6, 0.2, 0.1, \n",
    "                 0.0, 1.0]]  # Make sure this has 36 features\n",
    "\n",
    "# Function to test individual inputs\n",
    "def test_input(input_data, input_index):\n",
    "    input_data = np.array(input_data, dtype=np.float32)\n",
    "\n",
    "    # Print the shape of input_data for debugging\n",
    "    print(f\"Shape of input_data for input {input_index}: {input_data.shape}\")\n",
    "    \n",
    "    # Set the input tensor\n",
    "    interpreter.set_tensor(input_details[0]['index'], input_data)\n",
    "\n",
    "    # Invoke the model\n",
    "    interpreter.invoke()\n",
    "\n",
    "    # Get the output\n",
    "    output_data = interpreter.get_tensor(output_details[0]['index'])\n",
    "    print(f'TFLite model output for input {input_index}:')\n",
    "    print(output_data)\n",
    "\n",
    "    # Verify with the original Keras model\n",
    "    output_data_keras = model2.predict(input_data)\n",
    "    print(f'Original Keras model output for input {input_index}:')\n",
    "    print(output_data_keras)\n",
    "\n",
    "# Test the specific inputs\n",
    "test_input(input1_data, 1)\n",
    "test_input(input2_data, 2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to convert some hex values into an array for C programming\n",
    "import time, sys\n",
    "\n",
    "# Function to convert some hex values into an array for C programming\n",
    "def hex_to_c_array(hex_data, var_name):\n",
    "    c_str = \"\"\n",
    "\n",
    "    # Create header guard\n",
    "    c_str += '#ifndef ' + var_name.upper() + '_H\\n'\n",
    "    c_str += \"#define \" + var_name.upper() + '_H\\n\\n'\n",
    "\n",
    "# Time stamping of this model2 data in the generated file\n",
    "    localtime = time.asctime( time.localtime(time.time()) )\n",
    "    c_str += \" This model2 data was generated on \" + localtime+ '\\n\\n'\n",
    "    print(\"This model2 data was generated on:\", localtime)\n",
    "\n",
    "# Add information about the verisons of tools and packages used in generating this header file\n",
    "    c_str += \" Tools used:\\n Python:\" + str(sys.version) + \"\\n Numpy:\" + str(np.version.version) + \\\n",
    "          \"\\n TensorFlow/Keras Version: \" + str(tf.__version__) + \"\\n\\n\"\n",
    "    print(\"Tools used: Python:\", sys.version, \"\\n Numpy:\", np.version.version, \\\n",
    "         \"\\n TensorFlow/Keras Version:\", tf.__version__, \"\\n\\n\")\n",
    "\n",
    "# Training details of the model\n",
    "    c_str += ' Model details are:\\n'\n",
    "    c_str += ' NUM_OF_EPOCHS = ' + str(EPOCHS) + '\\n'\n",
    "    c_str += ' BATCH_SIZE    = ' + str(BATCH_SIZE) + '\\n*/\\n'\n",
    "    \n",
    "# Generate 'C' constants for the no. of nodes in each layer\n",
    "    c_str += '\\nconst int ' + 'DENSE1_SIZE' + ' = ' + str(DENSE_LAYERS[0]) + ';\\n'\n",
    "    c_str +=   'const int ' + 'DENSE2_SIZE' + ' = ' + str(DENSE_LAYERS[1]) + ';\\n'      \n",
    "    \n",
    "    # Add array length at the top of the file\n",
    "    c_str += '\\nconst unsigned int ' + var_name + '_len = ' + str(len(hex_data)) + ';\\n'\n",
    "\n",
    "    # Declare C variable\n",
    "    c_str += 'alignas(8) const unsigned char ' + var_name + '[] = {'\n",
    "    hex_array = []\n",
    "    for i, val in enumerate(hex_data):\n",
    "        # Construct string from hex\n",
    "        hex_str = format(val, '#04x')\n",
    "\n",
    "        # Add formating so each line stays within 80 characters\n",
    "        if (i + 1) < len(hex_data):\n",
    "          hex_str += ','\n",
    "        if (i + 1) % 12 == 0:\n",
    "          hex_str += '\\n'\n",
    "        hex_array.append(hex_str)\n",
    "\n",
    "    # Add closing brace\n",
    "    c_str += '\\n' + format(''.join(hex_array)) + '\\n};\\n\\n'\n",
    "\n",
    "    # Close out header guard\n",
    "    c_str += '#endif //' + var_name.upper() + '_H'\n",
    "\n",
    "    return c_str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This model2 data was generated on: Mon Oct 28 20:35:32 2024\n",
      "Tools used: Python: 3.11.0 (main, Oct 24 2022, 18:26:48) [MSC v.1933 64 bit (AMD64)] \n",
      " Numpy: 1.26.4 \n",
      " TensorFlow/Keras Version: 2.15.0 \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Write TFLite model to a C source (or header) file\n",
    "with open(\"PhishingWebsites_esp32\" + '.h', 'w') as file:\n",
    "  file.write(hex_to_c_array(tflite_model, \"PhishingWebsites_esp32\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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