{
 "cells": [
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <center>Malria Disease Prediction</center>\n",
    "\n",
    "## Overview:\n",
    "Malaria is a life-threatening disease caused by parasites transmitted through the bite of infected female Anopheles mosquitoes. Symptoms typically include fever, chills, and flu-like illness, and if left untreated, it can lead to severe complications and death, particularly in children under five and pregnant women. Prevention strategies include the use of insecticide-treated bed nets, indoor residual spraying, and antimalarial medications. Malaria remains a significant public health challenge, particularly in tropical and subtropical regions of the world.\n",
    "\n",
    "* We are trying to detect whether the cell/tissue is Infected or not.\n",
    "## Dataset Information:\n",
    "\n",
    "Malaria Infected Tissues Dataset - [Kaggle - Malaria Disease Detection](https://www.kaggle.com/datasets/iarunava/cell-images-for-detecting-malaria/data)\n",
    "\n",
    "The Malaria dataset contains a total of 27,558 cell images with equal instances of parasitized and uninfected cells from the thin blood smear slide images of segmented cells.\n",
    "\n",
    "### Training : Testing ::   22048 : 5510"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Importing required libraries\n",
    "* ```Numpy```: A fundamental package for scientific computing in Python. It provides support for multidimensional arrays, along with a collection of mathematical functions to operate on these arrays efficiently. NumPy is widely used in numerical and scientific computing tasks, including data manipulation, linear algebra, statistics, and signal processing.\n",
    "\n",
    "* ```Pandas```: A powerful library for data manipulation and analysis in Python. It offers data structures and functions for working with structured data, primarily in the form of dataframes. Dataframes are two-dimensional labeled arrays capable of holding heterogeneous data types. Pandas provides tools for reading and writing data from various file formats, reshaping and transforming data, and performing data analysis tasks such as grouping, filtering, and aggregation.\n",
    "\n",
    "* ```Matplotlib```: A plotting library for creating visualizations in Python. It provides a MATLAB-like interface for generating a wide range of static, interactive, and animated plots. Matplotlib is highly customizable and supports various plot types, including line plots, scatter plots, bar charts, histograms, and heatmaps.\n",
    "\n",
    "* ```TensorFlow```: TensorFlow is an open-source machine learning framework developed by Google. It provides a comprehensive ecosystem of tools, libraries, and resources for building and deploying machine learning models at scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "l9z2oNNdi80k"
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter('ignore')\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras.layers import Conv2D,MaxPool2D,Dropout,Flatten,Dense,BatchNormalization\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-21T15:45:41.987329Z",
     "iopub.status.busy": "2024-02-21T15:45:41.987025Z",
     "iopub.status.idle": "2024-02-21T15:45:41.991872Z",
     "shell.execute_reply": "2024-02-21T15:45:41.990881Z",
     "shell.execute_reply.started": "2024-02-21T15:45:41.987298Z"
    },
    "id": "U69K2DVKjbWm"
   },
   "source": [
    "# 2. Data Preproceessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Displaying Uninfected and Infected Cell tissues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2\n",
    "upic='C:/Users/prajw/Downloads/archive/Dataset/cell_images/Uninfected/C100P61ThinF_IMG_20150918_144104_cell_131.png'\n",
    "apic='C:/Users/prajw/Downloads/archive/Dataset/cell_images/Parasitized/C100P61ThinF_IMG_20150918_144104_cell_164.png'\n",
    "plt.figure(1, figsize = (15 , 7))\n",
    "plt.subplot(1 , 2 , 1)\n",
    "plt.imshow(cv2.imread(upic))\n",
    "plt.title('Uninfected Cell')\n",
    "plt.xticks([]) , plt.yticks([])\n",
    "\n",
    "plt.subplot(1 , 2 , 2)\n",
    "plt.imshow(cv2.imread(apic))\n",
    "plt.title('Infected Cell')\n",
    "plt.xticks([]) , plt.yticks([])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Training and Testing Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "MAd3M91Smb3j"
   },
   "outputs": [],
   "source": [
    "datagen = ImageDataGenerator(rescale=1/255.0, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 22048 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "trainDatagen = datagen.flow_from_directory(directory='C:/Users/prajw/Downloads/archive/Dataset/cell_images/',\n",
    "                                           target_size=(128,128),\n",
    "                                           class_mode = 'binary',\n",
    "                                           batch_size = 16,\n",
    "                                           subset='training')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "7I7zFilVoUDm",
    "outputId": "fe3e90da-2503-4e36-e206-6b8cacfffcba"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 5510 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "valDatagen = datagen.flow_from_directory(directory='C:/Users/prajw/Downloads/archive/Dataset/cell_images/',\n",
    "                                           target_size=(128,128),\n",
    "                                           class_mode = 'binary',\n",
    "                                           batch_size = 16,\n",
    "                                           subset='validation')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Convolutional Neural Networks Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "eGj3RRNRomKa"
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Conv2D(16,(3,3),activation='relu',input_shape=(128,128,3)))\n",
    "model.add(MaxPool2D(2,2))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Conv2D(32,(3,3),activation='relu'))\n",
    "model.add(MaxPool2D(2,2))\n",
    "model.add(Dropout(0.3))\n",
    "\n",
    "model.add(Conv2D(64,(3,3),activation='relu'))\n",
    "model.add(MaxPool2D(2,2))\n",
    "model.add(Dropout(0.3))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(64,activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(1,activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "3qesYVQMpqn6",
    "outputId": "ab924654-3993-48ea-f843-ba09426c5243"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 126, 126, 16)      448       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 63, 63, 16)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 63, 63, 16)        0         \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 61, 61, 32)        4640      \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 30, 30, 32)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 30, 30, 32)        0         \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 28, 28, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 14, 14, 64)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 14, 14, 64)        0         \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 12544)             0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 64)                802880    \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 64)                0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 65        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 826,529\n",
      "Trainable params: 826,529\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "3TOyOrioptrq"
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "early_stop = EarlyStopping(monitor='val_loss',patience=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "dSnFw-V2p2B9",
    "outputId": "e59014ac-3bf1-41c9-b7f7-5b2d84835949"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "1378/1378 [==============================] - 38s 24ms/step - loss: 0.5782 - accuracy: 0.6745 - val_loss: 0.1920 - val_accuracy: 0.9283\n",
      "Epoch 2/20\n",
      "1378/1378 [==============================] - 33s 24ms/step - loss: 0.1897 - accuracy: 0.9409 - val_loss: 0.1791 - val_accuracy: 0.9374\n",
      "Epoch 3/20\n",
      "1378/1378 [==============================] - 34s 25ms/step - loss: 0.1623 - accuracy: 0.9524 - val_loss: 0.1672 - val_accuracy: 0.9466\n",
      "Epoch 4/20\n",
      "1378/1378 [==============================] - 34s 25ms/step - loss: 0.1506 - accuracy: 0.9542 - val_loss: 0.1711 - val_accuracy: 0.9436\n",
      "Epoch 5/20\n",
      "1378/1378 [==============================] - 38s 27ms/step - loss: 0.1397 - accuracy: 0.9572 - val_loss: 0.1779 - val_accuracy: 0.9452\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(generator = trainDatagen,\n",
    "                             steps_per_epoch = len(trainDatagen),\n",
    "                              epochs =20,\n",
    "                              validation_data = valDatagen,\n",
    "                              validation_steps=len(valDatagen),\n",
    "                             callbacks=[early_stop])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "qWX7Iy53qTZy"
   },
   "outputs": [],
   "source": [
    "def plotLearningCurve(history,epochs):\n",
    "  epochRange = range(1,epochs+1)\n",
    "  plt.plot(epochRange,history.history['accuracy'])\n",
    "  plt.plot(epochRange,history.history['val_accuracy'])\n",
    "  plt.title('Model Accuracy')\n",
    "  plt.xlabel('Epoch')\n",
    "  plt.ylabel('Accuracy')\n",
    "  plt.legend(['Train','Validation'],loc='upper left')\n",
    "  plt.show()\n",
    "\n",
    "  plt.plot(epochRange,history.history['loss'])\n",
    "  plt.plot(epochRange,history.history['val_loss'])\n",
    "  plt.title('Model Loss')\n",
    "  plt.xlabel('Epoch')\n",
    "  plt.ylabel('Loss')\n",
    "  plt.legend(['Train','Validation'],loc='upper left')\n",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "jdB5PpS1sIhE",
    "outputId": "75956d00-c8c1-418a-dcf3-79895b908b05"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cQKQikwEqOzmkvu+WAcgMCks1aDdrW4P/3tPvRsPJ3jyX8N1338XTTz9t+NnT0xPh4eGGn9977z1s3LgRP/30EyZNmlTtcUaPHo1hw4YBAObNm4dFixbh0KFD6N27t1nqJCKqC41W1CJAmNbTUX2gqdwjUqoRUv8JDGQywMFOAZVSDpWdHCo7he6rssL3+u33aqNUGN63N+xXcftd39spoFTIGsVjRxiACADQuXNno5/z8/MxZ84cbN68GampqSgrK0NhYSFSUlJqPE6HDh0M3zs7O8PV1dWwxAQRWQchBMq0AmUagVKtFmUagTKNFqVaAU2FbaUabXk7LUo1AmX6tto77cs0WqPjVNynut9RptFWOURTeYjnThAp0zaeAKKQy0wLGfdqY9TeuG1VocRO3jgCiNQYgMzAUanA6XejJfm95uLs7Gz08xtvvIHt27fj448/RqtWreDo6IjnnnsOJSUlNR5HqVQa/SyTyaDVNp6uVyIpCCEMAaBUc+fDvVSjDwS67RrtnQBw93vG7e4+zl2BQivu2reaY94dSCpsuxNIdNsqBpvGFCbqQqmQVerFsFfITejtqE1wqfp9e4UcdgregN0YSB6AlixZgo8++ghpaWkIDw/Hv/71L3Tt2rXKtqWlpZg/fz6+/vprXL9+HaGhofjggw+MhlfmzJmDuXPnGu0XGhqKs2fP1ts5yGQysw1FNRZ//PEHRo8ejQEDBgDQ9Qj99ddf0hZFVEeFJRpczy7AtduFuJ5diJzC0ko9CkYf/vqQYdRDUXVvhUYrqgksd/a38LxQKzIZoJTLYaeQwU4ug1Kh/14OpUIGO4Wu58FoW3l7Zfl7+n0UcpnhWErDfsb71DWI2NvJoZCz94MkDkDr1q1DfHw8li5disjISCxcuBDR0dE4d+4cfHx8KrWfOXMmvvvuO3z11Vdo06YNtm3bhgEDBmDfvn3o2LGjoV379u2xY8cOw8+1uaWbjLVu3RobNmxATEwMZDIZ3nnnHfbkUKOVU1iK6+Xh5trtAsP3up8LcUtdc8+lFBRyXVCo6sO98of+nffsFHIo9UHC8P2d/RVymSFw6N+zU9wJFPrtimpCyt37VDxexZBy53i67+UMFWRhJE0GCxYswPjx4zFmzBgAwNKlS7F582YsX74c06ZNq9T+22+/xYwZM9C3b18AwMsvv4wdO3bgk08+wXfffWdoZ2dnZ3TrN5luwYIFePHFF9G9e3d4eXnhH//4B3Jzc6Uui2yQEAI31SW4fruwvAenoELYKcT124XIKy6753FcVHYI9HBEoLsjPJ3tjUKDsrzXoXKgqBxO7oSU6nsw7OR3ejuq7MGQyxgYiCQmWQAqKSnBkSNHMH36dMM2uVyOqKgo7N+/v8p9iouL4eDgYLTN0dERe/fuNdp2/vx5BAQEwMHBAd26dcP8+fNrvHW7uLgYxcXFhp+t+YN+9OjRGD16tOHnnj17QojK/fMhISHYuXOn0baJEyca/Xz3kFhVx8nOzq5zrWQbNFqB9NwiXY9NhWBz7XYBrmcX4kZ2IYpK79376Olsj0B3RzQrDzn6sNPMwwmBHo5wc1Te8xhEZDskC0BZWVnQaDTw9fU12u7r61vtfJ3o6GgsWLAAjz/+OFq2bInExERs2LABGs2dZ/BERkZi5cqVCA0NRWpqKubOnYvHHnsMycnJcHFxqfK48+fPrzRviIjMo6RMi9QcXbi5Vh5yDD052YVIzS6656RamQzwcVHpwoxRuNG9AtwdrW4eHhHVL4v6L8Znn32G8ePHo02bNpDJZGjZsiXGjBmD5cuXG9r06dPH8H2HDh0QGRmJ4OBgfP/99xg7dmyVx50+fTri4+MNP+fm5iIoKKj+ToTIitw9wVg/LKXv0UnPK0IVnYNG7OQy+Ls76MKNu67HppmHI5qVhx1/N0fY2/HOGSIyH8kCkJeXFxQKBdLT0422p6enVzt/x9vbGwkJCSgqKsLNmzcREBCAadOmoUWLFtX+Hnd3dzz44IO4cOFCtW1UKhVUKlXdToTIyuknGOuHpOoywVhlJ7+r18a4J8fX1YF35hBRg5IsANnb26NTp05ITExEXFwcAECr1SIxMbHGJw0DgIODAwIDA1FaWooff/wRgwcPrrZtfn4+Ll68iBEjRpizfCKroJ9gfKfX5v4nGDfz0AcbJ8M2ryb2Df/gNSGA4jwgPx3IS6vwNQ0oKwbsVICdY/lXB0DpoPtq9FIBygptKm63cwAUSt34HBFZHEmHwOLj4zFq1Ch07twZXbt2xcKFC6FWqw13hY0cORKBgYGYP38+AODgwYO4fv06IiIicP36dcyZMwdarRZvvfWW4ZhvvPEGYmJiEBwcjBs3bmD27NlQKBSG5RmIbEl9TzAOLO/NadAJxlotUHjrTpjJS6/ia/mrtKB+a5HJKwSiqsKUubZXEcrk5nsQKpEtkjQADRkyBJmZmZg1axbS0tIQERGBrVu3GiZGp6SkQC6/M+5fVFSEmTNn4tKlS2jSpAn69u2Lb7/9Fu7u7oY2165dw7Bhw3Dz5k14e3ujR48eOHDgALy9vRv69Ijq3d0TjI16cu5zgnGghyOCGnKCsaYMUGfc1VtTxdf8DEBbWvvjqlyBJr6Ai9+dr0pHoKxI1xNUVgSUFlX4ufDe2/WEVheySgsA3Db7n6RGcrsGCl93bbdzAOScjyUZIQBtGaAp1X2t+Kpym/770rt+1m/T3ONYmgr7lrev8/Hu2rfzWODRVyX7U8pEVfcu27jc3Fy4ubkhJycHrq6uRu8VFRXh8uXLaN68eaVb8smyNcZrW3GC8bUKE4vve4JxhaGqep9gXFpUQ09Nha/qLAAm/OfIqSnQxA9w8a35q72Tec9HiDtBqLaB6Z7bi2oXyDSN5IGOCvtahKYKvVZmC1+q6occtdoG/GCv0L7GD/sK72nKzLOvsKIH0j46FXjavHdg1/T5fTeLuguMyNpUN8FYH3bqMsG44rNv6m2CcU3za+4ONkU5tT+uTAE08ancY2P4Wh5snH0AO3vznlOta5TpPqCVEoRkraZC+KoQkkoL63+7uPO4EWhKdK/i6kutN/ogJGAcWKwpGNSFXKnrFZTbAYryr4ZtCt18Nf37crsKPyvutFMoy3+usK/Czng/o33v3qYwoQ4l4Oov6Z+MAYionlQ3wbhiT46pE4z1t4fX2wRjrRYovF0eYO4xFGXK/BqFqoaemgoBx6kp57bURK7Q9WiZu1erNjRl9whM5ugJq2Z7xZ5B/e+vrTp/uFcVDKr7cFfUPhjcsw5l1YGlpuPL5JyMXwcMQFRrPXv2REREBBYuXAhA97ToqVOnYurUqdXuI5PJsHHjRsOdfnVlruOYU9UTjO88D8fUCcbGd1CZeYJxQ86vqeqrgxv/A23pFHaAogmgatKwv1cI3bDQ3cFIJq8+eOh/ZjCgGjAA2YiYmBiUlpZi69atld77/fff8fjjj+PEiRPo0KFDrY95+PBhODs7m7NMzJkzBwkJCTh+/LjR9tTUVHh4eJj1d9XFb39mYunui7iWXWDSBOO7h6XMNsFY0vk1voC9ea8/USUymW64U6ohT7JaDEA2YuzYsRg4cCCuXbuGZs2aGb23YsUKdO7c2aTwA6BB76xrDIvbCiHwTkIyUm7dGfqxk8vg5+ZgNCxVcYKxn5sDVHYmDunY8vwaIqIGwgBkI5599ll4e3tj5cqVmDlzpmF7fn4+1q9fj2nTpmHYsGHYs2cPbt++jZYtW+Ltt9+u8flJdw+BnT9/HmPHjsWhQ4fQokULfPbZZ5X2+cc//oGNGzfi2rVr8PPzw/DhwzFr1iwolUqsXLnSsCabfk7LihUrMHr06EpDYElJSZgyZQr2798PJycnDBw4EAsWLECTJrru+dGjRyM7Oxs9evTAJ598gpKSEgwdOhQLFy6EUlm3IaWjKdlIuVUAJ3sFVozugiBPJ9MmGAsBFNzi/BoiokaAAcgchKj/B65VRelU6/FtOzs7jBw5EitXrsSMGTMMAWP9+vXQaDR44YUXsH79evzjH/+Aq6srNm/ejBEjRqBly5bo2rXrPY+v1Wrxt7/9Db6+vjh48CBycnKqnBvk4uKClStXIiAgAElJSRg/fjxcXFzw1ltvYciQIUhOTsbWrVuxY8cOAICbm1ulY6jVakRHR6Nbt244fPgwMjIyMG7cOEyaNAkrV640tNu1axf8/f2xa9cuXLhwAUOGDEFERATGjx9fq7/Z3RKOXQcARLf3Q2SLpnfe0JQB6sxaDEWlmza/xt7l3rd4u/gCDu6c50BEZCIGIHMoLQDmBTT87337hklzMF588UV89NFH+O2339CzZ08Auh6WgQMHIjg4GG+88Yah7eTJk7Ft2zZ8//33tQpAO3bswNmzZ7Ft2zYEBOj+FvPmzTNanBaAUe9TSEgI3njjDaxduxZvvfUWHB0d0aRJE9jZ2dU45LV69WoUFRXhm2++McxBWrx4MWJiYvDBBx8YHqTp4eGBxYsXQ6FQoE2bNujXrx8SExNrH4BKCnThJT8DZTmpaHLid7xhl4WBxUrgu+zyHpt0XfgxZX6No+e9Jw27+HF+DRFRPWIAsiFt2rRB9+7dsXz5cvTs2RMXLlzA77//jnfffRcajQbz5s3D999/j+vXr6OkpATFxcVwcqrd7bZnzpxBUFCQIfwAQLdu3Sq1W7duHRYtWoSLFy8iPz8fZWVl93xYVVW/Kzw83GgC9qOPPgqtVotz584ZAlD79u2hUNwZ8vH390dSUtKdh49pSnU9MpryZ4kUFunugvruDSDjBFCSZ9jXDsA/9N9cqqIomVw3d+ZeQ1FNfDm/hoioEWAAMgelk643Rorfa6KxY8di8uTJWLJkCVasWIGWLVviiSeewAcffIDPPvsMCxcuRFhYGJydnTF16lSUlJjvybP79+/H8OHDMXfuXERHR8PNzQ1r167FJ598cv8H15bfbl5aCBRmA2VFUMo0QHaKIejICm5BW5QLpCdXfYwyobvFNvvKnfBj5wi4+OJSYROcVTvByy8IXR9qWzngOHtxfg0RkQVhADIHmcxihisGDx6MKVOmYPXq1fjmm2/w8ssvQyaT4Y8//kBsbCxeeOEFALo5PX/++SfatWtXq+O2bdsWV69eRWpqKvz9dU/3PHDggFGbffv2ITg4GDNmzDBsu3LlilEbe3t7aDTlT5wV4s4j4QHDnVFtQ/ywcsUxqK+cgLOjEtCU4Y8dv0EulyPUSwHcvqwbviorBgpu3jl4xSfZVnzomEKpe8ZJKQAnAQz4AnArv1tK5QJ1iQb9/m8HCks1SOj/KBDkXqu/CRERNV4MQDamSZMmGDJkCKZPn47c3FyMHj0aANC6dWv88MMP2LdvHzw8PLBgwQKkp6fXOgBFRUXhwQcfxKhRo/DRRx8hNzfXKOjof0dKSgrWrlmNLg93xObNP2Pjxg0ABJCbCmhLEdLUCZcvXcLxHT+gmV9TuDg7QaUqHzJSZwK5NzC8Xw/MnqfEqFdex5zX/47Mm7cx+Z0PMWJgP/j6+d95EJrCXjf0pA85jh6A0hnwD9cNWd2tqAiwzwUC2wMV1gL79XQaCks1CGnqhPBmlSdlExGR5eGSvjZo7NixuH37NqKjow1zdmbOnImHH34Y0dHR6NmzJ/z8/Ex66rJcJsPGH9ejsECNrl27YtzYMfjnzDd1b+ZnAFnn0f+R1nht/AuYNHEiIjp1xr7fduCdV1/UreGTnwYU3MTAZ7qhd89u6PXci/AOexJrErbqnl8D6NYAcvSAk1cwtm1aj1v5xejSbySee2k6nno6GouXrQJ82wPeoYDKRbfit4u/bnjKwa38kfGyqsNPDTYe0w1vxkYEmm/JCSIikhRXg68CV4OvQGiNVy2uNIG4ws+m3AkFWYVH1pcPQRl9rbDdxMBSV1Vd28y8YkTO2wGtAHa90RPNvSxjqJOIyBZxNXiqmRC6YKMPMVXdEaUPNtp7L9ZpRKa4E2L0AUYfZioGHrnCIp5d8/PJG9AKIDzIneGHiMiKMABZEyGqDjP6QGP4uUwXgExRqXemYu+N8s6EYrl1jaomHNcNf8VFSPCcJyIiqjcMQJZAfyeUoXemYi/NXcHGFDJ5FYGmqmBjZxG9NeZ2OUuNE1ezoZDL8GwHBiAiImvCACQVfW9NVcNOdwcbk3trquupuWs7n1tTo03HdUtfPNrKC94uKomrISIic2IAqqM6zR1XZwEFWXeCjSlk8srPrZErq+7BscHeGnOoeE2FENjE4S8iIqvFAGQi/UriBQUFcHR0NG1nodE9qbgi+d13P911F5Q+2MjkDDb1rKBAt6CtUqnEiWs5uJylhoNSjmfaV78uGRERWSYGIBMpFAq4u7sjIyMDAODk5GTCs2EcAOdmgMwOUCjKe2tqmDQsAGhQfos51RchBAoKCpCRkQF3d3coFArDyu/PtPNDExX/Z0JEZG34X/Y60K9Urg9BZB3c3d3h5+eHMo0WP58sH/7qyOEvIiJrxABUBzKZDP7+/vDx8UFpKXtnrIFSqTSsHP/HxSxk5ZfA09kej7X2lrgyIiKqDwxA90GhUBg+NMl6bCof/uoX5g+lwrqea0RERDr8rztRBQUlZdh2Kg0Ah7+IiKwZAxBRBdtPp0NdokGQpyMefsBD6nKIiKieMAARVaB/9k9sOFd+JyKyZgxAROVuqUuw589MABz+IiKydgxAROU2n7yBMq3AQ4GuaOXjInU5RERUjxiAiMrdWfk9UOJKiIiovjEAEQG4eqsAR67chkwGxIRz+IuIyNoxABHhzsrv3Vs2ha+rg8TVEBFRfWMAIpsnhMDG8ocfxnL4i4jIJjAAkc07dSMXFzPVUNnJ0fshrvxORGQLGIDI5ulXfo9q6wtXB6XE1RARUUNgACKbptEK/HSi/OGHEZz8TERkKxiAyKYduHQTGXnFcHNUomeoj9TlEBFRA2EAIpumH/7qG+YPezv+z4GIyFbwv/hks4pKNdiaXL7yO4e/iIhsCgMQ2azEMxnIKy5DgJsDuoR4Sl0OERE1IAYgslkJ5Q8/jO0YCLmcK78TEdkSBiCySdkFJdh9LgMA1/4iIrJFDEBkk7YkpaFUI9DGzwWhflz5nYjI1jAAkU3SD3/FdWTvDxGRLWIAIptzPbsQhy7fgkwG9OfK70RENokBiGyOfuX3riGeCHB3lLgaIiKSAgMQ2ZxNx3RLXwzg8BcRkc1iACKbciY1F+fS82CvkKNPmL/U5RARkUQYgMim6Cc/92rjDTdHrvxORGSrJA9AS5YsQUhICBwcHBAZGYlDhw5V27a0tBTvvvsuWrZsCQcHB4SHh2Pr1q33dUyyHVqtwH+P64a/+OwfIiLbJmkAWrduHeLj4zF79mwcPXoU4eHhiI6ORkZGRpXtZ86ciS+++AL/+te/cPr0abz00ksYMGAAjh07Vudjku049Nct3MgpgouDHXq14crvRES2TCaEEFL98sjISHTp0gWLFy8GAGi1WgQFBWHy5MmYNm1apfYBAQGYMWMGJk6caNg2cOBAODo64rvvvqvTMauSm5sLNzc35OTkwNXV9X5PkxqJ6RtOYs2hqxjcuRk+fC5c6nKIiMjMTPn8lqwHqKSkBEeOHEFUVNSdYuRyREVFYf/+/VXuU1xcDAcHB6Ntjo6O2Lt3b52PqT9ubm6u0YusS3GZBptPpgLg8BcREUkYgLKysqDRaODr62u03dfXF2lpaVXuEx0djQULFuD8+fPQarXYvn07NmzYgNTU1DofEwDmz58PNzc3wysoKOg+z44am11nM5FbVAY/VwdEtmgqdTlERCQxySdBm+Kzzz5D69at0aZNG9jb22PSpEkYM2YM5PL7O43p06cjJyfH8Lp69aqZKqbGQv/ww/4RAVBw5XciIpsnWQDy8vKCQqFAenq60fb09HT4+flVuY+3tzcSEhKgVqtx5coVnD17Fk2aNEGLFi3qfEwAUKlUcHV1NXqR9cgtKkXiWd0k+NgILn1BREQSBiB7e3t06tQJiYmJhm1arRaJiYno1q1bjfs6ODggMDAQZWVl+PHHHxEbG3vfxyTrtTUpDSVlWrT2aYJ2/gy3REQE2En5y+Pj4zFq1Ch07twZXbt2xcKFC6FWqzFmzBgAwMiRIxEYGIj58+cDAA4ePIjr168jIiIC169fx5w5c6DVavHWW2/V+phkeyqu/C6TcfiLiIgkDkBDhgxBZmYmZs2ahbS0NERERGDr1q2GScwpKSlG83uKioowc+ZMXLp0CU2aNEHfvn3x7bffwt3dvdbHJNuSllOE/ZduAuDK70REdIekzwFqrPgcIOvx5Z6LmLflLLqEeGD9S92lLoeIiOqRRTwHiKghJJSv/B7LZ/8QEVEFDEBktc6n5+F0ai7s5DL048rvRERUAQMQWS395Oeeod7wcLaXuBoiImpMGIDIKgkhsOk4h7+IiKhqDEBklY5cuY1rtwvhbK9AVFveAUhERMYYgMgqbTymG/6KfsgPjvYKiashIqLGhgGIrE5JmRabk3QL5A7oyOEvIiKqjAGIrM6ePzORXVAKbxcVurf0krocIiJqhBiAyOro7/6K6cCV34mIqGoMQGRV8ovLsONMOgAgriOXviAioqoxAJFV2ZachqJSLVp4OSMs0E3qcoiIqJFiACKroh/+io3gyu9ERFQ9BiCyGhl5RfjjQhYAIDaCw19ERFQ9BiCyGv89kQqtADo+4I4QL2epyyEiokaMAYisxqby4a84Ln1BRET3wABEVuFSZj5OXsuBQi5Dvw5c+Z2IiGrGAERWIaF84dPHWnvBq4lK4mqIiKixYwAii6db+Z3DX0REVHsMQGTxjl3NxpWbBXBUKvB0O678TkRE98YARBZvk37l9/a+cFbZSVwNERFZAgYgsmilGi1+Pqlb+T2WK78TEVEtMQCRRdt7IQs31SVo6myPx1px5XciIqodBiCyaPrhr2c7+MNOwX/ORERUO/zEIIulLi7DtlO6ld85/EVERKZgACKLtf10OgpLNQhu6oSOQe5Sl0NERBaEAYgslmHl9/AArvxOREQmYQAii5SVX4zfz5ev/M7hLyIiMhEDEFmkzSdTodEKdGjmhpbeTaQuh4iILAwDEFkkw/AXl74gIqI6YAAii3PlphrHUrIhlwEx4Vz5nYiITMcARBZnU/nK74+28oKPi4PE1RARkSViACKLIoRAwjEOfxER0f1hACKLknQ9B5ey1HBQyhHdniu/ExFR3TAAkUVJOKYb/opq6wsXB6XE1RARkaViACKLodEK/PekLgDFcfiLiIjuAwMQWYx9F7OQmVcMdyclHn/QW+pyiIjIgjEAkcXQD3/1C/OHvR3/6RIRUd3xU4QsQmGJBluTUwEAcVz6goiI7hMDEFmEHWfSoS7RINDdEZ0e8JC6HCIisnAMQGQRNpUvfRHXMQByOVd+JyKi+8MARI3ebXUJdp/LBMC7v4iIyDwYgKjR25yUijKtQDt/V7T2dZG6HCIisgIMQNToVRz+IiIiMgcGIGrUrt4qwOG/bkMmA/qHc/iLiIjMgwGIGrWfTuie/fNI86bwc+PK70REZB4MQNRoVVz5fQCf/UNERGbEAESN1unUXJzPyIe9nRy9w/ykLoeIiKwIAxA1WpuO64a/nmrjA1eu/E5ERGYkeQBasmQJQkJC4ODggMjISBw6dKjG9gsXLkRoaCgcHR0RFBSE1157DUVFRYb358yZA5lMZvRq06ZNfZ8GmZlGK/BTeQCK5bN/iIjIzOyk/OXr1q1DfHw8li5disjISCxcuBDR0dE4d+4cfHx8KrVfvXo1pk2bhuXLl6N79+74888/MXr0aMhkMixYsMDQrn379tixY4fhZzs7SU+T6uDg5ZtIyy2Cq4MderXhyu9ERGRekvYALViwAOPHj8eYMWPQrl07LF26FE5OTli+fHmV7fft24dHH30Uzz//PEJCQvDMM89g2LBhlXqN7Ozs4OfnZ3h5eXk1xOmQGeknP/cN84fKTiFxNUREZG0kC0AlJSU4cuQIoqKi7hQjlyMqKgr79++vcp/u3bvjyJEjhsBz6dIlbNmyBX379jVqd/78eQQEBKBFixYYPnw4UlJSaqyluLgYubm5Ri+STlGpBr8kpQHg8BcREdUPycaGsrKyoNFo4Ovra7Td19cXZ8+erXKf559/HllZWejRoweEECgrK8NLL72Et99+29AmMjISK1euRGhoKFJTUzF37lw89thjSE5OhotL1csozJ8/H3PnzjXfydF92XU2A3nFZfB3c0Bkc0+pyyEiIisk+SRoU+zevRvz5s3Dv//9bxw9ehQbNmzA5s2b8d577xna9OnTB4MGDUKHDh0QHR2NLVu2IDs7G99//321x50+fTpycnIMr6tXrzbE6VA1EsqXvugfwZXfiYiofkjWA+Tl5QWFQoH09HSj7enp6fDzq/qZL++88w5GjBiBcePGAQDCwsKgVqsxYcIEzJgxA3J55Tzn7u6OBx98EBcuXKi2FpVKBZVKdR9nQ+aSU1CKXWe58jsREdUvyXqA7O3t0alTJyQmJhq2abVaJCYmolu3blXuU1BQUCnkKBS6CbJCiCr3yc/Px8WLF+Hv72+myqk+/ZKcihKNFqG+Lmjr7yp1OUREZKUkvT88Pj4eo0aNQufOndG1a1csXLgQarUaY8aMAQCMHDkSgYGBmD9/PgAgJiYGCxYsQMeOHREZGYkLFy7gnXfeQUxMjCEIvfHGG4iJiUFwcDBu3LiB2bNnQ6FQYNiwYZKdJ9Wefvgrliu/ExFRPZI0AA0ZMgSZmZmYNWsW0tLSEBERga1btxomRqekpBj1+MycORMymQwzZ87E9evX4e3tjZiYGPzzn/80tLl27RqGDRuGmzdvwtvbGz169MCBAwfg7c1nyTR2N7ILceDSLQBA/3AGICIiqj8yUd3YkQ3Lzc2Fm5sbcnJy4OrKYZiGsvS3i3j/l7PoGuKJ71+qehiUiIioOqZ8flvUXWBk3fQPP4zjyu9ERFTPGICoUTiXloezaXlQKmToy5XfiYionjEAUaOgn/zcM9QH7k72EldDRETWjgGIJKetsPI7n/1DREQNgQGIJHf4r1u4nl2IJio7PNXWR+pyiIjIBpgcgEJCQvDuu+/ec4FRotpKKO/96f2QHxyUXPmdiIjqn8kBaOrUqdiwYQNatGiBp59+GmvXrkVxcXF91EY2oKRMiy1JqQCAAbz7i4iIGkidAtDx48dx6NAhtG3bFpMnT4a/vz8mTZqEo0eP1keNZMV2n8tATmEpfFxUeKRFU6nLISIiG1HnOUAPP/wwFi1aZFhu4j//+Q+6dOmCiIgILF++vNq1uYgq2lQ+/NU/PAAKrvxOREQNpM5LYZSWlmLjxo1YsWIFtm/fjkceeQRjx47FtWvX8Pbbb2PHjh1YvXq1OWslK5NXVIodZ9IB8OGHRETUsEwOQEePHsWKFSuwZs0ayOVyjBw5Ep9++inatGljaDNgwAB06dLFrIWS9dmanIbiMi1aejujfQCXHCEiooZjcgDq0qULnn76aXz++eeIi4uDUqms1KZ58+YYOnSoWQok66V/+GFcRCBkMg5/ERFRwzE5AF26dAnBwcE1tnF2dsaKFSvqXBRZv/TcIuy7eBMAEMuHHxIRUQMzeRJ0RkYGDh48WGn7wYMH8b///c8sRZH1+++JGxAC6BTsgQeaOkldDhER2RiTA9DEiRNx9erVStuvX7+OiRMnmqUosn53hr8CJK6EiIhskckB6PTp03j44Ycrbe/YsSNOnz5tlqLIul3IyEfy9VzYyWXo14EBiIiIGp7JAUilUiE9Pb3S9tTUVNjZ1fmuerIhm8p7fx5/0Buezlz5nYiIGp7JAeiZZ57B9OnTkZOTY9iWnZ2Nt99+G08//bRZiyPrI4QwDH/FcviLiIgkYnKXzccff4zHH38cwcHB6NixIwDg+PHj8PX1xbfffmv2Asm6HE25jau3CuFkr8DT7XylLoeIiGyUyQEoMDAQJ0+exKpVq3DixAk4OjpizJgxGDZsWJXPBCKqKOFY+crv7f3gZM8hUyIikkadPoGcnZ0xYcIEc9dCVq5Uo8Xm8pXfY7n0BRERSajO/xf89OnTSElJQUlJidH2/v3733dRZJ1+P5+JW+oSeDWxx6MtufI7ERFJp05Pgh4wYACSkpIgk8kMq77rlzLQaDTmrZCshn7469kOAbBTmDz/noiIyGxM/hSaMmUKmjdvjoyMDDg5OeHUqVPYs2cPOnfujN27d9dDiWQN8ovL8OvpNABc+Z2IiKRncg/Q/v37sXPnTnh5eUEul0Mul6NHjx6YP38+Xn31VRw7dqw+6iQL9+upNBSVahHS1AnhzdykLoeIiGycyT1AGo0GLi4uAAAvLy/cuKEb1ggODsa5c+fMWx1ZjYTjun8nsVz5nYiIGgGTe4AeeughnDhxAs2bN0dkZCQ+/PBD2Nvb48svv0SLFi3qo0aycJl5xdh7PhMAh7+IiKhxMDkAzZw5E2q1GgDw7rvv4tlnn8Vjjz2Gpk2bYt26dWYvkCzfzydvQCuA8CB3NPdylrocIiIi0wNQdHS04ftWrVrh7NmzuHXrFjw8PDi0QVXSD39x5XciImosTJoDVFpaCjs7OyQnJxtt9/T0ZPihKl3OUuPE1Wwo5DI8y5XfiYiokTApACmVSjzwwAN81g/VWsIx3cKnj7bygreLSuJqiIiIdEy+C2zGjBl4++23cevWrfqoh6yIEAKbyld+5/AXERE1JibPAVq8eDEuXLiAgIAABAcHw9nZeFLr0aNHzVYcWbYT13Lw180COCoViG7vJ3U5REREBiYHoLi4uHoog6yRfvjr6Xa+cFZx5XciImo8TP5Umj17dn3UQVamTKPFzyfL7/7qyOEvIiJqXLgiJdWLPy7eRFZ+CTyd7fFYa2+pyyEiIjJicg+QXC6v8ZZ33iFGALCpfPirX5g/lFz5nYiIGhmTA9DGjRuNfi4tLcWxY8fw9ddfY+7cuWYrjCxXQUkZtp7Sr/zO4S8iImp8TA5AsbGxlbY999xzaN++PdatW4exY8eapTCyXNtPp6OgRIMgT0c8/ICH1OUQERFVYraxiUceeQSJiYnmOhxZsE2GpS+48jsRETVOZglAhYWFWLRoEQIDudK3rbulLsGeP3Urv8dG8N8DERE1TiYPgd296KkQAnl5eXBycsJ3331n1uLI8mw+eQNlWoGHAl3RyqeJ1OUQERFVyeQA9OmnnxoFILlcDm9vb0RGRsLDg/M9bF1CheEvIiKixsrkADR69Oh6KIOsQcrNAhy5chsyGRATzru/iIio8TJ5DtCKFSuwfv36StvXr1+Pr7/+2ixFkWXSL3zavWVT+Lo6SFwNERFR9UwOQPPnz4eXl1el7T4+Ppg3b55ZiiLLI4RAQnkA4uRnIiJq7EwOQCkpKWjevHml7cHBwUhJSTFLUWR5Tt3IxcVMNVR2cvR+iCu/ExFR42ZyAPLx8cHJkycrbT9x4gSaNm1qlqLI8uhXfo9q6wtXB6XE1RAREdXM5AA0bNgwvPrqq9i1axc0Gg00Gg127tyJKVOmYOjQoSYXsGTJEoSEhMDBwQGRkZE4dOhQje0XLlyI0NBQODo6IigoCK+99hqKioru65h0fzRagZ9O6O7+io3g5GciImr8TA5A7733HiIjI/HUU0/B0dERjo6OeOaZZ/Dkk0+aPAdo3bp1iI+Px+zZs3H06FGEh4cjOjoaGRkZVbZfvXo1pk2bhtmzZ+PMmTNYtmwZ1q1bh7fffrvOx6T7d+DSTWTkFcPNUYmeoT5Sl0NERHRPMiGEqMuO58+fx/Hjx+Ho6IiwsDAEBwebfIzIyEh06dIFixcvBgBotVoEBQVh8uTJmDZtWqX2kyZNwpkzZ4yW3Hj99ddx8OBB7N27t07HrEpubi7c3NyQk5MDV1dXk8/L1ryx/gR+OHINw7o+gPl/C5O6HCIislGmfH7XeSmM1q1bY9CgQXj22WfrFH5KSkpw5MgRREVF3SlGLkdUVBT2799f5T7du3fHkSNHDENaly5dwpYtW9C3b986H5PuT1GpBluTy1d+5/AXERFZCJMD0MCBA/HBBx9U2v7hhx9i0KBBtT5OVlYWNBoNfH19jbb7+voiLS2tyn2ef/55vPvuu+jRoweUSiVatmyJnj17GobA6nJMACguLkZubq7Ri2on8UwG8ovLEOjuiC4hnlKXQ0REVCsmB6A9e/YYelwq6tOnD/bs2WOWoqqze/duzJs3D//+979x9OhRbNiwAZs3b8Z77713X8edP38+3NzcDK+goCAzVWz99M/+6R8RALmcK78TEZFlMHkpjPz8fNjb21farlQqTeo58fLygkKhQHp6utH29PR0+PlV/RyZd955ByNGjMC4ceMAAGFhYVCr1ZgwYQJmzJhRp2MCwPTp0xEfH2/4OTc3lyGoFrILSrD7nG5yOdf+IiIiS2JyD1BYWBjWrVtXafvatWvRrl27Wh/H3t4enTp1MprQrNVqkZiYiG7dulW5T0FBAeRy45IVCgUA3ZOI63JMAFCpVHB1dTV60b1tSUpDqUagjZ8LQv1cpC6HiIio1kzuAXrnnXfwt7/9DRcvXsSTTz4JAEhMTMTq1avxww8/mHSs+Ph4jBo1Cp07d0bXrl2xcOFCqNVqjBkzBgAwcuRIBAYGYv78+QCAmJgYLFiwAB07dkRkZCQuXLiAd955BzExMYYgdK9jkvnoH34Y15G9P0REZFlMDkAxMTFISEjAvHnz8MMPP8DR0RHh4eHYuXMnPD1NmwQ7ZMgQZGZmYtasWUhLS0NERAS2bt1qmMSckpJi1OMzc+ZMyGQyzJw5E9evX4e3tzdiYmLwz3/+s9bHJPO4drsAh/66BZkM6M+V34mIyMLU+TlAerm5uVizZg2WLVuGI0eOQKPRmKs2yfA5QPf2790X8OHWc4hs7ol1f69+eJGIiKihNMhzgPbs2YNRo0YhICAAn3zyCZ588kkcOHCgrocjC7PpmG7piwEc/iIiIgtk0hBYWloaVq5ciWXLliE3NxeDBw9GcXExEhISTJoATZbtTGouzqXnwV4hR58wf6nLISIiMlmte4BiYmIQGhqKkydPYuHChbhx4wb+9a9/1Wdt1Ejpn/3Tq4033By58jsREVmeWvcA/fLLL3j11Vfx8ssvo3Xr1vVZEzViWq3AT8d1w1989g8REVmqWvcA7d27F3l5eejUqRMiIyOxePFiZGVl1Wdt1AgdvHwLqTlFcHGwQ682XPmdiIgsU60D0COPPIKvvvoKqamp+Pvf/461a9ciICAAWq0W27dvR15eXn3WSY3EpvLhrz4P+cFBqZC4GiIiorox+S4wZ2dnvPjii9i7dy+SkpLw+uuv4/3334ePjw/69+9fHzVSI1FcpsGWpFQAfPghERFZtjrfBg8AoaGh+PDDD3Ht2jWsWbPGXDVRI7XrbCZyi8rg5+qAR5o3lbocIiKiOruvAKSnUCgQFxeHn376yRyHo0ZqE1d+JyIiK2GWAETWL6ewFIlndSu/x0Zw6QsiIrJsDEBUK1uTU1FSpkVrnyZo58/lQYiIyLIxAFGtJJQvfRHXMRAyGYe/iIjIsjEA0T2l5RThwOWbALjyOxERWQcGILqnn05chxBAlxAPBHk6SV0OERHRfWMAonvSD3/FcukLIiKyEgxAVKPz6Xk4nZoLO7kM/bjyOxERWQkGIKqRfuX3nqHe8HC2l7gaIiIi82AAompptYLDX0REZJUYgKhaR1Ju43p2IZztFYhq6yt1OURERGbDAETVSjimG/6KfsgPjvZc+Z2IiKwHAxBVqaRMi83lK78P4MrvRERkZRiAqEp7/sxEdkEpvF1U6N7SS+pyiIiIzIoBiKqkv/srpkMAFFz5nYiIrAwDEFWSV1SK7afTAQBxHbn0BRERWR8GIKpk26l0FJdp0cLLGWGBblKXQ0REZHYMQFTJpvLhr9gIrvxORETWiQGIjGTkFeGPC1kAOPxFRETWiwGIjPz3RCq0Auj4gDuCmzpLXQ4REVG9YAAiI/rhrzgufUFERFaMAYgMLmbm4+S1HCjkMvTrwJXfiYjIejEAkcGm8qUvHmvtBa8mKomrISIiqj8MQAQAEEIg4bhu5XcOfxERkbVjACIAwLGr2Ui5VQBHpQJPt+PK70REZN0YgAjAneGv6Pa+cFbZSVwNERFR/WIAIpRqtPj5pG7l91iu/E5ERDaAAYiw90IWbqpL0NTZHo+14srvRERk/RiACAnlw1/PdvCHnYL/JIiIyPrx087GqYvL8Osp3crvHP4iIiJbwQBk47afTkdhqQbBTZ3QMchd6nKIiIgaBAOQjUvgyu9ERGSDGIBsWFZ+MX4/X77yewRXficiItvBAGTDNp9MhUYr0KGZG1p4N5G6HCIiogbDAGTDKg5/ERER2RIGIBv1V5Yax1KyIZcBMeFc+Z2IiGwLA5CN2lS+8Omjrbzg4+IgcTVEREQNiwHIBgkhsInDX0REZMMYgGxQ0vUcXMpSw0EpR3R7rvxORES2hwHIBiUc0w1/RbX1hYuDUuJqiIiIGh4DkI0p02jx35O6ABTH4S8iIrJRjSIALVmyBCEhIXBwcEBkZCQOHTpUbduePXtCJpNVevXr18/QZvTo0ZXe7927d0OcSqO37+JNZOYVw91Jiccf9Ja6HCIiIknYSV3AunXrEB8fj6VLlyIyMhILFy5EdHQ0zp07Bx8fn0rtN2zYgJKSEsPPN2/eRHh4OAYNGmTUrnfv3lixYoXhZ5VKVX8nYUH0z/7pF+YPe7tGkX+JiIganOSfgAsWLMD48eMxZswYtGvXDkuXLoWTkxOWL19eZXtPT0/4+fkZXtu3b4eTk1OlAKRSqYzaeXh4NMTpNGqFJRpsS04DAMRx5XciIrJhkgagkpISHDlyBFFRUYZtcrkcUVFR2L9/f62OsWzZMgwdOhTOzs5G23fv3g0fHx+Ehobi5Zdfxs2bN6s9RnFxMXJzc41e1mjHmXSoSzRo5uGITg8wEBIRke2SNABlZWVBo9HA19f4VmxfX1+kpaXdc/9Dhw4hOTkZ48aNM9reu3dvfPPNN0hMTMQHH3yA3377DX369IFGo6nyOPPnz4ebm5vhFRQUVPeTasTuPPsnAHI5V34nIiLbJfkcoPuxbNkyhIWFoWvXrkbbhw4davg+LCwMHTp0QMuWLbF792489dRTlY4zffp0xMfHG37Ozc21uhB0W12C3ecyAfDuLyIiIkl7gLy8vKBQKJCenm60PT09HX5+fjXuq1arsXbtWowdO/aev6dFixbw8vLChQsXqnxfpVLB1dXV6GVtNielokwr0M7fFa19XaQuh4iISFKSBiB7e3t06tQJiYmJhm1arRaJiYno1q1bjfuuX78excXFeOGFF+75e65du4abN2/C3992F/1MOKYb/orrGCBxJURERNKT/C6w+Ph4fPXVV/j6669x5swZvPzyy1Cr1RgzZgwAYOTIkZg+fXql/ZYtW4a4uDg0bdrUaHt+fj7efPNNHDhwAH/99RcSExMRGxuLVq1aITo6ukHOqbG5eqsA/7tyGzIZ0D+cw19ERESSzwEaMmQIMjMzMWvWLKSlpSEiIgJbt241TIxOSUmBXG6c086dO4e9e/fi119/rXQ8hUKBkydP4uuvv0Z2djYCAgLwzDPP4L333rPZZwH9dEL35OdHmjeFnxtXficiIpIJIYTURTQ2ubm5cHNzQ05OjsXPBxJC4JlP9+B8Rj4+HNgBg7tY1+RuIiIiPVM+vyUfAqP6dTo1F+cz8mFvJ0fvsJonlhMREdkKBiArt+m4bvjrqTY+cOXK70RERAAYgKyaRisqPPyQk5+JiIj0GICs2MFLN5GeWwxXBzv0asOV34mIiPQYgKyYfuX3vmH+UNkpJK6GiIio8WAAslJFpRr8kqRbT43DX0RERMYYgKzUrrMZyCsug7+bAyKbe0pdDhERUaPCAGSl9MNf/bnyOxERUSUMQFYop6AUu85y5XciIqLqMABZoS3JqSjRaBHq64K2/pb9JGsiIqL6wABkhfQrv8dy5XciIqIqMQBZmRvZhTh4+RYAoH84AxAREVFVGICsjH7l967NPdHMw0niaoiIiBonBiArox/+4uRnIiKi6jEAWZGzabk4m5YHpUKGvlz5nYiIqFoMQFYk4Zhu+KtnqA/cnewlroaIiKjxYgCyElqtwE/HOfxFRERUGwxAVuLwX7dwI6cITVR2eKqtj9TlEBERNWoMQFYi4bhu+Kv3Q35wUHLldyIiopowAFmBkjIttiSlAgAGdOTwFxER0b0wAFmB3ecykFNYCh8XFR5p0VTqcoiIiBo9BiArsKl8+Kt/eAAUXPmdiIjonhiALFxuUSm2n0kHAMRx+IuIiKhWGIAs3NbkNJSUadHS2xntA7jyOxERUW0wAFm4TRWe/SOTcfiLiIioNhiALFh6bhH2XbwJAIjlww+JiIhqjQHIgv33xA0IAXQK9sADTbnyOxERUW0xAFmwBMPwV4DElRAREVkWBiALdSEjD8nXc2Enl6FfBwYgIiIiUzAAWSj9yu+PP+gNT2eu/E5ERGQKBiALJITAphO64a9YDn8RERGZjAHIAh1NuY2rtwrhZK/A0+18pS6HiIjI4jAAWSD98Ffv9n5wsreTuBoiIiLLwwBkYUo1WmwuX/k9lktfEBER1QkDkIX5/XwmbqlL4NXEHo+25MrvREREdcEAZGE2lg9/PdshAHYKXj4iIqK64CeoBckvLsP202kAuPI7ERHR/WAAsiC/nkpDUakWIU2dEN7MTepyiIiILBYDkAVJOK4b/orryJXfiYiI7gcDkIXIzCvG3vOZAIA4rvxORER0XxiALMTPJ29AK4DwIHeEeDlLXQ4REZFFYwCyEIbhLy59QUREdN8YgCzA5Sw1TlzNhkIuw7Nc+Z2IiOi+MQBZgIRjuoVPH23lBW8XlcTVEBERWT4GoEZOCIFNx3UBiMNfRERE5sEA1MiduJaDv24WwFGpQHR7P6nLISIisgoMQI2cfvjr6Xa+cFZx5XciIiJzYABqxMo0Wvx8Uv/wQw5/ERERmUujCEBLlixBSEgIHBwcEBkZiUOHDlXbtmfPnpDJZJVe/fr1M7QRQmDWrFnw9/eHo6MjoqKicP78+YY4FbPaeyELWfkl8HS2x2OtvaUuh4iIyGpIHoDWrVuH+Ph4zJ49G0ePHkV4eDiio6ORkZFRZfsNGzYgNTXV8EpOToZCocCgQYMMbT788EMsWrQIS5cuxcGDB+Hs7Izo6GgUFRU11GmZxabyZ//0C/OHkiu/ExERmY3kn6oLFizA+PHjMWbMGLRr1w5Lly6Fk5MTli9fXmV7T09P+Pn5GV7bt2+Hk5OTIQAJIbBw4ULMnDkTsbGx6NChA7755hvcuHEDCQkJDXhm96egpAzbTulXfufwFxERkTlJGoBKSkpw5MgRREVFGbbJ5XJERUVh//79tTrGsmXLMHToUDg765aHuHz5MtLS0oyO6ebmhsjIyFofszHYfjodBSUaBHk64uEHPKQuh4iIyKpIeltRVlYWNBoNfH19jbb7+vri7Nmz99z/0KFDSE5OxrJlywzb0tLSDMe4+5j69+5WXFyM4uJiw8+5ubm1Pof6ssmw9AVXficiIjI3yYfA7seyZcsQFhaGrl273tdx5s+fDzc3N8MrKCjITBXWzS11Cfb8qVv5PZYrvxMREZmdpAHIy8sLCoUC6enpRtvT09Ph51fzQ//UajXWrl2LsWPHGm3X72fKMadPn46cnBzD6+rVq6aeilltPnkDZVqBhwJd0cqniaS1EBERWSNJA5C9vT06deqExMREwzatVovExER069atxn3Xr1+P4uJivPDCC0bbmzdvDj8/P6Nj5ubm4uDBg9UeU6VSwdXV1eglpY3H9EtfsPeHiIioPkj+aOH4+HiMGjUKnTt3RteuXbFw4UKo1WqMGTMGADBy5EgEBgZi/vz5RvstW7YMcXFxaNq0qdF2mUyGqVOn4v/+7//QunVrNG/eHO+88w4CAgIQFxfXUKdVZyk3C3A0JRsyGRATzru/iIiI6oPkAWjIkCHIzMzErFmzkJaWhoiICGzdutUwiTklJQVyuXFH1blz57B37178+uuvVR7zrbfeglqtxoQJE5CdnY0ePXpg69atcHBwqPfzuV/6hU+7t2wKX9fGXy8REZElkgkhhNRFNDa5ublwc3NDTk5Ogw6HCSEQteA3XMxU46PnOmBQZ2knYxMREVkSUz6/LfouMGtz6kYuLmaqobKTo/dDXPmdiIiovjAANSL6ld+j2vrCxUEpcTVERETWiwGokdBoBX46oXv4YWwEJz8TERHVJwagRmL/xZvIyCuGm6MSPUN9pC6HiIjIqjEANRIJ5Xd/9Q3zh70dLwsREVF94idtI1BUqsHW5PKV3zn8RUREVO8YgBqBxDMZyC8uQ6C7I7qEeEpdDhERkdVjAGoE9MNf/SMCIJdz5XciIqL6xgAkseyCEuw+lwGAa38RERE1FAYgiW1OSkWpRqCNnwtC/VykLoeIiMgmMABJbNMx3bN/4jqy94eIiKihMABJ6NrtAhz66xZkMqA/V34nIiJqMAxAEtI/+TmyuScC3B0lroaIiMh2MABJyDD8xcnPREREDYoBSCJnUnNxLj0P9go5+oT5S10OERGRTWEAkoh+5fdebbzh5siV34mIiBoSA5AEtBVWfufwFxERUcNjAJLAwcu3kJpTBBcHO/Rqw5XfiYiIGhoDkAQ2lS990echPzgoFRJXQ0REZHsYgBpYcZkGW5JSAfDhh0RERFJhAGpgu85mIreoDH6uDnikeVOpyyEiIrJJDEANbBNXficiIpIcA1ADyiksReIZ3crvsRFc+oKIiEgqDEANaGtyKko0WrT2aYJ2/q5Sl0NERGSzGIAa0E11CRyVCsR1DIRMxuEvIiIiqdhJXYAteaVnK4zqFgKNEFKXQkREZNMYgBqYs4p/ciIiIqlxCIyIiIhsDgMQERER2RwGICIiIrI5DEBERERkcxiAiIiIyOYwABEREZHNYQAiIiIim8MARERERDaHAYiIiIhsDgMQERER2RwGICIiIrI5DEBERERkcxiAiIiIyOZwafIqCCEAALm5uRJXQkRERLWl/9zWf47XhAGoCnl5eQCAoKAgiSshIiIiU+Xl5cHNza3GNjJRm5hkY7RaLW7cuAEXFxfIZDKzHjs3NxdBQUG4evUqXF1dzXrsxoDnZ/ms/Rx5fpbP2s+R51d3Qgjk5eUhICAAcnnNs3zYA1QFuVyOZs2a1evvcHV1tcp/2Ho8P8tn7efI87N81n6OPL+6uVfPjx4nQRMREZHNYQAiIiIim8MA1MBUKhVmz54NlUoldSn1gudn+az9HHl+ls/az5Hn1zA4CZqIiIhsDnuAiIiIyOYwABEREZHNYQAiIiIim8MARERERDaHAciM9uzZg5iYGAQEBEAmkyEhIeGe++zevRsPP/wwVCoVWrVqhZUrV9Z7nXVl6vnt3r0bMpms0istLa1hCjbR/Pnz0aVLF7i4uMDHxwdxcXE4d+7cPfdbv3492rRpAwcHB4SFhWHLli0NUG3d1OUcV65cWekaOjg4NFDFpvn888/RoUMHwwPWunXrhl9++aXGfSzp+gGmn6MlXb+qvP/++5DJZJg6dWqN7SztOurV5vws7RrOmTOnUr1t2rSpcR8prh8DkBmp1WqEh4djyZIltWp/+fJl9OvXD7169cLx48cxdepUjBs3Dtu2bavnSuvG1PPTO3fuHFJTUw0vHx+feqrw/vz222+YOHEiDhw4gO3bt6O0tBTPPPMM1Gp1tfvs27cPw4YNw9ixY3Hs2DHExcUhLi4OycnJDVh57dXlHAHdE1srXsMrV640UMWmadasGd5//30cOXIE//vf//Dkk08iNjYWp06dqrK9pV0/wPRzBCzn+t3t8OHD+OKLL9ChQ4ca21nidQRqf36A5V3D9u3bG9W7d+/eattKdv0E1QsAYuPGjTW2eeutt0T79u2Ntg0ZMkRER0fXY2XmUZvz27VrlwAgbt++3SA1mVtGRoYAIH777bdq2wwePFj069fPaFtkZKT4+9//Xt/lmUVtznHFihXCzc2t4YoyMw8PD/Gf//ynyvcs/frp1XSOlnr98vLyROvWrcX27dvFE088IaZMmVJtW0u8jqacn6Vdw9mzZ4vw8PBat5fq+rEHSEL79+9HVFSU0bbo6Gjs379foorqR0REBPz9/fH000/jjz/+kLqcWsvJyQEAeHp6VtvG0q9hbc4RAPLz8xEcHIygoKB79jY0FhqNBmvXroVarUa3bt2qbGPp16825whY5vWbOHEi+vXrV+n6VMUSr6Mp5wdY3jU8f/48AgIC0KJFCwwfPhwpKSnVtpXq+nExVAmlpaXB19fXaJuvry9yc3NRWFgIR0dHiSozD39/fyxduhSdO3dGcXEx/vOf/6Bnz544ePAgHn74YanLq5FWq8XUqVPx6KOP4qGHHqq2XXXXsLHOc6qotucYGhqK5cuXo0OHDsjJycHHH3+M7t2749SpU/W+aHBdJCUloVu3bigqKkKTJk2wceNGtGvXrsq2lnr9TDlHS7t+ALB27VocPXoUhw8frlV7S7uOpp6fpV3DyMhIrFy5EqGhoUhNTcXcuXPx2GOPITk5GS4uLpXaS3X9GICo3oSGhiI0NNTwc/fu3XHx4kV8+umn+PbbbyWs7N4mTpyI5OTkGsetLV1tz7Fbt25GvQvdu3dH27Zt8cUXX+C9996r7zJNFhoaiuPHjyMnJwc//PADRo0ahd9++63agGCJTDlHS7t+V69exZQpU7B9+/ZGPdG3rupyfpZ2Dfv06WP4vkOHDoiMjERwcDC+//57jB07VsLKjDEAScjPzw/p6elG29LT0+Hq6mrxvT/V6dq1a6MPFZMmTcLPP/+MPXv23PP/XVV3Df38/OqzxPtmyjneTalUomPHjrhw4UI9VXd/7O3t0apVKwBAp06dcPjwYXz22Wf44osvKrW11OtnyjnerbFfvyNHjiAjI8Ool1ij0WDPnj1YvHgxiouLoVAojPaxpOtYl/O7W2O/hndzd3fHgw8+WG29Ul0/zgGSULdu3ZCYmGi0bfv27TWO5Vu648ePw9/fX+oyqiSEwKRJk7Bx40bs3LkTzZs3v+c+lnYN63KOd9NoNEhKSmq01/FuWq0WxcXFVb5nadevOjWd490a+/V76qmnkJSUhOPHjxtenTt3xvDhw3H8+PEqw4ElXce6nN/dGvs1vFt+fj4uXrxYbb2SXb96nWJtY/Ly8sSxY8fEsWPHBACxYMECcezYMXHlyhUhhBDTpk0TI0aMMLS/dOmScHJyEm+++aY4c+aMWLJkiVAoFGLr1q1SnUKNTD2/Tz/9VCQkJIjz58+LpKQkMWXKFCGXy8WOHTukOoUavfzyy8LNzU3s3r1bpKamGl4FBQWGNiNGjBDTpk0z/PzHH38IOzs78fHHH4szZ86I2bNnC6VSKZKSkqQ4hXuqyznOnTtXbNu2TVy8eFEcOXJEDB06VDg4OIhTp05JcQo1mjZtmvjtt9/E5cuXxcmTJ8W0adOETCYTv/76qxDC8q+fEKafoyVdv+rcfZeUNVzHiu51fpZ2DV9//XWxe/ducfnyZfHHH3+IqKgo4eXlJTIyMoQQjef6MQCZkf6277tfo0aNEkIIMWrUKPHEE09U2iciIkLY29uLFi1aiBUrVjR43bVl6vl98MEHomXLlsLBwUF4enqKnj17ip07d0pTfC1UdW4AjK7JE088YThfve+//148+OCDwt7eXrRv315s3ry5YQs3QV3OcerUqeKBBx4Q9vb2wtfXV/Tt21ccPXq04YuvhRdffFEEBwcLe3t74e3tLZ566ilDMBDC8q+fEKafoyVdv+rcHRCs4TpWdK/zs7RrOGTIEOHv7y/s7e1FYGCgGDJkiLhw4YLh/cZy/WRCCFG/fUxEREREjQvnABEREZHNYQAiIiIim8MARERERDaHAYiIiIhsDgMQERER2RwGICIiIrI5DEBERERkcxiAiIhqQSaTISEhQeoyiMhMGICIqNEbPXo0ZDJZpVfv3r2lLo2ILBRXgycii9C7d2+sWLHCaJtKpZKoGiKydOwBIiKLoFKp4OfnZ/Ty8PAAoBue+vzzz9GnTx84OjqiRYsW+OGHH4z2T0pKwpNPPglHR0c0bdoUEyZMQH5+vlGb5cuXo3379lCpVPD398ekSZOM3s/KysKAAQPg5OSE1q1b46effqrfkyaiesMARERW4Z133sHAgQNx4sQJDB8+HEOHDsWZM2cAAGq1GtHR0fDw8MDhw4exfv167NixwyjgfP7555g4cSImTJiApKQk/PTTT2jVqpXR75g7dy4GDx6MkydPom/fvhg+fDhu3brVoOdJRGZS78utEhHdp1GjRgmFQiGcnZ2NXv/85z+FELpV7l966SWjfSIjI8XLL78shBDiyy+/FB4eHiI/P9/w/ubNm4VcLhdpaWlCCCECAgLEjBkzqq0BgJg5c6bh5/z8fAFA/PLLL2Y7TyJqOJwDREQWoVevXvj888+Ntnl6ehq+79atm9F73bp1w/HjxwEAZ86cQXh4OJydnQ3vP/roo9BqtTh37hxkMhlu3LiBp556qsYaOnToYPje2dkZrq6uyMjIqOspEZGEGICIyCI4OztXGpIyF0dHx1q1UyqVRj/LZDJotdr6KImI6hnnABGRVThw4ECln9u2bQsAaNu2LU6cOAG1Wm14/48//oBcLkdoaChcXFwQEhKCxMTEBq2ZiKTDHiAisgjFxcVIS0sz2mZnZwcvLy8AwPr169G5c2f06NEDq1atwqFDh7Bs2TIAwPDhwzF79myMGjUKc+bMQWZmJiZPnowRI0bA19cXADBnzhy89NJL8PHxQZ8+fZCXl4c//vgDkydPbtgTJaIGwQBERBZh69at8Pf3N9oWGhqKs2fPAtDdobV27Vq88sor8Pf3x5o1a9CuXTsAgJOTE7Zt24YpU6agS5cucHJywsCBA7FgwQLDsUaNGoWioiJ8+umneOONN+Dl5YXnnnuu4U6QiBqUTAghpC6CiOh+yGQybNy4EXFxcVKXQkQWgnOAiIiIyOYwABEREZHN4RwgIrJ4HMknIlOxB4iIiIhsDgMQERER2RwGICIiIrI5DEBERERkcxiAiIiIyOYwABEREZHNYQAiIiIim8MARERERDaHAYiIiIhszv8DRQLhMwf+5aYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotLearningCurve(history,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"malaria.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kaggle": {
   "accelerator": "gpu",
   "dataSources": [
    {
     "datasetId": 87153,
     "sourceId": 200743,
     "sourceType": "datasetVersion"
    }
   ],
   "dockerImageVersionId": 29943,
   "isGpuEnabled": true,
   "isInternetEnabled": false,
   "language": "python",
   "sourceType": "notebook"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}