CNN_Model.ipynb

{
 "cells": [
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.jpeg](attachment:image.jpeg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reference: https://towardsdatascience.com/develop-an-interactive-drawing-recognition-app-based-on-cnn-deploy-it-with-flask-95a805de10c0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "\n",
    "import keras\n",
    "import keras.utils as np_utils\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Flatten, Dropout\n",
    "from keras.layers.convolutional import Conv2D, MaxPooling2D\n",
    "import matplotlib.pyplot as plt\n",
    "#from keras import backend as K\n",
    "#K.set_image_dim_ordering('th')\n",
    "\n",
    "from sklearn.metrics import confusion_matrix, roc_auc_score, roc_curve, accuracy_score\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the data\n",
    "arm = np.load('data/arm.npy')\n",
    "#beard = np.load('data/beard.npy')\n",
    "#brain = np.load('data/brain.npy')\n",
    "ear = np.load('data/ear.npy')\n",
    "#elbow = np.load('data/elbow.npy')\n",
    "eye = np.load('data/eye.npy')\n",
    "face = np.load('data/face.npy')\n",
    "finger = np.load('data/finger.npy')\n",
    "hand = np.load('data/hand.npy')\n",
    "#knee = np.load('data/knee.npy')\n",
    "leg = np.load('data/leg.npy')\n",
    "\n",
    "mouth = np.load('data/mouth.npy')\n",
    "#nail = np.load('data/nail.npy')\n",
    "nose = np.load('data/nose.npy')\n",
    "#skull = np.load('data/skull.npy')\n",
    "\n",
    "tooth = np.load('data/tooth.npy')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "moustache = np.load('data/moustache.npy')\n",
    "toe = np.load('data/toe.npy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "arm = np.c_[arm, np.zeros(len(arm))]\n",
    "ear = np.c_[ear, np.ones(len(ear))]\n",
    "eye = np.c_[eye, 2*np.ones(len(eye))]\n",
    "face = np.c_[face, 3*np.ones(len(face))]\n",
    "finger = np.c_[finger, 4*np.ones(len(finger))]\n",
    "hand = np.c_[hand, 5*np.ones(len(hand))]\n",
    "leg = np.c_[leg, 5*np.ones(len(leg))]\n",
    "mouth = np.c_[hand, 5*np.ones(len(hand))]\n",
    "hand = np.c_[hand, 5*np.ones(len(hand))]\n",
    "hand = np.c_[hand, 5*np.ones(len(hand))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "eye = np.c_[eye, np.zeros(len(eye))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "arm = np.c_[arm,np.ones(len(arm))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "face = np.c_[face,2*np.ones(len(face))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "finger = np.c_[finger,3*np.ones(len(finger))]\n",
    "hand = np.c_[hand,4*np.ones(len(hand))]\n",
    "leg = np.c_[leg,5*np.ones(len(leg))]\n",
    "mouth = np.c_[mouth,6*np.ones(len(mouth))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "nose = np.c_[nose,7*np.ones(len(nose))]\n",
    "ear = np.c_[ear,8*np.ones(len(ear))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def plot_samples(input_array, rows=4, cols=5, title=''):\n",
    "    '''\n",
    "    Function to plot 28x28 pixel drawings that are stored in a numpy array.\n",
    "    Specify how many rows and cols of pictures to display (default 4x5).  \n",
    "    If the array contains less images than subplots selected, surplus subplots remain empty.\n",
    "    '''\n",
    "    fig, ax = plt.subplots(figsize=(cols,rows))\n",
    "    ax.axis('off')\n",
    "    plt.title(title)\n",
    "\n",
    "    for i in list(range(0, min(len(input_array),(rows*cols)) )):      \n",
    "        a = fig.add_subplot(rows,cols,i+1)\n",
    "        imgplot = plt.imshow(input_array[i,:784].reshape((28,28)), cmap='gray_r', interpolation='nearest')\n",
    "        plt.xticks([])\n",
    "        plt.yticks([])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 21 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_samples(eye, title='Sample eye drawings\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 21 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_samples(arm, title='Sample arm drawings\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 21 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_samples(face, title='Sample face drawings\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 21 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_samples(finger, title='Sample finger drawings\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 21 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_samples(hand, title='Sample hand drawings\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Sl6h4CU8Q7TkpKYkxY8YA8PDDDwPuvSI+n3iSkpLC6NGjAc8H9e233wLufKXs74wZM+xv2V+aSJx44okxHXNOBPqN1qxZA8CFF15o/ybXI7DTUKRITk7Ol49ZNShFUXxLTDSoYIgNfNJJJ/H8888D8NRTTwGwf//+TE0FAqlRo4YtNeuXZdxw+PvvvwG45557AHcVU8rAhkJWO4P58aKFnLNq1ar88ccfmbYVK1aM4cOHA9CvXz8Apk+fnu0YpUuXBlztQ7SjYNdLVoDkKTtw4EDr+wn0d8UK0fYffvhhmjRpAnjND5588kkARo0aZbUjqes0Y8YMu4z/5ptvAl6qS7yRRg3GGNu8IVCDaty4MQCTJ08GvHstnO9xbhQtWjRT3m1eiVtXFyE5OdlGpt58882Ae2OKmSFqZ0pKCuDGXkyZMgWArl27RnTc+UXiQpYuXZrtt5gHBw8eBGDMmDFh5SbJZyOfRyypXr16NgEF3hdPmjPu378fcG9oMRfENM0N+RJIyZZJkybZCPV48MknnwBucvZLL70EuE5x8KKjb7/9drt/4GKPIAJcFgeOp8xIJJFr0ahRI+vUFzMU4IorrgCw5rZ85wJNw+MlKSkpXwJKTTxFUXxL3DoLC1u3bg35hLnjjjsAr49cUlKSL0w7WVJ+4403ABg9enS2MhRixl500UW21Gvnzp0BbIBibohpGC8NKpj5JtSuXTti55LFkDJlyoTVzThavPrqq4AbCS3hBXPmzAHglVdeAcjUjSWYFiwBjKLpd+rUKWrjzQspKSlBtZl27doBnokn5X0jQbFixdRJrihKwSTuPqjc7HNxoEtBsBEjRtC+fXvA7XkPxLwU7qZNm2wGv6RvNG/e3C5Hi1NS+tnnRyPYvXs3EB8N6qyzzrK1kMTPFK2lc9GgTj755KgcPzckPELy6Bo3bswzzzwDeAGbos0HIpULSpYsaR3Pou1LGIlfNKicfJ4SNnLDDTdE/JxFixbNV3fsuEWSh4us5IiJ1KdPH+bNmwd4qw+xpmXLljaeZP78+QB2xSc/yOeUmppqq1VKQTT5IsSSwJwyqQ7ZvHnzqJwrVDvtWPDaa68B3jWYP38+9913HxC6bIp8ua+44go+++wzwBPiInSXLVsWVtZAIlC2bFkb9xYYy5gT6iRXFKXA4nsNKitFixaNew3sypUr21gsid0qUqSIrTstMUDizC9RooRduhWNaMWKFfa1aEu//fYbAPv27bPnEtPu1ltvjd6EcqBFixa2J5xoGJHWoCSMQT4DeTrHkjFjxvDcc88B3r3brl27PBWcGzZsGBMmTAC8dvWySCK5cAWBUqVKZSuBHAqNJFcUpcASFQ0qaz/2SBTK8hMffvghr7/+OuBl4efVH1akSBEbCCf+JXG816lTxy4KiB8ocGk7ViQnJ1vNTRy+7777blTOJcGZAwYMiMrxQ7F9+3YbQCtOetEYw6V27dph18cqTBQvXjxoLm64qAalKIpviYoGJYFfssIVr9W2aJGUlGTTGKQu0BdffGHbMMmKlDyV9+/fz5lnngl4wY21atXyRcBpbjzxxBMAdhUq0v35ZJVW8hLF5xVLHn74YesvkrSPYGVslbzz5JNP5qv6SFQE1CmnnAK4pTYKOvIFa9OmTZxHEh1kGd0vsTzRoEiRIjaMRYksUhr5eFETT1EU32LyEgJgjNkGxK4Hd2Sp6jhOrvZDgs8RCsc8w5oj6DwThBznmScBpSiKEkvUxFMUxbeogFIUxbeogFIUxbeogFIUxbeogFIUxbeogFIUxbeogFIUxbeogFIUxbfkKRevXLlyTiQ7PsSS1NRU0tLScu1EmMhzBFi6dGlaONHHiTzPcK8l6DwTgVDzzJOAqlatGkuWLInMqAKQqgeSeNugQYOInyPcY0Zjjlu2bLGti6LdbNQYE1a6QyTmuW3bNsBrdimNV6NNXu6PcOe5ceNGwLsXpT1YPInGPPPC2rVr+f777wH4z3/+E9FjBxJqnvFteZpB7969Abj44osBmDhxYrZ9tmzZArjttqU/mVRN8Dvz5s2zZVmkOF1BKKI/depUwOug27ZtW1vuOJDrr78egLPPPhvwOvX4CekQ1KNHD8DNwg+n83NBRNLfbrjhBnbs2AGEL6CkvK+UFxo9erTtsyfdjGfMmAEQVrkh9UEpiuJb4q5BHTx40DYLCGX+SJfXyZMn8/jjjwOJo0G1bt3aFkR78803ARg1alQ8hxQRDh06lOl9sP5/O3bssL3mHn300ZiM63ho1qxZpvezZs3i9ttvj9No4st7770HwPfff88555yTp/894YQTAM/8/+WXX2zvS2nLJd/3cLprqwalKIpvibsGtWLFCo4dOwbA+eefn+N+0rYJPPs2UShZsqTt2ir+tWeffdY2CkhUsrYTCtYlet68eXY/P1cdldZQ0g36k08+KbQalDQEKVeunPX9hou07SpZsiQAq1ev5t///nemfVavXg2oBqUoSoITdw1KGmAC1KtXL8f9UlNTAbexo7SWTiRuu+02AMaNGwfA9OnTfbGUnR/C0aBWrFhhfVPSSsvPSFPYaLXX8jPbt28HXK0XXI13+vTpAGzevBkIv7Gq+CenTp3K8OHDAU+7WrNmTdhjiruAWrNmjV1uDGW6SQfaRA1Ga9iwIeDN8dNPPy1wAkocpIGsWbPGXjNpwOBnKlSoAHideQoTH3zwAeBdVzHTwHNshyugatasCcAPP/zA4MGDAWznbfkuh4OaeIqi+Ja4a1AHDx60zmJRAYMhTvJED3CU4L+8Oh/9iDxpxbQLdv1Wr15NjRo1Yjqu/CBaw5EjR2xfw0ToXxgJpkyZkul9YMC0aFDSNzA3qlatCrj3+UsvvQRAmTJlAFi1alXYY1INSlEU3xJ3DerQoUMUK1Ys1/2ko610LU5UypUrB8CmTZviPJL8c/ToUSC4c1xYvXp1VPO4Ik2g32Xv3r1AwdegxN82d+7cTH/v2rUrH330EQC//vprno4pQdTFixe34UPLly8H8uaDiruAOnz4cEjn6b59+wBsW/FwnXR+RVp7B65eJirp6elAcAElX+7NmzfbHLxEQCL+wZuDPFQKKhKHKMn6khPbs2dPli1bBsDSpUsB2LBhAyeffDJAyNV0+cx27tzJhx9+CMDVV18NeInZjuOEdOuAmniKovgY32tQO3fuzPRepHeiIk+WtLS0OI8k/4Qy8SRaGMhzPlc8CWbiFXTEHBO3w5AhQwBo0qRJtn2rVKliX4trpmzZsoD73ZTXe/bsAVzzUe4F0cZ+//13IPSimKAalKIovsUXGlQoJ3lB1aD27Nljo23DWSTwI6F8UAVBgxItoLAgEf8jRowAoH379nTq1AnwwnxOOeUU/v77b8D7boqTfefOnfZvEnkO3sJWy5YtAejSpQsAjRs3zn1Mxz8dRVGU6OILDapo0aI5bs+qQYmNWxCQyoWJSjg+KGNMQqUnFUYf1BtvvAF4AZSiOXbr1o1rrrkGgO7duwNufS/JzwuFBPGuXbuWMWPGAJ5mJr6ucMpf+0JAhTJxsuZEJUqRupz4888/ATfnK9Hja0KZeGPHjgVc8zWRysoECzMo6IjzWgSJLFqde+65VhgdPnwYgCuvvDLH43z33Xfce++9APz444+AGx502WWXZdqvf//+YY9NTTxFUXyLLzSovIQZJLqJJxpUohXdC4Zot4FVDd5++23AixaWAnCJQkEw8STwUkoUVa9ePeT+999/P+BpUHI9g+VQLliwwDbJyKo5z5s3z5bmvuWWWwC33Ir8TcIKpIlGOKgGpSiKb4m7BnXw4ME8OckTPcxAcgqlxGwis3LlSsBbUp46dSp33XUX4OWvNW3aNC5jO14KQpiBNKm49tprARg5ciSQs+9HKg9MmDAB8MIAKleubCscdOzYEXDvX/kOStqWEPhd3bp1K+BWP5g5cybg9U3Mix857gIqPT09k2MyKzJpcaQnksM1GCKgEu2LG4xatWoBbucOcHuniQkuMV5y8ycKBcHEk5IoImgGDBgAuKVPxDwL5mKQ8ioSLb5mzRrrfpGCi8eOHePGG28Esi9gff311/a6y/195513WoF2PPFwauIpiuJb4q5B5eYkFymd6Kbd/v37AWyn1oLgJBenp5ir/fv3p27duoD3FA+lHfsRKVtcvHjxhNWgpHpE1r6FQ4cOZejQoYCn9dapU4eff/4Z8FrYP/fcc0DmEs3ieD/11FN55plngp735ptvtrFx0kUYvCKT4izPC6pBKYriW3yhQYWTi5fo4QXiUBZyW/pNBORpKXWB+vTpYzvKConYgQegdOnSzJ8/H4BJkyYBns/tzDPPDCsTPxDRJEMV94sU4kuaNWsWAAsXLgRg8eLFNoRAajLVqVPHdhKW72GwfoDh5I3u3r076Pf08ssvB+C1114DspeKDoVqUIqi+Ja4a1CHDh0KK1Az0X1Qn3/+OeD5bWRVJJEJluoiFVCFRNWgevXqxbBhwwD48ssv8328Ro0aAcfnh8krUrlSfkuDjlq1anHppZcCmVNWRNOSe7J06dLZjhmuBiWNEQKRulLPP/884KXB1K9fP9e5xF1ApaenhxUHlegmntR7FodhQSgjK9dNBBV4iwFCogqoxx57jEGDBgFeqZEVK1YAmUuJhEK+kL/99ht//fVXFEYZHpKke+jQIRstLuzcudOW8xUHejBEQIX6rh4+fDjo9RYTTxCBH46AUhNPURTfEhMNas+ePTz44IMAzJ49G/BKjaxfvz5khrSEGSRSyY6sHD582Kr2d955Z5xHEzkkkvirr76yfysoGhR4IQcSYJjXQEOJ6DbG2K4oS5YsAaBBgwaRGmauiNP7uuuuy5bBMHfuXBtCIG3fgyHVDIKZf0LFihVt/l8gp556KgC1a9cGvPvl7rvvznXsqkEpiuJbYqJBDRkyxNYHkpweaXEDcNNNN+X4vxLYmMhO8m+//dY6j1u0aBHn0UQO0aCkAYTjONk0qERPTToeZNlefC2BOWryt1hqUBK4Gaz91/Lly+0iVb169XI8xoYNGwCoWbNmjvtUqlSJTz/9FIAOHTpk2y65jd9//32YI4+ygJI4mddee80KpsB2yuFQEFbx5s6da28CWc0pCIiAEif5rl27sgmowNy2woIshEje2y+//GJ7wzVr1ixu4wrGqlWrbEyemLSBiPkncVOhciuvueYaFi1aBLh5fFk5/fTTAfLUyFVNPEVRfEtUNSiJv9ixY0eeTRtZ1jx48CCQ2GEGn332mY0xCTRtE50KFSpker9hw4YC5SQPB7lP9+/fb00YiXWTqgASiQ5uCRM/sXr1aqsVSR15qRm+efNmOz9xkofKIb3mmmtsDfNIoRqUoii+JaoaVGBeVqVKlfL0v1kjkmWpMpGQp86SJUts0F9BQorhi8awZMmSbJHGBUljFPbv32/DXrLmHgajadOmvPrqqwCUL18+mkPLMxs3bmTx4sVA8BK/WQnmaI8mqkEpiuJboqpB1alTB4BXX33V1gcKFykL+tFHHwHk+f/9gKzcjR07ltatW8d5NJFH/CmSy3XppZfaVZ8333wTyLvmnAiUKFGCp556CvCWzkuWLGn9baJFSvWD888/Pw6jDI9p06bZIOKKFSsCnu+sYsWKtouw+Bazpq1Em6gKKMnb6dmz53EfI9JOt3ggHS4KKpKUGsitt94ah5HEBmNM0JIkicgll1zCJZdcEu9h5IiaeIqi+BaTl/bbxphtwLroDSeqVHUc57TcdkrwOULhmGdYcwSdZ4KQ4zzzJKAURVFiiZp4iqL4FhVQiqL4FhVQiqL4FhVQiqL4FhVQiqL4FhVQiqL4FhVQiqL4ljylupQrV85J1OYFqamppKWl5doONpHnCLB06dK0cIL7Enme4V5L0HkmAqHmmScBVa1aNduVItEItwZ0Is8RwBgTVjRxIs8zL/W8dZ7+J9Q84964E7x6x/Pnzwegc+fO8RyOUohZuHAh4N2LgUjye+vWrUM2DyisbN68mc8++wyArl27RuSYvhBQ77zzDgD33Xcf4HaECFbAXVGizf/93/8B8NJLL+W4z7333kubNm0A794tjM0hsvK///2PKVOmAHDjjTcCUKRI/tzc6iRXFMW3+EKDkvZUgiYwK/Fi1KhRmX4HIj0ax40bx//+9z/Aq1c2a9asAt8gIiekOUpKSgq9e/cGvO9w586drcvmuuuuy/OxVYNSFMW3+EKDUpREQMpQ33vvvdZJLlrBsGHDePTRR+M2tnjywgsvAHDkyBH69+8PYJtETJo0KWTn8NxQDUpRFN+iGlQObN++neeeew7APhmLFy8ezyEVOmTJPyUlBYDu3btz4YUXxnFEHrKK16lTJwBGjBhBv379AE/TKugsW7YMgOeffx6Am266iRIlSgBYH12bNm3y1VdABVQOfPvttzz99NMAdOnSBYC6devGc0gJz5EjRzK9T0rybj9xqk6YMMHe8D/++CPgdZW+/vrrIzIO6Vd41113AXDBBRfQoUMHIO/9FwcPHgzA5MmT7biffPLJiIzTr0iHFwklkF5/w4cP55577gG8juBi/h0vauIpiuJbVIPKgcCnvQaN5o+ZM2cCWBOoUaNGAEycOJFff/0VgF69egFuBLd0LB4/fjwA7du3B7DmQ36RsJaff/4ZcB26AwYMAKBVq1aAGwktZlwo0/7cc88FoF27dkyYMAGAJ554AvA6Lhc07r77bgBWr14NeFH3K1eu5K233gI8zbJ69er5OpdqUIqi+BbVoHIgMHhUNai889dffwGun2fatGkA1sEtmtTgwYOtn69MmTKA64OKVB5XTogmJh11161bx6RJkwB4/fXXAVdrkzFJKIFocg0aNKB06dIAnHTSSXbb5MmTAVi8eDEA//jHP6I6j3gwbdo0XnvtNQAee+wxwO0oDe7nIhrTgw8+GJHzqYDKgVACavv27bz33nuAp8aXL1+ecuXKAXDmmWcCboZ5YWLVqlW8/PLLgPdFN8YwYsQIwGsBLp2Wf/vtN7p37w64cUQQnxWwqlWr2i/UAw88AMDXX39t8/LeffddAGvCJScn07FjRwDefvttwDUNxRR8//33gYIloDZs2ADAbbfdZtufy0rd6NGjAfjhhx+YMWMGELkVbzXxFEXxLapB5UAoDapPnz5WgwpF5cqVAWjWrBl33nkngHUAFwTERHrqqacAmD17NsWKFQO8JeiOHTsyZswYwHOu1qpVC3Cdq02aNInpmHNDNOLLL7/cagqyVP7xxx8D0KNHD/bt25fp/0qWLGkd7HJvPPPMMzEZczQ5duwYgI0GP3bsmNUk09LSAC9OsG3btnZhIVKoBqUoim9RDSoHgmlQEkz46aef0rdvX8B7eqSlpdknStbl11mzZjFx4kTAW8YeOnQoF1xwQa7jkHP+8ssvfPnllwAsX74807a+ffvGJYhUfEmHDh0C4Omnn6ZFixYAvPjiiwC0bNmSk08+GcBG5ouTPDk5OabjPV5knG3btgXcMIJgFTfatWsHYBcFfvvtN4CELm4nixhffPEF4Eb1i2/15ptvBrygTAlUjSSqQSmK4ltUg8qBYBrUn3/+CcCuXbuoX78+gF25k9+A9V1069YNcJ8wsjQrAWz9+vWzGlEwpDJhnz59ANi2bZvdVqVKFcBbLm/Xrl1MNaidO3cC8McffwDYVbqNGzfSsGFDwNM6HnnkEVspVZbmE53k5ORsaTvgLbcLkquWqBrUd999Z+/XW2+9FXDrO61YsQLwVjUff/xxAM4666yIj0EFVA4EE1CBRenr1auX7X8kn2vOnDnZtpUqVQqAAwcOAFizJxiLFi2yy9iNGzcGoHfv3vZ1pUqVwp9IFPjhhx8yvU9PTwfcUAG5kcVBfNppuTaYSThKlCjB7t27s/39nHPOAbzYKMkllITiRGHv3r2A6xg/44wzgMw5dWPHjgW8UAJxd0QDNfEURfEtqkHlQDANSpyB4DkEJSCxePHi1mmYNf8oPT3dPpXkqSOFvYKxbNkyG6Iwa9asTP/nB+bNmwd4XU7Wrl0LuGMU53hBbiLQuHFjhg4dCrhBu+BWQZAGAaJ1SLeiRENCYv744w8bSiIWwKFDh+yCj2iGoayB/KIalKIovkU1qBwIVs1AfEwbN27k/vvvB9zlf4AhQ4bQunVrgHwHq/Xq1YvbbrsNyFwzyS98+OGHANYhLukNrVq1KtCak9CpUyfrGJaQArle4Gkbe/bsif3g8oHkEkr6ztNPP53N8f/+++/bcJoePXpEfUz+u/t9QqhI8gEDBnDeeecBXpmQa665xsY1Pfzww4CXXHo8ZTf8KJgA1q9fb53k4rSXxOA333wzXsOKKbVr17ZmjaxoBSJCOmu0uV/ZtWsX4BXwk1VoeQgHMnbsWHvvy37RRE08RVF8iz8f0z4gUIMKps1cddVVAPz++++Aqx7L0rqECEjk9LBhw7jiiiuiOt5Y8fLLL9vPQzQnifO58sor4zauWBOqBI9oUGIK+Z2HHnoI8Pr+SUeWwK7Acp/PmzePkSNHxmxsqkEpiuJbfKFByRK8ZML7oUBcuAXrZFunTp3ssqs4jaWAfNOmTa12MXz4cCB4oGe8kMhwiXaXcIqzzjrL5l1JFPioUaNo3rw54OYkghdyUVBL3OYV0aBSU1PjO5Aw+Oabb+x1F99pnTp1su0XGJyZnz53eUU1KEVRfIsvNChZqhdfhh+exPkp+XvttdcCbiY/wLhx4xg0aBDglb3t2rWrzRSPZ+rK4sWLrT9N0jfExxQs36xYsWK2+qXkEkrYQe/evX0VUBovpE2WaFCvvPKKDbyV1J8aNWrkucVVJJH0pF69enH22WcDXoXMQMSPJlpWx44dY1r11BcCSpZqL7roojiPxCMSNckl0rpnz57WcS6O9BEjRjB16lTAMwUlujyWN+66deuy5ZVJkmvx4sXZtGkT4HVASU1NtfW7JUFaIstff/31qOZlJQqSHP3VV18BruDOStGiRRk4cCCA/R1LxNWwfPlya6oH65ozZMgQwMshjfVY1cRTFMW3xF2DOnbsmC3w5qes76SkJFsyJBImp3QIkSdSnz59rNknnWildG737t2tUzLaFC1aNFvks2iyJUqUsCbJ+vXrAdc0EDNOHOjy+YgZU9iRXMyffvoJgC1btthyOVu2bAFck0mi0UXrjGZOmyAlcuTcXbp0sSZ+VtavX2/LNUukvJiDsUI1KEVRfIsvNChxxkaqc2wk6NmzZ1QL+lepUsWmhkig3PTp04HMxe+izfXXX28z8l966SXAe8omJSVZ/5QUI2vSpInNzxJNav/+/QCceOKJMRt3IiCaZcWKFalYsSLgtd766aefbO6bhNfEAqlUINdOfFGBSDnj3r172wWTePjJwAcCyq+ULl06Zn3NpH229GSLNeLMl64reaUwCyaJsN6zZ481lUMhRQ+HDh1qu9vE6vObNGmSLd8jq3IVKlTItp+YdR999JF1NZx++ukxGWNW1MRTFMW3qAalKMeBLOhIV93q1avb7sSdO3cGyFTxQMz5cePGAW4349mzZ8dkrFKt4O6777YVCALLw0gIgXRSljCJtm3bZtovHqgGpSiKb4m7BhW4hO+HHDxFCYdRo0YBcPvttwMwaNAgWz8pWB0laaRwxx13AG63m1gthsycOROAzZs3s3nzZiBzpYKsXH311QC88cYb0R9cLqgGpSiKb4m7BnXCCSeQkpICUGBqJimFB6lKMX36dNvx+fvvvwe8PLZzzz3XlkeOR/6d+MvKli1r08oCOyNLKIH0emzatGlsBxiCuAso8JyKipLISIyT/PYLIoDatGmT73r5sUZNPEVRfIsJVPVy3dmYbcC66A0nqlR1HCfXNrcJPkcoHPMMa46g80wQcpxnngSUoihKLFETT1EU36ICSlEU36ICSlEU36ICSlEU36ICSlEU36ICSlEU36ICSlEU35KnVJdy5co5Uig/0UhNTSUtLS3X7geJPEeApUuXpoUT3JfI8wz3WoLOMxEINc88Cahq1arZkqXxRGpoT5kyBYBu3brlWte5QYMGYR3bL3M8XowxYUUT52eeUkNecrwC2bp1KwALFy4E4Lrrrjuuc4Qi3GsJ0b+eUr/9xx9/pF27dhE9tp/mGQxp2Fq7dm3AbUZ6PISapy+ShcPh2LFjgFtLWTqgSrH+K6+8MubtcAorgwYNss0dJGs/UFBJE8ibbroJgBkzZiRcgmpWNmzYAGCbmAZ+od5//33A7cortZZi2Xk3nkhtK0n2f/755yN+DvVBKYriW3yvQclTWmojL1u2jFatWgHwwgsvALFvJliYadiwIU888QQA48ePB6BHjx52u9QekmvTs2dP25g1Ubu/PP3004Db5QQ8sw6gZcuWgNuRR0yebt26xXaAcUKsmmjm86oGpSiKb/GtBjV37lzA7SwBXjPLDz74ICqOVyU8WrZsSePGjQF49tlnAbdVO7j15cUf9eKLLwKuxiWahZ9a2+cFWRQ4evRotm1SnK5mzZpMnToVKPgalGhMe/bsAbx669FANShFUXyLLzWoDz74gI4dOwLeE+qTTz4BMrcFl35e48aNs/3GxB81adKkmI23sNG7d28AbrzxRgC+/vprANtzDeCf//wnAOeccw7vvvsukLgaVDi0bduWkSNHAvD3338Dbnfqgsj69esB7/snnbGjga8E1IQJEwDXZGjUqBHgxVoEXmz5W//+/QE30Ouyyy4DoGvXrjEZ61dffQV4gnPIkCExOa8fkHifsmXLAvD2228DmQWU0L59e7v8vHv3bgDKlCkTg1HGln//+98888wzgHdPyEO2oPHbb79lel+zZs2onUtNPEVRfIsvNKiXX34ZgL59+wJwzTXXMHnyZABKlCgBwKpVqwBXa5In1MUXXwxASkqKNSliwd69e20gYsWKFWN2Xr9QvHhxwNMQ3nnnHQBGjhyZLZSgTZs2DB06FIDvvvsOgKuuuipWQ40Zl1xyCVWqVAEKvga1cePGTO9l3tFANShFUXxL3DWoKVOmcOeddwKeE/Xtt9+maNGiAHz++ecAtG7dGnCD/V555RXAazsdqo1zNBg0aJB1FEqqQ2HklltuAeDVV18FYOrUqdl8gOecc459nZqaGrOxxRpjDJUqVQI8X1tBJWtgpjFh5TMfF3ETUD///DMAt956K82aNQM8J3lSUhIrV64E4D//+Q/gJSTOmTMnLt1ZwU0IBRg1ahQDBgwAPAF6ww032Nykhx56KC7jizViVteqVQtwHyxZBVT58uVtnMzatWtjO8AYs2/fPgD7cFXyj5p4iqL4lrhpUM899xzgOsElTkaikNPS0qxJV6pUKQBmzpwJxKe3vXDPPfcAULlyZQYPHgzA6aefDrhPz8ceewzAalfiTC7odOnSBYBHH32UHTt2AJkz+s8880ygYJt4aWlprFixAvAi65X8oxqUoii+JeYalDjYZs2aBbjL0KedlrkA5Lhx4+zTdtmyZQDWARkPpB7QF198AcDQoUOtZhfoGJVibRJI2qFDh6iOa+zYsQC0aNGC6tWrR/VcofjXv/4FwMCBA5k/fz5ApuJtMraC7IOaM2eOvbcLYhhFvFANSlEU3xJzDUrqO23ZsgVwgzKzsmDBApuDd8EFF8RucDkgeX3yhOzcuTOHDx8GPK2gTZs2ttLkRRddFJVxyPK11GGSVJ/x48fHVYOSgNmyZctaLTNQgxJtsyD7oObMmUOFChUAqFu3bpxHU3CIuYCSHDbhyiuvzLbPd99956uSKikpKYCXa3bmmWdah6iU4giMBZFcpeOt0Qxw8OBBAD7++GMAJk6caCOUZZvkH8Y7YvmEE04A3FAQKU4XyJo1awDiKkSjzWeffWZNu2jGBRU21MRTFMW3xFyDEoezZMLL70AOHDjAySefHMNRBUc0ITFLR48ebbf9+uuvmfYNDH8Q53VemwVs27YNcMvlyrl27doFuFrbXXfdBXjL+vXq1cvT8aPNoUOHgoZWSB5lsGoHiY7cB+vXr1fneBRQDUpRFN8Scw1KluLFoehnJEtfUhfat29vt+3cuTPTvoFZ/JLykhuSzzd8+HDAbakFkJ6ebvMSe/bsCUCjRo1innOYV/bt25et/GtaWprVAvPjk/Mrc+bMsa9btGgRx5EUTGIuoMSMyRr75EdEQF199dVA5jFnNU0Dt4WK2ZKOIC+88IIVSNIdQ5zdAwcOTKgvs/QnXLNmTbZcPFlMgMyJw4lEqK4lIqDq1q1L5cqVYzWkuCLXW5CSSNHA349kRVEKNTHXoCT+qWrVqrE+dZ5ITU3l999/B+DBBx/Mtj2rBrV48WL7WuKgxEl+3333MWbMGADee+89AEqWLEm/fv0AL8cvUYvfSSG6I0eO2NAHISUlxZZrbtiwYczHFgmkQFvgQkh6ejoA8+bNA7y+jYUBiWeT6xrNBS3VoBRF8S0x16DS0tIAt0Sqn5HgQoA6depk2571qdG8eXObtb93717AayYwc+ZM2yhAqiD069fPF6EUkeDNN98E3AoG0uxCPoN33nnHhkVEs39aNPnhhx+AzEHFCxcuBLzecIUpxCCWgbeqQSmK4lviFmbg91W8wMz7atWqZdsu/fkkreGVV16x/jUJQQgMC5AmC4MGDYrKeOOBXEvxq9199912Ref1118H3B5xieqfkfAICQe58MIL7TZZvUtOTgaw3ZYLA/LdOOuss6J+rpgJKFGFJY/M7wIqcGlZnN7iGE9KSrJR5rJfYGS5JMeKudOuXTt69OgR9THHGiltLGES0tATvGj6Bg0a2GTiRENMOyEwcl8ElFzjkiVLxmxc8UYElJTqjiZq4imK4ltiokFt2rQpU8Qt+F+D6tSpE5s3bwa84FIpd3L48GFuuOEGwHP2V69e3ar7BTlrHzznv7Sbb9myJeDmCy5fvhzwQg9GjRoVhxFGhp9++inTe9Ggdu3axZIlSwDXrC1M7N6925q+sTDxVINSFMW3REyDEqk6fvx45s6dC2CfMps2bbL7Sba734t6lSpVioEDB8Z7GL5ECvjJIoCEToBXH0tCCv773//aEsjirxGHcvPmzWMy3uNF2oxJ51xpBLF582ZbA2vYsGGAq01eeumlAPa3hCXEsut1tAkMv4mFpZBvASX97Zo0aQK4K1iy2iGR1PXr16d+/fqApyaLOaQkHvKFlY428oUEb0FBHMxjxozhm2++AeCpp54CvCjsMWPG0KtXr5iM+XgQEy9rVdeKFSvavo0LFiwAYNGiRXz77bcAPPHEE4Db5Qbch7dEXfsZKecj86hQoYK91rItcHVbTTxFUQo1+dagxEkosT/XXnstDzzwAIDVmgKznWVZPmu5kkBCZY8r8UdMNAklkBCKG2+80UbHy+9HHnnELjJ88MEHgBeekHUZ308cPXrUWgdyH0slCvDy0MRKaNOmjc0WkFbwffr0AWD79u2+1qCmTZsGeAsaNWvWBNxwmi+//BLwOhoVL17cxv4Fiw+MNKpBKYriW/KtQQ0dOhTwwgamTZvGjBkz8ntY22VY8R9S82ndunUAPP7444CXk5cbrVq1Alztyq9s2rTJBhWLRiS/w0VCUCRH069ISeusfPvtt8yePRvwrlmDBg1sV6Fo1oESVINSFMW35FtNadCgAeC1Ztq7d69dtZGgPVl6DkSWqMVuz7otrw0HlNghPgjRgCTX7s8//7S+xR07dgBuipOkCElVCL+HmIC7Uin+FwnYFdLT0221BuHw4cPs27cP8D4f6SwtIQl+RXyCEkYRWJlBukZLkLKkccWKiNtRJUuWtCVy5bdSsJFCe4lacC8nCksCsMSshYqKj7VgEtTEUxTFt5i8LOkbY7YB66I3nKhS1XGcXBMAE3yOUDjmGdYcQeeZIOQ4zzwJKEVRlFiiJp6iKL5FBZSiKL5FBZSiKL5FBZSiKL5FBZSiKL5FBZSiKL5FBZSiKL5FBZSiKL5FBZSiKL7l/wFe5cekwLEgmgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x288 with 21 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_samples(leg, title='Sample leg drawings\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 21 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_samples(mouth, title='Sample mouth drawings\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6XGXpUzK0mzVrZjtTxIPu3bsD8NVXX1ltQLL0RVv6448/bDG3u+++G/DKqADWdHvhhReArJrIpk2bADewTspgJJO9e/favCzRYMVMiDSERM6BZs2a2SYL8hsFYbED3OV0aXAxefJkIDJNN+hI2Z8zzzwTKFi3FjmXJUf0iy++sOeGX6gGpShKYCnw7Upq39xzzz2A21ZJfD4SZPnWW29xzjnnFPRQeSLahfigRIOSFkd+M378eACrATzwwAO2D5podlKJYfbs2fZOVatWLcCtTiDtiaTmlTjZf/vtN1tLKLwIXBAaM/bs2ZMJEyYA2HKwsaYedevWzfqjPvzwQwCuvfZaH0YZO6INjho1yuamnX322ckckm9s2rTJnk+RLmjkhQRoSvWGoUOH+q5BxSygRBA8+eSTgJvMCq4T7oILLgDc/LFEk10gxaOkx7Jly2xpChHCxYoV4+GHH871M/LDidn7zDPPWIeimMQNGzYEXLNRVkakSB0kV0BJKZEBAwbYhY6C5kS2aNGCU089FfAim5MloJYtWwZ43XNat26d5++ZikicHmAXlQqC3HRlBb1bt2788ccfABx33HEF3j+oiacoSoCJSYNavXo1/fr1A7wiV2+88QaA78uM0SJRv4J09PWTBx54wDqEu3TpAkD79u2tyisakdxhMjMzbelUKY87f/582yFDnOpiJg8ZMsTGUEkE/XHHHZfUUr9ivl588cU219APRo8eDXg99pKFlB0RJ/2oUaNSrntQfsyZM8den6eddppv+5XI8p49e1o58PTTT/uyb9WgFEUJLDFpUHPmzLF5YY899hiQfM1JajANGjQIgObNmwP+FuaXANEJEybw4osvAl7RripVquSpQWUvebx///4DoqdlzC+++CI9evQAYPr06YAbrZvIbhrZkaBFefQLcdb64bSNlW3btpGWlgbA9ddfD8BRRx2VtPHEi6VLl1K7dm3A0+T9QDoztW3b1mpQ4uOKtnpJdlSDUhQlsMSkQUnaCniZ6clk//793HLLLYC3LC+S3E9EawIvOE2C+YoVK2bvwnLXkHSC1atXR5RrJqky4PVUE41F8g8TzcqVKwFsPav8+himImlpaTYkJGgpN36yY8eOAtcly4u77rrL+hTl+pDQmViJSUCJOQUwZswYwIsqBa8TicQDifPXL8S8lKaeQ4YMYerUqYDn7JRQBz9p3769fS7JlhLpvWfPHhs5Lqxevdo+FxOmWrVqgCuMpB289Mpbv3693V4c6PEQtNEggl+ErkSPH0xMmDDBhslI2MPByK5duwpscoFXa18KMkq5lnPPPdeey5KfV1ABpSaeoiiBJSYNqkOHDtZ527FjR8DLgWvTpg0zZ84E4MILLwSwwVvgBW+GVxgQ52949r90IpY22T///DMAc+fOteqjRGDXrFnTBjbG0xSSYMvwaFmJPL7jjjusxvjVV19l+dwNN9xgI91Fq1q3bp0NfgwPxgRXUwtK5rz0QJNwikWLFtmCgQcLW7duDYSrIt7s3r37AFfDI488YrXiYcOGAdCoUaNc9/H555/bktvZefXVV23QppwvS5cutVpVLKgGpShKYIlJgypatKgNiJRcqrFjxwKuI1l8RPfff3+WRz+oXLmy1WDEP3L22WcnLahOgtQaN25sC9XJEvUVV1wBeDlr2RGnuBRGEwdmkFoYSZUBWTZ+5ZVX7J02O+vWrePxxx8H4NJLLwXg8ssvT8AoC8bOnTt9XXYPKrt27TqgRtnixYutlSLF7MQCGjx4sPWrlitXDsi6kJOdH3/88YDKIcOGDcuyuBQtMefiiekhA5LHrVu32gmKExg8x7qYbuFs27YNyNrlQ5x5J554IuA5jWVlLGiE13WWHmLZY5+yI+U7zjvvvPgNrICULl0a8EznoUOH0rt3bwCb0Cw91lq2bGlXUcW8TwVOOOEE6yKQMiRNmjSxpo485tXSfM+ePbZIolQaldXuM88805o8yRTYOZl4V199NV988QWA/V2Fk046iSZNmgCeO+Xoo4+2/QFlgUiYPHnyAYs6CxYsKNCY1cRTFCWw+F4drHTp0rRq1crv3aYUErskqu2mTZusipyqPPDAA4Ab0S6daMXMl9fLli2zDtdUKlHyxhtvWC1JFieGDx9+gGkilTHKli17QGG9LVu2WI1CYsXkcd68edbcl2J9F110ka0IkigyMzMPqDBy44032syH7K6IxYsX2/81aNDA/l9yMbPXZl+9erXNgBBtsqAZAqpBKYoSWIJRX/UgQ/K5pJjdxIkTrZaRqkgtqjfffJMrr7wS8HxQskDx4YcfppTmJBhj7PK4PGZmZrJ48WLA1YDAC3URn2k4UsQPDvQpZmRk2FK74peVEtmJZOTIkTnmGErhRPEPSxhP4cKFc6ynJv5mOSdEU6xdu7b1v/qFalCKogQW1aDigFRQEPs91l6AQaR9+/Y2xUi6RUuaiB8NMYJCoUKF7O8YSUWMX375xa5sZ+9fmJmZaVcJJfUjnjlxuSH9CLMjfqlx48YBWVchc/Ihicbkd2WLnFABFUcKWhI3qKRq2+94kpcQK1SokG12mQpIk9wgoCaeoiiBxWTPwM9zY2M2AqviN5y4Ut1xnHwbm6X4HOHQmGdEcwSdZ4qQ6zyjElCKoiiJRE08RVECiwooRVECiwooRVECiwooRVECiwooRVECiwooRVECiwooRVECiwooRVECS1S5eOXLl3ekxEKqkZ6eTkZGRr6Fy1N5jgDz58/PiCT6OJXnGelvCTrPVCCveUYloGrUqGFr46QakTYiiPccpZbQBx98YOt8+9layhgTUbpDQeYplSM//fRTIP9WX1KzXKof1KlTJ6bjCtE0lTgUzlko2Dw//PBDwKtGIc0/EkVe89RqBglm4sSJANx55522DMtxxx2XzCFFjZT1lcJl0iBBuiZnp1evXoAn0ML7JCrJ56abbgKgW7duADz55JPJHE4W1AelKEpg8U2D+uGHHwC36JWUTU1VmzieiJkDXuGvVOPII4/M8lp6peWmQf36668AVKlSJa7jSiV+//132206ewOGZBHe9i0oqAalKEpg8U10r127FoCBAwfywgsvANgusz179kxa59+gIdpG8eLFbVPMVCN74f1//vknz+2XL18OBLtBabyR7+i0004DYNWqVRQpUgTwzonsXX8ThWhwyWjkkB+qQSmKElh806DatWsHuHeGJ554AvBaKW/evJmBAwf6daiUZsOGDYDX9jwVye47y0+Dkrb32dtuJxsJfxg6dCi33347cKB26BetW7cG3OsDoHr16vb5qFGjALj//vvjcuz8CLIG5bt3rkKFCgwbNizL/9544w3bIy5oJ2mi+fvvv4HUFlDZL+KMjIw8t5euyv/++2/cxhQNslAh4R07d+7k9ddfB+Cnn34C/DW3Jk+ezJw5cwCvucKUKVPsOfDRRx8ByRNQhQq5hpQ6yRVFUaIgruub1157LeD2uZ82bRoALVq0KPB+pQOq9POSO0AqIA7RihUrJnkksVOiRIksr3fu3Jnn9qJBSQR6shGnvYy7ePHirFy5EnAj/MHrMOwHHTt2tM9FWypfvrzt2isdjJOFmHiZmZlJHUdOpM6VrSjKIUdcNaimTZsCULp0ab766ivAHw3q5JNPBjzH/IsvvljgfSaKdevWAXD22WcneSSxs2vXriyv8/Mrli1bFoAVK1bEbUzR8Msvv2R53a1bN5577jkA7r77bsDtlnzWWWcV6DhLliwBYNmyZdStWxfImucmPqg1a9YU6DgFRSyQQ8JJHo7EeVSqVMlXB6kIpkGDBgFuB99TTz3Vt/3HA3GOSx5aly5dkjmcAhGtgLrooosAr7X25MmTgeS1hN++fXuW1+3atbNz6t+/P3DgHGPhiy++sM/T09MBKFmyJACO41gzX5zTq1evBnKPyI8HjuOwZ8+ehB0vWtTEUxQlsAQjCShK+vbtC8DHH38MuA7N2bNnA/6WLvGTb775JsvrCy64IEkjKTjZ55JfNYY77rgDgJdeegmAzp07AzBp0qSkLBa88847gJdTWL9+fWt6vfbaa4B7jjVr1gyI/ZwStwbkrJGJVi1IKEIiNajp06fbxYtkabR5oRqUoiiBJSU1KLHjJeevffv2DB8+HPCcnEHj22+/BaBy5coA1KtXL5nDiZn9+/fz1FNPAdCoUSMgf21QfJESwHvZZZcBbqEyWXaXfcUT8bUsWrQIyPobSGCm1EYaPHiw1bRuueWWqI4jYTDRFpCTiiDt27eP6nOx4DgOAM8//7xdxGjVqlXcjxstqkEpihJY4qpByR1r/fr1Vkr7yRVXXAFAy5YtbTVAWeELWiDklClTAK/6ZKry3nvv8dtvvwHw5ZdfRvXZc889F8D6C9u1a2f9PKJhFnRpPy9Gjx6d5fWll156wDaiHQ4dOpTHHnsMiF6D+uyzzwA3B1WQSg7NmzcH3NAYCdoVrVo0qETwyiuvAPD5559bv1uxYsUSdvxIiauAktKw//33Hy1btozbcYYMGcKJJ54IwEMPPQR4jtAgsGzZMpsYmqrOcUn47dOnD2eeeSZAzL+pmFZz5syx+7ruuusAWLBgwQEF8fzi888/z/L66quvPmAbMfXatWtno8q///57IHLhOXbs2CyvixQpYm9Q8ijR9QCNGzcG4Oeff45o/wVBrsmHH34YgCuvvDKwbhFQE09RlAATFw1q/fr1gOewrl27tlXl40HNmjVtiZfu3bsDXqeRICydivkCqatBpaWlAW4em5gHBaVMmTK8/fbbgOckHzNmDB06dPBl/9nJbkJJwObIkSNp27Yt4IUe3HXXXVaDeuSRRwCYOXNmRMeRvD7BcRyb7yaa6KZNm2wRR9Gc/AgOzYtPP/3Uao0NGjQAsItLQUU1KEVRAotvGpQ4xIcNG8bTTz8NwI4dOwA3IK9o0aJ+HSpHsvue7r33XgAWLlwY92Pnx6RJk2wvuKpVqyZ1LNEi+VnymzZu3Ng6evNi/vz5gJvaI/mXOZU4lsYBQrwCbffv32/9gJKac8455wCuhiNpU0OGDAHcNBXxE3333XeAF54guaCRIlpTdmSp//fffwe878dxHF9LZP/4448AXHXVVdbfJY78UqVK+XaceFBgASUrMtdccw3gVgyUE/jZZ58FsImS8USEkFxIl19+OeD+OOKITTRSvmL69Ok5OmRTgZEjRwKuox+ymqs5Ids1bNgQcC82OQ/EMRtO9lIt2Uu5+MWqVausQBBTSmKeqlSpwjPPPAN4q6zh45LVrT59+gBeo8vsyKqddLGR/NCMjAybZyece+651mEu54l8fvny5dSuXTuWaeaIlHupWLFiyggmQU08RVECS8walCy9ylKz9DybNm2aVZ2TwfHHH5/ldX7laOOJlI/NyMhIWee4lCGR3zS/RQcx2WS5fvPmzTZ3LxINKl4loaWSQjgSdnD88cdbLUa0JcdxbDkU6Vg0derUPI8huaFSnUBMq5yYMmWKLXMixxZHevZzOFakgoIsDgwbNixlNCdBNShFUQJLTBrUjh07bJcKyQIX30SFChV8GdjcuXMB7y4gd+RGjRrlGcgXbc+2eCLfSaFCheIaZhEvVqxYYaPGc9J+ckI0EInSfvvtt60GJZpIeIfhRPmgHnzwQavZ9OzZE4Bt27YBrqab3Sm9a9cuG+kt/s1+/frleQzxx8q+xOcVjmQ89O/f/4ASu+JM37Fjhy0HXBCWLl2a5fUpp5xS4H0mGtWgFEUJLDFpUJMnT7YrDuPHjwfy1pwyMzOtL0OaJ9SoUQOArl27Wptb7jh9+/blySefzHFfxYsXZ/DgwQDceeedB7wvlQ6EZGhQUpRfguAaNmyYJbVBkHmIr0OaTASFcB9KtCWKRWOUQEwgx8qNolUJ8eyuK7l1ksMpK74ZGRn06tUry/FPPPFEu9onPR3zCyCVOmWStiP+2czMTBvGIMHL48aNs335ZL/SR3LZsmW+VIjNroXNmDGD008/Pcv/9u7da6/hJk2aAG4F3KAQk4CSCxAiUxvT0tLsySHbT58+HXALhImw2rp1K+BG2UpXDYnilV5mzz//vP1B33jjDcBdshW1Wi4qcTgmMsRAIo9lfNJ1Jvwi3b17N+Cq82L6iLNWvtfchHOikQsIom9oKUv4r7zyil0syAn5DeWikPCEeFKrVi0A3nrrrVy3Wbt2rRWekcY9yU1aHuUcHjRokL1By7nRq1cvG5ojAkLwS0BJzJOE3HTt2tWGSMi5+dtvv9kQiDZt2uQ4nmSiJp6iKIElJg0qXAWUsqU5OfVEW+jRo4dVHyWfSUyv0aNH28JeItUvvPBCW9RMkLKyH330kQ0elDIREggIXnE06fQSz/Id4EVad+rUyY5HluIlfy38+xLzYu/evbYkrATSiZlRtGhRHnzwQcCbTzIId1hLuEakmpRosDNmzLBdS2rWrJllm4ULF9rAQXFcJ3O+4ZQvX/6AFu/RIprwoEGDbLMMyXB47bXXrJskezWB7KWAY0Wi8kWzHzx4sP2+hSZNmtgIdikoKNeodGXKD/ncokWL7EKCX6a6alCKogQXx3Ei/jvttNMcx3Gc6dOnO4ADOH369HH69OnjZGZmOpmZmY7jOM6ePXucPXv2OAMGDHAGDBjgAM6MGTOcGTNmOMkiNPaI5xgp3bp1c7p16+YATs+ePZ2ePXs6+/btc/bt25dlu+nTp2f53ho3bmzf2717t7N7927nxhtvdG688UYHcOrXr+/Ur1/fWbt2rbN27dqIxwPM82ueK1eutOPt3bu307t374jHkRfz5s1z5s2b55QrV86pWbOmU7NmTWfjxo3Oxo0bI/p8pL+lE8PvGQ/OOeccp1SpUk6pUqXs9/nzzz/b99u0aeO0adPGvjdt2jTHcRI3z127djm7du1yjj32WOfYY491mjRp4jRp0sTZs2dPnp+Tc7NEiRJOiRIlHMCpVq2aU61aNSctLc1JS0tz1q9fn+/x85pnTCZeo0aNbN3kHj16APD6668DbnE6cXYLbdu2jVhdTBUkqlj6qHXq1MmaaNlZsWIFnTp1yvK/8OqFEmcjzvQbbrjBJthKFchk9NGrUaOGdXZLHlrp0qWtSRJp1LeYwe+//z4A9913H+CavpMmTQIosDkVZAYNGmQXa6SO/kknnWTfFxNMkpIT3dRVzkXJR5TV5EaNGtnzW87H8HgxWXV0QqvvX3zxhS13dP3119vtZJVe+gRGk2eoJp6iKIElJg2qWLFidrlSyptI2EC5cuXs3VAcqtkd3gcDEyZMyPI6IyPD1rOWCGRZpp4wYYJdRBCH+aZNm+z7ErsljsVixYrZPK3s2miiEc1YHLpdunTh+eefB7x4H6le0bRpU3s3lQjxTz/91N6FJYxClr/HjRsXuNrx8aBBgwY2IyKn+crvLx2Yk4WEPUjM3t13321juaSkdteuXa0G9OabbwLYBZ1WrVpx8cUXA+4CCLha4eLFi4HYHOeqQSmKElgKXA/qxhtvzPJ4qCABpKJZTJw40fqLJFBPtKVbb73ValcSHPj444/nWbyufv36gHd3ShYSavDpp58CMGvWLBt0KKV/5XVuiMYk0dTxbKARVIIUnZ0fohEvX77cVoEQ39ntt99ut5PzXIKwAav5S0B2QfP/VINSFCWwpGRn4SAg/rWhQ4dG9TmpCtC2bVu7EigBrVLEv0SJErbpgx9Z7X7SuHFjW/dIxi2BfT/++KNd5ZHgv3r16iV8VUrxh8KFC9vVenn8/vvvWbJkCcABjSbigQqoJFGrVi2bE5aqyPK0FONL1aJ8SuScddZZcc/OCEdNPEVRAouRZeGINjZmI7AqfsOJK9Udxzk6v41SfI5waMwzojmCzjNFyHWeUQkoRVGURKImnqIogUUFlKIogUUFlKIogUUFlKIogUUFlKIogUUFlKIogUUFlKIogSWqVJfy5cs70iIq1UhPTycjI8Pkt10qzxFg/vz5GZEE96XyPCP9LUHnmQrkNc+oBFSNGjVsB5ZUI3vDwtxI5TkCGGMiiiZO5XlG+luCzjMVyGuemiwcUKQ+uVQozKtzs+I/K1asYPLkyYBXHbRs2bKA2wlaOidn72St+IsKKJ9YuHChLe6VW/OESNm1axc333wz4LVHz9504VDiv//+A6BUqVJxO4aUvrnzzjsBV0DlhYxF+hzefvvtB11jkCCgTnJFUQJLIDSodevWAV5H1YKWCU0Go0ePttpOQTWo4sWL21K7fnWZDSrSsVgK8C9ZsoSlS5cCsGzZMsBtMCHbSCE/P0lPT7emdN26dQEYMWIEF154IeC1xJJxpKen21ZR0kpr5MiRdmwDBw4EvKJ9SuyoBqUoSmAJhAbVt29fANvEUe6cqcS6des45phjfNuftCfasGGDb/sMGmvXrqVhw4aApylWr16dOnXqAG4DU/C0Gikx6zf//vuvLV8srbRuu+22A7YTh3jVqlWtv+nFF18E4Nlnn7XNTb/99lvAa8gpTSOU6FENSlGUwBIIDWrWrFkAVKlSJckjiZ1169ZRuXJl3/Z39NFurKU0AT0YGT9+POvXrwfchguQHP/jKaecYjUh0d4iRdrWP/HEE1xyySWAp4W1atUKgBkzZmRpdX6wsGPHDiC+oRZJF1DLli1jwYIFALz22mvJHUwMZGZmAjB//nx7YvqBOGYPZif5jBkzOO6444DkL4x06dKlwPto0KABANOmTQOwzQWaN2/O7NmzATj22GMLfJxkI4tB8p1J92kJ0fATNfEURQksSdegRo8ebbuRXn755UkeTfT8/PPPgLsEfc455/i2X3GS5xcwmMps3brVV7M4KIh5/tlnnwHQpEkTG3g7ZcqUpI0rP6T7tVyHOZmlv/zyC48++ijgWQ9jxowBVINSFOUQIxAalDR8TMV8s+nTp9vnfmpQchc+mMMMDnYkPGLAgAF06NABcP1uQKC6LYsV0LNnTwDrF8xJg3r11VftdSrX7UcffQTA/v37KVy4sK9jS5qAkpW7X3/9la5duyZrGAVGBFTdunWtWeYHchJs2bIFgD179tgVIyW1uOmmm6z59OyzzwLBEVCbN2/mrrvuAtwMBoDWrVvnuv0zzzxj5yKLAW+99Rbgmn//+9//fB2fmniKogSWpGlQAwYMAFxn8LXXXpusYcSMNDwVDcrvKOdq1apleZ2enk7t2rV9PYaSGIoVK2arHkgZnaDwwAMP8NNPPwHw4YcfAl5ZmZwoVaqUreSQPUJ+1qxZqkEpinLokHANasmSJYC3BNu/f3+buZ9KyDzEiS0FzPxCHKzC0qVLDzoNauPGjXYx4GBHfrt///0XcKs4SDBuMpB813feeYcnnngCwEbCR4rknlavXh1wNai7777bx1GqBqUoSoBJuAbVv39/wKtIKCsIqUZ4eAH4vyojOWESxLp06VIuvfRSX4+RbJYtW0aTJk2SPYyEkD3Hb+nSpUmtwCkrb0WKFKFz584F2pek9MjKvJ8kTECJSSQlKB566CEAjjzyyEQNwVdkiVViRrI7tQuKLPlK7pYUcTsYWLx4MeAucZ9wwglJHk1iyO543r59e1LGsX//fgDeffddwA0pKFeuXIH2Kc7y999/35qOfrkj1MRTFCWwJESDWr58ORdddBHgBSD6kT2eTESDklKx8aJevXqAp4EeDDz33HOAWxK3ffv2SR5NYti7d2+W14cdlpwIH6kYsmbNGgCGDx9e4H22aNECcH9PCTOQ6/vxxx/niCOOiHnfqkEpihJY4irGFy1aBHiFu8DL5k7l5eUVK1bYQmt+hxdkR8INxKmZyqxcuRKA9957D4DOnTunrA8yWoKgQc2cOZPHHnsMgMsuuwzwxwIQf9Nvv/1Gjx49ADclBtzzVkp6S1MJWfiJhLh8S+KAkwTJihUr8vXXXwNQq1ateBwyocQrQTgnxMTbvHkzf/31FwCVKlWK6zEjZfPmzYAXVV+mTJkDTr4NGzZY4SrdTqQC4wMPPJCQcQYBcU4LRYoUSdix58yZA7hxTrKoM2LECN+PU6lSJWsy3nfffYBr6t1xxx2AV9hOlJRITD818RRFCSwF1qCkG8Y333wDuNrT6NGjAc95lpaWxlFHHVXQQ+XK3r17reQ+7bTTADjjjDPidrxp06bZsIKaNWvG7TiQVVuSHnLx1qCkEFm/fv14/vnnAa/H27Zt26zmlBNissmy+tq1a615I+aEaFJ+dsEJOok08eRalCJ54o6oXbu2tWTieT0C1lk+efJkxo4dC8BVV10FeJ1wnnzyyXz3oxqUoiiBJSYxPm3aNB555BHACyDcunUr4IYRSH+wxx9/HMjfKTZ//nwA7rnnHsCNMBbfy9SpUwHyrIXUu3dv+vXrl2W7tLQ0gLgsY0+bNi3uznEh/E6b/S7sN7J/cWampaVZTVG67FaoUMFqSWXKlAGwRco2bdpku+/+888/gKslyf7E/3EoIv5DIV5dh9esWWObd0iXJClE165du4QVhZRzKSMjgzZt2gBet5sXXngBgI4dO+a7WKYalKIogSUmDWr06NH88MMPgFstELxOrE2bNo2o7KesavTp04enn34a8CR+y5Ytbc97SY2R44Qzc+ZMwM3vk4LtotHde++9gLtyIWkjBWXt2rUArFq1ymqH8SZcc4yXBrVr1y7AK5Y/YcIEwPUVFTRPS3H58ssvAbcrMUTffy9SJk2aZH2VEqLiZ7WQNWvW8N133wFeqeBVq1YBbs2y9PR0wLtWZIUXvPQtOd8+/vjjfHNxYxJQvXr1sk5pCRuI1OQRwSQOvLS0NBuOIA7ZUqVKsXz5csBTB2+88UYAjDHWnJT/1alTh0GDBgHY4luShDp27FjbQrugSPldSFz99HATb8+ePXE5hjgtJ06cCHhF1eT7VQ5EFof+/PNPIO/wmfnz51tHcadOnQD3PI4HN998sx2blBeWaw28mCVJ8D377LMP6NUnQmXp0qU2AViUgdWrV9vtRODUqFHDPkq5YCnBUqFCBbuoIkJLZMA111yT73zUxFMUJbDEpEFVqFCB888/H4Bhw4YBXtmU3JxeIkVFW5Lyoi+//LIN6grnwQcfBLDlgMeNGwe4pXXvv/9+wG03Dm6ZB1Fj5c4gkaynnnpqLFMsENLlQlThjIwM28JcVPunn346opbR4QsM2YP9/GDfvn02P0uii1Vzypt//vnHlkoRl0LHjh159dVXs2wnpswtt9xi3Re9evWK69iMMfZalMdffvkFcJf85Zz86quvABg5cmSe+xPN8LzzzgNcy0SuMalEEU1keLSoBqUoSmCJOVpM/EUiTUVTeeSRR6y0FVt2wYIFthupLD/LXTu3EqESHiC+peuvvx5w7xA7d+4EvOJ3OWlJEgYRL7JrM+vWrbN3rM8//xzwghYrVapky7sOGTIEcO+uEvqfF5KmYIyJS+2k8ePH28z2/O6misuUKVMOqM8V3gFa/FKysLN48WJb/UIKNSYS6W930kknWetDSE9Pt9dkdqpVq5b0XpUxC6j69esDXtkRSULM/gUIjRo1ArCRrDk1BcwysJBz+JNPPgE8k6148eKccsopQHxinPJCVmAqVqxoVwnFoTx9+nQrtETwiDkb7hCV1b8BAwbYHKXTTz8912PK6k/Dhg3jkmAtxeMAa7YreZNTFPbKlSvtKqgsNhQrVgxwV72TWT0zL2rUqGGd3EFETTxFUQJLgROCxLwSzWj58uX2rizOsypVqtCgQYMs/4sU6dYrS+HJRKJ/p06dSu/evQGvhMh5551nx5jXHal79+6AG14hJqE41eVzW7dutZqZLO/K5/ymQoUKVo2P19J3KjJ37lxbJFBK2koIy7x58zj++OMBz7RbsWIF+/btA1yHOXhF20TzVqJHNShFUQKL7ynVtWrVOihqPuVF3bp1baR7tBx++OGA6ywXH1peFRGaN28OxK9EcocOHTSsIAemTp2a60JLiRIlbLa+5Je1a9fOWgmKf6gGpShKYElO5XaFSy65xC5HSxrEf//9B7grmFI9wO9e9zmRip2d481DDz1k0zVkdVY0pNq1a0eUb6oUHBVQSUSc01JmRgkOxhhbYE1JHmriKYoSWEx4OYR8NzZmI7AqfsOJK9Udx8k30jHF5wiHxjwjmiPoPFOEXOcZlYBSFEVJJGriKYoSWFRAKYoSWFRAKYoSWFRAKYoSWFRAKYoSWFRAKYoSWFRAKYoSWKJKdSlfvrwT5Op7eZGenk5GRka+BY9SeY4A8+fPz4gkuC+V5xnpbwk6z1Qgr3lGJaBq1KjBvHnz/BlVHixcuND2gGvYsKEv+8yrrG44fsxRWrlL8u+5555boP1FgzEmomhiP+Yp7by/+eYbwG0sesEFFwBeX7R4EOlvCfE7Z//++2/ArekOkJmZydVXXw14tegLShDmGQ2O49i69m3btgWgbNmy+X4ur3kGMlm4V69etpD79OnTkzya6JG649IJOJECKt5s2bLF1o/67LPPDnhfmjmOGTMGgDZt2iRucHHmjz/+AOC+++6zbZsyMzPt+3IxHqpJxj///LPtMF6uXDnAa2UWK+qDUhQlsARSgypatKg1j1KRX3/9FcC2gT6YaN26tTVhpe2X1El6+OGHbZNIqcvdvHlz290kVZHqqdIirVixYrYmvTSWrVChQlJaSgWJcGvHL9eMalCKogSWQGpQJUuWtM05UwlxFq9duxY4uHxPwq233mq714ifUJqqgtdotXPnzoDbN/DOO+9M8Cj94dtvvwW8+UmV01GjRlG5cuWkjSuofPfdd7bbzTHHHOPLPlWDUhQlsARSgypRogQ7duxI9jCiYseOHTz88MOA19/uyiuvTOKI4sNtt93Gs88+C0C3bt0AbKfnsWPH2g410uuvf//+3HrrrYDXLTpVeOmllwD4v//7P8BbtUx1n1q8+O6772yYiV8E8ow5/PDD2b59e7KHERUdOnSwznFpfZ1qF2ReZGRkAHDDDTfYJXZp7f7yyy8D7uKGIMKrZcuWtinENddck7Dx+sHcuXMBuOKKKwAVTLkhzWtXr17te4t3NfEURQksgbzFlyxZMmVMPHEYp6Wl8cILLwD4ruYmk1mzZgFe8OHmzZtJS0sDvKaVOXHxxRcDrvkn4QgSaZ0KLdb/+usvNmzYAKANOfNh5syZ9rlqUIqiHDIEUoMqUaIEu3btArxUgkKFgiVL5a7x2GOPAW7AXteuXZM5JN/56KOP7BK7OL+/+uorTjjhhIj38fjjj9vFgkmTJgFw0UUX+TxS/5E0JdDGpvkh4RgVK1akdu3avu47kAIq3BkpScOS4xUEVq9ezeWXXw5AvXr1ABg+fHgyhxQXOnfuzKmnngpgHePRRku3bdvWXuCTJ08GUkNAKfkjSsQnn3wCuCa/3+Z7sNQSRVGUMAKpQYnWBFCkSJEkjiRnbr75Zvbt2wfAuHHjADc04mBj//79nHbaaUD0mpNw2GGH2bys2bNn+zY2JflIXNiWLVuAvBdNYkU1KEVRAktgNShxihcuXDjJo/H4+OOPAZgyZYotzHXcccclcUTxpXr16vz5558F3s+ZZ54JwJAhQwCs9nkwBbIeiki4iRQnbNKkie/HUA1KUZTAEshb2N69e7OkTSQbCXWQXLvTTz/dVpU8mDn22GNZvnx5gfdzxhlnANgcPkkJ+t///lfgfceL8GoamuKSlfT0dAC+/PJLAB599FEgPgG4gRRQe/bsCdRJsXjxYsAr+fr0008HLi4rHlStWtWWkHEcB4jtJBQBJcyZMwcItoBatmyZfV6rVq0kjiR49OvXD/ByLzt16hS3Yx38V5miKClLYDWoIIUXyB1fOOuss5I0ksTSsGFDW3JESvnWr18/6v1UqVIFgGrVqgGeadC4cWPKly8PELgCcFOmTLEBpnXr1k3yaILDhAkTGDFiBOBlUVSoUCFux1MNSlGUwKIaVAT88MMPAFSqVAnwNIGDnfCqDJJHF4sGJUiDAXGWf/rpp/Y9KY88YMCAA3xWiWTz5s0AvPvuu1xyySWA5uKBm94FcNNNN9nA2549e8b9uIEUUHv37g2Uk1xMvGReOMmgQoUKttSIOMu7dOkS8/6eeeYZwCtct3btWlasWAHAwIEDAbcki6wcJuMmJStSmzdvplevXgk/ftCQpGkplbNv3z4++OADIDGrm2riKYoSWAKpQRUqVIjdu3fn+N6mTZts19JEIUvOrVq1Suhxg4B0MnnttdcA1/wuaIya1DCXR/BCDs4//3zee+89wM15TBSiJUtViu7du0dVVuZgRbrzSB7lF198YUvvJALVoBRFCSy+a1Bz587l/vvvB7B3wmglbr169Xj99dcBr/fa77//Drh+IMl/a9myJQDt2rWLa5ldqXtzKDpLRcuRJhZz5szh7LPP9v045513HgC1a9dmwoQJQOI0qH379nHXXXcB2L5u3bt3T8ixg8xzzz1nNecnnngC8K65RKEalKIogcU3DWrevHmAuzQtgXlHHnlkTPsKT4FYtGgRgL1rjxgxwt5hJZt6yJAhdmVBVhv8QHLwJM0jSPmBiWDmzJncd999gBdaEW//Q82aNVm3bl1cj5GdcePGsXDhQsCrHJpXBdd58+bZVlqS8tSlSxeOPvroOI80MfTt2xeAHj162J6GvXv3TspYfBNQr776KuCaQTNmzACgbNmyMe3r5JNPts9FQIkJcNttt3HbbbcBXtmOpk2b2vwgPwVUeOE8CGbxvHggpXlbt25t89Ak+rtq1ap5flYWNx588EHAjSKXiOO88vjkt1y8eLGNs0kUv/32mx3b+eefn+t2Ev5wzjnnWMG0f/9+AIYOHcpzzz0HuL0DIecb2vz58wG3GWisN/B4IWEVIow6dOhgTbxk5Z6qiacoSmDxTYNasmQJ4OZvSX5VrJQtW5Y+ffoA7t0qP4oWLWojgP1ETDxB7pYHO2IulylTxmrDZcqUieiz99xzDwBvvvmm/Z/0OJTfNCdkYWX16tW89dZb0Q+6AFSpUsWa8VJKRJzl4Ia2ANx+++0AHHHEETY3cevWrYA7b3lfglmbN28OQLNmzfj+++8BeP/99wEYNGiQnXOyEQe4WCEdO3YEXNdJsnsYqgalKEpg8U2DkrukX05Ukeo5IX4OKRo3Y8YM3njjDV+OG07JkiUBr7Ps559/DniF6w5WJP2kTp06EWtO4PphRHOSu/Fff/1lna5r164FsC27ypUrZ1NcPvzwQ8D1fyS6M3PLli2tU1xSXbp06WIdxPJ9CKNHj7YZ/PL49ddfW9+dtGGSpgJjx461DnTJXxNNM1mIxtilSxcGDRoEwCOPPAJ4KUlBwDcBJQ7BFStW2JgZcXxOmDCB8ePHA16yabdu3WI6zpdffskDDzxgjwXuhSHqdTwQQSiO32nTptGsWbO4HS/ZiGN85MiRTJ8+Hcjb1P76668B10xr164d4P2+mZmZtva4mG7hJpwIBrlI4ln8LDcqVapkHcMy7o8//pgjjjgC8FajR48eDXimW3bEwS6PgwcPBmD9+vVUrFgRSH4DWnFbiBk3bNgwKzSDmHuoJp6iKIHFNw1K1MPrrrvOqrOiQe3du9fG0UTi9N63bx8bN24EvLbKL7zwAgALFiywWti0adMA4hLZHM5NN90EeHla559/Pi1atADgqKOOAlwnstydMjIyAOwcMjIy7P/E4fr222/7GhLhJ/Jdf//99za8Q1qgd+7cmdKlSwOe1iPdWho0aMCoUaMAL6SgcOHCvPjii4BbKhm8EspbtmyxxeCSXbBOzt82bdoAMH78+Cz5Z3Bg2EmkJHtu4UycOBFwNSdwz+VLL70UgA0bNgBeOE2ZMmVsNYM1a9YArpkunX7kvVtuuQWIT01y1aAURQksvmlQV155JeD2yBInodxpmzdvzqmnnhrxvnr06MGAAQOy/E9ywtLS0rjqqquAxPVVk7CJH3/8EXAduaK9ybL05s2brX9BthcHav369a1WKY9NmzaNehxz584FsHc8Ce3wO+BPfC8zZsyw2o9oS++8847dTr5/cSz36tUrzwhseS+acyHR1KtXzz5K9QrRFA4GGjduDLgde8ANypXu0dEi13fbtm2B2AOz80I1KEVRAovvKkijRo1o1KhRgfZx//33U7t2bcALW5CSsMlEqhlk1+4SRZ06dQAv4FHuYPHiyCOP5KmnngKga9eugOsTFN+a3DmlFPLBhpQmFr/UxRdfnMzh+IJoOdJCbdmyZdYKkBxICeP577//rD/qmGOOAdxUJ9G+xLcWzxSwQBasq1y5so1BUTxEIN1xxx0JP7aYkVdccUXCj50sTjrppCyPBxOFCxcGXFNWzNogoiaeoiiBxUhEaUQbG7MRWBW/4cSV6o7j5FsPI8XnCIfGPCOaI+g8U4Rc5xmVgFIURUkkauIpihJYVEApihJYVEApihJYVEApihJYVEApihJYVEApihJYVEApihJYVEApihJYVEApihJY/h/1EP6jPI8PCgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x288 with 21 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_samples(nose, title='Sample nose drawings\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 21 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_samples(ear, title='Sample ear drawings\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.concatenate((eye[:10000,-1], arm[:10000,-1], face[:10000,-1], finger[:10000,-1], hand[:10000,-1],  leg[:10000,-1], mouth[:10000, -1], nose[:10000, -1], ear[:10000, -1]), axis=0).astype('float32') # the last column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Merging arrays and splitting the features and labels\n",
    "X = np.concatenate((eye[:10000,:-1], arm[:10000,:-1], face[:10000,:-1], finger[:10000,:-1],  hand[:10000,:-1], leg[:10000, :-1], mouth[:10000, :-1], nose[:10000, :-1], ear[:10000, :-1]), axis=0).astype('float32') # all columns but the last\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test, y_train, y_test = train_test_split(X/255., y, test_size = 0.2, random_state =0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#one hot encoding the output\n",
    "# one hot encode outputs\n",
    "y_train_cnn = np_utils.to_categorical(y_train)\n",
    "y_test_cnn = np_utils.to_categorical(y_test)\n",
    "num_classes = y_test_cnn.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reshape to be [samples][pixels][width][height]\n",
    "X_train_cnn = X_train.reshape(X_train.shape[0], 28, 28,1).astype('float32')\n",
    "X_test_cnn = X_test.reshape(X_test.shape[0], 28, 28,1).astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def cnn_model():\n",
    "    # create model\n",
    "    model = Sequential()\n",
    "    model.add(Conv2D(30, (3, 3), input_shape=(28, 28,1), activation='relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    model.add(Conv2D(15, (3, 3), activation='relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    model.add(Dropout(0.2))\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(128, activation='relu'))\n",
    "    model.add(Dense(50, activation='relu'))\n",
    "    model.add(Dense(num_classes, activation='softmax'))\n",
    "    # Compile model\n",
    "    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/19\n",
      "360/360 [==============================] - 28s 75ms/step - loss: 1.1910 - accuracy: 0.6005 - val_loss: 0.5713 - val_accuracy: 0.8235\n",
      "Epoch 2/19\n",
      "360/360 [==============================] - 25s 71ms/step - loss: 0.5455 - accuracy: 0.8303 - val_loss: 0.4488 - val_accuracy: 0.8616\n",
      "Epoch 3/19\n",
      "360/360 [==============================] - 25s 70ms/step - loss: 0.4640 - accuracy: 0.8559 - val_loss: 0.4056 - val_accuracy: 0.8759\n",
      "Epoch 4/19\n",
      "360/360 [==============================] - 24s 68ms/step - loss: 0.4131 - accuracy: 0.8697 - val_loss: 0.3801 - val_accuracy: 0.8813\n",
      "Epoch 5/19\n",
      "360/360 [==============================] - 25s 68ms/step - loss: 0.3905 - accuracy: 0.8777 - val_loss: 0.3615 - val_accuracy: 0.8857\n",
      "Epoch 6/19\n",
      "360/360 [==============================] - 24s 68ms/step - loss: 0.3616 - accuracy: 0.8841 - val_loss: 0.3583 - val_accuracy: 0.8887\n",
      "Epoch 7/19\n",
      "360/360 [==============================] - 24s 68ms/step - loss: 0.3439 - accuracy: 0.8902 - val_loss: 0.3376 - val_accuracy: 0.8934\n",
      "Epoch 8/19\n",
      "360/360 [==============================] - 24s 68ms/step - loss: 0.3289 - accuracy: 0.8957 - val_loss: 0.3303 - val_accuracy: 0.8953\n",
      "Epoch 9/19\n",
      "360/360 [==============================] - 24s 68ms/step - loss: 0.3156 - accuracy: 0.8991 - val_loss: 0.3347 - val_accuracy: 0.8928\n",
      "Epoch 10/19\n",
      "360/360 [==============================] - 26s 71ms/step - loss: 0.3093 - accuracy: 0.8989 - val_loss: 0.3240 - val_accuracy: 0.8963\n",
      "Epoch 11/19\n",
      "360/360 [==============================] - 25s 69ms/step - loss: 0.2890 - accuracy: 0.9076 - val_loss: 0.3197 - val_accuracy: 0.8972\n",
      "Epoch 12/19\n",
      "360/360 [==============================] - 25s 70ms/step - loss: 0.2880 - accuracy: 0.9061 - val_loss: 0.3163 - val_accuracy: 0.8993\n",
      "Epoch 13/19\n",
      "360/360 [==============================] - 26s 71ms/step - loss: 0.2807 - accuracy: 0.9085 - val_loss: 0.3148 - val_accuracy: 0.9008\n",
      "Epoch 14/19\n",
      "360/360 [==============================] - 26s 72ms/step - loss: 0.2719 - accuracy: 0.9109 - val_loss: 0.3121 - val_accuracy: 0.9013\n",
      "Epoch 15/19\n",
      "360/360 [==============================] - 26s 71ms/step - loss: 0.2683 - accuracy: 0.9116 - val_loss: 0.3147 - val_accuracy: 0.8999\n",
      "Epoch 16/19\n",
      "360/360 [==============================] - 26s 71ms/step - loss: 0.2652 - accuracy: 0.9131 - val_loss: 0.3091 - val_accuracy: 0.9017\n",
      "Epoch 17/19\n",
      "360/360 [==============================] - 26s 72ms/step - loss: 0.2546 - accuracy: 0.9160 - val_loss: 0.3200 - val_accuracy: 0.8975\n",
      "Epoch 18/19\n",
      "360/360 [==============================] - 26s 72ms/step - loss: 0.2460 - accuracy: 0.9190 - val_loss: 0.3110 - val_accuracy: 0.9004\n",
      "Epoch 19/19\n",
      "360/360 [==============================] - 26s 72ms/step - loss: 0.2473 - accuracy: 0.9198 - val_loss: 0.3103 - val_accuracy: 0.9015\n",
      "Final CNN accuracy:  0.9014999866485596\n",
      "Wall time: 8min 4s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "np.random.seed(0)\n",
    "# build the model\n",
    "model_cnn = cnn_model()\n",
    "# Fit the model\n",
    "model_cnn.fit(X_train_cnn, y_train_cnn, validation_data=(X_test_cnn, y_test_cnn), epochs=19, batch_size=200)\n",
    "# Final evaluation of the model\n",
    "scores = model_cnn.evaluate(X_test_cnn, y_test_cnn, verbose=0)\n",
    "print('Final CNN accuracy: ', scores[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
      "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
     ]
    }
   ],
   "source": [
    "y_pred_cnn = model_cnn.predict_classes(X_test_cnn, verbose=0)\n",
    "\n",
    "from sklearn import metrics\n",
    "c_matrix = metrics.confusion_matrix(y_test, y_pred_cnn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "def confusion_matrix(confusion_matrix, class_names, figsize = (10,7), fontsize=14):\n",
    "    df_cm = pd.DataFrame(\n",
    "        confusion_matrix, index=class_names, columns=class_names, \n",
    "    )\n",
    "    fig = plt.figure(figsize=figsize)\n",
    "    try:\n",
    "        heatmap = sns.heatmap(df_cm, annot=True, fmt=\"d\")\n",
    "    except ValueError:\n",
    "        raise ValueError(\"Confusion matrix values must be integers.\")\n",
    "    heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=fontsize)\n",
    "    heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45, ha='right', fontsize=fontsize)\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_names = ['eye', 'arm', 'face', 'finger', 'hand', 'leg', 'mouth', 'nose', 'ear']\n",
    "confusion_matrix(c_matrix, class_names, figsize = (10,7), fontsize=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Misclassification when y_pred and y_test are different.\n",
    "misclassified = X_test[y_pred_cnn != y_test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x720 with 51 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_samples(misclassified, rows=10, cols=5, title='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.python.keras.layers import deserialize, serialize\n",
    "from tensorflow.python.keras.saving import saving_utils\n",
    "\n",
    "\n",
    "def unpack(model, training_config, weights):\n",
    "    restored_model = deserialize(model)\n",
    "    if training_config is not None:\n",
    "        restored_model.compile(\n",
    "            **saving_utils.compile_args_from_training_config(\n",
    "                training_config\n",
    "            )\n",
    "        )\n",
    "    restored_model.set_weights(weights)\n",
    "    return restored_model\n",
    "\n",
    "# Hotfix function\n",
    "def make_keras_picklable():\n",
    "\n",
    "    def __reduce__(self):\n",
    "        model_metadata = saving_utils.model_metadata(self)\n",
    "        training_config = model_metadata.get(\"training_config\", None)\n",
    "        model = serialize(self)\n",
    "        weights = self.get_weights()\n",
    "        return (unpack, (model, training_config, weights))\n",
    "\n",
    "    cls = Model\n",
    "    cls.__reduce__ = __reduce__\n",
    "\n",
    "# Run the function\n",
    "make_keras_picklable()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import pickle\n",
    "with open('model_cnn.pkl', 'wb') as file:\n",
    "      pickle.dump(model_cnn, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import flask\n",
    "from flask import Flask,render_template,url_for,request\n",
    "import pickle\n",
    "import base64\n",
    "import numpy as np\n",
    "import cv2\n",
    "import tensorflow as tf\n",
    "from tensorflow.python.keras.backend import set_session\n",
    "\n",
    "#session = keras.backend.get_session()\n",
    "#init = tf.global_variables_initializer()\n",
    "#session.run(init)\n",
    "\n",
    "#Initialize the useless part of the base64 encoded image.\n",
    "init_Base64 = 21;\n",
    "\n",
    "#Our dictionary\n",
    "label_dict = {0:'eye', 1:'arm', 2:'face', 3:'finger', 4:'hand', 5:'leg', 6:'mouth', 7:\"nose\", 8:\"ear\"}\n",
    "#'eye', 'arm', 'face', 'finger', 'hand', 'leg', 'mouth', 'nose', 'ear'\n",
    "\n",
    "\n",
    "#tf_config = some_custom_config\n",
    "sess = tf.compat.v1.Session()\n",
    "\n",
    "\n",
    "#Initializing the Default Graph (prevent errors)\n",
    "graph = tf.compat.v1.get_default_graph()\n",
    "tf.compat.v1.disable_eager_execution()\n",
    "\n",
    "#\n",
    "    \n",
    "    #Use pickle to load in the pre-trained model.\n",
    "with open(f'model_cnn.pkl', 'rb') as f:\n",
    "    set_session(sess)\n",
    "    model = pickle.load(f)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * Serving Flask app \"__main__\" (lazy loading)\n",
      " * Environment: production\n",
      "   WARNING: This is a development server. Do not use it in a production deployment.\n",
      "   Use a production WSGI server instead.\n",
      " * Debug mode: off\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)\n",
      "127.0.0.1 - - [09/Sep/2021 14:04:06] \"\u001b[37mGET / HTTP/1.1\u001b[0m\" 200 -\n",
      "127.0.0.1 - - [09/Sep/2021 14:04:14] \"\u001b[37mGET / HTTP/1.1\u001b[0m\" 200 -\n",
      "127.0.0.1 - - [09/Sep/2021 14:04:14] \"\u001b[36mGET /static/draw.js HTTP/1.1\u001b[0m\" 304 -\n",
      "127.0.0.1 - - [09/Sep/2021 14:04:42] \"\u001b[37mPOST /predict HTTP/1.1\u001b[0m\" 200 -\n",
      "127.0.0.1 - - [09/Sep/2021 14:04:56] \"\u001b[37mPOST /predict HTTP/1.1\u001b[0m\" 200 -\n",
      "127.0.0.1 - - [09/Sep/2021 14:05:10] \"\u001b[37mPOST /predict HTTP/1.1\u001b[0m\" 200 -\n",
      "127.0.0.1 - - [09/Sep/2021 14:05:23] \"\u001b[37mPOST /predict HTTP/1.1\u001b[0m\" 200 -\n",
      "127.0.0.1 - - [09/Sep/2021 14:05:48] \"\u001b[37mPOST /predict HTTP/1.1\u001b[0m\" 200 -\n",
      "127.0.0.1 - - [09/Sep/2021 14:06:03] \"\u001b[37mPOST /predict HTTP/1.1\u001b[0m\" 200 -\n",
      "127.0.0.1 - - [09/Sep/2021 14:06:06] \"\u001b[37mGET / HTTP/1.1\u001b[0m\" 200 -\n",
      "127.0.0.1 - - [09/Sep/2021 14:06:06] \"\u001b[36mGET /static/draw.js HTTP/1.1\u001b[0m\" 304 -\n",
      "127.0.0.1 - - [09/Sep/2021 14:06:23] \"\u001b[37mPOST /predict HTTP/1.1\u001b[0m\" 200 -\n",
      "127.0.0.1 - - [09/Sep/2021 14:06:29] \"\u001b[37mPOST /predict HTTP/1.1\u001b[0m\" 200 -\n",
      "127.0.0.1 - - [09/Sep/2021 14:06:37] \"\u001b[37mPOST /predict HTTP/1.1\u001b[0m\" 200 -\n",
      "127.0.0.1 - - [09/Sep/2021 14:06:54] \"\u001b[37mPOST /predict HTTP/1.1\u001b[0m\" 200 -\n",
      "127.0.0.1 - - [09/Sep/2021 14:07:08] \"\u001b[37mPOST /predict HTTP/1.1\u001b[0m\" 200 -\n",
      "127.0.0.1 - - [09/Sep/2021 14:07:11] \"\u001b[37mGET / HTTP/1.1\u001b[0m\" 200 -\n",
      "127.0.0.1 - - [09/Sep/2021 14:07:11] \"\u001b[36mGET /static/draw.js HTTP/1.1\u001b[0m\" 304 -\n",
      "127.0.0.1 - - [09/Sep/2021 14:07:31] \"\u001b[37mPOST /predict HTTP/1.1\u001b[0m\" 200 -\n",
      "127.0.0.1 - - [09/Sep/2021 14:07:47] \"\u001b[37mPOST /predict HTTP/1.1\u001b[0m\" 200 -\n",
      "127.0.0.1 - - [09/Sep/2021 14:08:09] \"\u001b[37mPOST /predict HTTP/1.1\u001b[0m\" 200 -\n",
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      "127.0.0.1 - - [09/Sep/2021 14:08:32] \"\u001b[37mPOST /predict HTTP/1.1\u001b[0m\" 200 -\n",
      "127.0.0.1 - - [09/Sep/2021 14:08:46] \"\u001b[37mPOST /predict HTTP/1.1\u001b[0m\" 200 -\n",
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      "127.0.0.1 - - [09/Sep/2021 14:09:13] \"\u001b[37mPOST /predict HTTP/1.1\u001b[0m\" 200 -\n",
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      "127.0.0.1 - - [09/Sep/2021 14:10:24] \"\u001b[37mPOST /predict HTTP/1.1\u001b[0m\" 200 -\n",
      "127.0.0.1 - - [09/Sep/2021 14:10:34] \"\u001b[37mPOST /predict HTTP/1.1\u001b[0m\" 200 -\n",
      "127.0.0.1 - - [09/Sep/2021 14:10:50] \"\u001b[37mPOST /predict HTTP/1.1\u001b[0m\" 200 -\n",
      "127.0.0.1 - - [09/Sep/2021 14:10:58] \"\u001b[37mPOST /predict HTTP/1.1\u001b[0m\" 200 -\n",
      "127.0.0.1 - - [09/Sep/2021 14:11:20] \"\u001b[37mPOST /predict HTTP/1.1\u001b[0m\" 200 -\n",
      "127.0.0.1 - - [09/Sep/2021 14:11:33] \"\u001b[37mPOST /predict HTTP/1.1\u001b[0m\" 200 -\n",
      "127.0.0.1 - - [09/Sep/2021 14:11:52] \"\u001b[37mPOST /predict HTTP/1.1\u001b[0m\" 200 -\n",
      "127.0.0.1 - - [09/Sep/2021 14:12:07] \"\u001b[37mGET / HTTP/1.1\u001b[0m\" 200 -\n",
      "127.0.0.1 - - [09/Sep/2021 14:12:07] \"\u001b[36mGET /static/draw.js HTTP/1.1\u001b[0m\" 304 -\n",
      "127.0.0.1 - - [09/Sep/2021 14:12:28] \"\u001b[37mPOST /predict HTTP/1.1\u001b[0m\" 200 -\n",
      "127.0.0.1 - - [09/Sep/2021 14:12:43] \"\u001b[37mPOST /predict HTTP/1.1\u001b[0m\" 200 -\n",
      "127.0.0.1 - - [09/Sep/2021 14:12:57] \"\u001b[37mPOST /predict HTTP/1.1\u001b[0m\" 200 -\n",
      "127.0.0.1 - - [09/Sep/2021 14:13:11] \"\u001b[37mPOST /predict HTTP/1.1\u001b[0m\" 200 -\n"
     ]
    }
   ],
   "source": [
    "#Initializing new Flask instance. Find the html template in \"templates\".\n",
    "app = flask.Flask(__name__, template_folder='templates')\n",
    "\n",
    "#First route : Render the initial drawing template\n",
    "@app.route('/')\n",
    "def home():\n",
    "    return render_template('draw.html')\n",
    "\n",
    "\n",
    "\n",
    "#Second route : Use our model to make prediction - render the results page.\n",
    "@app.route('/predict', methods=['POST'])\n",
    "def predict():\n",
    "        global graph\n",
    "        with graph.as_default():\n",
    "            if request.method == 'POST':\n",
    "                    final_pred = None\n",
    "                    #Preprocess the image : set the image to 28x28 shape\n",
    "                    #Access the image\n",
    "                    draw = request.form['url']\n",
    "                    #Removing the useless part of the url.\n",
    "                    draw = draw[init_Base64:]\n",
    "                    #Decoding\n",
    "                    draw_decoded = base64.b64decode(draw)\n",
    "                    image = np.asarray(bytearray(draw_decoded), dtype=\"uint8\")\n",
    "                    image = cv2.imdecode(image, cv2.IMREAD_GRAYSCALE)\n",
    "                    #Resizing and reshaping to keep the ratio.\n",
    "                    resized = cv2.resize(image, (28,28), interpolation = cv2.INTER_AREA)\n",
    "                    vect = np.asarray(resized, dtype=\"uint8\")\n",
    "                    vect = vect.reshape(1, 28, 28,1).astype('float32')\n",
    "                    #Launch prediction\n",
    "                    set_session(sess)\n",
    "                    my_prediction = model.predict(vect)\n",
    "                    #Getting the index of the maximum prediction\n",
    "                    index = np.argmax(my_prediction[0])\n",
    "                    #Associating the index and its value within the dictionnary\n",
    "                    final_pred = label_dict[index]\n",
    "\n",
    "        return render_template('results.html', prediction =final_pred)\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    app.run(debug=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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