{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build ML web app - assignment 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28)\n",
      "(10000, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "from tensorflow import keras\n",
    "\n",
    "(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n",
    "print(x_train.shape)\n",
    "print(x_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_input(img, ax):\n",
    "    ax.imshow(img, cmap='gray')\n",
    "    width, height = img.shape\n",
    "    thresh = img.max()/2.5\n",
    "    for x in range(width):\n",
    "        for y in range(height):\n",
    "            ax.annotate(str(round(img[x][y],2)), xy=(y,x),\n",
    "                        horizontalalignment='center',\n",
    "                        verticalalignment='center',\n",
    "                        color='white' if img[x][y]<thresh else 'black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure(figsize = (10,10)) \n",
    "ax = fig.add_subplot(111)\n",
    "visualize_input(x_train[0], ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '[2]')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "i = random.randint(0,60000)\n",
    "plt.imshow(x_train[i],cmap='gray') # Color map\n",
    "plt.title([y_train[i]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=uint8),\n",
       " array([5923, 6742, 5958, 6131, 5842, 5421, 5918, 6265, 5851, 5949],\n",
       "       dtype=int64))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many images are there in every digit?\n",
    "import numpy as np\n",
    "np.unique(y_train,return_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=uint8),\n",
       " array([ 980, 1135, 1032, 1010,  982,  892,  958, 1028,  974, 1009],\n",
       "       dtype=int64))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(y_test,return_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "# Normalization : Scaling down the value to a specific range(0-1)\n",
    "x_train=x_train/255\n",
    "x_test = x_test/255\n",
    "\n",
    "# AFter Normalization\n",
    "print(x_train.max())\n",
    "print(x_train.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Dense\n",
    "from keras.layers import Flatten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1500/1500 [==============================] - 13s 8ms/step - loss: 3.2063 - accuracy: 0.8986 - val_loss: 0.6879 - val_accuracy: 0.9189\n",
      "Epoch 2/10\n",
      "1500/1500 [==============================] - 12s 8ms/step - loss: 0.3889 - accuracy: 0.9335 - val_loss: 0.4031 - val_accuracy: 0.9345\n",
      "Epoch 3/10\n",
      "1500/1500 [==============================] - 12s 8ms/step - loss: 0.3103 - accuracy: 0.9388 - val_loss: 0.3946 - val_accuracy: 0.9368\n",
      "Epoch 4/10\n",
      "1500/1500 [==============================] - 12s 8ms/step - loss: 0.2950 - accuracy: 0.9385 - val_loss: 0.3703 - val_accuracy: 0.9377\n",
      "Epoch 5/10\n",
      "1500/1500 [==============================] - 12s 8ms/step - loss: 0.2744 - accuracy: 0.9439 - val_loss: 0.3204 - val_accuracy: 0.9419\n",
      "Epoch 6/10\n",
      "1500/1500 [==============================] - 12s 8ms/step - loss: 0.2417 - accuracy: 0.9481 - val_loss: 0.2982 - val_accuracy: 0.9454\n",
      "Epoch 7/10\n",
      "1500/1500 [==============================] - 13s 8ms/step - loss: 0.2174 - accuracy: 0.9538 - val_loss: 0.3228 - val_accuracy: 0.9504\n",
      "Epoch 8/10\n",
      "1500/1500 [==============================] - 11s 8ms/step - loss: 0.2152 - accuracy: 0.9564 - val_loss: 0.3443 - val_accuracy: 0.9463\n",
      "Epoch 9/10\n",
      "1500/1500 [==============================] - 12s 8ms/step - loss: 0.2122 - accuracy: 0.9578 - val_loss: 0.4409 - val_accuracy: 0.9382\n",
      "Epoch 10/10\n",
      "1500/1500 [==============================] - 14s 9ms/step - loss: 0.2056 - accuracy: 0.9590 - val_loss: 0.3617 - val_accuracy: 0.9479\n"
     ]
    }
   ],
   "source": [
    "model = keras.models.Sequential()\n",
    "model.add(Flatten(input_shape=(28,28))) # 784\n",
    "model.add(Dense(392,activation='relu'))\n",
    "model.add(Dense(10,activation='softmax'))\n",
    "\n",
    "model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])\n",
    "history = model.fit(x_train,y_train,epochs=10,validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1799f6b3e80>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1799f7b00d0>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "index_of_8 = np.where( y_train == 8)[0][0]\n",
    "image_of_8 = x_train[index_of_8]\n",
    "\n",
    "# plt.imshow(image_of_8, cmap='gray')\n",
    "# plt.show()\n",
    "\n",
    "if not os.path.exists('./tmp'):\n",
    "    os.makedirs('./tmp')\n",
    "plt.imsave('./tmp/8.png', image_of_8, cmap='gray')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 41ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaAAAAGdCAYAAABU0qcqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAbh0lEQVR4nO3df2xV9f3H8dcF6QWxvayU9vYKxQIqUwQzJl2DMIQKdI6BsASdf6AhGrC4KRM3nPxyW+qYEafrZImGYhRxbAMmWzBQbcm2FgPKiNE1lBSpoy3Yyb1QpBD6+f5BvN9daYFzubfv/ng+kk/Se8553/Pm47GvnntOT33OOScAADpYL+sGAAA9EwEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAE1dZN/BVra2tOnLkiFJTU+Xz+azbAQB45JzTiRMnFAqF1KtX++c5nS6Ajhw5oiFDhli3AQC4QnV1dRo8eHC76zvdR3CpqanWLQAAEuBS38+TFkAlJSW67rrr1LdvX+Xl5em99967rDo+dgOA7uFS38+TEkBvvvmmFi9erBUrVuj999/XmDFjNG3aNB09ejQZuwMAdEUuCcaNG+eKioqir8+dO+dCoZArLi6+ZG04HHaSGAwGg9HFRzgcvuj3+4SfAZ05c0Z79+5VQUFBdFmvXr1UUFCgysrKC7ZvaWlRJBKJGQCA7i/hAfTZZ5/p3LlzysrKilmelZWlhoaGC7YvLi5WIBCIDu6AA4CewfwuuKVLlyocDkdHXV2ddUsAgA6Q8N8DysjIUO/evdXY2BizvLGxUcFg8ILt/X6//H5/otsAAHRyCT8DSklJ0dixY1VWVhZd1traqrKyMuXn5yd6dwCALiopT0JYvHix5s2bp29+85saN26cnn/+eTU3N+uBBx5Ixu4AAF1QUgJo7ty5OnbsmJYvX66Ghgbdeuut2r59+wU3JgAAei6fc85ZN/G/IpGIAoGAdRsAgCsUDoeVlpbW7nrzu+AAAD0TAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABNXWTcAoOfq37+/55ry8nLPNaFQyHONJI0fP95zzaFDh+LaV0/EGRAAwAQBBAAwkfAAWrlypXw+X8wYOXJkoncDAOjiknIN6Oabb9bOnTv/fydXcakJABArKclw1VVXKRgMJuOtAQDdRFKuAR04cEChUEjDhg3Tfffdp8OHD7e7bUtLiyKRSMwAAHR/CQ+gvLw8lZaWavv27XrppZdUW1urCRMm6MSJE21uX1xcrEAgEB1DhgxJdEsAgE7I55xzydzB8ePHNXToUD333HOaP3/+BetbWlrU0tISfR2JRAghoIfg94C6t3A4rLS0tHbXJ/3ugAEDBuiGG25QTU1Nm+v9fr/8fn+y2wAAdDJJ/z2gkydP6uDBg8rOzk72rgAAXUjCA+jxxx9XRUWFDh06pH/+85+6++671bt3b917772J3hUAoAtL+Edwn376qe699141NTVp0KBBuv3221VVVaVBgwYlelcAgC4s4QG0cePGRL8lAMV3Ib0jf/D7/PPPPdfccccdnmvGjh3ruaa6utpzjSQ1NTXFVYfLw7PgAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmEj6H6QDLIwaNSquuh/+8Ieea4YOHRrXvry64YYbPNfk5OQkoZO2PfPMM55rbrrpJs81Pp/Pc81//vMfzzWSlJKSElcdLg9nQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAEzwNG93S5MmT46qbP39+gjtJnJaWFs81r732Wlz7imf+fvrTn8a1L6+cc55rSktL49pXU1NTXHW4PJwBAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMMHDSNHprVy50nPNkiVLEt9IO9avX++55tixY55rnn322Q7ZjyTdeuutnmvefvttzzUZGRmea+L5N/3xj3/0XIPk4wwIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACR5Gik6vf//+nmv69esX174++eQTzzU/+9nPPNfU19d7ronHiBEj4qp78sknPdcMGjTIc01zc7PnmngeTnv69GnPNUg+zoAAACYIIACACc8BtGvXLs2YMUOhUEg+n09btmyJWe+c0/Lly5Wdna1+/fqpoKBABw4cSFS/AIBuwnMANTc3a8yYMSopKWlz/erVq/XCCy9o7dq12r17t/r3769p06bxGSwAIIbnmxAKCwtVWFjY5jrnnJ5//nk99dRTmjlzpiTp1VdfVVZWlrZs2aJ77rnnyroFAHQbCb0GVFtbq4aGBhUUFESXBQIB5eXlqbKyss2alpYWRSKRmAEA6P4SGkANDQ2SpKysrJjlWVlZ0XVfVVxcrEAgEB1DhgxJZEsAgE7K/C64pUuXKhwOR0ddXZ11SwCADpDQAAoGg5KkxsbGmOWNjY3RdV/l9/uVlpYWMwAA3V9CAyg3N1fBYFBlZWXRZZFIRLt371Z+fn4idwUA6OI83wV38uRJ1dTURF/X1tZq3759Sk9PV05Ojh599FH94he/0PXXX6/c3FwtW7ZMoVBIs2bNSmTfAIAuznMA7dmzR3fccUf09eLFiyVJ8+bNU2lpqZ544gk1NzfroYce0vHjx3X77bdr+/bt6tu3b+K6BgB0eT7nnLNu4n9FIhEFAgHrNtCJ5OXlea55+eWX49rXTTfd5Lnmtdde81zz8MMPe66J5/+LtWvXeq6RpLvuustzzeeff+655pe//KXnmjVr1niugY1wOHzR6/rmd8EBAHomAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJz3+OAeho+/bt81xTVVUV177ieRr25MmTPdfceeednmvieQp0Tk6O55p4rVq1ynPNiy++mIRO0FVwBgQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAEwQQAMAEDyNFp9fS0uK5JhKJJKGTtoVCIc81f/rTnzzX+Hw+zzXOOc81kvTKK694rtmyZUtc+0LPxRkQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAEzyMFN3SJ598Yt1Cp/C3v/0trrpnn33Wc01dXV1c+0LPxRkQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAEzyMFJ1e7969PddMmDAhrn35fL646jrCX//6V881M2bMSEInQGJwBgQAMEEAAQBMeA6gXbt2acaMGQqFQvL5fNqyZUvM+vvvv18+ny9mTJ8+PVH9AgC6Cc8B1NzcrDFjxqikpKTdbaZPn676+vroeOONN66oSQBA9+P5JoTCwkIVFhZedBu/369gMBh3UwCA7i8p14DKy8uVmZmpG2+8UQsXLlRTU1O727a0tCgSicQMAED3l/AAmj59ul599VWVlZXpV7/6lSoqKlRYWKhz5861uX1xcbECgUB0DBkyJNEtAQA6oYT/HtA999wT/fqWW27R6NGjNXz4cJWXl2vKlCkXbL906VItXrw4+joSiRBCANADJP027GHDhikjI0M1NTVtrvf7/UpLS4sZAIDuL+kB9Omnn6qpqUnZ2dnJ3hUAoAvx/BHcyZMnY85mamtrtW/fPqWnpys9PV2rVq3SnDlzFAwGdfDgQT3xxBMaMWKEpk2bltDGAQBdm+cA2rNnj+64447o6y+v38ybN08vvfSS9u/fr/Xr1+v48eMKhUKaOnWqfv7zn8vv9yeuawBAl+c5gCZNmiTnXLvr33777StqCPiqjRs3eq6ZPXt2XPu62LFtrTP3BsSDZ8EBAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwk/E9yo+cIhUKeax544AHPNXPmzPFcE++To99//33PNf/6178818QzD5mZmZ5rgM6MMyAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmeBgp4jZlyhTPNU8//XQSOrnQU089FVfdb3/7W881s2bN8lwTz8NIP/roI881QGfGGRAAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATPIwUmjRpUlx1L7zwQmIbacf3vvc9zzU7d+6Ma1/BYNBzzfLly+Pal1eHDh3qkP0AHYUzIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACZ4GCl05513xlUXCAQ811RUVHiu2bZtm+eaPn36eK6RpO9+97uea+KZB5/P57nm2LFjnmuAzowzIACACQIIAGDCUwAVFxfrtttuU2pqqjIzMzVr1ixVV1fHbHP69GkVFRVp4MCBuuaaazRnzhw1NjYmtGkAQNfnKYAqKipUVFSkqqoq7dixQ2fPntXUqVPV3Nwc3eaxxx7TW2+9pU2bNqmiokJHjhzR7NmzE944AKBr83QTwvbt22Nel5aWKjMzU3v37tXEiRMVDof1yiuvaMOGDZo8ebIkad26dfr617+uqqoqfetb30pc5wCALu2KrgGFw2FJUnp6uiRp7969Onv2rAoKCqLbjBw5Ujk5OaqsrGzzPVpaWhSJRGIGAKD7izuAWltb9eijj2r8+PEaNWqUJKmhoUEpKSkaMGBAzLZZWVlqaGho832Ki4sVCASiY8iQIfG2BADoQuIOoKKiIn344YfauHHjFTWwdOlShcPh6Kirq7ui9wMAdA1x/SLqokWLtG3bNu3atUuDBw+OLg8Ggzpz5oyOHz8ecxbU2NioYDDY5nv5/X75/f542gAAdGGezoCcc1q0aJE2b96sd955R7m5uTHrx44dqz59+qisrCy6rLq6WocPH1Z+fn5iOgYAdAuezoCKioq0YcMGbd26VampqdHrOoFAQP369VMgEND8+fO1ePFipaenKy0tTY888ojy8/O5Aw4AEMNTAL300kuSpEmTJsUsX7dune6//35J0po1a9SrVy/NmTNHLS0tmjZtmn73u98lpFkAQPfhKYCcc5fcpm/fviopKVFJSUncTaFjtba2xlV3OcdDImriebDorFmzPNdI0m9+8xvPNZ9//rnnmpdfftlzzZc/AALdBc+CAwCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYiOsvoqJ7yczM7LB9HTt2zHPNjh07PNdMmDDBc028HnjgAc81b731VhI6AboWzoAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCY4GGk0Mcff9xh+/r+97/vucbn83mu+e9//+u5RpJKSko81+zcuTOufQE9HWdAAAATBBAAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATPAwUmj9+vVx1aWkpHiuWbZsmeeaPXv2eK75y1/+4rlGktasWRNXHQDvOAMCAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgwuecc9ZN/K9IJKJAIGDdBgDgCoXDYaWlpbW7njMgAIAJAggAYMJTABUXF+u2225TamqqMjMzNWvWLFVXV8dsM2nSJPl8vpixYMGChDYNAOj6PAVQRUWFioqKVFVVpR07dujs2bOaOnWqmpubY7Z78MEHVV9fHx2rV69OaNMAgK7P019E3b59e8zr0tJSZWZmau/evZo4cWJ0+dVXX61gMJiYDgEA3dIVXQMKh8OSpPT09Jjlr7/+ujIyMjRq1CgtXbpUp06davc9WlpaFIlEYgYAoAdwcTp37py766673Pjx42OW//73v3fbt293+/fvd6+99pq79tpr3d13393u+6xYscJJYjAYDEY3G+Fw+KI5EncALViwwA0dOtTV1dVddLuysjInydXU1LS5/vTp0y4cDkdHXV2d+aQxGAwG48rHpQLI0zWgLy1atEjbtm3Trl27NHjw4Itum5eXJ0mqqanR8OHDL1jv9/vl9/vjaQMA0IV5CiDnnB555BFt3rxZ5eXlys3NvWTNvn37JEnZ2dlxNQgA6J48BVBRUZE2bNigrVu3KjU1VQ0NDZKkQCCgfv366eDBg9qwYYO+853vaODAgdq/f78ee+wxTZw4UaNHj07KPwAA0EV5ue6jdj7nW7dunXPOucOHD7uJEye69PR05/f73YgRI9ySJUsu+Tng/wqHw+afWzIYDAbjyselvvfzMFIAQFLwMFIAQKdEAAEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADDR6QLIOWfdAgAgAS71/bzTBdCJEyesWwAAJMClvp/7XCc75WhtbdWRI0eUmpoqn88Xsy4SiWjIkCGqq6tTWlqaUYf2mIfzmIfzmIfzmIfzOsM8OOd04sQJhUIh9erV/nnOVR3Y02Xp1auXBg8efNFt0tLSevQB9iXm4Tzm4Tzm4Tzm4TzreQgEApfcptN9BAcA6BkIIACAiS4VQH6/XytWrJDf77duxRTzcB7zcB7zcB7zcF5XmodOdxMCAKBn6FJnQACA7oMAAgCYIIAAACYIIACAiS4TQCUlJbruuuvUt29f5eXl6b333rNuqcOtXLlSPp8vZowcOdK6raTbtWuXZsyYoVAoJJ/Ppy1btsSsd85p+fLlys7OVr9+/VRQUKADBw7YNJtEl5qH+++//4LjY/r06TbNJklxcbFuu+02paamKjMzU7NmzVJ1dXXMNqdPn1ZRUZEGDhyoa665RnPmzFFjY6NRx8lxOfMwadKkC46HBQsWGHXcti4RQG+++aYWL16sFStW6P3339eYMWM0bdo0HT161Lq1DnfzzTervr4+Ov7+979bt5R0zc3NGjNmjEpKStpcv3r1ar3wwgtau3atdu/erf79+2vatGk6ffp0B3eaXJeaB0maPn16zPHxxhtvdGCHyVdRUaGioiJVVVVpx44dOnv2rKZOnarm5uboNo899pjeeustbdq0SRUVFTpy5Ihmz55t2HXiXc48SNKDDz4YczysXr3aqON2uC5g3LhxrqioKPr63LlzLhQKueLiYsOuOt6KFSvcmDFjrNswJclt3rw5+rq1tdUFg0H361//Orrs+PHjzu/3uzfeeMOgw47x1Xlwzrl58+a5mTNnmvRj5ejRo06Sq6iocM6d/2/fp08ft2nTpug2H3/8sZPkKisrrdpMuq/Og3POffvb33Y/+tGP7Jq6DJ3+DOjMmTPau3evCgoKost69eqlgoICVVZWGnZm48CBAwqFQho2bJjuu+8+HT582LolU7W1tWpoaIg5PgKBgPLy8nrk8VFeXq7MzEzdeOONWrhwoZqamqxbSqpwOCxJSk9PlyTt3btXZ8+ejTkeRo4cqZycnG59PHx1Hr70+uuvKyMjQ6NGjdLSpUt16tQpi/ba1ekeRvpVn332mc6dO6esrKyY5VlZWfr3v/9t1JWNvLw8lZaW6sYbb1R9fb1WrVqlCRMm6MMPP1Rqaqp1eyYaGhokqc3j48t1PcX06dM1e/Zs5ebm6uDBg3ryySdVWFioyspK9e7d27q9hGttbdWjjz6q8ePHa9SoUZLOHw8pKSkaMGBAzLbd+Xhoax4k6Qc/+IGGDh2qUCik/fv36yc/+Ymqq6v15z//2bDbWJ0+gPD/CgsLo1+PHj1aeXl5Gjp0qP7whz9o/vz5hp2hM7jnnnuiX99yyy0aPXq0hg8frvLyck2ZMsWws+QoKirShx9+2COug15Me/Pw0EMPRb++5ZZblJ2drSlTpujgwYMaPnx4R7fZpk7/EVxGRoZ69+59wV0sjY2NCgaDRl11DgMGDNANN9ygmpoa61bMfHkMcHxcaNiwYcrIyOiWx8eiRYu0bds2vfvuuzF/viUYDOrMmTM6fvx4zPbd9Xhobx7akpeXJ0md6njo9AGUkpKisWPHqqysLLqstbVVZWVlys/PN+zM3smTJ3Xw4EFlZ2dbt2ImNzdXwWAw5viIRCLavXt3jz8+Pv30UzU1NXWr48M5p0WLFmnz5s165513lJubG7N+7Nix6tOnT8zxUF1drcOHD3er4+FS89CWffv2SVLnOh6s74K4HBs3bnR+v9+Vlpa6jz76yD300ENuwIABrqGhwbq1DvXjH//YlZeXu9raWvePf/zDFRQUuIyMDHf06FHr1pLqxIkT7oMPPnAffPCBk+See+4598EHH7hPPvnEOefcM8884wYMGOC2bt3q9u/f72bOnOlyc3PdF198Ydx5Yl1sHk6cOOEef/xxV1lZ6Wpra93OnTvdN77xDXf99de706dPW7eeMAsXLnSBQMCVl5e7+vr66Dh16lR0mwULFricnBz3zjvvuD179rj8/HyXn59v2HXiXWoeampq3NNPP+327Nnjamtr3datW92wYcPcxIkTjTuP1SUCyDnnXnzxRZeTk+NSUlLcuHHjXFVVlXVLHW7u3LkuOzvbpaSkuGuvvdbNnTvX1dTUWLeVdO+++66TdMGYN2+ec+78rdjLli1zWVlZzu/3uylTprjq6mrbppPgYvNw6tQpN3XqVDdo0CDXp08fN3ToUPfggw92ux/S2vr3S3Lr1q2LbvPFF1+4hx9+2H3ta19zV199tbv77rtdfX29XdNJcKl5OHz4sJs4caJLT093fr/fjRgxwi1ZssSFw2Hbxr+CP8cAADDR6a8BAQC6JwIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACb+D9hspvhEloFAAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import keras.utils as image\n",
    "\n",
    "img = image.load_img(path=\"./tmp/8.png\", color_mode= 'grayscale', target_size=(28, 28))\n",
    "img = image.img_to_array(img)\n",
    "plt.imshow(image.array_to_img(img), cmap=\"gray\")\n",
    "img = img.astype('float')/255\n",
    "test_img = img.reshape((1, 28, 28, 1))\n",
    "#img_class = model.predict_classes(test_img)\n",
    "img_class = np.argmax(model.predict(test_img), axis = 1)\n",
    "prediction = img_class[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 2s 6ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9807"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report\n",
    "\n",
    "y_pred = model.predict(x_test)\n",
    "y_pred = np.argmax(y_pred,axis=1)\n",
    "accuracy_score(y_pred,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 969,    0,    1,    2,    0,    2,    2,    0,    0,    2],\n",
       "       [   1, 1127,    0,    0,    1,    1,    3,    4,    2,    4],\n",
       "       [   0,    2, 1007,    2,    2,    0,    0,    6,    3,    0],\n",
       "       [   0,    0,    1,  991,    0,   11,    1,    1,    2,    3],\n",
       "       [   1,    0,    4,    0,  970,    1,    1,    0,    4,   15],\n",
       "       [   2,    0,    0,    3,    0,  868,    2,    1,    4,    5],\n",
       "       [   2,    2,    2,    0,    3,    3,  949,    0,    1,    1],\n",
       "       [   1,    1,    7,    5,    2,    0,    0, 1012,    2,   14],\n",
       "       [   2,    3,    9,    5,    0,    4,    0,    1,  952,    3],\n",
       "       [   2,    0,    1,    2,    4,    2,    0,    3,    4,  962]],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_pred,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99       978\n",
      "           1       0.99      0.99      0.99      1143\n",
      "           2       0.98      0.99      0.98      1022\n",
      "           3       0.98      0.98      0.98      1010\n",
      "           4       0.99      0.97      0.98       996\n",
      "           5       0.97      0.98      0.98       885\n",
      "           6       0.99      0.99      0.99       963\n",
      "           7       0.98      0.97      0.98      1044\n",
      "           8       0.98      0.97      0.97       979\n",
      "           9       0.95      0.98      0.97       980\n",
      "\n",
      "    accuracy                           0.98     10000\n",
      "   macro avg       0.98      0.98      0.98     10000\n",
      "weighted avg       0.98      0.98      0.98     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_pred,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"mnist.hdf5\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now it's time to use our hdf5 model file for prediction!"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "import streamlit as st\n",
    "from streamlit_drawable_canvas import st_canvas\n",
    "from tensorflow import keras\n",
    "import cv2\n",
    "import numpy as np\n",
    "model_new = keras.models.load_model('mnist.hdf5')\n",
    "\n",
    "st.title(\"MNIST Digit Recognizer\")\n",
    "\n",
    "SIZE = 192\n",
    "\n",
    "canvas_result = st_canvas(\n",
    "    fill_color=\"#ffffff\",\n",
    "    stroke_width=10,\n",
    "    stroke_color='#ffffff',\n",
    "    background_color=\"#000000\",\n",
    "    height=150,\n",
    "    width=150,\n",
    "    drawing_mode='freedraw',\n",
    "    key=\"canvas\",\n",
    ")\n",
    "\n",
    "if canvas_result.image_data is not None:\n",
    "    img_color = cv2.resize(canvas_result.image_data.astype('uint8'), (28, 28))\n",
    "    img_rescaling = cv2.resize(img_color, (SIZE, SIZE), interpolation=cv2.INTER_NEAREST)\n",
    "    st.write('Input Image')\n",
    "    st.image(img_rescaling)\n",
    "    \n",
    "if st.button('Predict'):\n",
    "    img_grey = cv2.cvtColor(img_color, cv2.COLOR_BGR2GRAY)\n",
    "    pred = model_new.predict(img_grey.reshape(1, 28, 28, 1))\n",
    "    st.write(f'result: {np.argmax(pred[0])}')\n",
    "    st.bar_chart(pred[0])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seperation of frontend and backend\n",
    "\n",
    "For this, we should use FastAPI and UviCorn."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deployment on streamlit cloud\n",
    "\n",
    "- https://docs.streamlit.io/streamlit-community-cloud/get-started/deploy-an-app"
   ]
  }
 ],
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