diff --git a/.env.example b/.env.example
index 9486334..d759c11 100644
--- a/.env.example
+++ b/.env.example
@@ -1,4 +1,4 @@
 APP_NAME=app
 SECRET_KEY=app
 FLASK_DEBUG=0
-MODEL_PATH=/app/mnist_model.h5
+MODEL_PATH=/app/mnist_model.keras
diff --git a/.env.test b/.env.test
index 7f7bf6d..7699210 100644
--- a/.env.test
+++ b/.env.test
@@ -1,4 +1,4 @@
 APP_NAME=app
 SECRET_KEY=app
 FLASK_DEBUG=0
-MODEL_PATH=../mnist_model.h5
+MODEL_PATH=../mnist_model.keras
diff --git a/Deep Learning MNIST prediction model with Keras.ipynb b/Deep Learning MNIST prediction model with Keras.ipynb
index 49fbfba..c0d305d 100644
--- a/Deep Learning MNIST prediction model with Keras.ipynb	
+++ b/Deep Learning MNIST prediction model with Keras.ipynb	
@@ -203,27 +203,39 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/10\n",
-      "54000/54000 [==============================] - 6s 108us/step - loss: 0.2693 - acc: 0.9224 - val_loss: 0.1361 - val_acc: 0.9588\n",
+      "Epoch 1/10\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-03-31 21:18:56.862940: W external/local_tsl/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 169344000 exceeds 10% of free system memory.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m422/422\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8670 - loss: 0.4630 - val_accuracy: 0.9535 - val_loss: 0.1597\n",
       "Epoch 2/10\n",
-      "54000/54000 [==============================] - 5s 86us/step - loss: 0.1112 - acc: 0.9667 - val_loss: 0.0912 - val_acc: 0.9732\n",
+      "\u001b[1m422/422\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9642 - loss: 0.1230 - val_accuracy: 0.9687 - val_loss: 0.1054\n",
       "Epoch 3/10\n",
-      "54000/54000 [==============================] - 3s 58us/step - loss: 0.0726 - acc: 0.9787 - val_loss: 0.0859 - val_acc: 0.9735\n",
+      "\u001b[1m422/422\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9777 - loss: 0.0754 - val_accuracy: 0.9762 - val_loss: 0.0829\n",
       "Epoch 4/10\n",
-      "54000/54000 [==============================] - 3s 58us/step - loss: 0.0529 - acc: 0.9838 - val_loss: 0.0764 - val_acc: 0.9747\n",
+      "\u001b[1m422/422\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9848 - loss: 0.0531 - val_accuracy: 0.9810 - val_loss: 0.0675\n",
       "Epoch 5/10\n",
-      "54000/54000 [==============================] - 3s 58us/step - loss: 0.0391 - acc: 0.9884 - val_loss: 0.0745 - val_acc: 0.9773\n",
+      "\u001b[1m422/422\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9887 - loss: 0.0384 - val_accuracy: 0.9807 - val_loss: 0.0637\n",
       "Epoch 6/10\n",
-      "54000/54000 [==============================] - 3s 62us/step - loss: 0.0299 - acc: 0.9912 - val_loss: 0.0699 - val_acc: 0.9803\n",
+      "\u001b[1m422/422\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9917 - loss: 0.0285 - val_accuracy: 0.9803 - val_loss: 0.0656\n",
       "Epoch 7/10\n",
-      "54000/54000 [==============================] - 3s 63us/step - loss: 0.0222 - acc: 0.9935 - val_loss: 0.0676 - val_acc: 0.9793\n",
+      "\u001b[1m422/422\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9940 - loss: 0.0220 - val_accuracy: 0.9805 - val_loss: 0.0666\n",
       "Epoch 8/10\n",
-      "54000/54000 [==============================] - 3s 59us/step - loss: 0.0170 - acc: 0.9951 - val_loss: 0.0732 - val_acc: 0.9795\n",
+      "\u001b[1m422/422\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9960 - loss: 0.0152 - val_accuracy: 0.9813 - val_loss: 0.0594\n",
       "Epoch 9/10\n",
-      "54000/54000 [==============================] - 3s 58us/step - loss: 0.0135 - acc: 0.9963 - val_loss: 0.0694 - val_acc: 0.9815\n",
+      "\u001b[1m422/422\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9970 - loss: 0.0121 - val_accuracy: 0.9783 - val_loss: 0.0678\n",
       "Epoch 10/10\n",
-      "54000/54000 [==============================] - 3s 58us/step - loss: 0.0106 - acc: 0.9972 - val_loss: 0.0704 - val_acc: 0.9812\n"
+      "\u001b[1m422/422\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9983 - loss: 0.0086 - val_accuracy: 0.9822 - val_loss: 0.0673\n"
      ]
     }
    ],
@@ -251,14 +263,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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Z4cOHM3PmTC5dukSDBg347LPPbKMwhSUuLo6qVasya9YsnnnmGSpVqkSfPn148cUXbetUNWzYkKioKD777DOOHz+Ol5cXDRs2ZNWqVbYr4B5++GEOHz7M22+/zZkzZ6hSpQqtWrVi/PjxtqvIRPLDYhSnf3qKSLEVHR3N3r17s52fIiJS0mgOkIhkce1tKw4cOMAXX3xB69atHVOQiEgB0wiQiGQREBBguz/VkSNHmDNnDqmpqezatYvatWs7ujwRkXzTHCARyaJt27YsWrSIhIQE3N3dad68OS+++KLCj4iUGhoBEhEREaejOUAiIiLidBSARERExOloDlA20tPTOXHiBBUqVMjV8vMiIiLiOIZh8PfffxMYGHjTBT8VgLJx4sQJgoODHV2GiIiI5MGxY8cICgq6YR8FoGxUqFABMP8Cvb29HVyNiIiI5ERSUhLBwcG27/EbUQDKRsZpL29vbwUgERGREiYn01c0CVpEREScjgKQiIiIOB0FIBEREXE6mgMkIiKFzmq1cvnyZUeXISVc2bJlcXV1LZB9KQCJiEihMQyDhIQEzp8/7+hSpJSoWLEi/v7++V6nTwFIREQKTUb4qVatGl5eXlpcVvLMMAxSUlI4ffo0AAEBAfnanwKQiIgUCqvVags/lStXdnQ5Ugp4enoCcPr0aapVq5av02GaBC0iIoUiY86Pl5eXgyuR0iTj9ym/c8oUgEREpFDptJcUpIL6fdIpsCJktcLGjXDyJAQEQMuWUECT2UVERCQXNAJURJYuhdBQuO8++Ne/zD9DQ812EREp/UJDQ5k+fXqO+2/YsAGLxVLoV9AtWLCAihUrFupnFEcKQEVg6VLo3Bn++MO+/fhxs10hSETkxqxW2LABFi0y/7RaC++zLBbLDbe4uLg87Xf79u306dMnx/1btGjByZMn8fHxydPnyY3pFFghs1ph0CAwjKyvGQZYLDB4MHTooNNhIiLZWbrU/P9o5n9EBgXBa69Bx44F/3knT560PV68eDFjx45l//79trby5cvbHhuGgdVqpUyZm3+dVq1aNVd1uLm54e/vn6v3SM5pBKiQbdyYdeQnM8OAY8fMfiIiYs8RI+j+/v62zcfHB4vFYnv+yy+/UKFCBVatWkXjxo1xd3fnu+++47fffqNDhw74+flRvnx5mjZtytq1a+32e+0pMIvFwv/+9z8eeeQRvLy8qF27NitWrLC9fu0psIxTVV9++SX16tWjfPnytG3b1i6wXblyhYEDB1KxYkUqV67M8OHDiY2NJTo6Old/B3PmzKFWrVq4ublRp04d3n33XdtrhmEQFxdH9erVcXd3JzAwkIEDB9pef/3116lduzYeHh74+fnRuXPnXH12UVEAKmSZfi8LpJ+IiLO42Qg6mCPohXk67HpGjBjB5MmT2bdvHw0aNCA5OZkHH3yQdevWsWvXLtq2bUv79u05evToDfczfvx4Hn30UXbv3s2DDz5I9+7dOXfu3HX7p6SkMHXqVN59912+/fZbjh49yrBhw2yvv/TSS7z//vvMnz+fTZs2kZSUxPLly3N1bMuWLWPQoEEMHTqUn376if/85z/06tWL9evXA/DJJ5/w6quv8sYbb3DgwAGWL19OWFgYAD/88AMDBw5kwoQJ7N+/n9WrV3Pvvffm6vOLjCFZJCYmGoCRmJiY732tX28Y5n+qN97Wr8/3R4mIFCsXL140fv75Z+PixYt5en9x+P/n/PnzDR8fn0w1rTcAY/ny5Td9b/369Y2ZM2fanoeEhBivvvqq7TlgjB492vY8OTnZAIxVq1bZfdZff/1lqwUwDh48aHvP7NmzDT8/P9tzPz8/4+WXX7Y9v3LlilG9enWjQ4cOOT7GFi1aGE8++aRdny5duhgPPvigYRiGMW3aNOO2224z0tLSsuzrk08+Mby9vY2kpKTrfl5+3ej3Kjff3xoBKmQtW5rnqq+3bIHFAsHBZj8REbmqOI+gN2nSxO55cnIyw4YNo169elSsWJHy5cuzb9++m44ANWjQwPa4XLlyeHt72271kB0vLy9q1aplex4QEGDrn5iYyKlTp2jWrJntdVdXVxo3bpyrY9u3bx8RERF2bREREezbtw+ALl26cPHiRWrWrMmTTz7JsmXLuHLlCgD/+Mc/CAkJoWbNmjz++OO8//77pKSk5Orzi4oCUCFzdTUn6kHWEJTxfPp0TYAWEblWTm/1lM9bQuVJuXLl7J4PGzaMZcuW8eKLL7Jx40bi4+MJCwsjLS3thvspW7as3XOLxUJ6enqu+hvZnSMsRMHBwezfv5/XX38dT09Pnn76ae69914uX75MhQoV2LlzJ4sWLSIgIICxY8fSsGHDYnkzXAWgItCxI3z8Mdxyi317UJDZXhhXMYiIlHQlaQR906ZN9OzZk0ceeYSwsDD8/f05fPhwkdbg4+ODn58f27dvt7VZrVZ27tyZq/3Uq1ePTZs22bVt2rSJ22+/3fbc09OT9u3bM2PGDDZs2MCWLVvYs2cPAGXKlCEyMpIpU6awe/duDh8+zNdff52PIyscugy+iHTsaF7qrpWgRURyJmMEvXNnM+xkHugobiPotWvXZunSpbRv3x6LxcKYMWNuOJJTWAYMGMCkSZO49dZbqVu3LjNnzuSvv/7K1e0jnn32WR599FHuvPNOIiMj+eyzz1i6dKntqrYFCxZgtVoJDw/Hy8uL9957D09PT0JCQli5ciW///479957L76+vnzxxRekp6dTp06dwjrkPFMAKkKurtC6taOrEBEpOTJG0LNbB2j69OIzgv7KK6/wxBNP0KJFC6pUqcLw4cNJSkoq8jqGDx9OQkICPXr0wNXVlT59+hAVFZWru6ZHR0fz2muvMXXqVAYNGkSNGjWYP38+rf//C6xixYpMnjyZIUOGYLVaCQsL47PPPqNy5cpUrFiRpUuXEhcXx6VLl6hduzaLFi2ifv36hXTEeWcxivrkYQmQlJSEj48PiYmJeHt7O7ocEZES6dKlSxw6dIgaNWrg4eGRr33pXop5k56eTr169Xj00UeZOHGio8spEDf6vcrN97dGgEREpNjTCHrOHDlyhK+++opWrVqRmprKrFmzOHToEP/6178cXVqxo0nQIiIipYSLiwsLFiygadOmREREsGfPHtauXUu9evUcXVqxoxEgERGRUiI4ODjLFVySPY0AiYiIiNNRABIRERGnowAkIiIiTkcBSERERJyOApCIiIg4HQUgERERcToKQCIiIoWgdevWDB482PY8NDSU6dOn3/A9FouF5cuX5/uzC2o/NxIXF0ejRo0K9TMKkwKQiIhIJu3bt6dt27bZvrZx40YsFgu7d+/O9X63b99Onz598lueneuFkJMnT9KuXbsC/azSRgFIREQkk969e7NmzRr+yHz31f83f/58mjRpQoMGDXK936pVq+Ll5VUQJd6Uv78/7u7uRfJZJZUCkIiISCb//Oc/qVq1KgsWLLBrT05OZsmSJfTu3ZuzZ8/SrVs3brnlFry8vAgLC2PRokU33O+1p8AOHDjAvffei4eHB7fffjtr1qzJ8p7hw4dz22234eXlRc2aNRkzZgyXL18GYMGCBYwfP54ff/wRi8WCxWKx1XztKbA9e/Zw//334+npSeXKlenTpw/Jycm213v27El0dDRTp04lICCAypUr069fP9tn5UR6ejoTJkwgKCgId3d3GjVqxOrVq22vp6Wl0b9/fwICAvDw8CAkJIRJkyYBYBgGcXFxVK9eHXd3dwIDAxk4cGCOPzsvdCsMEREpMoYBKSmO+WwvL7BYbt6vTJky9OjRgwULFjBq1Cgs//+mJUuWYLVa6datG8nJyTRu3Jjhw4fj7e3N559/zuOPP06tWrVo1qzZTT8jPT2djh074ufnx/fff09iYqLdfKEMFSpUYMGCBQQGBrJnzx6efPJJKlSowH//+19iYmL46aefWL16NWvXrgXAx8cnyz4uXLhAVFQUzZs3Z/v27Zw+fZp///vf9O/f3y7krV+/noCAANavX8/BgweJiYmhUaNGPPnkkzf/SwNee+01pk2bxhtvvMGdd97J22+/zcMPP8zevXupXbs2M2bMYMWKFXz00UdUr16dY8eOcezYMQA++eQTXn31VT788EPq169PQkICP/74Y44+N88MySIxMdEAjMTEREeXIiJSYl28eNH4+eefjYsXL9rakpMNw4xBRb8lJ+e89n379hmAsX79eltby5Ytjccee+y673nooYeMoUOH2p63atXKGDRokO15SEiI8eqrrxqGYRhffvmlUaZMGeP48eO211etWmUAxrJly677GS+//LLRuHFj2/Nx48YZDRs2zNIv837mzZtn+Pr6GsmZ/gI+//xzw8XFxUhISDAMwzBiY2ONkJAQ48qVK7Y+Xbp0MWJiYq5by7WfHRgYaLzwwgt2fZo2bWo8/fTThmEYxoABA4z777/fSE9Pz7KvadOmGbfddpuRlpZ23c/LkN3vVYbcfH/rFJiIiMg16tatS4sWLXj77bcBOHjwIBs3bqR3794AWK1WJk6cSFhYGJUqVaJ8+fJ8+eWXHD16NEf737dvH8HBwQQGBtramjdvnqXf4sWLiYiIwN/fn/LlyzN69Ogcf0bmz2rYsCHlypWztUVERJCens7+/fttbfXr18fV1dX2PCAggNOnT+foM5KSkjhx4gQRERF27REREezbtw8wT7PFx8dTp04dBg4cyFdffWXr16VLFy5evEjNmjV58sknWbZsGVeuXMnVceaWApCIiBQZLy9ITnbMltv5x7179+aTTz7h77//Zv78+dSqVYtWrVoB8PLLL/Paa68xfPhw1q9fT3x8PFFRUaSlpRXY39WWLVvo3r07Dz74ICtXrmTXrl2MGjWqQD8js7Jly9o9t1gspKenF9j+77rrLg4dOsTEiRO5ePEijz76KJ07dwbMu9jv37+f119/HU9PT55++mnuvffeXM1Byi3NARIRkSJjsUCmgYhi7dFHH2XQoEF88MEHvPPOO/Tt29c2H2jTpk106NCBxx57DDDn9Pz666/cfvvtOdp3vXr1OHbsGCdPniQgIACArVu32vXZvHkzISEhjBo1ytZ25MgRuz5ubm5YrdabftaCBQu4cOGCbRRo06ZNuLi4UKdOnRzVezPe3t4EBgayadMmW0jM+JzMc6K8vb2JiYkhJiaGzp0707ZtW86dO0elSpXw9PSkffv2tG/fnn79+lG3bl327NnDXXfdVSA1XksBSEREJBvly5cnJiaGkSNHkpSURM+ePW2v1a5dm48//pjNmzfj6+vLK6+8wqlTp3IcgCIjI7ntttuIjY3l5ZdfJikpyS7oZHzG0aNH+fDDD2natCmff/45y5Yts+sTGhrKoUOHiI+PJygoiAoVKmS5/L179+6MGzeO2NhY4uLi+PPPPxkwYACPP/44fn5+efvLycazzz7LuHHjqFWrFo0aNWL+/PnEx8fz/vvvA/DKK68QEBDAnXfeiYuLC0uWLMHf35+KFSuyYMECrFYr4eHheHl58d577+Hp6UlISEiB1XctnQITERG5jt69e/PXX38RFRVlN19n9OjR3HXXXURFRdG6dWv8/f2Jjo7O8X5dXFxYtmwZFy9epFmzZvz73//mhRdesOvz8MMP88wzz9C/f38aNWrE5s2bGTNmjF2fTp060bZtW+677z6qVq2a7aX4Xl5efPnll5w7d46mTZvSuXNn2rRpw6xZs3L3l3ETAwcOZMiQIQwdOpSwsDBWr17NihUrqF27NmBe0TZlyhSaNGlC06ZNOXz4MF988QUuLi5UrFiRN998k4iICBo0aMDatWv57LPPqFy5coHWmJnFMAyj0PZeQiUlJeHj40NiYiLe3t6OLkdEpES6dOkShw4dokaNGnh4eDi6HCklbvR7lZvvb40AiYiIiNNRABIRERGnowAkIiIiTkcBSERERJxOsQhAs2fPJjQ0FA8PD8LDw9m2bdt1+7755pu0bNkSX19ffH19iYyMzNK/Z8+ethvDZWxt27Yt7MMQEZFs6FobKUgF9fvk8AC0ePFihgwZwrhx49i5cycNGzYkKirqustvb9iwgW7durF+/Xq2bNlCcHAwDzzwAMePH7fr17ZtW06ePGnbbnaXXhERKVgZKwunOOrup1IqZfw+XbtydW45/DL48PBwmjZtaluPID09neDgYAYMGMCIESNu+n6r1Yqvry+zZs2iR48egDkCdP78eZYvX56nmnQZvIhIwTh58iTnz5+nWrVqeHl52VZSFsktwzBISUnh9OnTVKxY0baCdma5+f526ErQaWlp7Nixg5EjR9raXFxciIyMZMuWLTnaR0pKCpcvX6ZSpUp27Rs2bKBatWr4+vpy//338/zzz193QaXU1FRSU1Ntz5OSkvJwNCIici1/f3+AHN9UU+RmKlasaPu9yg+HBqAzZ85gtVqzLMXt5+fHL7/8kqN9DB8+nMDAQCIjI21tbdu2pWPHjtSoUYPffvuN5557jnbt2rFlyxa7O91mmDRpEuPHj8/fwYiISBYWi4WAgACqVatWqDe2FOdQtmzZbL/H86JE3wts8uTJfPjhh2zYsMFuNciuXbvaHoeFhdGgQQNq1arFhg0baNOmTZb9jBw5kiFDhtieJyUlERwcXLjFi4g4EVdX1wL74hIpCA6dBF2lShVcXV05deqUXfupU6duOrw1depUJk+ezFdffUWDBg1u2LdmzZpUqVKFgwcPZvu6u7s73t7edpuIiIiUXg4NQG5ubjRu3Jh169bZ2tLT01m3bh3Nmze/7vumTJnCxIkTWb16NU2aNLnp5/zxxx+cPXs22wlTIiIi4nwcfhn8kCFDePPNN1m4cCH79u2jb9++XLhwgV69egHQo0cPu0nSL730EmPGjOHtt98mNDSUhIQEEhISSE5OBiA5OZlnn32WrVu3cvjwYdatW0eHDh249dZbiYqKcsgxioiISPHi8DlAMTEx/Pnnn4wdO5aEhAQaNWrE6tWrbROjjx49iovL1Zw2Z84c0tLS6Ny5s91+xo0bR1xcHK6uruzevZuFCxdy/vx5AgMDeeCBB5g4cSLu7u5FemwiIiJSPDl8HaDiSOsAiYiIlDy5+f52+CkwERERkaKmACQiIiJORwFIREREnI4CkIiIiDgdBSARERFxOgpAIiIi4nQUgERERMTpKACJiIiI01EAEhEREaejACQiIiJORwFIREREnI4CkIiIiDgdBSARERFxOgpAIiIi4nQUgERERMTpKACJiIiI01EAEhEREaejACQiIiJORwFIREREnI4CkIiIiDgdBSARERFxOgpAIiIi4nQUgERERMTpKACJiIiI01EAEhEREaejACQiIiJORwFIREREnI4CkIiIiDgdBSARERFxOgpAIiIi4nQUgERERMTpKACJiIiI01EAEhEREaejACQiIiJORwFIREREnI4CkIiIiDgdBSARERFxOgpAIiIi4nQUgERERMTpKACJiIiI01EAEhEREaejACQiIiJORwFIREREnI4CkIiIiDgdBSARERFxOgpAIiIi4nQUgERERMTpKACJiIiI01EAEhEREaejACQiIiJORwFIREREnE6xCECzZ88mNDQUDw8PwsPD2bZt23X7vvnmm7Rs2RJfX198fX2JjIzM0t8wDMaOHUtAQACenp5ERkZy4MCBwj4MERERKSEcHoAWL17MkCFDGDduHDt37qRhw4ZERUVx+vTpbPtv2LCBbt26sX79erZs2UJwcDAPPPAAx48ft/WZMmUKM2bMYO7cuXz//feUK1eOqKgoLl26VFSHJSIiIsWYxTAMw5EFhIeH07RpU2bNmgVAeno6wcHBDBgwgBEjRtz0/VarFV9fX2bNmkWPHj0wDIPAwECGDh3KsGHDAEhMTMTPz48FCxbQtWvXm+4zKSkJHx8fEhMT8fb2zt8BioiISJHIzfe3Q0eA0tLS2LFjB5GRkbY2FxcXIiMj2bJlS472kZKSwuXLl6lUqRIAhw4dIiEhwW6fPj4+hIeHX3efqampJCUl2W0iIiJSejk0AJ05cwar1Yqfn59du5+fHwkJCTnax/DhwwkMDLQFnoz35WafkyZNwsfHx7YFBwfn9lBERESkBHH4HKD8mDx5Mh9++CHLli3Dw8Mjz/sZOXIkiYmJtu3YsWMFWKWIiIgUN2Uc+eFVqlTB1dWVU6dO2bWfOnUKf3//G7536tSpTJ48mbVr19KgQQNbe8b7Tp06RUBAgN0+GzVqlO2+3N3dcXd3z+NRiIiISEnj0BEgNzc3GjduzLp162xt6enprFu3jubNm1/3fVOmTGHixImsXr2aJk2a2L1Wo0YN/P397faZlJTE999/f8N9ioiIiPNw6AgQwJAhQ4iNjaVJkyY0a9aM6dOnc+HCBXr16gVAjx49uOWWW5g0aRIAL730EmPHjuWDDz4gNDTUNq+nfPnylC9fHovFwuDBg3n++eepXbs2NWrUYMyYMQQGBhIdHe2owxQREZFixOEBKCYmhj///JOxY8eSkJBAo0aNWL16tW0S89GjR3FxuTpQNWfOHNLS0ujcubPdfsaNG0dcXBwA//3vf7lw4QJ9+vTh/Pnz3HPPPaxevTpf84RERESk9HD4OkDFkdYBEhERKXlKzDpAIiIiIo6gACQiIiJORwFIREREnI4CkIiIiDgdBSARERFxOgpAIiIi4nQUgERERMTpKACJiIiI01EAEhEREaejACQiIiJORwFIREREnI4CkIiIiDgdBSARERFxOgpAIiIi4nQUgERERMTpKACJiIiI01EAEhEREaejACQiIiJORwFIREREnI4CkIiIiDgdBSARERFxOgpAIiIi4nQUgERERMTpKACJiIiI01EAEhEREaejACQiIiJORwFIREREnI4CkIiIiDgdBSARERFxOgpAIiIi4nQUgERERMTpKACJiIiI01EAEhEREaejACQiIiJORwFIREREnI4CkIiIiDgdBSARERFxOgpAIiIi4nQUgERERMTpKACJiIiI01EAEhEREaejACQiIiJORwGoCB09Cm3awK5djq5ERETEuSkAFaHRo+HrryEmBv7+29HViIiIOC8FoCI0fToEB8OBA/DUU2AYjq5IRETEOSkAFaFKlWDRInB1hQ8+gPnzHV2RiIiIc1IAKmIREfD88+bj/v1h717H1iMiIuKMFIAc4L//hQcegIsX4dFHISXF0RWJiIg4FwUgB3BxgXffBX9/+PlnGDDA0RWJiIg4FwUgB6lWzZwH5OICb78N773n6IpEREScR54C0LFjx/jjjz9sz7dt28bgwYOZN29egRXmDO67D8aONR8/9RT8+qtj6xEREXEWeQpA//rXv1i/fj0ACQkJ/OMf/2Dbtm2MGjWKCRMm5Gpfs2fPJjQ0FA8PD8LDw9m2bdt1++7du5dOnToRGhqKxWJh+vTpWfrExcVhsVjstrp16+aqpqI0ejS0bg0XLpjzgS5dcnRFIiIipV+eAtBPP/1Es2bNAPjoo4+444472Lx5M++//z4LFizI8X4WL17MkCFDGDduHDt37qRhw4ZERUVx+vTpbPunpKRQs2ZNJk+ejL+//3X3W79+fU6ePGnbvvvuu1wdX1FydYX334eqVeHHH2HoUEdXJCIiUvrlKQBdvnwZd3d3ANauXcvDDz8MQN26dTl58mSO9/PKK6/w5JNP0qtXL26//Xbmzp2Ll5cXb7/9drb9mzZtyssvv0zXrl1tn5+dMmXK4O/vb9uqVKmSi6MreoGB5qRogNdfh48/dmw9IiIipV2eAlD9+vWZO3cuGzduZM2aNbRt2xaAEydOULly5RztIy0tjR07dhAZGXm1GBcXIiMj2bJlS17Ksjlw4ACBgYHUrFmT7t27c/To0XztryhERcGIEebj3r3h998dW4+IiEhplqcA9NJLL/HGG2/QunVrunXrRsOGDQFYsWKF7dTYzZw5cwar1Yqfn59du5+fHwkJCXkpC4Dw8HAWLFjA6tWrmTNnDocOHaJly5b8fYObb6WmppKUlGS3OcKECdCiBSQlQdeukJbmkDJERERKvTJ5eVPr1q05c+YMSUlJ+Pr62tr79OmDl5dXgRWXF+3atbM9btCgAeHh4YSEhPDRRx/Ru3fvbN8zadIkxo8fX1QlXlfZsuatMho1gu3bzRGhV15xdFUiIiKlT55GgC5evEhqaqot/Bw5coTp06ezf/9+qlWrlqN9VKlSBVdXV06dOmXXfurUqRtOcM6tihUrctttt3Hw4MHr9hk5ciSJiYm27dixYwX2+blVvTpkzCN/9VVYscJhpYiIiJRaeQpAHTp04J133gHg/PnzhIeHM23aNKKjo5kzZ06O9uHm5kbjxo1Zt26drS09PZ1169bRvHnzvJSVreTkZH777TcCAgKu28fd3R1vb2+7zZEefhgGDzYf9+wJJWAKk4iISImSpwC0c+dOWrZsCcDHH3+Mn58fR44c4Z133mHGjBk53s+QIUN48803WbhwIfv27aNv375cuHCBXr16AdCjRw9Gjhxp65+WlkZ8fDzx8fGkpaVx/Phx4uPj7UZ3hg0bxjfffMPhw4fZvHkzjzzyCK6urnTr1i0vh+owL70ETZrAX39Bt25w+bKjKxIRESk98jQHKCUlhQoVKgDw1Vdf0bFjR1xcXLj77rs5cuRIjvcTExPDn3/+ydixY0lISKBRo0asXr3aNjH66NGjuLhczWgnTpzgzjvvtD2fOnUqU6dOpVWrVmzYsAGAP/74g27dunH27FmqVq3KPffcw9atW6latWpeDtVh3Nxg8WK4807YvBnGjYMXX3R0VSIiIqWDxTAMI7dvatCgAf/+97955JFHuOOOO1i9ejXNmzdnx44dPPTQQ/m6iqs4SEpKwsfHh8TERIefDluyxFwhGmD1avNyeREREckqN9/feToFNnbsWIYNG0ZoaCjNmjWzzdn56quv7EZoJP+6dIG+fc3Hjz8OuVhnUkRERK4jTyNAYN4D7OTJkzRs2NB2mmrbtm14e3sX63tv5URxGgEC8/5g4eGwe7d5A9U1a8xbaIiIiMhVufn+znMAypBxV/igoKD87KZYKW4BCGD/fmjc2Lxp6vjxV+8iLyIiIqZCPwWWnp7OhAkT8PHxISQkhJCQECpWrMjEiRNJT0/PU9FyY3XqwNy55uPx4+H/53yLiIhIHuTpKrBRo0bx1ltvMXnyZCIiIgD47rvviIuL49KlS7zwwgsFWqSYHnsMvv4a5s+Hf/0L4uMhh+tOioiISCZ5OgUWGBjI3LlzbXeBz/Dpp5/y9NNPc/z48QIr0BGK4ymwDBcuQLNm8PPP5hVhX3wBLnkaxxMRESldCv0U2Llz57Kd6Fy3bl3OnTuXl11KDpUrBx99BJ6e8OWX8PLLjq5IRESk5MlTAGrYsCGzZs3K0j5r1iwaNGiQ76LkxurXh5kzzcejRpkLJYqIiEjO5ekU2DfffMNDDz1E9erVbWsAbdmyhWPHjvHFF1/YbpNRUhXnU2AZDMOcE/TBBxAcbM4HqlTJ0VWJiIg4TqGfAmvVqhW//vorjzzyCOfPn+f8+fN07NiRvXv38u677+apaMkdi8W8KuzWW+HYMejVywxFIiIicnP5Xgcosx9//JG77roLq9VaULt0iJIwApRh1y64+25IS4Pp02HQIEdXJCIi4hiFPgIkxcedd8Irr5iPn30Wtm93bD0iIiIlgQJQKfD009CxI1y+DDExkJjo6IpERESKNwWgUsBigbfegtBQOHQI/v1vzQcSERG5kVytBN2xY8cbvn7+/Pn81CL5ULEiLF4MERHw8cfwxhvw1FOOrkpERKR4ylUA8vHxuenrPXr0yFdBknfNmsFLL8HQoTB4MDRvDg0bOroqERGR4qdArwIrLUrSVWDXMgx4+GFYuRJuuw127IDy5R1dlYiISOHTVWBOzGKBBQsgKAh+/RX69tV8IBERkWspAJVClSvDokXg6grvvQcLFzq6IhERkeJFAaiUuucemDDBfNyvn3n3eBERETEpAJViI0bAP/4BKSnw6KPmnyIiIqIAVKq5uMC774KfH+zdq9tkiIiIZFAAKuX8/OD9983J0f/7n3n3eBEREWenAOQE2rSBMWPMx//5Dxw44Nh6REREHE0ByEmMHQutWkFysjkf6NIlR1ckIiLiOApATsLV1TwVVqUKxMebd44XERFxVgpATuSWW+Cdd8zHs2bB0qWOrUdERMRRFICcTLt28N//mo+feMK8e7yIiIizUQByQs8/D3ffDYmJ0LUrpKU5uiIREZGipQDkhMqWhQ8/hIoVYds2eO45R1ckIiJStBSAnFRICMyfbz6eNs28e7yIiIizUAByYtHRMHCg+Tg2Fv74w6HliIiIFBkFICc3ZQo0bgznzkG3bnDlys3fY7XChg3mHec3bDCfi4iIlCQKQE7O3R0WL4YKFeC77yAu7sb9ly6F0FC47z7417/MP0NDdUm9iIiULApAQq1a5n3CAF58Edasyb7f0qXQuXPWU2XHj5vtCkEiIlJSKAAJYN4e4z//AcOAxx6DhAT7161W827yhpH1vRltgwfrdJiIiJQMCkBi8+qrEBYGp09D9+72YWbjxhtPkjYMOHbM7CciIlLcKQCJjacnfPQReHnB11+bp8MynDyZs33ktJ+IiIgjKQCJnbp1Yc4c83FcHHzzjfk4ICBn789pPxEREUdSAJIsevQw1wVKTzev9PrzT2jZEoKCwGLJ/j0WCwQHm/1ERESKOwUgydasWeZo0IkTZhiyWOC118zXrg1BGc+nTwdX1yItU0REJE8UgCRb5cub84E8PGDVKvN2GR07wscfwy232PcNCjLbO3Z0TK0iIiK5ZTGM7C5sdm5JSUn4+PiQmJiIt7e3o8txqDffhD59oEwZ+PZbaN7cvDps40ZzwnNAgHnaSyM/IiLiaLn5/lYAyoYC0FWGYc4D+vBDqF4d4uPB19fRVYmIiGSVm+9vnQKTG7JY4I03zNWijx6FJ57IfjFEERGRkkQBSG7K29ucD+TmBsuXw8yZjq5IREQkfxSAJEfuugumTjUfDxsGP/zg2HpERETyQwFIcqx/f4iOhsuXISYGEhMdXZGIiEjeKABJjlks8PbbEBICv/8OrVvDhg2OrkpERCT3FIAkV3x9zflAPj7mFWH33WeOCv36q6MrExERyTkFIMm1Zs3gwAF4+mlz/Z9PP4X69WHQIDh71tHViYiI3JzDA9Ds2bMJDQ3Fw8OD8PBwtm3bdt2+e/fupVOnToSGhmKxWJg+fXq+9yl5U7UqzJ4Ne/bAQw/BlSswYwbceiu88gqkpjq6QhERketzaABavHgxQ4YMYdy4cezcuZOGDRsSFRXF6dOns+2fkpJCzZo1mTx5Mv7+/gWyT8mfevVg5UpYswYaNIDz52HoUHNE6JNPtGaQiIgUTw5dCTo8PJymTZsya9YsANLT0wkODmbAgAGMGDHihu8NDQ1l8ODBDB48uMD2mUErQeeN1QoLF8KoUZCQYLbdc485ItS0qWNrExGR0q9ErASdlpbGjh07iIyMvFqMiwuRkZFs2bKl2OxTcs7V1Vwp+sABGDMGPD3hu+/MOUOPPWauJC0iIlIcOCwAnTlzBqvVip+fn127n58fCRnDB0W0z9TUVJKSkuw2ybvy5WHCBPPKsB49zLb334c6dczRob//dmx9IiIiDp8EXRxMmjQJHx8f2xYcHOzokkqFoCDzlNgPP0CrVnDpErz4ojlRet48c+K0iIiIIzgsAFWpUgVXV1dOnTpl137q1KnrTnAurH2OHDmSxMRE23bs2LE8fb5kr3FjWL/evI9Y7dpw+jT85z/QqBF8+aWjqxMREWfksADk5uZG48aNWbduna0tPT2ddevW0bx58yLdp7u7O97e3nabFCyLBTp0gJ9+gtdeMxdU3LsX2raFdu3MxyIiIkXFoafAhgwZwptvvsnChQvZt28fffv25cKFC/Tq1QuAHj16MHLkSFv/tLQ04uPjiY+PJy0tjePHjxMfH8/BgwdzvE9xLDc3GDgQDh6EZ56BsmVh9WrzEvqnnoJrBu9EREQKh+FgM2fONKpXr264ubkZzZo1M7Zu3Wp7rVWrVkZsbKzt+aFDhwwgy9aqVasc7zMnEhMTDcBITEzMz6FJDhw4YBgdOxqGuWKQYVSoYBgvvmgYKSmOrkxEREqa3Hx/O3QdoOJK6wAVvY0bYcgQc8I0QPXqMGkSdO0KLpqqLyIiOVAi1gESyaxlS/j+e3jvPfPqsaNHoXt3aN4cNm1ydHUiIlLaKABJseHiYoaeX3+FF14w1xPats1cTbpzZ/jtN0dXKCIipYUCkBQ7np7w3HPmitJ9+pjB6JNPzPuODRsGf/3l6ApFRKSkUwCSYsvfH954A+Lj4YEH4PJlmDbNXEhx5kzzuYiISF4oAEmxFxZmLpi4ahXcfjucO2deSn/HHbBihe44LyIiuacAJCVG27bw448wdy5UrWrOFerQAe6/H3budHR1IiJSkigASYlSpox5G42DB2HkSHB3hw0boEkT6NkTjh93dIUiIlISKABJieTtbd5Ydf9+6NbNPA22cCHcdhuMGwfJyY6uUEREijMFICnRQkLggw9g61aIiICUFJgwwQxCb78NVqujKxQRkeJIAUhKhfBwczXpJUugRg04eRJ69zbvRJ/p3rgiIiKAApCUIhaLuWDivn0wdSr4+JiTpiMjoX17+OUXR1coIiLFhQKQlDru7jB0qDlResAAcHWFlSvNy+b794czZxxdoYiIOJoCkJRaVarAjBmwdy88/LA5H2j2bHMhxZdfhtRUR1coIiKOogAkpV6dOvDpp/D119CoESQmwn//a95aY8kSLaQoIuKMFIDEadx3H/zwA8yfDwEBcOgQPPqoudL0K6/A6dOOrlBERIqKApA4FVdXc8HEAwcgLg68vMxTZEOHwi23QHS0eXsN3WdMRKR0UwASp1SunLlg4vHjMGcONGsGV66Yp8o6dICgIPPO83v3OrpSEREpDBbD0AyIayUlJeHj40NiYiLe3t6OLkeKyN69sGABvPOO/emwZs2gVy/o2hUqVnRUdSIicjO5+f5WAMqGApBzu3zZvPP8/Pnm5fNXrpjtHh7QsaMZhu6/H1w0fioiUqwoAOWTApBzsFrN1aNPnjQnRbdsac4Ryuz0aXjvPTMM/fTT1fbq1c25RD17mitPi4iI4ykA5ZMCUOm3dCkMGgR//HG1LSgIXnvNHOW5lmHAjh3m/cUWLYLz56++1rq1OSrUqZM5t0hERBxDASifFIBKt6VLzVtmXPubb7GYf378cfYhKMOlS7B8uRmG1q69up8KFSAmxgxDzZtf3Z+IiBQNBaB8UgAqvaxWCA21H/nJzGIxR4IOHcp6Oiw7R4+ak6bnz4fff7/aXqeOGYQefxwCAwukdBERuYncfH9rGqc4lY0brx9+wBzNOXbM7JcT1avD6NHmukIbNkBsrLm20P79MGIEBAfDQw/BJ59AWlqBHIKIiBQABSBxKidPFmy/DC4u0KqVeRl9QgL8738QEQHp6fDFF+Ypt8BAc95RfHxuqxYRkYKmACROJSCgYPtlp0IF6N0bvvsOfvnFHAkKDISzZ82bs955J9x1F8ycabaJiEjR0xygbGgOUOmVMQfo+PHsb4Ka2zlAOXXlCqxZY06c/vTTq7facHMzV57u1QseeKBgP1NExNloDpDIdbi6mpe6Q9artDKeT59e8EGkTBlo1868+/zJk+ZIUKNG5rygJUvgwQfN+UQjR8KvvxbsZ4uISFYKQOJ0OnY0L3W/5Rb79qCgm18CXxAqV4YBA2DXLnMbONBsO3ECJk82ryC75x546y34++/CrUVExFnpFFg2dArMOeRkJeiikpoKn31mXk6/erU5eRrMK8q6dDFPkd17r9YWEhG5Ea0DlE8KQOJIJ05cXVso8+mwmjXNIBQba15eLyIi9hSA8kkBSIoDw4AtW8yJ04sXQ3Ky2W6xQGSkGYaio8HT06FliogUGwpA+aQAJMXNhQvmYopvvw3ffHO1vWJF6NbNDENNmugUmUhpdurU1VFhV9esm4tL7tqvfa00/P9DASifFICkOPvtN1i40Fx08dixq+0+PuZcpozN3z/75xUrlo7/0YmUZklJ5g2Yt2+HbdvMP48eLdzPtFjyFpzy+lrnzubtggpSbr6/yxTsR4tIYatVCyZMgHHj4OuvzblCS5dCYqK5/fLLjd/v7p41IGUXlqpVMy/fF8dITzeXSUhNNbfsHhfF6x4ecMcd0KABNGxo/hkUpBBdkFJT4ccfrwad7dvN/46zu2FzaKj536XVam7p6VcfX7td+9rNGIa5ZtmVK4VymFnUr180n3M9GgHKhkaApKS5cAGOHDFvw3Hy5NUt8/OEBDh/Puf7tFjMEHS9kaTMj728Cu3Qio20NHMeVnKyuTxB5j+za0tJyV8AKaovobzw9TWDUMbWsKH5ZeYMvwf5ZbXCvn1Xg862bbB799XFUTMLCYGmTa9ujRtDfr6SMgeiGwWn3AarvL7WqBE0a5b348mOToHlkwKQlFYXL14NRdeGo8zB6fTpq5fi54S3d85GlSpVKpqRg/R0MxTmJKjktM3RN7MtW9YcvXNzM/+83uO8vH6j9yQmml/QGdsvv2QfziwWqF376ihRRjCqXt15R4sMAw4ftj+NtWOH+bt5rSpVzJDTrNnVwFOtWpGXXOIpAOWTApA4O6sV/vzz5kHp5Em4dCnn+3VzM4PQjUaV3NzyF1SSk7P/giko7u5Qvrx5z7fMf17b5ul5NUgUREApLiEiNdUcwdi92zxtk/Hnn39m39/b2/70WYMGEBYG5coVbd1F4dSpqyM7GduZM1n7lS9vjuZkBJ1mzczRnuLyMy7JFIDySQFIJGcMw5ysmV04ujY4nTtX9PW5uFw/oFyv7Wb9y5Yt+uMoCRISro4SZQSjffuyP7VjsZhz2a4NRqGh5s+sJMiYpJx53k52k5TLljWPMfPITt26uu9fYVEAyicFIJGCl5qas9NvV67kL6BkbvPw0L+qHSktzTxldm0wSkjIvn+FCuboUOZgFBZmtjvSpUtm7Znn7ezfn/0k5Xr17Ed2GjQwR/GkaCgA5ZMCkIhI4Tl92n5e0e7dsHfv9edZ1axpP6+oQQOzrTBGizJPUs4Y3bnZJOWM0Z277srfJGXJPwWgfFIAEhEpWpcvm4v8ZR4p2r0bjh/Pvn+5cldHizKCUViYuR5WTmVMUs58Gut6k5SrVrUf2WnSRJOUiyMFoHxSABIRKR7OnIE9e+yD0U8/madUsxMSkvVKtFq1zDk3mScpZ4Ses2ez7iNjknLmeTuapFwyKADlkwKQiEjxdeUKHDiQdW5R5pXRM/P0NNcuOnEi62tubmZIyjy6U6eOJimXVApA+aQAJCJS8vz1l/28oh9/NEeLLl40X8+YpJx5ZEeTlEsX3QpDREScjq8vtGplbhmsVvP+eWfOFI8ryqT4UAASKeGsVti40byEPCAAWrbU8L1IBldXuO02cxPJTAFIpARbuhQGDYI//rjaFhQEr70GHTs6ri4RkeKuhKy5KSLXWroUOne2Dz9gXjbcubP5uoiIZE8BSKQEslrNkZ/sLmHIaBs82OwnIiJZKQCJlEAbN2Yd+cnMMMxLgjduLLqaRERKEgUgkRLo5MmC7Sci4myKRQCaPXs2oaGheHh4EB4ezrZt227Yf8mSJdStWxcPDw/CwsL44osv7F7v2bMnFovFbmvbtm1hHoJIkQoIKNh+IiLOxuEBaPHixQwZMoRx48axc+dOGjZsSFRUFKdPn862/+bNm+nWrRu9e/dm165dREdHEx0dzU8//WTXr23btpw8edK2LVq0qCgOR6RItGxpXu11vaX5LRYIDjb7iYhIVg5fCTo8PJymTZsya9YsANLT0wkODmbAgAGMGDEiS/+YmBguXLjAypUrbW133303jRo1Yu7cuYA5AnT+/HmWL1+ep5q0ErSUBBlXgYH9ZOiMUPTxx7oUXkScS26+vx06ApSWlsaOHTuIjIy0tbm4uBAZGcmWLVuyfc+WLVvs+gNERUVl6b9hwwaqVatGnTp16Nu3L2ezu+Pd/0tNTSUpKcluEynuOnY0Q84tt9i3BwUp/IiI3IxDF0I8c+YMVqsVPz8/u3Y/Pz9++eWXbN+TkJCQbf+EhATb87Zt29KxY0dq1KjBb7/9xnPPPUe7du3YsmULrtkskTtp0iTGjx9fAEckUrQ6doQOHbQStIhIbpXKlaC7du1qexwWFkaDBg2oVasWGzZsoE2bNln6jxw5kiFDhtieJyUlERwcXCS1iuSXqyu0bu3oKkREShaHngKrUqUKrq6unDp1yq791KlT+Pv7Z/sef3//XPUHqFmzJlWqVOHgwYPZvu7u7o63t7fdJiIiIqWXQwOQm5sbjRs3Zt26dba29PR01q1bR/PmzbN9T/Pmze36A6xZs+a6/QH++OMPzp49S4CuCRYRERGKwWXwQ4YM4c0332ThwoXs27ePvn37cuHCBXr16gVAjx49GDlypK3/oEGDWL16NdOmTeOXX34hLi6OH374gf79+wOQnJzMs88+y9atWzl8+DDr1q2jQ4cO3HrrrURFRTnkGEVERKR4cfgcoJiYGP7880/Gjh1LQkICjRo1YvXq1baJzkePHsXF5WpOa9GiBR988AGjR4/mueeeo3bt2ixfvpw77rgDAFdXV3bv3s3ChQs5f/48gYGBPPDAA0ycOBF3d3eHHKOIiIgULw5fB6g40jpAIiIiJU9uvr8dPgIkIgLmnet1Ob+IFBUFIBFxuKVLYdAg+zvcBwXBa69pQUcRKRwOnwQtIs4t45YemcMPwPHjZvvSpY6pS0RKNwUgEXEYq9Uc+cluJmJG2+DBZj8RkYKkACQiDrNxY9aRn8wMA44dM/uJiBQkBSARcZiTJwu2n4hITikAiYjD5HRxdi3iLiIFTQFIRBymZUvzai+LJfvXLRYIDjb7iYgUJAUgEXEYV1fzUnfIGoIynk+frvWARKTgKQCJiEN17Agffwy33GLfHhRktmsdIBEpDFoIUUQcrmNH6NBBK0GLSNFRABKRYsHVFVq3dnQVIuIsFIBERAqQ7mkmUjIoAImIFBDd00yk5NAkaBGRAqB7momULApAIiL5pHuaiZQ8CkAiIvmke5qJlDwKQCIi+aR7momUPApAIiL5pHuaiZQ8CkAiIvmke5qJlDwKQCIi+aR7momUPApAIiIFoLTd08xqhQ0bYNEi809dwSaljRZCFBEpIKXlnmZa0FGcgcUwslu5wrklJSXh4+NDYmIi3t7eji5HRKTIZCzoeO03Q8apvJI4miXOIzff3zoFJiIigBZ0FOeiACQiIoAWdBTnogAkIiKAFnQU56IAJCIigBZ0FOeiq8BERAS4uqDj8ePZzwOyWMzXS9KCjlZryb8qTwqHRoBERAQofQs6Ll0KoaFw333wr3+Zf4aGmu0iCkAiImJTWhZ0zLic/9pJ3cePm+0KQaJ1gLKhdYBExNmV5FNHVqs50nO9K9oyTuUdOlRyjklyJjff35oDJCIiWbi6QuvWjq4ib3JzOX9JPUbJP50CExGRUkWX80tOaARIRERKldJ4OX9JPiVZXGkESERESpWMy/mvvZItg8UCwcEl53J+Xc1WOBSARESkVClNl/PrarbCowAkIiKlTmm4nF83py1cmgMkIiKlUseO0KFDyZ07UxqvZitOc5kUgEREpNQqyZfzl7ar2ZYuNUe0Moe6oCDzdKUjRuR0CkxERKQYKk1XsxXHuUwKQCIiIsVQabmarbjOZVIAEhERKYZKy9VsuZnLVJQUgERERIqp0nA1W3Gdy6RJ0CIiIsVYSb+arbjOZVIAEhERKeZK8tVsGXOZjh/Pfh6QxWK+XtRzmXQKTERERApNcZ3LpAAkIiIihao4zmXSKTAREREpdMVtLpMCkIiIiBSJ4jSXSafARERExOkUiwA0e/ZsQkND8fDwIDw8nG3btt2w/5IlS6hbty4eHh6EhYXxxRdf2L1uGAZjx44lICAAT09PIiMjOXDgQGEegoiIiJQgDg9AixcvZsiQIYwbN46dO3fSsGFDoqKiOH36dLb9N2/eTLdu3ejduze7du0iOjqa6OhofvrpJ1ufKVOmMGPGDObOncv3339PuXLliIqK4tKlS0V1WCIiIlKMWQwju6vyi054eDhNmzZl1qxZAKSnpxMcHMyAAQMYMWJElv4xMTFcuHCBlStX2truvvtuGjVqxNy5czEMg8DAQIYOHcqwYcMASExMxM/PjwULFtC1a9eb1pSUlISPjw+JiYl4e3sX0JGKiIhIYcrN97dDR4DS0tLYsWMHkZGRtjYXFxciIyPZsmVLtu/ZsmWLXX+AqKgoW/9Dhw6RkJBg18fHx4fw8PDr7jM1NZWkpCS7TUREREovhwagM2fOYLVa8fPzs2v38/MjISEh2/ckJCTcsH/Gn7nZ56RJk/Dx8bFtwcHBeToeERERKRkcPgeoOBg5ciSJiYm27dixY44uSURERAqRQwNQlSpVcHV15dSpU3btp06dwt/fP9v3+Pv737B/xp+52ae7uzve3t52m4iIiJReDg1Abm5uNG7cmHXr1tna0tPTWbduHc2bN8/2Pc2bN7frD7BmzRpb/xo1auDv72/XJykpie+///66+xQRERHn4vCVoIcMGUJsbCxNmjShWbNmTJ8+nQsXLtCrVy8AevTowS233MKkSZMAGDRoEK1atWLatGk89NBDfPjhh/zwww/MmzcPAIvFwuDBg3n++eepXbs2NWrUYMyYMQQGBhIdHZ2jmjIujNNkaBERkZIj43s7Rxe4G8XAzJkzjerVqxtubm5Gs2bNjK1bt9pea9WqlREbG2vX/6OPPjJuu+02w83Nzahfv77x+eef272enp5ujBkzxvDz8zPc3d2NNm3aGPv3789xPceOHTMAbdq0adOmTVsJ3I4dO3bT73qHrwNUHKWnp3PixAkqVKiAxWJxdDnFUlJSEsHBwRw7dkxzpooB/TyKF/08ihf9PIqXwvx5GIbB33//TWBgIC4uN57l4/BTYMWRi4sLQUFBji6jRNCk8eJFP4/iRT+P4kU/j+KlsH4ePj4+Oeqny+BFRETE6SgAiYiIiNNRAJI8cXd3Z9y4cbi7uzu6FEE/j+JGP4/iRT+P4qW4/Dw0CVpEREScjkaARERExOkoAImIiIjTUQASERERp6MAJCIiIk5HAUhybNKkSTRt2pQKFSpQrVo1oqOj2b9/v6PLkv83efJk273wxHGOHz/OY489RuXKlfH09CQsLIwffvjB0WU5JavVypgxY6hRowaenp7UqlWLiRMn5uw+UZJv3377Le3btycwMBCLxcLy5cvtXjcMg7FjxxIQEICnpyeRkZEcOHCgyOpTAJIc++abb+jXrx9bt25lzZo1XL58mQceeIALFy44ujSnt337dt544w0aNGjg6FKc2l9//UVERARly5Zl1apV/Pzzz0ybNg1fX19Hl+aUXnrpJebMmcOsWbPYt28fL730ElOmTGHmzJmOLs0pXLhwgYYNGzJ79uxsX58yZQozZsxg7ty5fP/995QrV46oqCguXbpUJPXpMnjJsz///JNq1arxzTffcO+99zq6HKeVnJzMXXfdxeuvv87zzz9Po0aNmD59uqPLckojRoxg06ZNbNy40dGlCPDPf/4TPz8/3nrrLVtbp06d8PT05L333nNgZc7HYrGwbNkyoqOjAXP0JzAwkKFDhzJs2DAAEhMT8fPzY8GCBXTt2rXQa9IIkORZYmIiAJUqVXJwJc6tX79+PPTQQ0RGRjq6FKe3YsUKmjRpQpcuXahWrRp33nknb775pqPLclotWrRg3bp1/PrrrwD8+OOPfPfdd7Rr187BlcmhQ4dISEiw+/+Wj48P4eHhbNmypUhq0M1QJU/S09MZPHgwERER3HHHHY4ux2l9+OGH7Ny5k+3btzu6FAF+//135syZw5AhQ3juuefYvn07AwcOxM3NjdjYWEeX53RGjBhBUlISdevWxdXVFavVygsvvED37t0dXZrTS0hIAMDPz8+u3c/Pz/ZaYVMAkjzp168fP/30E999952jS3Fax44dY9CgQaxZswYPDw9HlyOY/zBo0qQJL774IgB33nknP/30E3PnzlUAcoCPPvqI999/nw8++ID69esTHx/P4MGDCQwM1M9DdApMcq9///6sXLmS9evXExQU5OhynNaOHTs4ffo0d911F2XKlKFMmTJ88803zJgxgzJlymC1Wh1dotMJCAjg9ttvt2urV68eR48edVBFzu3ZZ59lxIgRdO3albCwMB5//HGeeeYZJk2a5OjSnJ6/vz8Ap06dsms/deqU7bXCpgAkOWYYBv3792fZsmV8/fXX1KhRw9ElObU2bdqwZ88e4uPjbVuTJk3o3r078fHxuLq6OrpEpxMREZFlaYhff/2VkJAQB1Xk3FJSUnBxsf+ac3V1JT093UEVSYYaNWrg7+/PunXrbG1JSUl8//33NG/evEhq0CkwybF+/frxwQcf8Omnn1KhQgXbeVofHx88PT0dXJ3zqVChQpb5V+XKlaNy5cqal+UgzzzzDC1atODFF1/k0UcfZdu2bcybN4958+Y5ujSn1L59e1544QWqV69O/fr12bVrF6+88gpPPPGEo0tzCsnJyRw8eND2/NChQ8THx1OpUiWqV6/O4MGDef7556lduzY1atRgzJgxBAYG2q4UK3SGSA4B2W7z5893dGny/1q1amUMGjTI0WU4tc8++8y44447DHd3d6Nu3brGvHnzHF2S00pKSjIGDRpkVK9e3fDw8DBq1qxpjBo1ykhNTXV0aU5h/fr12X5nxMbGGoZhGOnp6caYMWMMPz8/w93d3WjTpo2xf//+IqtP6wCJiIiI09EcIBEREXE6CkAiIiLidBSARERExOkoAImIiIjTUQASERERp6MAJCIiIk5HAUhEREScjgKQiMh1WCwWli9f7ugyRKQQKACJSLHUs2dPLBZLlq1t27aOLk1ESgHdC0xEiq22bdsyf/58uzZ3d3cHVSMipYlGgESk2HJ3d8ff399u8/X1BczTU3PmzKFdu3Z4enpSs2ZNPv74Y7v379mzh/vvvx9PT08qV65Mnz59SE5Otuvz9ttvU79+fdzd3QkICKB///52r585c4ZHHnkELy8vateuzYoVK2yv/fXXX3Tv3p2qVavi6elJ7dq1swQ2ESmeFIBEpMQaM2YMnTp14scff6R79+507dqVffv2AXDhwgWioqLw9fVl+/btLFmyhLVr19oFnDlz5tCvXz/69OnDnj17WLFiBbfeeqvdZ4wfP55HH32U3bt38+CDD9K9e3fOnTtn+/yff/6ZVatWsW/fPubMmUOVKlWK7i9ARPKuyG67KiKSC7GxsYarq6tRrlw5u+2FF14wDMMwAOOpp56ye094eLjRt29fwzAMY968eYavr6+RnJxse/3zzz83XFxcjISEBMMwDCMwMNAYNWrUdWsAjNGjR9ueJycnG4CxatUqwzAMo3379kavXr0K5oBFpEhpDpCIFFv33Xcfc+bMsWurVKmS7XHz5s3tXmvevDnx8fEA7Nu3j4YNG1KuXDnb6xEREaSnp7N//34sFgsnTpygTZs2N6yhQYMGtsflypXD29ub06dPA9C3b186derEzp07eeCBB4iOjqZFixZ5OlYRKVoKQCJSbJUrVy7LKamC4unpmaN+ZcuWtXtusVhIT08HoF27dhw5coQvvviCNWvW0KZNG/r168fUqVMLvF4RKViaAyQiJdbWrVuzPK9Xrx4A9erV48cff+TChQu21zdt2oSLiwt16tShQoUKhIaGsm7dunzVULVqVWJjY3nvvfeYPn068+bNy9f+RKRoaARIRIqt1NRUEhIS7NrKlCljm2i8ZMkSmjRpwj333MP777/Ptm3beOuttwDo3r0748aNIzY2lri4OP78808GDBjA448/jp+fHwBxcXE89dRTVKtWjXbt2vH333+zadMmBgwYkKP6xo4dS+PGjalfvz6pqamsXLnSFsBEpHhTABKRYmv16tUEBATYtdWpU4dffvkFMK/Q+vDDD3n66acJCAhg0aJF3H777QB4eXnx5ZdfMmjQIJo2bYqXlxedOnXilVdese0rNjaWS5cu8eqrrzJs2DCqVKlC586dc1yfm5sbI0eO5PDhw3h6etKyZUs+/PDDAjhyESlsFsMwDEcXISKSWxaLhWXLlhEdHe3oUkSkBNIcIBEREXE6CkAiIiLidDQHSERKJJ29F5H80AiQiIiIOB0FIBEREXE6CkAiIiLidBSARERExOkoAImIiIjTUQASERERp6MAJCIiIk5HAUhEREScjgKQiIiIOJ3/A/sy+m9lyh65AAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -290,20 +300,18 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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d7clkr72SL9etW5N2/K1bG266PmXffTepOZR3MENSk+rbNzlnX/jCtoRw8MFJ4rSdk+8O7iuBX0o6H5gFrAK2SjoIOBTomZZ7TNLREfGPzI0jYjIwGaC4uNj9GTvBA/jlzkcfweLFlZPD8uXJuj33hCOOgO98J0kOI0YkX9h11axZ8gW+117w8Y/XbZstW2D9+rolmCVLkve33673x96OlCTDFi2S952Z7tQp+XeZWUvo0aNpXMKaL7lMFquA/TPme6bLKkTEapKaBZLaAadFxJuSLgaejYh30nUPA0cAlZKFNZzaBvBzsqifzZuhpGRbYnj66W2/5vfZJ0kKX/1q8l5U1Pht4y1awN57J6+62rw5afJat27bPurzJd+8ub/Id3W5/Gc6GzhYUh+SJHE28LnMApK6Ausj4iPgapIrowBeBS6WdD1JM9RI4KYcxtrkeQC/HbdhAzzzzLbkMHt28uUKyS/e005LEsNRR8GBB+6abeR77JFcMrrffvmOxPIlZ8kiIrZIuhR4lOTS2SkRsVDSdUBJRDwIjAKulxQkzVBfTje/FzgOWEDS2f1IRDyUq1gt6aPI7BTMXG7bRCTnKbNJaeHCZF3LlnDYYXDZZUli+MQnoFu3/MZr1lC0u9y6UFxcHCUlJfkOY5dVtc8Ckg7Dpj4u09atsGBB5eSwKm1M7dAhSQjltYZhw7YfBNGs0EmaExHF2crlu4PbCkR5QmjKV0NFJB3Ps2cnr3//G+bMSa68AejZE44+eltyGDAgaYs3awpcs7Am6403tiWF8gSxdm2yrlWr5BLWYcPg8MOT5JDrsXfM8sE1C7MMb72V1BIyaw3lnffNmkG/fnDyyckYO8OGwcCBScIws4SThe12Nm+G0tLKtYYXXtg2QNuBByb3Nlx+eZIchgyp370NZk2Rk4Xt0rZuTRJBZlNSaWkythJA9+5JTWH8+OS9uDi5E9nM6sfJwnYZ1XVAz50L77yTrG/fPkkGX/takhiGD086pXfF+xrMCo2ThRWsunRAn3/+tsTw8Y/7LmGzXHGysEa1eXMyUujrr8Nrr9U+XT5InDugzfLPycJ2WmYCqC0JvP56MkJqdTp1SvoXuneHwYOT9969k2aloUPdAW2Wb04WVq3Nm5NmoLrUAGpKAB07JgPnde8OgwZtm+7evfL03nt7CGmzQudkUUA+/DAZbqOuj1Lckem6lH333doTQPmX/aBB1X/577OPE4DZ7sbJokD84Q9w7rmVH/m4M1q2TEYKbdUqea9pes89t1/etm31SaB7dycAs6bKyaIAPPkknHNOcnXPmWdm/4KvOl11vmVLXxVkZg3LySLPFiyAz34WPvYx+OtfkyeemZkVGv/+zKNXX4UxY5KmoEcecaIws8LlmkWerF8PJ56Y3H381FN+yJCZFTYnizx47z0YOxaWLYNHH01uMDMzK2Q5bYaSNEbSEknLJF1VzfpekmZImi/pSUk90+XHSpqX8Xpf0mdzGWtj2bo1eaDQ00/Db38Lo0blOyIzs+xyliwkNQduAU4E+gHjJfWrUuwGYGpEDAKuA64HiIiZETE4IgaTPIt7E/C3XMXaWCKSYbHvvx9+9rPkyiczs11BLmsWw4FlEfFyRHwATAfGVinTD3ginZ5ZzXqA04GHI2JTNet2KT/8Ifzv/8KVV8JXvpLvaMzM6i6XyaIHsCJjfmW6LFMpMC6dPhVoL6lLlTJnA3dVdwBJEyWVSCopKytrgJBz58474VvfSpqgfvSjfEdjZlY/+b509kpgpKTngJHAKmBr+UpJ+wIDgUer2zgiJkdEcUQUd+vWrTHi3SGPPAJf+AIcfzxMmeIb5sxs15PLq6FWAftnzPdMl1WIiNWkNQtJ7YDTIiJzVKIzgfsj4sMcxplTJSVw+unJFU9//KOH1TazXVMuf+POBg6W1EdSK5LmpAczC0jqKqk8hquBKVX2MZ4amqB2BS+9BCedBN26JXdnd+iQ74jMzHZMzpJFRGwBLiVpQloM3BMRCyVdJ+mUtNgoYImkpUB3YFL59pJ6k9RM/p6rGHPpjTfgU59KLpV95BHYd998R2RmtuMUEfmOoUEUFxdHSUlJvsMAkruyjz0WFi6EJ56Aww/Pd0RmZtWTNCciirOVc1drA/vww+T+iblzYfr0uiWKadOSp8I1a5a8T5uW6yjNzOrHw300oAiYOBEefhgmT4ZTTsm+zbRpyTab0rtIXnklmYfkMlszs0LgmkUD+s534I474Npr4eKL67bNNddsSxTlNm1KlpuZFQoniwbyf/8H3/8+XHRRkizq6tVX67fczCwfnCwawAMPwJe/DJ/5DNx6K0h137amock9ZLmZFRIni5309NMwfjwMG5Z0aLeoZy/QpEnJM68ztW2bLDczKxROFjth8WI4+WTYf3/485+TJ97V14QJSWd4r15JjaRXr2TendtmVkh8NdQOWr06eSRqq1bJA4y6dt3xfU2Y4ORgZoXNyWIHbNyYPBJ1/XqYNQv69Ml3RGZmueVkUU+bN8Opp8KiRcl4T0OG5DsiM7Pcc7Koh48+gvPOg5kzk0eijh6d74jMzBqHO7jr4cor4e67k4cXnXNOvqMxM2s8ThZ1dOONyXOzL78cvvGNfEdjZta46pQsJH1M0h7p9ChJl0vqlNvQCsdddyW1ijPOSBJGfW66MzPbHdS1ZnEfsFXSQcBkkudM/D5nURWQGTOSfoqRI2HqVD8S1cyaprp+9X2UPszoVOAXEfENYLd/nM+8ecmVT4cckgzp0bp1viMyM8uPuiaLDyWNB84D/pwua5mbkArD8uXJvRQdOyZDjndqMo1uZmbbq2uyuAA4ApgUEf+R1Af4bbaNJI2RtETSMklXVbO+l6QZkuZLelJSz4x1B0j6m6TFkhalj1ltFOvWJXdnv/9+8kjUnj2zb2Nmtjur030WEbEIuBxAUmegfUT8qLZtJDUHbgFGAyuB2ZIeTPdV7gZgakTcKek44Hrg8+m6qSTJ6TFJ7YCP6vG5dtimTcl4T8uXw2OPQf/+jXFUM7PCVteroZ6U1EHSXsBc4NeSfppls+HAsoh4OSI+AKYDY6uU6Qc8kU7PLF8vqR/QIiIeA4iIdyKiyiOCGt6WLckIss8+C7//PRx9dK6PaGa2a6hrM1THiHgLGEdSExgBfDLLNj2AFRnzK9NlmUrTfULSed5eUhfg48Cbkv4o6TlJP0lrKpVImiipRFJJWVlZHT9K9SKSZ1I8+CD84hcwblz2bczMmoq6JosWkvYFzmRbB3dDuBIYKek5YCSwCthK0jx2dLp+GHAgcH7VjSNickQUR0Rxt27ddiqQ738/GRr86quTpGFmZtvUNVlcBzwKvBQRsyUdCLyYZZtVJPdjlOuZLqsQEasjYlxEDAGuSZe9SVILmZc2YW0BHgCG1jHWevvNb5LnZ597rh86ZGZWnToli4j4Q0QMiohL0vmXI+K0LJvNBg6W1EdSK+Bs4MHMApK6SiqP4WpgSsa2nSSVVxeOAzI7xhvMCy/AF78In/oU3Hab7842M6tOXTu4e0q6X9Ib6eu+zMtcq5PWCC4lqZEsBu6JiIWSrpN0SlpsFLBE0lKgOzAp3XYrSRPUDEkLAAG/3oHPl1XfvnDnnXDvvdByt75zxMxsxykisheSHiMZ3qP83opzgAkRUTCDdBcXF0dJSUm+wzAz26VImhMRxdnK1bXPoltE3B4RW9LXHcDO9Sibmdkuo67JYp2kcyQ1T1/nAOtyGZiZmRWOuiaLC0kum30NWAOcTjWXspqZ2e6prldDvRIRp0REt4jYOyI+C2S7GsrMzHYTO/N0hq81WBRmZlbQdiZZ+I4EM7MmYmeSRfZrbs3MbLdQ6xDlkt6m+qQgoE1OIjIzs4JTa7KIiPaNFYiZmRWunWmGMjOzJsLJwszMsnKyMDOzrJwszMwsKycLMzPLysnCzMyycrIwM7OsnCzMzCwrJwszM8sqp8lC0hhJSyQtk3RVNet7SZohab6kJzOf6y1pq6R56evBXMZpZma1q3W4j50hqTlwCzAaWAnMlvRgRCzKKHYDMDUi7pR0HHA98Pl03XsRMThX8ZmZWd3lsmYxHFgWES9HxAfAdGBslTL9gCfS6ZnVrDczswKQy2TRA1iRMb8yXZapFBiXTp8KtJfUJZ1vLalE0rOSPlvdASRNTMuUlJWVNWTsZmaWId8d3FcCIyU9B4wEVgFb03W9IqIY+Bxwk6SPVd04IiZHRHFEFHfr1q3RgjYza2py1mdB8sW/f8Z8z3RZhYhYTVqzkNQOOC0i3kzXrUrfX5b0JDAEeCmH8ZqZWQ1yWbOYDRwsqY+kVsDZQKWrmiR1lVQew9XAlHR5Z0l7lJcBjgQyO8bNzKwR5SxZRMQW4FLgUWAxcE9ELJR0naRT0mKjgCWSlgLdgUnp8kOBEkmlJB3fP6xyFZWZmTUiRewej9IuLi6OkpKSfIdhZrZLkTQn7R+uVb47uM3MbBfgZGFmZlk5WZiZWVZOFmZmlpWThZmZZeVkYWZmWTlZmJlZVk4WZmaWlZOFmZll5WRhZmZZOVmYmVlWThZmZpaVk4WZmWXlZGFmZlk5WZiZWVZOFmZmlpWThZmZZZXTZCFpjKQlkpZJuqqa9b0kzZA0X9KTknpWWd9B0kpJv8xlnGZmVrucJQtJzYFbgBOBfsB4Sf2qFLsBmBoRg4DrgOurrP8eMCtXMZqZWd3ksmYxHFgWES9HxAfAdGBslTL9gCfS6ZmZ6yUdBnQH/pbDGM3MrA5ymSx6ACsy5lemyzKVAuPS6VOB9pK6SGoG3AhcWdsBJE2UVCKppKysrIHCNjOzqvLdwX0lMFLSc8BIYBWwFfgv4K8RsbK2jSNickQUR0Rxt27dch+tmVkT1SKH+14F7J8x3zNdViEiVpPWLCS1A06LiDclHQEcLem/gHZAK0nvRMR2neRmZpZ7uUwWs4GDJfUhSRJnA5/LLCCpK7A+Ij4CrgamAETEhIwy5wPFThRmZvmTs2aoiNgCXAo8CiwG7omIhZKuk3RKWmwUsETSUpLO7Em5isfMzHacIiLfMTSI4uLiKCkpyXcYZma7FElzIqI4W7l8d3CbmdkuwMnCzMyycrIwM7OsnCzMzCwrJwszM8vKycLMzLJysjAzs6ycLMzMLCsnCzMzy8rJwszMsnKyMDOzrJwszMwsKycLMzPLKpfPszCzPPnwww9ZuXIl77//fr5DsQLRunVrevbsScuWLXdoeycLs93QypUrad++Pb1790ZSvsOxPIsI1q1bx8qVK+nTp88O7cPNUGa7offff58uXbo4URgAkujSpctO1TSdLMx2U04Ulmln/z04WZiZWVY5TRaSxkhaImmZpKuqWd9L0gxJ8yU9KalnxvK5kuZJWijpS7mM06ypmzYNeveGZs2S92nTdm5/69atY/DgwQwePJh99tmHHj16VMx/8MEHddrHBRdcwJIlS2otc8sttzBtZ4O1OsnZM7glNQeWAqOBlcBsYHxELMoo8wfgzxFxp6TjgAsi4vOSWqWxbZbUDnge+ERErK7peH4Gt9k2ixcv5tBDD61T2WnTYOJE2LRp27K2bWHyZJgwYedj+e53v0u7du248sorKy2PCCKCZs2aVgPHli1baNEiP9cWVffvohCewT0cWBYRL0fEB8B0YGyVMv2AJ9LpmeXrI+KDiNicLt8jx3GaNWnXXFM5UUAyf801DX+sZcuW0a9fPyZMmED//v1Zs2YNEydOpLi4mP79+3PddddVlD3qqKOYN28eW7ZsoVOnTlx11VUUFRVxxBFH8MYbbwDw7W9/m5tuuqmi/FVXXcXw4cM55JBDeOaZZwB49913Oe200+jXrx+nn346xcXFzJs3b7vYrr32WoYNG8aAAQP40pe+RPkP6aVLl3LcccdRVFTE0KFDWb58OQA/+MEPGDhwIEVFRVyTnqzymAFee+01DjroIABuu+02PvvZz3LsscfyqU99irfeeovjjjuOoUOHMmjQIP785z9XxHH77bczaNAgioqKuOCCC9i4cSMHHnggW7ZsAWDDhg2V5htLLr+EewArMuZXpssylQLj0ulTgfaSugBI2l/S/HQfP6quViFpoqQSSSVlZWUN/gHMmoJXX63f8p0u+TAYAAARTElEQVT1wgsvcMUVV7Bo0SJ69OjBD3/4Q0pKSigtLeWxxx5j0aJF222zceNGRo4cSWlpKUcccQRTpkypdt8Rwb///W9+8pOfVCSeX/ziF+yzzz4sWrSI//7v/+a5556rdtuvfOUrzJ49mwULFrBx40YeeeQRAMaPH88VV1xBaWkpzzzzDHvvvTcPPfQQDz/8MP/+978pLS3l61//etbP/dxzz/HHP/6RGTNm0KZNGx544AHmzp3L448/zhVXXAFAaWkpP/rRj3jyyScpLS3lxhtvpGPHjhx55JEV8dx1112cccYZjV47yfcv9iuBkZKeA0YCq4CtABGxIiIGAQcB50nqXnXjiJgcEcURUdytW7fGjNtst3HAAfVbvrM+9rGPUVy8rdXjrrvuYujQoQwdOpTFixdXmyzatGnDiSeeCMBhhx1W8eu+qnHjxm1X5qmnnuLss88GoKioiP79+1e77YwZMxg+fDhFRUX8/e9/Z+HChWzYsIG1a9dy8sknA8mNbW3btuXxxx/nwgsvpE2bNgDstddeWT/3CSecQOfOnYEkqV111VUMGjSIE044gRUrVrB27VqeeOIJzjrrrIr9lb9fdNFF3H777UBS87jggguyHq+h5TJZrAL2z5jvmS6rEBGrI2JcRAwBrkmXvVm1DEmfxdE5jNWsyZo0KemjyNS2bbI8F/bcc8+K6RdffJGbb76ZJ554gvnz5zNmzJhq7wVo1apVxXTz5s1rbILZY489spapzqZNm7j00ku5//77mT9/PhdeeOEO3ZPQokULPvroI4Dtts/83FOnTmXjxo3MnTuXefPm0bVr11qPN3LkSJYuXcrMmTNp2bIlffv2rXdsOyuXyWI2cLCkPmmH9dnAg5kFJHWVVB7D1cCUdHlPSW3S6c7AUUDtl0WY2Q6ZMCHpzO7VC6TkvaE6t7N56623aN++PR06dGDNmjU8+uijDX6MI488knvuuQeABQsWVFtzee+992jWrBldu3bl7bff5r777gOgc+fOdOvWjYceeghIEsCmTZsYPXo0U6ZM4b333gNg/fr1APTu3Zs5c+YAcO+999YY08aNG9l7771p0aIFjz32GKtWJb+jjzvuOO6+++6K/ZW/A5xzzjlMmDAhL7UKyGGyiIgtwKXAo8Bi4J6IWCjpOkmnpMVGAUskLQW6A+W/ZQ4F/iWpFPg7cENELMhVrGZN3YQJsHw5fPRR8t4YiQJg6NCh9OvXj759+3Luuedy5JFHNvgxLrvsMlatWkW/fv34n//5H/r160fHjh0rlenSpQvnnXce/fr148QTT2TEiBEV66ZNm8aNN97IoEGDOOqooygrK+Mzn/kMY8aMobi4mMGDB/Ozn/0MgG984xvcfPPNDB06lA0bNtQY0+c//3meeeYZBg4cyPTp0zn44IOBpJnsm9/8JscccwyDBw/mG9/4RsU2EyZMYOPGjZx11lkNeXrqLGeXzjY2Xzprtk19Lp3d3W3ZsoUtW7bQunVrXnzxRU444QRefPHFvF2+uqOmT5/Oo48+WtF3sSN25tLZXetsmZnV0zvvvMPxxx/Pli1biAh+9atf7XKJ4pJLLuHxxx+vuCIqH3atM2ZmVk+dOnWq6EfYVd166635DiHvl86amdkuwMnCzMyycrIwM7OsnCzMzCwrJwsza3DHHnvsdjfY3XTTTVxyySW1bteuXTsAVq9ezemnn15tmVGjRpHtMvmbbrqJTRmjI37605/mzTffrGULy8bJwswa3Pjx45k+fXqlZdOnT2f8+PF12n6//far9Q7obKomi7/+9a906tRph/fX2CKiYtiQQuFkYbab++pXYdSohn199au1H/P000/nL3/5S8WDjpYvX87q1as5+uijK+57GDp0KAMHDuRPf/rTdtsvX76cAQMGAMlQHGeffTaHHnoop556asUQG5Dcf1A+vPm1114LwM9//nNWr17Nsccey7HHHgskw3CsXbsWgJ/+9KcMGDCAAQMGVAxvvnz5cg499FAuvvhi+vfvzwknnFDpOOUeeughRowYwZAhQ/jkJz/J66+/DiT3clxwwQUMHDiQQYMGVQwX8sgjjzB06FCKioo4/vjjgeT5HjfccEPFPgcMGMDy5ctZvnw5hxxyCOeeey4DBgxgxYoV1X4+gNmzZ/OJT3yCoqIihg8fzttvv80xxxxTaej1o446itLS0tr/UPXg+yzMrMHttddeDB8+nIcffpixY8cyffp0zjzzTCTRunVr7r//fjp06MDatWs5/PDDOeWUU2p8RvStt95K27ZtWbx4MfPnz2fo0KEV6yZNmsRee+3F1q1bOf7445k/fz6XX345P/3pT5k5cyZdu3attK85c+Zw++23869//YuIYMSIEYwcOZLOnTvz4osvctddd/HrX/+aM888k/vuu49zzjmn0vZHHXUUzz77LJK47bbb+PGPf8yNN97I9773PTp27MiCBcmoRBs2bKCsrIyLL76YWbNm0adPn0rjPNXkxRdf5M477+Twww+v8fP17duXs846i7vvvpthw4bx1ltv0aZNG77whS9wxx13cNNNN7F06VLef/99ioqK6vV3q42ThdluLv3x3OjKm6LKk8VvfvMbIGli+da3vsWsWbNo1qwZq1at4vXXX2efffapdj+zZs3i8ssvB2DQoEEMGjSoYt0999zD5MmT2bJlC2vWrGHRokWV1lf11FNPceqpp1aMADtu3Dj+8Y9/cMopp9CnTx8GDx4M1DwM+sqVKznrrLNYs2YNH3zwAX369AHg8ccfr9Ts1rlzZx566CGOOeaYijJ1Gca8V69eFYmips8niX333Zdhw4YB0KFDBwDOOOMMvve97/GTn/yEKVOmcP7552c9Xn00+Waohn72sJklxo4dy4wZM5g7dy6bNm3isMMOA5KB+crKypgzZw7z5s2je/fuOzQc+H/+8x9uuOEGZsyYwfz58znppJN2aD/lyoc3h5qHOL/sssu49NJLWbBgAb/61a92ehhzqDyUeeYw5vX9fG3btmX06NH86U9/4p577mFCA48G2aSTRfmzh195BSKS94kTnTDMGkK7du049thjufDCCyt1bJcPz92yZUtmzpzJK6+8Uut+jjnmGH7/+98D8PzzzzN//nwgGd58zz33pGPHjrz++us8/PDDFdu0b9+et99+e7t9HX300TzwwANs2rSJd999l/vvv5+jj677o3I2btxIjx7JAz/vvPPOiuWjR4/mlltuqZjfsGEDhx9+OLNmzeI///kPUHkY87lz5wIwd+7civVV1fT5DjnkENasWcPs2bMBePvttysS20UXXcTll1/OsGHDKh601FCadLJozGcPmzVF48ePp7S0tFKymDBhAiUlJQwcOJCpU6dmfZDPJZdcwjvvvMOhhx7Kd77znYoaSlFREUOGDKFv37587nOfqzS8+cSJExkzZkxFB3e5oUOHcv755zN8+HBGjBjBRRddxJAhQ+r8eb773e9yxhlncNhhh1XqD/n2t7/Nhg0bGDBgAEVFRcycOZNu3boxefJkxo0bR1FRUcXQ4qeddhrr16+nf//+/PKXv+TjH/94tceq6fO1atWKu+++m8suu4yioiJGjx5dUeM47LDD6NChQ06eedGkhyhv1iypUVQlJeP6m+2qPER507R69WpGjRrFCy+8QLNm29cFdmaI8iZds2jsZw+bmeXK1KlTGTFiBJMmTao2UeysJp0sGvvZw2ZmuXLuueeyYsUKzjjjjJzsP6fJQtIYSUskLZN0VTXre0maIWm+pCcl9UyXD5b0T0kL03U5eY5gPp89bJZru0sTszWMnf33kLNkIak5cAtwItAPGC+pX5ViNwBTI2IQcB1wfbp8E3BuRPQHxgA3ScrJvfr5evawWS61bt2adevWOWEYkCSKdevW0bp16x3eRy5vyhsOLIuIlwEkTQfGAosyyvQDvpZOzwQeAIiIpeUFImK1pDeAboBHAjOrg549e7Jy5UrKysryHYoViNatW9OzZ88d3j6XyaIHsCJjfiUwokqZUmAccDNwKtBeUpeIWFdeQNJwoBXwUtUDSJoITAQ4wL3SZhVatmxZceewWUPIdwf3lcBISc8BI4FVwNbylZL2BX4LXBAR213MGhGTI6I4Ioq7devWWDGbmTU5uaxZrAL2z5jvmS6rEBGrSWoWSGoHnBYRb6bzHYC/ANdExLM5jNPMzLLIZc1iNnCwpD6SWgFnAw9mFpDUVVJ5DFcDU9LlrYD7STq/d3xQezMzaxA5vYNb0qeBm4DmwJSImCTpOqAkIh6UdDrJFVABzAK+HBGbJZ0D3A4szNjd+RExjxpIKgNqH2Sm8HUF1uY7iALi81GZz8c2PheV7cz56BURWdvxd5vhPnYHkkrqctt9U+HzUZnPxzY+F5U1xvnIdwe3mZntApwszMwsKyeLwjI53wEUGJ+Pynw+tvG5qCzn58N9FmZmlpVrFmZmlpWThZmZZeVkUQAk7S9ppqRF6bDsX8l3TPkmqbmk5yT9Od+x5JukTpLulfSCpMWSjsh3TPkk6Yr0/8nzku6StONDqe6CJE2R9Iak5zOW7SXpMUkvpu8N+wBunCwKxRbg6xHRDzgc+HI1w7k3NV8BFuc7iAJxM/BIRPQFimjC50VSD+ByoDgiBpDc8Ht2fqNqdHeQPLoh01XAjIg4GJiRzjcoJ4sCEBFrImJuOv02yZdBj/xGlT/pQ7BOAm7Ldyz5JqkjcAzwG4CI+KB8/LQmrAXQRlILoC2wOs/xNKqImAWsr7J4LHBnOn0n8NmGPq6TRYGR1BsYAvwrv5Hk1U3AN4HtRhpugvoAZcDtabPcbZL2zHdQ+RIRq0gemvYqsAbYGBF/y29UBaF7RKxJp18Dujf0AZwsCkg68u59wFcj4q18x5MPkj4DvBERc/IdS4FoAQwFbo2IIcC75KCJYVeRtsWPJUmi+wF7pmPJWSqS+yEa/J4IJ4sCIaklSaKYFhF/zHc8eXQkcIqk5cB04DhJv8tvSHm1ElgZEeU1zXtJkkdT9UngPxFRFhEfAn8EPpHnmArB6+nzf8qfA/RGQx/AyaIASBJJm/TiiPhpvuPJp4i4OiJ6RkRvko7LJyKiyf5yjIjXgBWSDkkXHU/lRxM3Na8Ch0tqm/6/OZ4m3OGf4UHgvHT6POBPDX0AJ4vCcCTweZJf0fPS16fzHZQVjMuAaZLmA4OBH+Q5nrxJa1j3AnOBBSTfYU1q6A9JdwH/BA6RtFLSF4AfAqMlvUhS+/phgx/Xw32YmVk2rlmYmVlWThZmZpaVk4WZmWXlZGFmZlk5WZiZWVZOFmZZSNqacUnzPEkNdge1pN6Zo4eaFaoW+Q7AbBfwXkQMzncQZvnkmoXZDpK0XNKPJS2Q9G9JB6XLe0t6QtJ8STMkHZAu7y7pfkml6at8mIrmkn6dPqPhb5LapOUvT59xMl/S9Dx9TDPAycKsLtpUaYY6K2PdxogYCPySZLRcgF8Ad0bEIGAa8PN0+c+Bv0dEEcn4TgvT5QcDt0REf+BN4LR0+VXAkHQ/X8rVhzOrC9/BbZaFpHciol01y5cDx0XEy+lAkK9FRBdJa4F9I+LDdPmaiOgqqQzoGRGbM/bRG3gsfWgNkv4f0DIivi/pEeAd4AHggYh4J8cf1axGrlmY7ZyoYbo+NmdMb2VbX+JJwC0ktZDZ6cN+zPLCycJs55yV8f7PdPoZtj3qcwLwj3R6BnAJVDxjvGNNO5XUDNg/ImYC/w/oCGxXuzFrLP6lYpZdG0nzMuYfiYjyy2c7p6PBbgbGp8suI3my3TdInnJ3Qbr8K8DkdJTQrSSJYw3Vaw78Lk0oAn7ux6laPrnPwmwHpX0WxRGxNt+xmOWam6HMzCwr1yzMzCwr1yzMzCwrJwszM8vKycLMzLJysjAzs6ycLMzMLKv/D1uYNUefcNIQAAAAAElFTkSuQmCC",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "acc = history.history[\"acc\"]\n",
-    "validation_acc = history.history[\"val_acc\"]\n",
+    "acc = history.history[\"accuracy\"]\n",
+    "validation_acc = history.history[\"val_accuracy\"]\n",
     "\n",
     "plt.plot(epochs, acc, \"bo\", label=\"Training accuracy\")\n",
     "plt.plot(epochs, validation_acc, \"b\", label=\"Validation accuracy\")\n",
@@ -339,15 +347,29 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/5\n",
-      "60000/60000 [==============================] - 4s 68us/step - loss: 0.2569 - acc: 0.9266\n",
+      "\u001b[1m  1/469\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:26\u001b[0m 186ms/step - accuracy: 0.0781 - loss: 2.4094"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-03-31 21:19:29.842693: W external/local_tsl/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 188160000 exceeds 10% of free system memory.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8703 - loss: 0.4504\n",
       "Epoch 2/5\n",
-      "60000/60000 [==============================] - 3s 58us/step - loss: 0.1052 - acc: 0.9692\n",
+      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9659 - loss: 0.1192\n",
       "Epoch 3/5\n",
-      "60000/60000 [==============================] - 3s 57us/step - loss: 0.0691 - acc: 0.9789\n",
+      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9788 - loss: 0.0717\n",
       "Epoch 4/5\n",
-      "60000/60000 [==============================] - 3s 57us/step - loss: 0.0498 - acc: 0.9850\n",
+      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9858 - loss: 0.0498\n",
       "Epoch 5/5\n",
-      "60000/60000 [==============================] - 4s 59us/step - loss: 0.0373 - acc: 0.9890\n"
+      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9893 - loss: 0.0373\n"
      ]
     }
    ],
@@ -372,9 +394,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model.save(\"app/mnist_model.h5\")\n",
+    "model.save(\"app/mnist_model.keras\")\n",
     "del model\n",
-    "model = load_model(\"app/mnist_model.h5\")"
+    "model = load_model(\"app/mnist_model.keras\")"
    ]
   },
   {
@@ -393,7 +415,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "10000/10000 [==============================] - 1s 52us/step\n"
+      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 699us/step - accuracy: 0.9745 - loss: 0.0776\n"
      ]
     }
    ],
@@ -417,8 +439,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Final loss: 0.07552170741255396\n",
-      "Final accuracy: 0.9768\n"
+      "Final loss: 0.06699982285499573\n",
+      "Final accuracy: 0.9779999852180481\n"
      ]
     }
    ],
@@ -448,14 +470,12 @@
    "outputs": [
     {
      "data": {
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+      "image/png": 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TTQhbtmyJ+nrNmjXKzMzUzp07NWXKFIVCIf35z3/W2rVrNW3aNEnS6tWrdeONN6q2tlbf//734zc5AKBXu6L3gEKhkCQpPT1dkrRz506dOXNGRUVFkX3GjBmj4cOHq6amptPv0d7ernA4HLUBAJJfzAHq6OjQ4sWLNXnyZI0dO1aS1NzcrEGDBiktLS1q36ysLDU3N3f6fcrLyxUIBCJbbm5urCMBAHqRmANUWlqqffv2ad26dVc0QFlZmUKhUGRramq6ou8HAOgdYvqLqIsWLdLmzZu1bds2DRs2LPJ4MBjU6dOndfz48airoJaWFgWDwU6/l9/vl9/vj2UMAEAv5ukKyDmnRYsWacOGDdq6davy8vKinp8wYYIGDhyoysrKyGN1dXU6cOCACgsL4zMxACApeLoCKi0t1dq1a7Vp0yalpKRE3tcJBAIaPHiwAoGAHnzwQS1dulTp6elKTU3Vo48+qsLCQu6AAwBE8RSgVatWSZKmTp0a9fjq1as1f/58SdIf/vAH9evXT3PnzlV7e7uKi4v1xz/+MS7DAgCSh6cAXc4HFF511VWqqKhQRUVFzEMBiA+fz9et6wAv+Cw4AIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmIjpN6IC6B1OnTrVbccaPHhwtx0LyYErIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABB9GCiSx1atXx7QuLS3N85ply5bFdCz0XVwBAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAm+DBSIIlNnDgxpnVLlizxvGbatGkxHQt9F1dAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJPowUSGLvvvuu9QhAl7gCAgCYIEAAABOeAlReXq6JEycqJSVFmZmZmj17turq6qL2mTp1qnw+X9T28MMPx3VoAEDv5ylA1dXVKi0tVW1trT744AOdOXNGM2bMUFtbW9R+CxYs0OHDhyPbypUr4zo0AKD383QTwpYtW6K+XrNmjTIzM7Vz505NmTIl8vjVV1+tYDAYnwkBAEnpit4DCoVCkqT09PSox1977TVlZGRo7NixKisr08mTJ7v8Hu3t7QqHw1EbACD5xXwbdkdHhxYvXqzJkydr7Nixkcfvu+8+jRgxQjk5OdqzZ4+eeOIJ1dXV6e233+70+5SXl+uZZ56JdQwAQC/lc865WBYuXLhQf/3rX/Xxxx9r2LBhXe63detWTZ8+XfX19Ro1atRFz7e3t6u9vT3ydTgcVm5urkKhkFJTU2MZDQBgKBwOKxAIXPLneExXQIsWLdLmzZu1bdu2b4yPJBUUFEhSlwHy+/3y+/2xjAEA6MU8Bcg5p0cffVQbNmxQVVWV8vLyLrlm9+7dkqTs7OyYBgQAJCdPASotLdXatWu1adMmpaSkqLm5WZIUCAQ0ePBgNTQ0aO3atfrhD3+oa6+9Vnv27NGSJUs0ZcoUjR8/PiH/AgCA3snTe0A+n6/Tx1evXq358+erqalJP/3pT7Vv3z61tbUpNzdXd911l5566qnLfj/ncv/sEADQMyXkPaBLtSo3N1fV1dVeviUAoI/is+AAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYGWA9wIeecJCkcDhtPAgCIxfmf3+d/nnelxwWotbVVkpSbm2s8CQDgSrS2tioQCHT5vM9dKlHdrKOjQ4cOHVJKSop8Pl/Uc+FwWLm5uWpqalJqaqrRhPY4D+dwHs7hPJzDeTinJ5wH55xaW1uVk5Ojfv26fqenx10B9evXT8OGDfvGfVJTU/v0C+w8zsM5nIdzOA/ncB7OsT4P33Tlcx43IQAATBAgAICJXhUgv9+v5cuXy+/3W49iivNwDufhHM7DOZyHc3rTeehxNyEAAPqGXnUFBABIHgQIAGCCAAEATBAgAICJXhOgiooKfec739FVV12lgoIC/eMf/7AeqdutWLFCPp8vahszZoz1WAm3bds2zZo1Szk5OfL5fNq4cWPU8845LVu2TNnZ2Ro8eLCKioq0f/9+m2ET6FLnYf78+Re9PmbOnGkzbIKUl5dr4sSJSklJUWZmpmbPnq26urqofU6dOqXS0lJde+21GjJkiObOnauWlhajiRPjcs7D1KlTL3o9PPzww0YTd65XBOiNN97Q0qVLtXz5cn366afKz89XcXGxjhw5Yj1at7v55pt1+PDhyPbxxx9bj5RwbW1tys/PV0VFRafPr1y5Ui+88IJeeuklbd++Xddcc42Ki4t16tSpbp40sS51HiRp5syZUa+P119/vRsnTLzq6mqVlpaqtrZWH3zwgc6cOaMZM2aora0tss+SJUv07rvvav369aqurtahQ4c0Z84cw6nj73LOgyQtWLAg6vWwcuVKo4m74HqBSZMmudLS0sjXZ8+edTk5Oa68vNxwqu63fPlyl5+fbz2GKUluw4YNka87OjpcMBh0zz77bOSx48ePO7/f715//XWDCbvHhefBOefmzZvn7rzzTpN5rBw5csRJctXV1c65c//tBw4c6NavXx/Z59///reT5GpqaqzGTLgLz4Nzzv3gBz9wP//5z+2Gugw9/gro9OnT2rlzp4qKiiKP9evXT0VFRaqpqTGczMb+/fuVk5OjkSNH6v7779eBAwesRzLV2Nio5ubmqNdHIBBQQUFBn3x9VFVVKTMzUzfccIMWLlyoo0ePWo+UUKFQSJKUnp4uSdq5c6fOnDkT9XoYM2aMhg8fntSvhwvPw3mvvfaaMjIyNHbsWJWVlenkyZMW43Wpx30Y6YW+/PJLnT17VllZWVGPZ2Vl6T//+Y/RVDYKCgq0Zs0a3XDDDTp8+LCeeeYZ3X777dq3b59SUlKsxzPR3NwsSZ2+Ps4/11fMnDlTc+bMUV5enhoaGvTkk0+qpKRENTU16t+/v/V4cdfR0aHFixdr8uTJGjt2rKRzr4dBgwYpLS0tat9kfj10dh4k6b777tOIESOUk5OjPXv26IknnlBdXZ3efvttw2mj9fgA4f9KSkoi/zx+/HgVFBRoxIgRevPNN/Xggw8aToae4J577on887hx4zR+/HiNGjVKVVVVmj59uuFkiVFaWqp9+/b1ifdBv0lX5+Ghhx6K/PO4ceOUnZ2t6dOnq6GhQaNGjeruMTvV4/8ILiMjQ/3797/oLpaWlhYFg0GjqXqGtLQ0XX/99aqvr7cexcz51wCvj4uNHDlSGRkZSfn6WLRokTZv3qyPPvoo6te3BINBnT59WsePH4/aP1lfD12dh84UFBRIUo96PfT4AA0aNEgTJkxQZWVl5LGOjg5VVlaqsLDQcDJ7J06cUENDg7Kzs61HMZOXl6dgMBj1+giHw9q+fXuff30cPHhQR48eTarXh3NOixYt0oYNG7R161bl5eVFPT9hwgQNHDgw6vVQV1enAwcOJNXr4VLnoTO7d++WpJ71erC+C+JyrFu3zvn9frdmzRr3r3/9yz300EMuLS3NNTc3W4/WrX7xi1+4qqoq19jY6P7+97+7oqIil5GR4Y4cOWI9WkK1tra6Xbt2uV27djlJ7rnnnnO7du1yn3/+uXPOud/97ncuLS3Nbdq0ye3Zs8fdeeedLi8vz3311VfGk8fXN52H1tZW99hjj7mamhrX2NjoPvzwQ/e9733PXXfdde7UqVPWo8fNwoULXSAQcFVVVe7w4cOR7eTJk5F9Hn74YTd8+HC3detWt2PHDldYWOgKCwsNp46/S52H+vp696tf/crt2LHDNTY2uk2bNrmRI0e6KVOmGE8erVcEyDnnXnzxRTd8+HA3aNAgN2nSJFdbW2s9Ure7++67XXZ2ths0aJD79re/7e6++25XX19vPVbCffTRR07SRdu8efOcc+duxX766addVlaW8/v9bvr06a6urs526AT4pvNw8uRJN2PGDDd06FA3cOBAN2LECLdgwYKk+5+0zv79JbnVq1dH9vnqq6/cI4884r71rW+5q6++2t11113u8OHDdkMnwKXOw4EDB9yUKVNcenq68/v9bvTo0e6Xv/ylC4VCtoNfgF/HAAAw0ePfAwIAJCcCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwMT/AI0qpH2V+QWDAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -495,8 +515,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[6.5164194e-08 1.0987159e-10 5.4261386e-07 8.3557815e-08 9.7565311e-01\n",
-      "  4.1773944e-08 9.2240427e-08 2.0984227e-05 2.0405821e-06 2.4323082e-02]]\n"
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+      "[[5.1081292e-07 3.1363423e-09 2.1023111e-06 7.7408027e-09 9.9883264e-01\n",
+      "  4.4669033e-08 1.4959137e-05 2.1703441e-05 2.2524851e-07 1.1278475e-03]]\n"
      ]
     }
    ],
@@ -526,7 +547,7 @@
    "outputs": [],
    "source": [
     "APP_ROOT = os.path.dirname(os.path.abspath(\"Deep Learning MNIST prediction model with Keras.ipynb\"))\n",
-    "APP_STATIC = os.path.join(APP_ROOT, \"app/static\")\n",
+    "APP_STATIC = os.path.join(APP_ROOT, \"app/app/static\")\n",
     "\n",
     "filename = \"4.jpg\"\n",
     "path_to_file = os.path.join(APP_STATIC, filename)\n",
@@ -559,14 +580,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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jrSytNth37bTidRXhf1nSYjP7hplNlbRa0tYK+vgSM5uZfRAjM5sp6btqv9WHt0pam91eK2lLhb18Trus3Jy3srQq3ndtt+K1u7f8R9KdGv3E/x1JP6uih5y+rpb0avbzRtW9SXpaoy8DhzX62cg6SV+TtF3S25KelzSvjXr7L0mvS3pNo0Hrrqi3WzX6kv41SXuynzur3neJvirZb3zDDwiKD/yAoAg/EBThB4Ii/EBQhB8IivADQRF+ICjCDwT1NzI5nDbZl0E+AAAAAElFTkSuQmCC",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -579,12 +598,19 @@
    "execution_count": 23,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "array([[8.0255100e-09, 1.9455256e-05, 1.4459256e-05, 9.0474996e-06,\n",
-       "        9.9718273e-01, 7.5980934e-06, 1.1683321e-06, 1.8591178e-03,\n",
-       "        5.1540881e-04, 3.9107347e-04]], dtype=float32)"
+       "array([[8.2700979e-08, 1.6669065e-04, 4.8218979e-05, 2.3290573e-05,\n",
+       "        9.9914443e-01, 1.4074722e-06, 2.4940262e-05, 4.9085240e-04,\n",
+       "        4.4384862e-05, 5.5692170e-05]], dtype=float32)"
       ]
      },
      "execution_count": 23,
@@ -627,7 +653,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -641,7 +667,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,
diff --git a/Dockerfile b/Dockerfile
index 9f9d736..6943234 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -9,7 +9,7 @@ WORKDIR /app
 COPY requirements.txt /app
 RUN python3 -m venv .
 RUN python3 -m pip install pip==24.0
-RUN python3 -m pip install setuptools==69.1.1
+RUN python3 -m pip install setuptools==69.2.0
 RUN python3 -m pip install --no-cache-dir -r requirements.txt
 COPY ./app /app
 EXPOSE 5000
diff --git a/README.md b/README.md
index 8369001..de98e18 100644
--- a/README.md
+++ b/README.md
@@ -25,10 +25,10 @@ The code has been tested using:
 * [Flask] (3.0): a microframework for [Python] based on Werkzeug, Jinja 2 and good intentions.
 * [Gunicorn] (21.2): a [Python] [WSGI] HTTP Server for UNIX.
 * [NGINX] (1.25): a free, open-source, high-performance HTTP server, reverse proxy, and IMAP/POP3 proxy server.
-* [Docker] (25.0): an open platform for developers and sysadmins to build, ship, and run distributed applications, whether on laptops, data center VMs, or the cloud.
-* [Docker Compose] (2.24): a tool for defining and running multi-container [Docker] applications.
+* [Docker] (26.0): an open platform for developers and sysadmins to build, ship, and run distributed applications, whether on laptops, data center VMs, or the cloud.
+* [Docker Compose] (2.25): a tool for defining and running multi-container [Docker] applications.
 * [Keras] ([TensorFlow] built-in): a high-level neural networks [API], written in [Python] and capable of running on top of [TensorFlow].
-* [TensorFlow] (2.15): an open source software [Deep Learning] library for high performance numerical computation using data flow graphs.
+* [TensorFlow] (2.16): an open source software [Deep Learning] library for high performance numerical computation using data flow graphs.
 * [Matplotlib] (3.8): a plotting library for [Python] and its numerical mathematics extension [NumPy].
 * [NumPy] (1.26): a library for [Python], adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.
 * [Ruff] (0.3): An extremely fast Python linter and code formatter, written in Rust.
@@ -45,7 +45,7 @@ Command to configure virtual environment with [venv]:
 ~/deeplearning_flask$ python3 -m venv dlflask3
 ~/deeplearning_flask$ source dlflask3/bin/activate
 (dlflask3)~/deeplearning_flask$ python3 -m pip install pip==24.0
-(dlflask3)~/deeplearning_flask$ python3 -m pip install setuptools==69.1.1
+(dlflask3)~/deeplearning_flask$ python3 -m pip install setuptools==69.2.0
 (dlflask3)~/deeplearning_flask$ python3 -m pip install -r requirements_dev.txt
 ```
 
@@ -72,7 +72,7 @@ deeplearning_flask
 │   │       └── dlflask.html
 │   ├── config.py
 │   ├── Makefile
-│   ├── mnist_model.h5
+│   ├── mnist_model.keras
 │   ├── server.py
 │   └── tests
 │       ├── __init__.py
@@ -158,7 +158,12 @@ It is possible to execute tests of [Flask] microservice created with [pytest] fr
 ~/deeplearning_flask$ docker exec -it deeplearning_flask-web-1 /bin/bash
 ~/app# make test
 ...
-test_app.py ..                                                         [100%]
+============================= test session starts ==============================
+platform linux -- Python 3.10.14, pytest-8.1.1, pluggy-1.4.0
+rootdir: /app/tests
+collected 2 items
+
+test_app.py ..                                                           [100%]
 ```
 
 Those tests are also automatically executed with CI/CD workflows implemented with [GitHub Actions] for every push and pull request in the project repository.
@@ -171,50 +176,50 @@ A POST example using [curl] from outside [Docker] container is shown below:
                                  Dload  Upload   Total   Spent    Left  Speed
 100 11650  100   489  100 11161    321   7347  0:00:01  0:00:01 --:--:--  7664
 {
-  "most_probable_label": "4",
-  "predictions": [
-    {
-      "label": "0",
-      "probability": "8.025511e-09"
-    },
-    {
-      "label": "1",
-      "probability": "1.9455256e-05"
-    },
-    {
-      "label": "2",
-      "probability": "1.4459256e-05"
-    },
-    {
-      "label": "3",
-      "probability": "9.0475e-06"
-    },
-    {
-      "label": "4",
-      "probability": "0.9971827"
-    },
-    {
-      "label": "5",
-      "probability": "7.5980934e-06"
-    },
-    {
-      "label": "6",
-      "probability": "1.1683321e-06"
-    },
-    {
-      "label": "7",
-      "probability": "0.0018591178"
-    },
-    {
-      "label": "8",
-      "probability": "0.0005154088"
-    },
-    {
-      "label": "9",
-      "probability": "0.00039107347"
-    }
-  ],
-  "success": true
+   "most_probable_label" : "4",
+   "predictions" : [
+      {
+         "label" : "0",
+         "probability" : "8.270098e-08"
+      },
+      {
+         "label" : "1",
+         "probability" : "0.00016669065"
+      },
+      {
+         "label" : "2",
+         "probability" : "4.821898e-05"
+      },
+      {
+         "label" : "3",
+         "probability" : "2.3290573e-05"
+      },
+      {
+         "label" : "4",
+         "probability" : "0.99914443"
+      },
+      {
+         "label" : "5",
+         "probability" : "1.4074722e-06"
+      },
+      {
+         "label" : "6",
+         "probability" : "2.4940262e-05"
+      },
+      {
+         "label" : "7",
+         "probability" : "0.0004908524"
+      },
+      {
+         "label" : "8",
+         "probability" : "4.4384862e-05"
+      },
+      {
+         "label" : "9",
+         "probability" : "5.569217e-05"
+      }
+   ],
+   "success" : true
 }
 ```
 
diff --git a/app/config.py b/app/config.py
index a6c9b0d..35026c6 100644
--- a/app/config.py
+++ b/app/config.py
@@ -15,7 +15,7 @@ class DefaultConfig:
     else:
         raise ValueError("SECRET KEY NOT FOUND!")
 
-    MODEL_PATH = os.environ.get("MODEL_PATH") or "/app/mnist_model.h5"
+    MODEL_PATH = os.environ.get("MODEL_PATH") or "/app/mnist_model.keras"
 
     @staticmethod
     def init_app(app):
diff --git a/app/mnist_model.h5 b/app/mnist_model.h5
deleted file mode 100644
index 5e58776..0000000
Binary files a/app/mnist_model.h5 and /dev/null differ
diff --git a/app/mnist_model.keras b/app/mnist_model.keras
new file mode 100644
index 0000000..96cf69b
Binary files /dev/null and b/app/mnist_model.keras differ
diff --git a/requirements.txt b/requirements.txt
index 98cdd02..34e5bb9 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,8 +2,8 @@ Flask==3.0.2
 gunicorn==21.2.0
 numpy==1.26.4
 Pillow==10.2.0
-pytest==8.0.2
+pytest==8.1.1
 requests==2.31.0
-ruff==0.3.0
+ruff==0.3.4
 scikit-image==0.22.0
-tensorflow==2.15.0
+tensorflow==2.16.1
diff --git a/requirements_dev.txt b/requirements_dev.txt
index d3b2f03..1d3d0d5 100644
--- a/requirements_dev.txt
+++ b/requirements_dev.txt
@@ -1,3 +1,3 @@
 -r requirements.txt
-jupyterlab==4.1.2
+jupyterlab==4.1.5
 matplotlib==3.8.3