Plant_disease_detection.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "1MFpW2EJI0wZ"
      },
      "outputs": [],
      "source": [
        "## importing necessary libraries of tensorfllow\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras.preprocessing.image import ImageDataGenerator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GtMTaOwhXPhd",
        "outputId": "e5227fc0-3c9b-4151-dec1-909296dc42c2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "#mounting the drive on google colab for unzipping the file\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "ILBASazNK_ht"
      },
      "outputs": [],
      "source": [
        "#unzipping the PlantVillage file contains images of disease and healthy potato leaves\n",
        "import zipfile\n",
        "import os\n",
        "\n",
        "zip_train = zipfile.ZipFile('/content/drive/MyDrive/PlantVillage.zip', 'r')\n",
        "\n",
        "zip_train.extractall('/home')  # extracting in home file \n",
        "zip_train.close()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Umf9kNUqPP1a",
        "outputId": "c7f102c0-f92f-40e7-d086-dc852ca439fc"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Copying files: 2152 files [00:00, 4977.73 files/s]\n"
          ]
        }
      ],
      "source": [
        "import splitfolders # or import splitfolders\n",
        "input_folder = \"/home/PlantVillage\"\n",
        "output_folder='/tmp'\n",
        "\n",
        "#spiliting the image folder for validation and training folder\n",
        "splitfolders.ratio(input_folder, output=output_folder, seed=42, ratio=(.8, .2)) # taking validation ratio as 0.2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qcWmDaxCTsMQ",
        "outputId": "c6654a79-be99-4670-bada-f5cd2490dbba"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 1721 images belonging to 3 classes.\n",
            "Found 431 images belonging to 3 classes.\n"
          ]
        }
      ],
      "source": [
        "## ImageDataGenearator is used for Data Augmentation\n",
        "## Applyimg data Augmentation for incresing the traning size of image folder and also prevent overfitting \n",
        "\n",
        "#defining hyperparamter need for augmentation\n",
        "train_datagen = ImageDataGenerator(\n",
        "    rescale=1./255,\n",
        "    rotation_range=45,\n",
        "    width_shift_range=0.2,\n",
        "    height_shift_range=0.2,\n",
        "    shear_range=0.2,\n",
        "    zoom_range=0.2,\n",
        "    horizontal_flip=True,\n",
        "    fill_mode='nearest',\n",
        "    )\n",
        "\n",
        "## only rescaling the validation images\n",
        "val_datagen=ImageDataGenerator(\n",
        "    rescale=1./255\n",
        ")\n",
        "\n",
        "##appling the augmentaion on training folder\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "    '/tmp/train',# defining the path\n",
        "    target_size=(180,180),\n",
        "    batch_size=32,\n",
        "    class_mode='sparse',# three classes\n",
        "    )\n",
        "\n",
        "## applying the augmentation on val folder\n",
        "validation_generator = val_datagen.flow_from_directory(\n",
        "    '/tmp/val',# the path\n",
        "    target_size=(180,180),\n",
        "    batch_size=32,\n",
        "    class_mode='sparse',\n",
        "    )\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "Vy3cxYXZLeUu"
      },
      "outputs": [],
      "source": [
        "##importing libraries for hyperparamter and fine the tuning the models\n",
        "\n",
        "from tensorflow.keras.layers import Flatten\n",
        "\n",
        "import keras\n",
        "from keras.layers import Dense, Activation, Dropout, Reshape, Permute\n",
        "\n",
        "from tensorflow.keras.models import Sequential\n",
        "\n",
        "from tensorflow.keras.optimizers.legacy import Adam\n",
        "from tensorflow.keras import layers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GnWDv7JZoeEq",
        "outputId": "55a16d5f-26f8-40e0-c39a-bec4a372f7de"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "94765736/94765736 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "## importing the resnet 50 model as transfer learning model \n",
        "## ResNet50 is a deep convolutional neural network (CNN) architecture that was developed by Microsoft and is powerful image classification model \n",
        "from tensorflow.keras.applications import ResNet50\n",
        "\n",
        "\n",
        "##adding the dense layers  \n",
        "model=Sequential()\n",
        "\n",
        "## making all layers untrainable for first time\n",
        "resnet_50 = ResNet50(include_top=False, weights='imagenet', input_shape=(180,180,3))\n",
        "for layer in resnet_50.layers:\n",
        "    layer.trainable = False\n",
        "\n",
        "model.add(resnet_50)\n",
        "model.add(Flatten())# flattening \n",
        "model.add(Dense(128,activation='relu'))## adding the dense layers and takin relu as an activation for first two layers\n",
        "model.add(layers.Dropout(0.3)) # defining the dropout \n",
        "model.add(Dense(64,activation='relu'))\n",
        "model.add(layers.Dropout(0.3))\n",
        "model.add(Dense(3,activation='softmax'))## as there are three classes so 3 neurons for last layer and also sigmoid for multi class "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "WfVLipKdp1pt"
      },
      "outputs": [],
      "source": [
        "##compiling the model with Adam optimizer and defining the loss and metrics \n",
        "model.compile(optimizer=Adam(learning_rate=0.0001),loss='sparse_categorical_crossentropy',metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GvpLTMugpwwA",
        "outputId": "26e6543a-59c5-444c-9665-4c3da828874e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "54/54 [==============================] - 35s 481ms/step - loss: 1.1000 - accuracy: 0.4747 - val_loss: 0.7967 - val_accuracy: 0.7610\n",
            "Epoch 2/20\n",
            "54/54 [==============================] - 18s 324ms/step - loss: 0.9050 - accuracy: 0.5689 - val_loss: 0.8360 - val_accuracy: 0.5545\n",
            "Epoch 3/20\n",
            "54/54 [==============================] - 17s 320ms/step - loss: 0.8834 - accuracy: 0.5962 - val_loss: 0.6976 - val_accuracy: 0.7796\n",
            "Epoch 4/20\n",
            "54/54 [==============================] - 17s 324ms/step - loss: 0.8290 - accuracy: 0.6467 - val_loss: 0.7590 - val_accuracy: 0.6636\n",
            "Epoch 5/20\n",
            "54/54 [==============================] - 17s 316ms/step - loss: 0.7939 - accuracy: 0.6572 - val_loss: 0.6609 - val_accuracy: 0.7819\n",
            "Epoch 6/20\n",
            "54/54 [==============================] - 20s 362ms/step - loss: 0.7818 - accuracy: 0.6781 - val_loss: 0.7246 - val_accuracy: 0.7193\n",
            "Epoch 7/20\n",
            "54/54 [==============================] - 21s 388ms/step - loss: 0.7421 - accuracy: 0.7031 - val_loss: 0.7836 - val_accuracy: 0.6613\n",
            "Epoch 8/20\n",
            "54/54 [==============================] - 21s 387ms/step - loss: 0.7882 - accuracy: 0.6711 - val_loss: 0.7883 - val_accuracy: 0.6172\n",
            "Epoch 9/20\n",
            "54/54 [==============================] - 18s 338ms/step - loss: 0.7683 - accuracy: 0.6764 - val_loss: 0.8369 - val_accuracy: 0.6543\n",
            "Epoch 10/20\n",
            "54/54 [==============================] - 17s 314ms/step - loss: 0.7497 - accuracy: 0.6979 - val_loss: 0.8058 - val_accuracy: 0.6891\n",
            "Epoch 11/20\n",
            "54/54 [==============================] - 18s 342ms/step - loss: 0.7345 - accuracy: 0.7176 - val_loss: 0.7170 - val_accuracy: 0.7262\n",
            "Epoch 12/20\n",
            "54/54 [==============================] - 18s 339ms/step - loss: 0.7054 - accuracy: 0.7217 - val_loss: 0.6100 - val_accuracy: 0.7657\n",
            "Epoch 13/20\n",
            "54/54 [==============================] - 17s 310ms/step - loss: 0.7013 - accuracy: 0.7130 - val_loss: 0.6402 - val_accuracy: 0.7680\n",
            "Epoch 14/20\n",
            "54/54 [==============================] - 18s 336ms/step - loss: 0.7284 - accuracy: 0.7031 - val_loss: 0.6674 - val_accuracy: 0.7309\n",
            "Epoch 15/20\n",
            "54/54 [==============================] - 18s 335ms/step - loss: 0.7120 - accuracy: 0.7054 - val_loss: 0.7101 - val_accuracy: 0.7216\n",
            "Epoch 16/20\n",
            "54/54 [==============================] - 17s 311ms/step - loss: 0.6818 - accuracy: 0.7362 - val_loss: 0.6397 - val_accuracy: 0.7587\n",
            "Epoch 17/20\n",
            "54/54 [==============================] - 17s 322ms/step - loss: 0.7239 - accuracy: 0.7095 - val_loss: 0.6656 - val_accuracy: 0.7285\n",
            "Epoch 18/20\n",
            "54/54 [==============================] - 18s 321ms/step - loss: 0.6869 - accuracy: 0.7286 - val_loss: 0.6842 - val_accuracy: 0.7239\n",
            "Epoch 19/20\n",
            "54/54 [==============================] - 17s 313ms/step - loss: 0.6813 - accuracy: 0.7455 - val_loss: 0.6771 - val_accuracy: 0.7448\n",
            "Epoch 20/20\n",
            "54/54 [==============================] - 17s 312ms/step - loss: 0.6881 - accuracy: 0.7257 - val_loss: 0.6336 - val_accuracy: 0.7355\n"
          ]
        }
      ],
      "source": [
        "## fitting the model with training and validation image data \n",
        "history=model.fit(\n",
        "    train_generator,\n",
        "    epochs=20,#number of epochs\n",
        "    validation_data=validation_generator\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 735
        },
        "id": "s1_8qVtUaO6A",
        "outputId": "faa4ba73-5890-4842-f3d0-20b6a102ea5b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x79d05f716fe0>"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "##plotting the graph for accuracy of validation and training accuarcy\n",
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(8, 8))\n",
        "\n",
        "epochs_range= range(20)\n",
        "\n",
        "plt.plot( epochs_range, history.history['accuracy'], label=\"Training Accuracy\")\n",
        "\n",
        "plt.plot(epochs_range, history.history['val_accuracy'], label=\"Validation Accuracy\")\n",
        "\n",
        "plt.axis(ymin=0.4,ymax=1)\n",
        "\n",
        "plt.grid()\n",
        "\n",
        "plt.title('Model Accuracy')\n",
        "\n",
        "plt.ylabel('Accuracy')\n",
        "\n",
        "plt.xlabel('Epochs')\n",
        "\n",
        "plt.legend(['train', 'validation'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 735
        },
        "id": "nHOs7VWRRYi5",
        "outputId": "a9ca6010-4e88-4667-fecd-42f4e9a4b12a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x79d049cdf400>"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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KCvHx8QwYMIDw8PAKP3/srmN8sXUJR7IN+vfvf0pzYzkpNzeXqVOn6jqdha6Te3Sd3KPr5B5dJ/dVxLXKyspiz549hIaGEhgY6JFjWs3lcpGamkpYWBiGYVhdjiWysrIICgrivPPOO+Xv9a83x51NlQ+zAQEBBAQEnPK8w+GolB9IjWqZgflYNmCz64egGyrr78bX6Tq5R9fJPbpO7tF1cp8nr1V+fj6GYWCz2bDZqkZX0cKpBYVfV3Vks9kwDKPE75XSfO9Uz6tXiWLCAgjws+HCYP+JLKvLEREREalSLB2ZTUtLY+vWrUUf79ixg5UrVxIZGUn9+vUZPXo0+/bt47PPPivaZ+XKlUWvTU5OZuXKlfj7+9OyZcvKLt8thmEQXzOIrcnp7DmaSePYGlaXJCIiIlJlWBpmly5dSt++fYs+LpzbevPNNzN+/HgOHDhwykoZHTp0KNpetmwZEyZMoEGDBl69fnF8ZEGYPeb+ZGYREREROTtLpxn06dMHl8t1yp/x48cDMH78eGbOnFnsNSXt781BFiC+ptk1Yc/RTIsrEREREV+SkJBQbLVTwzCYNGnSafffuXMnhmEUvZNdVp46TmWo8jeAeYP4yCAAdh/VyKyIiIiU3YEDB6hZs6ZHjzl8+HCOHz9eLCTHx8dz4MABoqOjPXquiqAwWwnia5phds8xjcyKiIhI2cXFxVXKeex2e6Wdq7zUzaAS1C+YZrD7aGbRih8iIiJSRi4X5KRb86cU/4//97//pU6dOqes8HXppZdy6623smPHDi677DJiY2MJDQ2lS5cuTJs27YzH/Ps0g8WLF9OhQwcCAwPp3LkzK1asKLZ/fn4+t956Kw0bNiQoKIhmzZrx1ltvFX1+zJgxfPrpp/z4448YhoFhGMycObPEaQazZs2ia9euBAQEULt2bR599NFiix306dOH++67j0ceeYTIyEji4uIYM2aM29errDQyWwnqFYzMpmXncSIzl4hgf4srEhER8WG5GfB8HWvO/dh+8A9xa9err76ae++9lxkzZnDBBRcAcPToUX7//Xd++eUX0tLSGDRoEM8//zwBAQF89tlnDBkyhE2bNlG/fv2zHj8tLY2LL76Y/v3787///Y8dO3Zw//33F9vH6XRSr149vv32W6Kiopg/fz633347tWvX5pprruHhhx9mw4YNpKSk8MknnwAQGRnJ/v37ix1n3759DB48mOHDh/PZZ5+xceNGRo4cSWBgYLHA+umnnzJq1CgWLVrEggULGD58OL169aJ///5uXbOyUJitBEH+dsIdLlJyDXYfzVCYFRERqQZq1qzJoEGDmDBhQlGY/e6774iOjqZv376kpaXRq1evokUTnn32WX744Qd++ukn7rnnnrMef8KECTidTj766CMCAwNp1aoVe/fu5c477yzax+Fw8MwzzxR93LBhQxYsWMA333zDNddcQ2hoKEFBQWRnZ59xWsG4ceOIj4/n3XffxTAMmjdvzv79+/nXv/7FU089VfQ1tG3blqeffhqAJk2a8O677zJ9+nSF2aogKhBScs2bwNrWi7C6HBEREd/lCDZHSK06dylcf/31jBw5knHjxhEQEMAXX3zBddddh81mIy0tjWeffZZff/2VAwcOkJeXR2Zm5iltSU9nw4YNtG3btthSsD169Dhlv7Fjx/Lxxx+ze/duMjMzycnJoX379qX6OjZs2ECPHj2KLb3bq1cv0tLS2Lt3b9FIctu2bYu9rnbt2hw6dKhU5yothdlKEh3gYkeqoY4GIiIi5WUYbr/Vb7UhQ4bgcrmYPHkyXbp0Yc6cObzxxhsAPPnkk8yePZtXX32Vxo0bExQUxFVXXUVOTo7Hzv/VV1/x8MMP89prr9GjRw/CwsJ45ZVXWLRokcfO8Vd/X4bWMIxT5gx7msJsJYkq+KVpj8KsiIhItREYGMgVV1zBF198wdatW2nWrBkdO3bE6XSyaNEibr75Zi6//HLAnANbmt75LVq04PPPPycrK6todHbhwoXF9pk3bx49e/bkrrvuKnpu27Ztxfbx9/cnPz//rOf6/vvvcblcRaOz8+bNIywsjHr16rldc0VQN4NKEhVo3v2okVkREZHq5frrr2fy5Ml8/PHHXH/99UXPJyYm8sMPP7By5UpWrVrFsGHDSjWKOWzYMAzDYOTIkaxfv55ff/2VV199tdg+TZo0YenSpfzxxx9s3ryZJ598kiVLlhTbJyEhgdWrV7Np0yYOHz5Mbm7uKee666672LNnD/feey8bN27kxx9/5Omnn2bUqFFF82WtojBbSaICFGZFRESqo/PPP5/IyEg2bdrEsGHDip5/7rnnqFmzJj179mTIkCEMHDiQjh07un3c0NBQfv75Z9asWUOHDh14/PHHeemll4rt849//IMrrriCa6+9lm7dunHkyJFio7QAI0eOpFmzZnTu3JmYmBjmzZt3yrnq1q3Lr7/+yuLFi2nXrh133HEHt956K0888UQpr4bnaZpBJYkumGaw/3gWuflOHHb9HiEiIlId2Gy2U1pdAdSvX59p06YVG9m8++67i+3z92kHf+9X371791OWnP3rPgEBAXzyySdFbbcKvfDCC0XbMTExTJky5ZT6/n6u3r17s3jx4lP2KzRz5sxTnjvT0rueokRVScIcEOBnI9/p4sDxLKvLEREREakSFGYric04uXjCrqPpFlcjIiIiUjUozFai+IIwq3mzIiIiIp6hMFuJ4iPNRssKsyIiIiKeoTBbiQpHZtVrVkREpPT+fkOS+DZP/X0qzFai+ppmICIiUmp2ux3AoytjifUK/z4L/37LSq25KlF8ZEGYPaIwKyIi4i4/Pz+Cg4NJTk7G4XBY3qTfE5xOJzk5OWRlZVWJr6e0nE4nycnJBAcH4+dXvjiqMFuJ4muac2ZTsvI4kZFLjWDHWV4hIiIihmFQu3ZtduzYwa5du6wuxyNcLheZmZkEBQUVLQ9b3dhsNurXr1/ur19hthIF+duJCQsgOTWb3UczaBNcw+qSREREfIK/vz9NmjSpMlMNcnNzmT17Nueddx4OR/Uc3PL39/fIqLTCbCWrHxl8MszWU5gVERFxl81mIzAw0OoyPMJut5OXl0dgYGC1DbOeUv0maVisvtpziYiIiHiMwmwlU69ZEREREc9RmK1khSOz6jUrIiIiUn4Ks5VM0wxEREREPEdhtpIVhtl9xzPJzXdaXI2IiIiIb1OYrWS1wgLw97OR73Rx4HiW1eWIiIiI+DSF2UpmsxnEa1lbEREREY9QmLWA5s2KiIiIeIbCrAUUZkVEREQ8Q2HWAvWjQgC15xIREREpL4VZC2hkVkRERMQzFGYtoDArIiIi4hkKsxaIjzS7GZzIzOVERq7F1YiIiIj4LoVZCwT7+xEdGgDAnmManRUREREpK4VZi9SPVK9ZERERkfJSmLWI5s2KiIiIlJ/CrEUKw+yuIwqzIiIiImWlMGuR+IIwq16zIiIiImWnMGsRTTMQERERKT+FWYvUjzLD7L7jmeTlOy2uRkRERMQ3KcxaJDYsEH8/G/lOFwdOZFldjoiIiIhPUpi1iM1mEF9T7blEREREykNh1kKaNysiIiJSPgqzFlKYFRERESkfhVkLxSvMioiIiJSLwqyF6qvXrIiIiEi5KMxaqLA9l0ZmRURERMpGYdZC8TXNMHs8I5cTmbkWVyMiIiLiexRmLRQS4Ed0qD+gqQYiIiIiZaEwazHdBCYiIiJSdgqzFlN7LhEREZGyU5i1mMKsiIiISNkpzFosXu25RERERMpMYdZiDTQyKyIiIlJmCrMWK+w1u+9YJnn5TourEREREfEtCrMWiw0LxN9uI8/p4sCJLKvLEREREfEpCrMWs9kM6kUGAZo3KyIiIlJaCrNeQB0NRERERMpGYdYLKMyKiIiIlI3CrBdQmBUREREpG4VZL6AlbUVERETKRmHWC2hkVkRERKRsFGa9QOHI7PGMXE5k5lpcjYiIiIjvUJj1AqEBfkSF+ANqzyUiIiJSGgqzXqJwdFZhVkRERMR9CrNeokGU5s2KiIiIlJbCrJfQTWAiIiIipacw6yXUnktERESk9BRmvUR9zZkVERERKTWFWS9RGGb3Hssk3+myuBoRERER36Aw6yViwwPxt9vIc7o4cCLT6nJEREREfILCrJew2wzq1QwCNG9WRERExF0Ks15EvWZFRERESkdh1osUzpvddURhVkRERMQdCrNeRL1mRUREREpHYdaLaJqBiIiISOkozHoRjcyKiIiIlI7CrBepH2WG2WMZuaRk5VpcjYiIiIj3U5j1IqEBfkSF+AOaaiAiIiLiDoVZL6N5syIiIiLuU5j1Mpo3KyIiIuI+hVkvozArIiIi4j6FWS9zMsxmWlyJiIiIiPdTmPUymjMrIiIi4j6FWS9T2J5r77EM8p0ui6sRERER8W4Ks14mLjwQh90gN9/FgROaaiAiIiJyJgqzXsZuM6hXUzeBiYiIiLhDYdYLad6siIiIiHsUZr1Q/cggQCOzIiIiImejMOuFGkSGAGrPJSIiInI2CrNeKF4LJ4iIiIi4RWHWC9XXnFkRERERtyjMeqH4gjmzR9NzSM3KtbgaEREREe+lMOuFwgIdRIb4A7BH82ZFRERETkth1ktp3qyIiIjI2SnMeinNmxURERE5O4VZL6VesyIiIiJnpzDrpQpHZncpzIqIiIiclsKsl9KStiIiIiJnpzDrpQpHZvceyyDf6bK4GhERERHvpDDrpWrXCMJhN8jNd3EwJcvqckRERES8ksKsl7LbDOrVLGjPdURTDURERERKYmmYnT17NkOGDKFOnToYhsGkSZPO+pqZM2fSsWNHAgICaNy4MePHj6/wOq2iebMiIiIiZ2ZpmE1PT6ddu3aMHTvWrf137NjBRRddRN++fVm5ciUPPPAAt912G3/88UcFV2oNtecSEREROTM/K08+aNAgBg0a5Pb+77//Pg0bNuS1114DoEWLFsydO5c33niDgQMHVlSZlqmvVcBEREREzsjSMFtaCxYsoF+/fsWeGzhwIA888MBpX5OdnU12dnbRxykpKQDk5uaSm5tbIXX+XeF5Snu+OuEBAOw6kl5ptVqprNeputF1co+uk3t0ndyj6+Q+XSv36DqdWWmui0+F2YMHDxIbG1vsudjYWFJSUsjMzCQoKOiU17zwwgs888wzpzw/ZcoUgoODK6zWkkydOrVU++9NB/Bj28Hj/PrrrxVSkzcq7XWqrnSd3KPr5B5dJ/foOrlP18o9uk4ly8hw/11pnwqzZTF69GhGjRpV9HFKSgrx8fEMGDCA8PDwSqkhNzeXqVOn0r9/fxwOh9uvS83K5ZXVM0jLMzjvggGEBlTtv66yXqfqRtfJPbpO7tF1co+uk/t0rdyj63Rmhe+ku8On0lFcXBxJSUnFnktKSiI8PLzEUVmAgIAAAgICTnne4XBU+jdPac8Z6XBQM9jBsYxcDqbm0iK05K+xqrHi78YX6Tq5R9fJPbpO7tF1cp+ulXt0nUpWmmviU31me/TowfTp04s9N3XqVHr06GFRRRVPN4GJiIiInJ6lYTYtLY2VK1eycuVKwGy9tXLlSnbv3g2YUwRuuummov3vuOMOtm/fziOPPMLGjRsZN24c33zzDQ8++KAV5VeKwl6zWjhBRERE5FSWhtmlS5fSoUMHOnToAMCoUaPo0KEDTz31FAAHDhwoCrYADRs2ZPLkyUydOpV27drx2muv8eGHH1bJtlyFNDIrIiIicnqWzpnt06cPLpfrtJ8vaXWvPn36sGLFigqsyrsozIqIiIicnk/Nma2O6kdpSVsRERGR01GY9XKFI7N7j2WS7zz9KLaIiIhIdaQw6+Vq1wjCz2aQk+8kKSXL6nJEREREvIrCrJez2wzq1TT7y2rerIiIiEhxCrM+IF43gYmIiIiUSGHWBxTOm9VNYCIiIiLFKcz6ALXnEhERESmZwqwPUJgVERERKZnCrA+I1zQDERERkRIpzPqAwoUTDqflkJadZ3E1IiIiIt5DYdYHhAc6iAh2ABqdFREREfkrhVkfoXmzIiIiIqdSmPURas8lIiIiciqFWR+hkVkRERGRUynM+giFWREREZFTKcz6CIVZERERkVMpzPqIwl6ze49m4nS6LK5GRERExDsozPqI2jUC8bMZ5OQ7SUrNsrocEREREa+gMOsj/Ow26tYMAmD3EU01EBEREQGFWZ+iebMiIiIixSnM+pB49ZoVERERKUZh1ocUjszuUpgVERERARRmfYqmGYiIiIgUpzDrQ7SkrYiIiEhxCrM+pH6UGWYPp+WQnp1ncTUiIiIi1lOY9SHhgQ4igh0A7Dmm0VkRERERhVkfUzRvVr1mRURERBRmfU28bgITERERKaIw62N0E5iIiIjISQqzPkbtuUREREROUpj1MQqzIiIiIicpzPqYomkGxzJxOl0WVyMiIiJiLYVZH1O7RiB2m0FOnpNDqdlWlyMiIiJiKYVZH+Nnt1E3IgiAXUfSLa5GRERExFoKsz5I82ZFRERETAqzPihe7blEREREAIVZn9QgSiOzIiIiIqAw65M0zUBERETEpDDrg06G2UyLKxERERGxlsKsDyqcM3s4LZuMnDyLqxERERGxjsKsD6oR5KBGkAOAPRqdFRERkWpMYdZHad6siIiIiMKsz1KYFREREVGY9VnqNSsiIiKiMOuzNDIrIiIiojDrsxRmRURERBRmfdZfw6zT6bK4GhERERFrKMz6qNoRgdhtBjl5Tg6lZltdjoiIiIglFGZ9lMNuo25EEKCpBiIiIlJ9Kcz6MM2bFRERkepOYdaHxSvMioiISDWnMOvD6qvXrIiIiFRzCrM+TNMMREREpLpTmPVhCrMiIiJS3SnM+rDCMJucmk1mTr7F1YiIiIhUPoVZH1Yj2EF4oB8Ae45pdFZERESqH4VZH1c/qmCqwRGFWREREal+FGZ9nObNioiISHWmMOvj1GtWREREqjOFWR+nkVkRERGpzhRmfVyDyBBAYVZERESqJ4VZH/fXVcCcTpfF1YiIiIhULoVZH1c7IhC7zSA7z0lyWrbV5YiIiIhUKoVZH+ew26gTEQhoqoGIiIhUPwqzVUDRTWDqNSsiIiLVjMJsFaCOBiIiIlJdKcxWAfF/uQlMREREpDpRmK0CNDIrIiIi1ZXCbBWgMCsiIiLVlcJsFVAYZg+lZpOZk29xNSIiIiKVR2G2CqgR5CAs0A+APcc0OisiIiLVh8JsFWAYhtpziYiISLWkMFtFNIjSvFkRERGpfhRmq4h43QQmIiIi1ZDCbBVRX71mRUREpBpSmK0i1J5LREREqiOF2Srir2HW5XJZXI2IiIhI5VCYrSLqRARhMyA7z0lyarbV5YiIiIhUCoXZKsJht1EnIgjQVAMRERGpPhRmqxDNmxUREZHqRmG2ClGYFRERkepGYbYKUa9ZERERqW4UZqsQLWkrIiIi1Y3CbBWiaQYiIiJS3SjMViENoswweyg1m8ycfIurEREREal4CrNVSI0gB2GBfgDsPabRWREREan6FGarEMMwNNVAREREqhWF2SpGYVZERESqE4XZKkZhVkRERKoThdkqprDX7B6FWREREakGFGarGI3MioiISHWiMFvF/DXMulwui6sRERERqVgKs1VMnYggbAZk5TpJTsu2uhwRERGRCqUwW8X4+9moXSMI0LxZERERqfoUZqsgzZsVERGR6kJhtgoqDLO7jijMioiISNWmMFsF1Y/SyKyIiIhUDwqzVVB99ZoVERGRakJhtgrSnFkRERGpLhRmq6DCMJuUkk1Wbr7F1YiIiIhUHIXZKigi2EFYgB8Ae49pdFZERESqLoXZKsgwDBrFhADw5KR1HM/IsbgiERERkYqhMFtF/evC5oT421mw/QiXjZ3H1kNpVpckIiIi4nFeEWbHjh1LQkICgYGBdOvWjcWLF59239zcXP7973+TmJhIYGAg7dq14/fff6/Ean1Dz8bRfH9XT+pGBLHzSAaXj5vHnC3JVpclIiIi4lGWh9mvv/6aUaNG8fTTT7N8+XLatWvHwIEDOXToUIn7P/HEE/zf//0f77zzDuvXr+eOO+7g8ssvZ8WKFZVcufdrHhfOj/f0olODmqRm5TH8kyV8tmCn1WWJiIiIeIzlYfb1119n5MiRjBgxgpYtW/L+++8THBzMxx9/XOL+n3/+OY899hiDBw+mUaNG3HnnnQwePJjXXnutkiv3DdGhAUwY2Y0rOtYl3+niqR/X8eSkteTmO60uTURERKTc/Kw8eU5ODsuWLWP06NFFz9lsNvr168eCBQtKfE12djaBgYHFngsKCmLu3Lmn3T87O7vo45SUFMCcrpCbm1veL8EtheeprPP9nQ148bKWJEYH8+rULXy+cBfbDqXy9nXtqBHksKSmklh9nXyFrpN7dJ3co+vkHl0n9+lauUfX6cxKc10Ml8vlqsBazmj//v3UrVuX+fPn06NHj6LnH3nkEWbNmsWiRYtOec2wYcNYtWoVkyZNIjExkenTp3PppZeSn59fLLQWGjNmDM8888wpz0+YMIHg4GDPfkE+YM1Rg8+22MhxGtQKdDGyeT61gqyuSkREROSkjIwMhg0bxokTJwgPDz/jvpaOzJbFW2+9xciRI2nevDmGYZCYmMiIESNOOy1h9OjRjBo1qujjlJQU4uPjGTBgwFkvjqfk5uYydepU+vfvj8Nh7UjoYOCSA6n844sVHDiRxTsbA3nnunb0TIyytC7wruvkzXSd3KPr5B5dJ/foOrlP18o9uk5nVvhOujssDbPR0dHY7XaSkpKKPZ+UlERcXFyJr4mJiWHSpElkZWVx5MgR6tSpw6OPPkqjRo1K3D8gIICAgIBTnnc4HJX+zWPFOUvStn4kP97Ti398vowVu49zy2fLeeaSVtzQvYHVpQHec528na6Te3Sd3KPr5B5dJ/fpWrlH16lkpbkmlt4A5u/vT6dOnZg+fXrRc06nk+nTpxebdlCSwMBA6tatS15eHt9//z2XXnppRZdbpdQKC+TLkd25rH0d8p0unpi0lqd/XEuebgwTERERH2J5N4NRo0bxwQcf8Omnn7JhwwbuvPNO0tPTGTFiBAA33XRTsRvEFi1axMSJE9m+fTtz5szhwgsvxOl08sgjj1j1JfisQIedN65tzz8HNgPg0wW7GDF+CScyNRldREREfIPlc2avvfZakpOTeeqppzh48CDt27fn999/JzY2FoDdu3djs53M3FlZWTzxxBNs376d0NBQBg8ezOeff05ERIRFX4FvMwyDu/s2JjEmlAe/XsmcLYe5fNw8Pr65CwnRIVaXJyIiInJGlodZgHvuuYd77rmnxM/NnDmz2Me9e/dm/fr1lVBV9XJh6zjq1ezByM+Wsj05nUvHzuO9GzrSMzHa6tJERERETsvyaQbiPVrXrcGPd/eifXwEJzJzuemjxUxYtNvqskREREROS2FWiqkVHshXt3fnknZ1yHO6eOyHNTzz8zrdGCYiIiJeSWFWThHosPPWde15qH9TAD6Zt5NbP11KSpZuDBMRERHvojArJTIMg3svaMK46zsS6LAxa3MyV4ybz64j6VaXJiIiIlJEYVbOaHCb2nz7j57EhQey9VAal42dx8LtR6wuS0RERARQmBU3tKlXgx/v6UXbejU4lpHLjR8t4uslujFMRERErKcwK26JDQ/k69t7cFHb2uTmu/jX92v4zy/ryXe6rC5NREREqjGFWXFbkL+dd4d24IF+TQD4cO4Obvt0Cam6MUxEREQsojArpWIYBg/0a8q7wzoQ4GdjxqZkrnxvPnuOZlhdmoiIiFRDCrNSJhe3rcM3/+hBrbAANielcenYeSzZedTqskRERKSaUZiVMmsXH8FP95xD67rhHE3PYdgHC/l26R6ryxIREZFqRGFWyiWuRiDf/qMng9vEkZvv4p/freaNqZutLktERESqCYVZKTfzxrCO3HeBeWPY239uYeuhVIurEhERkepAYVY8wmYzGNW/KQNaxuJywXszt1tdkoiIiFQDCrPiUXf3bQzApJX71OFAREREKpzCrHhUu/gIzm0STb7TxX9na3RWREREKpbCrHjcXX3M0dmvl+7hUGqWxdWIiIhIVaYwKx7XvVEkHetHkJPn5KO5O6wuR0RERKowhVnxOMMwuOd8c3T2fwt2cSJDy92KiIhIxVCYlQrRt1ktmseFkZ6Tz/j5O60uR0RERKoohVmpEIZhFHU2+GT+DtKz8yyuSERERKoihVmpMIPb1CYhKpjjGbl8uXi31eWIiIhIFaQwKxXGbjO4s08iAP+dvZ3svHyLKxIREZGqRmFWKtTlHepRu0Ygh1Kz+W7ZXqvLERERkSpGYVYqlL+fjdvPawTA+7O2kZfvtLgiERERqUoUZqXCXdelPpEh/uw5mskvqw9YXY6IiIhUIQqzUuGC/O3cek5DAMbN3IrT6bK4IhEREakqFGalUtzYowFhAX5sTkpj2oYkq8sRERGRKkJhVipFeKCDm3o2AGDsjK24XBqdFRERkfJTmJVKM6JXQwIdNlbtPcG8rUesLkdERESqAIVZqTTRoQFc16U+YI7OioiIiJSXwqxUqtvPa4TDbrBg+xGW7TpmdTkiIiLi4xRmpVLViQjiig71ABin0VkREREpJ4VZqXR39EnEZsD0jYdYvz/F6nJERETEhynMSqVrGB3C4Da1AXhv1jaLqxERERFfpjArlrirT2MAJq/ez47D6RZXIyIiIr6qTGH2008/ZfLkyUUfP/LII0RERNCzZ0927drlseKk6mpZJ5wLmtfC6YL3Z2p0VkRERMqmTGH2+eefJygoCIAFCxYwduxYXn75ZaKjo3nwwQc9WqBUXXf1NUdnJ67Yy/7jmRZXIyIiIr6oTGF2z549NG5sBpFJkyZx5ZVXcvvtt/PCCy8wZ84cjxYoVVenBjXp3iiS3HwXH8zZbnU5IiIi4oPKFGZDQ0M5csRcwWnKlCn0798fgMDAQDIzNcIm7ru7YHT2y8W7OZKeY3E1IiIi4mv8yvKi/v37c9ttt9GhQwc2b97M4MGDAVi3bh0JCQmerE+quHMaR9OuXg1W7T3Bpwt20dzqgkRERMSnlGlkduzYsfTo0YPk5GS+//57oqKiAFi2bBlDhw71aIFStRmGUTR39n+L9pCZZ3FBIiIi4lPKNDIbERHBu+++e8rzzzzzTLkLkuqnf4tYmtQKZcuhNOYmGVxpdUEiIiLiM8o0Mvv7778zd+7coo/Hjh1L+/btGTZsGMeOHfNYcVI92GwGd/VNBGDmfhuZOfkWVyQiIiK+okxh9p///CcpKeYypGvWrOGhhx5i8ODB7Nixg1GjRnm0QKkehrStQ72aQaTlGXy7fJ/V5YiIiIiPKFOY3bFjBy1btgTg+++/5+KLL+b5559n7Nix/Pbbbx4tUKoHP7uN289NAOCDOTvIyXNaW5CIiIj4hDKFWX9/fzIyMgCYNm0aAwYMACAyMrJoxFaktK5oX4dwh4uDKdlMWqHRWRERETm7MoXZc845h1GjRvHss8+yePFiLrroIgA2b95MvXr1PFqgVB8BDjt965gjsu/N2ka+02VxRSIiIuLtyhRm3333Xfz8/Pjuu+947733qFu3LgC//fYbF154oUcLlOqlV6yLiCAHOw6n89vaA1aXIyIiIl6uTK256tevzy+//HLK82+88Ua5C5LqLcAON/Woz9t/bmPsjG1c1KY2hmFYXZaIiIh4qTKFWYD8/HwmTZrEhg0bAGjVqhWXXHIJdrvdY8VJ9XRjt/p8NHcnGw6kMGPTIc5vHmt1SSIiIuKlyjTNYOvWrbRo0YKbbrqJiRMnMnHiRG644QZatWrFtm3bPF2jVDMRwQ5u6N4AgHf/3IrLpbmzIiIiUrIyhdn77ruPxMRE9uzZw/Lly1m+fDm7d++mYcOG3HfffZ6uUaqhW89piL+fjeW7j7Nox1GryxEREREvVaYwO2vWLF5++WUiIyOLnouKiuLFF19k1qxZHitOqq9a4YFc09nsjDF2xlaLqxERERFvVaYwGxAQQGpq6inPp6Wl4e/vX+6iRAD+cV4idpvBnC2HWbXnuNXliIiIiBcqU5i9+OKLuf3221m0aBEulwuXy8XChQu54447uOSSSzxdo1RT8ZHBXNq+DgDjZmp0VkRERE5VpjD79ttvk5iYSI8ePQgMDCQwMJCePXvSuHFj3nzzTQ+XKNXZXX0SMQz4Y10SW5JOfTdAREREqrcyteaKiIjgxx9/ZOvWrUWtuVq0aEHjxo09WpxI41phDGwZx+/rDvLezG28fm17q0sSERERL+J2mB01atQZPz9jxoyi7ddff73sFYn8zd19G/P7uoP8uGo/D/RrSv2oYKtLEhERES/hdphdsWKFW/tptSbxtDb1anBe0xhmb07m/2Zv47nL21hdkoiIiHgJt8PsX0deRSrb3X0Smb05mW+X7uX+C5pQKzzQ6pJERETEC5TpBjCRyta1YSSdG9QkJ9/Jh3N3WF2OiIiIeAmFWfEJhmFw9/nmDYb/W7iLY+k5FlckIiIi3kBhVnxGn6YxtKwdTkZOPuPn77S6HBEREfECCrPiMwzD4O6+5ujs+Pk7ScvOs7giqZKcTpj9Ciz/zOpKRETEDQqzVdXuReZ/yM58qyvxqAtbx9EoOoQTmblMWLTL6nKkKlo3Ef78D/x0L6z80upqRETkLBRmq6IT++CLq8z/kDf/bnU1HmW3GdzRJxGAD+bsICu3aoV1sVhejvnvptDP95m/GIqIiNdSmK1qXC745QHITjE/3r/SymoqxOUd6lI3Iojk1Gy+XbbX6nKkKlnxGRzbASG1oOkgyM+Br4bB8d1WVyYiIqehMFvVrPoKtkw5+fHBNdbVUkEcdhu3n9cIgP+btY3cfKfFFUmVkJMOs142t3s/Ald+CHFtIOMwTLgOslOtrU9EREqkMFuVpB6E3/9lbje90HysgmEW4Nou8USH+rP3WCY/r9pvdTlSFSwcB2lJUDMBOt4MAaEw9CtzlPbQOph4e5Wbgy4iUhUozFYVLhf8MgqyTkDt9nDZe+bzKXsh46ilpVWEQIedW85pCMC4mdtwOl0WVyQ+LeMozHvb3D7/SfDzN7dr1IPrJoA9ADb9CtOfsa5GEREpkcJsVbH2e9g0GWwOuGwcBEeaI0xQZUdnb+zegLBAP7YeSmPK+oNWlyO+bM5r5jzzuDbQ6orin4vvApeONbfnvQUrJ1R+fSIicloKs1VB2iH49Z/m9nn/hNhW5nZcG/OxiobZsEAHw3smADB2xjZcLo3OShkc3wOLPzC3LxgDthJ+LLa9Gs592Nz++X7YvbDSyhMRkTNTmK0Kfv0nZB6F2DZw7qiTz8e1NR+raJgFGNGrIUEOO2v2nWD2lsNWlyO+aOaLkJ8NCedC4wtOv1/fx6H5xQUdDq6HY+pzLCLiDRRmfd36H2H9JDDscNlYsDtOfq6Kj8wCRIb4M7RrfQCe/nGtVgWT0jm0EVYVTBvoNwYM4/T72mxwxX9Pdjj4Uh0ORES8gcKsL0s/ApMfMrfPeRBqtyv++cIwe3gT5GZVbm2V6L4LGlOnRiA7j2Tw+A9rNN1A3Pfns+BymiOu9TqffX//kL90OFgP39+mDgciIhZTmPVlv/8L0pMhpoXZF/PvwutCUE1w5kHyxsqvr5JEBPvz9tAO2G0GP67cz7dLtZCCuGHPYtj4Cxg2uOAp919Xox4M/dLscLD5d5g2psJKFBGRs1OY9VUbf4U135r/EV82FvwCTt3HMKrFVAOAzgmRjOrfFICnflrL5iS9/Stn4HKdDKHtr4eYZqV7fb3OZtcQgPlvw4ovPFqeiIi4T2HWF2Ueg18eNLd73gt1O51+32pwE1ihO3sncm6TaLJyndwzYTmZOXr7V05jy1TYNc8cXe0zumzHaHOV2T0EzA4HuxZ4rj4REXGbwqwv+v0xSDsIUU2gz2Nn3reajMwC2GwGr1/TnpiwADYnpfHMz+usLkm8kdN5cvGDbrdDjbplP1afx6DFJeDMha/V4UBExAoKs75my9SCu68Ns5G7I/DM+/81zDqdFV6e1WLCAnjz2vYYBny1ZA8/rtxndUnibdZ+B0lrIaAGnDPq7Pufic0Gl79vvgOScUQdDkRELKAw60uyTsBP95nb3e+C+t3O/propmD3h5xUOF49Ro16NY7mnr6NAXhs4hp2Hk63uCLxGnk58Od/zO1z7jdXyiuvwg4HobHqcCAiYgGFWV8y5QlI3Q+RjeD8J9x7jd0BtVqY29VgqkGh+y9oQteESNJz8rnny+Vk5ylcCLDsE/OXutA46Han545boy5c9yX4BRZ0OHjac8cWEZEzUpj1Fdv+hOWfmduXvAv+we6/thrNmy3kZ7fx1tD21Ax2sHZfCi/8WnVbk4mbslNh1svmdu9HSvdvyB31OplTfwDmvwMr/ufZ44uISIkUZn1Bdir8dL+53fV2SOhVutdXo44Gf1W7RhCvXWMuJDF+/k7+WHfQ4orEUgvGmSt3RSZCx5sq5hxtroLzCno+//wA7JpfMecREZEiCrO+YNoYOLEbIurDBWV4+7IajswWOr95LLed0xCAR75bzb7jmRZXJJZIP2z2gwVzis5fl332tD6joeWlBR0OboBjOyvuXCIiojDr9XbMgSUfmtuXvAMBoaU/Rmwr8zFlL2Qc9VxtPuKRC5vTrl4NTmTmct+XK8jNr/pdHeRvZr8KOWlQuz20vKxiz2WzwWXvm+fKOAITroOslIo9p4hINaYw681y0uGne8ztTsOhUZ+yHSewBtRMMLer4eisv5+Nd4Z2JCzAj2W7jvH61M1WlySV6fhuWPqRud3vaTNsVjT/YHPJ29A4SN4A39+qDgciIhVEYdabTX/WfIsyvB70f7Z8x6rGUw0A6kcF8+KV5tzh92ZuY9bmZIsrkspin/0S5OdAw96QeH7lnTi8DgydYHY42DIFpj5VeecWEalGFGa91e6FsOh9c/uStyAwvHzHizNvhKquYRbgora1ub5bfQBGfb2SQylZFlckFS0scw/Gmm/MD/qNqfwC6naCy8aZ2wvePdmRREREPEZh1hvlZsKPdwMuaH8DNO5X/mNW85HZQk9e3JLmcWEcSc/hga9Xku90WV2SVKCW+7/FwGXOk63b0ZoiWl8JvR81t38ZBTvnWVOHiEgVpTDrjWY8B0e2QlhtGPicZ45ZGGYPb4Lc6jsiGeiw8+6wjgQ57MzfdoSxM7ZaXZJUEGPPQuJSVuIy7HD+k9YW0/tfZqAu7HBwdIe19YiIVCEKs95m71JYUNB4/eI3ISjCM8cNrwNBkeDMg+TqvYBA41qh/Oey1gC8OW0zi7Yfsbgi8TiXC9uf/wbA2f56iG5sbT02G1z2ntnhIPMofKkOByIinqIw603ysmHSXeByQttrodmFnju2YfxlqsFqzx3XR13ZqR5XdKyL0wX3fbWCo+k5VpcknrT5d2x7F5Nn+OM8959WV2Mq1uFgozociIh4iMKsN5n1kjkNIKQWXPii54+vebPFPHtpaxrFhJCUks3D367CqfmzVYMzH6Y9A8D2WgPM6TreIryOGWjV4UBExGMUZr3F/hUw901z+6LXIDjS8+eopsvank5IgB9jh3XE38/GnxsP8dFczWOsElZ/DckbcAXWYGuti6yu5lR1O5pTDkAdDkREPEBh1hvk5cCku8GVD60uh5aXVMx5ikZm14JTq2ABtKgdzlMXtwTgpd83snLPcWsLkvLJzYIZzwPg7Hk/uX4hFhd0Gq2vMJe9hYIOB3OtrUdExIcpzHqDOa/BoXUQHAWDX62480Q3AXsA5KTC8Z0Vdx4fc323+gxuE0ee08U9E5ZzIjPX6pKkrJZ+DCf2QFgdnJ1HWl3NmfX+F7S6oqDDwY3qcCAiUkYKs1Y7uAbmFATYwa9ASHTFncvugFotTp5XADAMgxeuaEu9mkHsPZbJ6Imrcbk0f9bnZKWc/LfU51FwBFlbz9kYhrmgQp0Of+lwcMLqqkREfI7CrJXyc83uBc48aH6xOUpT0XQTWIlqBDl4d1hH/GwGv645yP8W7ba6JCmt+e9AxhGIagLtr7e6Gvc4guC6L82b1JI3wnfqcCAiUloKs1aa96bZJiuoJlz0ujlSU9F0E9hptY+P4F8XNgfg2V/Ws36/+oD6jLRDJ/szX/Ak2P2srac0wmsXdDgIgq1TYf7bVlckIuJTFGatcmgDzHrZ3L7wJQiLrZzzamT2jG49pyHnN69FTp6TeyYsJz07z+qSxB2zX4HcdKjbCVpU0A2UFalOBxj4H3N7zXfW1iIi4mMUZq2Qn2dOL8jPgaYXQttrKu/csa3Mx5R9kK6Vr/7OZjN49ep2xIUHsv1wOk/+uNbqkuRsju6ApZ+Y2/3GVM47HBWh1RVg2CBpLZzYa3U1IiI+Q2HWCgvHwv7lEFADLn6jcv/zDQyHmg3N7SSNzpYkMsSft65rj82Aicv38d0yBQuvNuM5syNA4vnQ8Dyrqym74Eio18Xc3jLF2lpERHyIV4TZsWPHkpCQQGBgIN26dWPx4sVn3P/NN9+kWbNmBAUFER8fz4MPPkhWVlYlVVtOh7fAn8+Z2wOfM1cEqmyaanBW3RpF8UC/pgA8OWktWw+lWVyRlOjAaljzrbndb4ylpXhEkwHm42aFWRERd1keZr/++mtGjRrF008/zfLly2nXrh0DBw7k0KFDJe4/YcIEHn30UZ5++mk2bNjARx99xNdff81jjz1WyZWXgTMffrwb8rMh8QLocIM1degmMLfc3bcxPROjyMzN554Jy8nK1V3mXmf6v83H1ldC7XbW1uIJhWF2xyxzAQgRETkry8Ps66+/zsiRIxkxYgQtW7bk/fffJzg4mI8//rjE/efPn0+vXr0YNmwYCQkJDBgwgKFDh551NNcb2JZ+AHsWgX8YDHnLurl9Gpl1i91m8Oa17YkK8WfjwVSe/WW91SXJX+2ca979b/ODvo9bXY1nxLWBsDqQmwG7tCqYiIg7LO1fk5OTw7Jlyxg9enTRczabjX79+rFgwYISX9OzZ0/+97//sXjxYrp27cr27dv59ddfufHGG0vcPzs7m+zs7KKPU1LMdku5ubnk5lbOSk+5ubmEZCdhm2HerZx/wdM4Q+Kgks5/iugWOABX8ibyMlPBL9CaOv6m8O+jsv5e3FEzyM4rV7Xmlk+X88Wi3XRLiGBQ6zhLa/LG61TpXC7sU57CBuS3vxFneP1T/j356nWyJ16AbeXn5G/8HWeD3hV+Pl+9TpVN18l9ulbu0XU6s9JcF0vD7OHDh8nPzyc2tnhbqtjYWDZu3Fjia4YNG8bhw4c555xzcLlc5OXlcccdd5x2msELL7zAM888c8rzU6ZMITg4uPxfhDtcTnrt/hAjL4vk0JbMPxADv/5aOecusR4XF9pDCchPY96kjzgR3NC6WkowdepUq0s4Rb86Nqbtt/HId6tI3rycaC/I/954nSpL7eNL6bp/GXk2f6bldCD7DP+efO06xZ2IpBuQtfpHpuWdU2nv4PjadbKKrpP7dK3co+tUsoyMDLf39aHO4qaZM2fy/PPPM27cOLp168bWrVu5//77efbZZ3nyySdP2X/06NGMGjWq6OOUlBTi4+MZMGAA4eHhlVKza9F/8V+5CZcjmIibP2dwRINKOe+Z2I9/BDtnc07jGrjaD7a6HMD8LWzq1Kn0798fh8NhdTnF9M93csPHS1m++ziTDkXy1W1d8fezZpaON1+nSuHMw++/zwJg9LiHC/oMK3E3n71OOefhev09QnIOMbhbU4huUqGn89nrVMl0ndyna+UeXaczK3wn3R2Whtno6GjsdjtJSUnFnk9KSiIuruS3cp988kluvPFGbrvtNgDatGlDeno6t99+O48//jg2W/GAERAQQEBAwCnHcTgclfPNc2wnrllm9wJn36dwxDSu+HO6o3Zb2Dkbv+T14GX/iCrt76YUHA54Z1hHBr81hzX7Unhj+jaeuLilxTV533WqFMu/giNbICgS+7kPYD/LNfC56+SoCQ16wfYZOHZMh9qV833mc9fJIrpO7tO1co+uU8lKc00svQHM39+fTp06MX369KLnnE4n06dPp0ePHiW+JiMj45TAarfbAXC5XBVXbFnNeAEjN53Doc1wdr7F6mpOUkeDUqsbEcQrV5nX7cO5O5i+IeksrxCPy82EmS+Y2+c+BIE1rK2nojQdaD6q36yIyFlZ3s1g1KhRfPDBB3z66ads2LCBO++8k/T0dEaMGAHATTfdVOwGsSFDhvDee+/x1VdfsWPHDqZOncqTTz7JkCFDikKtVxn8Cvmdb2Nl/dvM1X28RVFHg7XgdFpbiw8Z0CqO4T0TAHjo21UcOJFpbUHVzeIPzNXrwutBl9usrqbiFLbo2jUfstx/q01EpDqyfM7stddeS3JyMk899RQHDx6kffv2/P7770U3he3evbvYSOwTTzyBYRg88cQT7Nu3j5iYGIYMGcJzzz1n1ZdwZoHhOAe+SLqVN3yVJLoJ2AMgJxWO74TIRlZX5DNGD27O0l1HWbsvhfu+XMF/b+xMzRB/q8uq+jKPw5zXzO2+o8HhBXfhVZSoRIhqDEe2wvYZ0PJSqysSEfFaXjFUeM8997Br1y6ys7NZtGgR3bp1K/rczJkzGT9+fNHHfn5+PP3002zdupXMzEx2797N2LFjiYiIqPzCfZndAbVamNuaalAqAX523h3akdAAP5bsPEbPF//kmZ/Xse+4Rmkr1Py3Ies4xDSHdkOtrqbiNSmYaqDVwEREzsgrwqxYRIsnlFlCdAgf3dyZlrXDyczN55N5O+n98gxGfb2STQdTrS6v6kk9CAvGmdsXPAU2L5xS5GlN+puPW6ZoKpCIyBkozFZnugmsXLo1imLyfefw2S1d6ZkYRZ7TxcQV+xj45mxuGb+EJTuPWl1i1THrJcjLhHpdoZl3tJKrcA16gX8opB+Cg6usrkZExGspzFZnGpktN8MwOK9pDBNGdufHu3sxuE0chgF/bjzE1e8v4Mr35jN1fRJOpxd22vAVR7bBsk/N7X5jrFsGurL5+UOjPua2phqIiJyWwmx1FtvKfEzZB+lHrK2lCmgXH8G46zsxfVRvhnaNx99uY9muY4z8bCkD35zNt0v3kJOnt4tL7c//gCvfvMM/oZfV1VSuohZdf1hbh4iIF1OYrc4Cw6FmwVK2SRqd9ZRGMaG8cEVb5v6rL3f0TiQswI8th9L453er6f3KDD6cs5207Dyry/QN+1fAuomAARc8bXU1la9xwbzZfcshLdnaWkREvJTCbHWnqQYVplZ4II8Oas680efz6KDmxIQFcOBEFv+ZvIFeL/7Jq39s4nBattVlegeXC07sM99On/MafDsC3u2C87/nA3CwwRCIa21xkRYIr10wt90FW7V+u4hISSzvMysWi2sLG35SmK1A4YEO7uidyIheCfywfB//nb2d7YfTeXfGVj6Ys51rOscz8txG1I8KtrrUypGbBckbIWktJK0zv/eS1kLmsVN2tQGbnXUZvqkfV/yxiQf7N8VuqyZzZgs1HQgHV5tdDdoPs7oaERGvozBb3WlkttIE+Nm5rmt9ru4cz9T1B3lv1nZW7TnO5wt38cWiXVzUtg539G5EqzpVZIlWl8tsqZW0zpzGcnCtGVoPbzHnwP6dYTcX84htjSu2Nc8vt/HjgSj8I2qzPyeLd2dsZfnuY7w9tAPRoQGV//VYpclAmP0KbP0T8nPNHtEiIlJEYba6KwyzyZvMEbOqvKqSl7DbDC5sXZuBreJYsP0I78/azuzNyfy8aj8/r9rPuU2iubN3Ij0SozB85c79vGzze+jvo60Zp7mxMKgmxLY2/8QVPMY0L/r++33NAT44sJxAh42Zd/Zi0Y4jjJ64hvnbjnDR23MYO6wjnRMiK/ELtFDdjhAcZV7LPYsg4RyrKxIR8SoKs9VdeB0IioTMo5C8Aep0sLqiasMwDHomRtMzMZp1+0/wf7O288vq/czZcpg5Ww7Trl4N7uidyIBWcd711npqUkFoXfuX0dbN4CzhpjbDBlFNzM4Zca0hto25HV7ntC22svPyeeG3jQD847xE4moEcmn7urSsHc6dXyxn66E0rv3vQkYPas6t5zT0ncBfVja7eSPY6q9g8x8KsyIif6MwW90Zhjk6u2OWOZqmMGuJVnVq8PbQDvxzYDM+mLOdr5fsYdXeE9z5xXIaRodw+3mNuKJjXQL8LFr56ugO+O0Rs7tA+mnuqg+sYYbVuNZmYI1tbS6Z7Agq1ak+nb+T3UczqBUWwD96Nyp6vklsGD/e3YvRE9fw06r9/GfyBpbsPMorV7cjPLCKv/XepCDMbpkCA561uhoREa+iMCvFw6xYKj4ymH9f2pr7L2jCp/N38umCXew4nM7oiWt4fepmbunVkGs71a78wmY8ZwYpAAyIalx8tDWuNYTXLfeCBkfSsnln+lYA/jmwGcH+xX9EhQT48dZ17enSMJJnf17PH+uS2HRwLuOu70TLOuHlOrdXa3yBOac4eSMc2wU1G1hdkYiI11CYFS1r64WiQgMYNaAZ/+idyJeLd/PR3B0cOJHFS79vZOyMrfSvbVBpi7qm7Id1P5jb102ARn3Bv2I6L7w1fQup2Xm0qhPOlR3rlbiPYRjc2L0BbevW4K4vlrPzSAaXj5vHs5e15prO8RVSl+WCakJ8N9g93/yloutIqysSEfEa6jMrf+losBacWqHKm4QE+HHbuY2Y9c++vHp1O5rUCiUtO48fdtqZv62SVm1b/IE5H7ZBL2h+UYUF2a2HUvli0W4AHr+oBbazzBNuFx/BL/eeQ99mMWTnOXnku9U88t0qsnJL6JRQFTQdYD5u0dK2IiJ/pTArEN0U7AGQkwrHd1pdjZTA38/GVZ3q8ccD53FdF3PE8vEf15ORU8ErieVkwLJPzO3ud1XoqZ7/dSP5Thf9W8bSMzHardfUDPHno5u78M+BzbAZ8M3SvVw+bj47D6dXaK2WaFKwtO2O2ebfi4iIAAqzAmD3g9iW5ramGng1m83gXwObUtPfxd5jmbzyx6aKPeGqL83FDGomQLNBFXaaOVuS+XPjIfxsBqMHNS/Va202g7v7NuZ/t3YjOtSfDQdSGPLOXKasT6qgai1SqwWE14O8LNg5x+pqRES8hsKsmLR4gs8IDfDj2kRzOsj4+TtZtutoxZzI6YRF75vb3e4wW0RVgHyni+cmbwDgxh4NaBQTWqbj9GwczS/3nkuXhJqkZudx95ermLTTRm5+FZk6YxiaaiAiUgKFWTHpJjCf0iLCxRUd6uBywSPfra6YeaLbppv9YwPCocMNnj9+gW+W7mHjwVRqBDm4/4Im5TpWXI1AJozszu3nmS29ZhywcePHSzl4IssTpVqvcKrB5inmCmsiIqIwKwU0MutzHhvUjJiwALYlp/POn1s8f4KF48zHDjdCQJjnjw+kZefx2hRzqsT9FzQhIti/3Md02G08NrgFY4e2I9DuYtnu41z8zhzmbT1c7mNbruF54BcIJ3abbbpERERhVgrEtjIfU/ZBeiXdJS/lUiPIwbOXtgbg/VnbWbvvhOcOfmgDbPvTXMGr2z88d9y/eW/mVg6n5dAwOoQbunu2d+qAlrE83Caf5nFhHE7L4caPFvHun1twOn14RNM/GBLONbc3/2FtLSIiXkJhVkwBYRBZsNpSkkZnfcWFreO4qE1t8p0uHvlutefmhxaOyja/uMIa9O89lsEHc3YAMHpQc/z9PP/jKCYIvr29K9d0rofTBa9O2cwtny7hWHqOx89VaZpo3qyIyF8pzMpJhVMNDqy2tg4plTGXtCIi2MH6Ayn836xt5T9g+mFY9bW5XYHtuF75YxM5eU66N4qkf8vYCjtPoMPOy1e14+Ur2xLgZ2PmpmQufmcuK/ccr7BzVqjCm8B2L4TM45aWIiLiDRRm5STNm/VJMWEBjBliThN5e/pWtiSllu+ASz+B/Gyo0wHqd/dAhadasfsYP67cj2HAExe1xCjnMrjuuKZLPD/c1YuEqGD2Hc/k6vfn8/mCnbh87UaqmgkQ3Qxc+eZUEBGRak5hVk5SRwOfdWn7OpzfvBY5+U4e+X41+WWdF5qXDUs+MLe732W2g/Iwl8vFfwpacV3ZsR6t69bw+DlOp2WdcH669xwubBVHbr6LJ39cx/1frSQ9u4IXn/A0tegSESmiMCsnFY7MHt4MuZnW1iKlYhgGz13emrAAP1bsPs4n83aU7UDrfoC0JAirDS0v82iNhX5dc5Blu44R5LDz8IBmFXKOMwkPdPDeDR154qIW2G0GP63az6Vj55V/RLsyFbbo2jJVS1CLSLWnMCsnhdWG4Cjz7ctDG6yuRkqpdo0gRg9uAcCrUzax60gpl3R1uWDBWHO760jwK3+brL/Lys3nhd/M761/9G5EXI1Aj5/DHYZhcNu5jfjq9u7Ehgew9VAal7w7jx9X7rOknlKr393s/5txGPYvt7oaERFLKczKSYahebM+bmjXeHo0iiIr18mj368p3XzQXfPh4GrwC4JOIyqkvvHzd7L3WCZx4YFFCxtYqUtCJJPvO5dejaPIzM3n/q9W8uSktWTnVcAiFJ5kd0BiX3NbUw1EpJpTmJXiFGZ9mmEYvHhlG4IcdhZsP8KXi/e4/+LCdlztroPgSI/Xdjgtm7F/bgXgnwObEezv5/FzlEV0aACf3dKNe89vDMDnC3dx9fsL2HM0w+LKzqJoNTD1mxWR6k1hVorTTWA+r0FUCA8PNOeiPv/rBvYfd2P+89HtsHGyud39zgqp681pm0nNzqN13XAu71C3Qs5RVnabwUMDmvHJiC5EBDtYvfcEF709h8mrD1hd2uk16W8+HlgJqQctLUVExEoKs1Jc4chs0lrdWOLDhvdMoGP9CNKy83j8BzemGyz6L+CCxv0gxvM3ZW1OSmXCot2A2YrLZqv4Vlxl0bdZLX659xzax0eQkpXH3ROW889vV3lnt4PQWlCno7m9Zaq1tYiIWEhhVoqLagL2AMhJg2NlvCNeLGe3Gbx8VVv87TZmbEpm0plubMo6ASs+N7craJGE53/dgNMFA1vF0r1RVIWcw1Pq1Qzm2zt6cE/fxhgGfLtsLxe/M5fVe49bXdqpmhZ2NdBUAxGpvhRmpTi7H8S2NLc11cCnNa4Vxn0XmPNAn/l5Pcmp2SXvuPxz85eXmOaQeL7H65i1OZmZm5Jx2A0eHdTC48evCA67jYcHNuPLkd2pXSOQHYfTuWLcfN6buQ1nWXv4VoTCqQbbZkKeDy/RKyJSDgqzcirdBFZl/KN3Ii1rh3M8I5cxP607dYf8PFj0f+Z29zs9vkhCXr6T5yavB+CmHgk0jA7x6PErWvdGUfx+/3kMbhNHntPFS79v5IaPFnHwRJbVpZlqd4CQWpCTCrsXWF2NiIglFGblVLoJrMpw2G28fFVb7DaDyWsO8Pvav90otGkynNgNQZHQ9lqPn//rpXvYnJRGRLCD+85v4vHjV4YawQ7GDuvIy1e2JchhZ/62I1z41uxTr6UVbLaTo7Nq0SUi1ZTCrJxKI7NVSuu6Nbijt9nT9ckf13I84y9vRy98z3zsfAs4gjx63tSsXF6fshmA+y9oQo1gh0ePX5kMw+CaLvFMvu8c2tStwfGMXO743zJGT1xDRo7FN4c1KVjaVi26RKSaUpiVU8W2Mh9T90P6YWtrEY+49/wmJMaEkJyazbO/FKzutm+5+da0zQFdbvP4OcfN3MaR9BwaRYdwQ/cGHj++FRrFhPL9nT25o3cihgFfLt7Nxe/MZe2+E9YVldgXbH5wZIvZYk1EpJpRmJVTBYRBZMHqTBqdrRICHXZevqodhgHfL9/LzE2HTi6S0PoKCK/t0fPtOZrBR3PNbhiPDW6Bw151ftT4+9l4dFBzvri1G7HhAWxPTufycfP4YPZ2a24OC6wB9XuY22rRJSLVUNX5H0Y8S1MNqpxODWoyomdDAN6YOAvXuh/MT1RAO66X/9hETp6TnolRXNCilseP7w16No7m9/vPY0DLWHLzXTz36wZu/mQxh1IsuDlMUw1EpBpTmJWS+VKYzUmHeW/BiTP0UhUAHh7YlPqRwQxI/wnDmQcNekGd9h49x7Jdx/h51X4MAx6/qAWGhzskeJOaIf78342deP7yNgQ6bMzZcpgL35rDtPVJlVtIYb/ZnXPNfw8iItWIwqyUzJc6Gkx/FqY+BVOftLoSrxfs78fLlzRmmP1PADY3vMGjx3e5XPynoBXX1Z3q0apODY8e3xsZhsGwbvX55d5zaVk7nKPpOdz22VKenLSWrNz8yikiuilENID8bNg+q3LOKSLiJRRmpWSFI7OHN0NuprW1nEl2Gqz8wtzeNkNL8Lqhe9pUahpp7HbGcMfiWmTmeC5w/bL6ACt2HyfY385DAzy/LK43a1wrlB/u7snIc82pHJ8v3MWQd+ay4UBKxZ/cMLQamIhUWwqzUrKw2hAcBa58OLTB6mpOb/VXkF0QFjKPQtJaa+vxdk5nUTuu7x0Xs/1oNm9M2+yRQ2fl5vPibxsBuKN3IrHhgR45ri8J8LPz+EUt+eyWrsSEBbDlUBqXjp3Hx3N34HJV8M1hhfNmt0yFij6XiIgXUZiVkhmG98+bdblg8Qfmtj3AfNw+07JyfMK2P83Rdv8wOlx6LwAfztnOqj3Hy33oj+ftYN/xTOLCAxl5bqNyH8+Xndc0ht/vP5cLmtciJ8/Jv39Zz/BPlpx+SWFPSDgH/IIgZR8klbDam4hIFaUwK6fn7WF25xxI3giOEDh3lPmcwuyZLRxrPna8iT5tE7msfR2cLnjku9Xk5JV9ikZyajbjZmwD4JELmxHkb/dEtT4tKjSAD2/uzL8vbUWAn41Zm5MZ9NZsZmw8VDEndARBo97mtqYaiEg1ojArp+ftN4Et+j/zsd110OISc3vXfMirwNEvX3Zogzkya9ig2+0APDWkFVEh/mxKSmXsjK1lPvQb0zaTlp1H23o1uKx9XU9V7PMMw+CmHgn8fO85NI8L43BaDiPGL2HMT+sq5uawohZdWtpWRKoPhVk5vcKR2aS13ndj1fE9sOlXc7vr7VCrBYTUgrxM2LvE2tq8VeHStc0vgpoJAESG+PPMpeaKb2NnbC3TzUqbDqby1eLdADxxUUtstqrbiqusmsaGMenuXgzvmQDA+Pk7uWzsPDYnpXr2RIVhdu9iyDjq2WOLiHgphVk5vagm5lzUnDQ4tsPqaopb+jG4nNDwPKjV3JzjW/gWq6YanCr9CKz+2tz+2yIJF7WpzcBWseQ5XTzy3Wry8kv3i8tzv27A6YJBrePo2jDSUxVXOYEOO2MuacUnw7sQHerPxoOpDHlnLp8t2Om5m8Mi4qFWS/PfxrY/PXNMEREvpzArp2f3g9iW5rY3TTXIzYLln5rbXW8/+XyjPuajwuypln4MeVlQu/3JpU8LGIbBs5e2JjzQjzX7TvDhXPd/cZm56RCzNyfjsBs8Oqi5h4uumvo2r8Vv959Hn2YxZOc5eerHddz26VKOpHloeoxWAxORakZhVs7MG28CWzcRMo5AjXhoOujk8w0LRmb3LYesE9bU5o3ycmBJQdeHHnebo9h/Uys8kCcvNn9xeWPqZrYnp539sPlOnptstm0b3jOBBlEhnqu5iosJC+CT4V14ekhL/O02pm88xIVvzWH25uTyH7yw3+zWaeCspEUbREQspDArZ+ZtN4G5XCdv/Op8izl6XCgiHqIam71xd86zpj5vtG4ipCVBaBy0vOy0u13VqR7nNTVHC//1/WqczjO/9f3lkj1sOZRGzWAH95zfxMNFV32GYTCiV0N+vKcXTWqFkpyazU0fL2b0xNXlG6Wt1xUCI8y+y3uXeqxeERFvpTArZ+ZtI7N7l8KBleZc3o43n/r5hpo3W4zLBQsK2nF1HQl+/qfd1TAMnr+8NSH+dpbsPMbnC3eddt+UrFzemGoutvBAv6bUCHJ4tOzqpEXtcH6+9xxu7N4AgC8X76HvqzP5ZN4Ocks5fxkwf8FrfIG5rRZdIlINKMzKmcWad7qTuh/SD1tbC8Di/5qPba6CkKhTP695s8Xtmg8HV4NfIHQacdbd69UMLpr7+tLvG9lzNKPE/cbO2MrR9BwaxYQwrFt9j5ZcHQU67Dx7WWu++UcPWtYOJyUrj2d+Xs9Fb89h3tYy/LsrWg1MLbpEpOpTmJUzCwiDyILVnKwenU1NgnU/mNtdR5a8T8NzAQMOb4KUA5VWmtdaOM58bHddyeG/BNd3a0DXhpFk5OTz2A9rTrnTfs/RDD6ZuxOAxwe3wGHXjxFP6dowkp/vPYfnLm9NzWAHm5PSuP7DRdzx+bLT/mJRosb9AMP8N5uyv8LqFRHxBvpfSM7OW+bNLv8UnLlQrwvU6VDyPkE1oU57c3vHrEorzSsd3QEbJ5vb3e50+2U2m8FLV7YlwM/GnC2H+XbZ3mKff/H3jeTkO+nVOIrzm9fyZMUC2G0G13drwMyH+zK8ZwJ2m8Hv6w7S7/VZvD5lE5k5btzUFRIN9Tqb2xqdFZEqTmFWzs4b5s3m55rtpQC6/uPM+2qqgWnR/wEuSLzA7MVbCg2jQxjVvykA//llPUkpWQAs23WUyasPYBjmAglGCZ0RxDNqBDsYc0krfr3vXHomRpGd5+TtP7dywWsz+XnV/rP3pm1S0NVAq4GJSBWnMCtn5w0jsxt+htQD5ipfLS89875/DbOeakbva7JOwIrPze0ed51539O49ZyGtK1Xg5SsPJ6YtBan08W/fzFbcV3bOZ4WtcM9Va2cQbO4ML64rRvvXd+RuhFB7D+Rxb1fruDa/y5k/f4zrNjWtGDe7PaZWuJZRKo0hVk5u8KR2cObITfTmhoWF/RJ7TT8jHfkAxDf3bzhKfUAHN5S4aV5pRX/M1dui25mjsyWgZ/dxstXtcVhN5i6Pon7v17Jqj3HCfa3M2pAUw8XLGdiGAaD2tRm+kO9GdW/KYEOG4t3HOXid+bwxKQ1HEvPOfVFcW3Ndmy56bBLrepEpOpSmJWzC4uD4Gizf+uhDZV//oNrYPd8sPmZvWXPxhEI8d3M7eo41cCZD4veN7e731niIgnuah4Xzt19GwPw8yrzRqK7+iRSKyyw3GVK6QU67Nx3QROmP9SHi9rWxumC/y3cTZ9XZ/LZgp3FlyI2DGjS39zWVAMRqcIUZuXsDMPaebOF7bhaDIHw2u69pjrPm904GY7vhqBIs4tBOd3VpzHN48IAqFMjkNvObVTuY0r51I0IYuywjnx1e3eax4VxIjOXp35cx8XvzGXBtiMndyxcDUz9ZkWkClOYFfdYFWYzjsLqb83trre7/7rCMLtzDuTnebwsr1bYjqvzCHAElftw/n423rquA+c2iebVq9sR6LCX+5jiGd0bRfHLvefw7GWtiQh2sPFgKkM/WMjdXyxn77EM89+BzQFHt8PhrVaXKyJSIRRmxT1W3QS28gvIy4TYNlC/h/uvq93OXNIzO8VcMay62Lccdi8wA0yX0/TiLYNmcWF8fms3ejaO9tgxxTP87DZu7N6AGQ/14cbuDbAZMHnNAS54bRZvzjlAfv2e5o4anRWRKkphVtxTODKbtBacZVhisyyc+Sdv/Oo6snRzP232ggUUgO0zPF+bt1r4nvnY+gr3p2RIlVAzxJ9nL2vN5PvOpVvDSLLznLw5bQtj95rTQlzqNysiVZTCrLgnqrHZISAnDY7tqJxzbpkKx3eZI6xtri7964vmzVaTxRNS9sO6ieZ2d/cXSZCqpUXtcL66vTvvDutAnRqB/JDeGoD87XPZvFurgYlI1aMwK+6x+0GtluZ2ZU01KLzxq+ON4B9c+tc36ms+7lkEOaVYCtRXLf4AnHlQv+fpV0iTasEwDC5uW4fpD/XhkvPPY6crDj/yeP3//svTP67leEYJrbxERHyUwqy4rzJvAju8BbZNBwzofGvZjhHZCGrEQ36OOY+0KsvJgGWfmNsalZUCQf52HuzflKgOFwPQx1jBpwt20ffVmfxv4S7yndV0URERqVIUZsV9RWF2dcWfa8mH5mPTCyGyYdmOYRjQsLe5XdVbdK3+CjKPQUQDaH6R1dWIlwlrY35PXBG2nqa1QjiWkcsTk9Zy8TtzWbzzqMXViYiUj8KsuK+yOhpkp8KKL8ztruW8I7869Jt1Ok/e+NXtDvPmN5G/atALHCH4Zx7i12sieOaSVoQH+rHhQArXf7SUTzfbyMipZi3sRKTKUJgV98W2BAxzmdi05Ao7jW3Nt5CTat50VjjvtawaFYzMHlwD6UfOvK+v2vanudSwfxh0uMHqasQb+QUU/WLnt20qN/dMYOY/+zKsW30MA5YfsXH/16uLryAmIuIjFGbFfQFh5jxUgKQKGp11ubAtK5hi0PV2sJXzWzS0VsGNay7YObvc5XmlhWPNx443QmC4tbWI92o6wHws6DcbGeLP85e34X+3dMZhuJi5+TBPTFqLy6V5tCLiWxRmpXQq+Caw6LT1GIc3g38otBvqmYNW5akGhzaYI7MYpVshTaqfJgVhdu9SSD9c9HTXhEhuburEZsBXS/bw1vQtFhUoIlI2CrNSOhUcZhslTzM32l3nuVHGqhxmC+fKNr+o7DfKSfUQXqfg368Ltk4r9qk2kS7GDGkBwJvTtvD1kt0WFCgiUjYKs1I6FXkT2Ik9xJ1Ybm57cpSxQU+w+cGxneafqiLjCKz+2tzucbe1tYhvKBydLWE1sKFd4rmnb2MAHvthLX9uTKrMykREykxhVkqncGT28GbIzfTooW3LPsHAhTPhPIhp5rkDB4RB3c7mdhVaDcy2/FPIy4La7aB+D6vLEV/QZKD5uHUa5J/aveChAU25smM98p0u7v5iBav2HK/c+kREykBhVkonLA6Co8HlhEPrPXfc3ExsK/8HgLPzbZ47bqEqNtXAcOZhW/aR+UH3u82euiJnU68zBEVC1gnYu/iUTxuGwYtXtuG8pjFk5uZzy/gl7DycbkGhVcucLcm8PmUTh9OyrS5FpEpSmJXSMYyKmTe7diJG5lEyHFG4CkePPKkwzO6YbfZl9XF1jy/CSEuC0DhodbnV5YivsNmhcT9ze/MfJe7isNt47/qOtK4bzpH0HG7+ZLFCWDn8se4gwz9Zwtt/bqXf67P4ZukedYwQ8TCFWSk9T4dZlwsW/x8AO2IuqJim//U6mx0SMg7DoXWeP35lcrlIPFQQRLreBn7+1tYjvqVpwS+LJcybLRQS4MfHw7sQHxnEriMZ3DJ+CenZWlShtGZtTubeCSvId7qICHZwPCOXR75bzdAPFrI9Oc3q8kSqDIVZKT1P3wS2dwkcWIXLHsDuqN6eOebf2R3mjWDg81MNjD0LiMjcicsvEDrdYnU54msSzwfDZk4TOr7ntLvVCgvk0xFdqRnsYPXeE9wzYbkWVSiFhduPcPtnS8nJdzK4TRwLR1/AY4ObE+iwsXD7US58cw5vTdtCdl6+1aWK+DyFWSm9opHZtZ55y36ROSrranUlOX5h5T/e6VSRebO2glFsV+urISTK4mrE5wRHQr2u5vYZRmcBGsWE8tHwLgQ6bMzYlMzjP2hRBXcs332MW8cvITvPyfnNa/HmtR0IdNi5/bxEpj7Ym95NY8jJd/LGtM1c9PZcluw8anXJIj5NYVZKL6ox+AVCbjoc21G+Y6UehPWTAMjvfGv5azuTwjC7az7k5VTsuSrKsV0Ym34FIL/rHRYXIz6r6elbdP1dx/o1eWdoR2wGfL10D29O06IKZ7J23wmGf7yY9Jx8ejWOYtz1HfH3O/lfbXxkMONHdOHtoR2IDvVn66E0rn5/AaMnruZERq6Flfu2tftOMGHRbvKd+mWrOlKYldKz+xUsEQscXF2+Yy37FJx55khR7Xblr+1MarWEkBjIzTCnNviiZeMxcJEc2tKz7cukeim8yXL7LLda7PVvGcuzl7UG4K3pW/hysRZVKMmWpFRu+ngxKVl5dG5Qkw9u6kyg49R7AAzD4JJ2dZg2qjfXdYkH4MvFe7jg9Vn8vGq/Rr9L6Vh6Djd9vJjHfljDuBlbrS5HLKAwK2XjiZvA8nJg6cfmdrd/lL+mszEMaFgwJ9cXpxrk5cCKz4GCG+VEyiq2FYTXhbxMjF3z3HrJ9d0acO/55qIKT0xay/QNWlThr3YeTuf6DxdxND2HtvVq8PGILgT7+53xNRHB/rx4ZVu++UcPEmNCOJyWzb1frmDE+CXsOZpRSZX7vpf/2MjRdPPdtjenb2Gl+iNXOwqzUjaeCLMbf4a0gxBSC1pc4pm6zsaX581u/AXSk3GFxnKwRgerqxFfZhjQpL+5uW3aWXY+aVT/plzVqWBRhQnLWbH7WEVV6FP2Hsvg+g8XcSg1m2axYXw6oivhgQ63X9+1YSS/3n8uD/Rrgr/dxsxNyQx4YzYfzN6um+7OYtmuo3y52LyRsX18BPlOFw9+vZKMHHXfqE4UZqVsPNHRYPEH5mPnEZXXXqowzO5bBlkplXNOTykYxXa2vwGXceYRH5GzKphqYNs61WyP5wbDMHjhijb0bhpDVq6TWz9dyo5qvqjCoZQsbvhwEfuOZ9IoOoT/3daNmiGl/3kW4GfngX5N+fX+c+nWMJLM3Hye+3UDl46dx+q9xz1feBWQm+/k8R/WAnBN53qMH9GFuPBAdhxO57nJGyyuTiqTwqyUTWxLwIDUA5CWXPrXH1gNuxeAzQ86jfB4eacVEQ+RjcCVD26+veoVkjfDzjlg2HC2v9HqaqQqaNQb7AEYx3cRmr3f7Zc57DbGXd+RNnVrcDQ9h5s/XkxyavVcVOFIWjbXf7iInUcyiI8M4ouR3YgJCyjXMRvXCuWr27vz8pVtqRHkYN3+FC4bO49nfl5Hmnr9FjN+3k42HkylZrCDRwe1ICLYn9euMe+9+GLRbk2FqUYUZqVsAsLMUAiQVIbR2cX/NR9bXALhtT1Xlzt8carBsk/MxyYDoUY9a2uRqsE/BBLOASD2xKpSvbRwUYX6kcHsPprBrZ9Wv0UVTmTkcuNHi9lyKI248EAm3Nad2jWCPHJswzC4pks80x/qzaXt6+B0wSfzdjLg9VlMW6+ABrDveCZvTNsMwOjBLYgsGA3v1TiaW89pCMC/vl+t1euqCYVZKbuyzpvNOAprvjW3u97u2ZrcURRmZ1X+ucsiNxNWfmFud9YiCeJBBauBxaaULswCxIQF8OktXYkM8Wf13hPcPWE5udVkfmdadh43f7KY9QdSiA7154uR3YiPDPb4eaJDA3jrug58ektX4iOD2H8ii9s+W8qd/1tGUkqWx8/nS575aR0ZOfl0SajJVR2L/4L/z4HNaBYbxuG0HB79frW6Q1QDCrNSdmUNsys+h7ws8/X1u3u+rrNJOBcwIHmD2efW2637AbJOQER9aKwuBuJBBTeBRaVtLtMc8obRIXx0c2cCHeZNS49NXFPlg0NmTj63jl/Cyj3HqRHk4PNbu5EYE1qh5+zdNIYpD/Tmjt6J2G0Gv609SL/XZvH5wl04q2Ff1Wnrk5iyPgk/m8F/LmuDzWYU+3ygw86b17XH325j2oZDRTeISdWlMCtlV5abwJz5sORDc7vr7eZd1ZUtOPJkT1tfGJ1d8pH52Gk42E7tWSlSZpGNcEU1xkY+xq65ZTpEh/o1GTvMXFTh22V7eaMKL6qQ54S7v1zJoh1HCQvw4/Nbu9KidnilnDvI386jg5rz8z3n0C4+gtTsPJ6ctJar3p/PpoOplVKDN8jIyePpn9YBcNu5jWgWV/KqkS1qh/PPgWYv7md/Wc/25LRKq1Eqn8KslF3hyOzhzW41XgfMFYeO74bACGh9VYWVdla+Mm/2wCrYt9S8Ua6DbvwSz3M2OBcAY/f8Mh/jghax/Ocy8+fB29O3MGFR1VtUITffyfjNNuZsPUKQw84nI7rQtl5EpdfRsk44E+/syTOXtCLE387y3ce56O05vPz7RrJy8yu9nsr29vSt7DueSd2IIO67oPEZ9731nIb0aBRFZm4+D36zqtpMg6mOFGal7MLiIDgaXE44tN691yz6P/Ox403g7/k5Zm4rDLM7ZrndlsgSSwtu/GoxBEJrWVuLVEmu+j0AMHYvKNdxhnWrz30XNAHgiUlrqtSNSvlOF//8fi1rjtnw97Px4c2d6ZwQaVk9dpvBzT0TmPZQbwa0jCXP6WLczG0MfHM2c7cctqyuirY5KZUP52wH4JlLWp11UQqbzeC1a9oRHujHqj3HeedPrQ5WVSnMStkZRunmzSZvhu0zAAO63FqhpZ1V/e5gD4CUfXDES3/AZaeevFGus8XXS6osV3xBmE1aU+7eyw/2a8I1nevhdME9X1aNRRWcThejJ65m8pqD2AwX717Xjl6No60uC4DaNYL4702d+b8bOxEXHsiuIxnc8NEiRn29kiNV7C5+p9PF4z+sIc/pon/LWPq1jHXrdXUigvjP5eb/U+/+uYVlu3z/e1JOpTAr5VOaMLukYJGEphdCzYQKK8ktjiCo383c9tapBqu/hpw0iGpS1EJJxOPCa5PmXwvD5YQ9i8t1KMMweO7yNvRpdnJRBV+eq+hyuXjm53V8s3QvNgNubuKkb7MYq8s6xcBWcUwddR4392iAYcDEFfvo9/osvlu2t8rckPfd8r0s2XmMIIedMZe0KtVrL2lXh8sKWpyN+mal+vVWQQqzUj7u3gSWlQIrJ5jb3Sxox1USb54363LBEnPFLzrfYs2NclJtHAk1b5ShjDeB/ZXDbmPssI60rVewqMInvrmogsvl4sXfN/Lpgl0YBrx0RWvaR3lvMAwLdPDMpa2ZeGdPmseFcSwjl4e/XcXIz5b5/FzRY+k5vPCruaLXg/2bUDei9P18n7m0NXUjgth1JINnf3ZzWpz4DIVZKZ/ahWF2LTjP8APzr6OMDftURmVnVzRvdo7ZZcGb7F0Ch9aBXyC0u87qaqSKOxLa3NzYVfabwP7qr4sq7DmayS3jfW9Rhbenb+X/ZpnzM/9zWWsua1/H4orc06F+TX6+9xweHdScAD8b0zYk8eSktT49Qvvibxs5lpFLs9gwRvRqWKZj1Ahy8No17TAM+HrpHv5Y5wNtGcVtCrNSPlGNwS8IctPh2I6S93G5Tq741fV2sHnJt13t9hBYA7JPwP6VVldT3NKCUdnWV5qtxEQqUNHI7L7lkJPhkWNGh55cVGHNvhPc9YXvLKrwweztRatLPXFRC67v1sDiikrHYbdxxzn1mdryd262T+GrJXv4v9nbrS6rTJbuPMrXS80+sc9d3hqHvez/f3RvFMXt55krVz76/WoOVfOFJ6oSL0kV4rNsdohtaW4fXF3yPttnmu27/EO9a5TRZi9YQIGCG9O8RMZRWDvR3NaKX1IJMvxjcIXVAWeu2QrOQxpGh/Dx8C4EOezM2pzMaB9YVOHzBTt5ruAt7Yf6N+W2cxtZXFEZLXqf+pvH87T/58RwnBd/28ivaw5YXVWp5OY7efyHtQBc1yXeIx0kRvVvSsva4RzLyOWf32l1sKpCYVbK72w3gS0uuPGr3VAIrJwG4277a4sub7FyAuRnm9e1bierq5HqwDCKWnR5aqpBofbxEbw7rAM2A75btpfXp2726PE96dule3jyR7Mh/119Ernn/DP3MfVaJ/bBjBcAsLny+U9iwXzTr1f6VIeJj+fuYFNSKpEh/vzrwuYeOWaAn523rmtPgJ+NWZuT+XzhLo8cV6ylMCvld6Ywe2wXbP7N3O7qJTd+/VWjvubj7oUee3u1XFyuk1MMdOOXVKKTYXaex499QYtYnitoj/TOn1v5YpH3BYifV+3nX9+b7y4N75nAPwc2w/DVf39/PGZO/XKEADAgbwbnN69Fdp6TkZ8tZc9RL/hZdxb7jmfyZsFqcqMHNadmiL/Hjt0kNoxHB5nh+LnJG9h6qPqsoFZVKcxK+Z2po8HSj8xFFRr1gZimlVqWW6ISIbwu5OfAnoVWVwM7ZsPRbeAfBm2utroaqUac9XuaG3uWQF6Ox48/tGt97i9YVOHJSWuZ6kWLKkxbn8SDX6/E6TLfzn56SEvfDbJbp8P6SWDYYNjXYPfHSFrL2PMdtKwdzuG0HG4Zv4QTmblWV3pGY35aR2ZuPl0TIrmqUz2PH//mHgmc2ySa7DwnD3y9kpw835jPLSVTmJXyq9USMCD1AKQln3w+NxOWf2Zue+OoLJgjn97UomvpR+Zj22sgoOQ1x0UqRFQTCI6CvEw4sLJCTvFAvyZc2zkepwvu/XI509YnkZJlbaiasyWZu75YTp7TxaXt6/Dc5W18N8jmZsGvD5vb3e6AhudCs0EABG34lo+GdyY2PIAth9K46wvvbdk1dX0SU9cn4Wcz+M/lrSvk78NmM3j16nZEBDtYuy+FN6d57/QXOTuFWSm/gFBzhBMg6S+js2u+g8xjUKO+uVCCtyoKsxbPm009CBsnm9udR1hbi1Q/hgENCkZnd5a/32zJpzDDSd+CRRVu+2wpbcdMoetz0xj634U8MWkNn8zbwZwtyew/nonTWbE35yzafoSRny0lJ9/Jha3ieO3qdthtPhpkAea/DUe3Q2gc9BltPtduqPm4+htqhzr46OYuBPvbmbf1CE/84H0tuzJy8hjzkzlveeR5jWgaW3G/1MeGB/JCwfSX92ZtY/GOoxV2LqlYZ17YWMRdcW3MZWEProHE8wvacf2f+bkut5qdA7xVw97m44FVZicBq1phrfgcnHlQr+vJecgilalBL9jws3kT2LmjKuQUDruNsdd35PEf1jJv62EOpWYX/Vmw/UixfYP97TSKCSExJvTkn1ohJESFEOgo38+UlXuOc8v4JWTlOunTLIa3h3bArxxtnyx3dAfMec3cHvjcyZttG/eD4GhIPwTb/qR10wG8M7QDIz9bytdL95AQHcKdfRKtq/tv3pq+hX3HM6kbEcR95zep8PMNalObqzrV47tle3nw65X89sC5hAc6Kvy84lkKs+IZcW1g3Q8n583uWWRu+wVCx5usre1swmIhpgUkbzDnrLa6rPJrcObDsk/N7S63Vv75ReDkyOzuheb3ZAX9Ehrs78cb17YHICUrl+3J6Ww7lMa25MI/6ew8nE5GTj5r96Wwdl9KsdcbBsTXDCaxMOjWKgy7IUSG+J/1ben1+1O46aNFpOfk06NRFO/f0Al/Px8Osi4X/PYvyMuChueZ/akL2R3m/PtF78GqL6HpAC5oEctTF7dkzM/reen3jdSPDOaitrWtq7/ApoOpfDTH7Ff+70tbEeRfOYMgTw9pyaIdR9hzNJMxP63j9WvaV8p5xXO8IsyOHTuWV155hYMHD9KuXTveeecdunbtWuK+ffr0YdasU98OHjx4MJMnT67oUuV0/n4TWOEiCW2u8o2m/436mGF2+0xrwuyWqXBiDwTVhJaXVv75RQBiW0NAwUIiB9dAnfYVfsrwQAft4yNoHx9R7PncfCd7jmawLTmdrX8NuofSSMnKY/fRDHYfzWDGpuRir4sIdhQF25OjuaHE1wzCz25j66FUbvxoESlZeXSsH8GHN3cu9yiv5Tb9Clv+AJsDBr92aheUdteZYXbjZMg8DkERDO/VkJ1HMhg/fyejvllJ7YhAOtavaUn5AE6niycmrSHP6WJAy1guaBFbaecOC3TwxjXtueb/FjBx+T4uaB7rFeFe3Gd5mP36668ZNWoU77//Pt26dePNN99k4MCBbNq0iVq1ap2y/8SJE8nJOXmn7ZEjR2jXrh1XX607vy1V+Lb44c3m213rfzQ/7jLSuppKo1Ef84e9Vf1mC9txtb8eHKVfd1zEI2x2qN/dDEa75ldKmD0dh91Go5hQGsWE0r/lyWDjcrk4nJbzl3CbXrS973gmxzNyWbbrGMt2Hfvb8QwSokI4kp7D0fQcWtcN55MRXQkJsPy/wfLJSYffHjW3e95bcteY2u1Ovvu0fhJ0Gg7Akxe3ZM/RDKZvPMTIT5cy6e5exEcGV1rpf/Xdsr0s2XmMYH87T1/SqtLP3zkhkrv6NObdGVt57Ic1dGpQk7gagZVeh5SN5e+rvP7664wcOZIRI0bQsmVL3n//fYKDg/n4449L3D8yMpK4uLiiP1OnTiU4OFhh1mqhsRASY7bh+u0Rc+5nfDdL/zMslYReYNjNmyeOVXIPzOO7YcsUc7vgPxkRyxRONaiAfrOeYBgGMWEBdG8UxfXdGvDUkJZ8ektX5v7rfNY/cyG/3ncu7wztwAP9mjCkXR1a1g4n0GEjN9/FlkNpHE3PoWlsKJ/d0o0aQVVgbuTsV+HEbqgRD+c9XPI+hgHtC24EW/VV0dN2m8HbQzvQqk44R9JzGGFRy66j6Tm88FvBwg79mlI3wppf6O/v14S29WpwIjOXh79dVeE3IIrnWPoraU5ODsuWLWP06NFFz9lsNvr168eCBQvcOsZHH33EddddR0hISImfz87OJjs7u+jjlBRz7lVubi65uZXzj7bwPJV1PqvYY1tj2z6jKJjldboVVym+Zkuvky0Qe91O2PYuJm/rn7ja31B5p17yCXZcOBPOJb9GApzl668u30/lpevknr9fJ6NeN/wA16755OXk+NTCHX4GNIkJoklMEBBT9LzT6eJAShbbk9M5cCKL/i1rEeZvlOp7wyu/nw5vwW/+OxhAXv/ncBn+p//50eJy/KaNwdi9gNxDm6FmQwD8bfD+9e256v8WsfVQGnd8vpQPb+xYrjnEpb1Wz09ez7GMXJrFhnJ917qWXuNXrmjNpe8tYO7Ww3w0dxvDezSosHN55feUFynNdTFcFvbl2L9/P3Xr1mX+/Pn06NGj6PlHHnmEWbNmsWjRojO+fvHixXTr1o1Fixaddo7tmDFjeOaZZ055fsKECQQHW/N2SlXVct/XNDlkzlvO8qvBlFZv4LL5zlt4zQ5MpPnBSeyt2Z1lCXdVyjkNZx4D1j1IYN4JFifcw4GaJX8fi1QWw5nH4DV34OfM4c/mL5AaVNfqkqQkLhc9t75ETNp6Doa3Y1GjUWf9xaPH1peplbqWjXGXsan2FcU+tzcd3lprJ8dp0C3GydBEZ6X8HrMtBd5eZ/4/8UDrPBp6QXvtuQcNvt1hx89w8VDbfOooKlgiIyODYcOGceLECcLDw8+4r+8kjRJ89NFHtGnT5rRBFmD06NGMGnWyxUxKSgrx8fEMGDDgrBfHU3Jzc5k6dSr9+/fH4agCb2udhrEuEyaZYdbR43YGnXdJqV5v9XUydteEzydRN3srsYMuNFfQqehzbvgRv1UncIXUosN1j9PBfvav2+rr5Ct0ndxT0nWynRgPO2fTO8GOs9Ngawv0Et72/WSsm4jfyvW4/AKJuuEjBtdMOPtr1mbAj3fQLGsFiYM+OCX8NmmbzB1frGBRso1z2zfjH+c1LFNt7l6r3Hwn745bAKRzbee63H1p5c+VLckgl4tD/1vBrM2H+fFgBN/d0Z2ACuh24W3fU96m8J10d1gaZqOjo7Hb7SQlFV/WMCkpibi4uDO+Nj09na+++op///vfZ9wvICCAgICAU553OByV/s1jxTkrVb3O5qPND3vX27CX8Wu17Do16A6OYIyMwziOboG41hV/zhVmOy6j4004Akv363+V/37yEF0n9xS7TgnnwM7Z2Pcuwt79H9YW5mW84vspKwWmPQWAce5DOGq52Y+11aXw28MYx3fhOLAMGvQo9ukBrevw9JAcnv5pHa9O3UJCTCgXt61T5jLPdq0+mr+NLYfSiQzxZ/TgltZf17949er2XPjmbDYmpfHOjO2MHtyiws7lFd9TXqg018TSG8D8/f3p1KkT06dPL3rO6XQyffr0YtMOSvLtt9+SnZ3NDTdU3txGOYvoxnDJu3DN5xB25l9GvJKfv9k0HipnadvDW8y+thjQ6eaKP5+Iu4puAptv9jAV7zLzBUg7CJGJ0PM+91/nHwwtLzO3V00ocZebeyYwolcCAKO+WXVKVwhP2Xssg7embQHgscEtiAj2r5DzlFVMWAAvXmm2nPzvnO3M33bY4orkTCzvZjBq1Cg++OADPv30UzZs2MCdd95Jeno6I0aYy3nedNNNxW4QK/TRRx9x2WWXERUVVdkly5l0vBGa+/DbkoVL21ZGi66ln5iPTQdCRP2KP5+Iu+p1NnuWph6AYzusrkb+6uAaWPS+uT34FXCUsn1UYVeDdZMgN7PEXZ64qCX9WtQiJ8/J7Z8tZfeRjLLXexpjflpPZm4+XRtGcmVH75yX3b9lLEO7xuNywcPfrLKk04O4x/Iwe+211/Lqq6/y1FNP0b59e1auXMnvv/9ObKzZV3D37t0cOHCg2Gs2bdrE3LlzufVWrZQkHlYYZnfOg7ycM+5aLrmZsPILc7vzLRV3HpGycARB3U7m9q751tYiJzmdMPkhswViy8ug8QWlP0b9nlCjPmSnmIsolMBuM3jrur+27FrMiQzPBbkp6w4ybUMSfjaD5y5rfdYV26z0xEUtSYgKZv+JLJ76ca3V5chpWB5mAe655x527dpFdnY2ixYtolu3bkWfmzlzJuPHjy+2f7NmzXC5XPTv37+SK5Uqr1ZLcx3z3HTYt7TizrNuEmQdN3tDNu5XcecRKauEgik3CrPeY+UX5lLhjhAY+HzZjmGzQbtrze2/9Jz9u5AAPz66uQtx4YFsS07nzi+WkZPnLNs5/yI9O48xP60D4PbzGtEk1gvaF5xBSIC59LLdZvDjyv38uHKf1SVJCbwizIp4DZsNGvU2tyty3mzhil+dbjZXXRLxNl6+eEK1k3EUppo3fdF3NNQox1vz7QqmGmybDqlJp90trkYgHw/vQoi/nfnbjvDEpDWUt5vn29O3sP9EFvVqBnHv+W7euGaxDvVrcu/5jQF4YtJa9h0veXqGWEdhVuTvCqcabK+gebMH18DexWDzgw43Vcw5RMorvpvZnu7YTjih0SjLTX8GMo+ay9J2u6N8x4pKhHpdzekKa745464t64Tz7rCO2Az4Zulexs3cVubTbjyYwodzzTnY/760FUH+vvOL/D19G9M+PoLUrDwe+malVgfzMgqzIn9XGGb3LjFb4Hha4ahs84shLPbM+4pYJSAMarczt3e7tyKjVJC9S2GZ2caPi18HN/pRn1W768zHM0w1KNS3eS3GXGL2gH3lj038vGp/qU/ndLp4/Ie15DtdXNgqjvOb+9bPPj+7jTevbU+wv52F24/y4dztVpckf6EwK/J3EfXNpR5d+Z6fL5idCqsLRkJ045d4u8JWdTvnWltHdebMh8mjAJc5PaBw+kd5tb4C7P6QtNZ8t+gsbuqRwC29zEUUHvq29C27vl22h2W7jhHsb+epIS3LVLLVEqJDeLqg9lf+2MT6/RUw2CFlojArUpKKatG15lvISYOoxtDwPM8eW8TT/tpvVqyx9GM4sAoCa0D/My8SVCpBNaHZIHN75ZduveTxi1rQr0UsOXlORpaiZdfR9Bxe+G0jAKP6N6VORFCZSvYG13SOZ0DLWHLzXTzw9QqycvOtLklQmBUpWdG82ZmeO6bLBUsKphh0vuWs66iLWK5+weI1hzdBWrK1tVRHaYdg+rPm9vlPQmgtzx6/8EawNd9Aft5ZdzdbdrWndd1wjpaiZdcLv27geEYuzePCGN4zoZxFW8swDF64og3RoQFsTkrj5d83WV1Spdh/PJPvlu1l1DcrScs++/dKZVOYFSlJw/MAAw6tP+PdvqWydykkrQF7wMn/RES8WXCk2a4ONG/WClOehOwTULt9xUxLatzPbEWYngzb/nTrJYUtu2rXMFt23fG/M7fsWrT9CN8u2wvAc5e3wc/u+7EjKjSAV642Vwf7eN4Opqw7WOVuCDuSls3k1Qd47Ic19H11Jj1f/JOHv13FxOX7WLzjiNXlncLP6gJEvFJwJNRua769t2MWtL2m/McsvPGr9RXm8UV8QYOe5i91u+ZDy0usrqb62DkXVn8FGHDR6xXTws/ugDZXw6L3zOVtmw5w62Wx4YF8dHMXrn5/Pgu2H+HxH9bw8lVtT1n8ICfPyROTzIUGhnatT6cGNT3+JVilb7Na3Ni9AZ8v3MXtny+jRpCDdvERdIiPoH1989Hblug9k7TsPBbvOMK8rUeYv+0IGw4Unw9sM6BtvQh6JkbRICrEoipPT2FW5HQa9THD7HYPhNmMo7BuormtG7/ElzToBUs+VL/ZypSfa670BdBpONTrVHHnanedGWY3/gqZxyEowq2XFbbsuvXTJXy7bC8J0SHc3bdxsX0+mb+LLYfSiArx518XNvN87RZ7bHALjqbnMG1DEicyc5m9OZnZm09Ox2kUHUL7+Ag61I+gfXxNmtcOw+ElI9NZufks332M+VuPMH/bYVbtPUH+30aXm8eF0SMxil6J0XRtFEl4oAe6aFQQhVmR02nUB+a9Zc6bdbnKN8d11VeQlwWxbaBeF09VKFLxCm8CO7gGsk6YNyJJxVo4DpI3QnAUXPBUxZ6rdjuzd23yBlg/yQzPburbvBbPXNKKJ39cxyt/bKJ+ZDBD2tUB4EgWvLvU7En72OAWPjVK6a4gfztjh7YjNzeHjck5rNxzjBW7j7Nyz3G2H04v+jNxhdmnOcDPRtt6NQoCbk1a1w6ttFrz8p2s2XeC+dvM8Lp05zGy/zY9pEFUMD0To+iZGE2PxCiiQwMqrb7yUpgVOZ347mbrmpS9cGQbRDc++2tK4nKdnGLQeYRu/BLfEhYHkYlwdBvsXuT2W9FSRif2wsyXzO3+/674KUmGAe2HmquLrfyyVGEW4MYeCew4nMHH83bw0LerqBMRSJvaoXy/00ZWrpNuDSO5omM5VivzVi6XOUjx279w5GfTpkEv2jS+gBv7ng8xvTmWkcvKvcdZufs4K/YcZ+XuY6Rk5bFk5zGW7DwGmItH1HDYmXxiJR0bRNIhPoI29WoQ7F/+aOZ0uth8KJV5W4+wYNthFm0/SurfbtyqFRZghtfG0fRMjKJezeByn9cqCrMip+MfbK6CtHMObJ9R9jC7cw4c2QL+oZ6ZeytS2Rr0NMPsrrkKsxXt99GQm27+Mt1uWOWcs801MG0M7FkIR7dDZKNSvfzxi1qw+2gG0zYkMfKzZfzj3ATWHbPhsBs8d3nrU+bS+rysE/DLKFj73cnntk03/wCE1aFm4vn0TexL3559IaQpTqeLHUfSC8KtOYK78WAqJ3JhyvpDTFl/CDA7RjSLDSuYmmCO4DaKDsFmO/M1dLlc7D76/+3dd3hUZd7/8fekh5JQQkKooYNUQYmAWABproCigORRcF0LBstiQd1V4KeP7oqr7vqwoK6gKwqIq8gKwtKV7tIFREAEBEI1JARSzJzfHzcJxIQwSWbmTPm8ritXTiZnznzn5szwyZm7nD3f5/UEa/ae5GRWbpF9YqLCTLeB8+G1Sa0qAfNvozArUprGN5gwum8FdL6vfMcouCrb9g6zqpKIv2nYDTZ9oPlmPW3PYtg5FxyhcPNfIMRL/StjEs173d6lsGUW3PhMme4eGuLgb3d2YMhba/j2UAYvL/gegN91S6JpfIC95x1YC/+6D04fMP9ONzwDLfvD3mWm/favgszDsHm6+cIBdToQ0qQHTZr0pEn7qxncqR4AGVnZ/OPThUTXu4KthzLYfDCdI6ez2XEkgx1HMvhw3QEAqkaFFQbbK+ubkFu9cgRHM7JZvffE+X6vJzmUfq5IqdHhoVzdqAbdzncduKJODKGXCcX+SmFWpDSNb4SlL8C+r8xKPGUdUXzmGOz8t9m++l731yfiDQX9Zg9vgtwsiPC90cx+Ly8b5j9ptpMfhNptvPv47YefD7Mz4PqxZQ7SlSLMlF2DJq3iyOlsakRajLq+bFd4fVr+L/DVRPjqFbCcUD0JbvsH1D8/BiKhNXQdDXnnzDR2e5fCnqVwbLt53RzeBF//xXxCl9QdmvYkumF3mlS16H9tEuHhZnDVkdPn2Hy+3+2mA+lsPZROZvYvfL37BF/vPlFYTlyVSE6cySlSYniogyvrV6drUxNeO9SvRkSYbww48zSFWZHS1OkAkbHmY6Ujm6FuGUcVb/wnOH8xg75qt/VEhSKeV60BxNQz/cd/+ubCoiLiPqv+aj7ir1Ibbnja+4/f8mYTtNL3m+4G5Vg2NyEmin/+tjOvL9pFCw4RHeGB6cTs8PN++PQ+OLjO/NxuGPSfCFExxfcNj4YmPcxXbyDjiOmmtnepuXp79gR8/yV8/yXhQK+IWoQ4lkKzXtD4ehJjY0lsG02/tokA5OU72ZWWeb7fremi8MPxLE6cycHhgDZ1YgvD69VJ1d3S39YfBeezFnFVSCg06g7ffWGm6CpLmHXmw4b3zbam4xJ/5nCYcLPtY9PVQGHWvU79YK7aAfR9qeSQ5GkRleCKQeaj8S0zyhVmAZolVOWvQ9szf/4h99Znl22fwBe/h5wMiIwxc/62u8P1+8ckQofh5svphLSt54PtUqwDa6mcexw2vW++HKFQ7ypo0tOE4bodCQ8NpU3dWNrUjeWuaxoCcPpsHnuOn6FprSrEVvLd6bK8KTiuP4tURHmXtt2zxPSriqoGrW91c1EiXlYQbtRv1r0sC74cC/k55r2m9W321dLh/MqE2+eYj8uDWXYGfPoA/OteE2TrJ8ODX5ctyP5aSIj5tK/7GBj5Bb88vpu1jceQf9V9ULMZWPnm6u/yl+DdXvBKI/j4btjwHqQfLDxMbKVwOjWsriB7EV2ZFbmcgjB7YK15gw+Pdu1+BQO/OqS4fh8RX5V0rfn+0zfwSw6E+c8clD7tu3mw+z8QEg79X7V36r4GXSG2gfkj/Lt50PZ2+2qx00//NSH25x/BEQLXPQXXPQmhbo5MEVU4GtsBZ5/+hIaHQ/qBwqu2/LDcdG/b8bn5AhN4m/SApj3NoMxI781T6+t0ZVbkcmo2hap1zJWTA2tdu0/6Qdi90GxfdY/nahPxlppNoXIts/jH4U12VxMYcrPMVVmAbo9AXDN76wkJgfZDzfaWmfbWYgdnvhnk9W5vE2RjG8DI+WZ2B3cH2ZJUa2Dm+R3yT3jyB7h3MdzwrLkq7Ag1Uzyufws+GgKvNDZdIARQmBW5PIfjwtXZfStcu8/G982I16Tu9v8HJeIOBf1mQUvbustXE82gutgG0P0Ju6sx2p/varB3CWSm2VuLN6UfhPdvgaUvmo/72ww23QoadrGnntAwM1PCDWPh3v/AUz/AkA+g0z0m9ObnmLluzxy//LGCgMKsiCvK0m82P8/MYgAa+CWBpWE38/1HhdkKO74LVr9ptvv92QzA8gU1m0C9zuaP8W2z7a7GO7Z/BlO6mT/SIqrAoCkw+F2IrmZ3ZRdEV4MrBsAtb8Ajm80yxDmnYcl4e+vyEQqzIq5ofL35fngznD1V+r675sOZo1A5Hlr+xuOliXhNwZXZg+vMvJtSPpYF8x430/Y172sm3fcl7YeZ74He1SDnDHyeCrNHmv6pdTrCA1+ZgXC+vDJWSKjpXw2waToc/MbeenyAwqyIK6rWhlotAcusCFaab9413zveBWERHi9NxGvir4CoWMg9Y6YYkvLZ9ol5HwmLMldlfU2b2yA0Ao5+C0cC9N/50EZ46zoTBnFA98fNx/k1m9hdmWvqdzaDiwHmP276+wYxhVkRVxV2NSil3+yJPef71Tqg4whvVCXiPSGh0OB8H0JN0VU+2afhP38w292fMCtJ+Zro6tCin9kOtKuzTiesfAPevQlO7YWYujDi39DzeQj1s6muek0wi/oc2WKm7wpiCrMirnKl3+yGaeZ7s5ugekNPVyTifZpvtmKWvWS6IdVoYmYw8FUFA8G2fRw4XUoyDsMHA2HxONPFo9UAeHClWRjHH1WpBT3O/2G05P9B1kl767GRwqyIqxp2M9OjnNpbZALrQnnZsPlDs33Vvd6tTcRbCgaBHVhtrnKJ645shfVvm+2bX/XtuXqb9oJKcZB13Mxs4O92fgGTu8K+ryC8Egx400yBVamG3ZVVzFX3QkIbyE6HJRPsrsY2CrMiroqKubCcbUlTdO2YA+d+NmvYN7vJq6WJeE1iewivbM7149/ZXY3/cDph3hgzS0DrW83k974sNBzanl/tassMe2upiNyz8O/HYFaKOWcT25tBXh3v9u1BXq4KDbswGGzjP+HQBnvrsYnCrEhZlNbVoGDFr04jTd9CkUAUGm4Gn4Dmmy2LzdPN6mkRVaDPS3ZX45qCWQ2+mw/n0m0tpVyObIG3r7/Q/avrI2YhgkCb+7thF2g3DLBg3hNB+YmJwqxIWVwcZi3rwu1p35rpikLCzCwGIoGsoKuBwqxrzp6CRePM9g3PQEwde+txVWJ7M4NFfo6Zi9VfOJ2w+v/gnZ5w4nuoUhvumgO9XwjcGWZumgARVeHwRtj0gd3VeJ3CrEhZ1Lva9LfKOg7Hdly4veAv/5Y3m2m8RALZxYPALv6jToqzLLNk7blTJhgmP2B3Ra5zOPxvztnMNPhwsJkxwpkHLfrDqNXQ5Ea7K/OsqrXNsrsAi8dffj70AKMwK1IWYREX/iMvmKIr5wxsmWW2teKXBIO6ncw8pGeOwqkf7K7Gd1kWLHzWzAjgCIGbX/O/6Z/aDjG1H1zr+//WuxaYQV57l0JYtGnvYR9B5Zp2V+Ydne+HWq3MH05LX7S7Gq9SmBUpq1/3m902G3IzzVQ7SdfZVZWI94RHQd2rzLa6Glzaspdg7d/N9oA3Td9GfxOTCI3PX9X01auzlmW6ccwYCmdPQkJbuH85XH1vYAzyclVouJklA8wYjsObbS3HmxRmRcqqIMz+uBLy8y4M/LrqHgjRS0qChOabLd3K1+GrV8x2v4lw5f/YW09FFMw5u2WG7w0usixY8AysesP8fM1D8LvFEN/S1rJsk3QttLkdsGB+8AwG0/+8ImUV3xoq1YS8LFj3llnWMzTywtKCIsGgMMzqymwx698x/RYBeo2H5PvtrKbiWt5sBhelHzDdDXxFQZBdN9n8fMtfoe/L5pODYNb7RTNrxk/fwJaP7K7GKxRmRcoqJAQaXW+2l75gvre+1f8n3xYpi/qdzSIi6QdKXkQkWG360FwRA7juSbj29/bW4w4RlaD1QLO92UfCkWXBgqeLBtlOI20tyWfEJML1T5ntReP8c1q1MlKYFSmPgq4Gv2Sb7xr4JcEmsirU6WC2D6yxtRSf8e2nMHe02b7mIbjxD/bW404FXQ22z4G8c7aWciHITjE/K8gWlzwK4prD2ROm73aAU5gVKY/G11/Yjm99YRJ5kWBS0NXgx5X21uELdi2AT+8zK3x1vNssjBBIg48adIXYBmaw63fz7KujWJD9m4JsScIioP9Es/3NO5C2zd56PExhVqQ8qidB9UZm+6p7Aus/LRFXFS6eEOSDwPYug4/vBucvZgnY37wReO8JISEXzTlr0/K2BXP2FgmyI+ypxR80vgGuGGT+wJr/ZEDPCa0wK1Jeg/5uVvPpqDdTCVINrgEccHI3nDlmdzX2OLAWZg43q2S1/A0MmhK4y1kXhNm9S83iBN5UEGTXv2V+HvCmgqwr+vyvWejnwBrYOsvuajxGYVakvBp2hRueDtzlEUUuJ7o6JLQ228F4dfbwJvjwDsg7C016wu1TITTM7qo8p2YTqNfZXOnbNtt7j2tZ8OVT54OswwTZjnd77/H9WWw9MxAR4D/PQfZpe+vxEIVZEREpv2Cdb/boDvjgVsjJMN0thk6HsEi7q/K8DucHgm2e4Z2PrQuD7NsoyJZTl1SzqE/WMVj+J7ur8QiFWRERKb9gDLMn98IHg+Dcz2Zp3+GzzPRVwaD1rWYp42PbPT+oyLJMX88iQfYuzz5mIAqLhP7nF/BY95b5QyzAKMyKiEj5NTgfZo9+a8JdoDt9EN4fAGeOQkIbSPnETFMWLKKrQ4t+ZtuTy9sWBNlv3kFB1g2a9jJ9uq38gBwMpjArIiLlVzUBajYFLDiwzu5qPCoyL52wD2+DjJ+gZjO467PgXCylYM7ZbR+bJb3dTUHWM/q+DGHRsH8lfPsvu6txK4VZERGpmMIpugJ4aduzJ+m65884ft4H1RrA3Z9DlXi7q7JH015QKQ6yjpuZDdzJsswKagVBduD/Kci6S7UG0P1xs/2fP0JOpr31uJHCrIiIVEygh9ns04TNuIOY7ENYVWrD3XMhtq7dVdknNNzMpwvunXO2MMj+g8Ige+X/uO/4Al0fNnOkZx6BFX+2uxq3UZgVEZGKKRgEdngz5JyxtRS3yzkDH96BI20rOWFV+SXlM6jRyO6q7Fcw5+x3893TV9qyYN7jFwXZSQqynhAeBf3ODwZbOxmOfWdvPW6iMCsiIhVTrb5Z6tTKh5/W212N++Rlw8w74eA6rKhYVjcZC3HN7K7KNyS2h/grzGIR2+dU7FgFQfa/73IhyKa4o0opSfPe0LyfWbHuy6cCYjCYwqyIiFRcoE3R9UuuWaJ231cQUYX8YR+TUamB3VX5DofDPcvbOp1Fg+ygvyvIekPflyE0EvatgB1z7K6mwhRmRUSk4gIpzDrz4dP7YPdCCIuC4bOw6nayuyrf03YIOELg4Doz925ZOZ2mj+zFQbbDcLeXKSWo0Qiu/b3ZXvgHv+8epDArIiIVVzAI7Kf/mo/n/ZXTCXMfNlerQsJh6IeQdK3dVfmmmERofKPZ3jqrbPd1OmH+4wqydrr2MajWEDIOwdev2l1NhSjMiohIxdVsApXjTR/KwxvtrqZ8CpZO3fwhOELhjmnQrJfdVfm2gjlnt8wwAdUVhUF2KgqyNgqPhr7nl7dd/X9wYre99VSAwqyIiFScwwFJfjxFl2XB4nEX5jcdNBla3WJ3Vb6v5c0QURXSD8CBNZff3+mEeWMuCrKTFWTt1KIfNOsNzjy/HgymMCsiIu5R0NXgRz8Ms1+9Cqv+arZ/8zq0H2pvPf4iohK0Hmi2LzcQrCDIbpjGhSB7p8dLlFI4HObqbGiEWQBj57/trqhcFGZFRMQ9CgaBHVzvmWVOPWXNJFj2otnu8xJcdY+99fibgq4G2+dA7tmS9/l1kL11ioKsr6jZBLo+YrYXPnvpf0MfpjArIiLuUasVRFWDvCw4stXualzz32nmP3CAG/8AXVLtrccfNehq5hnOzYRd84v/3umEeb8vGmQLpvUS39D9cYitD6cPwsrX7K6mzBRmRUTEPUJCLpqiyw+6GmyZBV+cn56o26Nw3ZP21uOvQkIuPedsYZB9DxNk31KQ9UURlcynEmC625RnqjUbKcyKiIj7+Mt8szvmwpxRgAVX3we9Jpj+g1I+BQF171LIPGK2LSd88ZgJso6Q80FWfZF9VqtboEkPyM+FBU/71WAwhVkREXGfgjB7YLXrUzV52+7F8MlvzfK7HVLMWvUKshVTswnU6wyWk5Dt/wLLSej8x2Hj+ybIDpqiIOvrHA7zWggJh93/gV1f2l2RyxRmRUTEfWq3h/DKkH0aju2wu5riflwJs1LMVEStb4UBb5qPyaXizg/oCtk6i/YH3yNk8wcKsv4mrtmFfuMLxkLeOXvrcZFewSIi4j6hYdAg2Wz7WleDQxvho6HwSzY07wu3vg0hoXZXFTha3wqhETiO7yTp5HIsdS3wT9c9CTF1zdzBK9+wuxqXKMyKiIh7NfTBxRN+3m+CbO4ZaHQd3PE+hEXYXVVgia5uJuEHLBzkD5gE7YbYXJSUWWQV6H1+qrqVr8OpffbW4wKFWRERca+Lw6wvDCI5lw4fDYGsY5DQBoZ9BOFRdlcVmK57Eme9ZP6blIrV5g67q5Hyan2r+aMvP+fC1HU+TGFWRETcq25HCI2ErONwco+9teTnwewRcPw7qJoIwz+GyKr21hTIarclf8Q8DlfvbHclUhEOB/R/FULCzNzB3//H7opKpTArIiLuFRYJ9a4223Z2NbAsM4/sD8vNoLThsyC2rn31iPiTWi3gmlFm+8unIC/b3npKoTArIiLu5wvzza58HTadH1F/+1RIbG9fLSL+6PqxUKU2/LwPVr9pdzWXpDArIiLuZ3eY/fZTWDLBbPf9M7Toa08dIv4ssuqFwWBf/8XMcOCDFGZFRMT96nc2/e1OH/T+f4AH1sFnD5rt5FGQfL93H18kkLS9HRpeC7+cgwXP2F1NiRRmRUTE/SIqQ2IHs+3Nq7OnfoCZd5pR2C36Q5//9d5jiwQihwP6TwRHKHz3BexZbHdFxSjMioiIZxR2NfDSILCzp+DDIXD2pOkfO/gfWhRBxB0SroDkB8zrKrqG3dUUE2Z3ASIiEqCSroXVf4MfvRBmf8mBWXfByd0QUw/unGWuDouIe/R8HkIjfPIPRF2ZFRERz6ifDDjg1F7ITPPc41gWzH0E9q+EiKqQ8jHEJHru8USCUXi0TwZZUJgVERFPia4GtduYbU/2m13xCmydafr0DXkPElp77rFExOcozIqIiOcULm3roTC7ZRYsf8ls3/wXaNrLM48jIj5LYVZERDzHk/PN/rgKPk81210fgavucf9jiIjPU5gVERHPaXA+zB7bbmYbcJcTu2HmcHDmQasB0GuC+44tIn5FYVZERDynSi2Ia262D6x1zzGzTsKHd0B2OtS9Cm57G0L035lIsNKrX0REPMud883mZZtFEX7eB9UawJ0zzChrEQlaCrMiIuJZhYPAKhhmnU6YMwoOroPIWEj5BKrEV7w+EfFrCrMiIuJZBVdmj2yBnMzyH2fZ/8L2TyEkDIZ+ALVauKc+EfFrCrMiIuJZsfWgWkOwnOaqanls/AC+ftVs3/JXaHy9++oTEb+mMCsiIp5Xkflmf1gOXzxmtrs/AVf+j7uqEpEAoDArIiKeV975Zo99B7PuBucv0OZ26PFH99cmIn5NYVZERDyvIMwe2gB551y7z5lj8NEdkHMa6l8DAyeBw+G5GkXELynMioiI59VoDFVqQ36uCbSXk3sWZgyD9APmvsM+gvAoz9cpIn5HYVZERDzP4XC9q4HTCZ/db0JvdHUzBVflmp6vUUT8ksKsiIh4h6uLJyx+Hnb+G0IjzBXZmk08X5uI+C2FWRER8Y6CGQ0Orof8vJL3+eZdWP2m2R446UIAFhG5BIVZERHxjlotIboG5J2Fw5uL/373Ypj/pNm+8Q/QbohXyxMR/6QwKyIi3hEScumuBmnfwuyRYOVD+zvhuie9Xp6I+CeFWRER8Z6SBoFlHIGPhkBuJiR1h1v+pim4RMRlYXYXICIiQaQgzB5YC858M+fsR0Mg4xDUbAZDP4CwCHtrFBG/oiuzIiLiPQltIaKqWQghbSv863fme6U4SJltpuISESkDhVkREfGe0DBokGy2Z98D338JoZFw5wyo0cje2kTELynMioiIdxV0Nfh5n/l+21tQv7N99YiIX1OYFRER7yqYbxag13hofattpYiI/9MAMBER8a56naHzA1AlHro9Znc1IuLnFGZFRMS7QkKg/yt2VyEiAULdDERERETEbynMioiIiIjfUpgVEREREb+lMCsiIiIifkthVkRERET8lk+E2UmTJpGUlERUVBTJycmsX7++1P3T09NJTU0lMTGRyMhImjdvzvz5871UrYiIiIj4Ctun5po1axZjxoxhypQpJCcn88Ybb9CnTx927dpFfHx8sf1zc3O56aabiI+P55NPPqFu3brs37+fatWqeb94EREREbGV7WH2tdde47777uOee+4BYMqUKcybN4+pU6fy9NNPF9t/6tSpnDp1itWrVxMeHg5AUlKSN0sWERERER9ha5jNzc1lw4YNPPPMM4W3hYSE0KtXL9asWVPifebOnUuXLl1ITU3l888/p1atWgwfPpyxY8cSGhpabP+cnBxycnIKf87IyAAgLy+PvLw8Nz+jkhU8jrcez1+pnVyjdnKN2sk1aifXqJ1cp7ZyjdqpdGVpF4dlWZYHaynV4cOHqVu3LqtXr6ZLly6Ftz/11FOsWLGCdevWFbtPy5Yt+fHHH0lJSeGhhx5iz549PPTQQzzyyCOMGzeu2P7jx49nwoQJxW7/6KOPqFSpknufkIiIiIhU2NmzZxk+fDinT58mJiam1H1t72ZQVk6nk/j4eN5++21CQ0Pp1KkThw4dYuLEiSWG2WeeeYYxY8YU/pyRkUH9+vXp3bv3ZRvHXfLy8li0aBE33XRTYdcIKU7t5Bq1k2vUTq5RO7lG7eQ6tZVr1E6lK/gk3RW2htm4uDhCQ0M5evRokduPHj1K7dq1S7xPYmIi4eHhRboUtGrVirS0NHJzc4mIiCiyf2RkJJGRkcWOEx4e7vWTx47H9EdqJ9eonVyjdnKN2sk1aifXqa1co3YqWVnaxNapuSIiIujUqRNLliwpvM3pdLJkyZIi3Q4u1q1bN/bs2YPT6Sy87fvvvycxMbFYkBURERGRwGb7PLNjxozhnXfe4f3332fnzp2MGjWKrKyswtkN7r777iIDxEaNGsWpU6d49NFH+f7775k3bx4vvfQSqampdj0FEREREbGJ7X1mhw4dyvHjx3n++edJS0ujQ4cOLFiwgISEBAAOHDhASMiFzF2/fn0WLlzI73//e9q1a0fdunV59NFHGTt2rF1PQURERERsYnuYBRg9ejSjR48u8XfLly8vdluXLl1Yu3ath6sSEREREV9nezcDEREREZHyUpgVEREREb+lMCsiIiIifkthVkRERET8lsKsiIiIiPgthVkRERER8VsKsyIiIiLitxRmRURERMRvKcyKiIiIiN/yiRXAvMmyLAAyMjK89ph5eXmcPXuWjIwMwsPDvfa4/kbt5Bq1k2vUTq5RO7lG7eQ6tZVr1E6lK8hpBbmtNEEXZjMzMwGoX7++zZWIiIiISGkyMzOJjY0tdR+H5UrkDSBOp5PDhw9TtWpVHA6HVx4zIyOD+vXrc/DgQWJiYrzymP5I7eQatZNr1E6uUTu5Ru3kOrWVa9ROpbMsi8zMTOrUqUNISOm9YoPuymxISAj16tWz5bFjYmJ0wrpA7eQatZNr1E6uUTu5Ru3kOrWVa9ROl3a5K7IFNABMRERERPyWwqyIiIiI+C2FWS+IjIxk3LhxREZG2l2KT1M7uUbt5Bq1k2vUTq5RO7lObeUatZP7BN0AMBEREREJHLoyKyIiIiJ+S2FWRERERPyWwqyIiIiI+C2FWRERERHxWwqzbjJp0iSSkpKIiooiOTmZ9evXl7r/7NmzadmyJVFRUbRt25b58+d7qVJ7vPzyy1x99dVUrVqV+Ph4Bg0axK5du0q9z3vvvYfD4SjyFRUV5aWK7TF+/Phiz7lly5al3ifYziWApKSkYu3kcDhITU0tcf9gOZe++uorbrnlFurUqYPD4WDOnDlFfm9ZFs8//zyJiYlER0fTq1cvdu/efdnjlvX9zR+U1lZ5eXmMHTuWtm3bUrlyZerUqcPdd9/N4cOHSz1meV6/vu5y59TIkSOLPee+ffte9riBdk5drp1Ker9yOBxMnDjxkscMxPPJUxRm3WDWrFmMGTOGcePGsXHjRtq3b0+fPn04duxYifuvXr2aO++8k3vvvZdNmzYxaNAgBg0axLfffuvlyr1nxYoVpKamsnbtWhYtWkReXh69e/cmKyur1PvFxMRw5MiRwq/9+/d7qWL7tG7dushzXrly5SX3DcZzCeCbb74p0kaLFi0C4I477rjkfYLhXMrKyqJ9+/ZMmjSpxN+/8sor/O1vf2PKlCmsW7eOypUr06dPH7Kzsy95zLK+v/mL0trq7NmzbNy4keeee46NGzfy6aefsmvXLgYMGHDZ45bl9esPLndOAfTt27fIc54xY0apxwzEc+py7XRx+xw5coSpU6ficDgYPHhwqccNtPPJYyypsM6dO1upqamFP+fn51t16tSxXn755RL3HzJkiHXzzTcXuS05Odl64IEHPFqnLzl27JgFWCtWrLjkPtOmTbNiY2O9V5QPGDdunNW+fXuX99e5ZDz66KNWkyZNLKfTWeLvg/FcAqzPPvus8Gen02nVrl3bmjhxYuFt6enpVmRkpDVjxoxLHqes72/+6NdtVZL169dbgLV///5L7lPW16+/KamdRowYYQ0cOLBMxwn0c8qV82ngwIFWjx49St0n0M8nd9KV2QrKzc1lw4YN9OrVq/C2kJAQevXqxZo1a0q8z5o1a4rsD9CnT59L7h+ITp8+DUCNGjVK3e/MmTM0bNiQ+vXrM3DgQLZv3+6N8my1e/du6tSpQ+PGjUlJSeHAgQOX3FfnknkNTp8+nd/+9rc4HI5L7heM59LF9u3bR1paWpHzJTY2luTk5EueL+V5fwtUp0+fxuFwUK1atVL3K8vrN1AsX76c+Ph4WrRowahRozh58uQl99U5BUePHmXevHnce++9l903GM+n8lCYraATJ06Qn59PQkJCkdsTEhJIS0sr8T5paWll2j/QOJ1OHnvsMbp160abNm0uuV+LFi2YOnUqn3/+OdOnT8fpdNK1a1d++uknL1brXcnJybz33nssWLCAyZMns2/fPrp3705mZmaJ+wf7uQQwZ84c0tPTGTly5CX3CcZz6dcKzomynC/leX8LRNnZ2YwdO5Y777yTmJiYS+5X1tdvIOjbty///Oc/WbJkCX/+859ZsWIF/fr1Iz8/v8T9dU7B+++/T9WqVbnttttK3S8Yz6fyCrO7AAk+qampfPvtt5ft+9OlSxe6dOlS+HPXrl1p1aoVb731Fi+88IKny7RFv379CrfbtWtHcnIyDRs25OOPP3bpr/hg9O6779KvXz/q1KlzyX2C8VwS98jLy2PIkCFYlsXkyZNL3TcYX7/Dhg0r3G7bti3t2rWjSZMmLF++nJ49e9pYme+aOnUqKSkplx2EGoznU3npymwFxcXFERoaytGjR4vcfvToUWrXrl3ifWrXrl2m/QPJ6NGj+eKLL1i2bBn16tUr033Dw8O58sor2bNnj4eq8z3VqlWjefPml3zOwXwuAezfv5/Fixfzu9/9rkz3C8ZzqeCcKMv5Up73t0BSEGT379/PokWLSr0qW5LLvX4DUePGjYmLi7vkcw72c+rrr79m165dZX7PguA8n1ylMFtBERERdOrUiSVLlhTe5nQ6WbJkSZErQRfr0qVLkf0BFi1adMn9A4FlWYwePZrPPvuMpUuX0qhRozIfIz8/n23btpGYmOiBCn3TmTNn2Lt37yWfczCeSxebNm0a8fHx3HzzzWW6XzCeS40aNaJ27dpFzpeMjAzWrVt3yfOlPO9vgaIgyO7evZvFixdTs2bNMh/jcq/fQPTTTz9x8uTJSz7nYD6nwHyS1KlTJ9q3b1/m+wbj+eQyu0egBYKZM2dakZGR1nvvvWft2LHDuv/++61q1apZaWlplmVZ1l133WU9/fTThfuvWrXKCgsLs1599VVr586d1rhx46zw8HBr27Ztdj0Fjxs1apQVGxtrLV++3Dpy5Ejh19mzZwv3+XU7TZgwwVq4cKG1d+9ea8OGDdawYcOsqKgoa/v27XY8Ba94/PHHreXLl1v79u2zVq1aZfXq1cuKi4uzjh07ZlmWzqWL5efnWw0aNLDGjh1b7HfBei5lZmZamzZtsjZt2mQB1muvvWZt2rSpcAT+n/70J6tatWrW559/bm3dutUaOHCg1ahRI+vcuXOFx+jRo4f15ptvFv58ufc3f1VaW+Xm5loDBgyw6tWrZ23evLnIe1ZOTk7hMX7dVpd7/fqj0topMzPTeuKJJ6w1a9ZY+/btsxYvXmx17NjRatasmZWdnV14jGA4py732rMsyzp9+rRVqVIla/LkySUeIxjOJ09RmHWTN99802rQoIEVERFhde7c2Vq7dm3h766//nprxIgRRfb/+OOPrebNm1sRERFW69atrXnz5nm5Yu8CSvyaNm1a4T6/bqfHHnussE0TEhKs/v37Wxs3bvR+8V40dOhQKzEx0YqIiLDq1q1rDR061NqzZ0/h73UuXbBw4UILsHbt2lXsd8F6Li1btqzE11lBWzidTuu5556zEhISrMjISKtnz57F2q9hw4bWuHHjitxW2vubvyqtrfbt23fJ96xly5YVHuPXbXW5168/Kq2dzp49a/Xu3duqVauWFR4ebjVs2NC67777ioXSYDinLvfasyzLeuutt6zo6GgrPT29xGMEw/nkKQ7LsiyPXvoVEREREfEQ9ZkVEREREb+lMCsiIiIifkthVkRERET8lsKsiIiIiPgthVkRERER8VsKsyIiIiLitxRmRURERMRvKcyKiIiIiN9SmBURCSIOh4M5c+bYXYaIiNsozIqIeMnIkSNxOBzFvvr27Wt3aSIifivM7gJERIJJ3759mTZtWpHbIiMjbapGRMT/6cqsiIgXRUZGUrt27SJf1atXB0wXgMmTJ9OvXz+io6Np3Lgxn3zySZH7b9u2jR49ehAdHU3NmjW5//77OXPmTJF9pk6dSuvWrYmMjCQxMZHRo0cX+f2JEye49dZbqVSpEs2aNWPu3LmFv/v5559JSUmhVq1aREdH06xZs2LhW0TElyjMioj4kOeee47BgwezZcsWUlJSGDZsGDt37gQgKyuLPn36UL16db755htmz57N4sWLi4TVyZMnk5qayv3338+2bduYO3cuTZs2LfIYEyZMYMiQIWzdupX+/fuTkpLCqVOnCh9/x44dfPnll+zcuZPJkycTFxfnvQYQESkjh2VZlt1FiIgEg5EjRzJ9+nSioqKK3P7ss8/y7LPP4nA4ePDBB5k8eXLh76655ho6duzI3//+d9555x3Gjh3LwYMHqVy5MgDz58/nlltu4fDhwyQkJFC3bl3uueceXnzxxRJrcDgc/PGPf+SFF14ATECuUqUKX375JX379mXAgAHExcUxdepUD7WCiIh7qc+siIgX3XjjjUXCKkCNGjUKt7t06VLkd126dGHz5s0A7Ny5k/bt2xcGWYBu3brhdDrZtWsXDoeDw4cP07Nnz1JraNeuXeF25cqViYmJ4dixYwCMGjWKwYMHs3HjRnr37s2gQYPo2rVruZ6riIg3KMyKiHhR5cqVi33s7y7R0dEu7RceHl7kZ4fDgdPpBKBfv37s37+f+fPns2jRInr27Elqaiqvvvqq2+sVEXEH9ZkVEfEha9euLfZzq1atAGjVqhVbtmwhKyur8PerVq0iJCSEFi1aULVqVZKSkliyZEmFaqhVqxYjRoxg+vTpvPHGG7z99tsVOp6IiCfpyqyIiBfl5OSQlpZW5LawsLDCQVazZ8/mqquu4tprr+XDDz9k/fr1vPvuuwCkpKQwbtw4RowYwfjx4zl+/DgPP/wwd911FwkJCQCMHz+eBx98kPj4ePr160dmZiarVq3i4Ycfdqm+559/nk6dOtG6dWtycnL44osvCsO0iIgvUpgVEfGiBQsWkJiYWOS2Fi1a8N133wFmpoGZM2fy0EMPkZiYyIwZM7jiiisAqFSpEgsXLuTRRx/l6quvplKlSgwePJjXXnut8FgjRowgOzub119/nSeeeIK4uDhuv/12l+uLiIjgmWee4ccffyQ6Opru3bszc+ZMNzxzERHP0GwGIiI+wuFw8NlnnzFo0CC7SxER8RvqMysiIiIifkthVkRERET8lvrMioj4CPX6EhEpO12ZFRERERG/pTArIiIiIn5LYVZERERE/JbCrIiIiIj4LYVZEREREfFbCrMiIiIi4rcUZkVERETEbynMioiIiIjf+v+UpVhmBW64dAAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "##plotting the graph for loss in traning and validation\n",
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(8, 8))\n",
        "\n",
        "epochs_range= range(20)\n",
        "\n",
        "plt.plot( epochs_range, history.history['loss'], label=\"Training loss\")\n",
        "\n",
        "plt.plot(epochs_range, history.history['val_loss'], label=\"Validation Loss\")\n",
        "\n",
        "plt.grid()\n",
        "\n",
        "plt.title('Model loss')\n",
        "\n",
        "plt.ylabel('loss')\n",
        "\n",
        "plt.xlabel('Epochs')\n",
        "\n",
        "plt.legend(['train', 'validation'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "8Cya1CuFcfwe"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.applications import ResNet50\n",
        "\n",
        "model_1=Sequential()\n",
        "\n",
        "resnet_50 = ResNet50(include_top=False, weights='imagenet', input_shape=(180,180,3))\n",
        "for layer in resnet_50.layers:\n",
        "    layer.trainable = False\n",
        "## decreasing the number of dense layers \n",
        "model_1.add(resnet_50)\n",
        "model_1.add(Flatten())\n",
        "model_1.add(Dense(64,activation='relu'))\n",
        "model_1.add(layers.Dropout(0.3))\n",
        "model_1.add(Dense(3,activation='softmax'))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "zo8Lcnsg90Li"
      },
      "outputs": [],
      "source": [
        "## recompiling the model with changes \n",
        "\n",
        "model_1.compile(optimizer=Adam(learning_rate=0.0001),loss='sparse_categorical_crossentropy',metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nPJ-CEv397dp",
        "outputId": "e308c592-0f69-4b21-eddd-fde7e54bc108"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "54/54 [==============================] - 21s 333ms/step - loss: 0.9671 - accuracy: 0.5578 - val_loss: 0.8788 - val_accuracy: 0.4733\n",
            "Epoch 2/20\n",
            "54/54 [==============================] - 17s 317ms/step - loss: 0.8447 - accuracy: 0.6165 - val_loss: 0.7416 - val_accuracy: 0.7053\n",
            "Epoch 3/20\n",
            "54/54 [==============================] - 17s 314ms/step - loss: 0.7822 - accuracy: 0.6601 - val_loss: 0.6648 - val_accuracy: 0.7703\n",
            "Epoch 4/20\n",
            "54/54 [==============================] - 24s 435ms/step - loss: 0.7535 - accuracy: 0.6601 - val_loss: 0.7492 - val_accuracy: 0.7077\n",
            "Epoch 5/20\n",
            "54/54 [==============================] - 25s 469ms/step - loss: 0.7420 - accuracy: 0.6641 - val_loss: 0.7053 - val_accuracy: 0.7285\n",
            "Epoch 6/20\n",
            "54/54 [==============================] - 17s 318ms/step - loss: 0.7343 - accuracy: 0.6612 - val_loss: 0.6758 - val_accuracy: 0.7378\n",
            "Epoch 7/20\n",
            "54/54 [==============================] - 17s 312ms/step - loss: 0.7189 - accuracy: 0.6653 - val_loss: 0.6958 - val_accuracy: 0.7285\n",
            "Epoch 8/20\n",
            "54/54 [==============================] - 17s 310ms/step - loss: 0.7150 - accuracy: 0.6746 - val_loss: 0.7300 - val_accuracy: 0.6937\n",
            "Epoch 9/20\n",
            "54/54 [==============================] - 17s 324ms/step - loss: 0.6967 - accuracy: 0.6827 - val_loss: 0.6821 - val_accuracy: 0.7285\n",
            "Epoch 10/20\n",
            "54/54 [==============================] - 18s 327ms/step - loss: 0.6923 - accuracy: 0.6967 - val_loss: 0.6259 - val_accuracy: 0.7657\n",
            "Epoch 11/20\n",
            "54/54 [==============================] - 17s 311ms/step - loss: 0.7006 - accuracy: 0.6874 - val_loss: 0.6487 - val_accuracy: 0.7471\n",
            "Epoch 12/20\n",
            "54/54 [==============================] - 17s 320ms/step - loss: 0.6866 - accuracy: 0.6961 - val_loss: 0.6344 - val_accuracy: 0.7494\n",
            "Epoch 13/20\n",
            "54/54 [==============================] - 17s 317ms/step - loss: 0.6969 - accuracy: 0.7147 - val_loss: 0.6364 - val_accuracy: 0.7633\n",
            "Epoch 14/20\n",
            "54/54 [==============================] - 17s 314ms/step - loss: 0.7068 - accuracy: 0.7071 - val_loss: 0.6990 - val_accuracy: 0.7169\n",
            "Epoch 15/20\n",
            "54/54 [==============================] - 18s 331ms/step - loss: 0.6800 - accuracy: 0.7310 - val_loss: 0.6962 - val_accuracy: 0.7169\n",
            "Epoch 16/20\n",
            "54/54 [==============================] - 18s 327ms/step - loss: 0.6939 - accuracy: 0.7333 - val_loss: 0.7708 - val_accuracy: 0.6798\n",
            "Epoch 17/20\n",
            "54/54 [==============================] - 17s 315ms/step - loss: 0.6810 - accuracy: 0.7507 - val_loss: 0.8284 - val_accuracy: 0.6334\n",
            "Epoch 18/20\n",
            "54/54 [==============================] - 17s 324ms/step - loss: 0.6724 - accuracy: 0.7420 - val_loss: 0.7875 - val_accuracy: 0.7169\n",
            "Epoch 19/20\n",
            "54/54 [==============================] - 17s 313ms/step - loss: 0.6601 - accuracy: 0.7490 - val_loss: 0.7101 - val_accuracy: 0.7169\n",
            "Epoch 20/20\n",
            "54/54 [==============================] - 18s 338ms/step - loss: 0.6890 - accuracy: 0.7374 - val_loss: 0.5505 - val_accuracy: 0.7889\n"
          ]
        }
      ],
      "source": [
        "## refittin the model \n",
        "history_1=model_1.fit(\n",
        "    train_generator,\n",
        "    epochs=20,\n",
        "    validation_data=validation_generator\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 735
        },
        "id": "yluhlPfj99f9",
        "outputId": "56944aaf-e343-47a7-9c6b-8a25f0545ce8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x79d049d49690>"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(8, 8))\n",
        "\n",
        "epochs_range= range(20)\n",
        "\n",
        "plt.plot( epochs_range, history_1.history['accuracy'], label=\"Training Accuracy\")\n",
        "\n",
        "plt.plot(epochs_range, history_1.history['val_accuracy'], label=\"Validation Accuracy\")\n",
        "\n",
        "plt.axis(ymin=0.4,ymax=1)\n",
        "\n",
        "plt.grid()\n",
        "\n",
        "plt.title('Model Accuracy')\n",
        "\n",
        "plt.ylabel('Accuracy')\n",
        "\n",
        "plt.xlabel('Epochs')\n",
        "\n",
        "plt.legend(['train', 'validation'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 735
        },
        "id": "_dJQLPFOTFJc",
        "outputId": "4b7a75a5-7d0e-4f54-a65b-ff60c16de9fe"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x79d049b88eb0>"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(8, 8))\n",
        "\n",
        "epochs_range= range(20)\n",
        "\n",
        "plt.plot( epochs_range, history.history['loss'], label=\"Training loss\")\n",
        "\n",
        "plt.plot(epochs_range, history.history['val_loss'], label=\"Validation Loss\")\n",
        "\n",
        "plt.grid()\n",
        "\n",
        "plt.title('Model loss')\n",
        "\n",
        "plt.ylabel('loss')\n",
        "\n",
        "plt.xlabel('Epochs')\n",
        "\n",
        "plt.legend(['train', 'validation'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "4rcPwek9-ATo"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.applications import ResNet50\n",
        "\n",
        "model_1=Sequential()\n",
        "\n",
        "resnet_50 = ResNet50(include_top=False, weights='imagenet', input_shape=(180,180,3))\n",
        "for layer in resnet_50.layers:\n",
        "    layer.trainable = False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t2AFygrECdIM",
        "outputId": "dab79bd5-3343-4f2d-cbc1-64015298231f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0 input_3 False\n",
            "1 conv1_pad False\n",
            "2 conv1_conv False\n",
            "3 conv1_bn False\n",
            "4 conv1_relu False\n",
            "5 pool1_pad False\n",
            "6 pool1_pool False\n",
            "7 conv2_block1_1_conv False\n",
            "8 conv2_block1_1_bn False\n",
            "9 conv2_block1_1_relu False\n",
            "10 conv2_block1_2_conv False\n",
            "11 conv2_block1_2_bn False\n",
            "12 conv2_block1_2_relu False\n",
            "13 conv2_block1_0_conv False\n",
            "14 conv2_block1_3_conv False\n",
            "15 conv2_block1_0_bn False\n",
            "16 conv2_block1_3_bn False\n",
            "17 conv2_block1_add False\n",
            "18 conv2_block1_out False\n",
            "19 conv2_block2_1_conv False\n",
            "20 conv2_block2_1_bn False\n",
            "21 conv2_block2_1_relu False\n",
            "22 conv2_block2_2_conv False\n",
            "23 conv2_block2_2_bn False\n",
            "24 conv2_block2_2_relu False\n",
            "25 conv2_block2_3_conv False\n",
            "26 conv2_block2_3_bn False\n",
            "27 conv2_block2_add False\n",
            "28 conv2_block2_out False\n",
            "29 conv2_block3_1_conv False\n",
            "30 conv2_block3_1_bn False\n",
            "31 conv2_block3_1_relu False\n",
            "32 conv2_block3_2_conv False\n",
            "33 conv2_block3_2_bn False\n",
            "34 conv2_block3_2_relu False\n",
            "35 conv2_block3_3_conv False\n",
            "36 conv2_block3_3_bn False\n",
            "37 conv2_block3_add False\n",
            "38 conv2_block3_out False\n",
            "39 conv3_block1_1_conv False\n",
            "40 conv3_block1_1_bn False\n",
            "41 conv3_block1_1_relu False\n",
            "42 conv3_block1_2_conv False\n",
            "43 conv3_block1_2_bn False\n",
            "44 conv3_block1_2_relu False\n",
            "45 conv3_block1_0_conv False\n",
            "46 conv3_block1_3_conv False\n",
            "47 conv3_block1_0_bn False\n",
            "48 conv3_block1_3_bn False\n",
            "49 conv3_block1_add False\n",
            "50 conv3_block1_out False\n",
            "51 conv3_block2_1_conv False\n",
            "52 conv3_block2_1_bn False\n",
            "53 conv3_block2_1_relu False\n",
            "54 conv3_block2_2_conv False\n",
            "55 conv3_block2_2_bn False\n",
            "56 conv3_block2_2_relu False\n",
            "57 conv3_block2_3_conv False\n",
            "58 conv3_block2_3_bn False\n",
            "59 conv3_block2_add False\n",
            "60 conv3_block2_out False\n",
            "61 conv3_block3_1_conv False\n",
            "62 conv3_block3_1_bn False\n",
            "63 conv3_block3_1_relu False\n",
            "64 conv3_block3_2_conv False\n",
            "65 conv3_block3_2_bn False\n",
            "66 conv3_block3_2_relu False\n",
            "67 conv3_block3_3_conv False\n",
            "68 conv3_block3_3_bn False\n",
            "69 conv3_block3_add False\n",
            "70 conv3_block3_out False\n",
            "71 conv3_block4_1_conv False\n",
            "72 conv3_block4_1_bn False\n",
            "73 conv3_block4_1_relu False\n",
            "74 conv3_block4_2_conv False\n",
            "75 conv3_block4_2_bn False\n",
            "76 conv3_block4_2_relu False\n",
            "77 conv3_block4_3_conv False\n",
            "78 conv3_block4_3_bn False\n",
            "79 conv3_block4_add False\n",
            "80 conv3_block4_out False\n",
            "81 conv4_block1_1_conv False\n",
            "82 conv4_block1_1_bn False\n",
            "83 conv4_block1_1_relu False\n",
            "84 conv4_block1_2_conv False\n",
            "85 conv4_block1_2_bn False\n",
            "86 conv4_block1_2_relu False\n",
            "87 conv4_block1_0_conv False\n",
            "88 conv4_block1_3_conv False\n",
            "89 conv4_block1_0_bn False\n",
            "90 conv4_block1_3_bn False\n",
            "91 conv4_block1_add False\n",
            "92 conv4_block1_out False\n",
            "93 conv4_block2_1_conv False\n",
            "94 conv4_block2_1_bn False\n",
            "95 conv4_block2_1_relu False\n",
            "96 conv4_block2_2_conv False\n",
            "97 conv4_block2_2_bn False\n",
            "98 conv4_block2_2_relu False\n",
            "99 conv4_block2_3_conv False\n",
            "100 conv4_block2_3_bn False\n",
            "101 conv4_block2_add False\n",
            "102 conv4_block2_out False\n",
            "103 conv4_block3_1_conv False\n",
            "104 conv4_block3_1_bn False\n",
            "105 conv4_block3_1_relu False\n",
            "106 conv4_block3_2_conv False\n",
            "107 conv4_block3_2_bn False\n",
            "108 conv4_block3_2_relu False\n",
            "109 conv4_block3_3_conv False\n",
            "110 conv4_block3_3_bn False\n",
            "111 conv4_block3_add False\n",
            "112 conv4_block3_out False\n",
            "113 conv4_block4_1_conv False\n",
            "114 conv4_block4_1_bn False\n",
            "115 conv4_block4_1_relu False\n",
            "116 conv4_block4_2_conv False\n",
            "117 conv4_block4_2_bn False\n",
            "118 conv4_block4_2_relu False\n",
            "119 conv4_block4_3_conv False\n",
            "120 conv4_block4_3_bn False\n",
            "121 conv4_block4_add False\n",
            "122 conv4_block4_out False\n",
            "123 conv4_block5_1_conv False\n",
            "124 conv4_block5_1_bn False\n",
            "125 conv4_block5_1_relu False\n",
            "126 conv4_block5_2_conv False\n",
            "127 conv4_block5_2_bn False\n",
            "128 conv4_block5_2_relu False\n",
            "129 conv4_block5_3_conv False\n",
            "130 conv4_block5_3_bn False\n",
            "131 conv4_block5_add False\n",
            "132 conv4_block5_out False\n",
            "133 conv4_block6_1_conv False\n",
            "134 conv4_block6_1_bn False\n",
            "135 conv4_block6_1_relu False\n",
            "136 conv4_block6_2_conv False\n",
            "137 conv4_block6_2_bn False\n",
            "138 conv4_block6_2_relu False\n",
            "139 conv4_block6_3_conv False\n",
            "140 conv4_block6_3_bn False\n",
            "141 conv4_block6_add False\n",
            "142 conv4_block6_out False\n",
            "143 conv5_block1_1_conv False\n",
            "144 conv5_block1_1_bn False\n",
            "145 conv5_block1_1_relu False\n",
            "146 conv5_block1_2_conv False\n",
            "147 conv5_block1_2_bn False\n",
            "148 conv5_block1_2_relu False\n",
            "149 conv5_block1_0_conv False\n",
            "150 conv5_block1_3_conv False\n",
            "151 conv5_block1_0_bn False\n",
            "152 conv5_block1_3_bn False\n",
            "153 conv5_block1_add False\n",
            "154 conv5_block1_out False\n",
            "155 conv5_block2_1_conv False\n",
            "156 conv5_block2_1_bn False\n",
            "157 conv5_block2_1_relu False\n",
            "158 conv5_block2_2_conv False\n",
            "159 conv5_block2_2_bn False\n",
            "160 conv5_block2_2_relu False\n",
            "161 conv5_block2_3_conv False\n",
            "162 conv5_block2_3_bn False\n",
            "163 conv5_block2_add False\n",
            "164 conv5_block2_out False\n",
            "165 conv5_block3_1_conv False\n",
            "166 conv5_block3_1_bn False\n",
            "167 conv5_block3_1_relu False\n",
            "168 conv5_block3_2_conv False\n",
            "169 conv5_block3_2_bn False\n",
            "170 conv5_block3_2_relu False\n",
            "171 conv5_block3_3_conv False\n",
            "172 conv5_block3_3_bn False\n",
            "173 conv5_block3_add False\n",
            "174 conv5_block3_out False\n"
          ]
        }
      ],
      "source": [
        "## all layers of resnset 50\n",
        "for i, layer in enumerate(resnet_50.layers):\n",
        "   print(i, layer.name, layer.trainable)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "l_StzgwgCn27"
      },
      "outputs": [],
      "source": [
        "for layer in resnet_50.layers[:165]:\n",
        "   layer.trainable = False\n",
        "for layer in resnet_50.layers[165:]:\n",
        "   layer.trainable = True\n",
        "\n",
        "for i, layer in enumerate(resnet_50.layers):\n",
        "   print(i, layer.name, layer.trainable)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "ytz5bgHHCWhX"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.applications import ResNet50\n",
        "\n",
        "model_2=Sequential()\n",
        "## fine tuning the last layers of resent 50 \n",
        "resnet_50 = ResNet50(include_top=False, weights='imagenet', input_shape=(180,180,3))\n",
        "for layer in resnet_50.layers[:165]:\n",
        "   layer.trainable = False\n",
        "for layer in resnet_50.layers[165:]:\n",
        "   layer.trainable = True\n",
        "model_2.add(resnet_50)\n",
        "model_2.add(Flatten())\n",
        "model_2.add(Dense(64,activation='relu'))\n",
        "model_2.add(layers.Dropout(0.3))\n",
        "model_2.add(Dense(3,activation='softmax'))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "UtQrURNNDZ0B"
      },
      "outputs": [],
      "source": [
        "# again compiling the model\n",
        "model_2.compile(optimizer=Adam(learning_rate=0.0001),loss='sparse_categorical_crossentropy',metrics=['accuracy'])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kp2pNmaYDl1p",
        "outputId": "cb8c3b2c-e4d4-418c-dc57-d244d2369d9e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "54/54 [==============================] - 24s 366ms/step - loss: 0.9929 - accuracy: 0.6746 - val_loss: 1.0320 - val_accuracy: 0.4640\n",
            "Epoch 2/20\n",
            "54/54 [==============================] - 17s 320ms/step - loss: 0.7068 - accuracy: 0.7223 - val_loss: 0.8181 - val_accuracy: 0.4988\n",
            "Epoch 3/20\n",
            "54/54 [==============================] - 23s 421ms/step - loss: 0.6598 - accuracy: 0.7478 - val_loss: 0.8140 - val_accuracy: 0.5313\n",
            "Epoch 4/20\n",
            "54/54 [==============================] - 17s 318ms/step - loss: 0.6503 - accuracy: 0.7571 - val_loss: 0.7402 - val_accuracy: 0.6195\n",
            "Epoch 5/20\n",
            "54/54 [==============================] - 18s 330ms/step - loss: 0.6062 - accuracy: 0.7838 - val_loss: 0.5568 - val_accuracy: 0.7981\n",
            "Epoch 6/20\n",
            "54/54 [==============================] - 17s 318ms/step - loss: 0.5917 - accuracy: 0.7827 - val_loss: 0.4503 - val_accuracy: 0.8190\n",
            "Epoch 7/20\n",
            "54/54 [==============================] - 17s 321ms/step - loss: 0.6221 - accuracy: 0.7542 - val_loss: 0.4723 - val_accuracy: 0.8167\n",
            "Epoch 8/20\n",
            "54/54 [==============================] - 19s 348ms/step - loss: 0.6726 - accuracy: 0.7548 - val_loss: 0.5825 - val_accuracy: 0.7285\n",
            "Epoch 9/20\n",
            "54/54 [==============================] - 18s 330ms/step - loss: 0.6446 - accuracy: 0.7420 - val_loss: 0.4628 - val_accuracy: 0.8260\n",
            "Epoch 10/20\n",
            "54/54 [==============================] - 17s 315ms/step - loss: 0.5856 - accuracy: 0.7891 - val_loss: 0.4797 - val_accuracy: 0.8097\n",
            "Epoch 11/20\n",
            "54/54 [==============================] - 17s 318ms/step - loss: 0.5770 - accuracy: 0.7687 - val_loss: 0.5435 - val_accuracy: 0.7703\n",
            "Epoch 12/20\n",
            "54/54 [==============================] - 17s 313ms/step - loss: 0.6027 - accuracy: 0.7751 - val_loss: 0.4729 - val_accuracy: 0.7958\n",
            "Epoch 13/20\n",
            "54/54 [==============================] - 17s 317ms/step - loss: 0.5931 - accuracy: 0.7838 - val_loss: 0.4190 - val_accuracy: 0.8515\n",
            "Epoch 14/20\n",
            "54/54 [==============================] - 18s 334ms/step - loss: 0.5732 - accuracy: 0.7902 - val_loss: 0.5104 - val_accuracy: 0.7889\n",
            "Epoch 15/20\n",
            "54/54 [==============================] - 17s 313ms/step - loss: 0.5355 - accuracy: 0.8024 - val_loss: 0.5467 - val_accuracy: 0.8213\n",
            "Epoch 16/20\n",
            "54/54 [==============================] - 17s 311ms/step - loss: 0.5901 - accuracy: 0.7926 - val_loss: 0.4910 - val_accuracy: 0.7564\n",
            "Epoch 17/20\n",
            "54/54 [==============================] - 18s 328ms/step - loss: 0.5622 - accuracy: 0.7926 - val_loss: 0.4478 - val_accuracy: 0.8399\n",
            "Epoch 18/20\n",
            "54/54 [==============================] - 17s 309ms/step - loss: 0.5580 - accuracy: 0.7949 - val_loss: 0.4174 - val_accuracy: 0.8538\n",
            "Epoch 19/20\n",
            "54/54 [==============================] - 17s 310ms/step - loss: 0.5699 - accuracy: 0.7775 - val_loss: 0.4776 - val_accuracy: 0.8260\n",
            "Epoch 20/20\n",
            "54/54 [==============================] - 18s 337ms/step - loss: 0.5455 - accuracy: 0.7943 - val_loss: 0.5701 - val_accuracy: 0.7610\n"
          ]
        }
      ],
      "source": [
        "## fitting the model\n",
        "history_2=model_2.fit(\n",
        "    train_generator,\n",
        "    epochs=20,\n",
        "    validation_data=validation_generator\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 735
        },
        "id": "Ay7yPoR3TWjn",
        "outputId": "6c97ffaa-31dd-4a12-c627-b1fb02a7db22"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x79d04b35ae60>"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(8, 8))\n",
        "\n",
        "epochs_range= range(20)\n",
        "\n",
        "plt.plot( epochs_range, history_2.history['accuracy'], label=\"Training Accuracy\")\n",
        "\n",
        "plt.plot(epochs_range, history_2.history['val_accuracy'], label=\"Validation Accuracy\")\n",
        "\n",
        "plt.grid()\n",
        "\n",
        "plt.title('Model Accuracy')\n",
        "\n",
        "plt.ylabel('Accuracy')\n",
        "\n",
        "plt.xlabel('Epochs')\n",
        "\n",
        "plt.legend(['train', 'validation'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 735
        },
        "id": "QFeO2D1TTPRU",
        "outputId": "60cbc182-29f1-468a-f411-bb53d8f898d0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x79d04b3d1b40>"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(8, 8))\n",
        "\n",
        "epochs_range= range(20)\n",
        "\n",
        "plt.plot( epochs_range, history_2.history['loss'], label=\"Training loss\")\n",
        "\n",
        "plt.plot(epochs_range, history_2.history['val_loss'], label=\"Validation Loss\")\n",
        "\n",
        "plt.grid()\n",
        "\n",
        "plt.title('Model loss')\n",
        "\n",
        "plt.ylabel('loss')\n",
        "\n",
        "plt.xlabel('Epochs')\n",
        "\n",
        "plt.legend(['train', 'validation'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kxxAGi15EY6Y",
        "outputId": "72534997-bb18-48b4-c7e7-02222b4f12f4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "58889256/58889256 [==============================] - 0s 0us/step\n",
            "input_5 False\n",
            "block1_conv1 False\n",
            "block1_conv2 False\n",
            "block1_pool False\n",
            "block2_conv1 False\n",
            "block2_conv2 False\n",
            "block2_pool False\n",
            "block3_conv1 False\n",
            "block3_conv2 False\n",
            "block3_conv3 False\n",
            "block3_pool False\n",
            "block4_conv1 False\n",
            "block4_conv2 False\n",
            "block4_conv3 False\n",
            "block4_pool False\n",
            "block5_conv1 True\n",
            "block5_conv2 True\n",
            "block5_conv3 True\n",
            "block5_pool True\n"
          ]
        }
      ],
      "source": [
        "## VGG 16 is another powerful image classification model and is 16 layers deep\n",
        "\n",
        "#importing the VGG 16 model\n",
        "from keras.applications.vgg16 import VGG16\n",
        "\n",
        "\n",
        "vgg_16 = VGG16(\n",
        "    weights='imagenet',\n",
        "    include_top = False,\n",
        "    input_shape=(180,180,3)\n",
        ")\n",
        "vgg_16.trainable = True\n",
        "\n",
        "set_trainable = False\n",
        "## making last layers of model to be trainable \n",
        "for layer in vgg_16.layers:\n",
        "  if layer.name == 'block5_conv1':\n",
        "    set_trainable = True\n",
        "  if set_trainable:\n",
        "    layer.trainable = True\n",
        "  else:\n",
        "    layer.trainable = False\n",
        "\n",
        "for layer in vgg_16.layers:\n",
        "  print(layer.name,layer.trainable)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "_3yWNj99QLEv"
      },
      "outputs": [],
      "source": [
        "from keras.applications.vgg16 import VGG16\n",
        "\n",
        "model_vgg=Sequential()\n",
        "vgg_16 = VGG16(\n",
        "    weights='imagenet',\n",
        "    include_top = False,\n",
        "    input_shape=(180,180,3)\n",
        ")\n",
        "\n",
        "vgg_16.trainable = True\n",
        "\n",
        "set_trainable = False\n",
        "##fine tuning the model \n",
        "for layer in vgg_16.layers:\n",
        "  if layer.name == 'block5_conv1':\n",
        "    set_trainable = True\n",
        "  if set_trainable:\n",
        "    layer.trainable = True\n",
        "  else:\n",
        "    layer.trainable = False\n",
        "\n",
        "## adding the VGG 16 model\n",
        "model_vgg.add(vgg_16)\n",
        "model_vgg.add(Flatten())\n",
        "model_vgg.add(Dense(64,activation='relu'))\n",
        "model_vgg.add(layers.Dropout(0.3))\n",
        "model_vgg.add(Dense(3,activation='softmax'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "eCiGEWv0QOTW"
      },
      "outputs": [],
      "source": [
        "## compiling the model with same hyperparamters \n",
        "model_vgg.compile(optimizer=Adam(learning_rate=0.0001),loss='sparse_categorical_crossentropy',metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Dl49YHKQRGG",
        "outputId": "cf5d6d60-552f-4189-c8a1-a5e6c849a558"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/25\n",
            "54/54 [==============================] - 33s 466ms/step - loss: 0.5396 - accuracy: 0.7740 - val_loss: 0.2744 - val_accuracy: 0.8817\n",
            "Epoch 2/25\n",
            "54/54 [==============================] - 17s 320ms/step - loss: 0.2881 - accuracy: 0.8879 - val_loss: 0.1233 - val_accuracy: 0.9675\n",
            "Epoch 3/25\n",
            "54/54 [==============================] - 18s 323ms/step - loss: 0.2234 - accuracy: 0.9204 - val_loss: 0.1234 - val_accuracy: 0.9374\n",
            "Epoch 4/25\n",
            "54/54 [==============================] - 18s 326ms/step - loss: 0.1317 - accuracy: 0.9587 - val_loss: 0.1765 - val_accuracy: 0.9513\n",
            "Epoch 5/25\n",
            "54/54 [==============================] - 18s 341ms/step - loss: 0.1207 - accuracy: 0.9617 - val_loss: 0.0991 - val_accuracy: 0.9722\n",
            "Epoch 6/25\n",
            "54/54 [==============================] - 18s 325ms/step - loss: 0.1216 - accuracy: 0.9582 - val_loss: 0.1401 - val_accuracy: 0.9536\n",
            "Epoch 7/25\n",
            "54/54 [==============================] - 17s 322ms/step - loss: 0.1220 - accuracy: 0.9640 - val_loss: 0.0928 - val_accuracy: 0.9698\n",
            "Epoch 8/25\n",
            "54/54 [==============================] - 18s 339ms/step - loss: 0.1330 - accuracy: 0.9500 - val_loss: 0.0668 - val_accuracy: 0.9768\n",
            "Epoch 9/25\n",
            "54/54 [==============================] - 18s 325ms/step - loss: 0.0819 - accuracy: 0.9750 - val_loss: 0.0583 - val_accuracy: 0.9768\n",
            "Epoch 10/25\n",
            "54/54 [==============================] - 18s 324ms/step - loss: 0.0647 - accuracy: 0.9773 - val_loss: 0.0907 - val_accuracy: 0.9652\n",
            "Epoch 11/25\n",
            "54/54 [==============================] - 19s 360ms/step - loss: 0.0702 - accuracy: 0.9762 - val_loss: 0.0478 - val_accuracy: 0.9791\n",
            "Epoch 12/25\n",
            "54/54 [==============================] - 17s 322ms/step - loss: 0.0642 - accuracy: 0.9785 - val_loss: 0.1577 - val_accuracy: 0.9490\n",
            "Epoch 13/25\n",
            "54/54 [==============================] - 18s 325ms/step - loss: 0.0999 - accuracy: 0.9721 - val_loss: 0.0520 - val_accuracy: 0.9745\n",
            "Epoch 14/25\n",
            "54/54 [==============================] - 19s 346ms/step - loss: 0.0429 - accuracy: 0.9843 - val_loss: 0.1033 - val_accuracy: 0.9791\n",
            "Epoch 15/25\n",
            "54/54 [==============================] - 18s 324ms/step - loss: 0.0674 - accuracy: 0.9762 - val_loss: 0.0438 - val_accuracy: 0.9814\n",
            "Epoch 16/25\n",
            "54/54 [==============================] - 18s 327ms/step - loss: 0.0549 - accuracy: 0.9785 - val_loss: 0.0633 - val_accuracy: 0.9745\n",
            "Epoch 17/25\n",
            "54/54 [==============================] - 19s 343ms/step - loss: 0.0600 - accuracy: 0.9814 - val_loss: 0.0250 - val_accuracy: 0.9861\n",
            "Epoch 18/25\n",
            "54/54 [==============================] - 18s 326ms/step - loss: 0.0351 - accuracy: 0.9866 - val_loss: 0.0523 - val_accuracy: 0.9838\n",
            "Epoch 19/25\n",
            "54/54 [==============================] - 19s 347ms/step - loss: 0.0356 - accuracy: 0.9901 - val_loss: 0.0291 - val_accuracy: 0.9907\n",
            "Epoch 20/25\n",
            "54/54 [==============================] - 18s 330ms/step - loss: 0.0374 - accuracy: 0.9878 - val_loss: 0.0390 - val_accuracy: 0.9884\n",
            "Epoch 21/25\n",
            "54/54 [==============================] - 18s 324ms/step - loss: 0.0298 - accuracy: 0.9895 - val_loss: 0.0428 - val_accuracy: 0.9838\n",
            "Epoch 22/25\n",
            "54/54 [==============================] - 19s 353ms/step - loss: 0.0362 - accuracy: 0.9895 - val_loss: 0.3067 - val_accuracy: 0.9281\n",
            "Epoch 23/25\n",
            "54/54 [==============================] - 18s 324ms/step - loss: 0.0389 - accuracy: 0.9866 - val_loss: 0.0398 - val_accuracy: 0.9838\n",
            "Epoch 24/25\n",
            "54/54 [==============================] - 18s 328ms/step - loss: 0.0323 - accuracy: 0.9884 - val_loss: 0.0800 - val_accuracy: 0.9722\n",
            "Epoch 25/25\n",
            "54/54 [==============================] - 19s 343ms/step - loss: 0.0312 - accuracy: 0.9872 - val_loss: 0.0150 - val_accuracy: 0.9930\n"
          ]
        }
      ],
      "source": [
        "## fitting the model \n",
        "history_vgg=model_vgg.fit(\n",
        "    train_generator,\n",
        "    epochs=25,\n",
        "    validation_data=validation_generator\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 735
        },
        "id": "1zZX_55SVIjv",
        "outputId": "3752b3ba-1d60-412d-f185-0c254fc85504"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x79d04a6c6fe0>"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#plotting the graphs of accuarcy\n",
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(8, 8))\n",
        "\n",
        "epochs_range= range(25)\n",
        "\n",
        "plt.plot( epochs_range, history_vgg.history['accuracy'], label=\"Training Accuracy\")\n",
        "\n",
        "plt.plot(epochs_range, history_vgg.history['val_accuracy'], label=\"Validation Accuracy\")\n",
        "\n",
        "plt.grid()\n",
        "\n",
        "plt.title('Model Accuracy')\n",
        "\n",
        "plt.ylabel('Accuracy')\n",
        "\n",
        "plt.xlabel('Epochs')\n",
        "\n",
        "plt.legend(['train', 'validation'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "hTLF5tsEXmFO",
        "outputId": "17ce52ed-2ddb-4b71-eb46-ca88567c1925"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x79d04a51d480>"
            ]
          },
          "execution_count": 33,
          "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": [
        "##plotting the graph for loss in traning and validation\n",
        "epochs_range= range(25)\n",
        "\n",
        "plt.plot( epochs_range, history_vgg.history['loss'], label=\"Training loss\")\n",
        "\n",
        "plt.plot(epochs_range, history_vgg.history['val_loss'], label=\"Validation Loss\")\n",
        "\n",
        "plt.grid()\n",
        "\n",
        "plt.title('Model loss')\n",
        "\n",
        "plt.ylabel('loss')\n",
        "\n",
        "plt.xlabel('Epochs')\n",
        "\n",
        "plt.legend(['train', 'validation'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qKPLsEo4XtQA",
        "outputId": "2df86c06-c5dc-45cf-b514-3161d81265af"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        }
      ],
      "source": [
        "## saving this high performance model \n",
        "from tensorflow.keras.models import load_model\n",
        "model_vgg.save('DiseaseDetectionvgg16.h5')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "GH_1xMpeYTIN"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.models import load_model\n",
        "# loading the model \n",
        "model_3=load_model('/content/DiseaseDetectionvgg16.h5')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p8GH9mHQiprM",
        "outputId": "fa6671c6-6fd0-4dc4-cb5a-5dc29193abf7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 2s 2s/step\n",
            "Predicted Disease: Potato___Late_blight\n"
          ]
        }
      ],
      "source": [
        "## this is end to end function from giving the image path to classify the potato leaf whether it is healthy or having any kind of disease\n",
        "\n",
        "import cv2\n",
        "import numpy as np\n",
        "\n",
        "def predict_potato_disease(image_path, model, class_labels):\n",
        "\n",
        "  # Read the image\n",
        "  image = cv2.imread(image_path)\n",
        "\n",
        "  # Check if image reading was successful\n",
        "  if image is None:\n",
        "      print(\"Error: Could not read image from\", image_path)\n",
        "      return None\n",
        "\n",
        "  # Preprocess the image\n",
        "  target_size = (180, 180)  # Adjust based on your model's training size\n",
        "  resized_image = cv2.resize(image, target_size)\n",
        "  normalized_image = resized_image.astype('float32') / 255.0\n",
        "\n",
        "  # Add a batch dimension (if the model expects batches)\n",
        "  image_batch = np.expand_dims(normalized_image, axis=0)\n",
        "\n",
        "  # Make prediction\n",
        "  predictions = model.predict(image_batch)\n",
        "\n",
        "  # Identify the most likely class\n",
        "  predicted_class = np.argmax(predictions)\n",
        "\n",
        "  # Map the class index to disease name\n",
        "  predicted_disease = class_labels[predicted_class]\n",
        "\n",
        "  return predicted_disease\n",
        "\n",
        "# Example usage (assuming model and class_labels are defined)\n",
        "class_names = list(train_generator.class_indices.keys())\n",
        "image_path = \"/tmp/val/Potato___Late_blight/051089cf-e083-4fdb-9256-aac12b8a55d2___RS_LB 4078.JPG\"\n",
        "predicted_disease = predict_potato_disease(image_path, model_3, class_names)\n",
        "\n",
        "if predicted_disease:\n",
        "  print(\"Predicted Disease:\", predicted_disease)\n",
        "else:\n",
        "  print(\"Error: Could not predict disease\")"
      ]
    }
  ],
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    "accelerator": "GPU",
    "colab": {
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