brain_tumor (1).ipynb

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      "source": [
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import os\n"
      ],
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      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Conv2D,Flatten,Dense,MaxPooling2D,Dropout\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "execution": {
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      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import ipywidgets as widgets\n",
        "import io\n",
        "from PIL import Image\n",
        "import tqdm\n",
        "from sklearn.model_selection import train_test_split\n",
        "import cv2\n",
        "from sklearn.utils import shuffle\n",
        "import tensorflow as tf\n",
        "from google.colab import drive"
      ],
      "metadata": {
        "execution": {
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      "execution_count": 3,
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    {
      "cell_type": "raw",
      "source": [
        "Folder paths"
      ],
      "metadata": {
        "id": "E-b__xT2OwDb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Mount Google Drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "# Update the file paths\n",
        "base_path = '/content/drive/MyDrive'  # Change this to the path where your data is stored on Google Drive\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GLvcsocEtRgg",
        "outputId": "d64d7159-06e2-4fd0-a6e3-749859800b12"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "X_train = []\n",
        "Y_train = []\n",
        "image_size = 150\n",
        "labels = ['glioma_tumor','meningioma_tumor','no_tumor','pituitary_tumor']\n",
        "for i in labels:\n",
        "    folderPath = os.path.join(base_path, 'Training', i)\n",
        "    for j in os.listdir(folderPath):\n",
        "        img = cv2.imread(os.path.join(folderPath,j))\n",
        "        img = cv2.resize(img,(image_size,image_size))\n",
        "        X_train.append(img)\n",
        "        Y_train.append(i)\n",
        "\n",
        "for i in labels:\n",
        "    folderPath = os.path.join(base_path, 'Testing', i)\n",
        "    for j in os.listdir(folderPath):\n",
        "        img = cv2.imread(os.path.join(folderPath,j))\n",
        "        img = cv2.resize(img,(image_size,image_size))\n",
        "        X_train.append(img)\n",
        "        Y_train.append(i)\n",
        "\n",
        "X_train = np.array(X_train)\n",
        "Y_train = np.array(Y_train)"
      ],
      "metadata": {
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      "execution_count": 8,
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      "cell_type": "code",
      "source": [
        "X_train,Y_train = shuffle(X_train,Y_train,random_state=101)\n",
        "X_train.shape"
      ],
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      "execution_count": 9,
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        {
          "output_type": "execute_result",
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            "text/plain": [
              "(3274, 150, 150, 3)"
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          "metadata": {},
          "execution_count": 9
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      "cell_type": "raw",
      "source": [
        "Train test split"
      ],
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        "id": "N-fxoJUxOwDb"
      }
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      "cell_type": "code",
      "source": [
        "X_train,X_test,y_train,y_test = train_test_split(X_train,Y_train,test_size=0.1,random_state=101)"
      ],
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      "execution_count": 10,
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      "cell_type": "code",
      "source": [
        "y_train_new = []\n",
        "for i in y_train:\n",
        "    y_train_new.append(labels.index(i))\n",
        "y_train=y_train_new\n",
        "y_train = tf.keras.utils.to_categorical(y_train)\n",
        "\n",
        "y_test_new = []\n",
        "for i in y_test:\n",
        "    y_test_new.append(labels.index(i))\n",
        "y_test=y_test_new\n",
        "y_test = tf.keras.utils.to_categorical(y_test)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T07:40:02.908265Z",
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      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Convolutional Neural Network"
      ],
      "metadata": {
        "id": "oWYPCYd5OwDb"
      }
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    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Conv2D(32,(3,3),activation = 'relu',input_shape=(150,150,3)))\n",
        "model.add(Conv2D(64,(3,3),activation='relu'))\n",
        "model.add(MaxPooling2D(2,2))\n",
        "model.add(Dropout(0.3))\n",
        "model.add(Conv2D(64,(3,3),activation='relu'))\n",
        "model.add(Conv2D(64,(3,3),activation='relu'))\n",
        "model.add(Dropout(0.3))\n",
        "model.add(MaxPooling2D(2,2))\n",
        "model.add(Dropout(0.3))\n",
        "model.add(Conv2D(128,(3,3),activation='relu'))\n",
        "model.add(Conv2D(128,(3,3),activation='relu'))\n",
        "model.add(Conv2D(128,(3,3),activation='relu'))\n",
        "model.add(MaxPooling2D(2,2))\n",
        "model.add(Dropout(0.3))\n",
        "model.add(Conv2D(128,(3,3),activation='relu'))\n",
        "model.add(Conv2D(256,(3,3),activation='relu'))\n",
        "model.add(MaxPooling2D(2,2))\n",
        "model.add(Dropout(0.3))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(512,activation = 'relu'))\n",
        "model.add(Dense(512,activation = 'relu'))\n",
        "model.add(Dropout(0.3))\n",
        "model.add(Dense(4,activation='softmax'))"
      ],
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      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Testing**"
      ],
      "metadata": {
        "id": "PD0IiOqt6nfu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Conv2D(32,(3,3),activation = 'relu',input_shape=(150,150,3)))\n",
        "model.add(Conv2D(64,(3,3),activation='relu'))\n",
        "model.add(MaxPooling2D(2,2))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Conv2D(64,(3,3),activation='relu'))\n",
        "model.add(Conv2D(64,(3,3),activation='relu'))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(MaxPooling2D(2,2))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Conv2D(128,(3,3),activation='relu'))\n",
        "model.add(MaxPooling2D(2,2))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Conv2D(128,(3,3),activation='relu'))\n",
        "model.add(Conv2D(256,(3,3),activation='relu'))\n",
        "model.add(Conv2D(128,(3,3),activation='relu'))\n",
        "model.add(MaxPooling2D(2,2))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(512,activation = 'relu'))\n",
        "model.add(Dropout(0.3))\n",
        "model.add(Dense(4,activation='softmax'))"
      ],
      "metadata": {
        "id": "SQpVQ_U86lY7"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
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        }
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_33 (Conv2D)          (None, 148, 148, 32)      896       \n",
            "                                                                 \n",
            " conv2d_34 (Conv2D)          (None, 146, 146, 64)      18496     \n",
            "                                                                 \n",
            " max_pooling2d_16 (MaxPooli  (None, 73, 73, 64)        0         \n",
            " ng2D)                                                           \n",
            "                                                                 \n",
            " dropout_24 (Dropout)        (None, 73, 73, 64)        0         \n",
            "                                                                 \n",
            " conv2d_35 (Conv2D)          (None, 71, 71, 64)        36928     \n",
            "                                                                 \n",
            " conv2d_36 (Conv2D)          (None, 69, 69, 64)        36928     \n",
            "                                                                 \n",
            " dropout_25 (Dropout)        (None, 69, 69, 64)        0         \n",
            "                                                                 \n",
            " max_pooling2d_17 (MaxPooli  (None, 34, 34, 64)        0         \n",
            " ng2D)                                                           \n",
            "                                                                 \n",
            " dropout_26 (Dropout)        (None, 34, 34, 64)        0         \n",
            "                                                                 \n",
            " conv2d_37 (Conv2D)          (None, 32, 32, 128)       73856     \n",
            "                                                                 \n",
            " conv2d_38 (Conv2D)          (None, 30, 30, 128)       147584    \n",
            "                                                                 \n",
            " conv2d_39 (Conv2D)          (None, 28, 28, 128)       147584    \n",
            "                                                                 \n",
            " max_pooling2d_18 (MaxPooli  (None, 14, 14, 128)       0         \n",
            " ng2D)                                                           \n",
            "                                                                 \n",
            " dropout_27 (Dropout)        (None, 14, 14, 128)       0         \n",
            "                                                                 \n",
            " conv2d_40 (Conv2D)          (None, 12, 12, 128)       147584    \n",
            "                                                                 \n",
            " conv2d_41 (Conv2D)          (None, 10, 10, 256)       295168    \n",
            "                                                                 \n",
            " max_pooling2d_19 (MaxPooli  (None, 5, 5, 256)         0         \n",
            " ng2D)                                                           \n",
            "                                                                 \n",
            " dropout_28 (Dropout)        (None, 5, 5, 256)         0         \n",
            "                                                                 \n",
            " flatten_4 (Flatten)         (None, 6400)              0         \n",
            "                                                                 \n",
            " dense_11 (Dense)            (None, 512)               3277312   \n",
            "                                                                 \n",
            " dense_12 (Dense)            (None, 512)               262656    \n",
            "                                                                 \n",
            " dropout_29 (Dropout)        (None, 512)               0         \n",
            "                                                                 \n",
            " dense_13 (Dense)            (None, 4)                 2052      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4447044 (16.96 MB)\n",
            "Trainable params: 4447044 (16.96 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
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        "history = model.fit(X_train,y_train,epochs=10,validation_split=0.1)"
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          "text": [
            "Epoch 1/10\n",
            "83/83 [==============================] - 12s 98ms/step - loss: 2.0566 - accuracy: 0.2946 - val_loss: 1.3571 - val_accuracy: 0.4034\n",
            "Epoch 2/10\n",
            "83/83 [==============================] - 8s 96ms/step - loss: 1.2140 - accuracy: 0.4455 - val_loss: 1.0987 - val_accuracy: 0.5424\n",
            "Epoch 3/10\n",
            "83/83 [==============================] - 8s 96ms/step - loss: 0.9942 - accuracy: 0.5560 - val_loss: 0.9099 - val_accuracy: 0.6237\n",
            "Epoch 4/10\n",
            "83/83 [==============================] - 8s 98ms/step - loss: 0.8611 - accuracy: 0.6375 - val_loss: 0.8187 - val_accuracy: 0.6373\n",
            "Epoch 5/10\n",
            "83/83 [==============================] - 8s 96ms/step - loss: 0.7747 - accuracy: 0.6767 - val_loss: 0.8236 - val_accuracy: 0.6136\n",
            "Epoch 6/10\n",
            "83/83 [==============================] - 8s 97ms/step - loss: 0.6690 - accuracy: 0.7269 - val_loss: 0.7291 - val_accuracy: 0.6508\n",
            "Epoch 7/10\n",
            "83/83 [==============================] - 8s 95ms/step - loss: 0.6006 - accuracy: 0.7510 - val_loss: 0.6417 - val_accuracy: 0.7051\n",
            "Epoch 8/10\n",
            "83/83 [==============================] - 8s 96ms/step - loss: 0.5214 - accuracy: 0.7797 - val_loss: 0.5311 - val_accuracy: 0.7729\n",
            "Epoch 9/10\n",
            "83/83 [==============================] - 8s 94ms/step - loss: 0.4248 - accuracy: 0.8325 - val_loss: 0.4969 - val_accuracy: 0.7898\n",
            "Epoch 10/10\n",
            "83/83 [==============================] - 8s 97ms/step - loss: 0.3564 - accuracy: 0.8714 - val_loss: 0.3760 - val_accuracy: 0.8678\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T07:56:38.337268Z",
          "iopub.execute_input": "2022-11-19T07:56:38.337826Z",
          "iopub.status.idle": "2022-11-19T07:56:38.414947Z",
          "shell.execute_reply.started": "2022-11-19T07:56:38.337783Z",
          "shell.execute_reply": "2022-11-19T07:56:38.413785Z"
        },
        "trusted": true,
        "id": "sKzs33VGOwDd"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#model.save('braintumor.h5')\n",
        "\n",
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "epochs = range(len(acc))\n",
        "fig = plt.figure(figsize=(14,7))\n",
        "plt.plot(epochs,acc,'r',label=\"Training Accuracy\")\n",
        "plt.plot(epochs,val_acc,'b',label=\"Validation Accuracy\")\n",
        "plt.legend(loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T07:59:08.340664Z",
          "iopub.execute_input": "2022-11-19T07:59:08.341048Z",
          "iopub.status.idle": "2022-11-19T07:59:08.586066Z",
          "shell.execute_reply.started": "2022-11-19T07:59:08.341014Z",
          "shell.execute_reply": "2022-11-19T07:59:08.585137Z"
        },
        "trusted": true,
        "id": "_pC4YlBoOwDd",
        "outputId": "eed10af6-dfbf-42bf-d4d5-130bf7187e4c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        }
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "epochs = range(len(loss))\n",
        "fig = plt.figure(figsize=(14,7))\n",
        "plt.plot(epochs,loss,'r',label=\"Training loss\")\n",
        "plt.plot(epochs,val_loss,'b',label=\"Validation loss\")\n",
        "plt.legend(loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T08:00:16.058966Z",
          "iopub.execute_input": "2022-11-19T08:00:16.059362Z",
          "iopub.status.idle": "2022-11-19T08:00:16.289095Z",
          "shell.execute_reply.started": "2022-11-19T08:00:16.059328Z",
          "shell.execute_reply": "2022-11-19T08:00:16.288046Z"
        },
        "trusted": true,
        "id": "heZT7_xsOwDd",
        "outputId": "39b46777-0288-4cec-d110-d9d5b971e822",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        }
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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6++23NXHiRI0ePVpVq1bVli1btGXLFknSd999pzfeeEPffvutGjZsqB07dmjp0qX5el4AAAAAAJzmdwHOkSOZmz152qFDmTuF5yg0VAoL0y3dumnIyJGaNWuW2rdvL8mWT1155ZWKiYlRTEyMHn744WN3u/feezVlyhSNHj06XwHOtGnTtHr1ak2ZMkUVK1aUJL300ksnza156qmnjp2vXr26Hn74YX377bd69NFHFRERoRIlSigkJESxsbE5PtfXX3+tpKQkjRgxQsX//xfw7rvvqmvXrnrllVdUoUIFSVKpUqX07rvvKjg4WPXq1dOll16q6dOn5zvAGTp0qAYMGKBrrrlGkvTKK69oxowZevPNN/Xee+9p8+bNql27ts4991y5XC5Vq1bt2H03b96s2NhYdezYUaGhoapatWq+/h4BAAAAAPAGLKFyQvHiqle9utqcfbY+++wzSdL69es1Z84c3XrrrZKktLQ0Pf/882rUqJFKly6tEiVKaMqUKdq8eXO+nmLVqlWqUqXKsfBGklq3bn3ScaNGjVLbtm0VGxurEiVK6Kmnnsr3cxz/XE2aNDkW3khS27ZtlZ6erjVr1hy7rmHDhgoODj52OS4uTrt27crXcyQkJGjbtm1q27Ztluvbtm2rVatWSbJlWkuWLFHdunV133336Zdffjl23FVXXaXExESdccYZuv322zV+/HgdPXq0QF8nAAAAAABO8bsAJzLSOmGcOOV7fvD/g45br7hC3333nQ4ePKjPP/9cNWvWVLt27SRJQ4YM0VtvvaUBAwZoxowZWrJkiTp16qSUlJRC+7uaP3++rr/+enXp0kWTJk3S4sWL9eSTTxbqcxwvNDQ0y2WXy6X09PRCe/yzzjpLGzZs0PPPP6/ExERdffXV6tmzpySpSpUqWrNmjd5//31FRETonnvu0fnnn1+gGTwAAAAAADjF75ZQ/X+nbu/2/wKvbtdO97/0kr7++muNGDFCd99997F5OHPnzlX37t11ww03SLKZNmvXrlWDBg3y9RT169fXli1btH37dsXFxUmSfv/99yzHzJs3T9WqVdOTTz557LpNmzZlOaZYsWJKS0vL87mGDx+uw4cPH+vCmTt3roKCglS3bt181ZuX6OhoVaxYUXPnzj0WcmU8z/FLoaKjo9WrVy/16tVLPXv2VOfOnbVv3z6VLl1aERER6tq1q7p27aq+ffuqXr16Wr58uc4666xCqREAAAAAgKLidwGOT/j/tOMSxYqpV8+eevzxx5WQkKA+ffocO6R27doaO3as5s2bp1KlSun111/Xzp078x3gdOzYUXXq1NFNN92kIUOGKCEhIUtQk/Ecmzdv1rfffqsWLVroxx9/1Pjx47McU716dW3YsEFLlixR5cqVFRUVddL24ddff72effZZ3XTTTRo4cKB2796te++9VzfeeOOx+TeF4ZFHHtGzzz6rmjVrqmnTpvr888+1ZMkSffXVV5Kk119/XXFxcWrWrJmCgoI0ZswYxcbGqmTJkho+fLjS0tLUqlUrRUZG6ssvv1RERESWOTkAAAAAAHgrv1tC5ROCgqSICEnSrddco/3796tTp05Z5tU89dRTOuuss9SpUye1b99esbGx6tGjRwGeIkjjx49XYmKiWrZsqdtuu00vvvhilmO6deumBx98UP369VPTpk01b948Pf3001mOufLKK9W5c2d16NBB5cqVy3Yr88jISE2ZMkX79u1TixYt1LNnT1144YV69913C/CXkrf77rtP/fv310MPPaRGjRpp8uTJmjhxomrXri3JdtR69dVXdfbZZ6tFixbauHGjfvrpJwUFBalkyZL6+OOP1bZtWzVu3FjTpk3TDz/8oDJlyhRqjQAAAAAAFAWX2+12O11EXhISEhQTE6P4+HhFR0dnuS0pKUkbNmxQjRo1FB4e7lCFp2DzZmnXLqlCBalKFaerwQl89nUFAAAAAPApuWUex6MDxykZg3oOH3a2DgAAAAAA4PUIcJxyfIBTiDsxAQAAAAAA/0OA45SwMCk4WHK7pcREp6sBAAAAAABejADHKcfvd84yKgAAAAAAkAu/CXB8YBbzyQhwvJZPvp4AAAAAAH7L5wOc4OBgSVJKSorDlZyCEiXsTwIcr3PkyBFJUmhoqMOVAAAAAAAghThdwOkKCQlRZGSkdu/erdDQUAUF+VAmlVFrUpJ06JAU4vPfDp/ndrt15MgR7dq1SyVLljwWEAIAAAAA4CSfTwxcLpfi4uK0YcMGbdq0yelyCu7AAenoUWntWikiwulq8H8lS5ZUbGys02UAAAAAACDJDwIcSSpWrJhq167tm8uo3ntPmjRJuu8+6Z57nK4GsmVTdN4AAAAAALyJXwQ4khQUFKTw8HCnyyi4OnWkTZukX3+V+vd3uhoAAAAAAOCFfGhgjJ9q1cr+/OMPiZ2PAAAAAABANghwnNa0qVSsmLRnj7Rhg9PVAAAAAAAAL0SA47SwMAtxJOvCAQAAAAAAOAEBjjc4fhkVAAAAAADACQhwvAEBDgAAAAAAyAUBjjfICHAWL5Z8cSt0AAAAAABQpAhwvEHNmlKZMlJysrR0qdPVAAAAAAAAL0OA4w1cLqllSzvPMioAAAAAAHACAhxvwRwcAAAAAACQAwIcb0GAAwAAAAAAckCA4y0yllCtWyft2+dsLQAAAAAAwKsQ4HiL0qWl2rXt/IIFztYCAAAAAAC8CgGON2EZFQAAAAAAyAYBjjchwAEAAAAAANkgwPEmGQHOggWS2+1sLQAAAAAAwGsQ4HiTJk2ksDBp717pn3+crgYAAAAAAHgJAhxvUqyY1KyZnWcZFQAAAAAA+D8CHG/DHBwAAAAAAHACAhxvQ4ADAAAAAABOQIDjbTICnCVLpORkR0sBAAAAAADegQDH29SoIZUtK6WkWIgDAAAAAAACHgGOt3G5WEYFAAAAAACyIMDxRi1b2p8EOAAAAAAAQAQ43okOHAAAAAAAcBwCHG+U0YHzzz/S3r3O1gIAAAAAABxHgOONSpWS6tSx8wsWOFsLAAAAAABwHAGOt2IZFQAAAAAA+D8CHG9FgAMAAAAAAP6PAMdbZQQ4CxZIbreztQAAAAAAAEcR4Hirxo2lsDBp3z5p/XqnqwEAAAAAAA4iwPFWxYpJZ51l51lGBQAAAABAQCPA8WbMwQEAAAAAACLA8W4EOAAAAAAAQAQ43i0jwFmyREpKcrQUAAAAAADgHAIcb1a9ulSunJSaaiEOAAAAAAAISAQ43szlYhkVAAAAAAAgwPF6BDgAAAAAAAQ8AhxvR4ADAAAAAEDAI8Dxdi1a2J///ivt3u1sLQAAAAAAwBEEON6uZEmpXj07v2CBo6UAAAAAAABnEOD4ApZRAQAAAAAQ0AhwfAEBDgAAAAAAAY0AxxdkBDgLFkjp6c7WAgAAAAAAPI4Axxc0aiSFh0sHDkjr1jldDQAAAAAA8DACHF8QGio1b27nWUYFAAAAAEDAIcDxFczBAQAAAAAgYBHg+AoCHAAAAAAAAhYBjq/ICHCWLpUSE52tBQAAAAAAeBQBjq+oWlWqUEE6elRavNjpagAAAAAAgAcR4PgKl4tlVAAAAAAABCgCHF9CgAMAAAAAQEAiwPElBDgAAAAAAAQkAhxf0qKFLaXauFHatcvpagAAAAAAgIcQ4PiS6Gipfn07TxcOAAAAAAABgwDH17CMCgAAAACAgEOA42sIcAAAAAAACDgEOL4mI8BZsEBKT3e2FgAAAAAA4BEEOL7mzDOlyEgpIUFas8bpagAAAAAAgAcQ4PiakBCpeXM7zzIqAAAAAAACAgGOL2IODgAAAAAAAYUAxxcR4AAAAAAAEFAIcHxRRoCzbJl05IiztQAAAAAAgCJXoABn8ODBatGihaKiolS+fHn16NFDa/IxSHfMmDGqV6+ewsPD1ahRI/3000+nXDAkVa4sxcVJaWnSokVOVwMAAAAAAIpYgQKcWbNmqW/fvvr99981depUpaam6uKLL9bhw4dzvM+8efN07bXX6tZbb9XixYvVo0cP9ejRQytWrDjt4gOWyyW1bGnnWUYFAAAAAIDfc7ndbvep3nn37t0qX768Zs2apfPPPz/bY3r16qXDhw9r0qRJx64755xz1LRpUw0bNixfz5OQkKCYmBjFx8crOjr6VMv1L4MHS088IV11lTR6tNPVAAAAAACAU5DfzOO0ZuDEx8dLkkqXLp3jMfPnz1fHjh2zXNepUyfNnz8/x/skJycrISEhywknYJAxAAAAAAAB45QDnPT0dD3wwANq27atzjzzzByP27FjhypUqJDlugoVKmjHjh053mfw4MGKiYk5dqpSpcqplum/zj7bllJt3izl8ncJAAAAAAB83ykHOH379tWKFSv07bffFmY9kqTHH39c8fHxx05btmwp9OfwedHRUoMGdn7BAmdrAQAAAAAAReqUApx+/fpp0qRJmjFjhipXrpzrsbGxsdq5c2eW63bu3KnY2Ngc7xMWFqbo6OgsJ2SDZVQAAAAAAASEAgU4brdb/fr10/jx4/Xrr7+qRo0aed6ndevWmj59epbrpk6dqtatWxesUpyMAAcAAAAAgIAQUpCD+/btq6+//lrff/+9oqKijs2xiYmJUUREhCSpd+/eqlSpkgYPHixJuv/++9WuXTu99tpruvTSS/Xtt9/qzz//1EcffVTIX0oAyghwFi6U0tOloNOaSQ0AAAAAALxUgT7xf/DBB4qPj1f79u0VFxd37DRq1Khjx2zevFnbt28/drlNmzb6+uuv9dFHH6lJkyYaO3asJkyYkOvgY+RTw4ZSZKSUkCCtXu10NQAAAAAAoIi43G632+ki8pLfPdEDUrt20uzZ0mefSTff7HQ1AAAAAACgAPKbebDmxtcxBwcAAAAAAL9HgOPrCHAAAAAAAPB7BDi+LiPAWb5cOnLE2VoAAAAAAECRIMDxdZUrSxUrSmlp0l9/OV0NAAAAAAAoAgQ4/oBlVAAAAAAA+DUCHH9AgAMAAAAAgF8jwPEHBDgAAAAAAPg1Ahx/cPbZUlCQtGWLtH2709UAAAAAAIBCRoDjD0qUkBo2tPN04QAAAAAA4HcIcPwFy6gAAAAAAPBbBDj+ggAHAAAAAAC/RYDjLzICnIULpbQ0Z2sBAAAAAACFigDHXzRoYLNwDh2SVq1yuhoAAAAAAFCICHD8RXCw7UYlsYwKAAAAAAA/Q4DjT5iDAwAAAACAXyLA8ScEOAAAAAAA+CUCHH+SEeCsWGGzcAAAAAAAgF8gwPEnFStKlStL6enSX385XQ0AAAAAACgkBDj+hmVUAAAAAAD4HQIcf0OAAwAAAACA3yHA8TcEOAAAAAAA+B0CHH/TvLkUHCxt3WonAAAAAADg8whw/E3x4tKZZ9p5unAAAAAAAPALBDj+iGVUAAAAAAD4FQIcf0SAAwAAAACAXyHA8UcZAc6ff0ppac7WAgAAAAAAThsBjj+qV0+KipIOH5ZWrnS6GgAAAAAAcJoIcPxRcLDUooWdZxkVAAAAAAA+jwDHXzEHBwAAAAAAv0GA468IcAAAAAAA8BsEOP6qZUv7c+VK6eBBZ2sBAAAAAACnhQDHX8XFSVWqSG637UYFAAAAAAB8FgGOP2MZFQAAAAAAfoEAx58R4AAAAAAA4BcIcPzZ8QGO2+1sLQAAAAAA4JQR4Piz5s2l4GBp+3bpv/+crgYAAAAAAJwiAhx/FhkpNWpk5xcscLYWAAAAAABwyghw/B1zcAAAAAAA8HkEOP6OAAcAAAAAAJ9HgOPvMgKcP/+Ujh51thYAAAAAAHBKCHD8Xb16UnS0dOSItHKl09UAAAAAAIBTQIDj74KCpBYt7DzLqAAAAAAA8EkEOIGAOTgAAAAAAPg0ApxAQIADAAAAAIBPI8AJBBkBzt9/SwkJztYCAAAAAAAKjAAnEFSoIFWrJrndthsVAAAAAADwKQQ4gYJlVAAAAAAA+CwCnEBBgAMAAAAAgM8iwAkUxwc4breztQAAAAAAgAIhwAkUZ50lhYRIO3ZIW7Y4XQ0AAAAAACgAApxAEREhNW5s51lGBQAAAACATyHACSTMwQEAAAAAwCcR4AQSAhwAAAAAAHwSAU4gyQhw/vpLSk11thYAAAAAAJBvBDiBpE4dKSZGSkyUVqxwuhoAAAAAAJBPBDiBJChIatnSzrOMCgAAAAAAn0GAE2iYgwMAAAAAgM8hwAk0BDgAAAAAAPgcApxAkxHgrF4txcc7WwsAAAAAAMgXApxAU66cVKOG5HZLCxc6XQ0AAAAAAMgHApxAxDIqAAAAAAB8CgFOICLAAQAAAADApxDgBKLjAxy329laAAAAAABAnghwAlGzZlJoqLRrl7Rpk9PVAAAAAACAPBDgBKLwcKlJEzvPMioAAAAAALweAU6gYg4OAAAAAAA+gwAnUBHgAAAAAADgMwhwAlVGgLNokZSa6mwtAAAAAAAgVwQ4gap2balUKSkpSVq2zOlqAAAAAABALghwApXLJbVsaedZRgUAAAAAgFcjwAlkzMEBAAAAAMAnEOAEMgIcAAAAAAB8AgFOIGvRwv5cs0bav9/ZWgAAAAAAQI4IcAJZuXLSGWfY+YULna0FAAAAAADkiAAn0LGMCgAAAAAAr0eAE+gIcAAAAAAA8HoEOIHu+ADH7Xa2FgAAAAAAkC0CnEDXtKkUGirt2SNt2OB0NQAAAAAAIBsEOIEuPNxCHIllVAAAAAAAeCkCHGQuo1qwwNk6AAAAAABAtghwwCBjAAAAAAC8HAEOMgOcRYuklBRnawEAAAAAACchwIFUq5ZUurSUnCwtW+Z0NQAAAAAA4AQEOJBcLqllSzvPMioAAAAAALwOAQ4Mc3AAAAAAAPBaBDgwBDgAAAAAAHgtAhyYjCVUa9dK+/c7WwsAAAAAAMiCAAemTBkbZixJCxY4WwsAAAAAAMiCAAeZWEYFAAAAAIBXIsBBJgIcAAAAAAC8EgEOMh0f4LjdztYCAAAAAACOIcBBpiZNpGLFpL17pX//dboaAAAAAADwfwQ4yBQWJjVrZudZRgUAAAAAgNcgwEFWzMEBAAAAAMDrEOAgKwIcAAAAAAC8DgEOssoIcBYvlpKTna0FAAAAAABIOoUAZ/bs2eratasqVqwol8ulCRMm5Hr8zJkz5XK5Tjrt2LHjVGtGUTrjDKlsWSklRVq61OlqAAAAAACATiHAOXz4sJo0aaL33nuvQPdbs2aNtm/ffuxUvnz5gj41PMHlklq2tPMsowIAAAAAwCuEFPQOl1xyiS655JICP1H58uVVsmTJfB2bnJys5OOW7yQkJBT4+XAaWrWSfvrJApx773W6GgAAAAAAAp7HZuA0bdpUcXFxuuiiizR37txcjx08eLBiYmKOnapUqeKhKiGJQcYAAAAAAHiZIg9w4uLiNGzYMH333Xf67rvvVKVKFbVv316LFi3K8T6PP/644uPjj522bNlS1GXieBlLqNavl/budbYWAAAAAABQ8CVUBVW3bl3VrVv32OU2bdron3/+0RtvvKGRI0dme5+wsDCFhYUVdWnISalSUp060tq10oIF0iksmQMAAAAAAIXHkW3EW7ZsqfXr1zvx1MgvllEBAAAAAOA1HAlwlixZori4OCeeGvlFgAMAAAAAgNco8BKqQ4cOZeme2bBhg5YsWaLSpUuratWqevzxx7V161aNGDFCkvTmm2+qRo0aatiwoZKSkvTJJ5/o119/1S+//FJ4XwUKX0aAs2CB5Hbb9uIAAAAAAMARBQ5w/vzzT3Xo0OHY5f79+0uSbrrpJg0fPlzbt2/X5s2bj92ekpKihx56SFu3blVkZKQaN26sadOmZXkMeKHGjaWwMGnfPhtmXLu20xUBAAAAABCwXG632+10EXlJSEhQTEyM4uPjFR0d7XQ5gaNNG2n+fGnkSOmGG5yuBgAAAAAAv5PfzMORGTjwEczBAQAAAADAKxDgIGcEOAAAAAAAeAUCHOQsI8BZskRKSnK0FAAAAAAAAhkBDnJWvbpUrpyUmmohDgAAAAAAcAQBDnLmcrGMCgAAAAAAL0CAg9wR4AAAAAAA4DgCHOSuZUv7kwAHAAAAAADHEOAgdxkBzr//Srt3O1sLAAAAAAABigAHuStZUqpb184vWOBoKQAAAAAABCoCHOSNOTgAAAAAADiKAAd5I8ABAAAAAMBRBDjIW0aAs2CBlJ7ubC0AAAAAAAQgAhzkrXFjKTxcOnBAWrfO6WoAAAAAAAg4BDjIW2iodNZZdp5lVAAAAAAAeBwBDvLn+GVUAAAAAADAowhwkD8MMgYAAAAAwDEEOMifjABn6VIpKcnZWgAAAAAACDAEOMifatWk8uWl1FRp8WKnqwEAAAAAIKAQ4CB/XC6WUQEAAAAA4BACHOQfAQ4AAAAAAI4gwPGQo0edrqAQEOAAAAAAAOAIAhwPufBC6aqrpJkzJbfb6WpOUYsWtpRqwwZp926nqwEAAAAAIGAQ4HjA2rXS7NnS2LFShw5So0bS++9LBw86XVkBxcRI9erZebpwAAAAAADwGAIcD6hTR1q2TLrzTql4cWnlSqlvX6lSJenee6VVq5yusABYRgUAAAAAgMcR4HhIo0bSsGHS1q3SW29ZqHPwoPTuu1KDBrbEatw4H5iVQ4ADAAAAAIDHEeB4WEyMdN991nXzyy9S9+5SUJD066/SlVdKNWpIL74o7dzpdKU5yAhwFiyQ0tOdrQUAAAAAgABBgOOQoCDpooukCRNsJvDjj0tly0r//Sc99ZRUpYp0/fXS/PleNvS4USMpIkKKj7fhPgAAAAAAoMgR4HiBqlWll16y8GbkSGtySU2Vvv5aatNGat5c+vRT6cgRpyuVFBJiBUksowIAAAAAwEMIcLxIWJh0ww3S779Lf/4p3XyzFB4uLV4s3XabVLmy9NBD0vr1DhfKHBwAAAAAADyKAMdLNW8uffaZdeUMGWKzcfbvl15/XapdW+rSRfrxRyktzYHiCHAAAAAAAPAoAhwvV6aM9PDD0rp10qRJ0iWX2PU//yxddpmFOUOGSHv3erCo1q3tz0WLLFECAAAAAABFigDHRwQHS5deKv30k4U5/ftLJUvaAORHH7XlVbfcIv31lweKqVzZJi1LtqZr0CAvm7QMAAAAAIB/IcDxQbVqSa+9Jm3dKn3yidSsmZSUJH3+uXT22dI559gw5OTkIixi0CDp+eft/LPPSo89RogDAAAAAEARIcDxYZGR0q23WtfNvHnSdddJoaE2mqZ3b9uK/IknpM2bi+DJXS7rwslYQvXqq9K990rp6UXwZAAAAAAABDYCHD/gctlYmq++krZskV54wVY57d4tDR5sA5B79JCmTSuCJpkHH5Q+/NCKeO89S5QcmawMAAAAAID/IsDxMxUqSE8+abNxxo2TLrzQmmK+/1666CKpfn3p7bel+PhCfNI77pBGjLBBPcOHWytQSkohPgEAAAAAAIGNAMdPhYRIl19uXTd//y316ydFRUlr1kj33y9VqiTddZe0fHkhPeENN0ijR9sartGjpSuvtME8AAAAAADgtBHgBID69aV33rGhx++/LzVsKB0+bCufGjeW2rWzzCU19TSf6IorrNUnPNz2PL/sMnsiAAAAAABwWghwAkhUlHT33dZ1M2OG1LOnrXqaPVvq1UuqVk167jlp+/bTeJJLLpF+/lkqXlyaPl3q1KmQ12sBAAAAABB4CHACkMsltW8vjRkjbdokPfOMzc7Zvl0aOFCqWtUCndmzT3Hocfv2tnYrJkaaO9cG8ezdW7hfBAAAAAAAAYQAJ8BVqmRdN5s3S998I517rnT0qC2patdOatJEGjZMOnSogA98zjnW5lO2rO1z3r69tGNHUXwJAAAAAAD4PQIcSJKKFZOuuUaaM0dassQ2loqMtOVWd99tQc/999sQ5Hxr1szaeOLipBUrpPPPt33OAQAAAABAgRDg4CRNmtiA461bpTfekGrXlhISbPvxevWkiy+2WcVpafl4sPr1LRWqVk1at0467zxp/foi/xoAAAAAAPAnBDjIUcmS0gMPSKtXS1OmSF272vycqVOlHj2swWbjxnw8UM2aFuLUrm1Dd84/3/Y2BwAAAAAA+UKAgzwFBVnXzcSJ0r//SgMGSKVL2/KqVq2kBQvy8SBVqthyqjPPtGnJ7dpJixcXee0AAAAAAPgDAhwUSPXq0ssvS0uXSk2bSrt22XzicePycefYWGnmTKl5c2nPHqlDB2n+/CKtFwAAAAAAf0CAg1NSubI11HTpIiUmSj17SkOH5mPb8TJlpOnTbbur+Hjpooss1AEAAAAAADkiwMEpi4qyYcZ9+1pw88gjtmPV0aN53DEmRpo8WerYUTp8WLrkEunnnz1SMwAAAAAAvogAB6clJER65x3pzTdtwPGHH9qw44SEPO5YvLj0ww92cFKS1L17PtdhAQAAAAAQeAhwcNpcLun++6Xx46XISGuuOfdcacuWPO4YHi59953Uq5eUmipdfbX05ZceqRkAAAAAAF9CgINC0727NGuWzSrO2KFq0aI87hQaKn31ldSnj5SWJvXubW08AAAAAADgGAIcFKqzz5b++CNzt/DzzrOVUrkKDpY+/TRzmM5dd0lvvOGRegEAAAAA8AUEOCh0VatKv/0mXXyxdOSIdea8/XYedwoKsmE6jz5ql/v3l55/Ph/bWgEAAAAA4P8IcFAkYmKkSZOk22+3DOb++6X77rNVUjlyuaSXX7bgRpKeeUZ6/HFCHAAAAABAwCPAQZEJDbVxNq++apffeUfq0UM6dCiXO7lc0lNPSa+/bpdfecWSn/T0oi4XAAAAAACvRYCDIuVySY88Io0ZY5tOTZoknX++tG1bHnd88EFLf1wu6d13pVtvzaN9BwAAAAAA/0WAA4/o2VOaMUMqV05avNh2qFq6NI873XGHNGKEzccZPly67jrbbhwAAAAAgABDgAOPOecc26GqXj3pv/+kc8+VJk/O40433CCNHm3rsUaPlq68UkpK8ki9AAAAAAB4CwIceFSNGtK8eVKHDjYL57LLpA8+yONOV14pTZhga7B++EHq2lU6fNgT5QIAAAAA4BUIcOBxpUpZ502fPjbW5p57pIcfzmNOcZcu0k8/ScWLS9OmSZ06SfHxnioZAAAAAABHEeDAEcWKSZ99Jr3wgl1+7TWbk3PkSC536tBBmjrV9iifO1fq2FHau9cj9QIAAAAA4CQCHDjG5ZKefFL6+msLdMaPl9q3l3bsyOVOrVvbNOSyZaU//8zHHQAAAAAA8H0EOHDctddK06dLZcpICxfasOOVK3O5Q7Nm0qxZUlyctGKF7Uu+ZYvH6gUAAAAAwNMIcOAVzj1Xmj9fql1b2rRJatPGRt3kqEEDac4cqVo1ad066bzzpH/+8Vi9AAAAAAB4EgEOvEbt2hbinHeelJAgXXKJ9OmnudyhZk1p9uzM1Oe886S///ZYvQAAAAAAeAoBDrxKmTI2p/j666WjR6XbbpMefzyXHaqqVrUQ58wzpe3bpXbtpCVLPFkyAAAAAABFjgAHXicsTBo5UnrmGbv88ss2JycxMYc7xMZKM2dKzZtLe/bYblW//+6pcgEAAAAAKHIEOPBKLpf03HPSF19IoaHS6NHShRdKu3fncIcyZWwSctu20oED0kUXWagDAAAAAIAfIMCBV+vdW/rlF6lkSZuPc8450urVORwcEyNNmSJ17CgdOmRDdCZP9mS5AAAAAAAUCQIceL327S28qVFD+vdf26Eqx+aa4sWlH36QLrtMSkqSunWTxo3zYLUAAAAAABQ+Ahz4hHr1pD/+sA6c/fuliy+WRozI4eDwcAttrr5aSk21P7/80qP1AgAAAABQmAhw4DPKlZN+/VW66irLZW66SXr2Wcntzubg0FDp66+lPn2ktDRbi/XRR54uGQAAAACAQkGAA58SESF9+61tLS5JgwZJN94oJSdnc3BwsPTpp9I991jKc+ed0htveLReAAAAAAAKAwEOfE5QkPTSS9LHH1tG89VXtunU3r05HPzuu9Ijj9jl/v2lF17IoW0HAAAAAADvRIADn3XbbdLPP0vR0dKcOVLr1tL69dkc6HJJr7xi7TqS9PTT0hNPEOIAAAAAAHwGAQ582kUXSfPmSVWrSuvW2ZDj337L5kCXy4Kb116zyy+/LN1/v5Se7tF6AQAAAAA4FQQ48HkNG9oOVWefbcuoLrxQ+uabHA7u318aNswCnXfesTaetDSP1gsAAAAAQEER4MAvxMZKs2ZJPXpIKSnSdddJL76YwyqpO++UvvjC5uN8/rl0/fW2rRUAAAAAAF6KAAd+IzJSGjtWeughu/zUU9Itt1igc5Ibb5RGj7btxkeNkq68UkpK8mi9AAAAAADkFwEO/EpwsDR0qPT++9ZgM3y41LmztH9/NgdfeaU0YYIUFib98IN07rnS998zFwcAAAAA4HUIcOCX7r7bMpkSJaQZM6Q2baQNG7I5sEsX28qqRAnpr79sDdaZZ1ryk23rDgAAAAAAnkeAA7/VpYvtSFWpkrR6tdSqlfT779kc2KGDbWH12GO2J/mqVdLNN0s1a0pvvCEdOuTx2gEAAAAAOB4BDvxakya2Q1XTptLu3ZbVjB2bzYGxsdLgwdLmzdIrr9jl//6zXauqVpWeecYeAAAAAAAABxDgwO9VqiTNmSNdeqnNKb7qKunVV3PYoSomRnr0UVtv9dFHUq1aNkDn+eelatWke++VNm709JcAAAAAAAhwBDgICCVK2Lzifv3s8oAB0l135bJ7eHi4dPvttvZqzBipeXMpMVF6910LdW64QVq2zFPlAwAAAAACHAEOAkZIiPTOO9Jbb0kulzXYXHaZFB+fy52Cg6WePaWFC6Vp06SOHaW0NOmrr2x91qWXWntPtu08AAAAAAAUDgIcBJz77rNunMhI6ZdfbPfwzZvzuJPLJV14oTR1qvTnn7YOKyhI+ukn6fzzpbZt2YIcAAAAAFBkCHAQkLp1k2bPtlnFK1bYDlVz5+bzzs2bS6NHS2vWSHfeKRUrJs2fzxbkAAAAAIAiQ4CDgNW8ue1Q1aiRtGOHdeK0ayeNH2+rpPJUq5Y0bJgNNR4wgC3IAQAAAABFhgAHAa1qVem336TevW1GzuzZ0hVXSLVrW/6S63ycDHFx0ssvswU5AAAAAKDIuNxu75++mpCQoJiYGMXHxys6OtrpcuCn/vtPev996cMPpX377LoSJaRbbrHdw2vVyucDJSVJI0faXuXr19t1ERHSrbdKDz0kVa9eFOUDAAAAAHxQfjMPAhzgBEeOSF9+Kb35pq2IkmyG8WWXSQ88IHXoYJfzlJZm67Fefln66y+7LjhYuuYaW3LVqFERfQUAAAAAAF+R38yDJVTACSIjpTvukFaulKZMkS65xHYJ/+EH24iqSRPps8+s0SZXuW1B3rgxW5ADAAAAAPKNAAfIgcslXXyx7RS+apV0zz0W7ixfbquhMsbbbN+ejwc6cQtylyvrFuQTJ7IFOQAAAAAgRyyhAgpg/37pk0+kd96Rtmyx60JDbVXU/ffbzlb5sm6dNHRo1i3HGzSQHn1UuvZa25ocAAAAAOD3imwJ1ezZs9W1a1dVrFhRLpdLEyZMyPM+M2fO1FlnnaWwsDDVqlVLw4cPL+jTAl6hVCnpkUekf/+VRo+W2rSRUlNtZvHZZ1tDzbhx+diGvHZtm5Z8/Bbkf/8t9eljW5C/+SZbkAMAAAAAjilwgHP48GE1adJE7733Xr6O37Bhgy699FJ16NBBS5Ys0QMPPKDbbrtNU6ZMKXCxgLcICbGVUHPnSgsWSNdfb9fNmSNdeaXtWPXaa9KBA3k80PFbkL/8cuYW5A8+yBbkAAAAAIBjTmsJlcvl0vjx49WjR48cjxkwYIB+/PFHrVix4th111xzjQ4cOKDJkyfn63lYQgVfsHWr9MEH0rBh0t69dl3x4tLNN0v33WdNN3lKSpJGjJCGDMm6Bfltt9kW5NWqFVn9AAAAAADP85pdqObPn6+OHTtmua5Tp06aP39+jvdJTk5WQkJClhPg7SpVkl54wWbjfPyx1LChdPiw9O67Ut26Uteu0vTpeWw6FR5uW2CtXm1rtJo3lxITbehOzZrSDTfYFGUAAAAAQEAp8gBnx44dqlChQpbrKlSooISEBCUmJmZ7n8GDBysmJubYqUqVKkVdJlBoMhpmli+3jacuvdRCm0mTbCfxxo2lTz+1XCZHwcG2RostyAEAAAAA8tJtxB9//HHFx8cfO23J2O4H8CEul+UukyZJa9ZI/frZkqoVKyzgqVpVeuopadu2PB6ELcgBAAAAIOAVeYATGxurnTt3Zrlu586dio6OVkRERLb3CQsLU3R0dJYT4Mvq1LFVUP/9Z7uHV6sm7dkjvfiinb/hBstnctW8uS2rWrPGllkVKybNny917y41aiR98UXmluQAAAAAAL9S5AFO69atNX369CzXTZ06Va1bty7qpwa8TsmSNot4/Xpp7Fjp3HOlo0dtZVSLFnZ57Fi7LkdsQQ4AAAAAAafAAc6hQ4e0ZMkSLVmyRJJtE75kyRJt3rxZki1/6t2797Hj77rrLv3777969NFHtXr1ar3//vsaPXq0HnzwwcL5CgAfFBJi243PmWNjbm64QQoNtW3Jr7rKcpihQ6X9+3N5kLy2IH/xRenIEY99TQAAAACAolPgbcRnzpypDh06nHT9TTfdpOHDh6tPnz7auHGjZs6cmeU+Dz74oP7++29VrlxZTz/9tPr06ZPv52QbcQSC7dttG/IPPrDlVZIUGWmNNffdZztZ5Sq7Lcgztsa68UYbjAwAAAAA8Cr5zTwKHOA4gQAHgSQpSfr6a1sJdfyO4V26SA88YIORXa5cHiAtTfr2W+nJJ6VNm+y6xo2tpeeii4qwcgSi3bulH3+0UUylSjldDQAAAOB78pt5eOUuVEAgCw+XbrlFWrpUmj5d6to1c+Opiy+WzjxT+vjjXFZHBQdL118vrV5t3TgxMdKyZXbnzp2zpkLAKXK7peHDpXr1pJtvltq3z2PJHwAAAIDTQoADeCmXS7rgAtslfO1aW0ZVooTNK77jDqlKFemJJ6StW3N4gPBw6eGHpX/+sdad0FBpyhSpaVPp1lvz2L8cyNn69dYJdvPN0r59dt2yZdYlxvxsAAAAoGgQ4AA+oFYt6a23bEbx669L1avbB+fBg+38dddJCxbkcOcyZaQ33rDkp2dPKT1d+uwz283q2Wf5xI18S02111yjRtKvv1pG+Mor0qJFtnzq999tKVVSktOVAgAAAP6HAAfwITExtsnU+vXSuHHS+efbluPffCO1aiW1aSONHp3DNuS1akljxthWV61b2xqsQYPs+o8+ymPvcgS633+XzjrLur6Skmyc0ooV0qOPSs2aSZMnW4fYr79KvXpZ2AMAAACg8DDEGPBxixZZd84332R+aK5SxWaSlC5tDTilS59wvpRbZeZOVPRzD8n17z92pwYNpFdftXUwuU5JRiBJSLB52O+9Z3Nvypa1hq7rrz/5ZTJzpnTJJRbwXHedNHKkFMSvCQAAAIBcsQsVEGB27JCGDZPef992BsqP4GC3SoUnqXTiVpVJ36XS2qfSscVU5oImKl2vQo4BUHQ0GU8g+P57qW/fzDlLvXtLr71mIU5OfvxR6tHDGrruvFP64ANeKwAAAEBuCHCAAJWUJP3wg7Rxo83J2bvX/jzxfI67WOVDcHBmqJNjl08256Oi+DDvC7Ztk+6915bpSVLNmhYOduyYv/uPGiVde6117DzyiM3J4fsOAAAAZC+/mUeIB2sC4AHh4dJVV+V9XFJSNsHO2j3a++1U7VuySftUWvuCympv5SbaF1Vd++KDtXevlJgopaVZl09+O30yZAQ/OYU8Od1G8OMZ6enShx9Kjz1mS6eCgy2AeeYZKSIi/4/Tq5d08KB0++2ZO9k/+WTR1Y2cpadL27dLlSo5XQkAAABOFx04AE7255+2BfmsWXa5bFnbserOO5V4NFT79+fc2ZNT109i4qmXExoqdepkwULbtoXzJSKrlStte/p58+xyy5bSxx9LjRuf+mO+8YbUv7+df+st6b77Tr9O5N+ePdJll0kLF9pw8yuvdLoiAAAAZIclVABOj9tta7EefVRas8auq1PH1sN0717glpjExJxDntyWep0Y/Jx3ngU5l1xCV05hSEqSXnzRvq2pqbaT1EsvSffcYx04p2vgQOm55+z8559Lffqc/mMibxs3Wui5dq1drl1b+vtvKYS+WwAAAK9DgAOgcKSmSp98Yh04GWumzj1XGjrU9i4vYomJ0j//WAfHF19k7rTVqJEFOVdfzYfSUzVrlnXdZHzI79rVdpuqUqXwnsPtlh56yLpxgoJsPk7PnoX3+DjZ0qVS58422LxqVenwYQtFv/jCBlEDAADAu+Q382CDVwC5Cw2V7r5bWr/eBplEREi//Sadc450zTXShg1F+vQREdKZZ9pyng0bbGVXiRLS8uW2lXWdOrbT0eks0Qo0+/ZJt91mW82vXSvFxkpjx9quU4UZ3kjWJfXaa/Z86em2vfjkyYX7HMg0c6Z0/vkW3px5pi2Je+QRu+355213MAAAAPgmAhwA+RMdLb3wgn3i79PHPpmPGiXVq2ctFvv2FXkJlSrZUNzNm62UsmUt1LnnHql6dWnwYOnAgSIvw2e53dK330r160uffmrX3XWXtGqVzUcpqiVpLpftYtWrl3VQXXGFNGdO0TxXIBs71pZNJSRYiDNnjv3M9O1rPyvr10tfful0lQAAADhVBDgACqZyZRtmsmiR7SudkiK9/rpUq5b9mZxc5CWUKmXNQJs2Se+8I1WrJu3aJT3xhJ1/7DHrQECmjRulSy+17b137bIQZ84c614qWbLonz84WBoxwmpITLQ///yz6J83ULz3ni0nTEmxgGzKlMzva4kSNspKsi6cjGWIAAAA8C0EOABOTdOm0i+/SD//bGs19u+3Tpz69W3LGw+M14qMlPr1k9atk0aOlBo2tO6DV16xjpy77rKug0B29KjNn2nY0L5VxYrZUOHFi22UkScVKyaNGWNLtw4etDktf//t2Rr8jdstPfWU/Ry43faaHz1aCg/Petw990jly0v//ms/KwAAAPA9BDgATp3LZZ/ClyyxQcdxcbamqVcvqU0bae5cj5QRGirdcIO0bJk0caLUurU1An34oVS3ro3qWbzYI6V4lcWLbVRR//7SkSO2rGbpUumZZ6SwMGdqioiw71HLljZYt2NHCxVQcEeP2myhF1+0y4MGSe+/n/3uYcWLZ3bhvPACXTgAAAC+iAAHwOkLDpZuvdVaYZ57zj4t/v67tXhceaVd7wFBQbaT0ty50uzZUpcuNjh31CjprLNs6/FZszzSHOSow4dtcG2LFtJff9lSmo8/lmbMsJFFTouKymzc2r5duvBCaetWp6vyLUeOSJdfLn32mb3uP/pIevrp3OcY3X23VKGCZaxffOG5WgEAAFA4CHAAFJ7ixa29Y9062586KEgaN05q0EC67z5pzx6PlOFySeedJ/34ozUHXXedlTJ5si3fadPGdlxKT/dIOR41ZYoFI0OHSmlp1gy1apV1agR50Tt+6dK2Aq9WLZvPc9FFmbvUI3cZnUuTJtlSqXHjpNtvz/t+kZHSgAF2/oUXbF4OAAAAfIcX/XcegN+Ii7P1S8uWWRvM0aM2bbhmTRtQ48E9v5s0kb76yjKlu++2pUO//y716CE1amSDdf1hOcmuXbateufOFohUrWof8L/91rYJ90ZxcdK0aTYXe9Uq20EpPt7pqrzb5s3W2DZ/vg3znjZN6t49//e/6y57PWzaJA0fXmRlAgAAoAgQ4AAoOg0bWhvMtGk29DghwbaIqlvX9jP2YAvMGWfYfJBNm6THH7dd0f/+W7rpJsuV3n7blh75GrfbNgWrV0/6+mvrsnnwQWnlStvpydtVq2Yvj3LlbGbPpZf65vfBE5Yvt/lOq1db6DVnjtS2bcEeIyLCfgQlm51DFw4AAIDvIMABUPQuvNCGsYwYYZ88t2yRbrzRhrTMmOHRUipUkF56yToZXn7ZLm/ZIt1/v4UJgwZJ+/Z5tKRTtm6d/dXecottAta0qfTHH7abe4kSTleXf3Xr2nKqmBibX3TFFR7Zjd6nzJ5tywK3bbMVifPmWT56Ku64w7qfNm+2GToAAADwDQQ4ADwjKMhCm7VrLUGJipIWLZIuuMAmD69a5dFyYmJsHsjGjdKwYdahs3ev9OyztvzooYek//7zaEn5lpJi3RONGln+FREhvfqqtGCBdPbZTld3apo2tcHGxYtbmHPddbbyDtL48dLFF9vysrZtrfOmSpVTf7yICOtCk+x1RFgGAADgGwhwAHhWxqfH9eulvn1tB6tJkyyNuOsuaedOj5YTHi7deae0Zo3Ni2nSxJbwvP66hTq33mq3eYv586XmzaWnnrIP3hdfLK1YYbtOhYY6Xd3pad1amjBBKlbMBvPeeqt/DpouiGHDpJ497XvdrZs0daoNgD5dt98uVapkIeWnn57+4wEAAKDoEeAAcEb58tK779qwlh49bMukDz+0bYmef97jg1BCQmzHpsWLrROkXTsbbvzZZ1L9+rYb+sKFHi0pi4QEqV8/68BYsUIqW9bGCE2ebEGTv+jYURo92nK9ESNsaZu/b/ueHbfbusHuvttCrNtvl777zvLPwhAentmF89JLUlJS4TwuAAAAig4BDgBn1a1ra0Rmz7aZOIcO2VbkderYdN60NI+W43LZTk4zZ9qckW7d7MP0uHFSy5YWMEyb5tlQYcIEm3vy3nv2vH362CDb66+3ev1N9+7SF1/Y1/buu9ZtFEiOHrVmtEGD7PIzz1i2GRJSuM9z2202kmrrVumTTwr3sQEAAFD4CHAAeIfzzrP9vb/5Rqpe3aa13nKL1KyZDUVxQOvW0vffW8dL7972AXr6dOmiiyzM+e67os2Xtm61gb6XX27na9Wy8Ojzz6UyZYrueb3B9dfbrmGSdYi88oqz9XhKYqItmfroIwuwPvhAeu65ognqwsKkJ56w84MH04UDAADg7QhwAHiPoCDpmmusvWToUKlkSds7uVMnm3L74os2BNnDGja0jpD166X77rNlLH/+aR+0GzSwGSKFOQg2Pd3Ci/r1rTkpJMQ+aC9bZrtOBYq77soMbh57zMIMf7Zvn4WD339v4crYsfZ3UJRuucUGIm/bZqERAAAAvJfL7fb+6QIJCQmKiYlRfHy8oqOjnS4HgKfs2ye98IKto0lNzby+USNLT666ylIOD9u9W3rnHStr/367rmJFqX9/26I5KurUH3vFCnuM+fPtcqtW9sG6cePTr9tXPfWUZXcul83FueEGpysqfFu22NK9v/+2HdImTpTOP98zz/3hhxYUxcZK//5beHN2AAAAkD/5zTwIcAB4vz17rC1h7FhbQ3T8/tINGliY07OndOaZHh0Kc/Cg9PHH0muvWQeDJJUqZcOG771XKlcu/4+VlGRZ1Suv2JcXFWVLh+6+2wb6BjK324YZv/OO/V2MHWtzr/3F339bk9l//1kQOHmyZZSekpJiI6c2bZLeeEN64AHPPTcAAAAIcAD4q337rD1h7FibjXN8Z07duplhTpMmHgtzkpOlr76y8CVjhVdEhA2JfeghqVq13O8/c6Z13axbZ5e7dbOBxZUrF2nZPiU93Zb7fPGFbTP+4482UNrXzZ0rde1qnVz16ll4k9frpSh8/LG9BitUsC6cyEjP1wAAABCoCHAA+L8DB6QffrAwZ8qUrINoatXKDHPOOssjYU5amu0YNXiw9Ndfdl1IiHTdddKjj9osnePt2yc98ohtVS5JcXG2LOvyy/1zd6nTdfSojUj67jsLGKZOldq0cbqqUzdxom1dn5QknXOONGmSc8OpU1OtC2fjRuso69/fmToAAAACEQEOgMCSkGBtGWPGSD//nHVLnRo1MsOcFi2KPB1xu6Vff7UgZ/r0zOu7dbNhvOecI337rS0L2r3bbrv7bjs+JqZIS/N5ycm2zfiUKfZ3NXOmzbf2NZ98It15p3UWXXaZNGqU810vn35qXWPly1sXTvHiztYDAAAQKAhwAASuQ4ekn36yMOfHH21v5gxVq0pXXmkDkFu1sp2vitDChba0atw4C3Yk2yV940Y736CBDSlu27ZIy/ArR47YzJjffrM5Q7Nn2/IjX+B226yjZ56xy7fcYkOEQ0KcrUuyLpx69Sy8GTJEevhhpysCAAAIDAQ4ACBJhw/bYJGxY2251eHDmbdVqmRhTs+elqAUYZizZo306qvSyJH2QblYMenpp21pVbFiRfa0fis+XrrgAmnRIpsVNGeOBWPeLC3NhltnbIf+5JPS889713K5zz+3UKlcOWnDBrpwAAAAPIEABwBOlJhoa2/GjrUBJAcPZt4WFyddcYWFOeedV2RbP/33n3XjdO5sM0dw6vbssa22V62Sata0ECcuzumqspeUJF1/vX3vXS7p7bdttzJvc/SodeH88491jj36qNMVAQAA+D8CHADITVKSTcEdO9a2KI+Pz7ytfPnMMKddO+9Y34Jsbd1qeduGDbaL/MyZzg0CzsmBAza3Z/Zs67b68ktbweetvvhC6tNHKlvW/l5LlHC6IgAAAP9GgAMA+ZWSIk2bZmHOhAm2p3OGsmVtW6iePaUOHaTQUMfKRPb+/ddCnG3bbEb19OlSVJTTVZmtW6VLLpGWL5eio+3l1aGD01Xl7uhRm820bp0N1n7sMacrAgAA8G8EOABwKlJTbQupsWOl8eOlvXszbytVSurRw9onLryQ4TVe5O+/bTnV3r3WNPXzz1JEhLM1rV5tw5Y3b5ZiY20UU5MmztaUXyNHSr17S6VL28BtbwnEAAAA/BEBDgCcrqNHbU3O2LE2vCRjz2/J9rDu3t3CnIsuksLCHCsT5q+/bLBxQoLUpYvlb05lbL//Ll16qbRvn806mjLF+4csH+/oUalhQ2ntWunFF6UnnnC6IgAAAP9FgAMAhSktzabkjhljYc6OHZm3RUdLXbtamHPxxc63fgSwOXOs6yUxUbr6aunrr4tsHnWOfvzRXgqJiVLLltKkSbark6/56ivphhus8WzjRnuZAwAAoPDlN/Mouj1zAcCfBAdL7dtL771nW0nNnm17QlesaC0fX31ly6vKl5euvVb67jvpyBGnqw44551nnTehodLo0dKdd0qe/DXF559bY1Zios2++fVX3wxvJOmaa2xHqv37bdcsAAAAOIsOHAA4Henptl5m7Fg7bdmSeVtkpK2j6dnT1vSwnY/HfPeddeCkp0sPPCC9/rpt311U3G7p5ZczlxrddJP08ce+P/P6m2+k666TSpa0LpyYGKcrAgAA8D904ACAJwQFSW3aWEKwcaOFOQ8/LFWrZh04Y8ZIvXpZZ86VV9on4oMHna7a7115pfTZZ3b+zTel554ruudKT5fuvz8zvBkwwDpxfD28kSwEq1/ftkJ/6y2nqwEAAAhsdOAAQFFwu22q7tixFuL8+2/mbWFhtr6mVy+bnVO8uHN1+rl33pHuu8/Ov/aa1L9/4T5+crLt1jR6tF1+800Lc/zJqFG2nComxjLKkiWdrggAAMC/MMQYALyF2y0tWZIZ5qxbl3lbRIR02WUW5nTpwgDkIvDSS9KTT9r5jz+WbrutcB43Pl66/HJpxgzrthkxwoIOf5OeLjVuLK1cKT37rDRwoNMVAQAA+BcCHADwRm63tGyZtWx8+23WzpwSJaRu3SzM6dSJrckLidstPfaY9OqrNgfnm2/sr/h0bN9uTVRLl0pRUTY4+cILC6debzRmjC2nio62LpxSpZyuCAAAwH8Q4ACAt8tYZjVqlAU6mzdn3hYTY7taXXONJQP+MFDFQW63dM890rBhUkiINGGCzZc+FWvXWr62caNUoYL0889Ss2aFWa33SU+XmjaVli+Xnn5aGjTI6YoAAAD8BwEOAPgSt9sGII8aZe0O27Zl3lamjHTFFdY20q6dJRAosPR0m1fz1VfW3PTzz1KHDgV7jIULbaXbnj1SrVrSlCnSGWcUTb3e5rvvbEO1qCgLr0qXdroiAAAA/0CAAwC+Kj1d+u03C3PGjpV27cq8rXx5+xTdq5d07rm2CxbyLTVVuuoq6fvvbcXatGlSq1b5u+/kyba71ZEjUvPm0k8/2bcjUKSnW6fRsmU2U+iFF5yuCAAAwD8Q4ACAPzh6VJo1y8Kc776T9u3LvK1iRUsjevWSzjnHBrwgT0lJNjd6+nSb5TJrltSoUe73GTlSuuUW+3ZcfLF9K0qU8Ey93mT8eGsGK1HCunDKlHG6IgAAAN+X38yDX90CgDcLCbEZOB99JO3YYet+brrJZuRs2ya99ZbUpo1Uvbr0yCPSn3/acizkKDzcZuC0bi3t3y9ddFHWjcGO53ZLQ4bY0qujR6Xrr5d++CEwwxvJxjI1bSodOmTbsgMAAMBz6MABAF+UnGwDWEaNkiZOtE/UGWrWtC2DevWy/Z/pzMnWgQNS+/a2k1TVqrZqrUqVzNvT06WHH5beeMMuP/SQ7WQV6KvWvv/egpwSJaQNG6SyZZ2uCAAAwLexhAoAAkViog1kGTVKmjTJLmeoV8+CnF69pPr1navRS+3aJZ1/vrRmjVSnjjR7tu0slZIi9eljW45L0tChFuDAupLOPltatEgaMEB6+WWnKwIAAPBtBDgAEIgOHbIQZ9QoW26VnJx5W6NGmWFOrVrO1ehltmyxedCbN0tNmlhD06232oDjkBBp+HBbOoVMP/wgdesmFS9uXTjlyjldEQAAgO8iwAGAQJeQYOtdRo2y5VZHj2bedtZZFuRcfbXNzwlw69dL551nY4ZCQ223quLFpXHjbGgxsnK7pZYtbeTSI4/Y0jIAAACcGgIcAECmfftsC6FRo6Rff5XS0jJva9XKwpyrrpIqV3auRoctXy61a2eDjcuVs1VpZ5/tdFXe68cfbTevyEjrwgmkLdUBAAAKEwEOACB7u3fbPtijRtke2sf/M3DuudI110g9e9owmACzbJn0xRfSPffYLGjkzO223esXLLD5QEOHOl0RAACAbyLAAQDkbft2aexYC3Pmzs28PijItmjq1Uu64gq2GkK2fv5Z6tJFioiQ/v1Xio11uiIAAADfQ4ADACiYLVukMWMszFmwIPP64GCpY0cLc3r0kEqVcqxEeBe3W2rdWvrjD+nBB6XXX3e6IgAAAN9DgAMAOHUbNkijR1uYs3hx5vWhoVKnThbmdOsm8Z4c8KZMkTp3lsLDrQsnLs7pigAAAHwLAQ4AoHCsXWtBzqhR0sqVmdeHhdn6mV697M+oKOdqhGPcbqltW2n+fOn++6U333S6IgAAAN9CgAMAKHwrV2aGOWvXZl5frJjUoYN15XTtKlWp4lyN8LipU2279bAw68KpWNHpigAAAHwHAQ4AoOi43dLSpRbkjB0rrV+f9fZmzSzM6dbNzrtcztQJj3C7pfPOsznY994rvf220xUBAAD4DgIcAIBnuN3S6tXSDz9IEydK8+Zl3Zq8UiXryunWzbp0wsOdqxVFZvp0m3UdFib984992wEAAJA3AhwAgDN27ZJ++snCnClTpCNHMm8rXtyGIHfrZnNzypVzrk4UKrdbatdOmjNH6ttXevddpysCAADwDQQ4AADnJSVJM2ZYmDNxorRtW+ZtQUFSmzaZ3Tl167LUysfNmCFdcIGNRFq/nlFIAAAA+UGAAwDwLm63tGhRZpizZEnW22vXzpyb06aNFBLiSJk4Pe3bS7NmSXffLb3/vtPVAAAAeD8CHACAd9u8WZo0ycKcX3+VUlMzbytd2pZYdetmS6547/cZs2ZZiBMaal04Vas6XREAAIB3I8ABAPiOhATpl18szPnxR2nfvszbQkOzblFOIuD1LrjAllPdeac0bJjT1QAAAHg3AhwAgG86etR2svrhB+n776V167Le3qRJ5lKrs86yWTrwKnPmSOefb9nbunVStWpOVwQAAOC9CHAAAP5hzZrMuTnz5knp6Zm3VayYOQT5ggvYotyLdOxoW4vffrv00UdOVwMAAOC9CHAAAP5nz57MLconT5YOH868LTJSuvhiC3MuvVQqX965OqG5c6Vzz7VZ1GvXSjVqOF0RAACAdyLAAQD4t6QkaebMzO6crVszb3O5pNatM+fm1K/PFuUOuPhiaepU6dZbpU8+cboaFKXERGnwYNtcrlYtqU4dqW5dO8XF8eMHAEBuCHAAAIHD7bZPjhlhzqJFWW+vWTNzbk5GWwiK3Pz5tiN8cLB14ZxxhtMVoSgsXy5dd520YkX2t5cokTXQqVvXLtepY7cBABDoCHAAAIFry5asW5SnpGTeVqqUbVHetavUubMUE+NcnQGgc2dpyhTp5pulzz5zuhoUpvR06Z13pAEDpORkqUIFqX9/accOG121Zo20YUPWsVUnqlQp+3CnenUL/gAACAQEOAAASNLBg7aOZ+JEC3X27s28LSREat8+c6lV9epOVem3/vhDOucc+zC+erUtr4Hv277dQrkpU+zyZZdJn3568uiplBTpn38yA521azPP79mT8+MXK2avlYxA5/iAp0yZovu6AABwAgEOAAAnSkuzdT0TJ9o25atXZ729cWMLci66yFKHsDBn6vQzXbpIP/8s3XSTNHy409XgdE2caHON9uyxjd9ef126666Cz7nZty9roJMR8KxbZx09OSld+uSOnbp1LfDhRxYA4IsIcAAAyMvatRbkTJwo/fZb1rUeERFS27a2PfmFF0pnncXsnFO0YIHUqpUUFGSZWe3aTleEU3HkiPTQQ9KwYXa5SRPp66+lBg0K93nS0qTNm7MPd7Zsyfl+QUHWRHd8x07G+UqVGKQMAPBeBDgAABTE3r22RflPP0kzZkg7d2a9PTpaatfOAp0LLpDOPNM+MSJfLrtM+vFH6cYbpREjnK4GBbV4sQ0qzmhae+gh6cUXPd/xcviwdeicGO6sWWOrJXNSvHjm4OQTu3eiojxXPwAA2SHAAQDgVLnd0qpVNgB5+nTbrvzAgazHlC0rdeiQGejUrs2v+HPx559SixaWef39t314hvdLT7clUk88IaWm2pbgX3xhqwy9idttmWt2s3b+/de6enISF3dyx07dutbNQ9MdAMATCHAAACgsaWm2Tfmvv9ppzhxrBThepUqZYc4FF0hVqzpSqjfr1s1WrF1/vfTll05Xg7xs3Wpzi6ZPt8vdu0uffGLZpS9JTbUQJ7twZ9eunO8XGip17Ghzm04czgwAvmrxYls5ftVVhb8EFqeOAAcAgKKSkiItXJgZ6Mybl3WrckmqWTMzzOnQwfZYDnCLFknNm1sXzsqVUr16TleEnIwfL912mw0ajoyU3nzTLvtbk9mBA9nP2lm7VkpKsmOqV7flf3zQAeDLli+XBg6Uxo2zy8HB0u23S889R0jtDQhwAADwlMREC3EyAp2FC09es9GwYeZA5HbtpJIlHSnVaT16SN9/L117rQ3AhXc5fFh64AHrtJFsdvfXXwfekrf0dGnpUvsN9T//2AissWO9b+kYAOTl778tpBk92i67XPbe/tdfdjkqSnr8cXvvj4hwrMyAR4ADAIBTEhJsmVXGDJ2lS7PeHhRk/3vK6NA591ybshoAliyRmjWz/0CuWEFXgzf5809b3rZ2rX1/Hn1UGjRIKlbM6cqcs2ePdMUV9uMcHCy99550551OVwUAeVuzxt7Dv/nG5oRJ0tVXS88+a//2zp4t9e+fGeRUrSq99JL9goU9GjyPAAcAAG+xZ48NQs7o0FmzJuvtoaG2z3ZGoHPOOZ7f3seDrrjCluj06iV9+63T1SAtTRoyRHr6aenoURvnNHKkrfyDlJxsywxGjrTL/ftLr75qgQ4AeJv166Xnn7dZc+npdt0VV1hw07hx1mPT0y3gefxxacsWu+7ss214/XnnebbuQEeAAwCAt9q61bYqz+jQ2bw56+3h4daVkxHoNG/uV9vhLFsmNWliXR7Ll9vqMjhjyxbb2n3WLLvcs6f04YdS6dLO1uVt3G7bNv3pp+1y9+7SV18FTOMcAB+wcaP0wgs2eD1jFXfXrrZ8qlmz3O+bmGizzgYPlg4etOsuv1x65RXbZBNFjwAHAABf4HZLGzZkduf8+qvth3y8qCibm5MxQ+fMM32+v7lnT+m772zGSMa6fHjWmDHSHXfYIN/ixaV33pH69PG/QcWF6dtv7e8oOdk+EP3wg3UsAYBTNm+2pU+ffmpdlJJ0ySUW3LRoUbDH2rnTBh1/9JF154SESPfcIz3zjFSmTKGXjuMQ4AAA4IvcbmnVqszunJkz7RP28cqWtfUtGR06tWv73Kfu5cszW7mXLZMaNXK2nkBy8KB03332W1pJatnSuklq1XK0LJ8xf7514OzeLVWsKE2alPdvtwGgsG3dah0zH3+cuRHmRRdZcNO69ek99t9/S488Iv30k10uWVJ66impXz+/XuHtKAIcAAD8QVqaTf7N6M6ZM8e2CjpepUqZYc4FF9gkQh9w9dXWBXLllbbDD4reH3/YoOJ//rHM74knbC5CaKjTlfmWDRukyy6zDznFi9sMia5dna4KQCDYsUN6+WVp2DDrBpTsdzrPPVf4c2umTZMeesh+0SJJZ5xhz92zp8/93sjrEeAAAOCPUlJsm/KMQGfevMxfvWWoWTMzzOnQQapQwZla87BypXXeuN2WUTVp4nRF/istzX5TO3Cgna9a1Ybynn++05X5rgMHLIScOtU+yLz2mm3Dy4caAEVh1y4boP7++zazRrJxeYMGFe3Q+bQ06YsvrANn+3a7rk0be88755yie95AQ4ADAEAgSEy0ECcj0Fm4MHN6YYZataS2be3Upo1Uv77XzNC55hpp1CgbljhunNPV+KeNG21Q8W+/2eVrrpE++MBa4nF6UlOle++1wc+SdNddNkvIj2aOA3DYnj3S0KH23nLkiF13zjm209SFF3ouND50yOoYMiSzjl697JcDNWp4pgZ/RoADAEAgSkiwZVYZM3SWLj35mFKlbIF8mzYW6rRsKUVGer5W2RKUM8+0LpzFi6WmTR0pw299842FCgkJNgv7vfekG26gS6Qwud3SG29IDz9s5y++2AZzx8Q4XRkAX7Zvn23n/dZbFp5INpR40CCpUyfn3se3bbNunOHD7T2vWDHp/vttSS6/GDh1BDgAAEDav9+mrs6da506f/yR2XudISTEkpPju3Q8uLXOdddZ0NC9uzRhgsee1q/Fx9uwyS+/tMutW9v5M85wti5/9v339lo+ckRq0ED68UepenWnqwLgaw4csC2933jDwnfJBqUPGiRdeqn3BPBLllhwPX26XS5Txmaq3XUXc9VOBQEOAAA4WWqq/a9r3jwLdebOtV+nnahatayBTqNGUnBwkZS0erXUsKFtWfrXX9JZZxXJ0wSMuXOty2bjRlsp98wz0pNPsqzHExYtsmHG27ZJ5ctbqMOMCAD5cfCg9PbbtkwpY/PJxo1tOHH37t4T3BzP7ZZ+/tmCnFWr7Lo6dWxWT7du3lmztyLAAQAAeXO7pc2bM8OcefNsu4n09KzHRUXZJ9GMZVfnnGPXFZIbbrCtrLt2lSZOLLSHDShHj9pMhBdesG9f9er2d9qmjdOVBZatW+11vHixbbf7xRc2JwKFZ/du6d13rdOJv1v4ukOHbHnrq6/asinJXtsDB9oujV4ysi5XR49Kn3xivzDYvduua9/ewqjmzR0tzWcUaYDz3nvvaciQIdqxY4eaNGmid955Ry1btsz22OHDh+vmm2/Ocl1YWJiSkpLy/XwEOAAAeFBCgi21yujS+f13+9Xg8YKCrCvn+C6datVO+ddta9fabOX0dJvDfPbZhfB1BJB//7XtwX//3S7feKN9wOW/Tc44dMi+Hxlh5Asv2HwIfht9elJT7YPuwIG2TFCS7rjDuhbCwhwtDSiwI0dsoPwrr2SGHnXq2Ov76quLrOm1SCUk2Dbjb7whZXzcv/FG6cUXpSpVnK3N2xVZgDNq1Cj17t1bw4YNU6tWrfTmm29qzJgxWrNmjcqXL3/S8cOHD9f999+vNWvWZD6py6UKBdjSlAAHAAAHpaVJK1Zk7dLZuPHk4ypWzBroNG1aoIXwvXvb1taXXipNmlRo1fs1t9v+zvr1s4wtJsY+EFx7rdOVIS1NevRRG0Iq2ev7o48IGk7Vzz9L/fvbkktJql1bWr/efgZatpS++06qXNnZGoH8SEy094LBg6WdO+26mjVtfsy11/rHctfNmy20/uoruxweLj30kDRgQKE27/qVIgtwWrVqpRYtWujdd9+VJKWnp6tKlSq699579dhjj510/PDhw/XAAw/oQMZCvlNAgAMAgJfZti0zzJk719aLHD2a9ZjISPtklbHsqnVr2wErB+vWWRdOWpo1AOXQ3Iv/279fuvtu24Zdks47z8KcatWcrQtZDRtmAVtamnT++dK4cTbsE/mzerUFNz//bJfLlZNeekm6+WZp6lQbHL1/v10/apTUoYOz9QI5SU62ZUYvvZQ5eq56dVt2dOON/hHcnGjhQgtu5syxyxUq2DDmW27xz6/3dOQ38yjQirqUlBT99ddf6tixY+YDBAWpY8eOmj9/fo73O3TokKpVq6YqVaqoe/fuWrlyZa7Pk5ycrISEhCwnAADgRSpWlK66yvqkFyyw9QwzZ1qfdJcutpfokSN23UsvWVtN6dI2rfiOO2wwSMavz/+vdm2bhSNZCzlyNnu21KSJfWANDrYlOjNmEN54o7vukn76yZazzZ5t46PWrnW6Ku934ID04IO2UvPnn62Z7+GHLei97TZ73XfuLP35pzX77d4tXXSR9NprWd5WAMelpEgffijVqmVh7rZttpzoo4+kNWssjPTXMKNFC2nWLGn8ePv6d+6U7rzTfmYnT3a6Ot9UoA6cbdu2qVKlSpo3b55at2597PpHH31Us2bN0h9//HHSfebPn69169apcePGio+P19ChQzV79mytXLlSlXPocxw4cKCee+65k66nAwcAAB+Rnm6/Oj++S2fdupOPK18+s0OnTRv9U7K56jYOU1qa7X7ODj5ZpaZauDV4sH1IrVnTWtRbtXK6MuRl5UrLMTdtska0ceNsyCeySkuzLoWnnpL27LHruna1YKZ27ezvc+SIBWUjR9rlq6+WPv1UKlHCMzUD2UlNlUaMsOHymzbZdZUq2a6At9wSeMspU1KsI/G55zKHNV98sTRkiO22FeiKZAnVqQQ4J0pNTVX9+vV17bXX6vnnn8/2mOTkZCUnJ2f5YqpUqUKAAwCAL9u1y1KZjFk6f/5p/6M7XliYbon5Tp/vulSdmu3U5ClBtjYCWrfOBuMuXGiXb7lFevNN5gn4kp07pR49bNh0aKj9Br5PH6er8h4zZkgPPGAb4Um2pPKNN6ROnfK+r9stvf++3f/oUWv2GzfOhsICnnT0qAXrgwbZgHlJio21mTC3327zYALZ/v3WrPv22xZyBQVZF9Lzz0txcU5X55wiWUJVtmxZBQcHa2fGtKX/27lzp2JjY/P1GKGhoWrWrJnWr1+f4zFhYWGKjo7OcgIAAD6ufHmpe3fbK3XuXNuuYu5cu9yjhwU1ycl6ate9ClGqpiyuoPnlu9knsJtvtl/L//33yVuc+zm3W/rsM6lZMwtvSpaURo+2DgPCG99SoYL066+29XVqqr2sn3wy4F7SJ/n3X9su+YILLLwpVco+3C1dmr/wRrIdvvr2tVWbcXHW8dSiReZOYEBRS0uTvv7awsM+fex1Xb68DTL/91/p3nsJbyT7+R461Jp0r7rK3v8+/dQ67AYNkg4fdrpC71agAKdYsWJq3ry5pk+ffuy69PR0TZ8+PUtHTm7S0tK0fPlyxQVyvAYAAKx/vE0b6ZFHbIH8zp3S2rU6Y/izuqmu7Yf9rJ6z1pPhw+1Xlw0bSmXL2lqUF1+0T2t+/L+9ffvsP7i33mpfZvv29gH3qqucrgynKiLCPuQ99ZRdfuklC3QSE52tywkHD1pXQoMG1i0THGwhzLp19mG3AJvYHdO2rfTXX9K551pG3L279PTT9uEaKArp6RaqN2pkXZJr19qg8ldeseDmwQft5x5ZnXGG/b3NnWvLpQ8ftp246tSxf/L5mc3eKW0jftNNN+nDDz9Uy5Yt9eabb2r06NFavXq1KlSooN69e6tSpUoaPHiwJGnQoEE655xzVKtWLR04cEBDhgzRhAkT9Ndff6lBgwb5ek52oQIAILBs3Gi/jTt6VPptyHy13T/JZun88cfJn3SDg20iYsb25W3a2IRIH/frr7b19NatNuDyhRdsiGtwsNOVobCMGGEDeVNTbde1iROtS8ffpafbvJrHH5e2b7frLrzQlgSeeWbhPEdqqv28vP22Xe7UyYKz0qUL5/GB9HRpwgQLHVassOtKlbLX3b330iFZEG63hTmPPWb//kv2z/prr1lnXiAosm3EJendd9/VkCFDtGPHDjVt2lRvv/22Wv1/el779u1VvXp1DR8+XJL04IMPaty4cdqxY4dKlSql5s2b64UXXlCzZs0K/YsBAAD+4447pI8/tg9206b9/8rUVFtXkTEYee5cSzhOVLly1kCnSZNT+3W+A1JSrGNgyBD7T22dOvbBs3lzpytDUZg9W7r8cuu2qlZNmjSp8EIMbzR/vnT//ZmznGrWtA9p3brZMqjC9tVX1ryXmCjVqGGdPk2bFv7zIHC43dIPP1hws2SJXRcTY9vd33+/ncepSUqS3nnHGmzj4+26yy6zldb16ztbW1Er0gDH0whwAAAIPJs2WRdOaqp9yD3vvBwO3LIlM9CZN8/+R31i73VkpLU4ZAQ6rVt75a/iV6+2FvxFi+zyHXfY/ITixZ2tC0Vr3Tr7kLJ2rf3WfvRo2yLbn/z3n/12/auv7HJUlC0ju//+ot+NZ+lS6YorbDlLeLgNj77xxqJ9Tvgft9u2tH/mGVumJ9nr+IEHbJlUqVKOludX9uyx3aqGDbNO3OBg+/dw4ECbK+SPCHAAAIDPu+su6cMPpQ4dbElRvhw+bL/ezwh05s2TDhw4+bj69TMDnTZtpLp1i6YFIB/cbus2euAB6xQoXdpmNl9+uSPlwAH79tkg35kzbVeWd96R7rnH6apOX2KiDSx9+WXb7tvlsuHNL75oO/N4yv79Fo7+/LNd7tvXwtFixTxXA3yT2y398ot13GRsuly8uHTffdJDD9m8GxSNNWukAQOk77+3y1FRNjfrgQf8byA0AQ4AAPB5mzdLtWpZF87MmVK7dqfwIOnp9r/A4wOdNWtOPq506ayBTosW1rlTxPbssTkoGf9B7dhR+uILqWLFIn9qeJmUFOnOO22Ap2TdKa+95ptzj9xuacwYm1G+ebNd17at9NZbzi0HTE+33+oPGmSX27SxGvlZQ3b++Uf68kub1/TPP3ZdRITUr5+9rsuVc7a+QDJzpoVlGd2pVatKgwdL11xjgbc/IMABAAB+4Z57pA8+sPBm5sxCetA9e2wYR0ags2CBLb4/XkiI7d3dpk3mPJ1KlQqpADN1qg0q3rHDOgEGD7bfLPrLf0hRcG637V7z+ON2+bLLbAaSLw1EXbzYwqc5c+xylSo20+nqqx1rcsti0iTphhtsxkZsrC1Zy3GJJgLK/v32ehg50jL/DMWL2yylAQM82zmGTOnptgTziSdsSaZkv2d5/XXbdc7XEeAAAAC/8N9/Nug0JcWWUXXoUARPkpJis3P+H+ik/TZfSdv3KVlhSlJ45im2hpLObK7k+s2UVLuRkuJqKCk1WElJOnZKTlaWyzndlrHSS7LVXF9/zXBVZBozxsK9pCSbwf3DD96/udrOndKTT0qffWZBVESEfeB95BGPNLMVyPr1tkRxxQrLal9/3TorvCFggmelpEiTJ9uucD/8YJclC9IvvNB+Dnv0kEqUcLRM/N+RI9Ibb9iyzEOH7LorrrDgu1YtZ2s7HQQ4AADAb/TrJ733njXBDB6cdzhyurcdPeq5r+2ee6w7wds+4MJ5f/whde9uwUhcnH249MbdyJKTbbvu55+XDh6066691j5QeXPodPiwLV/89lu7fP31NuCYn0X/53ZbgD5ihH3/9+7NvO3MMy20ue66Qm+6RCHascPmEn3yiXXn9Otns8N8FQEOAADwG1u3WhdOcrLnnzskxIYlhoe5FR6UrLC0IwpPSVB44n6Fpx0+vj9HYUpWeMkIhVcspfAq5RReI07hcaUVHuFSWNj/H+e4U40aUqNGnv+a4Ds2bbJlVCtWWLDw1VfWDeAN3G5bjtS/v3W0SNLZZ9ucmzZtnK0tv9xuq/fhh23zusaNbavxmjWdrgxFYdMmm2szYoTt+pYhNtYCmxtvtI43OrF8x4oVNtvq/fd9ey4RAQ4AAPArb74pvfuuFBqaGYBkF4qc7m3H3x4WZgFOttLTpVWrsm5hvm7dyceVLZt1OPLZZ9vaEiCfEhKkXr1smYfLJb36qg30dPJD5sqVtnXy1Kl2OTbWuuN69/bNGU6zZtmMnl27pJIlLSjr0sXpqlAY4uOlsWMttJk9O/P6iAhbRnfjjTY8Psf3esADCHAAAAA8bffuzMHI8+ZZj/6JbUOhodJZZ1mY07q1TWGsVo1f+SJXR4/aYOD337fLt99uywpDQz1bx759tmzhgw+sY6VYMevAeeIJ3xq0nJ2tW6WePaXff7cfx4EDpaee8s1AKtClptrW3yNH2g5/GTPqXS6bo3bjjdKVV/r+axb+gwAHAADAaSkptu9pRqAzd64t3D9RuXLWmdOiReapQgXP1wuv5nbbjIcHH7QGsAsvtM6CkiWL/rmPHpWGDZOeecZ26pGse2HIEP9abpScbH+/H3xgly+7zEIAT/wd4/S43fZ2O3Kk9M031k2VoUEDC22uv9675zIhcBHgAAAAeBu3W9q4MTPM+eMPadmy7KcmV6mSNdBp3pxPkZBkc2euucaG8NarJ/34o3TGGUX3fFOnWqixcqVdbtTIljRecEHRPafThg+X7rrLAp1atWwuDvOqvNOWLbbkbeRI6e+/M68vVy5zrs1ZZ9HkCO9GgAMAAOALkpKkpUttudWff9qfq1ZZ2HOiOnWyduo0a8aWOQFq6VLrDvnvPxuz9P33hT84eP16m7UzcaJdLlPGdpq6/fbAmBfy11+2zGbTJvsx+/RTC87gvIMHpe++s9BmxozMt8uwMBvyfeON0sUXe36JIXCqCHAAAAB81cGDthZg4cLM04YNJx8XHCw1bJi1U6dRIz61BIht26Ru3SxoCAuTPv/ctu8+XQkJ0gsvWJdNaqqFNX372uybUqVO//F9yd699neaMaz5wQdte3R+xDzv6FFp2jQLbcaPlxITM29r185Cm549pZgY52oEThUBDgAAgD/ZsyezQyfjlN08nbAwqWnTrKFO3bpMYvVThw9LN9wgTZhglwcOtDk1p7JcJC3Nlg498UTm/JBOnaQ33pDq1y+kgn1QWpr09NO2y5YknX++NHo0Y6o8ZelS20Hq66+zvuXVrZs516Z6dcfKAwoFAQ4AAIA/c7utBeP4QGfhQunAgZOPLVHCZugcH+pUr85QCD+Rni499pgNFJbsA+0nn0jh4fl/jDlzbJerxYvtcp060uuv21bavEzM+PHSTTdZg1zFiraE55xznK7KP23bZoHNiBHS8uWZ15cpYx1RN95ob2O8NuEvCHAAAAACjdst/fNP1kBn0SLpyJGTjy1b9uSdr2JjPV8zCs3HH0v33GNLTdq2tcChXLnc77Npk/Too9ZRItnyk2eftSVTxYoVfc2+ZvVq6YorbExVaKj09tvSnXcSJBSGw4ftNTtihDR9ugWTkr0Ou3aVeveWOnfmdQn/RIADAAAA+zS/alVmoPPnn7YmITX15GMrV84a6px9duANPfFx06fb4N34eNuZ6scfbaeqEx0+bLNchgyxOdoul3THHTakOK/QJ9AdPCjdcott4S5JffpI778vRUQ4WpZPSkuzIcQjR1pH0+HDmbe1bWuhzVVX8TYE/0eAAwAAgOwlJ9v25cd36vz9d/Y7X9WqlbVLp1kzqXhxz9eMfFu1Srr0Upt7XbKkBQ0XXmi3ud22NGXAAGnrVruuXTsbWNy0qUMF+yC3Wxo61JaupafbNtXffccslvxascJCm6++ynwdSlLNmhba3HCDBZBAoCDAAQAAQP4dOnTyzlf//nvycUFBWXe+OvtsqXFj1jV4md27bTvlefNsF6lhw+zbdP/90vz5dkz16hZCXHEFS4BO1fTptrX4nj1S6dLSt99KF13kdFXeaccO6ZtvLLjJmLUkWXfNNdfYXJtzzuG1iMBEgAMAAIDTs3dv5s5XGX9u23byccWKSU2a2FqdiAibnhsenvX8qVxmr+bTkpQk3Xqrddwcr3hx22mqf/+CDTpG9jZvtmVrf/5p+eaLL1qHE0GEjd/6/nsLbX75xZZMSfajfeml1m3TpYttngcEMgIcAAAAFL7sdr7av79onis4OGugUxihUF6XM64LCfGLT+ButzRokG0vLtkH5sGDbRclFJ6kJOnee233L0m6/HLbkj0QP7okJEgLFlhwOHaszQzKcM451mnTq5ftKAXAEOAAAACg6LndttRq4UJrRUhOlhIT7RNtximvy8dfl5Li9FdkgoKkGjWkbt3s03ibNhYo+aj5863zpnFjpyvxbx9/LPXrZy/junVtV6X69Z2uqmikp9ucpWXLbC56xmnDhqzH1ahhM21uvFGqXduZWgFvR4ADAAAA35Oenn0IlJ8g6HTvk5ycc11ly1qY06OH1LEjWw4hRwsW2JKq//6TSpSwTpwrr3S6qtNz6JC0fLkFNBmBzbJldn12KleWLrnEOr7atvWLZjagSBHgAAAAAAWRnm6tE4mJNrxjwQJpwgTphx+yLhOLjJQ6d7Yw57LL2OMYJ9m1ywbzzphhlx991GbjhIQ4W1de3G5p06asHTXLlkn//JP9JnVhYTbTvEkTOzVubCeWRwEFQ4ADAAAAFIbUVGnOHAtzJkyQtmzJvC04WGrf3sKc7t2lKlWcqRFe5+hRGxY9ZIhdvvBC24WpXDln68pw5Iht531iWJOQkP3xcXGZQU3GqU4d7w+lAF9AgAMAAAAUNrfb9kDOCHOWL896e/PmFub06GGtCawdCXhjxkg33ywdPmz53nffSS1aeO753W7LHI9f/rR0qbRuXfZdNaGh9tJt3DhrZ423BE+APyLAAQAAAIra+vW2T/KECdLcuVk/EdesaQOQe/Sw7Xd8eAgyTs/KldIVV0hr10rFiknvv29bvBe2xER7ruM7apYty3mjuAoVMgOajLCmXj0LcQB4DgEOAAAA4Em7dtm8nAkTpKlTsw5FLl8+cwjyhRfaduUIKPHx0k03Wd4nSXfcIb39ts2RKSi3W9q27eTlT2vW2CinE4WE2G5Yx3fUNGliAQ4A5xHgAAAAAE45eFCaMsXCnEmT7NN7hhIlbIueHj2kLl2kkiUdKhKelp4uvfyy9NRTFsK0bCmNHZv76KSkJOnvv7Muf1q2TNq7N/vjy5Y9eVZNvXqnFhQB8AwCHAAAAMAbpKZKs2Zlzs3ZujXztpAQqUOHzCHIlSo5VCQ8acoU6brrpH37bLbMqFE2C3vHjqwhzdKl0urVUlrayY8RHGzBzPHLn5o0kWJjGb0E+BoCHAAAAMDbuN3Sn39mhjl//5319hYtLMy5/HL7dM4ncb+1YYPNxVmyxMKY0qWl3buzP7Z06ZNn1TRowEo8wF8Q4AAAAADebu3azCHI8+dnHYJcp07mjlatWklBQQ4ViaKSmCjddZc0YoRdDgqyb/uJs2oqVSLLA/wZAQ4AAADgS3bskCZOtDBn+nQpJSXzttjYzCHIF1zAQBM/4nZLixbZfJyGDaXISKcrAuBpBDgAAACAr0pIkCZPtjDnxx/tcoaoKBt+3KOHDUOOiXGqSgBAISDAAQAAAPxBSoo0Y4aFOd9/L23fnnlbaKh15PToYR06FSs6VSUA4BQR4AAAAAD+Jj1dWrgwcwjy6tVZb2/VygYg9+gh1a3rQIEAgIIiwAEAAAD83erVmWHOH39kva1evcwhyC1aMAQZALwUAQ4AAAAQSLZtyxyC/OuvUmpq5m1xcbbUqmFD23+6QQPpjDNs/2oAgKMIcAAAAIBAFR8v/fyzNH689NNP0qFDJx8TFmZdOg0aZA12ataUQkI8XzMABCgCHAAAAABScrI0c6btVb1ypfT339KqVVJSUvbHFytm83MyAp2McKdWLRuaDAAoVAQ4AAAAALKXliZt2pQZ6Bx/OnIk+/uEhEh16mTt1mnYUKpd20IfAMApIcABAAAAUDDp6dLmzRbknBjuZLcMS7I5OrVrn7wUq25dW6YFAMgVAQ4AAACAwuF2S1u2ZA10MgKehITs7xMUZMuuTlyKVbeuFBHh2foBwIsR4AAAAAAoWm637X51YrfOypXSgQPZ3ycoyHbAygh2MsKdevWkyEiPlg8A3oAABwAAAIAz3G5px46Tu3VWrpT27cv+Pi6XVL36yUux6teXSpTwaPkA4EkEOAAAAAC8i9st7d6dtWMn4/zu3Tnfr1q1k5di1a4tlSplwQ8A+DACHAAAAAC+Y/du2978xOVYO3bkfJ/oaOvaqVEj83T8ZTp3APgAAhwAAAAAvm/vXgt2TuzY2bYt7/uWKZNzuFOtmhQeXuTlA0BeCHAAAAAA+K/ERGnTJmnDhszTxo2Z53OatXO8uLjsw53q1aUqVaTQ0CL+IgCAAAcAAABAIEtIyBronBjwHDqU+/2Dg6XKlbMPd2rUkCpWtB21AOA05TfzCPFgTQAAAADgGdHRUuPGdjqR221Ls3IKeDZulJKTrcNn06bsH79YMalq1ZyXaJUrx4BlAIWKAAcAAABAYHG5pLJl7XT22Sffnp4u7dyZc/fO5s1SSoq0fr2dshMZmX3nTsb5UqWK7usD4JdYQgUAAAAABXH0qLR1a84Bz7Zt1uWTm5iYrIFO5cpSbGzWE9ukAwGBGTgAAAAA4ITkZOvSySng2b07f49TrNjJoU7GKS4u83yFClJERJF+SQCKDjNwAAAAAMAJYWFS7dp2ys6hQ1l30Nq4Udq+3U47dtjpwAFbprV5s53yEhOTfbhz4qlsWRvQDMDnEOAAAAAAgCeVKCE1bGinnCQl2RyejEDn+HDnxFNyshQfb6c1a3J/7uBgqXz5vLt6YmOtTpZwAV6DAAcAAAAAvE14uFStmp1y43ZbcJNbwJMRAO3ZI6WlZXb75CUyMn9dPRUqSKGhhfN1A8gRAQ4AAAAA+CqXSypZ0k716+d+bGqqzd/JLtw58bpDh6QjR6R//7VTXsqWPbmbJ7tTVFRhfNVAQCLAAQAAAIBAEBoqVaxop7wcOpS5hCu37p6dO21Xrj177LRiRe6PW7x4zuHO8afSpVm+BZyAAAcAAAAAkFWJEnaqWTP349LTpb17s+/o2bYtc7nW9u0WCh0+LK1fb6fcZOzAFRdngVNOQU+5cgxlRsAgwAEAAAAAnJqgIAtRypWTGjXK/dhDh7IGOjmd9u3L/w5cGUOZ8+roiY21UAjwYQQ4AAAAAICiV6JE7turZ0hOzuzkOf50YkfPrl0FG8pcpkzu3TwZp8jIwvl6gUJGgAMAAAAA8B5hYfnbgevoUQtx8uro2bHDBjjv3Wun5ctzf9zo6JNDnVKl7PqYmJNPGdfT4YMiRoADAAAAAPA9ISH5G8qcnm7LsnLr5sk4JSZKCQl2WrOmYPWEh2cf7OR13fHXR0QwvBk5IsABAAAAAPivoCDb5rxs2dzn9LjdFtxk18Fz4IAUH2+3x8dnPR0+bPdPSrLTzp2nXmtIyOmHQCVK2NcMv0OAAwAAAACAy5UZhtSrl//7HT0qHTx4crCTXdiT0/UJCRYgHT2audTrdL6O40OeEwOf6GhbphYWZsu+Mk55Xc7rmJAQuoeKGAEOAAAAAACnKiTEZuSUKnXqj5Gebp08pxMCxcdbAOR2Z172JJfr5MAnP8HQ6QZHxYpJdepIVat69ut1AAEOAAAAAABOCgqSoqLsVLnyqT2G221LuPIKexISbJv2lBTb8SvjfHaXczsmOfnk509OPvl6TxgyRHr4Yc8/r4cR4AAAAAAA4OtcLhuCHBEhxcYW/fO53baNe0FCn1MJivJzTIUKRf/1egECHAAAAAAAUDAuly0fCwmRihd3upqAwGhqAAAAAAAAL0eAAwAAAAAA4OUIcAAAAAAAALwcAQ4AAAAAAICXI8ABAAAAAADwcgQ4AAAAAAAAXo4ABwAAAAAAwMsR4AAAAAAAAHg5AhwAAAAAAAAvR4ADAAAAAADg5QhwAAAAAAAAvBwBDgAAAAAAgJcjwAEAAAAAAPByBDgAAAAAAABejgAHAAAAAADAyxHgAAAAAAAAeDkCHAAAAAAAAC9HgAMAAAAAAODlCHAAAAAAAAC8HAEOAAAAAACAlyPAAQAAAAAA8HIhTheQH263W5KUkJDgcCUAAAAAAACFJyPryMg+cuITAc7BgwclSVWqVHG4EgAAAAAAgMJ38OBBxcTE5Hi7y51XxOMF0tPTtW3bNkVFRcnlcjldzilJSEhQlSpVtGXLFkVHRztdDhzC6wASrwPwGoDhdQCJ1wEMrwNIvA4Cmdvt1sGDB1WxYkUFBeU86cYnOnCCgoJUuXJlp8soFNHR0fwwgtcBJPE6AK8BGF4HkHgdwPA6gMTrIFDl1nmTgSHGAAAAAAAAXo4ABwAAAAAAwMsR4HhIWFiYnn32WYWFhTldChzE6wASrwPwGoDhdQCJ1wEMrwNIvA6QN58YYgwAAAAAABDI6MABAAAAAADwcgQ4AAAAAAAAXo4ABwAAAAAAwMsR4AAAAAAAAHg5AhwAAAAAAAAvR4BTiN577z1Vr15d4eHhatWqlRYsWJDr8WPGjFG9evUUHh6uRo0a6aeffvJQpSgKgwcPVosWLRQVFaXy5curR48eWrNmTa73GT58uFwuV5ZTeHi4hypGURg4cOBJ39N69erleh/eC/xP9erVT3oduFwu9e3bN9vjeS/wfbNnz1bXrl1VsWJFuVwuTZgwIcvtbrdbzzzzjOLi4hQREaGOHTtq3bp1eT5uQf9vAWfl9jpITU3VgAED1KhRIxUvXlwVK1ZU7969tW3btlwf81T+XYGz8no/6NOnz0nf086dO+f5uLwf+Ja8XgfZ/T/B5XJpyJAhOT4m7wcgwCkko0aNUv/+/fXss89q0aJFatKkiTp16qRdu3Zle/y8efN07bXX6tZbb9XixYvVo0cP9ejRQytWrPBw5Sgss2bNUt++ffX7779r6tSpSk1N1cUXX6zDhw/ner/o6Ght37792GnTpk0eqhhFpWHDhlm+p7/99luOx/Je4J8WLlyY5TUwdepUSdJVV12V4314L/Bthw8fVpMmTfTee+9le/urr76qt99+W8OGDdMff/yh4sWLq1OnTkpKSsrxMQv6fws4L7fXwZEjR7Ro0SI9/fTTWrRokcaNG6c1a9aoW7dueT5uQf5dgfPyej+QpM6dO2f5nn7zzTe5PibvB74nr9fB8d//7du367PPPpPL5dKVV16Z6+PyfhDg3CgULVu2dPft2/fY5bS0NHfFihXdgwcPzvb4q6++2n3ppZdmua5Vq1buO++8s0jrhOfs2rXLLck9a9asHI/5/PPP3TExMZ4rCkXu2WefdTdp0iTfx/NeEBjuv/9+d82aNd3p6enZ3s57gX+R5B4/fvyxy+np6e7Y2Fj3kCFDjl134MABd1hYmPubb77J8XEK+n8LeJcTXwfZWbBggVuSe9OmTTkeU9B/V+Bdsnsd3HTTTe7u3bsX6HF4P/Bt+Xk/6N69u/uCCy7I9RjeD0AHTiFISUnRX3/9pY4dOx67LigoSB07dtT8+fOzvc/8+fOzHC9JnTp1yvF4+J74+HhJUunSpXM97tChQ6pWrZqqVKmi7t27a+XKlZ4oD0Vo3bp1qlixos444wxdf/312rx5c47H8l7g/1JSUvTll1/qlltukcvlyvE43gv814YNG7Rjx44sP+sxMTFq1apVjj/rp/J/C/ie+Ph4uVwulSxZMtf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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prediction"
      ],
      "metadata": {
        "id": "2QKBCdxNOwDd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "img = cv2.imread('/content/drive/MyDrive/Testing/no_tumor/image(18).jpg')\n",
        "img = cv2.resize(img,(150,150))\n",
        "img_array = np.array(img)\n",
        "img_array.shape"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T08:02:27.811000Z",
          "iopub.execute_input": "2022-11-19T08:02:27.811444Z",
          "iopub.status.idle": "2022-11-19T08:02:27.836705Z",
          "shell.execute_reply.started": "2022-11-19T08:02:27.811404Z",
          "shell.execute_reply": "2022-11-19T08:02:27.835457Z"
        },
        "trusted": true,
        "id": "bxMCdqHjOwDe",
        "outputId": "3faf60c6-bf2f-4e8f-e840-a1dd0f819858",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150, 150, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "img_array = img_array.reshape(1,150,150,3)\n",
        "img_array.shape"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T08:03:03.760314Z",
          "iopub.execute_input": "2022-11-19T08:03:03.761043Z",
          "iopub.status.idle": "2022-11-19T08:03:03.767644Z",
          "shell.execute_reply.started": "2022-11-19T08:03:03.761003Z",
          "shell.execute_reply": "2022-11-19T08:03:03.766627Z"
        },
        "trusted": true,
        "id": "V8RKfdkqOwDe",
        "outputId": "29a17551-5b22-4c99-bedf-d0f071f26ad2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 150, 150, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing import image\n",
        "img = image.load_img('/content/drive/MyDrive/Testing/no_tumor/image(18).jpg')\n",
        "plt.imshow(img,interpolation='nearest')\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T08:04:17.672532Z",
          "iopub.execute_input": "2022-11-19T08:04:17.672961Z",
          "iopub.status.idle": "2022-11-19T08:04:17.900139Z",
          "shell.execute_reply.started": "2022-11-19T08:04:17.672925Z",
          "shell.execute_reply": "2022-11-19T08:04:17.899171Z"
        },
        "trusted": true,
        "id": "t6kn7_CKOwDe",
        "outputId": "f778c05f-226b-47c5-d309-94981edbf9ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        }
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a=model.predict(img_array)\n",
        "indices = a.argmax()\n",
        "indices"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T08:05:21.269023Z",
          "iopub.execute_input": "2022-11-19T08:05:21.269738Z",
          "iopub.status.idle": "2022-11-19T08:05:21.520121Z",
          "shell.execute_reply.started": "2022-11-19T08:05:21.269701Z",
          "shell.execute_reply": "2022-11-19T08:05:21.519186Z"
        },
        "trusted": true,
        "id": "3XPVn3O6OwDe",
        "outputId": "f9cab988-8b20-4b93-fd45-92129fbdecce",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 438ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('model.h5')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ob4b4L11llI",
        "outputId": "ed6a05c5-0459-481f-8b5d-9d079691ecdb"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3079: 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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the current working directory\n",
        "current_directory = os.getcwd()\n",
        "\n",
        "# Print the directory where the model is saved\n",
        "print(\"Model saved in directory:\", current_directory)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sCdjMQ0M3AOF",
        "outputId": "d97b5fae-225f-45a2-bda7-100cce480e9b"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model saved in directory: /content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the model from the native Keras format file\n",
        "# model = keras.models.load_model('model.h5')\n",
        "model = keras.models.load_model('model.h5')\n",
        "\n",
        "a=model.predict(img_array)\n",
        "indices = a.argmax()\n",
        "indices\n",
        "\n",
        "# Assuming you have some input data for prediction\n",
        "# Replace 'input_data' with your actual input data\n",
        "# input_data = np.array([[1, 2, 3, 4]])  # Example input data\n",
        "\n",
        "# Perform prediction using the loaded model\n",
        "# predictions = model.predict(img_array)\n",
        "\n",
        "# Print the predictions\n",
        "# print(predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f9w0Grfs3YUj",
        "outputId": "d1f3f415-d24f-44f1-cb60-0ed51e712dad"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 134ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import ipywidgets as widgets\n",
        "from IPython.display import display\n",
        "from google.colab import drive\n",
        "import shutil\n"
      ],
      "metadata": {
        "id": "lV69rPxwBVPN"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "upload_widget = widgets.FileUpload()\n",
        "\n",
        "# Display the file upload widget\n",
        "display(upload_widget)\n",
        "\n",
        "# Retrieve the uploaded file\n",
        "uploaded_file = next(iter(upload_widget.value.values()))\n",
        "\n",
        "# Define the destination path in Google Drive\n",
        "destination_path = '/content/drive/MyDrive/UploadedImages/'\n",
        "\n",
        "# Save the uploaded file to the destination path\n",
        "with open(destination_path + uploaded_file['metadata']['name'], 'wb') as f:\n",
        "    f.write(uploaded_file['content'])\n",
        "\n",
        "# Print the image path\n",
        "print(\"Image Path:\", destination_path + uploaded_file['metadata']['name'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277,
          "referenced_widgets": [
            "978b3fed757d4665b3e711d6ad62bbdd",
            "17efcb64e1344a2eac65841c2d47eb42",
            "a5c14e748d114722a57e35f22a013e1d"
          ]
        },
        "id": "aXu8bnllBZNb",
        "outputId": "5e886e35-eaf7-4637-8483-70210697d2ab"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "FileUpload(value={}, description='Upload')"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "978b3fed757d4665b3e711d6ad62bbdd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "StopIteration",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-24-b64332717f73>\u001b[0m in \u001b[0;36m<cell line: 7>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# Retrieve the uploaded file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0muploaded_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mupload_widget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# Define the destination path in Google Drive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mStopIteration\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "lBQdymywCT_t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "uploaded_file = next(iter(upload_widget.value.values()))\n",
        "\n",
        "    # Define the destination path in Google Drive\n",
        "destination_path = '/content/drive/MyDrive/UploadedImages/'"
      ],
      "metadata": {
        "id": "AiVi5qUcCtRj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "uploaded_file"
      ],
      "metadata": {
        "id": "VOLKvo8qDCSW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "destination_path = '/content/drive/MyDrive/UploadedImages/'\n",
        "\n",
        "if not os.path.exists(destination_path):\n",
        "    os.makedirs(destination_path)"
      ],
      "metadata": {
        "id": "vUE2zI8VDluD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import ipywidgets as widgets\n",
        "from IPython.display import display\n",
        "\n",
        "# Create a file upload widget\n",
        "upload_widget = widgets.FileUpload()\n",
        "\n",
        "# Display the file upload widget\n",
        "display(upload_widget)"
      ],
      "metadata": {
        "id": "M8SYyjlKDp_Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if len(upload_widget.value) > 0:\n",
        "    # Retrieve the uploaded file\n",
        "    uploaded_file = next(iter(upload_widget.value.values()))\n",
        "\n",
        "    # Save the uploaded file to the destination path\n",
        "    with open(destination_path + uploaded_file['metadata']['name'], 'wb') as f:\n",
        "        f.write(uploaded_file['content'])\n",
        "\n",
        "    # Print the image path\n",
        "    print(\"Image Path:\", destination_path + uploaded_file['metadata']['name'])\n",
        "else:\n",
        "    print(\"No file uploaded.\")"
      ],
      "metadata": {
        "id": "TGxJpO7oDwNA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "img = cv2.imread(destination_path + uploaded_file['metadata']['name'])\n",
        "img = cv2.resize(img,(150,150))\n",
        "img_array = np.array(img)\n",
        "img_array.shape"
      ],
      "metadata": {
        "id": "G82t0pNnD76W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "img_array = img_array.reshape(1,150,150,3)\n",
        "img_array.shape"
      ],
      "metadata": {
        "id": "ge7Se7vfEMrr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing import image\n",
        "img = image.load_img(destination_path + uploaded_file['metadata']['name'])\n",
        "plt.imshow(img,interpolation='nearest')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "0H4gGbYJEQeB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.models.load_model('model.keras')\n",
        "\n",
        "a=model.predict(img_array)\n",
        "indices = a.argmax()\n",
        "indices"
      ],
      "metadata": {
        "id": "vi1AXj6kEcep"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}