{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!unzip DATASET-JPG.zip -d /content/dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VbTgMw6NF3Dp",
        "outputId": "4fb137ab-e6ca-46b2-81f6-83d2a4123e12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "unzip:  cannot find or open DATASET-JPG.zip, DATASET-JPG.zip.zip or DATASET-JPG.zip.ZIP.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip DATASET JPG.zip -d /content/dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vxdpYiN2HD-n",
        "outputId": "87818367-2b28-4ac6-8146-5883ee6260b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "unzip:  cannot find or open DATASET, DATASET.zip or DATASET.ZIP.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls /content"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zwt2SAPrHek4",
        "outputId": "c7cc2328-75c3-482d-9916-3d0f454fcc25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sample_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip \"DATASET JPG.zip\" -d /content/dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fzuab4slHoYs",
        "outputId": "4c4f0d58-5503-460d-fdfa-1b441bf7460f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "unzip:  cannot find or open DATASET JPG.zip, DATASET JPG.zip.zip or DATASET JPG.zip.ZIP.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls \"/content/dataset/DATASET JPG\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5i0-OkOBH2oP",
        "outputId": "6cca4053-ca81-49d9-d60f-a8a088d79405"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ls: cannot access '/content/dataset/DATASET JPG': No such file or directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "base_dir = \"/content/dataset/DATASET JPG\"\n",
        "\n",
        "datagen = ImageDataGenerator(\n",
        "    rescale=1./255,\n",
        "    validation_split=0.2\n",
        ")\n",
        "\n",
        "train_data = datagen.flow_from_directory(\n",
        "    base_dir,\n",
        "    target_size=(128,128),\n",
        "    batch_size=32,\n",
        "    class_mode='binary',\n",
        "    subset='training'\n",
        ")\n",
        "\n",
        "val_data = datagen.flow_from_directory(\n",
        "    base_dir,\n",
        "    target_size=(128,128),\n",
        "    batch_size=32,\n",
        "    class_mode='binary',\n",
        "    subset='validation'\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "1tMWsOceH77E",
        "outputId": "3528a2a1-5013-4b02-b2d6-12ae0c5ea40c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "[Errno 2] No such file or directory: '/content/dataset/DATASET JPG'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m/tmp/ipykernel_8964/1313620328.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m )\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m train_data = datagen.flow_from_directory(\n\u001b[0m\u001b[1;32m     12\u001b[0m     \u001b[0mbase_dir\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0mtarget_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m128\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[0;32m/usr/local/lib/python3.12/dist-packages/keras/src/legacy/preprocessing/image.py\u001b[0m in \u001b[0;36mflow_from_directory\u001b[0;34m(self, directory, target_size, color_mode, classes, class_mode, batch_size, shuffle, seed, save_to_dir, save_prefix, save_format, follow_links, subset, interpolation, keep_aspect_ratio)\u001b[0m\n\u001b[1;32m   1134\u001b[0m         \u001b[0mkeep_aspect_ratio\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1135\u001b[0m     ):\n\u001b[0;32m-> 1136\u001b[0;31m         return DirectoryIterator(\n\u001b[0m\u001b[1;32m   1137\u001b[0m             \u001b[0mdirectory\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1138\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/keras/src/legacy/preprocessing/image.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, directory, image_data_generator, target_size, color_mode, classes, class_mode, batch_size, shuffle, seed, data_format, save_to_dir, save_prefix, save_format, follow_links, subset, interpolation, keep_aspect_ratio, dtype)\u001b[0m\n\u001b[1;32m    454\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mclasses\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    455\u001b[0m             \u001b[0mclasses\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[0;32m--> 456\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0msubdir\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdirectory\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    457\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdirectory\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubdir\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[1;32m    458\u001b[0m                     \u001b[0mclasses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubdir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/content/dataset/DATASET JPG'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\n",
        "\n",
        "model = Sequential([\n",
        "    Conv2D(32, (3,3), activation='relu', input_shape=(128,128,3)),\n",
        "    MaxPooling2D(2,2),\n",
        "\n",
        "    Conv2D(64, (3,3), activation='relu'),\n",
        "    MaxPooling2D(2,2),\n",
        "\n",
        "    Conv2D(128, (3,3), activation='relu'),\n",
        "    MaxPooling2D(2,2),\n",
        "\n",
        "    Flatten(),\n",
        "    Dense(128, activation='relu'),\n",
        "    Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "model.compile(\n",
        "    optimizer='adam',\n",
        "    loss='binary_crossentropy',\n",
        "    metrics=['accuracy']\n",
        ")\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "id": "7CFbl2ZDIYV0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(\n",
        "    train_data,\n",
        "    validation_data=val_data,\n",
        "    epochs=10\n",
        ")"
      ],
      "metadata": {
        "id": "xjG7tA_-I7oI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Accuracy\n",
        "plt.plot(history.history['accuracy'], label='Train Accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.title('Model Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "# Loss\n",
        "plt.plot(history.history['loss'], label='Train Loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
        "plt.title('Model Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "3RZvAr6NJomh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('/content/face_mask_model.keras')"
      ],
      "metadata": {
        "id": "fiS00p5WKYQA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(32, (3,3), activation='relu', input_shape=(224,224,3)))\n",
        "model.add(MaxPooling2D(2,2))\n",
        "\n",
        "model.add(Conv2D(64, (3,3), activation='relu'))\n",
        "model.add(MaxPooling2D(2,2))\n",
        "\n",
        "model.add(Conv2D(128, (3,3), activation='relu'))\n",
        "model.add(MaxPooling2D(2,2))\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Dense(1, activation='sigmoid'))"
      ],
      "metadata": {
        "id": "7Yz8GaC7CeRZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(\n",
        "    optimizer='adam',\n",
        "    loss='binary_crossentropy',\n",
        "    metrics=['accuracy']\n",
        ")"
      ],
      "metadata": {
        "id": "G4hp2B6BCqZf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "print(os.listdir('/content'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pdPx-TwdDfT0",
        "outputId": "9cf6f921-268c-4306-a03c-489543394bcb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['.config', 'sample_data']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "id": "_uekLBtGFV2e",
        "outputId": "68664558-a5e8-402a-8571-079127082478"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-36f75184-e2ab-4d28-9319-61037e918e4f\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-36f75184-e2ab-4d28-9319-61037e918e4f\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving DATASET JPG.zip to DATASET JPG.zip\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import zipfile\n",
        "\n",
        "with zipfile.ZipFile('DATASET JPG.zip', 'r') as zip_ref:\n",
        "    zip_ref.extractall('/content')"
      ],
      "metadata": {
        "id": "4tvOpBTzFhKl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "print(os.listdir('/content'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z1cBTQq6FkRw",
        "outputId": "cee43e8f-8d85-4951-8718-781da16bc3e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['.config', '217.jpg', '028.jpg', '199.jpg', '086.jpg', '011.jpg', '161.jpg', '152.jpg', '192.jpg', '203.jpg', '186.jpg', '095.jpg', '248.jpg', '254.jpg', '149.jpg', '043.jpg', '103.jpg', 'DATASET JPG.zip', '079.jpg', '094.jpg', '012.jpg', '229.jpg', '207.jpg', '165.jpg', 'DATASET JPG (1).rar', '053.jpg', '111.jpg', '139.jpg', '183.jpg', '172.jpg', '100.jpg', 'DATASET JPG', 'DATASET JPG.rar', '035.jpg', 'sample_data']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(os.listdir('/content/DATASET JPG'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NYh3z8l4FmHH",
        "outputId": "5c29330b-9248-46d3-914f-98f56205c538"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['withmask', 'without']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "datagen = ImageDataGenerator(rescale=1./255, validation_split=0.2)\n",
        "\n",
        "train_data = datagen.flow_from_directory(\n",
        "    '/content/DATASET JPG',\n",
        "    target_size=(128,128),\n",
        "    batch_size=32,\n",
        "    class_mode='binary',\n",
        "    subset='training'\n",
        ")\n",
        "\n",
        "val_data = datagen.flow_from_directory(\n",
        "    '/content/DATASET JPG',\n",
        "    target_size=(128,128),\n",
        "    batch_size=32,\n",
        "    class_mode='binary',\n",
        "    subset='validation'\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3A1G7GeNFtDK",
        "outputId": "6a5f4a15-c265-4fd4-ba97-156308a3fcb9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 40 images belonging to 2 classes.\n",
            "Found 9 images belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n",
        "\n",
        "model = Sequential([\n",
        "    Conv2D(32, (3,3), activation='relu', input_shape=(128,128,3)),\n",
        "    MaxPooling2D(2,2),\n",
        "\n",
        "    Conv2D(64, (3,3), activation='relu'),\n",
        "    MaxPooling2D(2,2),\n",
        "\n",
        "    Conv2D(128, (3,3), activation='relu'),\n",
        "    MaxPooling2D(2,2),\n",
        "\n",
        "    Flatten(),\n",
        "    Dense(128, activation='relu'),\n",
        "    Dropout(0.5),\n",
        "    Dense(1, activation='sigmoid')\n",
        "])"
      ],
      "metadata": {
        "id": "pF_LRUcpGDqT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(\n",
        "    optimizer='adam',\n",
        "    loss='binary_crossentropy',\n",
        "    metrics=['accuracy']\n",
        ")"
      ],
      "metadata": {
        "id": "nCWSGFECGGYv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(\n",
        "    train_data,\n",
        "    validation_data=val_data,\n",
        "    epochs=10\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XfEFHTzsGI5j",
        "outputId": "3033aec2-5fdf-4671-a254-757006153a52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2s/step - accuracy: 0.4000 - loss: 0.7687 - val_accuracy: 0.5556 - val_loss: 0.9139\n",
            "Epoch 2/10\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 371ms/step - accuracy: 0.7000 - loss: 0.6598 - val_accuracy: 0.4444 - val_loss: 0.6388\n",
            "Epoch 3/10\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1s/step - accuracy: 0.6750 - loss: 0.6374 - val_accuracy: 0.5556 - val_loss: 0.6257\n",
            "Epoch 4/10\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1s/step - accuracy: 0.6250 - loss: 0.6224 - val_accuracy: 0.5556 - val_loss: 0.6946\n",
            "Epoch 5/10\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 367ms/step - accuracy: 0.6250 - loss: 0.5779 - val_accuracy: 0.8889 - val_loss: 0.4352\n",
            "Epoch 6/10\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1s/step - accuracy: 0.9500 - loss: 0.3001 - val_accuracy: 0.8889 - val_loss: 0.3111\n",
            "Epoch 7/10\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 380ms/step - accuracy: 1.0000 - loss: 0.1811 - val_accuracy: 1.0000 - val_loss: 0.1642\n",
            "Epoch 8/10\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 0.0574 - val_accuracy: 1.0000 - val_loss: 0.0859\n",
            "Epoch 9/10\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step - accuracy: 1.0000 - loss: 0.0356 - val_accuracy: 1.0000 - val_loss: 0.0399\n",
            "Epoch 10/10\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step - accuracy: 0.9750 - loss: 0.1514 - val_accuracy: 1.0000 - val_loss: 0.0503\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# رسم الدقة\n",
        "plt.figure(figsize=(8,5))\n",
        "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.title('Model Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.savefig('accuracy_plot.png')\n",
        "plt.show()\n",
        "\n",
        "# رسم الخسارة\n",
        "plt.figure(figsize=(8,5))\n",
        "plt.plot(history.history['loss'], label='Training Loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
        "plt.title('Model Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.savefig('loss_plot.png')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 957
        },
        "id": "swAfFHsJHFAi",
        "outputId": "94a4ca8d-f0d2-4665-d924-519500fc8380"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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rq+tVP58bBc9rbX+zY4FzSq9evTh06BDdunVjx44d1KpVizFjxgDQqlUroqKieP755zl58iT33XcfL774oqH1Xo/CrBPYzS5Y77v8VcKGz+H8YWMLEhEREQDWrl3L448/zgMPPECVKlUICwvjyJEjTq3B39+f0NBQNm3alLHOarWydevWLB+zYsWKpKen8/vvv2esO3fuHHv37qVSpUoZ6yIiInj66af5+eefeeGFF5g0aVLGZyEhIfTo0YPvvvuO0aNH88UXX2S5npykYQZOYi/bAko1gUPLYckw6PSt0SWJiIgUeGXLluXnn3+mXbt2mEwmhg4dmuWv9m/Hs88+y8iRIylTpgwVKlRgzJgxXLhw4aauSu/YsQNfX9+M9yaTiWrVqtG+fXt69+7NxIkT8fX1ZfDgwRQtWpT27dsDMHDgQFq1akW5cuW4cOECy5cvp2LFigAMGzaMmjVrUrlyZVJSUpg7d27GZ7mNwqyzmEzQYgRMaAC758CRNRDZ0OiqRERECrRRo0bxxBNPUL9+fYKDg3nllVeIi4tzeh2vvPIKp0+fpnv37lgsFvr06UOLFi2wWP57vHCjRo0yvbdYLKSnpzNlyhSee+452rZtS2pqKo0aNWL+/PkZQx6sViv9+vXj+PHj+Pn50bJlSz755BPAMVfukCFDOHLkCJ6entx9991Mnz49+088G5jsRg/YcLK4uDj8/f25ePEifn45fyNWWloa8+fPp3Xr1o5fnrmDYPNXEFYF+miqrvzqqn6XfE99XvDk9z5PTk7m8OHDlCxZEg8PD6PLyRVsNhtxcXH4+flhNufsSE2bzUbFihV55JFHePvtt3O0LaPc6HfsVvKaxsw6W5NXwd0fTl+eqktEREQKvKioKCZNmsS+ffvYsWMHffv25fDhw3Tp0sXo0nI9hVln8w6Gxq84Xi97G5Kd/1WGiIiI5C5ms5mpU6dSu3ZtGjRowI4dO1i6dGmuHaeam2jMrBFq94ZNX8H5g46pupq9aXRFIiIiYqCIiAjWrl1rdBl5kq7MGsHFDVq863itqbpEREREskxh1ijlWkKpxmBNdUzVJSIiIiK3TGHWKCYTtBgJJvPfU3WJiIiIyC1RmDVSaCWo2dPxeuFgsFmNrUdEREQkj1GYNZqm6hIRERHJMoVZo3kHwz0vO15rqi4RERGRW6IwmxvU6QOFSkPCWVgzyuhqRERECrTGjRszcODAjPeRkZGMHj36hvuYTCZmz559221n13EKEoXZ3OCfU3WtH6epukRERLKgXbt2tGzZ8pqfrV69GpPJxJ9//nnLx920aRN9+vS53fIyeeONN6hevfpV60+dOkWrVq2yta1/mzp1KgEBATnahjMpzOYWmqpLRETktjz55JMsWbKE48ePX/XZlClTqFWrFlWrVr3l44aEhODl5ZUdJf6nsLAw3N3dndJWfqEwm1uYTNBihKbqEhGR3Mluh9QEYxa7/aZKbNu2LSEhIUydOjXT+kuXLvHTTz/x5JNPcu7cOTp37kzRokXx8vKiSpUq/PDDDzc87r+HGezfv59GjRrh4eFBpUqVWLJkyVX7vPLKK5QrVw4vLy9KlSrF0KFDSUtLAxxXRt988022b9+OyWTCZDJl1PzvYQY7duzg3nvvxdPTk6CgIPr06cOlS5cyPn/88cfp0KEDH330EeHh4QQFBdGvX7+MtrLi6NGjtG/fHh8fH/z8/HjkkUc4c+ZMxufbt2+nSZMm+Pr64ufnR82aNdm8eTMAUVFRtGvXjsDAQLy9valcuTLz58/Pci03Q4+zzU1CKzum6tr8FSwcAn1WgNlidFUiIiKQlggjihjT9qsnwc37PzdzcXGhe/fuTJ06lddeew2TyQTATz/9hNVqpXPnzly6dImaNWvyyiuv4Ofnx7x58+jWrRulS5emTp06/9mGzWbjoYceIjQ0lN9//52LFy9mGl97ha+vL1OnTqVIkSLs2LGD3r174+vry8svv0ynTp3YuXMnCxcuZOnSpQD4+/tfdYyEhARatGhBvXr12LRpE2fPnqVXr170798/U2Bfvnw54eHhLF++nAMHDtCpUyeqV69O7969//N8rnV+V4LsypUrSU9Pp1+/fnTq1IkVK1YA0LVrV2rUqMH48eOxWCxs27YNV1dXAPr160dqaiqrVq3C29ubXbt24ePjc8t13AqF2dymyauwYyac/tMxVded3Y2uSEREJM944okn+PDDD1m5ciWNGzcGHEMMOnbsiL+/P/7+/rz44osZ2z/77LMsWrSIH3/88abC7IoVK9izZw+LFi2iSBFHuB8xYsRV41xff/31jNeRkZG8+OKLTJ8+nZdffhlPT098fHxwcXEhLCzsum1NmzaN5ORkvvnmG7y9HWF+7NixtGvXjvfff5/Q0FAAAgMDGTt2LBaLhQoVKtCmTRuWLVuWpTC7bNkyduzYweHDh4mIiADgm2++oXLlymzatInatWtz9OhRXnrpJSpUqABA2bJlM/Y/evQoHTt2pEqVKgCUKlXqlmu4VQqzuc2VqboWv+aYqqtSB/DwM7oqEREp6Fy9HFdIjWr7JlWoUIH69eszefJkGjduzIEDB1i9ejVvvfUWAFarlREjRvDjjz9y4sQJUlNTSUlJuekxsfv27SMiIiIjyALUq1fvqu1mzJjBZ599xsGDB7l06RLp6en4+d3av+e7d++mWrVqGUEWoEGDBthsNvbu3ZsRZitXrozF8vc3ueHh4ezYseOW2vpnmxERERlBFqBSpUoEBASwe/duateuzaBBg+jVqxfffvstTZs25eGHH6Z06dIADBgwgL59+7J48WKaNm1Kx44dszRO+VZozGxupKm6REQktzGZHF/1G7FcHi5ws5588klmzZpFfHw8U6ZMoXTp0txzzz0AfPjhh3z66ae88sorLF++nG3bttGiRQtSU1Oz7Ue1fv16unbtSuvWrZk7dy5//PEHr732Wra28U9XvuK/wmQyYbPZcqQtcMzE8Ndff9GmTRt+++03KlWqxC+//AJAr169OHToEN26dWPHjh3UqlWLMWPG5FgtoDCbO/17qq4LRwwtR0REJC955JFHMJvNTJs2jW+++YYnnngiY/zs2rVrad++PY899hjVqlWjVKlS7Nu376aPXa5cOY4dO8apU6cy1m3YsCHTNuvWraNEiRK89tpr1KpVi7JlyxIVFZVpGzc3N6zWGz/GvmLFimzfvp2EhISMdWvXrsVsNlO+fPmbrvlWVKxYkWPHjnHs2LGMdbt27SI2NpZKlSplrCtXrhzPP/88ixcv5sEHH2TKlCkZn0VERPD000/z888/88ILLzBp0qQcqfUKhdnc6p9TdS0eanQ1IiIieYaPjw+dOnViyJAhnDp1iscffzzjs7Jly7JkyRLWrVvH7t27eeqppzLdqf9fGjduTLly5ejRowfbt29n9erVvPbaa5m2KVu2LEePHmX69OkcPHiQzz77LOPK5RWRkZEcPnyYbdu2ERMTQ0pKylVtde3aFQ8PD3r06MHOnTtZvnw5zz77LN26dcsYYpBVVquVbdu2ZVp2795N06ZNqVKlCl27dmXr1q1s3LiR7t27c88991CrVi2SkpLo378/K1asICoqirVr17Jp0yYqVqwIwMCBA1m0aBGHDx9m69atLF++POOznKIwm1tpqi4REZEse/LJJ7lw4QItWrTINL719ddf584776RFixY0btyYsLAwOnTocNPHNZvNzJo1i6SkJOrUqUOvXr149913M21z//338/zzz9O/f3+qV6/OunXrGDo084Wpjh070rJlS5o0aUJISMg1pwfz8vJi0aJFnD9/ntq1a/PQQw9x3333MXbs2Fv7YVzDpUuXqFGjRqalXbt2mEwmfv31VwIDA2nUqBFNmzalVKlSzJgxAwCLxcK5c+fo3r075cqV45FHHqFVq1a8+eabgCMk9+vXj4oVK9KyZUvKlSvH559/ftv13ojJbr/Jydvyibi4OPz9/bl48eItD8TOirS0NObPn0/r1q2vGtNyU+Y+D5snQ1hVTdWVh9x2v0ueoz4vePJ7nycnJ3P48GFKliyJh4eH0eXkCjabjbi4OPz8/DCbdT3wdt3od+xW8pp6Irdr8hq4+1+eqmua0dWIiIiI5CoKs7ndlam6AJa9BSnxxtYjIiIikosozOYFdfpAoVKOqbpWf2x0NSIiIiK5hsJsXuDiBs01VZeIiIjIvynM5hXlW/09VdeSYUZXIyIiBUQBu09cnCi7frcUZvOKf07VtetXOLLW6IpERCQfuzJDQ2JiosGVSH515Ylo/3wUb1a4ZEcx4iShlaHm446puhYO1lRdIiKSYywWCwEBAZw9exZwzHlqusXHyuY3NpuN1NRUkpOTNTXXbbLZbERHR+Pl5YWLy+3FUYXZvKbJa7Bj1t9Tdd3ZzeiKREQknwoLCwPICLQFnd1uJykpCU9PzwIf7LOD2WymePHit/2zVJjNa65M1bX4NcdUXZU7gLuv0VWJiEg+ZDKZCA8Pp3DhwqSlpRldjuHS0tJYtWoVjRo1ypcPynA2Nze3bLnCrTCbF9XpA5u/gvOHYPUoaDrc6IpERCQfs1gstz2uMT+wWCykp6fj4eGhMJuLaMBHXqSpukREREQAhdm8q3wrKHkPWFM0VZeIiIgUWAqzeZXJBC1HaqouERERKdAMD7Pjxo0jMjISDw8P6taty8aNG2+4/ejRoylfvjyenp5ERETw/PPPk5yc7KRqc5krU3WBY6oum9XQckRERESczdAwO2PGDAYNGsTw4cPZunUr1apVo0WLFtedAmTatGkMHjyY4cOHs3v3br766itmzJjBq6++6uTKc5Emr4G7399TdYmIiIgUIIaG2VGjRtG7d2969uxJpUqVmDBhAl5eXkyePPma269bt44GDRrQpUsXIiMjad68OZ07d/7Pq7n52pWpusAxVVdKvLH1iIiIiDiRYVNzpaamsmXLFoYMGZKxzmw207RpU9avX3/NferXr893333Hxo0bqVOnDocOHWL+/Pl063b9BwekpKSQkpKS8T4uLg5wzBXnjDnzrrSRo23d+QQum77CdOEw1pUfYWvyes61JTfFKf0uuYr6vOBRnxc86nPnuZWfsWFhNiYmBqvVSmhoaKb1oaGh7Nmz55r7dOnShZiYGBo2bIjdbic9PZ2nn376hsMMRo4cyZtvvnnV+sWLF+Pl5XV7J3ELlixZkqPHDwtsT90Lo2H9OFbERpDoHpKj7cnNyel+l9xHfV7wqM8LHvV5zktMTLzpbfPUQxNWrFjBiBEj+Pzzz6lbty4HDhzgueee4+2332bo0KHX3GfIkCEMGjQo431cXBwRERE0b94cPz+/HK85LS2NJUuW0KxZs5ydYNneCtu0rViOrOI+20qsra89VEOcw2n9LrmG+rzgUZ8XPOpz57nyTfrNMCzMBgcHY7FYOHPmTKb1Z86cyXgW9L8NHTqUbt260atXLwCqVKlCQkICffr04bXXXrvmI9Hc3d1xd3e/ar2rq6tTfxGd0l7LkTDxbsx75mA+sREiG+Rse/KfnP17JsZTnxc86vOCR32e827l52vYDWBubm7UrFmTZcuWZayz2WwsW7aMevXqXXOfxMTEqwLrlcfr2e32nCs2rwi7A+7s4XitqbpERESkADB0NoNBgwYxadIkvv76a3bv3k3fvn1JSEigZ8+eAHTv3j3TDWLt2rVj/PjxTJ8+ncOHD7NkyRKGDh1Ku3bt9MzoK+59/e+purb/YHQ1IiIiIjnK0DGznTp1Ijo6mmHDhnH69GmqV6/OwoULM24KO3r0aKYrsa+//jomk4nXX3+dEydOEBISQrt27Xj33XeNOoXc58pUXYtfd0zVVak9uPsaXZWIiIhIjjD8BrD+/fvTv3//a362YsWKTO9dXFwYPnw4w4cPd0JleVidp2DzZDh/CFaPgqb6eYmIiEj+ZPjjbCUHuLhB83ccr9ePgwtHDC1HREREJKcozOZX5VtDyXvAmgJLdGVWRERE8ieF2fzKZIIWI8Bkhl2z4chaoysSERERyXYKsznMbrdzNsmgxjVVl4iIiORzCrM5bNqm47y33cLEVYex2gyYC7fJa5qqS0RERPIthdkc9sfRWKx2Ex8t2U+nies5eu7mnzWcLXxCHFN1gWOqrpR457YvIiIikoMUZnPYhx3voGtpK97uFjZHXaDVp6uYvvGoc59YVucpKFQKLp1xTNUlIiIikk8ozOYwk8lEncJ25varT52ShUhItTL45x30/mYz0fEpzilCU3WJiIhIPqUw6yTFAj35ofddvNq6Am4WM0t3n6Xl6FUs+uu0cwoo3xpKNtJUXSIiIpKvKMw6kcVsok+j0sx5tgEVw/04l5DKU99u4aWfthOfnJazjZtM0GLk31N1Ra3L2fZEREREnEBh1gAVwvyY3a8+T99TGpMJftpynFafrub3Q+dytuGrpuqy5Wx7IiIiIjlMYdYg7i4WBreqwIw+9SgW6MnxC0k8OmkDIxfsJiU9B+eDvTJV16ntsH1azrUjIiIi4gQKswarU7IQCwc2olOtCOx2mLjyEO3HrmX3qbicadAnBBq95HitqbpEREQkj1OYzQV83F14/6GqTOpeiyBvN/acjqf92LVMXHkwZx60UPfpv6fqWvNJ9h9fRERExEkUZnORZpVCWfR8I5pWDCXVamPkgj10/mIDx85n84MW/jlV17qxmqpLRERE8iyF2Vwm2MedSd1r8kHHqni7Wdh45DwtR6/ix83HsvdBC5qqS0RERPIBhdlcyGQy8UjtCBYObETtyEASUq28PPNP+ny7hZhL2fSgBU3VJSIiIvmAwmwuFlHIi+l96jG4VQVcLSaW7DpDy9GrWLrrTPY0oKm6REREJI9TmM3lLGYTT99Tml/7NaRCmC8xl1Lp9c1mBs/6k0sp6bffgKbqEhERkTxMYTaPqFTEj1/7N+CpRqUwmWD6pmO0+nQVm4+cv70Da6ouERERycMUZvMQdxcLQ1pX5Ifed1E0wJNj55N4ZOJ63l+4h9T02xgiUPcpCCypqbpEREQkz1GYzYPuKhXEwoF381DNYtjsMH7FQdqPW8ve01m8quriDi3edbxeNxYuRGVfsSIiIiI5SGE2j/L1cOWjh6sx4bGaFPJ2Y/epONqNWcOXqw9hy8qDFjJN1TUs+wsWERERyQEKs3lcyzvCWDjwbu6rUJhUq4135u2my5cbOH7hFh+0oKm6REREJA9SmM0HCvt68GWPWox8sApebhY2HDpPq9GrmbXl+K09aCHsDrizu+O1puoSERGRPEBhNp8wmUx0rlOcBc/dTc0SgcSnpPPCT9vp+91Wziek3vyBmrz+j6m6fsi5gkVERESygcJsPlMiyJsfn6rHSy3K42I2sfCv0zT/ZBW/7bnJBy1kmqrrTU3VJSIiIrmawmw+ZDGb6NekDLP7NaBsYR9iLqXwxNTNDPl5Bwk386AFTdUlIiIieYTCbD52R1F//vdsQ3o1LAnADxuP0vqz1WyJunDjHV3cofk7jteaqktERERyMYXZfM7D1cLrbSsxrXddivh7EHUukYcnrOPDRf/xoIUKbf6eqmvpcOcVLCIiInILFGYLiPqlg1n4fCMevLMoNjuMW36QBz5fy/4z1xkT+8+puv76RVN1iYiISK6kMFuA+Hm4MuqR6nze9U4CvFz562Qcbcas4as1h6/9oAVN1SUiIiK5nMJsAdS6SjiLBzaicfkQUtNtvD13F4999TsnYpOu3lhTdYmIiEgupjBbQBX282DK47V594E78HS1sO7gOVqOXsUvf/zrQQtXTdV1yZiCJW+I3ge75ugqvoiIOI3CbAFmMpnoWrcE85+7mxrFA4hPTuf5GdvpP+0PLvzzQQuaqktuxG6HA0vhu44wrjb82A1Wf2x0VSIiUkAozAolg7356al6vNCsHC5mE/N2nKLF6FWs2HvWsUGmqbrGaKoucUhNhM2TYVxdR5A9sPTvz1Z/BBeOGFaaiIgUHAqzAoCLxcyz95Xll2caUKawD2fjU3h8yiZen72DxNR0x1RdkXdrqi6Biydg6RvwSSWY+zzE7AU3X7jrGRjwh+P3JD0ZFrxidKUiIlIAKMxKJlWK+TP32Yb0bBAJwHcbjtLmszX8cSwWWv5zqq71htYpBji+BWY+CZ9WdQw3SboAgZHQ8j0YtMvx+1GoFLT5GMyusG8h7JlvdNUiIpLPKczKVTxcLQxvV5nvnqxLuL8Hh2MSeGjCekb96Ya1RjfHRpqqq2CwpsPOn+HLZvDlvbBzJtjSoURDeHQaPLsV7uoLHn5/7xNSHur3d7xe8IpjOIKIiEgOUZiV62pYNpiFAxvRoXoRrDY7n/12gB6Hm2Nz9YFT2zRVV36WdAHWjIZPq8HMnnB8I1jcoFoXeGoV9JznGHpitlx7/0YvgX8EXDyqm8FERCRHKczKDfl7ujL60RqM7VIDf09X1pwy8VFyewDsmqor/4nZD3MHwahKjrHRccfBKxjuGQwDd8ID4yG82n8fx83bMewAYN1nEHMgZ+sWEZECS2FWbkrbqkVYNLARjcqF8GVac47YQjFdOkP8sg+NLk1ul90OB3+D7x+GsbVg81eQlgihd0D7z+H5v6DJEPANvbXjVmgLZZqBNRXmv+hoR0REJJspzMpNC/P34OuetRnavjof2h8DwG3jOBav22hwZZIlaUmwZSp8fhd8+wDsXwyYoHxr6DEXnl4DNbqCq0fWjm8yQesPwOIOh5bDrtnZWLyIiIiDwqzcEpPJRLd6kQx69nn+dK2KO2mkLhhK/2lbiU1M/e8DiPHiTsKytxxDCf73HETvATcfqPs0PLsFOv8AJe92hNHbVagUNHze8Xrhq5ASf/vHFBER+QeFWcmS0oV9qfj4WOyYaGvZwOkdy2kxehWr9kUbXZpcz4mtMKsXjK7iuCkr6TwEFIcWIxxTa7V6H4JKZ3+7DQc6pvCKPwkr3sv+44uISIGmMCtZ5lq0GqY7uwPwruf3nI1LovvkjQz7dSdJqVaDqxPAMbXWX7PhqxYwqQns+Ony1FoNoNN3MGAb1OsHHv45V4OrJ7S6PLZ6w3g4syvn2hIRkQJHYVZuz71Dwc2X8raDfFxuNwDfrI+izWer2X4s1tjaCrKkWFj7GXxWHX7qAcc2OB5kUPVR6LMCes6Hiu2uP7VWdivX3HFDmN2qm8FERCRbuRhdgORxPiFwz0uwZBgPnv+Kwt0W8cKvBzgUk8CD49fxWN3iVC0WQGSwFyWCvAnydsOUHWMx5dpiDsDvE2DbNEhLcKzzCoJaT0LtJ8E3zLjaWo50zJoQtRb+nAHVHjWuFhERyTcUZuX21X0aNk+GC0doePpbFg18haG//sX/tp/k6/VRQFTGpr7uLpQI9iIyyNuxBHsTGeRFZLCCbpbZ7XB4peMr/H0L/15fuLLj6VxVHnJ81W+0gOKOhyksexMWvw7lWoJngNFViYhIHqcwK7fPxR2avwMzHoN1Ywio2YMxnWvQrmo4K/ZFcyQmgahziZy8mER8Sjo7T8Sx80TcVYfxcXfJuIIbGeT1j7DrTbCPgu5V0pIcY2A3jIez/xiHWq6lI8SWvCd7ZiTITvX6O54cF7MPlr8LrTVPsYiI3B6FWckeFdpC5N1wZDUsGQ4PT6F55TCaV/77a+3kNCvHzidy5FwiR2ISOHLu8hLjCLqX/iPolsgIuI7AWzLYmxJBXoT4uBesoBt/GjZ96bgannjOsc7V2zEnbJ2nILiMsfXdiIsbtP4IvrnfcQ7Vu0KR6kZXJSIieZjCrGQPk8kxJnLC3fDXz1CnD5Sol2kTD1cLZUN9KRvqe9XuyWlWjl9I5HBMIlH/CLlHziVwItYRdP86GcdfJ68Out5uFsfV3GsMXwjxzUdB9+QfsGEC7JwFtjTHOv8IqPsU1OiWd76yL3UP3NHRcR7zXoAnl4BZ96KKiEjWKMxK9gmrAnd2h61fw8LB0Hv5TYcUD1cLZQr7Uqbw1UE3Jd3KsfNJma7mRp27HHQvJJGQamXXqTh2nbo66HpdDrolrzF8oXBeCLo2K+yZ5xhKcHTd3+sj7nIMJajQFix58K9x83dh32I4sRn++BZq9jC6IhERyaPy4L+Ckqvd+zrs/BlObYM/p0P1Lrd9SHcXC2UK+1CmsM9Vn6WkWzl+4UrQ/Xv4QtS5RI5fSCQx1cruU3HsvkbQ9XS1UCLI6/Jwhb8Db8ncEHSTL8LWb2HjRIg96lhndoHKD8JdT0PRmsbVlh38wqHJEFj0Kiwd7gjl3kFGVyUiInmQwqxkL5/CGVN1sfRNqHg/uF8dQrOLu4uF0iE+lA65uo3UdBvHLyRmGrJwJfAev5BIUpqVPafj2XP66kesXgm6kUHelAj2omSQd6agazbnUNA9dxB+nwjbvofUS5eLKQS1noDavRwhML+o8xT88T2c/QuWvQH3jzG6IhERyYMUZiX7/WOqLlaMhIbPg2eg8ybov8zNxUypEB9KXSfonoj9x9CFK1d2zyVw/ELSDYOuh6vZEXL/MWThyhXeUF+PWw+6drvjxrkN42HvAuDyAwVCKjqGElR9JHdMrZXdLC7Q5mOY0hK2fgM1ukNEbaOrEhGRPEZhVrLfP6fqWj/WsWACr0LgHQJewY6vlL2CHe+9gx0T+3sH//25V6EcDb9uLmZKBjuutP5bmtXGiQtJHD6XQNQ/Qu6RmASOXUgiOc12w6BbopA3xQt5knLBTNTKQ4QFOG5EK+zrToivO0He7ljMJkhLhp0zLz/ideffBynb3BFiSzXJfVNrZbcS9RwzGmz7HuYNcjydzMn/0yMiInmbwqzkjAptHXfY7/4fJMcCdsc0UlemkvpPJsfV3OuF3X+HYc9C2XYjlKvF7JgNIdgbymf+7ErQvTIu93BMwuXZFxI5dj6R5DQbe8/Es/dMPGBm1ekDVx2/sCmW3p7Ledi+mAD7RQBSzR4cLHI/Zys9jleRio7gm2bFy60A/BVt+ibsmQun/4RNX0HdPkZXJCIieUgB+JdSDGEyQfuxjsWaBonnITEGEqIhIcYRahMuv0+MgYRzf3+edAGwQ9J5xxKz92YadExN9c+wm/H6Shi+Eowvv89C+M0UdP8l3Xp56MK5RA6djWPdH7vwCy3GuYQ0zsalEBi/mwdT5tDOvA43mxWAk/ZCfJ3egh+sTYg74AMHYoH1Gcf0cXch5PIV3X9e3S3s65HpfSEvt5wbx5vTfELgvmGOabp+ewcqd3CMvRYREbkJCrOS8yyu4BvqWG6GNd0RYq8Xdv8dhjPC74XLr/fdXDuegdcPu97BmV97BTnO4wZcLGZKXL5RrH7JAAqd20nrlhVxPbTUMZTgwhq4/A16YuGaHCrbnd1+jfBLtPFgfApn45OJjk/hbHwKZ+NSSEqzciklnUsp6RyOSbhh2xaziWAft6tCbuGMIOyR8drDNRd+jV+zJ/zxnWMu3cVD4cGJRlckIiJ5hMKs5D4WF8eVuZu9OmdNd4TYm73ym3ieTOH33P6ba8cj4AZh91/r7GZKnV2Iy/ihEBvl2N/sApU6wF198SpWizuAO27Q3KWUdEe4jUsm+pIj4Gb+M5mYSymcS0jFarNzJi6FM3Ep/3kavh4u173C+8/3AV6uzpuezGxx3Aw26T7HlG53dofIBs5pW0RE8jSFWcn7LC6Or6p9QoCK/729zeoIsZnC7pUAfK0rv+fBbnOM/U2OhXNXj4P9N1egypU3noGOK4+1e4F/0Zs+LR93F3zcXa55k9o/pVltnE9IvRxykx1/Xr7CG33liu/lEJySbiM+OZ345HQORt/4aq+rxUSwT+aru9e+6uuOu0s2XO0tWhNqPg5bpjiGHDy9+j+vhouIiCjMSsFjtvx9FZUK/729zQpJsf+68nv5au+1wnDiObDbiHcPx+veF7DU6ApuXjl2Oq4WM6F+HoT6eQD+193ObrcTn5L+j7DrGNYQHX91+L2QmEaa1c6pi8mcupj8nzX4e7pmhNsiAZ7UKxVEo3IhhPi639rJ3DcMds+B6N2OoRkNBtza/iIiUuAozIr8F7Pl8g1lQRBS/r+3t9lIS7jAb8tW0/rONlhcc8fVRZPJhJ+HK34ertd8mto/pabbiLl0jSu8/3h/ZUm12riYlMbFpDT2n3U86GHmluMA3FHUj3vKhdC4fGFqRATgYvmPxxt7FXLMbjCnP6x4D+7oeEtXs0VEpOBRmBXJbmYzePjl6Tli3VzMFAnwpEjAjR/WYLfbuZiUlunq7r4z8azaH83OE3EZy7jlB/H1cKFhmWAalw+hUbkQwv2vc+zqXeGPb+HY747H3T7ydQ6coYiI5BcKsyKSZSaTiQAvNwK83Cgb6pux/uWWFYiOT2HVvmhW7otm1f5oYhPTWLDzNAt2ngagQpgv95QL4Z7yIdQqUQg3l8tXbc1mx81gExvBrtlwYBmUuc+AsxMRkbxAYVZEckSIrzsdaxajY81iWG12/jwey8p90azYG83247EZT1GbuOoQ3m4W6pV2XLW9p1wIEWFVoM5T8Pt4mP8SPLPe8WQ5ERGRf1GYFZEcZzGbqFE8kBrFAxnYtBwXElJZfSCGFXvPsmpfNDGXUlm6+wxLd58BoFSINy1KP8hAj5m4nz8I6z6DRi8ZfBYiIpIb/cfdGDlv3LhxREZG4uHhQd26ddm4ceMNt4+NjaVfv36Eh4fj7u5OuXLlmD9/vpOqFZHsEOjtxv3VijDqkepsfLUpc59tyEstylMnshAWs4lD0QmM3xDNS3GPAJCy/EN+WrqGwzEJ2O12g6sXEZHcxNArszNmzGDQoEFMmDCBunXrMnr0aFq0aMHevXspXPjqCfNTU1Np1qwZhQsXZubMmRQtWpSoqCgCAgKcX7yIZAuz2cQdRf25o6g//ZqU4WJSGusOxLBibzQr997LuuTl1LfsImDlMJosfYHihbwyhiPUKx2El5u+YBIRKcgM/Vdg1KhR9O7dm549ewIwYcIE5s2bx+TJkxk8ePBV20+ePJnz58+zbt06XC9PdxQZGenMkkUkh/l7utKqSjitqoRjt9s5sjsQ60/NaWbZQgv7Vhadv5Nv1kfxzfoo3Cxm6pQsdHn6rxDKFPZx3lPLREQkVzAszKamprJlyxaGDBmSsc5sNtO0aVPWr19/zX3mzJlDvXr16NevH7/++ishISF06dKFV155BYvl2k8gSklJISXl70d8xsXFAZCWlkZaWlo2ntG1XWnDGW1J7qF+zz7FylaFu/rC+jF8XmgGyxp3ZsWRBFbti+F4bDJrDsSw5kAM787fTbi/B43KBtGobDD1SgXh6+G8/8Spzwse9XnBoz53nlv5GRsWZmNiYrBarYSGhmZaHxoayp49e665z6FDh/jtt9/o2rUr8+fP58CBAzzzzDOkpaUxfPjwa+4zcuRI3nzzzavWL168GC+vnHsq078tWbLEaW1J7qF+zx4WaxXudS2EV9wxSvz+JnWLPESdCnA2GXbHmth9wcSBOBOnLiYzY/MJZmw+gdlkp5QvVAiwUTHATlEv50z9qz4veNTnBY/6POclJibe9LYmu0F3U5w8eZKiRYuybt066tWrl7H+5ZdfZuXKlfz+++9X7VOuXDmSk5M5fPhwxpXYUaNG8eGHH3Lq1KlrtnOtK7MRERHExMTg5+eXzWd1tbS0NJYsWUKzZs0yhkZI/qd+z36mPXNxmfU4dosb6X1WQ6HSmT5PSrWy8ch5Vu0/x+r9MRw+l/k/hCE+btxdNphGZYNpUDqIAK/s7Rf1ecGjPi941OfOExcXR3BwMBcvXvzPvGbYldng4GAsFgtnzpzJtP7MmTOEhYVdc5/w8HBcXV0zDSmoWLEip0+fJjU1FTc3t6v2cXd3x9396vkpXV1dnfqL6Oz2JHdQv2ejOzrA9qaYDizFdfEQeOznTJdaXV1daVq5CE0rFwHg6LlEVu47y8p90aw9cI7oS6n8/MdJfv7jJGYTVI8IoHH5wtxTLoQqRf0xm7Pnsq36vOBRnxc86vOcdys/X8Om5nJzc6NmzZosW7YsY53NZmPZsmWZrtT+U4MGDThw4AA2my1j3b59+wgPD79mkBWRfMRkglYfgMUdDv4Gu3694ebFg7zoVi+SL3vUZtvwZnzfqy697y5JuVAfbHbYejSWUUv20X7cWmq9u5SB0//glz+Oc+5Syg2PKyIiuYuhsxkMGjSIHj16UKtWLerUqcPo0aNJSEjImN2ge/fuFC1alJEjRwLQt29fxo4dy3PPPcezzz7L/v37GTFiBAMGDDDyNETEWYJKQ8OBsPJ9WDgEyjQFd5//3M3dxUKDMsE0KBPMa23gZGwSK/dFs3JvNGsPxHA+IZXZ204ye9tJTCaoUtQ/Y4aEasUCcLEYPiW3iIhch6FhtlOnTkRHRzNs2DBOnz5N9erVWbhwYcZNYUePHsVs/vsfkYiICBYtWsTzzz9P1apVKVq0KM899xyvvPKKUacgIs7W8HnYPh1ioxyhtvnbt3yIIgGedK5TnM51ipNmtbE16kLGo3Z3nYrjz+MX+fP4Rcb8dgB/T1calg3mnnKOuW1D/Txy4KRERCSrDJ9tvH///vTv3/+an61YseKqdfXq1WPDhg05XJWI5FquntD6Q5j2CGz4HKp3gcIVs344i5m6pYKoWyqIl1tW4GxcMqv2Ox61u3p/DBeT0pj35ynm/em4ybRiuF9GsK0VGYirrtqKiBjK8DArInLLyrWA8m1g7zyY9yI8Pjfb5t0q7OfBQzWL8VDNYlhtdrYdi3UMSdgXzZ/HY9l9Ko7dp+KYsPIgPu4u1C8dROPyhWlQKiBb2hcRkVujMCsieVOr9xw3gkWtgT9/hGqdsr0Ji9lEzRKB1CwRyKBm5Th3KYU1lx+1u2pfNOcSUlm86wyLdzlmZakSaKZFSzu6x1lExHn0/ZiI5E0BxeGelxyvF78OSbE53mSQjzvtqxflk07V2fRaU/7XvyEvNCtHrRKBmE2w44KZn/84keN1iIjI3xRmRSTvqvcsBJWFhLOwfIRTmzabTVQp5s+z95VlZt/6DG5ZHoBRSw9wKSXdqbWIiBRkCrMikne5uEGbjxyvN02CU9sNK6VrnQiCPezEXEpl/IoDhtUhIlLQKMyKSN5WqjFUfhDsNpj3AvzjoSrO5OZipn0JR9uTVh/m+IWbf664iIhkncKsiOR9Ld4FNx84vgm2fWdYGVUC7dQtGUhquo33F+41rA4RkYJEYVZE8j6/ItB4iOP1kuGQeN6QMkwmeLVVeUwm+N/2k2yJumBIHSIiBYnCrIjkD3WfgsKVIOk8LHvTsDIqhfvxcM1iALwzbxd2u92wWkRECgKFWRHJHyyu0OZjx+stX8PxzYaV8mLz8ni5WfjjaCxztp80rA4RkYJAYVZE8o8S9aFaF8AO8waBzWpIGYX9POh7T2kA3l+wh+Q0Y+oQESkIFGZFJH9p9hZ4+Dum6do82bAyejcqRRF/D05eTObL1YcMq0NEJL9TmBWR/MUnBO4d6ni97G24dNaQMjxcLbzSqgIAn684yNn4ZEPqEBHJ7xRmRST/qfUEhFeHlIuwZJhhZdxfrQjVIwJITLXy8aJ9htUhIpKfKcyKSP5jtkCbUYAJtv8AUesMKcNkMjG0bSUAftxyjL9OXjSkDhGR/ExhVkTyp2I1oWYPx+t5L4A1zZAyapYIpG3VcOx2eGfubk3VJSKSzRRmRST/um84eBaCs7vg94mGlTG4VQXcXMysP3SOJbvOGFaHiEh+pDArIvmXVyFodvkBCitGQpwxc74WC/SiV8OSAIxcsIfUdJshdYiI5EcKsyKSv1V/DIrVgdRLsOhVw8p4pkkZgn3cORyTwLcbogyrQ0Qkv1GYFZH8zWx2PBnMZIa/foGDyw0pw8fdhReblwPg06X7uJCQakgdIiL5jcKsiOR/4VWhTh/H6/kvQnqKIWU8XCuCCmG+xCWn8+my/YbUICKS3yjMikjB0ORV8AmFcwdg3RhDSrCY/56q69sNURw4e8mQOkRE8hOFWREpGDz8ofk7jterPoILxoxbbVAmmKYVC2O12Rk5f7chNYiI5CcKsyJScFR5GCLvhvQkWDjEsDJebV0RF7OJZXvOsmZ/jGF1iIjkBwqzIlJwmEzQ+iMwu8DeebBvkSFllArxoVu9EgC8M28XVpsepCAiklUKsyJSsBSuAHc943g9/yVISzKkjOfuK4u/pyt7TsczY9MxQ2oQEckPFGZFpOC55xXwKwqxUbDmE0NKCPBy47n7ygIwasle4pONedyuiEhepzArIgWPuw+0GOF4veYTOHfQkDK61StBqWBvYi6lMm65MTWIiOR1CrMiUjBVag+l7wNrqmO4gd3541ZdLWZebV0RgMlrDnPsfKLTaxARyesUZkWkYDKZoPWHYHGDg8tg9xxDyrivYmEalAki1WrjvYV7DKlBRCQvU5gVkYIrqDQ0GOh4vXAIpDj/IQYmk4nX21TCZIJ5f55i85HzTq9BRCQvU5gVkYLt7kEQUALiTsCqDwwpoWK4H51qRQDw9txd2DRVl4jITctSmD127BjHjx/PeL9x40YGDhzIF198kW2FiYg4hasntLocYtePg7PGfNU/qHk5vN0sbD9+kV+3nzCkBhGRvChLYbZLly4sX74cgNOnT9OsWTM2btzIa6+9xltvvZWtBYqI5LjyLaF8a7Clw/wXDbkZrLCvB880KQPABwv3kpRqdXoNIiJ5UZbC7M6dO6lTpw4AP/74I3fccQfr1q3j+++/Z+rUqdlZn4iIc7R8D1w84chq2DHTkBKebFiSogGenLqYzKTVhwypQUQkr8lSmE1LS8Pd3R2ApUuXcv/99wNQoUIFTp06lX3ViYg4S2AJaPSC4/Xi1yD5otNL8HC1MLhVBQDGrzjImbhkp9cgIpLXZCnMVq5cmQkTJrB69WqWLFlCy5YtATh58iRBQUHZWqCIiNPUHwBBZeDSGVg+wpAS2lYN587iASSlWflw0V5DahARyUuyFGbff/99Jk6cSOPGjencuTPVqlUDYM6cORnDD0RE8hwXd2j9keP1xi/g1J9OL8FkMjG0bSUAZm09zs4Tzr9CLCKSl2QpzDZu3JiYmBhiYmKYPHlyxvo+ffowYcKEbCtORMTpSjeByg+A3QbzXgCbzekl1CgeSPvqRbDbHVN12Q24IU1EJK/IUphNSkoiJSWFwMBAAKKiohg9ejR79+6lcOHC2VqgiIjTtRgBbj5wfCNs+96QEl5uWQF3FzO/Hz7Por/OGFKDiEhekKUw2759e7755hsAYmNjqVu3Lh9//DEdOnRg/Pjx2VqgiIjT+RWBxoMdr5cOh0TnP5WraIAnfRqVAmDkgt2kpGuqLhGRa8lSmN26dSt33303ADNnziQ0NJSoqCi++eYbPvvss2wtUETEEHWfhpCKkHgOlhkzf/bT95QmxNedqHOJfLMuypAaRERyuyyF2cTERHx9fQFYvHgxDz74IGazmbvuuouoKP0HV0TyAYsrtPnY8XrLVDixxekleLu78FLz8gB89tt+zl1KcXoNIiK5XZbCbJkyZZg9ezbHjh1j0aJFNG/eHICzZ8/i5+eXrQWKiBgmsgFUfRSww9xBYHP+V/0daxajUrgf8cnpjF663+nti4jkdlkKs8OGDePFF18kMjKSOnXqUK9ePcBxlbZGjRrZWqCIiKGavw3u/nBqG2yZ4vTmLea/p+qatvEo+8/EO70GEZHcLEth9qGHHuLo0aNs3ryZRYsWZay/7777+OSTT7KtOBERw/kUhvuGOl4vewsuRTu9hHqlg2heKRSrzc6783c7vX0RkdwsS2EWICwsjBo1anDy5EmOHz8OQJ06dahQoUK2FScikivUegLCqzkecbtkmCElDGldEVeLiRV7o1m5z/mBWkQkt8pSmLXZbLz11lv4+/tTokQJSpQoQUBAAG+//TY2AyYYFxHJUWYLtBkFmGD7NIha7/QSSgZ7071eJADvzN1FulX/rRURgSyG2ddee42xY8fy3nvv8ccff/DHH38wYsQIxowZw9ChQ7O7RhER4xWrBXd2d7ye9wJY051ewoB7yxLo5cr+s5f4YdMxp7cvIpIbZSnMfv3113z55Zf07duXqlWrUrVqVZ555hkmTZrE1KlTs7lEEZFcoukb4FkIzv4FGyc6vXl/L1cGNi0HwCdL9hGXnOb0GkREcpsshdnz589fc2xshQoVOH/e+U/KERFxCq9CjkALsHwkxJ1yegld6handIg35xNSGffbAae3LyKS22QpzFarVo2xY8detX7s2LFUrVr1tosSEcm1anSDYrUhNR4Wv+b05l0tZl5v45iqa8raIxw9l+j0GkREchOXrOz0wQcf0KZNG5YuXZoxx+z69es5duwY8+fPz9YCRURyFbPZ8WSwLxrDzlmOcbSlGju1hMblQ7i7bDCr98cwcsFuxj9W06nti4jkJlm6MnvPPfewb98+HnjgAWJjY4mNjeXBBx/kr7/+4ttvv83uGkVEcpfwalC7t+P1vBch3bmPmTWZTLzephJmEyzYeZrfD51zavsiIrlJlueZLVKkCO+++y6zZs1i1qxZvPPOO1y4cIGvvvoqO+sTEcmd7n0NvAvDuf2w/uphVzmtfJgvj9YpDsA783Zjs9mdXoOISG6Q5TArIlKgefhD83ccr1d+CBedP1XWoGbl8HV3YceJi/zyxwmnty8ikhsozIqIZFXVR6BEQ0hPwmLAzWDBPu70u7cMAB8s2kNiqvPnvhURMZrCrIhIVplM0OYjMLtg3jefwhe3O72Ex+tHUizQkzNxKUxcecjp7YuIGO2WZjN48MEHb/h5bGzs7dQiIpL3FK4Id/WFdWOofmwynGkHxWo4rXkPVwtDWlWk37StTFx1kEfrRBDu7+m09kVEjHZLV2b9/f1vuJQoUYLu3bvnVK0iIrnTPYOxFyqNZ9oFXKa2gj9/cmrzrauEUTsykOQ0Gx8u2uvUtkVEjHZLV2anTJmSU3WIiORd7j6kP76Q85M6Ehr/J/zcC05sgeZvg8U1x5u/MlVX+3Fr+XnrCR6vH0nVYgE53q6ISG6gMbMiItnBM5ANpQdhbfCC4/3v4+Hr+yH+jFOarxYRwIM1igLw9txd2O2aqktECgaFWRGR7GIyY2s8BB6dBu5+cHQdfHEPHNvolOZfalkeD1czm45cYMHO005pU0TEaAqzIiLZrUIb6P0bhFSA+FMwpTVs+gpy+GppuL8nfRqVBmDkgt0kp1lztD0RkdxAYVZEJCcEl4VeS6FSe7ClwbxB8Gt/SEvO0WafvqcUoX7uHDufxNR1R3K0LRGR3EBhVkQkp7j7wsNfQ7O3wGSGbd/B5BYQm3NPC/Nyc+GlFhUAGPfbAWIupeRYWyIiuYHCrIhITjKZoMFz0O0X8CwEp7Y5xtEeWpFjTT5Yoyh3FPUjPiWdT5bsy7F2RERyA4VZERFnKNUYnloJ4dUh8Rx8+wCs/TRHxtGazSaGtqkEwA8bj7L3dHy2tyEiklsozIqIOEtAcXhiEVR/DOw2WDIMfuoBKdkfNuuWCqJl5TBsdnhnnqbqEpH8K1eE2XHjxhEZGYmHhwd169Zl48abm8Zm+vTpmEwmOnTokLMFiohkF1cPaD8W2owCsyvs+hW+bAoxB7K9qSGtK+BmMbN6fwwr9kZn+/FFRHIDw8PsjBkzGDRoEMOHD2fr1q1Uq1aNFi1acPbs2Rvud+TIEV588UXuvvtuJ1UqIpJNTCao/ST0nA++4RC9ByY1gT3zsrWZEkHePN4gEnBcnU2z2rL1+CIiuYHhYXbUqFH07t2bnj17UqlSJSZMmICXlxeTJ0++7j5Wq5WuXbvy5ptvUqpUKSdWKyKSjSLqQJ+VULw+pMTB9C7w2ztgy775Yfs1KUMhbzcORifww8aj2XZcEZHcwsXIxlNTU9myZQtDhgzJWGc2m2natCnr16+/7n5vvfUWhQsX5sknn2T16tU3bCMlJYWUlL+npomLiwMgLS2NtLS02zyD/3alDWe0JbmH+r3gyXKfexSCLrMwL3sDy6aJsOpDbMe3Yu0wATwDb7suLxcY0KQUb8zdwydL9tG6cmH8PV1v+7iiv+cFkfrceW7lZ2xomI2JicFqtRIaGpppfWhoKHv27LnmPmvWrOGrr75i27ZtN9XGyJEjefPNN69av3jxYry8vG655qxasmSJ09qS3EP9XvBkvc8bUKyEiWpHJ+NyaBlJYxuwseQA4ryK33ZNfnYI87RwOjGNF6cs44FIDTfITvp7XvCoz3NeYmLiTW9raJi9VfHx8XTr1o1JkyYRHBx8U/sMGTKEQYMGZbyPi4sjIiKC5s2b4+fnl1OlZkhLS2PJkiU0a9YMV1ddDSko1O8FT/b0eWvsZx7FPrMH3rFRND74LtY2o7Df8fBt1+dfLoYnv9nK2rMWXut0N5FB3rd9zIJOf88LHvW581z5Jv1mGBpmg4ODsVgsnDlzJtP6M2fOEBYWdtX2Bw8e5MiRI7Rr1y5jnc3muMLg4uLC3r17KV26dKZ93N3dcXd3v+pYrq6uTv1FdHZ7kjuo3wue2+7zYjWgzwr4uTemA0tx+bUvnP4Tmr8Nlqwf975K4dxTLoSV+6L5aMkBJnarlfUaJRP9PS941Oc571Z+vobeAObm5kbNmjVZtmxZxjqbzcayZcuoV6/eVdtXqFCBHTt2sG3btozl/vvvp0mTJmzbto2IiAhnli8ikjO8CkGXH6HRS473v4+Hr++H+DM33u8/vN6mIhaziUV/nWH9wXPZUKiIiPEMn81g0KBBTJo0ia+//prdu3fTt29fEhIS6NmzJwDdu3fPuEHMw8ODO+64I9MSEBCAr68vd9xxB25ubkaeiohI9jFb4N7X4dFp4O4HR9c5HoN77Obm4b6WsqG+dK7j+J/+d+btwmrTgxREJO8zPMx26tSJjz76iGHDhlG9enW2bdvGwoULM24KO3r0KKdOnTK4ShERg1RoA71/g5AKEH8KprSGTV9l+TG4zzcth6+HC3+djGPW1uPZXKyIiPPlihvA+vfvT//+/a/52YoVK26479SpU7O/IBGR3CS4LPRaCr/2czwxbN4gOLEV2nzseKLYLQjycefZe8swYv4ePly0lzZVwvF2zxX/FIiIZInhV2ZFROQmuPvCw19Ds7fAZIZt38HkFhB76w9C6FE/khJBXkTHpzBh5cEcKFZExHkUZkVE8gqTCRo8B91+Ac9CcGobTLwHDi6/pcO4u1gY0qoCAF+sOsTJ2KQcKFZExDkUZkVE8ppSjeGplRBeHZLOw3cPwprRtzSOtkXlMOqULERKuo0PFl77ITUiInmBwqyISF4UUByeWATVHwO7DZYOh596QEr8Te1uMpkY2qYSJhPM3naSbcdic7ZeEZEcojArIpJXuXpA+7HQZhSYXR03h33ZFGL239TuVYr582CNYgC8PXcX9izOkCAiYiSFWRGRvMxkgtpPQs/54BsO0Xtg0r2wZ95N7f5yy/J4ulrYEnWBuX9qGkQRyXsUZkVE8oOIOtBnJRSvDylxML0L/PYO2Kw33C3Uz4On73E8Bvy9BXtITrvx9iIiuY3CrIhIfuEbCj3mQN2+jverPoRpj0Di+Rvu1qdRKcL8PDgRm8TktYedUKiISPZRmBURyU8srtDqPXjgC3DxhANL4YvGcHrHdXfxdLPwcsvyAHy+/CDR8SlOKlZE5PYpzIqI5EfVOkGvJRBQAmKj4Mtm8OeP1928Q/WiVC3mz6WUdEYt2evEQkVEbo/CrIhIfhVWBfqsgDJNIT0Jfu4NCwaDNe2qTc1mE0PbVgJgxqZj7D4V5+RiRUSyRmFWRCQ/8yoEXX6ERi853v8+Hr6+H+LPXLVp7chCtKkSjs0O78zTVF0ikjcozIqI5HdmC9z7Ojw6Ddx84eg6+OIeOLbxqk0Ht6qAm8XM2gPn+G3PWQOKFRG5NQqzIiIFRYU20Gc5hFSA+FMwpTVs+irTY3AjCnnRs2EkAO/O302a1WZQsSIiN0dhVkSkIAkuC72WQqX2YEuDeYPg1/6QlpSxSf8mZQjyduNQdALfbYgysFgRkf+mMCsiUtC4+8LDX0Ozt8Bkhm3fweSWEHsUAF8PVwY1LwfA6KX7iU1MNbJaEZEbUpgVESmITCZo8Bx0+wU8C8GpbTDxHji4HIBOtSIoH+rLxaQ0Plt2wNhaRURuQGFWRKQgK9UYnloJ4dUh6Tx89yCsGY2L2cTrbSsC8M36IxyKvmRomSIi16MwKyJS0AUUhycWQfXHwG6DpcPhpx7cXdyDJuVDSLfZGTF/j9FViohck8KsiIiAqwe0HwttRoHZFXb9CpPu4436bljMJpbuPsO6AzFGVykichWFWRERcTCZoPaT0HM++IZDzF5K/NyOt8s7ZjR4a+4urDY9SEFEcheFWRERySyiDvRZCcXrQ0ocXQ4PZojHTPadvshPm48ZXZ2ISCYKsyIicjXfUOgxB+r2BeApfmay64d8sWgLl1LSDS5ORORvCrMiInJtFldo9R488AV2F08aW7YzNe0lZs6bb3RlIiIZFGZFROTGqnXC1GsJid7FKG6OptP2Jzm//hujqxIRARRmRUTkZoRVwbPfara518LTlEqhRc/CglfAmmZ0ZSJSwCnMiojITTF5FcK120+MSe/gWPH7BPj6fog/Y2hdIlKwKcyKiMhNq1ysEMeqD6J36iASTZ5wdB18cQ8c32J0aSJSQCnMiojILXmxeXnWutSlbfLbxPmWhvhT8HU7OLTC6NJEpABSmBURkVtS2M+DvveU5pC9CA+mvIW15D2QlgDfPwy75xpdnogUMAqzIiJyy3o3KkURfw8OxJmYWHQkVGgL1lT4sTts+8Ho8kSkAFGYFRGRW+bhauGVVhUAGLvqGGdbToRqXcBuhdlPw+8TDa5QRAoKhVkREcmS+6sVoXpEAImpVnp9t41jjT7MeGIYC16GlR+A3W5skSKS7ynMiohIlphMJt7pcAf+nq78efwibceuY2nxgdB4iGOD5e/CotcUaEUkRynMiohIlt1R1J95AxpSLSKAi0lp9Pp2CyMT22NtPtKxwYZx8Gt/sKYbW6iI5FsKsyIicluKBXrx01P16NkgEoCJqw7RaXt1Ypt/CiYzbPsOZvaE9BRjCxWRfElhVkREbpubi5nh7Sozvuud+Lq7sDnqAvcuK8pfDceAxQ12z4EfHoXUBKNLFZF8RmFWRESyTasq4fzv2YZUCvfjfEIqbZcGMrP8KOyu3nDwN/j2AUi6YHSZIpKPKMyKiEi2igz25udn6tOlbnHsdnhxayGG+b+Lzd0fjv0OU9vCpbNGlyki+YTCrIiIZDsPVwsjHqjCp49Wx8vNwrfHC9PFOpxUzxA4sxMmt4DYo0aXKSL5gMKsiIjkmPbVizKnf0PKhfqw4VIYLWKHEOceDucPweSWEL3P6BJFJI9TmBURkRxVprAPv/ZryEM1i3HYHkazi69xwqU4xJ2AKS3h5DajSxSRPExhVkREcpynm4WPHq7GBw9V5aJrMG0vvcpuU2lIPAdft4Mja40uUUTyKIVZERFxmkdqRTC7XwMCQ8J5OGkIG2wVISUO+3cPwr7FRpcnInmQwqyIiDhVhTA/5vRvSJNqZeiR+gpLrTUwpSdjn94Zdsw0ujwRyWMUZkVExOl83F347NHqDO1wJwNsLzDbWh+TLR37rF6webLR5YlIHuJidAEiIlIwmUwmHrurBNUjAuj3nRfx8Z/TzWUpzH0ee9JFTHc/b3SJIpIH6MqsiIgY6o6i/swZ0Ig15QYzLv1+AEzL3iBl4TCw2w2uTkRyO4VZERExnL+nKxO61cKj5Vu8l94FAPcNn3L+x/5gsxpcnYjkZgqzIiKSK5hMJp5sWJLmfUbwnktfbHYThXZ/x5FJXbGnpxpdnojkUgqzIiKSq9xZPJCnnn+LiYVfJc1uIfLUAnaNvp+ES/FGlyYiuZDCrIiI5DqB3m481fclFlcbTZLdjcqX1nPgkxYcPHbS6NJEJJdRmBURkVzJbDbR5sHuHG71LZfwopr1L5K/bM3/1v1pdGkikosozIqISK5W6a6WpHWbw0WzP5VNh6m4sBMjflhKcppuDBMRhVkREckDAkvXxufppcS7hVLGfJLue57imc9+4lD0JaNLExGDKcyKiEieYClcDt9nlpLkG0kxUwzvx73MC2O+53/bNY5WpCBTmBURkbwjoDieTy0hLaQyIaaLTDW9wZTpMxj2605S0jXsQKQgUpgVEZG8xacwrk/Mxx5RF39TIt+5jeTQ73N5aPx6jp1PNLo6EXEyhVkREcl7PAMwdfsFSt+LlymFyW4fUuTUElp/tprFf502ujoRcSKFWRERyZvcvKHzdKjUHjfS+dztM1qkLaPPt1t4Z+4u0qw2oysUESdQmBURkbzLxR0emgI1umHBxkeuE3nCsoAv1xym08T1nIxNMrpCEclhCrMiIpK3mS1w/xio1x+AYa7f8orHz2w9eoE2n61m+d6zBhcoIjlJYVZERPI+kwmavwP3vg5AX2YyJmA6sYkp9JyyiQ8W7iFdww5E8iWFWRERyR9MJmj0ErT+CIB2yf/j5yLfYcHK5ysO0uXL3zkTl2xwkSKS3RRmRUQkf6nTGx6YCCYLNc4vZE3JqQS6Wdl4+DxtPlvN2gMxRlcoItlIYVZERPKfao9Cp2/B4k74qWWsLT6R6qEuxFxK5bGvfufTpfux2uxGVyki2UBhVkRE8qcKbeCxmeDmg9fxNfzs/T49a/hjt8MnS/fx+JSNxFxKMbpKEblNCrMiIpJ/lWwE3eeAZyDmk1sYfu4lxrUrgqerhdX7Y2jz2Wo2Hj5vdJUichsUZkVEJH8rVhN6LgCfMDi7izabH2de9wjKFPbhTFwKnSdtYPyKg9g07EAkT8oVYXbcuHFERkbi4eFB3bp12bhx43W3nTRpEnfffTeBgYEEBgbStGnTG24vIiJC4YrwxEIIKAEXjlBqTkfmdArigRpFsdrsvL9wD72+2cyFhFSjKxWRW2R4mJ0xYwaDBg1i+PDhbN26lWrVqtGiRQvOnr32JNcrVqygc+fOLF++nPXr1xMREUHz5s05ceKEkysXEZE8pVBJeGIRhFSE+FN4fdeOUQ2svPdgFdxczPy25yxtx6zhj6MXjK5URG6B4WF21KhR9O7dm549e1KpUiUmTJiAl5cXkydPvub233//Pc888wzVq1enQoUKfPnll9hsNpYtW+bkykVEJM/xC4ee86FoTUg6j+mb+3k0JIpfnqlPZJAXJ2KTeGTieiavOYzdrmEHInmBi5GNp6amsmXLFoYMGZKxzmw207RpU9avX39Tx0hMTCQtLY1ChQpd8/OUlBRSUv6+WzUuLg6AtLQ00tLSbqP6m3OlDWe0JbmH+r3gUZ/nIa6+0HkmlpndMR9Zjf27jpR/8Ct+fropr83+iwV/neGtubvYcCiGkR0q4+fpes3DqM8LHvW589zKz9hkN/B/PU+ePEnRokVZt24d9erVy1j/8ssvs3LlSn7//ff/PMYzzzzDokWL+Ouvv/Dw8Ljq8zfeeIM333zzqvXTpk3Dy8vr9k5ARETyLLMtlVpHPif84lZsmPmjRB+OBdZn9WkTs6PMWO0mgtzt9CxnJcLH6GpFCpbExES6dOnCxYsX8fPzu+G2hl6ZvV3vvfce06dPZ8WKFdcMsgBDhgxh0KBBGe/j4uIyxtn+1w8nO6SlpbFkyRKaNWuGq+u1/+9e8h/1e8GjPs+jbG2xzR2AeceP3Bk1keoVStGmzRN0OX6RATO2cyI2mU93ufJ66wp0rl0Mk8mUsav6vOBRnzvPlW/Sb4ahYTY4OBiLxcKZM2cyrT9z5gxhYWE33Pejjz7ivffeY+nSpVStWvW627m7u+Pu7n7VeldXV6f+Ijq7Pckd1O8Fj/o8r3F1PPrWMwDTxi+wLHoZS1o8Ne9+gfkDGvHCT9tYuvssw/+3my1HLzLiwSr4uGf+p1N9XvCoz3Perfx8Db0BzM3NjZo1a2a6eevKzVz/HHbwbx988AFvv/02CxcupFatWs4oVURE8iuzGVp9AI1edrz/7W1YMhR/Txcmda/Fa60rYjGbmLP9JPePWcOe0zd/xUhEcp7hsxkMGjSISZMm8fXXX7N792769u1LQkICPXv2BKB79+6ZbhB7//33GTp0KJMnTyYyMpLTp09z+vRpLl26ZNQpiIhIXmcywb2vQfN3He/XjYH/DcBkt9G7USl+fOouwv09OBSTQIdxa/lx8zFj6xWRDIaPme3UqRPR0dEMGzaM06dPU716dRYuXEhoaCgAR48exWz+O3OPHz+e1NRUHnrooUzHGT58OG+88YYzSxcRkfymfn/w8If/DYCt30BKPDzwBTVLFGLegLt5fsY2Vu6L5uWZf7LhYAz19E2ziOEMD7MA/fv3p3///tf8bMWKFZneHzlyJOcLEhGRguvObuDuC7N6wV+/OALtI99SyNuLKY/XZvzKg3y8eC8//3GS9Z4WqtVLoFx4gNFVixRYhg8zEBERyXUqd4Au08HVCw4shW8fgKRYzGYT/ZqU4ftedxHi48apJBMPTvidxX+dNrpikQJLYVZERORayjSFbrPB3R+ObYCv28KlaADqlQ7i12fqUcrXzqWUdPp8u4WPF+/FatNTw0ScTWFWRETkeorXhZ7zwDsETu+AKS0h1nHzV4ivO/0rWel+V3EAxvx2gJ5TNxGbmGpkxSIFjsKsiIjIjYRVgZ4LwT8Czh2AyS0h5gAAFjMMbVOB0Z2q4+FqZtW+aNqNXcPOExcNLlqk4FCYFRER+S/BZeCJhRBUFuKOw+QWcPrPjI871CjKz30bULyQF8fOJ9Fx/DpmbTluYMEiBYfCrIiIyM3wL+YItOHVIDEGl+/aU+jS3oyPKxXx43/9G9KkfAgp6TZe+Gk7w37dSWq6zcCiRfI/hVkREZGb5R0MPf4HxetjSomn/oEPMC95HeIdj2X393Llqx61ee6+sgB8sz6KzpM2cCYu2ciqRfI1hVkREZFb4eEPj83CVrYlFnsalo0T4NOqsHAIxJ/GbDbxfLNyfNWjFr4eLmyJukDbMWvYePi80ZWL5EsKsyIiIrfKzQvrw9+yrvRL2IrWhvRk2PA5fFoNFgyG+NPcVzGU//VvSPlQX6LjU+gyaQNT1h7Gbtf0XSLZSWFWREQkK0wmov2qYO0xH7r9AhF1HaH29/GXQ+0rRLrF8Uu/+txfrQjpNjtv/m8XA2dsIynVanT1IvmGwqyIiMjtMJmg9L3wxCLHQxYi7rocaifAp9XwWvoqn7YuzNC2lbCYTfy67SQPfL6WqHMJRlcuctPSrTaOnks0uoxrUpgVERHJDiYTlG7imPGg+6+OUGtNgY0TMX1WgyfjPufHLiUI9nFjz+l42o1Zw/I9Z42uWuQ/nYxNosuk3+n0xfpc+VAQhVkREZHsZDJBqcZ/h9ri9S6H2i+o+UtjVt4xn/uKphOXnM4TX29i9NJ92PQYXMmlluw6Q+vPVrPxyHnik9PZczre6JKuojArIiKSE66E2p4LoPscKF4frKl4b5vMl7G9mF5sJqH2c4xeup/e32zmYlKa0RWLZEhJt/Lm//6i9zebiU1Mo0pRf+Y+25C7SgUZXdpVFGZFRERykskEpe6BnvMdc9SWaIDJmspdMT+z1msQ77pNZdee3dw/dg17TscZXa0Ih2MS6Dh+HVPWHgHgyYYlmdW3PpHB3sYWdh0KsyIiIs5gMkHJRpdD7Vwo0RCLLY2u5sWs9HieXhfH0nfcHH7ddsLoSqUA+3XbCdp+tpqdJ+II9HLlqx61GNq2Em4uuTcy5t7KRERE8quSd0PPeY5QG3k3bqTTzWUpi8wDiJ/5LKNn/UaaVY/BFedJTE3npZ+289z0bSSkWqlTshDzn7ub+yqGGl3af3IxugAREZECq+TdjuXIGuwr3sPtyGoec1lG6p8rWHGwBTW6vkVw0bJGVyn53O5TcfSftpWD0QmYTDDg3rIMuK8sFrPJ6NJuiq7MioiIGC2yIabH58Lj8zgXUhc3k5VmifPxn3QX0T/0hdijRlco+ZDdbue7DVG0H7eWg9EJhPq5M63XXTzfrFyeCbKgMCsiIpJ7RDYkqN9iTjwwi62WqriSTsjeaVg/rYF9znMKtZJtLial0W/aVl6fvZPUdBtNyocwf8Dd1Cud+2Yr+C8KsyIiIrlM0WpNKffycj4uNpo11spY7OmYtk7F/lkNmDMALkQZXaLkYX8cvUCbz1Yzf8dpXC0mXm9Tka961CbIx93o0rJEYVZERCQX8nF3YdCTj7Or2bc8kjqM1dY7MNnSYevXMOZOmPMsXDhidJmSh9hsdiasPMjDE9Zz/EISEYU8mfl0fXrdXQpzHhpW8G+6AUxERCSXMplM9GlUmjuK9KD/D1UpmbiDF91/oZ7tT9j6DWybBtU6Q6MXITDS6HIlF4u5lMKgH7ezal80AG2qhjPywSr4ebgaXNnt05VZERGRXK5+mWD+92xD0ovWoXPyYDqmvsHRwLvAlg5/fAtjasKv/eD8YaNLlVxo7YEYWn26mlX7onF3MTPywSqM7VwjXwRZUJgVERHJE4oGeDLjqXo8WjuCLbZyNDo1gPeKfEZ6ySaXQ+13jlA7ux+cP2R0uZILpFttfLRoL4999TvR8SmULezDnP4N6VynOCZT3h1W8G8KsyIiInmEh6uF9zpW5b0Hq+BmMTPhUDDNowdy7IFfofR9YLfCtu9gTC2F2gLuZGwSnSdtYOzyA9jt8GjtCOb0b0j5MF+jS8t2CrMiIiJ5zKN1ivPj0/UI9/fgUEwCLWYlM6/aOHhyKZRp+q9Q+wycO2h0yeJES3adodWnq9l05AI+7i581rkG73WsiqebxejScoTCrIiISB5UPSKA/z3bkHqlgkhMtdJv2lZG7PAhvfNP0GsZlGl2OdR+D2Nrwy99FWrzuZR0K2/M+Yve32zmYlIaVYv5M29AQ+6vVsTo0nKUwqyIiEgeFezjzrdP1uGpRqUA+GLVIbp9tZFzAVXgsZnQ6zco29wRardPg7G14JenFWrzocMxCTz4+TqmrjsCQK+GJZn5dH1KBHkbW5gTKMyKiIjkYS4WM0NaV2RclzvxcrOw/tA52o1Zw/ZjsVCsJnT96XKobQF2G2z/wRFqf34KYg4YXb5kg9l/nKDtZ6v562QcgV6uTH68Fq+3rYSbS8GIeQXjLEVERPK5NlXD+bVfA0oFe3PyYjIPT1jPDxsvP/62WE3o+iP0/keo/XM6jKsNP/eBmP3GFi9Zkpiazos/bWfgjG0kpFqpW7IQC55rxL0VQo0uzakUZkVERPKJsqG+zO7fgGaVQkm12hjy8w4Gz/qT5DSrY4OiV0LtcijX8nKonQHj6sCs3gq1ecjuU3G0G7OGmVuOYzbBc/eVZVrvuwjz9zC6NKdTmBUREclH/DxcmfhYTV5qUR6TCaZvOkanies5GZv090ZF74QuM6DPCijXyhFqd/z4d6iN3mdY/XJjdrudbzdE0X7cWg5GJxDq5873ve7i+WblsOThR9LeDoVZERGRfMZsNtGvSRm+7lmHAC9Xth+/SNsxa1h3ICbzhkVqQJfpjlBbvvW/Qm0vhdpc5mJSGs98v5Whs3eSmm6jSfkQ5g+4m3qlg4wuzVAKsyIiIvlUo3Ih/K9/QyoX8eN8QiqPffU7E1cexG63Z96wSA3o/AP0WQnl2wB22PGTI9TOfBKi9xpSv/xt69ELtP50NQt2nsbVYuL1NhX5qkdtgnzcjS7NcAqzIiIi+VhEIS9m9a1PxzuLYbPDyAV76DdtK5dS0q/euEh16DwNnloFFdoCdtg5E8bVhZlPwNk9zi6/wLPZ7ExYeZBHJqznRGwSxQt5MfPp+vS6uxTmAjqs4N8UZkVERPI5D1cLHz1clbc73IGrxcT8HafpMG4tB6MvXXuH8Grw6Pf/CrWz4PO74KeecHa3U+svqGIupfD41E28t2AP6TY7bauGM3dAQ6pFBBhdWq6iMCsiIlIAmEwmut1Vgul96hHq586Bs5doP3Yti/46ff2dMkLt6r9D7V8/w+f14NsH4fcv4Pxhp51DQbL2QAytPl3Nqn3ReLiaee/BKozpXAM/D1ejS8t1FGZFREQKkJolAvnfsw2pU7IQl1LSeerbLXywcA9Wm/36O4VXdYTap9dAxXaAHQ4ugwUvwWfVHY/LXfQaHF4F1jRnnUq+lG618dGivTz21e9Ex6dQLtSHOf0b8mid4phMGlZwLS5GFyAiIiLOVdjXg+971WXk/D1MXnuYz1ccZMeJi3z2aA0Cvd2uv2NYFej0nePJYXvnwb7FcHQ9xOxzLOvHgrsflG7ieDhD2WbgU9h5J5bHnYhN4rkf/mBz1AUAOteJYFjbyni6WQyuLHdTmBURESmAXC1mhrWrRLUIfwbP2sHq/TG0HbOGid1qckdR/xvvHFwGgp+DBs9BUiwc/A32L4b9SyAxBnb96lgAitwJ5VpA2eYQXh3M+lL4Whb/dZqXZv7JxaQ0fN1dGPFgFdpVK2J0WXmCwqyIiEgB1r56UcqH+fLUt1uIOpdIx/HrePeBKjxUs9jNHcAzAO540LHYbHByK+xbBPsXwantjvcnt8KKkeBd2BFqyzWHUk3Awy9Hzy0vSEm3MnL+HqauOwJA1WL+jO18J8WDvIwtLA9RmBURESngKoT5Mad/Q56fsY3f9pzlxZ+2s+3YBYa1rYybyy1cSTWboVgtx3LvaxB/2nHFdt8iOLQCEs7Ctu8ci9kVStS7PByhOQSXhQI2JvRQ9CWe/eEP/joZB0Dvu0vyUosKt/YzF4VZERERAX9PV77sXosxvx1g9LJ9fLfhKH+djGN815qE+Xtk7aC+YXBnd8eSngJR6xxDEfYvgnMHHDeMHV4Fi1+DwEhHsC3XHEo0BNcstplH/PLHcV7/ZScJqVYCvVz5+JFq3Fsh1Oiy8iSFWREREQEcj8F9rmlZqhbz57npf/DH0VjajlnNuC53UrfUbT4y1cXdcWNY6SbQcgScO/j3VduotXDhCGyc6FhcvaBUY8cV27LNwb9odpxerpCQks6wX/9i1tbjANQtWYhPH62R9f9hEIVZERERyaxJhcL879mGPPXtFvacjqfLl7/zauuKPNEgMvumhwoqDUF94a6+kHLJMQxh/yLHldv4U7B3vmMBCK3iuGJbtoVjCIM5b97dv+tkHP1/2Mqh6ATMJhhwX1mevbcsFj3J67YozIqIiMhVSgR588szDRj885/8uu0kb8/dxfZjsbzXsQpebtkcH9x9oGJbx2K3w+k/HdN+7V8ExzfDmR2OZfXH4FkIyjR1zJBQ+l7wKpS9teQAu93OdxuieHveblLTbYT6ufPpozW463avdgugMCsiIiLX4elmYXSn6lSPCODdebuZs/0ke0/HM7FbTSKDvXOmUZPJ8eSx8Gpwz0uQEAMHljqGIxxcBknnYcePjsVkhmJ1/r5qG1o5191EdjExjcE//8mCnY4nrd1boTAfPVyNQjeaz1duicKsiIiIXJfJZKJng5JULuLPM99vZe+ZeNqNXcMzjctQprAPkUFeRBTywsM1h7769w6Gao86Fms6HN94eeqvxXB2Fxzb4FiWvQV+xRwPaijXAkreA27GTm+1JeoCA374gxOxSbhaTLzSsgJPNiypJ3llM4VZERER+U91ShZi3oCGPPP9VrZEXeD9hXsyfR7u70GJIC9KFPKmRPDlP4O8KBHkha+Ha/YUYXGBEvUdS7M3Ifbo5ZvIFsPhlRB3HLZMcSwWdyh5998zJARGZk8NN8FmszNx1SE+WrwXq81O8UJejO1Sg6rFApxWQ0GiMCsiIiI3JdTPgx9638XX646w7VgsR84lEHUukUsp6Zy6mMypi8lsOHT+qv2CvN0uB9u/A26JIG9KFPKikLdb1q9UBhSH2r0cS1oSHF7tGGe7bzFcPOoYnnBgKSx4CYLL/z0cofhdYMmmgP0v0fEpDPpxG6v3xwDQrloRRjxwR/YFermKwqyIiIjcNDcXM70blcp4b7fbOZ+QypFziRw9n8CRmESOnk/kyLkEjp5L5FxCasay9WjsVcfzdXe56kruldAb6uuB+Wbv9Hf1dITVcs2htR2i9/w9HOHoBojZ61jWjQF3f8cUYeVaOG4m8ymcLT+bNftjeP7HbUTHp+DhauaNdpXpVDsidw8rsNshLdHxWOLk2Ov/mXzR8frBieAZaFi516IwKyIiIllmMpkI8nEnyMedmiWuDjnxyWlEnUsk6tzfAffIuQSOnk/k1MVk4lPS2Xkijp0n4q7a193FTIkgL4oX8ibyH0E3MsibIgEeuFiu86QskwkKV3QsDQdC0gU4+NvlBzYsgcQY2DXbsWCCIjUcwbZscwiv7niS2S1It9r4ZOk+Pl9xELsdyoX6MLbLnZQL9b2l42SZ3Q6pl24+kP77M1vazbeVcE5hVkRERAoOXw9X7ijqzx1F/a/6LDnNyrHziRw5l0jU5SELV4Lu8QtJpKTb2HfmEvvOXLpqXxeziWKBnhQPcgTd4oW8iAzyJjLYi2KB/7ohzTMQ7ujoWGxWOPnH5au2i+DUdji51bGsGAk+oVCmmeMKb6km4OF3w/M7EZvEcz/8weaoCwB0rlOcYW0r4el2izfE2e2QEpe1QJp8EWzpt9bev5ldwCMAPAP+8af/1eu8c990YgqzIiIiYggPVwtlQ30pe40rmGlWGydjkxzDF84lZAq8UecTSU23ceScIwiv+te+JhOE+3lQPMgRcK/8eeXKrk+xWo6HL9z7GsSdggNLHOH20Aq4dAa2fedYzK5Qot7lm8hagF+JTO0s+us0L8/8k4tJafi6uzDigcq0K+8Nl45mLZDabbf3A7W43Vwgvdafbt65blqzm6UwKyIiIrmOq8V8eeysNxCS6TObzc6Z+OTL43MTLgfexEw3pJ28mMzJ69yQFuzjlnEl1xF076NE/XaUaOVCYMxmTFces3v+IBxe5VgWv4ZLYEmqmSPhf4v56+gJvM+d5TtTAiFeSRR2TcY8Ow6w396Ju3hkPZC6eubZQHo7FGZFREQkTzGbTYT7exLu70m90pm/9r6ZG9JiLjmWa96Q5uFCiaDWlAh+mOoRMdRK20ypC2vwO7MR04XDRHIYzkFlgCsjCWxAyj8O4up1G4HUI9t+TgWFwqyIiIjkG/91Q1pcchpHb3RDWvLfN6TNA6AaUA1vnqCx6y6q2PeRYHcn3c2fdnUrUqlUiatDq4u7M0+5wFOYFRERkQLDL8s3pJmYl1aTedSkbslAPn30TsL8dRU1N1CYFREREeG/b0iLioln0bIVPNmxFh7ubgZUKNdyaxOpiYiIiBRArhYzJQp5UcwbLDf7IAdxCoVZEREREcmzFGZFREREJM9SmBURERGRPEthVkRERETyLIVZEREREcmzFGZFREREJM9SmBURERGRPEthVkRERETyrFwRZseNG0dkZCQeHh7UrVuXjRs33nD7n376iQoVKuDh4UGVKlWYP3++kyoVERERkdzE8DA7Y8YMBg0axPDhw9m6dSvVqlWjRYsWnD179prbr1u3js6dO/Pkk0/yxx9/0KFDBzp06MDOnTudXLmIiIiIGM3F6AJGjRpF79696dmzJwATJkxg3rx5TJ48mcGDB1+1/aeffkrLli156aWXAHj77bdZsmQJY8eOZcKECVdtn5KSQkpKSsb7uLg4ANLS0khLS8uJU8rkShvOaEtyD/V7waM+L3jU5wWP+tx5buVnbGiYTU1NZcuWLQwZMiRjndlspmnTpqxfv/6a+6xfv55BgwZlWteiRQtmz559ze1HjhzJm2++edX6xYsX4+XllfXib9GSJUuc1pbkHur3gkd9XvCozwse9XnOS0xMvOltDQ2zMTExWK1WQkNDM60PDQ1lz54919zn9OnT19z+9OnT19x+yJAhmcJvXFwcERERNG/eHD8/v9s8g/+WlpbGkiVLaNasGa6urjnenuQO6veCR31e8KjPCx71ufNc+Sb9Zhg+zCCnubu74+7uftV6V1dXp/4iOrs9yR3U7wWP+rzgUZ8XPOrznHcrP19Dw2xwcDAWi4UzZ85kWn/mzBnCwsKuuU9YWNgtbf9vdrsduLXEfzvS0tJITEwkLi5Ov/gFiPq94FGfFzzq84JHfe48V3Laldx2I4aGWTc3N2rWrMmyZcvo0KEDADabjWXLltG/f/9r7lOvXj2WLVvGwIEDM9YtWbKEevXq3VSb8fHxAERERNxW7SIiIiKSs+Lj4/H397/hNoYPMxg0aBA9evSgVq1a1KlTh9GjR5OQkJAxu0H37t0pWrQoI0eOBOC5557jnnvu4eOPP6ZNmzZMnz6dzZs388UXX9xUe0WKFOHYsWP4+vpiMply7LyuuDJG99ixY04Zoyu5g/q94FGfFzzq84JHfe48drud+Ph4ihQp8p/bGh5mO3XqRHR0NMOGDeP06dNUr16dhQsXZtzkdfToUczmv6fDrV+/PtOmTeP111/n1VdfpWzZssyePZs77rjjptozm80UK1YsR87lRvz8/PSLXwCp3wse9XnBoz4veNTnzvFfV2SvMNlvZjCCZFlcXBz+/v5cvHhRv/gFiPq94FGfFzzq84JHfZ47Gf4EMBERERGRrFKYzWHu7u4MHz78mtODSf6lfi941OcFj/q84FGf504aZiAiIiIieZauzIqIiIhInqUwKyIiIiJ5lsKsiIiIiORZCrMiIiIikmcpzOawcePGERkZiYeHB3Xr1mXjxo1GlyQ5ZOTIkdSuXRtfX18KFy5Mhw4d2Lt3r9FliRO99957mEymTI/blvzpxIkTPPbYYwQFBeHp6UmVKlXYvHmz0WVJDrFarQwdOpSSJUvi6elJ6dKlefvtt9E99LmDwmwOmjFjBoMGDWL48OFs3bqVatWq0aJFC86ePWt0aZIDVq5cSb9+/diwYQNLliwhLS2N5s2bk5CQYHRp4gSbNm1i4sSJVK1a1ehSJIdduHCBBg0a4OrqyoIFC9i1axcff/wxgYGBRpcmOeT9999n/PjxjB07lt27d/P+++/zwQcfMGbMGKNLEzQ1V46qW7cutWvXZuzYsQDYbDYiIiJ49tlnGTx4sMHVSU6Ljo6mcOHCrFy5kkaNGhldjuSgS5cuceedd/L555/zzjvvUL16dUaPHm10WZJDBg8ezNq1a1m9erXRpYiTtG3bltDQUL766quMdR07dsTT05PvvvvOwMoEdGU2x6SmprJlyxaaNm2asc5sNtO0aVPWr19vYGXiLBcvXgSgUKFCBlciOa1fv360adMm0993yb/mzJlDrVq1ePjhhylcuDA1atRg0qRJRpclOah+/fosW7aMffv2AbB9+3bWrFlDq1atDK5MAFyMLiC/iomJwWq1Ehoamml9aGgoe/bsMagqcRabzcbAgQNp0KABd9xxh9HlSA6aPn06W7duZdOmTUaXIk5y6NAhxo8fz6BBg3j11VfZtGkTAwYMwM3NjR49ehhdnuSAwYMHExcXR4UKFbBYLFitVt599126du1qdGmCwqxIjujXrx87d+5kzZo1RpciOejYsWM899xzLFmyBA8PD6PLESex2WzUqlWLESNGAFCjRg127tzJhAkTFGbzqR9//JHvv/+eadOmUblyZbZt28bAgQMpUqSI+jwXUJjNIcHBwVgsFs6cOZNp/ZkzZwgLCzOoKnGG/v37M3fuXFatWkWxYsWMLkdy0JYtWzh79ix33nlnxjqr1cqqVasYO3YsKSkpWCwWAyuUnBAeHk6lSpUyratYsSKzZs0yqCLJaS+99BKDBw/m0UcfBaBKlSpERUUxcuRIhdlcQGNmc4ibmxs1a9Zk2bJlGetsNhvLli2jXr16BlYmOcVut9O/f39++eUXfvvtN0qWLGl0SZLD7rvvPnbs2MG2bdsyllq1atG1a1e2bdumIJtPNWjQ4Kpp9/bt20eJEiUMqkhyWmJiImZz5shksViw2WwGVST/pCuzOWjQoEH06NGDWrVqUadOHUaPHk1CQgI9e/Y0ujTJAf369WPatGn8+uuv+Pr6cvr0aQD8/f3x9PQ0uDrJCb6+vleNifb29iYoKEhjpfOx559/nvr16zNixAgeeeQRNm7cyBdffMEXX3xhdGmSQ9q1a8e7775L8eLFqVy5Mn/88QejRo3iiSeeMLo0QVNz5bixY8fy4Ycfcvr0aapXr85nn31G3bp1jS5LcoDJZLrm+ilTpvD44487txgxTOPGjTU1VwEwd+5chgwZwv79+ylZsiSDBg2id+/eRpclOSQ+Pp6hQ4fyyy+/cPbsWYoUKULnzp0ZNmwYbm5uRpdX4CnMioiIiEiepTGzIiIiIpJnKcyKiIiISJ6lMCsiIiIieZbCrIiIiIjkWQqzIiIiIpJnKcyKiIiISJ6lMCsiIiIieZbCrIiIiIjkWQqzIiIFlMlkYvbs2UaXISJyWxRmRUQM8Pjjj2Myma5aWrZsaXRpIiJ5iovRBYiIFFQtW7ZkypQpmda5u7sbVI2ISN6kK7MiIgZxd3cnLCws0xIYGAg4hgCMHz+eVq1a4enpSalSpZg5c2am/Xfs2MG9996Lp6cnQUFB9OnTh0uXLmXaZvLkyVSuXBl3d3fCw8Pp379/ps9jYmJ44IEH8PLyomzZssyZMydnT1pEJJspzIqI5FJDhw6lY8eObN++na5du/Loo4+ye/duABISEmjRogWBgYFs2rSJn376iaVLl2YKq+PHj6dfv3706dOHHTt2MGfOHMqUKZOpjTfffJNHHnmEP//8k9atW9O1a1fOnz/v1PMUEbkdJrvdbje6CBGRgubxxx/nu+++w8PDI9P6V199lVdffRWTycTTTz/N+PHjMz676667uPPOO/n888+ZNGkSr7zyCseOHcPb2xuA+fPn065dO06ePEloaChFixalZ8+evPPOO9eswWQy8frrr/P2228DjoDs4+PDggULNHZXRPIMjZkVETFIkyZNMoVVgEKFCmW8rlevXqbP6tWrx7Zt2wDYvXs31apVywiyAA0aNMBms7F3715MJhMnT57kvvvuu2ENVatWzXjt7e2Nn58fZ8+ezeopiYg4ncKsiIhBvL29r/raP7t4enre1Haurq6Z3ptMJmw2W06UJCKSIzRmVkQkl9qwYcNV7ytWrAhAxYoV2b59OwkJCRmfr127FrPZTPny5fH19SUyMpJly5Y5tWYREWfTlVkREYOkpKRw+vTpTOtcXFwIDg4G4KeffqJWrVo0bNiQ77//no0bN/LVV18B0LVrV4YPH06PHj144403iI6O5tlnn6Vbt26EhoYC8MYbb/D0009TuHBhWrVqRXx8PGvXruXZZ5917omKiOQghVkREYMsXLiQ8PDwTOvKly/Pnj17AMdMA9OnT+eZZ54hPDycH374gUqVKgHg5eXFokWLeO6556hduzZeXl507NiRUaNGZRyrR48eJCcn88knn/Diiy8SHBzMQw895LwTFBFxAs1mICKSC5lMJn755Rc6dOhgdCkiIrmaxsyKiIiISJ6lMCsiIiIieZbGzIqI5EIaASYicnN0ZVZERERE8iyFWRERERHJsxRmRURERCTPUpgVERERkTxLYVZERERE8iyFWRERERHJsxRmRURERCTPUpgVERERkTzr/ylZflHvUMevAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "\n",
        "files.download('accuracy_plot.png')\n",
        "files.download('loss_plot.png')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "UZmHFEn6HJ_R",
        "outputId": "bec796c0-0665-4dc9-ef1a-e76cbc1f646f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_2a1433ed-28ee-475e-a403-eb8482fb45ce\", \"accuracy_plot.png\", 35543)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_20265676-f71c-4f85-ae97-610d5b9ebfc8\", \"loss_plot.png\", 32609)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.utils import plot_model\n",
        "from IPython.display import Image, display\n",
        "\n",
        "plot_model(\n",
        "    model,\n",
        "    to_file='cnn_model_full.png',\n",
        "    show_shapes=True,\n",
        "    show_layer_names=True,\n",
        "    expand_nested=True,\n",
        "    dpi=200\n",
        ")\n",
        "\n",
        "display(Image(filename='cnn_model_full.png'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "2L6vVn7mJ8dy",
        "outputId": "387e2245-0ce2-4b2a-be87-d28fb65d60f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "id": "As1j2LwwKLmg",
        "outputId": "5b13e06e-726d-44db-9b0f-9a5f69f58746",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_2\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_2\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_6 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m126\u001b[0m, \u001b[38;5;34m126\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │           \u001b[38;5;34m896\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_6 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m63\u001b[0m, \u001b[38;5;34m63\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_7 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m61\u001b[0m, \u001b[38;5;34m61\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m18,496\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_7 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_8 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │        \u001b[38;5;34m73,856\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_8 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ flatten_2 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25088\u001b[0m)          │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │     \u001b[38;5;34m3,211,392\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │           \u001b[38;5;34m129\u001b[0m │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">126</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">126</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">63</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">63</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">61</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">61</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ flatten_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25088</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │     <span style=\"color: #00af00; text-decoration-color: #00af00\">3,211,392</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">129</span> │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
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              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m9,914,309\u001b[0m (37.82 MB)\n"
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              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">9,914,309</span> (37.82 MB)\n",
              "</pre>\n"
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          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m3,304,769\u001b[0m (12.61 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3,304,769</span> (12.61 MB)\n",
              "</pre>\n"
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          },
          "metadata": {}
        },
        {
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          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
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            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m6,609,540\u001b[0m (25.21 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">6,609,540</span> (25.21 MB)\n",
              "</pre>\n"
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          },
          "metadata": {}
        }
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    },
    {
      "cell_type": "code",
      "source": [
        "# =========================================\n",
        "# NLP Classification Assignment\n",
        "# =========================================\n",
        "\n",
        "# 1) Import libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import re\n",
        "import string\n",
        "import nltk\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "nltk.download('stopwords')\n",
        "from nltk.corpus import stopwords\n",
        "\n",
        "# =========================================\n",
        "# 2) Download dataset\n",
        "# SMS Spam Collection Dataset\n",
        "# =========================================\n",
        "url = \"https://raw.githubusercontent.com/justmarkham/pycon-2016-tutorial/master/data/sms.tsv\"\n",
        "\n",
        "df = pd.read_csv(url, sep='\\t', header=None, names=['label', 'message'])\n",
        "\n",
        "print(\"First 5 rows:\")\n",
        "print(df.head())\n",
        "print(\"\\nDataset shape:\", df.shape)\n",
        "print(\"\\nClass distribution:\")\n",
        "print(df['label'].value_counts())\n",
        "\n",
        "# =========================================\n",
        "# 3) Data preprocessing\n",
        "# =========================================\n",
        "stop_words = set(stopwords.words('english'))\n",
        "\n",
        "def preprocess_text(text):\n",
        "    # Convert to lowercase\n",
        "    text = text.lower()\n",
        "\n",
        "    # Remove numbers\n",
        "    text = re.sub(r'\\d+', '', text)\n",
        "\n",
        "    # Remove punctuation\n",
        "    text = text.translate(str.maketrans('', '', string.punctuation))\n",
        "\n",
        "    # Remove extra spaces\n",
        "    text = re.sub(r'\\s+', ' ', text).strip()\n",
        "\n",
        "    # Remove stopwords\n",
        "    words = text.split()\n",
        "    words = [word for word in words if word not in stop_words]\n",
        "\n",
        "    return \" \".join(words)\n",
        "\n",
        "df['clean_message'] = df['message'].apply(preprocess_text)\n",
        "\n",
        "print(\"\\nSample after preprocessing:\")\n",
        "print(df[['message', 'clean_message']].head())\n",
        "\n",
        "# Convert labels to numbers\n",
        "# ham = 0, spam = 1\n",
        "df['label_num'] = df['label'].map({'ham': 0, 'spam': 1})\n",
        "\n",
        "# =========================================\n",
        "# 4) Split the dataset\n",
        "# =========================================\n",
        "X = df['clean_message']\n",
        "y = df['label_num']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y,\n",
        "    test_size=0.2,\n",
        "    random_state=42,\n",
        "    stratify=y\n",
        ")\n",
        "\n",
        "print(\"\\nTraining size:\", len(X_train))\n",
        "print(\"Testing size:\", len(X_test))\n",
        "\n",
        "# =========================================\n",
        "# 5) Feature representation methods\n",
        "#    Method 1: Bag of Words\n",
        "#    Method 2: TF-IDF\n",
        "# =========================================\n",
        "\n",
        "# -------- Bag of Words --------\n",
        "bow_vectorizer = CountVectorizer()\n",
        "X_train_bow = bow_vectorizer.fit_transform(X_train)\n",
        "X_test_bow = bow_vectorizer.transform(X_test)\n",
        "\n",
        "print(\"\\nBag of Words shape:\", X_train_bow.shape)\n",
        "\n",
        "# -------- TF-IDF --------\n",
        "tfidf_vectorizer = TfidfVectorizer()\n",
        "X_train_tfidf = tfidf_vectorizer.fit_transform(X_train)\n",
        "X_test_tfidf = tfidf_vectorizer.transform(X_test)\n",
        "\n",
        "print(\"TF-IDF shape:\", X_train_tfidf.shape)\n",
        "\n",
        "# =========================================\n",
        "# 6) Train and evaluate models\n",
        "#    Model 1: Naive Bayes\n",
        "#    Model 2: Logistic Regression\n",
        "# =========================================\n",
        "\n",
        "def evaluate_model(model, X_train, X_test, y_train, y_test, title):\n",
        "    model.fit(X_train, y_train)\n",
        "    y_pred = model.predict(X_test)\n",
        "\n",
        "    acc = accuracy_score(y_test, y_pred)\n",
        "    print(f\"\\n{'='*50}\")\n",
        "    print(title)\n",
        "    print(f\"{'='*50}\")\n",
        "    print(\"Accuracy:\", acc)\n",
        "    print(\"\\nClassification Report:\")\n",
        "    print(classification_report(y_test, y_pred))\n",
        "    print(\"Confusion Matrix:\")\n",
        "    print(confusion_matrix(y_test, y_pred))\n",
        "\n",
        "    return acc\n",
        "\n",
        "results = []\n",
        "\n",
        "# -------- Naive Bayes with Bag of Words --------\n",
        "nb_bow = MultinomialNB()\n",
        "acc1 = evaluate_model(\n",
        "    nb_bow, X_train_bow, X_test_bow, y_train, y_test,\n",
        "    \"Naive Bayes with Bag of Words\"\n",
        ")\n",
        "results.append((\"Naive Bayes\", \"Bag of Words\", acc1))\n",
        "\n",
        "# -------- Naive Bayes with TF-IDF --------\n",
        "nb_tfidf = MultinomialNB()\n",
        "acc2 = evaluate_model(\n",
        "    nb_tfidf, X_train_tfidf, X_test_tfidf, y_train, y_test,\n",
        "    \"Naive Bayes with TF-IDF\"\n",
        ")\n",
        "results.append((\"Naive Bayes\", \"TF-IDF\", acc2))\n",
        "\n",
        "# -------- Logistic Regression with Bag of Words --------\n",
        "lr_bow = LogisticRegression(max_iter=1000)\n",
        "acc3 = evaluate_model(\n",
        "    lr_bow, X_train_bow, X_test_bow, y_train, y_test,\n",
        "    \"Logistic Regression with Bag of Words\"\n",
        ")\n",
        "results.append((\"Logistic Regression\", \"Bag of Words\", acc3))\n",
        "\n",
        "# -------- Logistic Regression with TF-IDF --------\n",
        "lr_tfidf = LogisticRegression(max_iter=1000)\n",
        "acc4 = evaluate_model(\n",
        "    lr_tfidf, X_train_tfidf, X_test_tfidf, y_train, y_test,\n",
        "    \"Logistic Regression with TF-IDF\"\n",
        ")\n",
        "results.append((\"Logistic Regression\", \"TF-IDF\", acc4))\n",
        "\n",
        "# =========================================\n",
        "# 7) Compare results\n",
        "# =========================================\n",
        "results_df = pd.DataFrame(results, columns=['Model', 'Feature Method', 'Accuracy'])\n",
        "print(\"\\nFinal Results Comparison:\")\n",
        "print(results_df)\n",
        "\n",
        "# Plot results\n",
        "plt.figure(figsize=(8,5))\n",
        "plt.bar(range(len(results_df)), results_df['Accuracy'])\n",
        "plt.xticks(range(len(results_df)),\n",
        "           [f\"{m}\\n{f}\" for m, f in zip(results_df['Model'], results_df['Feature Method'])],\n",
        "           rotation=15)\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.title(\"Model Comparison\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "-yMYuQpR6nd8",
        "outputId": "efb64ccb-6d88-4d63-ba7f-ac5e2ca51fa4"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First 5 rows:\n",
            "  label                                            message\n",
            "0   ham  Go until jurong point, crazy.. Available only ...\n",
            "1   ham                      Ok lar... Joking wif u oni...\n",
            "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
            "3   ham  U dun say so early hor... U c already then say...\n",
            "4   ham  Nah I don't think he goes to usf, he lives aro...\n",
            "\n",
            "Dataset shape: (5572, 2)\n",
            "\n",
            "Class distribution:\n",
            "label\n",
            "ham     4825\n",
            "spam     747\n",
            "Name: count, dtype: int64\n",
            "\n",
            "Sample after preprocessing:\n",
            "                                             message  \\\n",
            "0  Go until jurong point, crazy.. Available only ...   \n",
            "1                      Ok lar... Joking wif u oni...   \n",
            "2  Free entry in 2 a wkly comp to win FA Cup fina...   \n",
            "3  U dun say so early hor... U c already then say...   \n",
            "4  Nah I don't think he goes to usf, he lives aro...   \n",
            "\n",
            "                                       clean_message  \n",
            "0  go jurong point crazy available bugis n great ...  \n",
            "1                            ok lar joking wif u oni  \n",
            "2  free entry wkly comp win fa cup final tkts st ...  \n",
            "3                u dun say early hor u c already say  \n",
            "4        nah dont think goes usf lives around though  \n",
            "\n",
            "Training size: 4457\n",
            "Testing size: 1115\n",
            "\n",
            "Bag of Words shape: (4457, 7431)\n",
            "TF-IDF shape: (4457, 7431)\n",
            "\n",
            "==================================================\n",
            "Naive Bayes with Bag of Words\n",
            "==================================================\n",
            "Accuracy: 0.97847533632287\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.99      0.99       966\n",
            "           1       0.95      0.89      0.92       149\n",
            "\n",
            "    accuracy                           0.98      1115\n",
            "   macro avg       0.97      0.94      0.95      1115\n",
            "weighted avg       0.98      0.98      0.98      1115\n",
            "\n",
            "Confusion Matrix:\n",
            "[[959   7]\n",
            " [ 17 132]]\n",
            "\n",
            "==================================================\n",
            "Naive Bayes with TF-IDF\n",
            "==================================================\n",
            "Accuracy: 0.9641255605381166\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      1.00      0.98       966\n",
            "           1       1.00      0.73      0.84       149\n",
            "\n",
            "    accuracy                           0.96      1115\n",
            "   macro avg       0.98      0.87      0.91      1115\n",
            "weighted avg       0.97      0.96      0.96      1115\n",
            "\n",
            "Confusion Matrix:\n",
            "[[966   0]\n",
            " [ 40 109]]\n",
            "\n",
            "==================================================\n",
            "Logistic Regression with Bag of Words\n",
            "==================================================\n",
            "Accuracy: 0.9802690582959641\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      1.00      0.99       966\n",
            "           1       1.00      0.85      0.92       149\n",
            "\n",
            "    accuracy                           0.98      1115\n",
            "   macro avg       0.99      0.93      0.95      1115\n",
            "weighted avg       0.98      0.98      0.98      1115\n",
            "\n",
            "Confusion Matrix:\n",
            "[[966   0]\n",
            " [ 22 127]]\n",
            "\n",
            "==================================================\n",
            "Logistic Regression with TF-IDF\n",
            "==================================================\n",
            "Accuracy: 0.9659192825112107\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      1.00      0.98       966\n",
            "           1       1.00      0.74      0.85       149\n",
            "\n",
            "    accuracy                           0.97      1115\n",
            "   macro avg       0.98      0.87      0.92      1115\n",
            "weighted avg       0.97      0.97      0.96      1115\n",
            "\n",
            "Confusion Matrix:\n",
            "[[966   0]\n",
            " [ 38 111]]\n",
            "\n",
            "Final Results Comparison:\n",
            "                 Model Feature Method  Accuracy\n",
            "0          Naive Bayes   Bag of Words  0.978475\n",
            "1          Naive Bayes         TF-IDF  0.964126\n",
            "2  Logistic Regression   Bag of Words  0.980269\n",
            "3  Logistic Regression         TF-IDF  0.965919\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}