Fake_QR_detector_Clash_of_Titans.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/SrishBansal/Fake_QR_Code_Detection/blob/main/Fake_QR_detector_Clash_of_Titans.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gK7-PMc1Y8hh",
        "outputId": "03ba9ec4-c561-4cbf-b721-996e357fc4a1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: openpyxl in /usr/local/lib/python3.10/dist-packages (3.1.5)\n",
            "Requirement already satisfied: et-xmlfile in /usr/local/lib/python3.10/dist-packages (from openpyxl) (1.1.0)\n",
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "import torch.nn as nn\n",
        "import math\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import cv2\n",
        "from google.colab.patches import cv2_imshow\n",
        "import matplotlib.pyplot as plt\n",
        "from torchvision import datasets, transforms\n",
        "import torch\n",
        "import os\n",
        "from torchvision.models import MobileNetV2\n",
        "#print(os.listdir(test_dir))\n",
        "!pip install openpyxl\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "def conv_bn(inp, oup, stride):\n",
        "    return nn.Sequential(\n",
        "        nn.Conv2d(inp, oup, 3, stride, 1, bias=False),\n",
        "        nn.BatchNorm2d(oup),\n",
        "        nn.ReLU6(inplace=True)\n",
        "    )\n",
        "\n",
        "\n",
        "def conv_1x1_bn(inp, oup):\n",
        "    return nn.Sequential(\n",
        "        nn.Conv2d(inp, oup, 1, 1, 0, bias=False),\n",
        "        nn.BatchNorm2d(oup),\n",
        "        nn.ReLU6(inplace=True)\n",
        "    )\n",
        "\n",
        "\n",
        "def make_divisible(x, divisible_by=8):\n",
        "    import numpy as np\n",
        "    return int(np.ceil(x * 1. / divisible_by) * divisible_by)\n",
        "\n",
        "\n",
        "class InvertedResidual(nn.Module):\n",
        "    def __init__(self, inp, oup, stride, expand_ratio):\n",
        "        super(InvertedResidual, self).__init__()\n",
        "        self.stride = stride\n",
        "        assert stride in [1, 2]\n",
        "\n",
        "        hidden_dim = int(inp * expand_ratio)\n",
        "        self.use_res_connect = self.stride == 1 and inp == oup\n",
        "\n",
        "        if expand_ratio == 1:\n",
        "            self.conv = nn.Sequential(\n",
        "                # dw\n",
        "                nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),\n",
        "                nn.BatchNorm2d(hidden_dim),\n",
        "                nn.ReLU6(inplace=True),\n",
        "                # pw-linear\n",
        "                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),\n",
        "                nn.BatchNorm2d(oup),\n",
        "            )\n",
        "        else:\n",
        "            self.conv = nn.Sequential(\n",
        "                # pw\n",
        "                nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),\n",
        "                nn.BatchNorm2d(hidden_dim),\n",
        "                nn.ReLU6(inplace=True),\n",
        "                # dw\n",
        "                nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),\n",
        "                nn.BatchNorm2d(hidden_dim),\n",
        "                nn.ReLU6(inplace=True),\n",
        "                # pw-linear\n",
        "                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),\n",
        "                nn.BatchNorm2d(oup),\n",
        "            )\n",
        "\n",
        "    def forward(self, x):\n",
        "        if self.use_res_connect:\n",
        "            return x + self.conv(x)\n",
        "        else:\n",
        "            return self.conv(x)\n",
        "\n",
        "\n",
        "class MobileNetV2(nn.Module):\n",
        "    def __init__(self, n_class=1000, input_size=224, width_mult=1.):\n",
        "        super(MobileNetV2, self).__init__()\n",
        "        block = InvertedResidual\n",
        "        input_channel = 32\n",
        "        last_channel = 1280\n",
        "        interverted_residual_setting = [\n",
        "            # t, c, n, s\n",
        "            [1, 16, 1, 1],\n",
        "            [6, 24, 2, 2],\n",
        "            [6, 32, 3, 2],\n",
        "            [6, 64, 4, 2],\n",
        "            [6, 96, 3, 1],\n",
        "            [6, 160, 3, 2],\n",
        "            [6, 320, 1, 1],\n",
        "        ]\n",
        "\n",
        "        # building first layer\n",
        "        assert input_size % 32 == 0\n",
        "        # input_channel = make_divisible(input_channel * width_mult)  # first channel is always 32!\n",
        "        self.last_channel = make_divisible(last_channel * width_mult) if width_mult > 1.0 else last_channel\n",
        "        self.features = [conv_bn(3, input_channel, 2)]\n",
        "        # building inverted residual blocks\n",
        "        for t, c, n, s in interverted_residual_setting:\n",
        "            output_channel = make_divisible(c * width_mult) if t > 1 else c\n",
        "            for i in range(n):\n",
        "                if i == 0:\n",
        "                    self.features.append(block(input_channel, output_channel, s, expand_ratio=t))\n",
        "                else:\n",
        "                    self.features.append(block(input_channel, output_channel, 1, expand_ratio=t))\n",
        "                input_channel = output_channel\n",
        "        # building last several layers\n",
        "        self.features.append(conv_1x1_bn(input_channel, self.last_channel))\n",
        "        # make it nn.Sequential\n",
        "        self.features = nn.Sequential(*self.features)\n",
        "\n",
        "        # building classifier\n",
        "        self.classifier = nn.Linear(self.last_channel, n_class)\n",
        "\n",
        "        self._initialize_weights()\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.features(x)\n",
        "        x = x.mean(3).mean(2)\n",
        "        x = self.classifier(x)\n",
        "        return x\n",
        "\n",
        "    def _initialize_weights(self):\n",
        "        for m in self.modules():\n",
        "            if isinstance(m, nn.Conv2d):\n",
        "                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n",
        "                m.weight.data.normal_(0, math.sqrt(2. / n))\n",
        "                if m.bias is not None:\n",
        "                    m.bias.data.zero_()\n",
        "            elif isinstance(m, nn.BatchNorm2d):\n",
        "                m.weight.data.fill_(1)\n",
        "                m.bias.data.zero_()\n",
        "            elif isinstance(m, nn.Linear):\n",
        "                n = m.weight.size(1)\n",
        "                m.weight.data.normal_(0, 0.01)\n",
        "                m.bias.data.zero_()\n",
        "\n",
        "\n",
        "def mobilenet_v2(pretrained=True):\n",
        "    model = MobileNetV2(width_mult=1)\n",
        "\n",
        "    if pretrained:\n",
        "        try:\n",
        "            from torch.hub import load_state_dict_from_url\n",
        "        except ImportError:\n",
        "          from torch.utils.model_zoo import load_url as load_state_dict_from_url\n",
        "          state_dict = load_state_dict_from_url(\n",
        "              'http/s/47tyzpofuuyyv1b/mobilenetv2_1.0-f2a8633.pth.tar?dl=1', progress=True)\n",
        "          model.load_state_dict(state_dict)\n",
        "          return model\n",
        "\n",
        "\n",
        "if __name__ == '__main__':\n",
        "    net = mobilenet_v2(True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import google.generativeai as genai\n",
        "\n",
        "# Set your API key\n",
        "genai.configure(api_key=\"AIzaSyC4Rlj0RHCqBsyvIJKbDx7bZ8mh2lYEJTQ\")\n",
        "\n",
        "def get_user_prompt():\n",
        "    \"\"\"Prompts the user to enter a prompt.\"\"\"\n",
        "    prompt = input(\"Enter your prompt (or type 'quit' to exit): \")\n",
        "    return prompt\n",
        "\n",
        "def generate_response(prompt):\n",
        "    \"\"\"Generates a response using the Gemini API.\"\"\"\n",
        "    try:\n",
        "        model = genai.GenerativeModel(\"gemini-1.5-flash\")\n",
        "        response = model.generate_content(prompt)\n",
        "        return response.text\n",
        "    except Exception as e:\n",
        "        return f\"Error: {e}\"\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    while True:\n",
        "        user_prompt = get_user_prompt()\n",
        "        if user_prompt.lower() == \"quit\":\n",
        "            print(\"Goodbye!\")\n",
        "            break\n",
        "        response = generate_response(user_prompt)\n",
        "        print(response)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 993
        },
        "id": "xoubWV_y2imn",
        "outputId": "3d5667b9-3741-4d6f-9c20-347035f1b8b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter your prompt (or type 'quit' to exit): What am I doing ?\n",
            "Please provide me with more context! I need more information to understand what you're doing. \n",
            "\n",
            "For example, tell me:\n",
            "\n",
            "* **What are you currently doing?** Are you working, studying, playing a game, or something else?\n",
            "* **What is your goal?** What are you trying to achieve?\n",
            "* **What are you feeling?** Are you happy, sad, confused, or something else?\n",
            "\n",
            "The more information you give me, the better I can understand what you're doing and help you. 😊 \n",
            "\n",
            "Enter your prompt (or type 'quit' to exit): I am currently playing\n",
            "Please tell me what you are playing! I need more information to understand what you are asking. \n",
            "\n",
            "For example, you could tell me:\n",
            "\n",
            "* **The name of the game:** \"I am currently playing **Mario Kart 8 Deluxe**.\"\n",
            "* **The type of game:** \"I am currently playing **a board game**.\"\n",
            "* **What you are doing in the game:** \"I am currently playing **a level in Super Mario Odyssey**.\"\n",
            "\n",
            "The more information you give me, the better I can understand and respond to your request! \n",
            "\n",
            "Enter your prompt (or type 'quit' to exit): WHat is India's capital?\n",
            "India's capital city is **New Delhi**. \n",
            "\n",
            "Enter your prompt (or type 'quit' to exit): How is OpenAI ?\n",
            "OpenAI is a leading artificial intelligence research and deployment company, known for developing cutting-edge technologies and models, including:\n",
            "\n",
            "**Strengths:**\n",
            "\n",
            "* **Advanced AI Models:** OpenAI has created highly sophisticated AI models like GPT-3, DALL-E, and ChatGPT, showcasing remarkable capabilities in natural language processing, image generation, and conversational AI.\n",
            "* **Research Excellence:** OpenAI invests heavily in research and development, pushing the boundaries of AI and making significant contributions to the field.\n",
            "* **Industry Impact:** OpenAI's technologies are being adopted by various industries, including tech, healthcare, finance, and education, driving innovation and efficiency.\n",
            "* **Strong Funding:** OpenAI has secured significant funding from prominent investors, enabling its continued development and expansion.\n",
            "* **Ethical Considerations:** OpenAI prioritizes responsible AI development and addresses ethical concerns related to bias, safety, and societal impact.\n",
            "\n",
            "**Challenges:**\n",
            "\n",
            "* **Ethical Concerns:**  The potential for misuse of OpenAI's powerful models, including the creation of deepfakes and biased outputs, raises ethical concerns.\n",
            "* **Job Displacement:** The widespread adoption of AI, especially in automation, could lead to job displacement and economic inequalities.\n",
            "* **Privacy and Data Security:**  OpenAI's models rely on massive datasets, raising concerns about data privacy and security.\n",
            "* **Black Box Nature:**  The inner workings of some AI models, particularly deep learning, remain opaque, making it difficult to understand their decision-making processes.\n",
            "* **Regulation and Governance:**  The rapid evolution of AI necessitates clear regulations and governance frameworks to ensure responsible development and deployment.\n",
            "\n",
            "**Overall, OpenAI is a powerful force in the AI landscape, driving significant advancements and shaping the future of technology. However, it's crucial to address ethical considerations, mitigate potential risks, and ensure responsible AI development for the benefit of society.**\n",
            "\n",
            "**In addition to the above, here are some further points to consider:**\n",
            "\n",
            "* **OpenAI's mission is to ensure that artificial general intelligence (AGI) benefits all of humanity.** This ambition drives their research and development efforts.\n",
            "* **OpenAI has released various APIs and tools that allow developers to integrate their AI technologies into their own applications.** This democratization of AI fosters wider adoption and innovation.\n",
            "* **OpenAI is actively involved in collaborations with other institutions and researchers to advance AI research and address global challenges.**\n",
            "\n",
            "It's important to stay informed about OpenAI's latest advancements and to engage in discussions about the ethical and societal implications of AI.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nWH1onjbEBZW"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-jt-MrDCcQXy"
      },
      "outputs": [],
      "source": [
        "#from google.colab import drive\n",
        "#drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WxmqeqwncXXx"
      },
      "source": [
        "### Exploring data for our case study"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WJ5fMvHrcg6x"
      },
      "outputs": [],
      "source": [
        "import zipfile\n",
        "fpath = '/content/drive/MyDrive/qr.zip'#upload the path\n",
        "with zipfile.ZipFile(fpath, 'r') as zip_ref:\n",
        "  zip_ref.extractall()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YAu3QGrocqm7"
      },
      "outputs": [],
      "source": [
        "train_dir = \"/content/qr/train\"\n",
        "test_dir = \"/content/qr/test\"\n",
        "labels = pd.read_csv(\"/content/qr/labels.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Xs6azpbhX8Iw"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AhvGfJa74buC"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.utils import image_dataset_from_directory\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GfsWonL0MkcT",
        "outputId": "d7174435-1a0c-4b86-d2a2-a3e1eb419430"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 410 files belonging to 2 classes.\n",
            "Using 328 files for training.\n",
            "Found 410 files belonging to 2 classes.\n",
            "Using 82 files for validation.\n"
          ]
        }
      ],
      "source": [
        "img_dim = 224\n",
        "train_ds_gen = image_dataset_from_directory(\n",
        "                  train_dir,\n",
        "                  image_size = (img_dim, img_dim),\n",
        "                  batch_size=32,\n",
        "                  seed = 42,\n",
        "                  shuffle=True,\n",
        "                  validation_split=0.2,\n",
        "                  subset='training'\n",
        ")\n",
        "\n",
        "val_ds_gen = image_dataset_from_directory(\n",
        "                  train_dir,\n",
        "                  image_size = (img_dim, img_dim),\n",
        "                  batch_size=32,\n",
        "                  seed = 42,\n",
        "                  shuffle=True,\n",
        "                  validation_split=0.2,\n",
        "                  subset='validation'\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QzU-v8-CYNCL"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lnVuf25h840C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "79f1333b-20df-4f21-dbf8-8c16f4a64fc4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224_no_top.h5\n",
            "\u001b[1m9406464/9406464\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n"
          ]
        }
      ],
      "source": [
        "from tensorflow.keras.applications import MobileNetV2\n",
        "model_base = MobileNetV2(weights='imagenet', include_top=False, input_shape=(224, 224, 3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "y3ygy1E9qFzV"
      },
      "outputs": [],
      "source": [
        "\n",
        "model_base.trainable = False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cWttXaOHqF3a"
      },
      "outputs": [],
      "source": [
        "model_base.trainable = True\n",
        "for layer in model_base.layers[:-5]:\n",
        "  layer.trainable = False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WLLtibE0ZEFT"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.layers import Flatten"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h-TI_oN40hVc"
      },
      "outputs": [],
      "source": [
        "num_classes = 10\n",
        "model_upd = Sequential()\n",
        "model_upd.add(model_base)\n",
        "model_upd.add(Dense(128,activation='relu'))\n",
        "model_upd.add(Flatten())\n",
        "model_upd.add(Dense(num_classes, activation='softmax'))\n",
        "#model_upd.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OtHGoLd1ad3p"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OTj8bx553hmb"
      },
      "outputs": [],
      "source": [
        "model_upd.compile(optimizer='adam',\n",
        "                  loss='sparse_categorical_crossentropy',\n",
        "                  metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Lqn5RvevasLQ"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.callbacks import EarlyStopping"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "y7TGglQ32HsP"
      },
      "outputs": [],
      "source": [
        "callback = EarlyStopping(monitor='loss', patience=3, restore_best_weights=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UGL5Rk3QbFi6"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k7a-L9Gh0wTZ",
        "outputId": "55bc1306-4cb6-4a43-a8c4-3770d8044d0d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m32s\u001b[0m 2s/step - accuracy: 0.4660 - loss: 3.8294 - val_accuracy: 0.8125 - val_loss: 0.4116\n",
            "Epoch 2/3\n",
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 2s/step - accuracy: 0.8541 - loss: 0.3729 - val_accuracy: 0.5938 - val_loss: 1.0770\n",
            "Epoch 3/3\n",
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 2s/step - accuracy: 0.8976 - loss: 0.2056 - val_accuracy: 0.4444 - val_loss: 1.3143\n"
          ]
        }
      ],
      "source": [
        "epochs=3\n",
        "history = model_upd.fit(train_ds_gen,\n",
        "                        validation_data=val_ds_gen,\n",
        "                        epochs=epochs,\n",
        "                        callbacks=[callback],\n",
        "                        validation_steps = 1\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DF-ElHlHi5ix"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "isZfEATjjAoJ"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yPhT5dkGT8xI"
      },
      "outputs": [],
      "source": [
        "from PIL import Image\n",
        "img = Image.open( \"/content/qr/test/benign_64599.png\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "neLg66-kYZvK",
        "outputId": "e77d9051-2ab9-4ea1-861e-40ae2bc80e8a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   ClassId       Name\n",
              "0        1     benign\n",
              "1        2  malicious"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-09de24e3-6a18-404c-9c0b-98a36df67688\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ClassId</th>\n",
              "      <th>Name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>benign</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>malicious</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-09de24e3-6a18-404c-9c0b-98a36df67688')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-09de24e3-6a18-404c-9c0b-98a36df67688 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-09de24e3-6a18-404c-9c0b-98a36df67688');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-3eb354e5-7ef0-4f96-847f-7dd8d1bba82d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3eb354e5-7ef0-4f96-847f-7dd8d1bba82d')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3eb354e5-7ef0-4f96-847f-7dd8d1bba82d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "labels",
              "summary": "{\n  \"name\": \"labels\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"ClassId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 2,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"malicious\",\n          \"benign\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "labels.index = labels.index.astype(str)\n",
        "labels.sort_index(inplace=True)\n",
        "labels.reset_index(drop=True, inplace=True)\n",
        "labels.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "67DddM2Y47Sa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3faf98df-d721-45ea-dc25-fb4961d34020"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.4/50.4 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m18.1/18.1 MB\u001b[0m \u001b[31m87.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m318.7/318.7 kB\u001b[0m \u001b[31m24.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m94.6/94.6 kB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.4/76.4 kB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.9/77.9 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m141.9/141.9 kB\u001b[0m \u001b[31m12.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.8/10.8 MB\u001b[0m \u001b[31m102.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.8/62.8 kB\u001b[0m \u001b[31m5.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.5/71.5 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m130.2/130.2 kB\u001b[0m \u001b[31m11.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install gradio --quiet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YGUJ7KA4jSLC"
      },
      "outputs": [],
      "source": [
        "from keras.preprocessing.image import img_to_array\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "id": "VpNaFjt0xCr4",
        "outputId": "322c4f59-ceb9-4ad6-f71a-d340f12b7c18"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=1 size=410x410>"
            ],
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\n"
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "from PIL import Image\n",
        "img_path=\"/content/qr/train/benign/benign_76507.png\"\n",
        "img = Image.open(img_path)\n",
        "img"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "82k7CHhaydRI"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gC1mg_z0qq0y"
      },
      "outputs": [],
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Input, Rescaling, Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n",
        "from keras.callbacks import EarlyStopping"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2klnu-L_xSVN",
        "outputId": "a1422a01-2c81-416b-ee49-9a34cbf06b3b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        }
      ],
      "source": [
        "\n",
        "model_cnn = Sequential()\n",
        "model_cnn.add(Rescaling(1./255))  # Rescale before convolution\n",
        "model_cnn.add(Conv2D(128, (3, 3), activation='relu'))\n",
        "model_cnn.add(MaxPooling2D((2, 2)))\n",
        "model_cnn.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "model_cnn.add(Conv2D(128, (3, 3), activation='relu'))\n",
        "model_cnn.add(MaxPooling2D((2, 2)))\n",
        "\n",
        "# Calculate the output size of the convolutional layers\n",
        "# Assuming img_dim is 128\n",
        "output_size = 128 // 2 // 2  # Two MaxPooling layers with pool size (2, 2)\n",
        "output_size = output_size * output_size * 128  # 128 filters in the last Conv2D layer\n",
        "\n",
        "model_cnn.add(Flatten())\n",
        "# Adjust the input size of the first dense layer\n",
        "model_cnn.add(Dense(64 , activation = 'relu', input_shape=(output_size,)))\n",
        "model_cnn.add(Dropout(0.2))\n",
        "model_cnn.add(Dense(128 , activation = 'relu'))\n",
        "model_cnn.add(Dense(num_classes , activation = 'softmax'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "t-AOuaynzJ-c"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lmOZDmks0gqc",
        "outputId": "ec3e167c-f331-49e1-b645-ed60ac74b180"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RangeIndex(start=0, stop=2, step=1)\n"
          ]
        }
      ],
      "source": [
        "from keras.preprocessing.image import img_to_array\n",
        "\n",
        "# ... (rest of your code)\n",
        "\n",
        "# Resize the image to the required size\n",
        "img = img.resize((128, 128))\n",
        "\n",
        "# Ensure the image has 3 channels (RGB)\n",
        "if img.mode != 'RGB':\n",
        "    img = img.convert('RGB')\n",
        "\n",
        "# Convert the image to an array\n",
        "img_array = img_to_array(img)\n",
        "\n",
        "# Reshape the array to match the model's input shape\n",
        "img_array = img_array.reshape((1, 128, 128, 3))\n",
        "\n",
        "# Make a prediction using the CNN model\n",
        "prediction = model_cnn.predict(img_array, verbose=0).flatten()\n",
        "print(labels['Name'].index)\n",
        "labels['Name'] = labels['Name'].reset_index(drop=True)\n",
        "\n",
        "# Create a dictionary with class names and their corresponding probabilities\n",
        "def return_predictions(prediction, labels, num_classes):\n",
        "    pred_list = {labels['Name'][i]: float(prediction[i]) for i in range(min(num_classes, len(labels['Name'])))}\n",
        "\n",
        "    return pred_list\n",
        "\n",
        "# ... (rest of your code)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NcNyJtS9ROHs"
      },
      "outputs": [],
      "source": [
        "#defining function to return predictions that we will use with Gradio\n",
        "def classify_image(inp):\n",
        "  img = inp.resize((128,128))\n",
        "  img_array = img_to_array(img)\n",
        "  img_array = img_array.reshape((1, 128, 128, 3))\n",
        "  prediction = model_cnn.predict(img_array, verbose=0).flatten()\n",
        "  # return labels['Name'][prediction.argmax()] -- for single prediction\n",
        "  pred_list =  {labels['Name'][i]: float(prediction[i]) for i in range(num_classes)}\n",
        "  return pred_list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aPL16C7ezQwZ"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pJmNnhpC02uo"
      },
      "outputs": [],
      "source": [
        "import gradio as gr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e4RtzNgMzUM3"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "id": "qe2uM2464zh7",
        "outputId": "83857cf6-2db4-4fdd-97c1-23fd5cbf74cc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
            "\n",
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Running on public URL: https://4e2811bd4bc666abea.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><iframe src=\"https://4e2811bd4bc666abea.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "image = gr.Image(type=\"pil\", height=300,width=300)\n",
        "label = gr.Label(num_top_classes=3)\n",
        "\n",
        "interface = gr.Interface(fn=classify_image,\n",
        "                         inputs=image,\n",
        "                        #  outputs=\"text\",\n",
        "                         outputs=label,\n",
        "                         allow_flagging = \"never\",\n",
        "                         title=\"Fake QR Detection\",\n",
        "                         description=\"This app classifies malicious and benign qr code from an uploaded image.\",\n",
        "                         theme='soft'\n",
        "                         )\n",
        "\n",
        "interface.launch()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HESXHvEznhCc"
      },
      "source": [
        "# New Section"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}