diff --git a/Handwritten Equation Solver/Dataset/README.md b/Handwritten Equation Solver/Dataset/README.md
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+## Handwritten Equation Solver - Dataset
+
+The dataset used for training and testing the models can be accessed from the following sources:
+- [Direct Download](https://cainvas-static.s3.amazonaws.com/media/user_data/Yuvnish17/data.zip)
+- [Kaggle Dataset](https://www.kaggle.com/datasets/xainano/handwrittenmathsymbols/data) 
diff --git a/Handwritten Equation Solver/Images/README.md b/Handwritten Equation Solver/Images/README.md
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+## Handwritten Equation Solver - Images
+
+This folder contains images used in the Jupyter notebooks for visualizations and analysis related to handwritten equation solver and streamlit application.
+
+**Math characters recognized using CNN 👇🏻**
+
+![img7](https://github.com/abhisheks008/DL-Simplified/assets/103712713/d6470e02-b5a7-4243-a007-bc0331c8f93e)
+
+----
+
+**Visual representation of the CNN model's performance through Confusion Matrix**
+
+![img3](https://github.com/abhisheks008/DL-Simplified/assets/103712713/aeadc8bd-e6ad-4538-b929-cfd603a562df)
+
+
+----
+
+
+**Visual representation of the RNN model's performance through Confusion Matrix**
+
+![img5](https://github.com/abhisheks008/DL-Simplified/assets/103712713/354867a3-4afb-4361-825a-8ff661228da9)
+
+----
+
+**Streamlit Interface**
+
+![image](https://github.com/Sgvkamalakar/Hand-Written-Equation-Solver/assets/103712713/aa618778-7d44-4665-877d-24221182f673)
+
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diff --git a/Handwritten Equation Solver/Models/Notebook.ipynb b/Handwritten Equation Solver/Models/Notebook.ipynb
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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Import libraries"
+      ],
+      "metadata": {
+        "id": "smxUl9vwhlpp"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 88,
+      "metadata": {
+        "id": "88MM0FEGtsmr"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import os\n",
+        "import random\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "from sklearn.metrics import confusion_matrix\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.preprocessing import LabelEncoder\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.keras.models import Sequential\n",
+        "from tensorflow.keras.layers import Conv2D,SimpleRNN, MaxPooling2D, Flatten, Dense, Dropout\n",
+        "from tensorflow.keras.models import load_model\n",
+        "from sklearn.preprocessing import LabelEncoder\n",
+        "import cv2\n",
+        "from google.colab.patches import cv2_imshow"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "MeB6iJPZtr_X"
+      },
+      "outputs": [],
+      "source": [
+        "def extractImages(datadir):\n",
+        "    # Get the data\n",
+        "    imagesData = []\n",
+        "    imagesLabel = []\n",
+        "    for folder in os.listdir(datadir):\n",
+        "        path = os.path.join(datadir, folder)\n",
+        "        for images in os.listdir(path):\n",
+        "            img = cv2.imread(os.path.join(path, images), cv2.IMREAD_GRAYSCALE)\n",
+        "            img = cv2.resize(img, (32, 32))\n",
+        "            imagesData.append(img)\n",
+        "            imagesLabel.append(folder)\n",
+        "\n",
+        "    combined = list(zip(imagesData, imagesLabel))\n",
+        "    random.shuffle(combined)\n",
+        "    imagesData, imagesLabel = zip(*combined)\n",
+        "\n",
+        "    return (imagesData, imagesLabel)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Loading the data"
+      ],
+      "metadata": {
+        "id": "vtg9pNMyhaiY"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "IgnRysUluAI4",
+        "outputId": "66b4c4cb-1f27-4f4c-9c3e-8cb52b9f5f9c"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "--2024-06-01 11:22:03--  https://cainvas-static.s3.amazonaws.com/media/user_data/Yuvnish17/data.zip\n",
+            "Resolving cainvas-static.s3.amazonaws.com (cainvas-static.s3.amazonaws.com)... 52.219.156.107, 52.219.156.67, 16.12.40.11, ...\n",
+            "Connecting to cainvas-static.s3.amazonaws.com (cainvas-static.s3.amazonaws.com)|52.219.156.107|:443... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 28437489 (27M) [application/x-zip-compressed]\n",
+            "Saving to: ‘data.zip’\n",
+            "\n",
+            "data.zip            100%[===================>]  27.12M  8.35MB/s    in 3.2s    \n",
+            "\n",
+            "2024-06-01 11:22:07 (8.35 MB/s) - ‘data.zip’ saved [28437489/28437489]\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "!wget https://cainvas-static.s3.amazonaws.com/media/user_data/Yuvnish17/data.zip\n",
+        "!unzip -qo data.zip"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "X1mEsU4qx-ax"
+      },
+      "outputs": [],
+      "source": [
+        "def download_csv(datadir):\n",
+        "    imagesData = []\n",
+        "    imagesLabel = []\n",
+        "    for folder in os.listdir(datadir):\n",
+        "        path = os.path.join(datadir, folder)\n",
+        "        for image in os.listdir(path):\n",
+        "            img = cv2.imread(os.path.join(path, image), cv2.IMREAD_GRAYSCALE)\n",
+        "            img = cv2.resize(img, (32, 32))\n",
+        "            img = img.flatten()\n",
+        "            imagesData.append(img)\n",
+        "            imagesLabel.append(folder)\n",
+        "\n",
+        "    combined = list(zip(imagesData, imagesLabel))\n",
+        "    random.shuffle(combined)\n",
+        "    imagesData, imagesLabel = zip(*combined)\n",
+        "\n",
+        "    return np.array(imagesData), np.array(imagesLabel)\n",
+        "\n",
+        "data_dir = '/content/data/dataset'\n",
+        "\n",
+        "imagesData, imagesLabel = download_csv(data_dir)\n",
+        "\n",
+        "X = np.array(imagesData)\n",
+        "X = X / 255.0\n",
+        "Y = np.array(imagesLabel)\n",
+        "df_X = pd.DataFrame(X)\n",
+        "df_X['label'] = Y\n",
+        "df_X.to_csv('data.csv', index=False)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "img=cv2.imread('/content/data/dataset/div/%_1_1.png')\n",
+        "cv2_imshow(img)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 45
+        },
+        "id": "oH62z51g8weC",
+        "outputId": "c3df6283-fa51-4406-b090-0020041f1893"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<PIL.Image.Image image mode=RGB size=28x28>"
+            ],
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAIAAAD9b0jDAAAAcElEQVR4nM3UMRKAMAwDQZv//zkpqGNJR0GuZ0eGGXqtVXndXVWnZx8gyq5B59shKotROZOgThegzu0xahag5swM9bsANf8+Lvq+UDML9T9RgKb9hKa3WyhIoGCmRlkTymYKFHdE8cwJ/dKEspkCxW3HLScy22FyyAAAAABJRU5ErkJggg==\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 235
+        },
+        "id": "OcBkl3uAzKSM",
+        "outputId": "3753e255-1bfe-45c0-9f67-87a4362b54d3"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df"
+            },
+            "text/html": [
+              "\n",
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+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
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+              "      <th>0</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
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+              "      <td>1.0</td>\n",
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+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>div</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>sub</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>...</td>\n",
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+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
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+              "      <td>1.0</td>\n",
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+              "      <td>4</td>\n",
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+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 1025 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e613654a-9080-46b3-8a44-5bc00b5eed2a')\"\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-e613654a-9080-46b3-8a44-5bc00b5eed2a 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-e613654a-9080-46b3-8a44-5bc00b5eed2a');\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-416c4e55-4ba5-4cab-8926-56ac53e0275f\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-416c4e55-4ba5-4cab-8926-56ac53e0275f')\"\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-416c4e55-4ba5-4cab-8926-56ac53e0275f button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "     0    1    2    3    4    5    6    7    8    9  ...  1015  1016  1017  \\\n",
+              "0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  ...   1.0   1.0   1.0   \n",
+              "1  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  ...   1.0   1.0   1.0   \n",
+              "2  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  ...   1.0   1.0   1.0   \n",
+              "3  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  ...   1.0   1.0   1.0   \n",
+              "4  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  ...   1.0   1.0   1.0   \n",
+              "\n",
+              "   1018  1019  1020  1021  1022  1023  label  \n",
+              "0   1.0   1.0   1.0   1.0   1.0   1.0      6  \n",
+              "1   1.0   1.0   1.0   1.0   1.0   1.0    div  \n",
+              "2   1.0   1.0   1.0   1.0   1.0   1.0    sub  \n",
+              "3   1.0   1.0   1.0   1.0   1.0   1.0      5  \n",
+              "4   1.0   1.0   1.0   1.0   1.0   1.0      4  \n",
+              "\n",
+              "[5 rows x 1025 columns]"
+            ]
+          },
+          "execution_count": 85,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df=pd.read_csv('data.csv')\n",
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "yb58U4n2uHR-",
+        "outputId": "b187b9d6-f572-4dbb-a128-8d13882f0ee3"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Number of images:  7600\n",
+            "Labels: ['5', '4', '3', '6', '7', '1', '8', 'mul', 'div', 'add', '0', 'sub', '9', '2']\n"
+          ]
+        }
+      ],
+      "source": [
+        "imagesData = []\n",
+        "imagesLabel = []\n",
+        "data_dir = '/content/data/dataset'\n",
+        "imagesData, imagesLabel = extractImages(data_dir)\n",
+        "print(\"Number of images: \",len(imagesData))\n",
+        "print(\"Labels:\",list(set(imagesLabel)))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ykn--lpmvONo",
+        "outputId": "764eb79d-8013-438d-80d4-0780eafce270"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Number of images in the folder \"/content/data/dataset/0\": 595\n",
+            "Number of images in the folder \"/content/data/dataset/1\": 562\n",
+            "Number of images in the folder \"/content/data/dataset/2\": 433\n",
+            "Number of images in the folder \"/content/data/dataset/3\": 541\n",
+            "Number of images in the folder \"/content/data/dataset/4\": 526\n",
+            "Number of images in the folder \"/content/data/dataset/5\": 433\n",
+            "Number of images in the folder \"/content/data/dataset/6\": 581\n",
+            "Number of images in the folder \"/content/data/dataset/7\": 533\n",
+            "Number of images in the folder \"/content/data/dataset/8\": 554\n",
+            "Number of images in the folder \"/content/data/dataset/9\": 546\n",
+            "Number of images in the folder \"/content/data/dataset/add\": 596\n",
+            "Number of images in the folder \"/content/data/dataset/sub\": 655\n",
+            "Number of images in the folder \"/content/data/dataset/mul\": 577\n",
+            "Number of images in the folder \"/content/data/dataset/div\": 468\n"
+          ]
+        }
+      ],
+      "source": [
+        "import os\n",
+        "\n",
+        "def count_images_in_folder(folder_path):\n",
+        "    valid_image_extensions = {\".jpg\", \".png\"}\n",
+        "\n",
+        "    files = os.listdir(folder_path)\n",
+        "\n",
+        "    image_count = sum(1 for file in files if os.path.splitext(file)[1].lower() in valid_image_extensions)\n",
+        "\n",
+        "    return image_count\n",
+        "\n",
+        "\n",
+        "folders = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'add','sub','mul','div']\n",
+        "image_count={}\n",
+        "for i in folders:\n",
+        "  folder_path = '/content/data/dataset/'+i\n",
+        "  x = count_images_in_folder(folder_path)\n",
+        "  image_count[i] = x\n",
+        "  print(f'Number of images in the folder \"{folder_path}\": {x}')\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "vyfrxOXxwml6",
+        "outputId": "5e5f1b1a-807d-4e48-d665-b2a28ed3b683"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "7600\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(sum(list(image_count.values())))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "folders = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'add', 'sub', 'mul', 'div']\n",
+        "images = []\n",
+        "\n",
+        "for folder in folders:\n",
+        "    folder_path = '/content/data/dataset/' + folder\n",
+        "    image_files = os.listdir(folder_path)\n",
+        "    if image_files:\n",
+        "        random_image_file = random.choice(image_files)\n",
+        "        image = cv2.imread(os.path.join(folder_path, random_image_file), cv2.IMREAD_GRAYSCALE)\n",
+        "        image = cv2.resize(image, (32, 32))\n",
+        "        images.append(image)\n",
+        "\n",
+        "images = np.array(images)\n",
+        "images = images / 255.0\n",
+        "images = np.expand_dims(images, axis=-1)\n",
+        "\n",
+        "\n",
+        "\n",
+        "fig, axes = plt.subplots(3, 5, figsize=(15, 9))\n",
+        "axes = axes.flatten()\n",
+        "\n",
+        "for i, ax in enumerate(axes):\n",
+        "    if i < len(images):\n",
+        "        ax.imshow(images[i].squeeze(), cmap='gray')\n",
+        "        ax.set_title(f\"{folders[i]}\")\n",
+        "        ax.axis('off')\n",
+        "    else:\n",
+        "        ax.axis('off')\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 789
+        },
+        "id": "b9i4GGmxuX7x",
+        "outputId": "c43ca79c-b5fa-4a3a-b219-d8537dad2a88"
+      },
+      "execution_count": 99,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1500x900 with 15 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "WdUTSsNTv02W",
+        "outputId": "fa463f02-ba99-46c4-f59f-07ca281a5889"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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bbyIiIgKJiYno1KkTrKyscPbsWWzcuBELFiyQbo2xZMkS9OzZE15eXsjJycGKFSug0WikxtbYdu7cKR31uF9gYCDq16+P0NBQzJs3D507d8brr7+OzMxMLF68GA0aNMDJkyel8Q0aNMD06dPx4Ycf4vnnn0evXr2gVqtx9OhRuLu7Sz8zUNlGjRqFr7/+GkOGDEF8fDzq1q2Ln3/+GYcOHcL8+fMfeXHA7NmzsWvXLjz//PN4++23pSapadOmsn318vLCRx99hGnTpiE1NRU9evRA1apVcf78eWzevBmjRo3CpEmTjLG79KQyxSVgRKZw5swZMXLkSFG3bl1hbW0tqlatKoKCgsSiRYvEnTt3pHF3794Vs2fPFvXq1RNWVlaiTp06Ytq0abIxQghRWFgopkyZImrUqCHs7OxESEiIOHfu3EMvPX/w8ufiS5n37dsnLUtPTxehoaGiatWqAsBjL0PPyckREydOFO7u7sLKyko0bNhQfPbZZ7JLj4UwzKXnYWFhsmXnz58XAMRnn31W6n5t3LhRWnb69GkRHBwsHBwcRI0aNcTIkSPFiRMnZJcwF1u/fr1o0qSJUKvVolmzZuK3334TvXv3Fk2aNCmRdfny5cLf31/Y2tqKqlWrCl9fX/Huu++Ky5cvCyGESEhIEP379xceHh5CrVYLV1dX0bVrV3Hs2LEyvQ+lXXp+/37d/z48uB8PetSl5w++/ttvvxUNGzYUarVaNGnSRKxatUrMnDlTlPZP9sqVK0XLli2FWq0W1apVE+3atRPR0dHSek9PTxEaGvrY/XuY0j77B2VkZIihQ4eKGjVqCGtra+Hr61vq+4FSfq4hJiZG+Pv7C2tra1G/fn2xbNmyh+7rL7/8Ip577jlhb28v7O3tRZMmTURYWJhISUmR7VdZ/6zT00MlxBMyQ42InlotWrSAi4tLiXk+RERlwTk7RGQ27t69i4KCAtmy/fv348SJE4+9dQYR0cPwyA4RmY3U1FQEBwfjjTfegLu7O/7++28sW7YMjo6O+Ouvv2S3VCAiKitOUCYis1GtWjX4+/vjm2++wdWrV2Fvb4/Q0FB88sknbHSIqNx4ZIeIiIgUjXN2iIiISNHY7BAREZGicc4O7t135fLly6hatWqF7olERERExiOEQE5ODtzd3UvcjPZ+bHYAXL58ucSdm4mIiOjJcPHiRdSuXfuh69nsANLPmV+8eBEajcbEaYiIiKgssrOzUadOnUfelgRgswPg/+40rdFo2OwQERE9YR43BYUTlImIiEjR2OwQERGRorHZISIiIkVjs0NERESKxmaHiIiIFI3NDhERESkamx0iIiJSNDY7REREpGhsdoiIiEjR2OwQERGRorHZISIiIkVjs0NERESKxmaHiIiIFI3NDhERESkamx0iIiJStCqmDkBERGQKKpVx6ghhnDr0cDyyQ0RERIrGZoeIiIgUjc0OERERKRqbHSIiIlI0NjtERESkaGx2iIiISNHY7BAREZGisdkhIiIiRWOzQ0RERIrGZoeIiIgUjc0OERERKRqbHSIiIlI0NjtERESkaGx2iIiISNHY7BAREZGisdkhIiIiRWOzQ0RERIrGZoeIiIgUjc0OERERKRqbHSIiIlI0NjtERESkaGx2iIiISNHY7BAREZGisdkhIiIiRTN5s3Pp0iW88cYbcHZ2hq2tLXx9fXHs2DFpvRACM2bMQM2aNWFra4vg4GCcPXtWto3r169jwIAB0Gg0cHJywvDhw5Gbm2vsXSEiIiIzZNJm58aNGwgKCoKVlRV27tyJ06dP44svvkC1atWkMZGRkVi4cCGWLVuGuLg42NvbIyQkBHfu3JHGDBgwAKdOnUJ0dDS2bduGAwcOYNSoUabYJSIiIjIzKiGEMFXxqVOn4tChQzh48GCp64UQcHd3xzvvvINJkyYBAHQ6Hdzc3LB69Wr069cPycnJ8PHxwdGjR9G6dWsAwK5du/Dyyy/jf//7H9zd3R+bIzs7G46OjtDpdNBoNIbbQSIiMlsqlXHqmO5bVvnK+v1t0iM7v/32G1q3bo0+ffrA1dUVLVu2xIoVK6T158+fR3p6OoKDg6Vljo6OaNu2LWJjYwEAsbGxcHJykhodAAgODoaFhQXi4uJKrZuXl4fs7GzZg4iIiJTJpM3Ov//+i6VLl6Jhw4aIiorC6NGjMW7cOKxZswYAkJ6eDgBwc3OTvc7NzU1al56eDldXV9n6KlWqoHr16tKYB0VERMDR0VF61KlTx9C7RkRERGbCpM1OUVERWrVqhblz56Jly5YYNWoURo4ciWXLllVq3WnTpkGn00mPixcvVmo9IiIiMh2TNjs1a9aEj4+PbJm3tzfS0tIAAFqtFgCQkZEhG5ORkSGt02q1yMzMlK0vKCjA9evXpTEPUqvV0Gg0sgcRERmPSmWcBxFg4mYnKCgIKSkpsmVnzpyBp6cnAKBevXrQarXYs2ePtD47OxtxcXEICAgAAAQEBCArKwvx8fHSmL1796KoqAht27Y1wl48Gv9CExERmVYVUxafOHEiAgMDMXfuXLz22ms4cuQIli9fjuXLlwMAVCoVJkyYgI8++ggNGzZEvXr18MEHH8Dd3R09evQAcO9IUOfOnaXTX3fv3sWYMWPQr1+/Ml2JRURERMpm0kvPAWDbtm2YNm0azp49i3r16iE8PBwjR46U1gshMHPmTCxfvhxZWVl47rnnsGTJEjRq1Egac/36dYwZMwZbt26FhYUFevfujYULF8LBwaFMGSrz0nNe2khEVJI5/NtoDhmoYsr6/W3yZsccsNkhIjIuc/i30RwyUMU8Eb+zQ0RERFTZ2OwQERGRorHZISIiIkUz6dVYRPR04RwJIjIFHtkhIiIiRWOzQ0RERIrG01hEREQmwNO6xsMjO0RERKRobHaIiIhI0djsEBERkaKx2SEiIiJFY7NDREREisZmh4iIiBSNzQ4REREpGn9n5ynA33IgIqKnGY/sEBERkaKx2SEiIiJFY7NDREREisY5O0RERsZ5dETGxSM7REREpGhsdoiIiEjR2OwQERGRonHODhHRU4ZzhuhpwyM7REREpGhsdoiIiEjReBqLngo8bE9E9PTikR0iIiJSNDY7REREpGhsdoiIiEjROGeHiJ4qnL9F9PThkR0iIiJSNDY7REREpGhsdoiIiEjROGeHyEg4V4SIyDR4ZIeIiIgUjc0OERERKRqbHSIiIlI0NjtERESkaGx2iIiISNHY7BAREZGisdkhIiIiRWOzQ0RERIrGZoeIiIgUzaTNzqxZs6BSqWSPJk2aSOvv3LmDsLAwODs7w8HBAb1790ZGRoZsG2lpaQgNDYWdnR1cXV0xefJkFBQUGHtXiIiIyEyZ/HYRTZs2xe+//y49r1Ll/yJNnDgR27dvx8aNG+Ho6IgxY8agV69eOHToEACgsLAQoaGh0Gq1+PPPP3HlyhUMGjQIVlZWmDt3rtH3hYiIiMyPyZudKlWqQKvVlliu0+nw7bffYt26dXjppZcAAKtWrYK3tzcOHz6MZ599Frt378bp06fx+++/w83NDS1atMCHH36IKVOmYNasWbC2tjb27hAREZGZMfmcnbNnz8Ld3R3169fHgAEDkJaWBgCIj4/H3bt3ERwcLI1t0qQJPDw8EBsbCwCIjY2Fr68v3NzcpDEhISHIzs7GqVOnHlozLy8P2dnZsgcREREpk0mbnbZt22L16tXYtWsXli5divPnz+P5559HTk4O0tPTYW1tDScnJ9lr3NzckJ6eDgBIT0+XNTrF64vXPUxERAQcHR2lR506dQy7Y0RERGQ2THoaq0uXLtJ/N2/eHG3btoWnpyd++ukn2NraVlrdadOmITw8XHqenZ3NhoeIiEihTH4a635OTk5o1KgRzp07B61Wi/z8fGRlZcnGZGRkSHN8tFptiauzip+XNg+omFqthkajkT2IiIhImcyq2cnNzcU///yDmjVrwt/fH1ZWVtizZ4+0PiUlBWlpaQgICAAABAQEICkpCZmZmdKY6OhoaDQa+Pj4GD0/ERERmR+TnsaaNGkSunXrBk9PT1y+fBkzZ86EpaUl+vfvD0dHRwwfPhzh4eGoXr06NBoNxo4di4CAADz77LMAgE6dOsHHxwcDBw5EZGQk0tPT8f777yMsLAxqtdqUu0ZERERmwqTNzv/+9z/0798f165dg4uLC5577jkcPnwYLi4uAIAvv/wSFhYW6N27N/Ly8hASEoIlS5ZIr7e0tMS2bdswevRoBAQEwN7eHoMHD8acOXNMtUtERERkZlRCCGHqEKaWnZ0NR0dH6HQ6g8/fUakMurmHetSnaA4ZTM0c3gNzyGBq5vAeMIPp6zODedRXgrJ+f5vVnB0iIiIiQ2OzQ0RERIrGZoeIiIgUjc0OERERKRqbHSIiIlI0NjtERESkaGx2iIiISNHY7BAREZGisdkhIiIiRWOzQ0RERIpm0ntjERERkek8Lbes4JEdIiIiUjQ2O0RERKRobHaIiIhI0fRudtasWYPt27dLz9999104OTkhMDAQFy5cMGg4IiIioorSu9mZO3cubG1tAQCxsbFYvHgxIiMjUaNGDUycONHgAYmIiIgqQu+rsS5evIgGDRoAALZs2YLevXtj1KhRCAoKQvv27Q2dj4iIiKhC9D6y4+DggGvXrgEAdu/ejY4dOwIAbGxscPv2bcOmIyIiIqogvY/sdOzYESNGjEDLli1x5swZvPzyywCAU6dOoW7duobOR0RERFQheh/ZWbx4MQICAnD16lX88ssvcHZ2BgDEx8ejf//+Bg9IREREVBEqIUz9u4aml52dDUdHR+h0Omg0GoNu2xx+ndIcMpiaObwH5pDB1MzhPWAG09dnBvOoby4ZKqKs39/l+p2dgwcP4o033kBgYCAuXboEAPj+++/xxx9/lC8tERERUSXRu9n55ZdfEBISAltbWyQkJCAvLw8AoNPpMHfuXIMHJCIiIqoIvZudjz76CMuWLcOKFStgZWUlLQ8KCkJCQoJBwxERERFVlN7NTkpKCl544YUSyx0dHZGVlWWITEREREQGo3ezo9Vqce7cuRLL//jjD9SvX98goYiIiIgMRe9mZ+TIkRg/fjzi4uKgUqlw+fJlrF27FpMmTcLo0aMrIyMRERFRuen9o4JTp05FUVEROnTogFu3buGFF16AWq3GpEmTMHbs2MrISERERFRu5f6dnfz8fJw7dw65ubnw8fGBg4ODobMZDX9np/IzmJo5vAfmkMHUzOE9YAbT12cG86hvLhkqoqzf33of2SlmbW0NHx+f8r6cnjJP+l8opeDnQERPI72bnZ49e0JVyr+YKpUKNjY2aNCgAV5//XU0btzYIAGJiIiIKkLvCcqOjo7Yu3cvEhISoFKpoFKpcPz4cezduxcFBQXYsGED/Pz8cOjQocrIS0RERKQXvY/saLVavP766/jqq69gYXGvVyoqKsL48eNRtWpVrF+/Hm+99RamTJnC20cQERGRyek9QdnFxQWHDh1Co0aNZMvPnDmDwMBA/Pfff0hKSsLzzz//xPzIICcoKz+Dqeszg3nUZwbzqM8M5lHfXDJURKXdCLSgoAB///13ieV///03CgsLAQA2NjalzushIiIiMja9T2MNHDgQw4cPx3vvvYdnnnkGAHD06FHMnTsXgwYNAgDExMSgadOmhk1KREREVA56Nztffvkl3NzcEBkZiYyMDACAm5sbJk6ciClTpgAAOnXqhM6dOxs2KREREVE5lPtHBYF758oAGHyei7Fxzo7yM5i6PjOYR31mMI/6zGAe9c0lQ0VU+o8KAk9+k0NERETKV65m5+eff8ZPP/2EtLQ05Ofny9YlJCQYJBgRERGRIeh9NdbChQsxdOhQuLm54fjx42jTpg2cnZ3x77//okuXLpWRkYiIiKjc9G52lixZguXLl2PRokWwtrbGu+++i+joaIwbNw46na4yMhIRERGVm97NTlpaGgIDAwEAtra2yMnJAXDvkvQff/yx3EE++eQTqFQqTJgwQVp2584dhIWFwdnZGQ4ODujdu7d0Bdj9eUJDQ2FnZwdXV1dMnjwZBQUF5c5BREREyqJ3s6PVanH9+nUAgIeHBw4fPgwAOH/+PMp7YdfRo0fx9ddfo3nz5rLlEydOxNatW7Fx40bExMTg8uXL6NWrl7S+sLAQoaGhyM/Px59//ok1a9Zg9erVmDFjRrlyEBERkQIJPQ0fPlzMmjVLCCHEV199JWxtbUVwcLBwcnISw4YN03dzIicnRzRs2FBER0eLdu3aifHjxwshhMjKyhJWVlZi48aN0tjk5GQBQMTGxgohhNixY4ewsLAQ6enp0pilS5cKjUYj8vLyypxBp9MJAEKn0+md/3HuXXBX+Q9meHQGU9dnBvOozwzmUZ8ZzKO+uWSoiLJ+f+t9ZGf58uWYPn06ACAsLAwrV66Et7c35syZg6VLl+rdbIWFhSE0NBTBwcGy5fHx8bh7965seZMmTeDh4YHY2FgAQGxsLHx9feHm5iaNCQkJQXZ2Nk6dOqV3FiIiIlIevS89t7CwkO52DgD9+vVDv379ylV8/fr1SEhIwNGjR0usS09Ph7W1NZycnGTL3dzckJ6eLo25v9EpXl+87mHy8vKQl5cnPS/+cUQiIiJSnnL9zs6dO3dw8uRJZGZmoqioSLbulVdeKdM2Ll68iPHjxyM6Oho2NjbliVFuERERmD17tlFrEhERkWno3ezs2rULgwYNwn///VdinUqlku58/jjx8fHIzMxEq1atpGWFhYU4cOAAvvrqK0RFRSE/Px9ZWVmyozsZGRnQarUA7k2WPnLkiGy7xVdrFY8pzbRp0xAeHi49z87ORp06dcqUm4iIiJ4ses/ZGTt2LPr06YMrV66gqKhI9ihrowMAHTp0QFJSEhITE6VH69atMWDAAOm/rayssGfPHuk1KSkpSEtLQ0BAAAAgICAASUlJyMzMlMZER0dDo9HAx8fnobXVajU0Go3sQURERMqk95GdjIwMhIeHl5gro6+qVauiWbNmsmX29vZwdnaWlg8fPhzh4eGoXr06NBoNxo4di4CAADz77LMA7t1d3cfHBwMHDkRkZCTS09Px/vvvIywsDGq1ukL5iIiISBn0bnZeffVV7N+/H15eXpWRR+bLL7+EhYUFevfujby8PISEhGDJkiXSektLS2zbtg2jR49GQEAA7O3tMXjwYMyZM6fSsxEREdGTQXXvOvuyu3XrFvr06QMXFxf4+vrCyspKtn7cuHEGDWgMZb1FfHmoVAbd3EM96lNkBtPXZwbzqM8M5lGfGcyjvrlkqIiyfn/rfWTnxx9/xO7du2FjY4P9+/dDdd87pVKpnshmh4iIiJRL72Zn+vTpmD17NqZOnSr7vR0iIiIic6R3t5Kfn4++ffuy0SEiIqIngt4dy+DBg7Fhw4bKyEJERERkcHqfxiosLERkZCSioqLQvHnzEhOU582bZ7BwRERERBWld7OTlJSEli1bAgD++usv2TqVsaZ1ExEREZWR3s3Ovn37KiMHERERUaXgLGMiIiJStDIf2enVq1eZxm3atKncYYiIiIgMrczNjqOjY2XmICIiIqoUZW52Vq1aVZk5iIiIiCoF5+wQERGRorHZISIiIkVjs0NERESKxmaHiIiIFK1MzU6rVq1w48YNAMCcOXNw69atSg1FREREZChlanaSk5Nx8+ZNAMDs2bORm5tbqaGIiIiIDKVMl563aNECQ4cOxXPPPQchBD7//HM4ODiUOnbGjBkGDUhERERUESohhHjcoJSUFMycORP//PMPEhIS4OPjgypVSvZJKpUKCQkJlRK0MmVnZ8PR0RE6nQ4ajcag2zbWvVEf9Skyg+nrM4N51GcG86jPDOZR31wyVERZv7/L1Ozcz8LCAunp6XB1da1wSHPBZkf5GUxdnxnMoz4zmEd9ZjCP+uaSoSLK+v2t913Pi4qKKhSMiIiIyJj0bnYA4J9//sH8+fORnJwMAPDx8cH48ePh5eVl0HBEREREFaX37+xERUXBx8cHR44cQfPmzdG8eXPExcWhadOmiI6OroyMREREROWm95ydli1bIiQkBJ988ols+dSpU7F7925OUH6AOZwPZQbT12cG86jPDOZRnxnMo765ZKiIsn5/631kJzk5GcOHDy+xfNiwYTh9+rS+myMiIiKqVHo3Oy4uLkhMTCyxPDExUVFXaBEREZEy6D1BeeTIkRg1ahT+/fdfBAYGAgAOHTqETz/9FOHh4QYPSERERFQRejc7H3zwAapWrYovvvgC06ZNAwC4u7tj1qxZGDdunMEDEhEREVWE3hOU75eTkwMAqFq1qsECmQInKCs/g6nrM4N51GcG86jPDOZR31wyVESl/ajg/Z70JoeIiIiUT+8JykRERERPEjY7REREpGhsdoiIiEjR9Gp27t69iw4dOuDs2bOVlYeIiIjIoPRqdqysrHDy5MnKykJERERkcHqfxnrjjTfw7bffVkYWIiIiIoPT+9LzgoICrFy5Er///jv8/f1hb28vWz9v3jyDhSMiIiKqKL2bnb/++gutWrUCAJw5c0a2TmWsXyciIiIiKiO9m519+/ZVRg4iIiKiSlHuS8/PnTuHqKgo3L59GwBQgbtOEBEREVUavZuda9euoUOHDmjUqBFefvllXLlyBQAwfPhwvPPOOwYPSERERFQRejc7EydOhJWVFdLS0mBnZyct79u3L3bt2mXQcEREREQVpfecnd27dyMqKgq1a9eWLW/YsCEuXLhgsGBEREREhqD3kZ2bN2/KjugUu379OtRqtV7bWrp0KZo3bw6NRgONRoOAgADs3LlTWn/nzh2EhYXB2dkZDg4O6N27NzIyMmTbSEtLQ2hoKOzs7ODq6orJkyejoKBA390iIiIihdK72Xn++efx3XffSc9VKhWKiooQGRmJF198Ua9t1a5dG5988gni4+Nx7NgxvPTSS+jevTtOnToF4N4ps61bt2Ljxo2IiYnB5cuX0atXL+n1hYWFCA0NRX5+Pv7880+sWbMGq1evxowZM/TdLSIiIlIqoaekpCTh6uoqOnfuLKytrcWrr74qvL29hZubmzh37py+myuhWrVq4ptvvhFZWVnCyspKbNy4UVqXnJwsAIjY2FghhBA7duwQFhYWIj09XRqzdOlSodFoRF5eXplr6nQ6AUDodLoK538QYJwHMzw6g6nrM4N51GcG86jPDOZR31wyVERZv7/1PrLTrFkznDlzBs899xy6d++OmzdvolevXjh+/Di8vLzK3XQVFhZi/fr1uHnzJgICAhAfH4+7d+8iODhYGtOkSRN4eHggNjYWABAbGwtfX1+4ublJY0JCQpCdnS0dHSIiIqKnm94TlAHA0dER06dPN0iApKQkBAQE4M6dO3BwcMDmzZvh4+ODxMREWFtbw8nJSTbezc0N6enpAID09HRZo1O8vnjdw+Tl5SEvL096np2dbZB9ISIiIvNTrmbnxo0b+Pbbb5GcnAwA8PHxwdChQ1G9enW9t9W4cWMkJiZCp9Ph559/xuDBgxETE1OeWGUWERGB2bNnV2oNIiIiMg96n8Y6cOAA6tati4ULF+LGjRu4ceMGFi5ciHr16uHAgQN6B7C2tkaDBg3g7++PiIgI+Pn5YcGCBdBqtcjPz0dWVpZsfEZGBrRaLQBAq9WWuDqr+HnxmNJMmzYNOp1Oely8eFHv3ERERPRk0LvZCQsLQ9++fXH+/Hls2rQJmzZtwr///ot+/fohLCyswoGKioqQl5cHf39/WFlZYc+ePdK6lJQUpKWlISAgAAAQEBCApKQkZGZmSmOio6Oh0Wjg4+Pz0BpqtVq63L34QURERAql78xnGxsb8ffff5dY/vfffwsbGxu9tjV16lQRExMjzp8/L06ePCmmTp0qVCqV2L17txBCiLfeekt4eHiIvXv3imPHjomAgAAREBAgvb6goEA0a9ZMdOrUSSQmJopdu3YJFxcXMW3aNL1y8Gos5WcwdX1mMI/6zGAe9ZnBPOqbS4aKKOv3t95zdlq1aoXk5GQ0btxYtjw5ORl+fn56bSszMxODBg3ClStX4OjoiObNmyMqKgodO3YEAHz55ZewsLBA7969kZeXh5CQECxZskR6vaWlJbZt24bRo0cjICAA9vb2GDx4MObMmaPvbhEREZFCqe51do928uRJ6b+Tk5Px7rvvYuzYsXj22WcBAIcPH8bixYvxySefoG/fvpWXtpJkZ2fD0dEROp3O4Ke0VCqDbu6hHvUpMoPp6zODedRnBvOozwzmUd9cMlREWb+/y9TsWFhYQKVS4XFDVSoVCgsL9U9rYmx2lJ/B1PWZwTzqM4N51GcG86hvLhkqoqzf32U6jXX+/HmDBSMiIiIypjI1O56enpWdg4iIiKhSlOtHBS9fvow//vgDmZmZKCoqkq0bN26cQYIRERERGYLezc7q1avx5ptvwtraGs7OzlDdd8JPpVKx2SEiIiKzonez88EHH2DGjBmYNm0aLCz0/k1CIiIiIqPSu1u5desW+vXrx0aHiIiIngh6dyzDhw/Hxo0bKyMLERERkcGV6Xd27ldYWIiuXbvi9u3b8PX1hZWVlWz9vHnzDBrQGPg7O8rPYOr6zGAe9ZnBPOozg3nUN5cMFWHQ39m5X0REBKKioqTbRTw4QZmIiIjInOjd7HzxxRdYuXIlhgwZUglxiIiIiAxL7zk7arUaQUFBlZGFiIiIyOD0bnbGjx+PRYsWVUYWIiIiIoPT+zTWkSNHsHfvXmzbtg1NmzYtMUF506ZNBgtHREREVFF6NztOTk7o1atXZWQhIiIiMji9m51Vq1ZVRg4iIiKiSsGfQSYiIiJF0/vITr169R75ezr//vtvhQIRERERGZLezc6ECRNkz+/evYvjx49j165dmDx5sqFyERERERmE3s3O+PHjS12+ePFiHDt2rMKBiIiIiAzJYHN2unTpgl9++cVQmyMiIiIyCIM1Oz///DOqV69uqM0RERERGYTep7Fatmwpm6AshEB6ejquXr2KJUuWGDQcERERUUXp3ez06NFD9tzCwgIuLi5o3749mjRpYqhcRERERAahd7Mzc+bMyshBREREVCn4o4JERESkaGU+smNhYfHIHxMEAJVKhYKCggqHIiIiIjKUMjc7mzdvfui62NhYLFy4EEVFRQYJRURERGQoZW52unfvXmJZSkoKpk6diq1bt2LAgAGYM2eOQcMRERERVVS55uxcvnwZI0eOhK+vLwoKCpCYmIg1a9bA09PT0PmIiIiIKkSvZken02HKlClo0KABTp06hT179mDr1q1o1qxZZeUjIiIiqpAyn8aKjIzEp59+Cq1Wix9//LHU01pERERE5kYlhBBlGWhhYQFbW1sEBwfD0tLyoeM2bdpksHDGkp2dDUdHR+h0Omg0GoNu+zEXsBnMoz5FZjB9fWYwj/rMYB71mcE86ptLhooo6/d3mY/sDBo06LGXnhMRERGZmzI3O6tXr67EGERERESVg7+gTERERIrGZoeIiIgUjc0OERERKRqbHSIiIlI0NjtERESkaGx2iIiISNHY7BAREZGisdkhIiIiRTNpsxMREYFnnnkGVatWhaurK3r06IGUlBTZmDt37iAsLAzOzs5wcHBA7969kZGRIRuTlpaG0NBQ2NnZwdXVFZMnT0ZBQYExd4WIiIjMlEmbnZiYGISFheHw4cOIjo7G3bt30alTJ9y8eVMaM3HiRGzduhUbN25ETEwMLl++jF69eknrCwsLERoaivz8fPz5559Ys2YNVq9ejRkzZphil4iIiMjcCDOSmZkpAIiYmBghhBBZWVnCyspKbNy4URqTnJwsAIjY2FghhBA7duwQFhYWIj09XRqzdOlSodFoRF5eXpnq6nQ6AUDodDoD7s09925/VvkPZnh0BlPXZwbzqM8M5lGfGcyjvrlkqIiyfn+b1ZwdnU4HAKhevToAID4+Hnfv3kVwcLA0pkmTJvDw8EBsbCwAIDY2Fr6+vnBzc5PGhISEIDs7G6dOnSq1Tl5eHrKzs2UPIiIiUiazaXaKioowYcIEBAUFoVmzZgCA9PR0WFtbw8nJSTbWzc0N6enp0pj7G53i9cXrShMREQFHR0fpUadOHQPvDREREZkLs2l2wsLC8Ndff2H9+vWVXmvatGnQ6XTS4+LFi5Vek4iIiEyjiqkDAMCYMWOwbds2HDhwALVr15aWa7Va5OfnIysrS3Z0JyMjA1qtVhpz5MgR2faKr9YqHvMgtVoNtVpt4L0gIiIic2TSIztCCIwZMwabN2/G3r17Ua9ePdl6f39/WFlZYc+ePdKylJQUpKWlISAgAAAQEBCApKQkZGZmSmOio6Oh0Wjg4+NjnB0hIiIis2XSIzthYWFYt24dfv31V1StWlWaY+Po6AhbW1s4Ojpi+PDhCA8PR/Xq1aHRaDB27FgEBATg2WefBQB06tQJPj4+GDhwICIjI5Geno73338fYWFhPHpDRERElXlB2OMBKPWxatUqaczt27fF22+/LapVqybs7OxEz549xZUrV2TbSU1NFV26dBG2traiRo0a4p133hF3794tcw5eeq78DKauzwzmUZ8ZzKM+M5hHfXPJUBFl/f5W3dvZp1t2djYcHR2h0+mg0WgMum2VyqCbe6hHfYrMYPr6zGAe9ZnBPOozg3nUN5cMFVHW72+zuRqLiIiIqDKw2SEiIiJFY7NDREREisZmh4iIiBSNzQ4REREpGpsdIiIiUjQ2O0RERKRobHaIiIhI0djsEBERkaKx2SEiIiJFY7NDREREisZmh4iIiBSNzQ4REREpGpsdIiIiUjQ2O0RERKRobHaIiIhI0djsEBERkaKx2SEiIiJFY7NDREREisZmh4iIiBSNzQ4REREpGpsdIiIiUjQ2O0RERKRobHaIiIhI0djsEBERkaKx2SEiIiJFY7NDREREisZmh4iIiBSNzQ4REREpGpsdIiIiUjQ2O0RERKRobHaIiIhI0djsEBERkaKx2SEiIiJFY7NDREREisZmh4iIiBSNzQ4REREpGpsdIiIiUjQ2O0RERKRobHaIiIhI0djsEBERkaKx2SEiIiJFY7NDREREimbSZufAgQPo1q0b3N3doVKpsGXLFtl6IQRmzJiBmjVrwtbWFsHBwTh79qxszPXr1zFgwABoNBo4OTlh+PDhyM3NNeJeEBERkTkzabNz8+ZN+Pn5YfHixaWuj4yMxMKFC7Fs2TLExcXB3t4eISEhuHPnjjRmwIABOHXqFKKjo7Ft2zYcOHAAo0aNMtYuEBERkbkTZgKA2Lx5s/S8qKhIaLVa8dlnn0nLsrKyhFqtFj/++KMQQojTp08LAOLo0aPSmJ07dwqVSiUuXbpU5to6nU4AEDqdruI78gDAOA9meHQGU9dnBvOozwzmUZ8ZzKO+uWSoiLJ+f5vtnJ3z588jPT0dwcHB0jJHR0e0bdsWsbGxAIDY2Fg4OTmhdevW0pjg4GBYWFggLi7uodvOy8tDdna27EFERETKZLbNTnp6OgDAzc1NttzNzU1al56eDldXV9n6KlWqoHr16tKY0kRERMDR0VF61KlTx8DpiYiIyFyYbbNTmaZNmwadTic9Ll68aOpIREREVEnMttnRarUAgIyMDNnyjIwMaZ1Wq0VmZqZsfUFBAa5fvy6NKY1arYZGo5E9iIiISJnMttmpV68etFot9uzZIy3Lzs5GXFwcAgICAAABAQHIyspCfHy8NGbv3r0oKipC27ZtjZ6ZiIiIzE8VUxbPzc3FuXPnpOfnz59HYmIiqlevDg8PD0yYMAEfffQRGjZsiHr16uGDDz6Au7s7evToAQDw9vZG586dMXLkSCxbtgx3797FmDFj0K9fP7i7u5tor4iIiMisVN4FYY+3b98+AaDEY/DgwUKIe5eff/DBB8LNzU2o1WrRoUMHkZKSItvGtWvXRP/+/YWDg4PQaDRi6NChIicnR68cvPRc+RlMXZ8ZzKM+M5hHfWYwj/rmkqEiyvr9rbq3s0+37OxsODo6QqfTGXz+jkpl0M091KM+RWYwfX1mMI/6zGAe9ZnBPOqbS4aKKOv3t9nO2SEiIiIyBDY7REREpGhsdoiIiEjR2OwQERGRorHZISIiIkVjs0NERESKxmaHiIiIFI3NDhERESkamx0iIiJSNDY7REREpGhsdoiIiEjR2OwQERGRorHZISIiIkVjs0NERESKxmaHiIiIFI3NDhERESkamx0iIiJSNDY7REREpGhsdoiIiEjR2OwQERGRorHZISIiIkVjs0NERESKxmaHiIiIFI3NDhERESkamx0iIiJSNDY7REREpGhsdoiIiEjR2OwQERGRorHZISIiIkVjs0NERESKxmaHiIiIFI3NDhERESkamx0iIiJSNDY7REREpGhsdoiIiEjR2OwQERGRorHZISIiIkVjs0NERESKxmaHiIiIFI3NDhERESkamx0iIiJSNDY7REREpGiKaXYWL16MunXrwsbGBm3btsWRI0dMHYmIiIjMgCKanQ0bNiA8PBwzZ85EQkIC/Pz8EBISgszMTFNHIyIiIhNTRLMzb948jBw5EkOHDoWPjw+WLVsGOzs7rFy50tTRiIiIyMSe+GYnPz8f8fHxCA4OlpZZWFggODgYsbGxJkxGRERE5qCKqQNU1H///YfCwkK4ubnJlru5ueHvv/8u9TV5eXnIy8uTnut0OgBAdnZ25QWtZOYQnRlMX58ZzKM+M5hHfWYwj/qVmaH4e1sI8chxT3yzUx4RERGYPXt2ieV16tQxQRrDcHQ0dQJmMIf6zGAe9ZnBPOozg3nUN0aGnJwcOD6iyBPf7NSoUQOWlpbIyMiQLc/IyIBWqy31NdOmTUN4eLj0vKioCNevX4ezszNUKlWl5n2c7Oxs1KlTBxcvXoRGo2EGE2UwdX1mMI/6zGAe9ZnBPOqbS4b7CSGQk5MDd3f3R4574psda2tr+Pv7Y8+ePejRoweAe83Lnj17MGbMmFJfo1aroVarZcucnJwqOal+NBqNyf8gMYPp6zODedRnBvOozwzmUd9cMhR71BGdYk98swMA4eHhGDx4MFq3bo02bdpg/vz5uHnzJoYOHWrqaERERGRiimh2+vbti6tXr2LGjBlIT09HixYtsGvXrhKTlomIiOjpo4hmBwDGjBnz0NNWTxK1Wo2ZM2eWOM3GDE9XfWYwj/rMYB71mcE86ptLhvJQicddr0VERET0BHvif1SQiIiI6FHY7BAREZGisdkhIiIiRWOzQ0RET4zU1FSoVCokJiY+dMz+/fuhUqmQlZVV6Xnq1q2L+fPnV3odfc2aNQstWrQwyLbat2+PCRMmADDf/X0cNjtmZvHixahbty5sbGzQtm1bHDlyxGi1Dxw4gG7dusHd3R0qlQpbtmwxWm3g3m08nnnmGVStWhWurq7o0aMHUlJSjJph6dKlaN68ufSDWQEBAdi5c6dRM9zvk08+gUqlkv6hMYZZs2ZBpVLJHk2aNDFa/WKXLl3CG2+8AWdnZ9ja2sLX1xfHjh0zWv26deuWeB9UKhXCwsKMUr+wsBAffPAB6tWrB1tbW3h5eeHDDz987D2ADC0nJwcTJkyAp6cnbG1tERgYiKNHjxo1A5mPo0ePYtSoUaaOoTc2O2Zkw4YNCA8Px8yZM5GQkAA/Pz+EhIQgMzPTKPVv3rwJPz8/LF682Cj1HhQTE4OwsDAcPnwY0dHRuHv3Ljp16oSbN28aLUPt2rXxySefID4+HseOHcNLL72E7t2749SpU0bLUOzo0aP4+uuv0bx5c6PXbtq0Ka5cuSI9/vjjD6PWv3HjBoKCgmBlZYWdO3fi9OnT+OKLL1CtWjWjZTh69KjsPYiOjgYA9OnTxyj1P/30UyxduhRfffUVkpOT8emnnyIyMhKLFi0ySv1iI0aMQHR0NL7//nskJSWhU6dOCA4OxqVLl4yag8yDi4sL7OzsTB1Df4LMRps2bURYWJj0vLCwULi7u4uIiAijZwEgNm/ebPS698vMzBQARExMjElzVKtWTXzzzTdGrZmTkyMaNmwooqOjRbt27cT48eONVnvmzJnCz8/PaPVKM2XKFPHcc8+ZNMODxo8fL7y8vERRUZFR6oWGhophw4bJlvXq1UsMGDDAKPWFEOLWrVvC0tJSbNu2Tba8VatWYvr06QapsXPnThEUFCQcHR1F9erVRWhoqDh37py0Pi4uTrRo0UKo1Wrh7+8vNm3aJACI48ePS2O2b98uGjZsKGxsbET79u3FqlWrBABx48aNMmXYuHGjaNasmbCxsRHVq1cXHTp0ELm5uaX+3evevbsYPHiw9NzT01PMmTNH9OvXT9jZ2Ql3d3fx1Vdf6fUetGvXTowZM0aMHz9eODk5CVdXV7F8+XKRm5srhgwZIhwcHISXl5fYsWOHEEKIVatWCUdHR9k2Nm/eLO7/Si/v3+Pc3FwxcOBAYW9vL7Rarfj8889l74Onp6f48ssvhRBC9O/fX7z22muy1+fn5wtnZ2exZs0avWtXJh7ZMRP5+fmIj49HcHCwtMzCwgLBwcGIjY01YTLT0el0AIDq1aubpH5hYSHWr1+PmzdvIiAgwKi1w8LCEBoaKvvzYExnz56Fu7s76tevjwEDBiAtLc2o9X/77Te0bt0affr0gaurK1q2bIkVK1YYNcP98vPz8cMPP2DYsGFGu1lwYGAg9uzZgzNnzgAATpw4gT/++ANdunQxSn0AKCgoQGFhIWxsbGTLbW1tDXa07+bNmwgPD8exY8ewZ88eWFhYoGfPnigqKkJubi66du0KHx8fxMfHY9asWZg0aZLs9RcvXkSvXr3QrVs3JCYmYsSIEZg6dWqZ61+5cgX9+/fHsGHDkJycjP3796NXr156nS787LPP4Ofnh+PHj2Pq1KkYP368dCSwrNasWYMaNWrgyJEjGDt2LEaPHo0+ffogMDAQCQkJ6NSpEwYOHIhbt27ptV19TZ48GTExMfj111+xe/du7N+/HwkJCaWOHTBgALZu3Yrc3FxpWVRUFG7duoWePXtWak69mbrbonsuXbokAIg///xTtnzy5MmiTZs2Rs8DEx/ZKSwsFKGhoSIoKMjotU+ePCns7e2FpaWlcHR0FNu3bzdq/R9//FE0a9ZM3L59WwghjH5kZ8eOHeKnn34SJ06cELt27RIBAQHCw8NDZGdnGy2DWq0WarVaTJs2TSQkJIivv/5a2NjYiNWrVxstw/02bNggLC0txaVLl4xWs7CwUEyZMkWoVCpRpUoVoVKpxNy5c41Wv1hAQIBo166duHTpkigoKBDff/+9sLCwEI0aNaqUelevXhUARFJSkvj666+Fs7Oz9HdBCCGWLl0qO7Izbdo04ePjI9vGlClTynxkJz4+XgAQqampJdaV9chO586dZWP69u0runTp8tja99e5/0hmQUGBsLe3FwMHDpSWXblyRQAQsbGxlXZkJycnR1hbW4uffvpJWnbt2jVha2tb6pGdu3fviho1aojvvvtOGt+/f3/Rt29fveoaA4/skFkKCwvDX3/9hfXr1xu9duPGjZGYmIi4uDiMHj0agwcPxunTp41S++LFixg/fjzWrl1b4v+mjaVLly7o06cPmjdvjpCQEOzYsQNZWVn46aefjJahqKgIrVq1wty5c9GyZUuMGjUKI0eOxLJly4yW4X7ffvstunTpAnd3d6PV/Omnn7B27VqsW7cOCQkJWLNmDT7//HOsWbPGaBkA4Pvvv4cQArVq1YJarcbChQvRv39/WFgY5uvj7Nmz6N+/P+rXrw+NRoO6desCANLS0pCcnIzmzZvL/i48eJQ1OTkZbdu2lS3T50isn58fOnToAF9fX/Tp0wcrVqzAjRs39NqHB+sFBAQgOTlZr23cPzfP0tISzs7O8PX1lZYV3+uxMudw/vPPP8jPz5e9n9WrV0fjxo1LHV+lShW89tprWLt2LYB7R+l+/fVXDBgwoNIylhebHTNRo0YNWFpaIiMjQ7Y8IyMDWq3WRKlMY8yYMdi2bRv27duH2rVrG72+tbU1GjRoAH9/f0RERMDPzw8LFiwwSu34+HhkZmaiVatWqFKlCqpUqYKYmBgsXLgQVapUQWFhoVFy3M/JyQmNGjXCuXPnjFazZs2a8PHxkS3z9vY2+uk0ALhw4QJ+//13jBgxwqh1J0+ejKlTp6Jfv37w9fXFwIEDMXHiRERERBg1h5eXF2JiYpCbm4uLFy/iyJEjuHv3LurXr2+Q7Xfr1g3Xr1/HihUrEBcXh7i4OAD3Th0ag6WlJaKjo7Fz5074+Phg0aJFaNy4Mc6fPw8LC4sSp7Pu3r1bKTmsrKxkz1UqlWxZ8enToqIio+Z6nAEDBmDPnj3IzMzEli1bYGtri86dO5sky6Ow2TET1tbW8Pf3x549e6RlRUVF2LNnj9Hni5iKEAJjxozB5s2bsXfvXtSrV8/UkQDc+xzy8vKMUqtDhw5ISkpCYmKi9GjdujUGDBiAxMREWFpaGiXH/XJzc/HPP/+gZs2aRqsZFBRU4mcHzpw5A09PT6NlKLZq1Sq4uroiNDTUqHVv3bpV4uiJpaUlioqKjJqjmL29PWrWrIkbN24gKioK3bt3r/A2r127hpSUFLz//vvo0KEDvL29ZUdVvL29cfLkSdy5c0dadvjwYdk2vL29S/xEx4NjHkelUiEoKAizZ8/G8ePHYW1tjc2bN8PFxQVXrlyRxhUWFuKvv/4q8foH6x0+fBje3t56ZdCHi4sLcnJyZFeqPup3h8rKy8sLVlZWUsMJ3LsysnjeWGkCAwNRp04dbNiwAWvXrkWfPn1KNG7mQDF3PVeC8PBwDB48GK1bt0abNm0wf/583Lx5E0OHDjVK/dzcXNn/vZ8/fx6JiYmoXr06PDw8Kr1+WFgY1q1bh19//RVVq1ZFeno6AMDR0RG2traVXh8Apk2bhi5dusDDwwM5OTlYt24d9u/fj6ioKKPUr1q1Kpo1ayZbZm9vD2dn5xLLK8ukSZPQrVs3eHp64vLly5g5cyYsLS3Rv39/o9QHgIkTJyIwMBBz587Fa6+9hiNHjmD58uVYvny50TIA9xrdVatWYfDgwahSxbj/XHbr1g0ff/wxPDw80LRpUxw/fhzz5s3DsGHDjJojKioKQgg0btwY586dw+TJk9GkSROD/LtUrVo1ODs7Y/ny5ahZsybS0tJkk4tff/11TJ8+HSNHjsS0adOQmpqKzz//XLaNt956C1988QUmT56MESNGID4+HqtXry5zhri4OOzZswedOnWCq6sr4uLicPXqVXh7e8Pe3h7h4eHYvn07vLy8MG/evFJ/qPDQoUOIjIxEjx49EB0djY0bN2L79u3lfVseq23btrCzs8N7772HcePGIS4uTq99fhgHBwcMHz4ckydPhrOzM1xdXTF9+vTHnrJ8/fXXsWzZMpw5cwb79u2rcI5KYdIZQ1TCokWLhIeHh7C2thZt2rQRhw8fNlrtffv2CQAlHvdPxqtMpdUGIFatWmWU+kIIMWzYMOHp6Smsra2Fi4uL6NChg9i9e7fR6pfG2BOU+/btK2rWrCmsra1FrVq1RN++fWWXAhvL1q1bRbNmzYRarRZNmjQRy5cvN3qGqKgoAUCkpKQYvXZ2drYYP3688PDwEDY2NqJ+/fpi+vTpIi8vz6g5NmzYIOrXry+sra2FVqsVYWFhIisry2Dbj46OFt7e3kKtVovmzZuL/fv3yy6QiI2NFX5+fsLa2lq0aNFC/PLLLyUuPd+6dato0KCBUKvV4vnnnxcrV64s8wTl06dPi5CQEOHi4iLUarVo1KiRWLRokRDi3mXUo0ePFtWrVxeurq4iIiKi1AnKs2fPFn369BF2dnZCq9WKBQsW6PUelPZ3/P6JwMXuf182b94sGjRoIGxtbUXXrl3F8uXLDXLpeU5OjnjjjTeEnZ2dcHNzE5GRkQ+99LzY6dOnBQDh6elptJ9m0JdKCCP/HCcRERGREXHODhERESkamx0iIiJSNDY7REREpGhsdoiIiEjR2OwQERGRorHZISIiIkVjs0NERESKxmaHyAy0b98eEyZMeOSYunXrYv78+UbJ86APPvgAo0aNKtdrVSoVtmzZUubx+/fvh0qlKvWXas2RvvtHwOnTp1G7dm3Z7Q6IKhObHSIDGDJkCFQqVYmHMW+eWVnS09OxYMECTJ8+XVp2//5aWVnBzc0NHTt2xMqVK0vcu+nKlSvo0qVLmesFBgbiypUrcHR0BACsXr0aTk5OBtkXU7l48SKGDRsGd3d3WFtbw9PTE+PHj8e1a9dMHU2SmpoKlUplkHssPY6Pjw+effZZzJs3r9JrEQFsdogMpnPnzrhy5YrsYS43MwXKfxfpb775BoGBgSVuwlm8v6mpqdi5cydefPFFjB8/Hl27dkVBQYE0TqvVQq1Wl7metbU1tFqtdJfnJ92///6L1q1b4+zZs/jxxx9x7tw5LFu2TLrJ7/Xr1yu1vrHuHn6/styBe+jQoVi6dKnszwpRZWGzQ2QgarUaWq1W9ii+S3lMTAzatGkDtVqNmjVrYurUqY/8Rz4zMxPdunWDra0t6tWrh7Vr15YYk5WVhREjRsDFxQUajQYvvfQSTpw4Ia2fNWsWWrRogW+++Qb16tWDjY0NAODnn3+Gr68vbG1t4ezsjODg4EeeTli/fj26dev20P2tVasWWrVqhffeew+//vordu7cKbsp4YOnef7880+0aNECNjY2aN26NbZs2SI7onD/aaz9+/dj6NCh0Ol00pGkWbNmAQCWLFmChg0bwsbGBm5ubnj11Vcfug/Xrl1D//79UatWLdjZ2cHX1xc//vijbEz79u0xbtw4vPvuu6hevTq0Wq1Uq9jZs2fxwgsvwMbGBj4+PoiOjn5ozWJhYWGwtrbG7t270a5dO3h4eKBLly74/fffcenSJdkRs7p16+LDDz9E//79YW9vj1q1amHx4sWy7ZX3c9+1axeee+45ODk5wdnZGV27dsU///wjva64MW/ZsiVUKhXat28P4N7NUOfMmYPatWtDrVajRYsW2LVrl/S64iNCGzZsQLt27WBjY4O1a9fiwoUL6NatG6pVqwZ7e3s0bdoUO3bskF7XsWNHXL9+HTExMY99D4kqis0OUSW7dOkSXn75ZTzzzDM4ceIEli5dim+//RYfffTRQ18zZMgQXLx4Efv27cPPP/+MJUuWIDMzUzamT58+yMzMxM6dOxEfH49WrVqhQ4cOsiMF586dwy+//IJNmzYhMTERV65cQf/+/TFs2DAkJydj//796NWrFx52i7zr16/j9OnTaN26dZn29aWXXoKfnx82bdpU6vrs7Gx069YNvr6+SEhIwIcffogpU6Y8dHuBgYGYP38+NBqNdLRs0qRJOHbsGMaNG4c5c+YgJSUFu3btwgsvvPDQ7dy5cwf+/v7Yvn07/vrrL4waNQoDBw7EkSNHZOPWrFkDe3t7xMXFITIyEnPmzJEamqKiIvTq1QvW1taIi4vDsmXLHpkduPf+RUVF4e2334atra1snVarxYABA7BhwwbZ+//ZZ5/Bz88Px48fx9SpUzF+/HhZU1Wezx0Abt68ifDwcBw7dgx79uyBhYUFevbsKZ12LH4vfv/9d1y5ckX6DBcsWIAvvvgCn3/+OU6ePImQkBC88sorOHv2rGx/irMmJycjJCQEYWFhyMvLw4EDB5CUlIRPP/0UDg4O0nhra2u0aNECBw8efOR7SGQQpr0PKZEyDB48WFhaWgp7e3vp8eqrrwohhHjvvfdE48aNZXcDXrx4sXBwcBCFhYVCCPldj1NSUgQAceTIEWl8cnKyACDdbfjgwYNCo9GIO3fuyHJ4eXmJr7/+Wghx767HVlZWIjMzU1ofHx8vAIjU1NQy7dfx48cFAJGWllZif7t3717qa/r27Su8vb2l57jvTs1Lly4Vzs7O4vbt29L6FStWyO5ivW/fPtkdq1etWiUcHR1lNX755Reh0WhEdnZ2mfajNKGhoeKdd96Rnrdr104899xzsjHPPPOMmDJlihDi3h3Qq1SpIi5duiSt37lzp2z/HnT48OFHrp83b54AIDIyMoQQ9+4o3blzZ9mYvn37ii5dugghyv+5l+bq1asCgEhKShJCCHH+/PkSdxMXQgh3d3fx8ccfy5Y988wz4u2335a9bv78+bIxvr6+YtasWY/M0LNnTzFkyJBHjiEyhCom6bCIFOjFF1/E0qVLpef29vYAgOTkZAQEBMjmoAQFBSE3Nxf/+9//4OHhIdtOcnIyqlSpAn9/f2lZkyZNZJN0T5w4gdzcXDg7O8tee/v2bdmpCU9PT7i4uEjP/fz80KFDB/j6+iIkJASdOnXCq6++imrVqpW6T7dv3wYA6VRIWQghHjrfJiUlBc2bN5dtr02bNmXedrGOHTvC09MT9evXR+fOndG5c2f07NkTdnZ2pY4vLCzE3Llz8dNPP+HSpUvIz89HXl5eifHNmzeXPa9Zs6Z0RC05ORl16tSBu7u7tD4gIKBMecVDjpyV5sFtBgQESFfhlfdzB+6dgpsxYwbi4uLw33//SUd00tLS0KxZs1KzZGdn4/LlywgKCpItDwoKkp06A1Di6N+4ceMwevRo7N69G8HBwejdu3eJ99fW1ha3bt0qtTaRIbHZITIQe3t7NGjQwCi1cnNzUbNmTezfv7/EuvubouKGq5ilpSWio6Px559/Yvfu3Vi0aBGmT5+OuLi4UidT16hRAwBw48aNEl+eD5OcnFzpE7OrVq2KhIQE7N+/H7t378aMGTMwa9YsHD16tNQrtz777DMsWLAA8+fPh6+vL+zt7TFhwoQSk3etrKxkz1UqVYmry/TRoEEDqFQqJCcno2fPniXWJycno1q1amV+b8v7uQNAt27d4OnpiRUrVsDd3R1FRUVo1qyZwSYwP1hzxIgRCAkJwfbt27F7925ERETgiy++wNixY6Ux169fh5eXl0HqEz0K5+wQVTJvb2/ExsbK/u/+0KFDqFq1KmrXrl1ifJMmTVBQUID4+HhpWUpKiux3Z1q1aoX09HRUqVIFDRo0kD2KG5SHUalUCAoKwuzZs3H8+HFYW1tj8+bNpY718vKCRqPB6dOny7Sve/fuRVJSEnr37l3q+saNGyMpKQl5eXnSsqNHjz5ym9bW1igsLCyxvEqVKggODkZkZCROnjyJ1NRU7N27t9RtHDp0CN27d8cbb7wBPz8/1K9fH2fOnCnTPhXz9vbGxYsXceXKFWnZ4cOHH/kaZ2dndOzYEUuWLJGOkhVLT0/H2rVr0bdvX9mRsAe3efjwYXh7ewMo/+d+7do1pKSk4P3330eHDh3g7e2NGzduyMZYW1sDgOy91mg0cHd3x6FDh2RjDx06BB8fn0fuOwDUqVMHb731FjZt2oR33nkHK1askK3/66+/0LJly8duh6ii2OwQVbK3334bFy9exNixY/H333/j119/xcyZMxEeHg4Li5J/BRs3bozOnTvjzTffRFxcHOLj4zFixAjZBNfg4GAEBASgR48e2L17N1JTU/Hnn39i+vTpOHbs2EOzxMXFYe7cuTh27BjS0tKwadMmXL16VfoyfZCFhQWCg4Pxxx9/lFiXl5eH9PR0XLp0CQkJCZg7dy66d++Orl27YtCgQaVu7/XXX0dRURFGjRqF5ORkREVF4fPPPweAh576qlu3LnJzc7Fnzx78999/uHXrFrZt24aFCxciMTERFy5cwHfffYeioiI0bty41G00bNhQOqKVnJyMN998ExkZGQ99n0oTHByMRo0aYfDgwThx4gQOHjwou5LqYb766ivk5eUhJCQEBw4cwMWLF7Fr1y507NgRtWrVwscffywbf+jQIURGRuLMmTNYvHgxNm7ciPHjx0sZyvO5V6tWDc7Ozli+fDnOnTuHvXv3Ijw8XDbG1dUVtra22LVrFzIyMqDT6QAAkydPxqeffooNGzYgJSUFU6dORWJiopTpYSZMmICoqCicP38eCQkJ2Ldvn+zPWWpqKi5duoTg4ODHvodEFWbiOUNEivCoCbtCCLF//37xzDPPCGtra6HVasWUKVPE3bt3pfX3T1AWQogrV66I0NBQoVarhYeHh/juu++Ep6enNEFZCCGys7PF2LFjhbu7u7CyshJ16tQRAwYMkCYTz5w5U/j5+clynD59WoSEhAgXFxehVqtFo0aNxKJFix65bzt27BC1atWSJlMX7y8AAUBUqVJFuLi4iODgYLFy5UrZOCFEiQm6hw4dEs2bNxfW1tbC399frFu3TgAQf//9txCi5ARlIYR46623hLOzswAgZs6cKQ4ePCjatWsnqlWrJmxtbUXz5s3Fhg0bHroP165dE927dxcODg7C1dVVvP/++2LQoEGyz+zBz0AIIbp37y4GDx4sPU9JSRHPPfecsLa2Fo0aNRK7du165ATkYqmpqWLw4MHCzc1N+qzGjh0r/vvvP9k4T09PMXv2bNGnTx9hZ2cntFqtWLBggWxMeT53IYSIjo4W3t7eQq1Wi+bNm4v9+/eXyL5ixQpRp04dYWFhIdq1ayeEEKKwsFDMmjVL1KpVS1hZWQk/Pz+xc+dO6TUPm9g8ZswY4eXlJdRqtXBxcREDBw6U7e/cuXNFSEjII983IkNRCaHHzDkieuoIIdC2bVtMnDgR/fv3N/j2165dK/2WzoOXZz9t6tatiwkTJjz21iFPuvz8fDRs2BDr1q0rMfmZqDJwgjIRPZJKpcLy5cuRlJRkkO199913qF+/PmrVqoUTJ05gypQpeO211576RudpkpaWhvfee4+NDhkNmx0ieqwWLVqgRYsWBtlWeno6ZsyYgfT0dNSsWRN9+vQpMW+FlK14UjWRsfA0FhERESkar8YiIiIiRWOzQ0RERIrGZoeIiIgUjc0OERERKRqbHSIiIlI0NjtERESkaGx2iIiISNHY7BAREZGisdkhIiIiRft/l16NsIt23IsAAAAASUVORK5CYII=\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "plt.bar(image_count.keys(), image_count.values(), color='blue')\n",
+        "plt.xlabel('Folders (Digits and Operators)')\n",
+        "plt.ylabel('Number of Images')\n",
+        "plt.title('Count of Images in Each Folder')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 464
+        },
+        "id": "FX8GCpCrwL6k",
+        "outputId": "b5a73947-f823-4f20-ad77-ccc967e5e1a0"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "7600\n",
+            "8\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "def display_image(image, label):\n",
+        "    plt.imshow(image, cmap='gray')\n",
+        "    plt.title(f'Label: {label}')\n",
+        "    plt.axis('off')\n",
+        "    plt.show()\n",
+        "\n",
+        "print(len(imagesLabel))\n",
+        "x=int(input())\n",
+        "display_image(imagesData[x], imagesLabel[x])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "G1TeDetlxQHg"
+      },
+      "outputs": [],
+      "source": [
+        "label_encoder = LabelEncoder()\n",
+        "y = label_encoder.fit_transform(imagesLabel)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "PoesMhx-9Ceh",
+        "outputId": "e6b341cd-12ff-41bf-e282-842fca643de1"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "{0: '0', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5', 6: '6', 7: '7', 8: '8', 9: '9', 10: 'add', 11: 'div', 12: 'mul', 13: 'sub'}\n"
+          ]
+        }
+      ],
+      "source": [
+        "label_mapping = {i: label for i, label in enumerate(label_encoder.classes_)}\n",
+        "print(label_mapping)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "lybQjgRrxJYY"
+      },
+      "outputs": [],
+      "source": [
+        "X=np.array(imagesData)\n",
+        "Y=np.array(y)\n",
+        "X=X/255.0"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "srQkaL1PxdIF",
+        "outputId": "6f6fd7e3-4c0c-45c9-b04b-f9c634a2f418"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(7600, 32, 32)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ],
+      "source": [
+        "X.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "hBVZVVT2xekT",
+        "outputId": "60744b8a-9167-4d0f-96dc-2286beb0c73e"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(7600, 32, 32, 1)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ],
+      "source": [
+        "X = np.expand_dims(X, axis=-1)\n",
+        "X.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "gkPsN5szxyx3",
+        "outputId": "c42d9e44-9ad2-40da-a860-1981df536acf"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Training set size: 6080\n",
+            "Testing set size: 1520\n"
+          ]
+        }
+      ],
+      "source": [
+        "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42)\n",
+        "\n",
+        "print(f'Training set size: {len(X_train)}')\n",
+        "print(f'Testing set size: {len(X_test)}')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "hxfmcBnH7EKO",
+        "outputId": "6d142755-7578-4dba-a092-4a529e3f42f8"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "14"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 14
+        }
+      ],
+      "source": [
+        "len(np.unique(y))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## CNN Model"
+      ],
+      "metadata": {
+        "id": "yD4BSrDohXUQ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "61KAn3eZ4D1h",
+        "outputId": "d306ce77-2c07-47f8-81f4-2370ede8c872"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Model: \"sequential_1\"\n",
+            "_________________________________________________________________\n",
+            " Layer (type)                Output Shape              Param #   \n",
+            "=================================================================\n",
+            " conv2d_3 (Conv2D)           (None, 30, 30, 64)        640       \n",
+            "                                                                 \n",
+            " max_pooling2d_3 (MaxPoolin  (None, 15, 15, 64)        0         \n",
+            " g2D)                                                            \n",
+            "                                                                 \n",
+            " conv2d_4 (Conv2D)           (None, 13, 13, 64)        36928     \n",
+            "                                                                 \n",
+            " max_pooling2d_4 (MaxPoolin  (None, 6, 6, 64)          0         \n",
+            " g2D)                                                            \n",
+            "                                                                 \n",
+            " conv2d_5 (Conv2D)           (None, 4, 4, 64)          36928     \n",
+            "                                                                 \n",
+            " max_pooling2d_5 (MaxPoolin  (None, 2, 2, 64)          0         \n",
+            " g2D)                                                            \n",
+            "                                                                 \n",
+            " dropout_1 (Dropout)         (None, 2, 2, 64)          0         \n",
+            "                                                                 \n",
+            " flatten_1 (Flatten)         (None, 256)               0         \n",
+            "                                                                 \n",
+            " dense_2 (Dense)             (None, 128)               32896     \n",
+            "                                                                 \n",
+            " dense_3 (Dense)             (None, 14)                1806      \n",
+            "                                                                 \n",
+            "=================================================================\n",
+            "Total params: 109198 (426.55 KB)\n",
+            "Trainable params: 109198 (426.55 KB)\n",
+            "Non-trainable params: 0 (0.00 Byte)\n",
+            "_________________________________________________________________\n"
+          ]
+        }
+      ],
+      "source": [
+        "cnn_model=Sequential()\n",
+        "cnn_model.add(Conv2D(64,(3,3),input_shape=(32,32,1),activation='relu'))\n",
+        "cnn_model.add(MaxPooling2D((2, 2)))\n",
+        "cnn_model.add(Conv2D(64, (3, 3), activation='relu'))\n",
+        "cnn_model.add(MaxPooling2D((2, 2)))\n",
+        "cnn_model.add(Conv2D(64, (3, 3), activation='relu'))\n",
+        "cnn_model.add(MaxPooling2D((2, 2)))\n",
+        "cnn_model.add(Dropout(0.5))\n",
+        "cnn_model.add(Flatten())\n",
+        "cnn_model.add(Dense(128, activation='relu'))\n",
+        "cnn_model.add(Dense(14, activation='softmax'))\n",
+        "\n",
+        "cnn_model.summary()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Efo6WvuA2za0",
+        "outputId": "e46c0d82-c9ac-496a-e3bc-5038989ea7e7"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/30\n",
+            "190/190 [==============================] - 15s 76ms/step - loss: 2.0170 - acc: 0.3324 - val_loss: 0.9832 - val_acc: 0.7020\n",
+            "Epoch 2/30\n",
+            "190/190 [==============================] - 15s 76ms/step - loss: 0.8992 - acc: 0.7164 - val_loss: 0.5667 - val_acc: 0.8230\n",
+            "Epoch 3/30\n",
+            "190/190 [==============================] - 15s 77ms/step - loss: 0.6354 - acc: 0.7998 - val_loss: 0.3939 - val_acc: 0.8875\n",
+            "Epoch 4/30\n",
+            "190/190 [==============================] - 15s 77ms/step - loss: 0.5013 - acc: 0.8382 - val_loss: 0.2901 - val_acc: 0.9230\n",
+            "Epoch 5/30\n",
+            "190/190 [==============================] - 15s 78ms/step - loss: 0.4158 - acc: 0.8674 - val_loss: 0.2568 - val_acc: 0.9217\n",
+            "Epoch 6/30\n",
+            "190/190 [==============================] - 15s 77ms/step - loss: 0.3575 - acc: 0.8845 - val_loss: 0.2154 - val_acc: 0.9434\n",
+            "Epoch 7/30\n",
+            "190/190 [==============================] - 14s 76ms/step - loss: 0.3250 - acc: 0.8974 - val_loss: 0.1973 - val_acc: 0.9467\n",
+            "Epoch 8/30\n",
+            "190/190 [==============================] - 14s 76ms/step - loss: 0.3004 - acc: 0.9081 - val_loss: 0.2063 - val_acc: 0.9421\n",
+            "Epoch 9/30\n",
+            "190/190 [==============================] - 17s 87ms/step - loss: 0.2801 - acc: 0.9140 - val_loss: 0.1545 - val_acc: 0.9533\n",
+            "Epoch 10/30\n",
+            "190/190 [==============================] - 15s 77ms/step - loss: 0.2447 - acc: 0.9230 - val_loss: 0.1567 - val_acc: 0.9507\n",
+            "Epoch 11/30\n",
+            "190/190 [==============================] - 14s 76ms/step - loss: 0.2252 - acc: 0.9289 - val_loss: 0.1467 - val_acc: 0.9592\n",
+            "Epoch 12/30\n",
+            "190/190 [==============================] - 15s 76ms/step - loss: 0.2151 - acc: 0.9275 - val_loss: 0.1147 - val_acc: 0.9658\n",
+            "Epoch 13/30\n",
+            "190/190 [==============================] - 14s 74ms/step - loss: 0.1983 - acc: 0.9347 - val_loss: 0.1634 - val_acc: 0.9428\n",
+            "Epoch 14/30\n",
+            "190/190 [==============================] - 14s 74ms/step - loss: 0.1915 - acc: 0.9388 - val_loss: 0.1142 - val_acc: 0.9645\n",
+            "Epoch 15/30\n",
+            "190/190 [==============================] - 14s 75ms/step - loss: 0.1787 - acc: 0.9434 - val_loss: 0.1128 - val_acc: 0.9671\n",
+            "Epoch 16/30\n",
+            "190/190 [==============================] - 15s 78ms/step - loss: 0.1669 - acc: 0.9465 - val_loss: 0.1403 - val_acc: 0.9566\n",
+            "Epoch 17/30\n",
+            "190/190 [==============================] - 15s 78ms/step - loss: 0.1613 - acc: 0.9493 - val_loss: 0.1122 - val_acc: 0.9691\n",
+            "Epoch 18/30\n",
+            "190/190 [==============================] - 15s 77ms/step - loss: 0.1517 - acc: 0.9484 - val_loss: 0.0898 - val_acc: 0.9730\n",
+            "Epoch 19/30\n",
+            "190/190 [==============================] - 15s 78ms/step - loss: 0.1527 - acc: 0.9495 - val_loss: 0.0938 - val_acc: 0.9691\n",
+            "Epoch 20/30\n",
+            "190/190 [==============================] - 15s 77ms/step - loss: 0.1352 - acc: 0.9559 - val_loss: 0.1017 - val_acc: 0.9651\n",
+            "Epoch 21/30\n",
+            "190/190 [==============================] - 17s 87ms/step - loss: 0.1485 - acc: 0.9503 - val_loss: 0.1190 - val_acc: 0.9632\n",
+            "Epoch 22/30\n",
+            "190/190 [==============================] - 14s 73ms/step - loss: 0.1281 - acc: 0.9566 - val_loss: 0.0805 - val_acc: 0.9763\n",
+            "Epoch 23/30\n",
+            "190/190 [==============================] - 14s 76ms/step - loss: 0.1293 - acc: 0.9604 - val_loss: 0.0899 - val_acc: 0.9704\n",
+            "Epoch 24/30\n",
+            "190/190 [==============================] - 15s 78ms/step - loss: 0.1090 - acc: 0.9623 - val_loss: 0.1098 - val_acc: 0.9625\n",
+            "Epoch 25/30\n",
+            "190/190 [==============================] - 15s 78ms/step - loss: 0.1205 - acc: 0.9617 - val_loss: 0.0870 - val_acc: 0.9711\n",
+            "Epoch 26/30\n",
+            "190/190 [==============================] - 15s 77ms/step - loss: 0.1064 - acc: 0.9646 - val_loss: 0.0969 - val_acc: 0.9697\n",
+            "Epoch 27/30\n",
+            "190/190 [==============================] - 14s 76ms/step - loss: 0.1112 - acc: 0.9640 - val_loss: 0.0941 - val_acc: 0.9737\n",
+            "Epoch 28/30\n",
+            "190/190 [==============================] - 15s 77ms/step - loss: 0.1083 - acc: 0.9633 - val_loss: 0.0888 - val_acc: 0.9724\n",
+            "Epoch 29/30\n",
+            "190/190 [==============================] - 15s 77ms/step - loss: 0.1034 - acc: 0.9664 - val_loss: 0.0661 - val_acc: 0.9776\n",
+            "Epoch 30/30\n",
+            "190/190 [==============================] - 15s 80ms/step - loss: 0.0977 - acc: 0.9671 - val_loss: 0.0771 - val_acc: 0.9757\n"
+          ]
+        }
+      ],
+      "source": [
+        "cnn_model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['acc'])\n",
+        "cnn_history = cnn_model.fit(X_train, y_train,epochs=30, validation_data=(X_test, y_test))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "IN-hkeQ_3ysM",
+        "outputId": "0fdfa0c6-4287-457f-d951-43cb6eb2d195"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "plt.plot(cnn_history.history['acc'])\n",
+        "plt.plot(cnn_history.history['val_acc'])\n",
+        "plt.title('Model Accuracy')\n",
+        "plt.ylabel('Accuracy')\n",
+        "plt.xlabel('Epoch')\n",
+        "plt.legend(['Train', 'Test'], loc='best')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Nr9Wb-rT8oP7",
+        "outputId": "852b79db-8c19-4fa6-b3e0-8645630e15ad"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "48/48 [==============================] - 1s 18ms/step - loss: 0.0771 - acc: 0.9757\n",
+            "Test accuracy of the loaded model: 97.57%\n"
+          ]
+        }
+      ],
+      "source": [
+        "loss, accuracy = cnn_model.evaluate(X_test, y_test)\n",
+        "print(f'Test accuracy of the loaded model: {accuracy * 100:.2f}%')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "TL7C9hXz9Q93",
+        "outputId": "44928007-b618-4fe0-b049-89d76631e6d8"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "238/238 [==============================] - 4s 17ms/step - loss: 0.0265 - acc: 0.9926\n",
+            "Accuracy of the model: 99.26%\n"
+          ]
+        }
+      ],
+      "source": [
+        "loss, accuracy = cnn_model.evaluate(X,y)\n",
+        "print(f'Accuracy of the model: {accuracy * 100:.2f}%')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(y.shape)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "isvPvInyqGyx",
+        "outputId": "1bd79d5c-6dc2-4cb2-93ca-cb197c5caa83"
+      },
+      "execution_count": 78,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(7600,)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "predictions = cnn_model.predict(X)\n",
+        "predictions_flat = np.argmax(predictions, axis=1)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7pgpT4t1p3FP",
+        "outputId": "8fdda35c-9414-4f2d-8d66-d88a2437d290"
+      },
+      "execution_count": 86,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "238/238 [==============================] - 6s 25ms/step\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cm = confusion_matrix(y, predictions_flat)\n",
+        "plt.figure(figsize=(8, 6))\n",
+        "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
+        "plt.axis('off')\n",
+        "plt.title(\"CNN Confusion Matrix\")\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 529
+        },
+        "id": "XiOwFEouqUph",
+        "outputId": "88722e71-298f-49cf-9dc8-ab19b89a3d91"
+      },
+      "execution_count": 97,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x600 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cm = confusion_matrix(y, predictions_flat)\n",
+        "\n",
+        "# Print confusion matrix\n",
+        "print(\"Confusion Matrix:\")\n",
+        "print(cm)"
+      ],
+      "metadata": {
+        "id": "8sPx_VouqRW9"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "oFe8aABO331B",
+        "outputId": "84710c81-c3c3-4cb0-d776-21e681b4c104"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
+            "  saving_api.save_model(\n"
+          ]
+        }
+      ],
+      "source": [
+        "cnn_model.save('cnn_model.h5')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from tensorflow.keras.models import load_model\n",
+        "cnn_model = load_model('cnn_model.h5')"
+      ],
+      "metadata": {
+        "id": "36p47RW-8J9r"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "folders = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'add', 'sub', 'mul', 'div']\n",
+        "images = []\n",
+        "\n",
+        "for folder in folders:\n",
+        "    folder_path = '/content/data/dataset/' + folder\n",
+        "    image_files = os.listdir(folder_path)\n",
+        "    if image_files:\n",
+        "        random_image_file = random.choice(image_files)\n",
+        "        image = cv2.imread(os.path.join(folder_path, random_image_file), cv2.IMREAD_GRAYSCALE)\n",
+        "        image = cv2.resize(image, (32, 32))\n",
+        "        images.append(image)\n",
+        "\n",
+        "images = np.array(images)\n",
+        "images = images / 255.0\n",
+        "images = np.expand_dims(images, axis=-1)\n",
+        "\n",
+        "predictions = cnn_model.predict(images)\n",
+        "predicted_labels = np.argmax(predictions, axis=1)\n",
+        "\n",
+        "fig, axes = plt.subplots(3, 5, figsize=(15, 9))\n",
+        "axes = axes.flatten()\n",
+        "\n",
+        "for i, ax in enumerate(axes):\n",
+        "    if i < len(images):\n",
+        "        ax.imshow(images[i].squeeze(), cmap='gray')\n",
+        "        ax.set_title(f\"Acutal '{folders[i]}': Predicted as {label_mapping[predicted_labels[i]]}\")\n",
+        "        ax.axis('off')\n",
+        "    else:\n",
+        "        ax.axis('off')\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 808
+        },
+        "id": "RsS1vui7vEGf",
+        "outputId": "7b93ff47-6756-40a0-9d40-0e0e1da1ca04"
+      },
+      "execution_count": 95,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "1/1 [==============================] - 0s 56ms/step\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1500x900 with 15 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import cv2\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from tensorflow.keras.models import load_model  # type: ignore\n",
+        "\n",
+        "labels = {0: '0', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5', 6: '6', 7: '7', 8: '8', 9: '9', 10: '+', 11: '/', 12: '*', 13: '-'}\n",
+        "\n",
+        "model = load_model('cnn_model.h5')\n",
+        "\n",
+        "def predict(image_path):\n",
+        "    image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\n",
+        "\n",
+        "    _, binary_image = cv2.threshold(image, 128, 255, cv2.THRESH_BINARY_INV)\n",
+        "\n",
+        "    contours, _ = cv2.findContours(binary_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
+        "\n",
+        "    bounding_boxes = [cv2.boundingRect(contour) for contour in contours]\n",
+        "    sorted_indices = sorted(range(len(bounding_boxes)), key=lambda i: bounding_boxes[i][0])\n",
+        "    sorted_contours = [contours[i] for i in sorted_indices]\n",
+        "\n",
+        "    rois = []\n",
+        "    padding = 15\n",
+        "\n",
+        "    for contour in sorted_contours:\n",
+        "        x, y, w, h = cv2.boundingRect(contour)\n",
+        "        x_start = max(0, x - padding)\n",
+        "        y_start = max(0, y - padding)\n",
+        "        x_end = min(image.shape[1], x + w + padding)\n",
+        "        y_end = min(image.shape[0], y + h + padding)\n",
+        "\n",
+        "        roi = image[y_start:y_end, x_start:x_end]\n",
+        "        roi = cv2.resize(roi, (32, 32))\n",
+        "        rois.append(roi)\n",
+        "\n",
+        "    rois = np.array(rois)\n",
+        "    rois = rois / 255.0\n",
+        "    rois = np.expand_dims(rois, axis=-1)\n",
+        "\n",
+        "    predictions = model.predict(rois)\n",
+        "    predicted_labels = np.argmax(predictions, axis=1)\n",
+        "\n",
+        "    image_color = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)\n",
+        "    for i, contour in enumerate(sorted_contours):\n",
+        "        x, y, w, h = cv2.boundingRect(contour)\n",
+        "        label = labels[predicted_labels[i]]\n",
+        "        cv2.rectangle(image_color, (x, y), (x+w, y+h), (0, 255, 0), 2)\n",
+        "        cv2.putText(image_color, label, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 0, 0), 2)\n",
+        "\n",
+        "    plt.figure(figsize=(10, 10))\n",
+        "    plt.imshow(cv2.cvtColor(image_color, cv2.COLOR_BGR2RGB))\n",
+        "    plt.axis('off')\n",
+        "    plt.show()\n",
+        "\n",
+        "    equation = ''.join(labels[predicted_labels[i]] for i in range(len(predicted_labels)))\n",
+        "    print(f'Predicted equation: {equation}')\n",
+        "    print(f'Result: {eval(equation)}')"
+      ],
+      "metadata": {
+        "id": "yHdJU2M-1vTX"
+      },
+      "execution_count": 48,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "predict('test2.png')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 556
+        },
+        "id": "HNkpXezz2fAc",
+        "outputId": "c36d66b2-5807-4a36-8558-b1a3b7e9905a"
+      },
+      "execution_count": 50,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "1/1 [==============================] - 0s 30ms/step\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x1000 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Predicted equation: 10-4\n",
+            "Result: 6\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## RNN model"
+      ],
+      "metadata": {
+        "id": "jGy93AlliG95"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "rnn_model = Sequential()\n",
+        "rnn_model.add(SimpleRNN(64, input_shape=(32, 32), activation='relu', return_sequences=True))\n",
+        "rnn_model.add(SimpleRNN(64, activation='relu', return_sequences=True))\n",
+        "rnn_model.add(SimpleRNN(64, activation='relu'))\n",
+        "rnn_model.add(Dropout(0.5))\n",
+        "rnn_model.add(Flatten())\n",
+        "rnn_model.add(Dense(128, activation='relu'))\n",
+        "rnn_model.add(Dense(14, activation='softmax'))\n",
+        "\n",
+        "rnn_model.summary()"
+      ],
+      "metadata": {
+        "id": "Ms45bdmC3kDl",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "5730f6fc-f8a3-4225-c7b1-19b0ed6a5df2"
+      },
+      "execution_count": 71,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Model: \"sequential_13\"\n",
+            "_________________________________________________________________\n",
+            " Layer (type)                Output Shape              Param #   \n",
+            "=================================================================\n",
+            " simple_rnn_48 (SimpleRNN)   (None, 32, 64)            6208      \n",
+            "                                                                 \n",
+            " simple_rnn_49 (SimpleRNN)   (None, 32, 64)            8256      \n",
+            "                                                                 \n",
+            " simple_rnn_50 (SimpleRNN)   (None, 64)                8256      \n",
+            "                                                                 \n",
+            " dropout_22 (Dropout)        (None, 64)                0         \n",
+            "                                                                 \n",
+            " flatten_2 (Flatten)         (None, 64)                0         \n",
+            "                                                                 \n",
+            " dense_12 (Dense)            (None, 128)               8320      \n",
+            "                                                                 \n",
+            " dense_13 (Dense)            (None, 14)                1806      \n",
+            "                                                                 \n",
+            "=================================================================\n",
+            "Total params: 32846 (128.30 KB)\n",
+            "Trainable params: 32846 (128.30 KB)\n",
+            "Non-trainable params: 0 (0.00 Byte)\n",
+            "_________________________________________________________________\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "rnn_model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['acc'])\n",
+        "rnn_history = rnn_model.fit(X_train, y_train,epochs=30,validation_data=(X_test,y_test))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Q59oOXKHi9nP",
+        "outputId": "dac3e5b9-36b4-49e1-add6-85e280dfdee1"
+      },
+      "execution_count": 72,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/30\n",
+            "190/190 [==============================] - 10s 29ms/step - loss: 2.5937 - acc: 0.1104 - val_loss: 2.4177 - val_acc: 0.1632\n",
+            "Epoch 2/30\n",
+            "190/190 [==============================] - 6s 30ms/step - loss: 2.3089 - acc: 0.2044 - val_loss: 2.1401 - val_acc: 0.2809\n",
+            "Epoch 3/30\n",
+            "190/190 [==============================] - 5s 29ms/step - loss: 2.0693 - acc: 0.2757 - val_loss: 1.8304 - val_acc: 0.3651\n",
+            "Epoch 4/30\n",
+            "190/190 [==============================] - 5s 24ms/step - loss: 1.8540 - acc: 0.3433 - val_loss: 1.7100 - val_acc: 0.3875\n",
+            "Epoch 5/30\n",
+            "190/190 [==============================] - 6s 34ms/step - loss: 1.7172 - acc: 0.3964 - val_loss: 1.5053 - val_acc: 0.4408\n",
+            "Epoch 6/30\n",
+            "190/190 [==============================] - 5s 25ms/step - loss: 1.5748 - acc: 0.4400 - val_loss: 1.4741 - val_acc: 0.4572\n",
+            "Epoch 7/30\n",
+            "190/190 [==============================] - 5s 25ms/step - loss: 1.4843 - acc: 0.4798 - val_loss: 1.4243 - val_acc: 0.5072\n",
+            "Epoch 8/30\n",
+            "190/190 [==============================] - 7s 35ms/step - loss: 1.4072 - acc: 0.5089 - val_loss: 1.2261 - val_acc: 0.5691\n",
+            "Epoch 9/30\n",
+            "190/190 [==============================] - 5s 25ms/step - loss: 1.3395 - acc: 0.5426 - val_loss: 1.2867 - val_acc: 0.5717\n",
+            "Epoch 10/30\n",
+            "190/190 [==============================] - 6s 31ms/step - loss: 1.2279 - acc: 0.5831 - val_loss: 1.1782 - val_acc: 0.6026\n",
+            "Epoch 11/30\n",
+            "190/190 [==============================] - 7s 39ms/step - loss: 1.2169 - acc: 0.5908 - val_loss: 1.0904 - val_acc: 0.6257\n",
+            "Epoch 12/30\n",
+            "190/190 [==============================] - 5s 24ms/step - loss: 1.0365 - acc: 0.6451 - val_loss: 1.0692 - val_acc: 0.6368\n",
+            "Epoch 13/30\n",
+            "190/190 [==============================] - 6s 34ms/step - loss: 1.0546 - acc: 0.6375 - val_loss: 1.0351 - val_acc: 0.6368\n",
+            "Epoch 14/30\n",
+            "190/190 [==============================] - 5s 25ms/step - loss: 0.9793 - acc: 0.6633 - val_loss: 0.9700 - val_acc: 0.6559\n",
+            "Epoch 15/30\n",
+            "190/190 [==============================] - 6s 33ms/step - loss: 0.9672 - acc: 0.6719 - val_loss: 0.8323 - val_acc: 0.7151\n",
+            "Epoch 16/30\n",
+            "190/190 [==============================] - 5s 27ms/step - loss: 0.8926 - acc: 0.6929 - val_loss: 1.0558 - val_acc: 0.6526\n",
+            "Epoch 17/30\n",
+            "190/190 [==============================] - 5s 24ms/step - loss: 0.8813 - acc: 0.6923 - val_loss: 0.8094 - val_acc: 0.6961\n",
+            "Epoch 18/30\n",
+            "190/190 [==============================] - 7s 35ms/step - loss: 0.9868 - acc: 0.6584 - val_loss: 0.8027 - val_acc: 0.7138\n",
+            "Epoch 19/30\n",
+            "190/190 [==============================] - 5s 24ms/step - loss: 0.8138 - acc: 0.7155 - val_loss: 0.7851 - val_acc: 0.7184\n",
+            "Epoch 20/30\n",
+            "190/190 [==============================] - 5s 26ms/step - loss: 0.8325 - acc: 0.7094 - val_loss: 0.8258 - val_acc: 0.7296\n",
+            "Epoch 21/30\n",
+            "190/190 [==============================] - 6s 32ms/step - loss: 0.7737 - acc: 0.7262 - val_loss: 0.7317 - val_acc: 0.7250\n",
+            "Epoch 22/30\n",
+            "190/190 [==============================] - 5s 24ms/step - loss: 0.8379 - acc: 0.7158 - val_loss: 0.7578 - val_acc: 0.7263\n",
+            "Epoch 23/30\n",
+            "190/190 [==============================] - 6s 32ms/step - loss: 0.6765 - acc: 0.7479 - val_loss: 0.7891 - val_acc: 0.7138\n",
+            "Epoch 24/30\n",
+            "190/190 [==============================] - 5s 25ms/step - loss: 0.6659 - acc: 0.7594 - val_loss: 0.6488 - val_acc: 0.7757\n",
+            "Epoch 25/30\n",
+            "190/190 [==============================] - 5s 24ms/step - loss: 0.6988 - acc: 0.7518 - val_loss: 0.8508 - val_acc: 0.7000\n",
+            "Epoch 26/30\n",
+            "190/190 [==============================] - 7s 36ms/step - loss: 0.6773 - acc: 0.7579 - val_loss: 0.6005 - val_acc: 0.7849\n",
+            "Epoch 27/30\n",
+            "190/190 [==============================] - 5s 25ms/step - loss: 0.6154 - acc: 0.7768 - val_loss: 0.6202 - val_acc: 0.7796\n",
+            "Epoch 28/30\n",
+            "190/190 [==============================] - 5s 28ms/step - loss: 0.6462 - acc: 0.7646 - val_loss: 0.6515 - val_acc: 0.7711\n",
+            "Epoch 29/30\n",
+            "190/190 [==============================] - 6s 32ms/step - loss: 0.6857 - acc: 0.7599 - val_loss: 0.6659 - val_acc: 0.7632\n",
+            "Epoch 30/30\n",
+            "190/190 [==============================] - 5s 25ms/step - loss: 0.6499 - acc: 0.7673 - val_loss: 1.0640 - val_acc: 0.6441\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "rnn_model.save('rnn_model.h5')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "iZ10CPr6srGw",
+        "outputId": "7fde2439-2b41-403f-bb31-2fa68e5c5c71"
+      },
+      "execution_count": 93,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
+            "  saving_api.save_model(\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.plot(rnn_history.history['acc'])\n",
+        "plt.plot(rnn_history.history['val_acc'])\n",
+        "plt.title('Model Accuracy')\n",
+        "plt.ylabel('Accuracy')\n",
+        "plt.xlabel('Epoch')\n",
+        "plt.legend(['Train', 'Test'], loc='best')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "sboFdMqapVMr",
+        "outputId": "c738c056-0ca8-4cc9-beb4-0b2d186b9729"
+      },
+      "execution_count": 73,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "predictions = rnn_model.predict(X)\n",
+        "predictions_flat = np.argmax(predictions, axis=1)\n",
+        "cm = confusion_matrix(y, predictions_flat)\n",
+        "plt.figure(figsize=(8, 6))\n",
+        "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
+        "plt.title(\"RNN Confusion Matrix\")\n",
+        "plt.axis('off')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 547
+        },
+        "id": "SFvOTo2PpnY_",
+        "outputId": "3b61e48c-412f-48bb-e9ea-d50bbc73819a"
+      },
+      "execution_count": 98,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "238/238 [==============================] - 2s 9ms/step\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x600 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "fig, axes = plt.subplots(1, 2, figsize=(18, 4))\n",
+        "\n",
+        "axes[0].plot(cnn_history.history['acc'], label='Training Accuracy')\n",
+        "axes[0].plot(cnn_history.history['val_acc'], label='Validation Accuracy')\n",
+        "axes[0].set_title('CNN Model Accuracy')\n",
+        "axes[0].set_xlabel('Epoch')\n",
+        "axes[0].set_ylabel('Accuracy')\n",
+        "axes[0].legend(loc='best')\n",
+        "\n",
+        "axes[1].plot(rnn_history.history['acc'], label='Training Accuracy')\n",
+        "axes[1].plot(rnn_history.history['val_acc'], label='Validation Accuracy')\n",
+        "axes[1].set_title('RNN Model Accuracy')\n",
+        "axes[1].set_xlabel('Epoch')\n",
+        "axes[1].set_ylabel('Accuracy')\n",
+        "axes[1].legend(loc='best')\n",
+        "\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 361
+        },
+        "id": "wZJTq_u7q7iW",
+        "outputId": "8b12e9fd-7e13-49e9-985b-0e4b0d38b8c2"
+      },
+      "execution_count": 92,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1800x400 with 2 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Conclusion\n",
+        "\n",
+        "In conclusion, the handwritten equation solver utilizing deep learning techniques, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), has demonstrated promising accuracy levels. The CNN-based approach achieved an impressive accuracy of 97.57%, while the RNN-based method achieved a respectable accuracy of 76.32%.\n",
+        "\n",
+        "The CNN model excelled in accurately recognizing and classifying individual handwritten characters within the equations. Its ability to effectively capture spatial hierarchies and local patterns in the input images contributed to its high accuracy. With its deep layers and convolutional operations, the CNN was capable of learning complex features directly from the pixel values, enabling robust classification of handwritten characters."
+      ],
+      "metadata": {
+        "id": "TM5-QxNUs1w6"
+      }
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/Handwritten Equation Solver/Models/README.md b/Handwritten Equation Solver/Models/README.md
new file mode 100644
index 000000000..83a62e8db
--- /dev/null
+++ b/Handwritten Equation Solver/Models/README.md	
@@ -0,0 +1,45 @@
+## Handwritten Equation Solver - Deep Learning Models
+
+This folder contains the trained models and the Jupyter notebook used for training and evaluating the models for the Handwritten Equation Solver project.
+
+**Math characters recognized using CNN 👇🏻**
+
+![img7](https://github.com/abhisheks008/DL-Simplified/assets/103712713/d6470e02-b5a7-4243-a007-bc0331c8f93e)
+
+
+## Contents
+
+- `cnn_model.h5`: Trained convolutional neural network model saved in HDF5 format.
+- `rnn_model.h5`: Trained recurrent neural network (RNN) model saved in HDF5 format.
+- `Notebook.ipynb` : Jupyter notebook for data preprocessing, model training, evaluation, and experimentation.
+
+## Model Comparison Plot
+
+![img1](https://github.com/abhisheks008/DL-Simplified/assets/103712713/f6aadfb9-874c-4734-b5c3-e55792ba4218)
+
+
+The comparison plot above shows the performance of the CNN, and RNN models in recognizing math characters. 
+
+----
+
+The notebook `Notebook.ipynb` covers the following topics:
+
+- Data Loading and Preprocessing: Includes steps to load and preprocess the dataset, such as resizing images, normalizing pixel values, and augmenting data.
+- CNN Model Training: Contains code for building, training, and evaluating the CNN model.
+- RNN Model Training: Contains code for building, training, and evaluating the RNN model.
+- Model Evaluation: Includes metrics and visualization for evaluating the performance of both models.
+- Experiments: Allows for experimentation with different architectures, hyperparameters, and preprocessing techniques.
+
+### ✒️ **Signature**
+
+<p align="center">
+  <img src="https://github.com/sgvkamalakar.png" height="200" width="200"/>
+</p>
+<p align="center">
+  Kamalakar Satapathi
+</p>
+
+ 
+Connect with me on [![LinkedIn](https://img.shields.io/badge/-Kamalakar_Satapathi-0077B5?style=flat-square&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/sgvkamalakar)
+
+Explore my codes [![GitHub](https://img.shields.io/badge/-Sgvkamalakar-181717?style=flat-square&logo=github)](https://github.com/sgvkamalakar)
diff --git a/Handwritten Equation Solver/Models/cnn_model.h5 b/Handwritten Equation Solver/Models/cnn_model.h5
new file mode 100644
index 000000000..619758cb2
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diff --git a/Handwritten Equation Solver/Models/rnn_model.h5 b/Handwritten Equation Solver/Models/rnn_model.h5
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index 000000000..9bbae440b
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diff --git a/Handwritten Equation Solver/README.md b/Handwritten Equation Solver/README.md
new file mode 100644
index 000000000..435946d2b
--- /dev/null
+++ b/Handwritten Equation Solver/README.md	
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+## Handwritten Equation Solver using DL with Web App
+
+<p align="center">
+<img src="https://github.com/abhisheks008/DL-Simplified/assets/103712713/98412d7d-f0e7-4e87-bfa5-d3d401736d1d" width=300 height=300/>
+</p>
+
+### 🎯 **Goal**
+
+The main goal of this project is to develop a Deep Learning (DL) model that can accurately solve handwritten mathematical equations. This project utilizes Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to recognize and interpret handwritten characters and equations.
+
+### 🧵 **Dataset**
+
+The dataset used for training and testing the models can be accessed from the following sources:
+- [Direct Download](https://cainvas-static.s3.amazonaws.com/media/user_data/Yuvnish17/data.zip)
+- [Kaggle Dataset](https://www.kaggle.com/datasets/xainano/handwrittenmathsymbols/data) 
+
+
+### 🧾 **Description**
+
+The Handwritten Equation Solver project addresses the challenge of accurately interpreting and solving handwritten mathematical equations, which is a non-trivial task due to the inherent variability in handwriting. Traditional OCR systems often fail to effectively handle these variations, leading to errors in character recognition and equation interpretation.
+
+
+### 📜 **Repo Structure**
+
+```bash
+Handwritten Equation Solver
+|- Dataset
+  |- README.md
+|- Images
+  |- img1.png
+  |- img2.png
+       :
+       :
+  |- img11.png
+  |- README.md
+|- Models
+  |- Notebook.ipynb
+  |- cnn_model.h5
+  |- rnn_model.h5
+  |- README.md
+|- Web_app
+  |- temp
+    |- temp.png
+  |- app.py
+  |- predict.py
+  |- Demo.mp4
+  |- README.md
+|- requirements.txt
+```
+
+
+### 🧮 **What I had done!**
+
+- Data Preparation:
+
+    - The dataset comprises images of handwritten mathematical symbols and equations. The dataset is sourced from Kaggle [Dataset](https://www.kaggle.com/datasets/xainano/handwrittenmathsymbols/data) and can also be downloaded directly from [here](https://cainvas-static.s3.amazonaws.com/media/user_data/Yuvnish17/data.zip)
+
+    ![img](https://github.com/abhisheks008/DL-Simplified/assets/103712713/3cebe29a-4626-49dc-8ef8-c1376078d038)
+
+    - Preprocessing steps include resizing images, normalizing pixel values, and augmenting data to improve model robustness.
+
+    ![img0](https://github.com/abhisheks008/DL-Simplified/assets/103712713/befbdbde-9f56-4d04-bed3-d6775ee1c49f)
+
+
+
+- Character Recognition with CNNs:
+
+    - Model Architecture: The CNN model is designed to capture spatial hierarchies and patterns in the input images. It consists of multiple convolutional layers followed by pooling layers and fully connected layers.
+
+   
+    - Training: The model is trained on individual character images to classify each character accurately. The CNN achieved an impressive accuracy of `97.57%` in recognizing handwritten characters.
+
+    ![img4](https://github.com/abhisheks008/DL-Simplified/assets/103712713/f8b129c6-df32-4071-8c65-4879dd7c66e1)
+
+    
+    - CNNs are highly effective in capturing local patterns and spatial features, making them ideal for character recognition tasks.
+
+- Preprocessing for Equation Recognition
+
+  Before feeding the images into the model for equation recognition, several preprocessing steps are performed to enhance the clarity and uniformity of the input data:
+
+  1. Grayscale Conversion: The input image is converted to grayscale to simplify the processing.
+  2. Thresholding: Binary thresholding is applied to convert the image to a binary format, making it easier to detect contours.
+  3. Contour Detection: Contours of the characters are detected to identify individual symbols.
+  4. Bounding Box Extraction: Bounding boxes are created around each detected contour to isolate individual characters.
+  4. Padding and Resizing: Each character is padded and resized to a uniform size (e.g., 32x32 pixels) to match the input requirements of the CNN model.
+
+  These preprocessing steps ensure that the input to the model is consistent, leading to more accurate character recognition and equation interpretation.
+
+- Implementation
+
+  The implementation involves integrating both CNN model to create a pipeline that processes input images, recognizes individual characters, and interprets the sequence to solve equations. The steps include:
+  - Preprocessing: Input images are preprocessed to enhance clarity and uniformity.
+  - Character Recognition: The CNN model identifies and classifies individual characters in the image.
+  - Sequence Interpretation: The RNN model interprets the sequence of recognized characters to form and solve the equation.
+  - Output: The final result is displayed, showing the interpreted equation and its solution.
+
+-  Web application
+
+    A Streamlit application has been developed to provide a user-friendly interface for solving hand written math equations using DL models. The application allows users to use their free hand to draw or write the math equation on a canvas and upload the drawing to the model. The interface includes features like stroke width adjustment, real-time updates, and a prediction button that processes the image and displays the equation and its solution.
+
+
+   ![image](https://github.com/Sgvkamalakar/Hand-Written-Equation-Solver/assets/103712713/da75072d-482d-4b4f-8b2e-9fafd519f1be)
+
+
+
+### 🚀 **Models Implemented**
+
+- Convolutional Neural Netowrk (CNN)
+- Recurrent Neural Netowrk (RNN)
+
+### 📚 **Libraries Needed**
+
+- TensorFlow
+- Streamlit
+- OpenCV
+- Numpy
+- Pandas
+- Seaborn
+- Sklearn
+- Matplotlib
+
+### 🎥 Demo
+
+https://github.com/Sgvkamalakar/Hand-Written-Equation-Solver/assets/103712713/356ea492-0f6e-41f5-9612-bfd583534ca3
+
+
+
+### ⚙️ **Usage**
+
+1. **Exploring Notebooks**: Navigate to the `Models/` directory to explore Jupyter notebooks. These notebooks cover data analysis, preprocessing, model training, and evaluation steps.
+   -  Navigate to the `Models/Notebook.ipynb`
+   -  Run all the cells
+2. **Trained Models**: The `Models/` directory contains trained Deep Learning models saved in HDF5 format. These models can be loaded and used for making predictions.
+3. **Streamlit App:** The `Web_app/app.py` contains the source code for the Streamlit web application. To run the app locally, follow the instructions below:
+
+    ```bash
+    pip install -r requirements.txt
+    cd Web_app
+    streamlit run app.py
+    ```
+
+    
+### 📈 **Performance of the Models based on the Accuracy Scores**
+
+
+
+<div align="center">
+<table>
+  <tr>
+    <th>Model</th>
+    <th>Validation Accuracy</th>
+  </tr>
+  <tr>
+    <td>CNN</td>
+    <td>97.57%</td>
+  </tr>
+  <tr>
+    <td>RNN</td>
+    <td>76.32%</td>
+  </tr>
+</table>
+</div>
+
+![img1](https://github.com/abhisheks008/DL-Simplified/assets/103712713/f6aadfb9-874c-4734-b5c3-e55792ba4218)
+
+
+### 📢 **Conclusion**
+
+In conclusion, the handwritten equation solver utilizing deep learning techniques, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), has demonstrated promising accuracy levels. The CNN-based approach achieved an impressive accuracy of `97.57%`, while the RNN-based method achieved a respectable accuracy of `76.32%`.
+
+The `CNN model` excelled in accurately recognizing and classifying individual handwritten characters within the equations. Its ability to effectively capture spatial hierarchies and local patterns in the input images contributed to its high accuracy. With its deep layers and convolutional operations, the CNN was capable of learning complex features directly from the pixel values, enabling robust classification of handwritten characters.
+
+
+### ✒️ **Signature**
+
+<p align="center">
+  <img src="https://github.com/sgvkamalakar.png" height="200" width="200"/>
+</p>
+<p align="center">
+  Kamalakar Satapathi
+</p>
+
+ 
+Connect with me on [![LinkedIn](https://img.shields.io/badge/-Kamalakar_Satapathi-0077B5?style=flat-square&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/sgvkamalakar)
+
+Explore my codes [![GitHub](https://img.shields.io/badge/-Sgvkamalakar-181717?style=flat-square&logo=github)](https://github.com/sgvkamalakar)
diff --git a/Handwritten Equation Solver/Web_app/Demo.mp4 b/Handwritten Equation Solver/Web_app/Demo.mp4
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index 000000000..aa67f37e2
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diff --git a/Handwritten Equation Solver/Web_app/README.md b/Handwritten Equation Solver/Web_app/README.md
new file mode 100644
index 000000000..50c5b448f
--- /dev/null
+++ b/Handwritten Equation Solver/Web_app/README.md	
@@ -0,0 +1,51 @@
+## Handwritten Equation Solver using DL - Streamlit Application 
+
+
+### Overview 
+
+The Handwritten Equation Solver is a Streamlit-based web application that leverages deep learning models to recognize and solve handwritten mathematical equations. Users can draw or write equations freehand on a canvas within the application, and the model will process the input to provide real-time solutions.
+
+### 📝 Features
+- Interactive Drawing Canvas: Users can draw or write mathematical equations directly on the canvas using their mouse or touchscreen.
+- Adjustable Stroke Width: Allows users to adjust the width of their drawing strokes for better accuracy.
+- Real-Time Updates: The canvas updates in real-time to reflect the user's input.
+- Equation Prediction: A prediction button processes the drawn equation, recognizes individual characters, and solves the equation.
+- Visual Feedback: Displays the recognized equation and its solution along with bounding boxes around detected characters.
+
+### 🎥 Demo
+
+https://github.com/Sgvkamalakar/Hand-Written-Equation-Solver/assets/103712713/356ea492-0f6e-41f5-9612-bfd583534ca3
+
+
+### 🪜Usage 
+
+- Drawing the Equation:
+Use the canvas in the main interface to draw or write the mathematical equation. Adjust the stroke width if necessary.
+- Predicting the Equation:
+Click the "Predict" button to process the drawn equation.
+- Viewing the Result:
+The recognized equation and its solution will be displayed below the canvas.
+
+
+### 🎓 Results 
+
+![image](https://github.com/abhisheks008/DL-Simplified/assets/103712713/85ea644e-1bea-407d-886c-d1741532f073)
+
+----
+
+![image](https://github.com/abhisheks008/DL-Simplified/assets/103712713/39d66a71-2455-4818-96aa-5dbc6bed14db)
+
+
+### ✒️ **Signature**
+
+<p align="center">
+  <img src="https://github.com/sgvkamalakar.png" height="200" width="200"/>
+</p>
+<p align="center">
+  Kamalakar Satapathi
+</p>
+
+ 
+Connect with me on [![LinkedIn](https://img.shields.io/badge/-Kamalakar_Satapathi-0077B5?style=flat-square&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/sgvkamalakar)
+
+Explore my codes [![GitHub](https://img.shields.io/badge/-Sgvkamalakar-181717?style=flat-square&logo=github)](https://github.com/sgvkamalakar)
diff --git a/Handwritten Equation Solver/Web_app/app.py b/Handwritten Equation Solver/Web_app/app.py
new file mode 100644
index 000000000..1db336195
--- /dev/null
+++ b/Handwritten Equation Solver/Web_app/app.py	
@@ -0,0 +1,89 @@
+import streamlit as st      
+from streamlit_drawable_canvas import st_canvas
+from PIL import Image
+import numpy as np
+import cv2 
+from predict import predict
+import base64
+st.set_page_config(page_title="Equation Solver", page_icon="logo.png",initial_sidebar_state="auto")
+
+LOGO_IMAGE = "logo.png"
+
+st.markdown(
+    """
+    <style>
+    .container {
+        display: flex;
+    }
+    .logo-text {
+        font-weight:700;
+        font-size:50px ;
+        color: black ;
+        margin-left:-15px;
+        padding: 15px ;
+    }
+    .logo-img {
+        float:right;
+        width: 100px;
+        height:100px;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+st.markdown(
+    f"""
+    <div class="container">
+        <img class="logo-img" src="data:image/png;base64,{base64.b64encode(open(LOGO_IMAGE, "rb").read()).decode()}">
+        <p class="logo-text">Equation Solver using CNN</p>
+    </div>
+    """,
+    unsafe_allow_html=True
+)
+stroke_color = "black"
+bg_color = "white"
+drawing_mode = "freedraw"
+realtime_update = True
+
+
+with st.sidebar:
+    stroke_width=st.slider(label="Adjust Stroke Width",value=5,min_value=3,max_value=10)
+
+    st.markdown("<p style='text-align: center; font-size: 1.2em; color: white;'><a href='https://github.com/Sgvkamalakar/Hand-Written-Equation-Solver'>Source Code</a></p>", unsafe_allow_html=True)
+    st.markdown("<p style='text-align: center; font-size: 1.2em; color: white;'><a href='https://github.com/sgvkamalakar'>Explore my Codes</a></p>", unsafe_allow_html=True)
+    st.markdown("<p style='text-align: center; font-size: 1.2em; color: white;'><a href='https://www.linkedin.com/in/sgvkamlakar'>Connect with me on LinkedIn</a></p>", unsafe_allow_html=True)
+data = st_canvas(
+    stroke_width=stroke_width,
+    stroke_color=stroke_color,
+    background_color=bg_color,
+    update_streamlit=realtime_update,
+    height=300,
+    width=700,
+    drawing_mode=drawing_mode,
+    key="full_app",
+)
+
+if st.button('Predict'):
+    path='temp/temp.png'
+    cv2.imwrite(path, data.image_data)
+    eq,res=predict(path)
+    st.write(f"#### Equation: **{eq}**")
+    x=str(res)
+    res_length=len(x)
+    width = 100
+    font_size = 35
+    if res_length > 3:
+        width = 150
+        font_size = 25
+
+    padding_top = max(5, (40 - font_size) / 2)
+    padding_bottom = max(5, (40 - font_size) / 2)
+    style = f"background-color: grey; height: 70px; width: {width}px; border-radius: 5px; padding-top: {padding_top}px; padding-bottom: {padding_bottom}px; margin-left: 150px; text-align: center;"
+
+    label_style = "font-weight: bold; color: white; font-size: {font_size}px;"
+
+    label_style = label_style.format(font_size=font_size)
+
+    st.subheader("Result")
+    st.markdown(f"<div style='{style}'><label style='{label_style}'>{res}</label></div>", unsafe_allow_html=True)
diff --git a/Handwritten Equation Solver/Web_app/logo.png b/Handwritten Equation Solver/Web_app/logo.png
new file mode 100644
index 000000000..c724208c8
Binary files /dev/null and b/Handwritten Equation Solver/Web_app/logo.png differ
diff --git a/Handwritten Equation Solver/Web_app/predict.py b/Handwritten Equation Solver/Web_app/predict.py
new file mode 100644
index 000000000..990a9fe64
--- /dev/null
+++ b/Handwritten Equation Solver/Web_app/predict.py	
@@ -0,0 +1,55 @@
+import cv2
+import numpy as np
+from tensorflow.keras.models import load_model  # type: ignore
+import streamlit as st 
+
+labels = {0: '0', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5', 6: '6', 7: '7', 8: '8', 9: '9', 10: '+', 11: '/', 12: '*', 13: '-'}
+
+model = load_model('../Models/cnn_model.h5')  
+
+
+def predict(image_path):
+    image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
+
+    _, binary_image = cv2.threshold(image, 128, 255, cv2.THRESH_BINARY_INV)
+
+    contours, _ = cv2.findContours(binary_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+    bounding_boxes = [cv2.boundingRect(contour) for contour in contours]
+    sorted_indices = sorted(range(len(bounding_boxes)), key=lambda i: bounding_boxes[i][0])
+    sorted_contours = [contours[i] for i in sorted_indices]
+
+    rois = []
+    padding = 10  
+
+    for contour in sorted_contours:
+        x, y, w, h = cv2.boundingRect(contour)
+        x_start = max(0, x - padding)
+        y_start = max(0, y - padding)
+        x_end = min(image.shape[1], x + w + padding)
+        y_end = min(image.shape[0], y + h + padding)
+        
+        roi = image[y_start:y_end, x_start:x_end]
+        roi = cv2.resize(roi, (32, 32))
+        rois.append(roi)
+
+    rois = np.array(rois)
+    rois = rois / 255.0
+    rois = np.expand_dims(rois, axis=-1)
+
+    predictions = model.predict(rois)
+    predicted_labels = np.argmax(predictions, axis=1)
+
+    image_color = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
+    for i, contour in enumerate(sorted_contours):
+        x, y, w, h = cv2.boundingRect(contour)
+        label = labels[predicted_labels[i]]
+        cv2.rectangle(image_color, (x, y), (x+w, y+h), (0, 255, 0), 2)
+        cv2.putText(image_color, label, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 0, 0), 2)
+
+    st.image(cv2.cvtColor(image_color, cv2.COLOR_BGR2RGB))
+    equation = ''.join(labels[predicted_labels[i]] for i in range(len(predicted_labels)))
+    return equation.replace('*','x'), eval(equation)
+
+
+
diff --git a/Handwritten Equation Solver/requirements.txt b/Handwritten Equation Solver/requirements.txt
new file mode 100644
index 000000000..d30eace25
--- /dev/null
+++ b/Handwritten Equation Solver/requirements.txt	
@@ -0,0 +1,9 @@
+tensorflow==2.12.0
+streamlit
+streamlit-drawable-canvas==0.9.3
+opencv-python==4.7.0.68
+numpy
+pandas
+matplotlib
+scikit-learn
+seaborn