Handwritten digit recognition using MNIST dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing Necessary Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import cv2\n",
    "from PIL import Image\n",
    "import tensorflow as tf\n",
    "\n",
    "# Set the random seeds\n",
    "tf.random.set_seed(3)\n",
    "\n",
    "from tensorflow import keras\n",
    "from keras.datasets import mnist\n",
    "from tensorflow.math import confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading The MNIST Data (Image Data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The dataset already has processed data and has split into train and test sets\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28) (60000,) (10000, 28, 28) (10000,)\n"
     ]
    }
   ],
   "source": [
    "# The images have already been converted to a numpy array, so no need to convert them\n",
    "\n",
    "# Shape of numpy arrays\n",
    "print(x_train.shape, y_train.shape, x_test.shape, y_test.shape)\n",
    "# 60,000 images, dimension - 28 x 28 -> x_train (all images are grayscale images)\n",
    "# 60,000 labels - corresponding to x_train images -> y_train\n",
    "# Grayscale image has only 1 channel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "# Printing an image from the x_train dataset\n",
    "print(x_train[7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28)\n"
     ]
    }
   ],
   "source": [
    "# The image is already in a numpy array form, so we can easily convert it back to an image\n",
    "print(x_train[7].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "# Displaying the image\n",
    "plt.imshow(x_train[7])\n",
    "plt.show()\n",
    "\n",
    "# Printing the corresponding label\n",
    "print(y_train[7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "# You can do this for any other image of the dataset too\n",
    "plt.imshow(x_train[24])\n",
    "plt.show()\n",
    "\n",
    "# Printing the corresponding label\n",
    "print(y_train[24])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Image Labels Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((60000,), (10000,))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We observe that the image and their labels are correct\n",
    "y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 5 6 7 8 9]\n",
      "[0 1 2 3 4 5 6 7 8 9]\n"
     ]
    }
   ],
   "source": [
    "# Unique values in y_train and y_test\n",
    "print(np.unique(y_train))\n",
    "print(np.unique(y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scaling The Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can use these labels as it is or else we can also apply \"One Hot Encoding\"\n",
    "# All images have same 28 x 28 dimension in this dataset, if not; we have to resize all these images to some common dimension\n",
    "\n",
    "# Pixel range - 0 to 255 -> instead of having this, we will have values ranging from 0 to 1 (NORMALIZATION)\n",
    "x_train = x_train/255\n",
    "x_test = x_test/255\n",
    "# 0/255 - 0 (min value)\n",
    "# 255/255 - 1 (max value)\n",
    "# All other values in between 0 & 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.14901961\n",
      "  0.16862745 0.41176471 1.         0.99215686 0.99215686 0.99215686\n",
      "  0.99215686 0.99215686 0.68235294 0.02352941 0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.16862745 0.54509804 0.87843137\n",
      "  0.88627451 0.98823529 0.99215686 0.98823529 0.98823529 0.98823529\n",
      "  0.98823529 0.98823529 0.98823529 0.61960784 0.05490196 0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.69803922 0.98823529 0.98823529\n",
      "  0.98823529 0.98823529 0.99215686 0.98823529 0.98823529 0.98823529\n",
      "  0.98823529 0.98823529 0.98823529 0.98823529 0.23137255 0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.42745098 0.98823529 0.98823529\n",
      "  0.90196078 0.51764706 0.52156863 0.51764706 0.51764706 0.74117647\n",
      "  0.98823529 0.98823529 0.98823529 0.98823529 0.23137255 0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.01568627 0.11372549 0.11372549\n",
      "  0.09411765 0.         0.         0.         0.         0.05490196\n",
      "  0.88627451 0.98823529 0.98823529 0.6745098  0.02745098 0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.33333333\n",
      "  0.95294118 0.98823529 0.98823529 0.56470588 0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.34509804 0.74117647\n",
      "  0.98823529 0.98823529 0.98823529 0.05490196 0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.35686275 0.83137255 0.96862745 0.98823529\n",
      "  0.98823529 0.98823529 0.8        0.03529412 0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.1254902  0.49019608 0.75686275\n",
      "  0.75686275 0.75686275 0.99215686 0.98823529 0.98823529 0.98823529\n",
      "  0.93333333 0.4        0.10980392 0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.17647059 0.87058824 0.98823529 0.98823529\n",
      "  0.98823529 0.98823529 0.99215686 0.98823529 0.98823529 0.98823529\n",
      "  0.69411765 0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.17647059 0.8745098  0.99215686 0.99215686\n",
      "  0.99215686 0.99215686 1.         0.99215686 0.99215686 0.99215686\n",
      "  0.99215686 0.29019608 0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.12156863 0.48235294 0.20392157\n",
      "  0.17254902 0.17254902 0.17254902 0.17254902 0.56078431 0.98823529\n",
      "  0.98823529 0.29019608 0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.05882353 0.98823529\n",
      "  0.98823529 0.29019608 0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.3372549  0.98823529\n",
      "  0.98823529 0.29019608 0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.01960784 0.29411765 0.03529412 0.         0.         0.\n",
      "  0.         0.         0.         0.38431373 0.94901961 0.98823529\n",
      "  0.98823529 0.29019608 0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.23921569\n",
      "  0.71764706 0.98823529 0.11372549 0.         0.         0.\n",
      "  0.         0.07058824 0.36078431 0.9372549  0.98823529 0.98823529\n",
      "  0.95294118 0.25490196 0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.81568627\n",
      "  0.98823529 0.98823529 0.57647059 0.5254902  0.5254902  0.5254902\n",
      "  0.5254902  0.79607843 0.99215686 0.98823529 0.98823529 0.7372549\n",
      "  0.3254902  0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.81568627\n",
      "  0.98823529 0.98823529 0.98823529 0.98823529 0.98823529 0.98823529\n",
      "  0.98823529 0.98823529 0.99215686 0.90196078 0.6        0.03137255\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.19215686\n",
      "  0.61568627 0.98823529 0.98823529 0.98823529 0.98823529 0.98823529\n",
      "  0.85098039 0.81176471 0.57254902 0.17647059 0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.02745098 0.40392157 0.92156863 0.98823529 0.6745098  0.40392157\n",
      "  0.09411765 0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]]\n"
     ]
    }
   ],
   "source": [
    "# Checking the range\n",
    "print(x_train[7])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Building The Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We are building a normal and simple neural network\n",
    "# Setting up the layers of the Neural Network\n",
    "model = keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape = (28, 28)),\n",
    "    # Creating a dense layer with 50 number of neurons\n",
    "    # Activation function - relu (most widely used and efficient activation function)\n",
    "    # relu - rectified linear unit\n",
    "    keras.layers.Dense(50, activation = 'relu'),\n",
    "    # Creating another layer\n",
    "    keras.layers.Dense(50, activation = 'relu'),\n",
    "    # Creating the output layer\n",
    "    # No of neurons = 10 because we have 10 classes in labels [0 to 9]\n",
    "    keras.layers.Dense(10, activation = 'sigmoid')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compiling the neural network - layers\n",
    "model.compile(optimizer = 'adam',\n",
    "              loss = 'sparse_categorical_crossentropy',\n",
    "              # For one hot encoding, loss function will be \"categorical_crossentropy\"\n",
    "              metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training The Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/17\n",
      "1875/1875 [==============================] - 3s 1ms/step - loss: 0.2943 - accuracy: 0.9154\n",
      "Epoch 2/17\n",
      "1875/1875 [==============================] - 3s 1ms/step - loss: 0.1307 - accuracy: 0.9602\n",
      "Epoch 3/17\n",
      "1875/1875 [==============================] - 3s 1ms/step - loss: 0.0953 - accuracy: 0.9714\n",
      "Epoch 4/17\n",
      "1875/1875 [==============================] - 3s 1ms/step - loss: 0.0786 - accuracy: 0.9757\n",
      "Epoch 5/17\n",
      "1875/1875 [==============================] - 3s 1ms/step - loss: 0.0655 - accuracy: 0.9798\n",
      "Epoch 6/17\n",
      "1875/1875 [==============================] - 3s 1ms/step - loss: 0.0564 - accuracy: 0.9821\n",
      "Epoch 7/17\n",
      "1875/1875 [==============================] - 3s 1ms/step - loss: 0.0483 - accuracy: 0.9842\n",
      "Epoch 8/17\n",
      "1875/1875 [==============================] - 3s 1ms/step - loss: 0.0434 - accuracy: 0.9858\n",
      "Epoch 9/17\n",
      "1875/1875 [==============================] - 3s 1ms/step - loss: 0.0379 - accuracy: 0.9878\n",
      "Epoch 10/17\n",
      "1875/1875 [==============================] - 3s 1ms/step - loss: 0.0344 - accuracy: 0.9890\n",
      "Epoch 11/17\n",
      "1875/1875 [==============================] - 3s 1ms/step - loss: 0.0315 - accuracy: 0.9898\n",
      "Epoch 12/17\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0271 - accuracy: 0.9912\n",
      "Epoch 13/17\n",
      "1875/1875 [==============================] - 3s 1ms/step - loss: 0.0263 - accuracy: 0.9914\n",
      "Epoch 14/17\n",
      "1875/1875 [==============================] - 3s 1ms/step - loss: 0.0228 - accuracy: 0.9920\n",
      "Epoch 15/17\n",
      "1875/1875 [==============================] - 3s 1ms/step - loss: 0.0210 - accuracy: 0.9933\n",
      "Epoch 16/17\n",
      "1875/1875 [==============================] - 3s 1ms/step - loss: 0.0208 - accuracy: 0.9930\n",
      "Epoch 17/17\n",
      "1875/1875 [==============================] - 3s 1ms/step - loss: 0.0192 - accuracy: 0.9931\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1b7160c5050>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training our neural network\n",
    "# epochs - how many times the neural network has to go through the data\n",
    "model.fit(x_train, y_train, epochs = 17)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accuracy On Test Data- Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 1ms/step - loss: 0.1073 - accuracy: 0.9767\n",
      "0.9767000079154968\n"
     ]
    }
   ],
   "source": [
    "# Evaluating the model\n",
    "loss, accuracy = model.evaluate(x_test, y_test)\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "Training data accuracy = 99.31% \n",
    "\n",
    "Test data accuracy = 97.67%\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "print(x_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n"
     ]
    }
   ],
   "source": [
    "# First data point in x_test\n",
    "plt.imshow(x_test[0])\n",
    "plt.show()\n",
    "\n",
    "print(y_test[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 0s 1ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(10000, 10)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predicting labels for test data\n",
    "y_pred = model.predict(x_test)\n",
    "y_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4.3696980e-04 5.2653332e-03 2.1859625e-02 7.7805442e-01 3.6606394e-07\n",
      " 1.5428791e-06 2.4497684e-09 9.9999934e-01 8.4597943e-04 1.5558797e-02]\n"
     ]
    }
   ],
   "source": [
    "print(y_pred[0])\n",
    "# Each value given below shows probablity for each label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "NOTE : model.predict() gives the prediction probability of each class for that particular data point\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting the prediction probabilities to class labels\n",
    "label_for_1_test_image = np.argmax(y_pred[0])\n",
    "label_for_1_test_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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5, 1, 3, 2, 2, 3, 0, 6, 4, 3, 7, 6, 9, 0, 4, 8, 1, 4, 0, 6, 1, 2, 6, 9, 2, 2, 3, 5, 5, 1, 0, 7, 7, 9, 6, 2, 9, 4, 7, 0, 2, 3, 4, 0, 0, 8, 8, 8, 5, 1, 3, 7, 4, 9, 8, 8, 9, 0, 9, 8, 9, 0, 2, 6, 5, 6, 7, 4, 7, 5, 4, 1, 3, 5, 3, 1, 2, 2, 4, 5, 6, 1, 2, 3, 4, 6, 0, 1, 2, 4, 5, 6, 7, 8, 1, 7, 2, 4, 1, 4, 1, 4, 9, 6, 8, 4, 5, 3, 7, 8, 8, 3, 3, 5, 6, 7, 0, 6, 1, 6, 8, 7, 0, 1, 5, 0, 8, 6, 0, 1, 5, 8, 4, 2, 3, 9, 7, 6, 9, 1, 9, 0, 6, 7, 1, 2, 3, 9, 7, 4, 5, 5, 3, 7, 5, 3, 1, 8, 2, 2, 3, 0, 2, 9, 4, 9, 7, 0, 2, 7, 4, 9, 9, 2, 5, 9, 8, 3, 8, 6, 7, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 0, 7, 2, 6, 5, 5, 3, 7, 8, 6, 6, 6, 6, 4, 3, 8, 8, 3, 0, 1, 9, 0, 5, 4, 1, 9, 1, 2, 7, 0, 1, 3, 8, 2, 9, 2, 7, 4, 2, 6, 5, 5, 9, 9, 1, 1, 5, 7, 6, 8, 2, 9, 4, 3, 1, 9, 0, 9, 3, 6, 8, 7, 0, 1, 0, 5, 8, 2, 7, 7, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 2, 1, 2, 1, 3, 9, 9, 8, 5, 3, 7, 0, 7, 7, 5, 7, 9, 9, 4, 7, 0, 3, 4, 1, 5, 8, 1, 4, 8, 4, 1, 8, 6, 6, 4, 6, 0, 5, 5, 3, 3, 5, 7, 2, 5, 9, 6, 9, 2, 6, 2, 1, 2, 0, 8, 3, 8, 3, 0, 8, 7, 4, 9, 5, 0, 9, 7, 0, 0, 4, 6, 0, 9, 1, 6, 2, 7, 6, 8, 3, 5, 2, 1, 8, 3, 8, 6, 1, 0, 2, 1, 4, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 7, 6, 4, 7, 6, 2, 3, 4, 8, 7, 8, 6, 9, 8, 3, 2, 2, 8, 4, 8, 5, 6, 5, 0, 2, 0, 1, 1, 2, 9, 6, 8, 2, 1, 0, 6, 5, 2, 9, 7, 6, 3, 9, 2, 7, 1, 8, 3, 8, 1, 9, 5, 5, 0, 1, 1, 9, 8, 2, 6, 0, 4, 5, 0, 2, 1, 8, 6, 7, 5, 9, 9, 3, 0, 3, 1, 4, 4, 0, 4, 9, 0, 1, 2, 3, 5, 6, 7, 8, 0, 8, 2, 3, 5, 6, 7, 8, 9, 0, 8, 2, 3, 5, 6, 7, 8, 9, 9, 7, 0, 9, 0, 1, 5, 8, 8, 0, 9, 3, 2, 7, 8, 4, 6, 1, 0, 4, 9, 4, 2, 0, 5, 0, 1, 6, 9, 3, 2, 9, 1, 6, 0, 1, 1, 8, 7, 7, 6, 3, 6, 0, 7, 2, 4, 1, 7, 0, 6, 7, 1, 2, 5, 8, 1, 8, 2, 8, 7, 6, 8, 7, 8, 6, 2, 9, 3, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 8, 9, 3, 7, 0, 3, 1, 6, 8, 4, 1, 5, 6, 4, 2, 7, 8, 1, 3, 4, 3, 4, 7, 2, 0, 5, 0, 1, 9, 2, 3, 2, 3, 5, 5, 7, 8, 4, 9, 9, 7, 1, 1, 9, 0, 7, 8, 3, 4, 8, 6, 3, 8, 0, 9, 6, 2, 8, 0, 1, 0, 6, 2, 3, 8, 9, 0, 7, 2, 3, 4, 5, 5, 2, 8, 5, 4, 6, 6, 6, 7, 9, 1, 8, 2, 1, 5, 3, 4, 7, 9, 4, 0, 0, 0, 1, 2, 3, 4, 8, 6, 7, 8, 9, 0, 1, 2, 3, 4, 3, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 9, 0, 1, 3, 1, 5, 1, 2, 4, 9, 8, 4, 6, 8, 0, 1, 1, 9, 2, 6, 6, 8, 7, 4, 2, 9, 7, 0, 2, 1, 0, 3, 6, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 8, 6, 5, 9, 7, 0, 2, 3, 4, 3, 8, 5, 1, 5, 2, 3, 0, 1, 2, 1, 3, 2, 6, 5, 3, 0, 7, 2, 7, 4, 6, 4, 0, 5, 9, 8, 8, 9, 5, 3, 1, 7, 4, 7, 6, 5, 4, 0, 0, 6, 6, 2, 0, 6, 3, 7, 7, 4, 4, 3, 9, 2, 8, 9, 6, 0, 9, 5, 3, 8, 8, 7, 1, 4, 0, 4, 8, 5, 2, 3, 9, 0, 1, 9, 1, 5, 1, 7, 4, 8, 6, 2, 1, 6, 8, 8, 0, 1, 2, 3, 4, 7, 8, 9, 0, 1, 2, 3, 4, 6, 7, 8, 9, 0, 1, 2, 3, 4, 7, 8, 9, 1, 4, 5, 3, 8, 0, 9, 5, 4, 8, 0, 8, 4, 6, 7, 0, 7, 7, 1, 6, 9, 1, 3, 6, 2, 3, 8, 2, 3, 8, 9, 5, 8, 8, 7, 1, 7, 1, 1, 0, 3, 4, 2, 6, 4, 7, 4, 2, 7, 4, 2, 9, 2, 7, 9, 2, 1, 3, 6, 5, 3, 4, 8, 5, 9, 6, 9, 0, 6, 3, 0, 8, 1, 6, 0, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 7, 8, 9, 0, 1, 2, 3, 4, 7, 2, 5, 1, 6, 4, 3, 9, 9, 0, 9, 7, 1, 6, 4, 3, 6, 2, 0, 9, 8, 6, 5, 7, 0, 0, 1, 7, 4, 3, 2, 4, 1, 3, 7, 6, 4, 7, 7, 7, 9, 8, 4, 3, 8, 2, 8, 3, 5, 8, 0, 5, 4, 7, 1, 3, 1, 7, 9, 6, 2, 0, 9, 1, 7, 3, 3, 9, 1, 6, 4, 3, 9, 8, 2, 1, 8, 6, 4, 1, 5, 5, 6, 5, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 6, 9, 7, 0, 2, 3, 4, 3, 8, 5, 1, 3, 0, 1, 2, 1, 3, 2, 0, 3, 2, 6, 4, 0, 5, 9, 9, 8, 9, 5, 3, 1, 7, 4, 7, 0, 0, 6, 6, 6, 3, 7, 4, 2, 8, 9, 8, 7, 1, 4, 0, 4, 8, 5, 2, 3, 9, 0, 1, 9, 1, 5, 1, 7, 6, 1, 2, 1, 6, 8, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 1, 2, 3, 5, 6, 7, 8, 1, 0, 4, 5, 6, 6, 3, 4, 4, 3, 8, 1, 0, 6, 4, 9, 7, 2, 9, 2, 0, 9, 3, 3, 9, 1, 5, 2, 3, 1, 6, 7, 3, 7, 8, 4, 0, 2, 4, 0, 2, 4, 7, 8, 0, 7, 0, 6, 9, 3, 2, 4, 8, 6, 0, 5, 7, 5, 1, 0, 8, 1, 6, 7, 2, 9, 7, 9, 5, 6, 5, 2, 6, 2, 8, 1, 7, 5, 5, 7, 3, 5, 0, 1, 1, 3, 8, 4, 9, 4, 5, 1, 8, 6, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 3, 5, 3, 2, 9, 3, 2, 1, 4, 5, 5, 2, 3, 2, 1, 3, 9, 7, 2, 1, 2, 8, 9, 1, 8, 8, 7, 8, 1, 0, 0, 6, 7, 7, 8, 7, 5, 0, 6, 1, 5, 7, 4, 6, 1, 2, 5, 0, 7, 9, 9, 0, 3, 4, 4, 8, 4, 1, 8, 6, 5, 9, 0, 0, 0, 3, 7, 1, 6, 4, 6, 0, 4, 5, 4, 1, 3, 8, 6, 3, 9, 9, 5, 9, 3, 7, 8, 5, 6, 4, 7, 6, 2, 2, 0, 9, 4, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 6, 4, 2, 6, 4, 7, 5, 5, 4, 7, 2, 9, 3, 9, 3, 8, 2, 0, 9, 5, 6, 0, 1, 0, 6, 5, 3, 5, 3, 8, 0, 0, 3, 4, 1, 5, 3, 0, 8, 3, 0, 6, 2, 7, 8, 1, 7, 1, 3, 8, 5, 4, 2, 0, 9, 7, 6, 7, 4, 1, 6, 2, 6, 7, 1, 9, 8, 0, 6, 9, 4, 9, 9, 6, 2, 3, 7, 1, 9, 2, 2, 5, 3, 7, 8, 0, 1, 2, 3, 4, 7, 8, 9, 0, 1, 2, 3, 4, 7, 8, 9, 0, 1, 7, 8, 9, 8, 9, 2, 6, 1, 3, 5, 4, 8, 2, 6, 4, 3, 4, 5, 9, 2, 0, 3, 9, 4, 9, 7, 3, 8, 7, 4, 4, 9, 8, 5, 8, 2, 6, 6, 2, 3, 1, 3, 2, 7, 3, 1, 9, 0, 1, 1, 3, 5, 0, 7, 8, 1, 5, 1, 4, 6, 0, 0, 4, 9, 1, 6, 6, 9, 0, 7, 6, 1, 1, 0, 1, 2, 3, 4, 2, 2, 3, 4, 5, 6, 2, 0, 1, 2, 7, 8, 6, 3, 9, 2, 1, 9, 3, 9, 6, 1, 7, 2, 4, 4, 5, 7, 0, 0, 1, 6, 6, 8, 2, 7, 7, 2, 4, 2, 1, 6, 1, 0, 6, 9, 8, 3, 9, 6, 3, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 3, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 6, 8, 9, 9, 0, 1, 2, 4, 4, 3, 7, 4, 4, 4, 0, 3, 8, 7, 5, 8, 2, 1, 7, 5, 3, 8, 5, 2, 5, 1, 1, 6, 2, 1, 3, 8, 6, 4, 2, 6, 2, 5, 5, 0, 2, 8, 0, 6, 8, 1, 7, 9, 1, 9, 2, 6, 7, 6, 6, 8, 7, 4, 9, 2, 1, 3, 3, 0, 5, 5, 8, 0, 2, 7, 9, 7, 0, 2, 7, 9, 1, 7, 8, 0, 3, 5, 3, 6, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 7, 8, 9, 6, 4, 2, 6, 4, 7, 8, 9, 2, 9, 3, 9, 3, 0, 0, 1, 0, 4, 2, 6, 3, 5, 3, 0, 3, 4, 1, 5, 3, 0, 8, 3, 0, 6, 1, 7, 8, 0, 9, 2, 6, 7, 1, 9, 6, 9, 4, 9, 9, 6, 7, 1, 2, 5, 3, 7, 8, 0, 1, 2, 4, 5, 6, 7, 8, 9, 0, 1, 3, 4, 5, 6, 7, 8, 0, 1, 3, 4, 7, 8, 9, 7, 5, 5, 1, 9, 9, 7, 1, 0, 0, 5, 9, 7, 1, 7, 2, 2, 3, 6, 8, 3, 2, 0, 0, 6, 1, 7, 5, 8, 6, 2, 9, 4, 8, 8, 7, 1, 0, 8, 7, 7, 5, 8, 5, 3, 4, 6, 1, 1, 5, 5, 0, 7, 2, 3, 6, 4, 1, 2, 4, 1, 5, 4, 2, 0, 4, 8, 6, 1, 9, 0, 2, 5, 6, 9, 3, 6, 3, 6, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 5, 6, 7, 8, 1, 0, 9, 5, 7, 5, 1, 8, 6, 9, 0, 4, 1, 9, 3, 8, 4, 4, 7, 0, 1, 9, 2, 8, 7, 8, 2, 5, 9, 6, 0, 6, 3, 5, 3, 3, 3, 9, 8, 1, 1, 0, 6, 1, 0, 0, 6, 2, 1, 1, 3, 2, 7, 7, 8, 8, 7, 8, 4, 6, 0, 7, 0, 7, 0, 3, 6, 8, 7, 1, 3, 9, 9, 3, 7, 2, 4, 9, 4, 3, 6, 2, 2, 5, 3, 2, 5, 3, 9, 4, 1, 7, 2, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 0, 1, 2, 7, 5, 3, 4, 4, 0, 0, 6, 8, 6, 6, 5, 7, 2, 3, 4, 4, 9, 1, 4, 0, 7, 9, 5, 7, 2, 3, 1, 4, 4, 0, 9, 9, 6, 1, 8, 3, 3, 7, 3, 9, 8, 8, 4, 7, 7, 6, 2, 1, 9, 8, 7, 8, 8, 7, 2, 2, 3, 9, 3, 3, 5, 5, 0, 7, 4, 5, 6, 5, 1, 4, 1, 1, 2, 8, 2, 6, 1, 5, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 8, 0, 6, 0, 1, 2, 3, 7, 9, 4, 7, 1, 9, 1, 7, 7, 4, 0, 0, 1, 7, 5, 7, 1, 3, 3, 3, 1, 6, 9, 7, 1, 3, 0, 7, 6, 0, 8, 9, 7, 3, 5, 4, 8, 1, 5, 9, 0, 6, 3, 3, 8, 1, 4, 7, 5, 2, 0, 0, 1, 7, 8, 9, 6, 8, 8, 2, 3, 6, 1, 8, 9, 5, 2, 0, 1, 2, 3, 4, 6, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 6, 6, 7, 8, 9, 7, 4, 6, 1, 4, 0, 9, 9, 3, 7, 8, 4, 7, 5, 8, 6, 3, 2, 2, 0, 5, 8, 6, 0, 3, 8, 1, 0, 3, 0, 4, 7, 4, 9, 0, 9, 0, 7, 1, 7, 1, 6, 6, 3, 6, 2, 8, 7, 6, 4, 9, 9, 5, 3, 7, 4, 3, 0, 4, 6, 6, 1, 1, 3, 2, 1, 0, 0, 1, 2, 3, 4, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 1, 2, 3, 4, 7, 8, 9, 0, 8, 3, 9, 5, 5, 2, 6, 8, 4, 1, 7, 1, 3, 3, 5, 6, 9, 1, 1, 1, 2, 1, 2, 0, 7, 7, 5, 8, 2, 9, 8, 6, 7, 3, 4, 6, 8, 7, 0, 4, 2, 7, 7, 5, 4, 3, 4, 2, 8, 1, 5, 1, 0, 2, 3, 3, 5, 7, 0, 6, 8, 6, 3, 9, 9, 8, 2, 7, 7, 1, 0, 1, 7, 8, 9, 0, 1, 3, 3, 4, 5, 6, 7, 8, 0, 1, 2, 3, 4, 7, 8, 9, 7, 8, 6, 4, 1, 9, 3, 8, 4, 4, 7, 0, 1, 9, 2, 8, 7, 8, 2, 6, 0, 6, 5, 3, 3, 8, 9, 1, 4, 0, 6, 1, 0, 0, 6, 2, 1, 1, 7, 7, 8, 4, 6, 0, 7, 0, 3, 6, 8, 7, 1, 5, 2, 4, 9, 4, 3, 6, 4, 1, 7, 2, 6, 6, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6]\n"
     ]
    }
   ],
   "source": [
    "# Converting prediction probabilities to class labels for all test data points\n",
    "y_pred_labels = [np.argmax(i) for i in y_pred]\n",
    "print(y_pred_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "y_test --> true labels\n",
    "\n",
    "y_pred_labels --> predicted labels\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Building The Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(\n",
      "[[ 971    1    0    1    1    0    1    0    3    2]\n",
      " [   0 1118    3    3    0    1    2    2    6    0]\n",
      " [   3    1 1007    8    1    1    1    4    6    0]\n",
      " [   0    0    5  989    0    3    0    3    8    2]\n",
      " [   1    0    4    1  967    0    4    2    1    2]\n",
      " [   2    0    0   35    2  834   14    0    4    1]\n",
      " [   2    2    1    2    4    1  946    0    0    0]\n",
      " [   1    3    7    7    1    0    0 1001    5    3]\n",
      " [   3    0    1    6    3    1    5    2  949    4]\n",
      " [   3    2    1    9   11    3    0    6    6  968]], shape=(10, 10), dtype=int32)\n"
     ]
    }
   ],
   "source": [
    "# Building the confusion matrix\n",
    "confusion = confusion_matrix(y_test, y_pred_labels)\n",
    "print(confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 25.722222222222214, 'Predicted Labels')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Building a heatmap with the above values\n",
    "plt.figure(figsize = (9, 5))\n",
    "# fmt = d -> because all the values in confusion matrix are integers\n",
    "sns.heatmap(confusion, annot = True, fmt = 'd', cmap = 'Greens')\n",
    "plt.ylabel('True Labels')\n",
    "plt.xlabel('Predicted Labels')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Building A Predictive System"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_image_path = 'input image.png'\n",
    "\n",
    "# Read the image and convert it to a numpy array\n",
    "input_image = cv2.imread(input_image_path)\n",
    "type(input_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[0 0 0]\n",
      "  [0 0 0]\n",
      "  [0 0 0]\n",
      "  ...\n",
      "  [0 0 0]\n",
      "  [0 0 0]\n",
      "  [0 0 0]]\n",
      "\n",
      " [[0 0 0]\n",
      "  [0 0 0]\n",
      "  [0 0 0]\n",
      "  ...\n",
      "  [0 0 0]\n",
      "  [0 0 0]\n",
      "  [0 0 0]]\n",
      "\n",
      " [[0 0 0]\n",
      "  [0 0 0]\n",
      "  [0 0 0]\n",
      "  ...\n",
      "  [0 0 0]\n",
      "  [0 0 0]\n",
      "  [0 0 0]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0 0 0]\n",
      "  [0 0 0]\n",
      "  [0 0 0]\n",
      "  ...\n",
      "  [0 0 0]\n",
      "  [0 0 0]\n",
      "  [0 0 0]]\n",
      "\n",
      " [[0 0 0]\n",
      "  [0 0 0]\n",
      "  [0 0 0]\n",
      "  ...\n",
      "  [0 0 0]\n",
      "  [0 0 0]\n",
      "  [0 0 0]]\n",
      "\n",
      " [[0 0 0]\n",
      "  [0 0 0]\n",
      "  [0 0 0]\n",
      "  ...\n",
      "  [0 0 0]\n",
      "  [0 0 0]\n",
      "  [0 0 0]]]\n"
     ]
    }
   ],
   "source": [
    "print(input_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(input_image)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(329, 329, 3)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_image.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(329, 329)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert the input image into a grayscale image and resizing it\n",
    "# cvtCOLOR -> convert the color\n",
    "# Color conversion\n",
    "gray = cv2.cvtColor(input_image, cv2.COLOR_RGB2GRAY)\n",
    "gray.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resizing the image\n",
    "resize_img = cv2.resize(gray, (28,28))\n",
    "resize_img.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(resize_img)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Scaling the input image\n",
    "resize_img = resize_img/255\n",
    "type(resize_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Telling the predicting model that only one image label has to be predicted and image dimension is 28 x 28\n",
    "reshaped_image = np.reshape(resize_img, [1, 28, 28])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 26ms/step\n",
      "[[1.9981008e-09 5.2914375e-01 9.9997234e-01 9.9976528e-01 1.4823379e-13\n",
      "  8.9316638e-03 1.0954111e-14 9.9999315e-01 1.2601516e-02 4.8958047e-08]]\n"
     ]
    }
   ],
   "source": [
    "input_pred = model.predict(reshaped_image)\n",
    "print(input_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n"
     ]
    }
   ],
   "source": [
    "input_pred_label = np.argmax(input_pred)\n",
    "print(input_pred_label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "The model has correctly predicted the image. Now, consolidaing all the above code in a single cell and building the final working digit recognition model.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PREDICTIVE SYSTEM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Image path: C:\\Users\\FATHIMA ISMAIL\\OneDrive\\Desktop\\Bharat Intern Internship\\Bharat Intern TASK 3\\input image.png\n",
      "\n",
      "Image Given:\n"
     ]
    },
    {
     "data": {
      "image/png": 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BWznlVy6X0dfXh0wmI/W98vl8mDJlCpqbm5HL5aAoitRKeeBKysrvgpWVlHGq3GiGSQypUW0wpwtG4lTDSOyzXC6b77Blq6frug5N08zP0GRCyhinaZr0u2ajBYumadKnRkeKscq2upIyvk8yzTStruqHipWuAIN9ozca8eo+IiKyLYYUERHZlnRIbd26FVdddRXa2trgcDjwzDPPVNwvhMC9996L1tZW+P1+tLe34/3336/Ypq+vD8uWLUM4HEY0GsWKFSuQyWQGNREiIhp9pEMqm81i7ty5WLdu3Snvf/jhh/GTn/wEjz76KLZt24ZgMIhFixZVtBNftmwZdu/ejZdeegkbN27E1q1bcfPNN1ufBRERjUrSF04sWbIES5YsOeV9Qgj86Ec/wj333IOrr74aAPCLX/wCLS0teOaZZ3DdddfhnXfewQsvvIA33ngDF110EQDgpz/9Ka644gr84Ac/QFtb2yCmQ0REo8mQfiZ14MABJBIJtLe3m1+LRCKYP38+Ojs7AQCdnZ2IRqNmQAFAe3s7nE4ntm3bNpSHQ0RENW5IL0FPJBIAgJaWloqvt7S0mPclEgk0NzdXHoTbjYaGBnObk6mqWlFmJ5VKDeVhExGRTdXE1X1r165FJBIxbxMnThzpQyIiomEwpCEVi8UAAN3d3RVf7+7uNu+LxWLo6empuL9UKqGvr8/c5mRr1qxBMpk0bx999NFQHjYREdnUkIbUlClTEIvFsGnTJvNrqVQK27ZtQzweBwDE43H09/dj+/bt5javvPIKdF3H/PnzT/m4iqIgHA5X3IjsZmCFgTPdRmq/I3mzerx2MdjjrbX52on0Z1KZTAb79u0z/33gwAHs2LEDDQ0NmDRpEm677Tb80z/9Ez75yU9iypQp+Pa3v422tjZ85StfAQCcf/75WLx4MW666SY8+uij0DQNq1atwnXXXccr+2hEyZZFOvkFRqYK+mBfkIzH0HW9ZsoiWZn3wDFG6alqH+PkUlWDqUUp+9waz41sUV0G1sdJh9Sbb76Jv/zLvzT/vXr1agDA8uXL8fjjj+Ob3/wmstksbr75ZvT39+MLX/gCXnjhhYpCmE888QRWrVqFBQsWwOl0YunSpfjJT34yBNMhkjewrpxsBfV0Og1d16WK0wohoGkastksisWipWKi6XQaQoiaa9VhpcCsUcxX9gyKoiiIRqNmSxO32y1VYFYIIVUc1lAqldDf34/u7m4cOXIE2Wy26scoFovo7++Xqpw+2jlEDX4nUqkUIpHISB8GjRIDWzhY6b1lFHmVfbdtFImVLYDqcrngdrvNF91aWklZ4Xa7EYvFEI1GpdqpKIqCCRMmIB6PY8mSJYhEIlKtOoxOAqqqSj1Hmqbh6NGjeOyxx7B7925kMpmq5270ZEun00gmk2MiqJLJ5BnfgLAKOo15QghLqxmHw2FWUbe6XyundoyVXy2E01AwgikQCMDv91c9b6/Xi2AwCJ/PB4/HA7fbLRVSRt+vQqFQ9c+HEAKlUgnZbBb9/f04fvw4UqlU1c+x8eZFtrP1aMaQIrJopD47GGufWRhvBIwVZLUhZaw0jdWmlX5lsp8rGW94jDcSRouRap8vXdct990arWri76SIiGhsYkgREZFtMaSIiMi2GFJERGRbDCkiIrIthhQREdkWQ4qIiGyLIUVERLbFkCIiIttixQkisi2jSoTT6ayoHlEN2e1PZhTFLZfLUmWRjDFW23CMtYoiZ8OQIqJhYaUskVEOyefzoa6uDsFgUKp2XygUgs/nk6rZZ9A0Dfl8HtlsFqVSqaoxRu2+XC4HTdOkSxwxoD6OIUVE55yxEnI6ndKV5v1+P1pbWzF9+nREIpGqx3s8HtTX16OpqUm6Uj1wondeIpFAb29v1QVfjdp9fX19SKVSUFUVmqZJBY9sD6rRjiFFROecy+WCoijweDxSqxqHw4FIJII5c+bg8ssvR3Nzc9XjnU5nxWpKppcUACQSCezcuRMHDhxANputepyu68hkMjhy5AgymQxUVbXU7JFOYEgR0bBxOp1SIWWswPx+P0KhEMLhcNXjHQ4H3G63VA+qgTRNQ6FQQC6XqzqkjM+k8vl8xek+so4hRUTDysrnUsbFEzIhN3CMVVZadVjtQEynxpAiopoyVpo90gn8OykiIrIthhQREdkWQ4qIiGyLIUVERLbFkCIiIttiSBERkW0xpIiIyLYYUkREZFsMKSIisi1WnCCic87pdMLj8cDr9UoVejWKxBoV1GWqqJ9c9VzXdalqFQNLIlXbT2rgOBoaDCkiOucURUF9fT0ikQgURal6nMPhQDgcRiQSgc/nk6qiPjCkSqWSdDmlYrFoFpjN5XJVj9N1HYVCwWx8SIPDkCKic87v96O5uRltbW0IBoNVj3M4HAgGgxg3bhyCwSAURZEuGCuEgKZpsodsNjxMp9NIp9NVj9N1HcViUbqPFJ0aQ4qIzjljJdXW1oZwOFz1OIfDgUAggPr6evh8PiiKUvWKyKhIXiqVLLXMUFUV+XxeaiVldNYtFotcSQ0RhhQRnXNOp9Ps7SR7us/r9cLtdsPlcpntN6oxsBW7lbCw8pmUsU+26hg6vLqPiIhsiyFFRES2xZAiIiLbYkgREZFtMaSIiMi2GFJERGRbDCkiIrIthhQREdkWQ4qIiGyLFSeIaFjIFngdalYrQAysXEHDjyFFRFU7uf1FtTwej9mqw+v1Su3PKIsku1+jPJGmacjlctJBo6oqNE2TrvvHskhDiyFFRFVxuVzw+/2oq6uDx+ORCo2pU6di9uzZmD17NhobG6X26/F40NraKlVcFgDK5TL6+/vxwQcfYPfu3cjn81Jhs3PnTrz33ntIJBLI5/NVjxNCoFwuo1gssq/UEGBIEVFVnE4nFEUxezvJBEZLSwsmT56M6dOnY9y4cdL7DQQC8Hg8UuN0XUcul8Phw4fx5z//GalUSio0Dh48iK6uLvT19aFYLErtGzjRw4qrqcFjSBFRVRwOB1wuF7xer3RIBQIBhEIhRCIRRCIR6X3LrtyA/+sjlc1mcfToUSSTSamQ6u/vRzabhaqqKJVKsofMgBoi0lf3bd26FVdddRXa2trgcDjwzDPPVNz/ta99zTxvbdwWL15csU1fXx+WLVuGcDiMaDSKFStWIJPJDGoiRHRuDfydNlq5y9xcLhdcLhfcbrf0TbbRocE49VYqlcxGhNXeyuWy2RPKyo2GhvQzn81mMXfuXKxbt+602yxevBhdXV3m7Ze//GXF/cuWLcPu3bvx0ksvYePGjdi6dStuvvlm+aMnIqJRTfp035IlS7BkyZIzbqMoCmKx2Cnve+edd/DCCy/gjTfewEUXXQQA+OlPf4orrrgCP/jBD9DW1iZ7SERENEqdkz/m3bx5M5qbmzFjxgysXLkSvb295n2dnZ2IRqNmQAFAe3s7nE4ntm3bdsrHU1UVqVSq4kZERKPfkIfU4sWL8Ytf/AKbNm3C97//fWzZsgVLliwx2y8nEgk0NzdXjHG73WhoaEAikTjlY65du9b8wDUSiWDixIlDfdhERGRDQ35133XXXWf+/+zZszFnzhxMmzYNmzdvxoIFCyw95po1a7B69Wrz36lUikFFRDQGnPPafVOnTkVTUxP27dsHAIjFYujp6anYplQqoa+v77SfYymKgnA4XHEjIqLR75yH1OHDh9Hb24vW1lYAQDweR39/P7Zv325u88orr0DXdcyfP/9cHw4REdUQ6dN9mUzGXBUBwIEDB7Bjxw40NDSgoaEBDzzwAJYuXYpYLIb9+/fjm9/8JqZPn45FixYBAM4//3wsXrwYN910Ex599FFomoZVq1bhuuuu45V9RERUQXol9eabb+LCCy/EhRdeCABYvXo1LrzwQtx7771wuVzYuXMnvvzlL+O8887DihUrMG/ePPzhD3+AoijmYzzxxBOYOXMmFixYgCuuuAJf+MIX8G//9m9DNysiIhoVpFdSl1122Rn/mvrFF18862M0NDRg/fr1srsmohFmlEZyuVxSZYpktz+ZlUKtRsUIo+KErutSj8PisPbA2n1EVBWHwwGPxwO/349gMChVqsjv98Pj8Vgub1QsFs0SRdUqFArI5XLI5XLI5/PI5XJSwWNlnzT0GFJEdFYOhwNutxuBQACNjY2or6+Hy+WqenxDQwOCwaCloNJ1HdlsFtls1vx7y2oUCgUcPXoUx44dQ29vr3SB2Ww2C03TGFIjjCFFRFVxuVyoq6vD+PHj0dzcLBVSra2tCIfD8Hg8UuOMArHJZBJHjx6FqqpVh4aqqjh8+DCOHDmCRCKB/v5+6dN9XEmNPIYUEVXl5AroMmEz2NbxRrdbmZWU8RnUwJtM4DCc7OGc/50UEY0+gw2d4cK2GbWPIUVERLbFkCIiIttiSBERkW0xpIiIyLYYUkREZFsMKSIisi2GFBER2RZDioiIbIshRUREtsWQIiIi22LtPiI6K6Nmn9vths/ng9/vl6rdpygK3G43HA6HdP08XdeRz+eRTCaRy+WqHl8sFpFOp1EoFMyafyyRVHsYUkRjjNPplK6953Q64fF4EAwG0djYiHHjxsHtrv7lo76+Hj6fDw6HQ6oSuRACmqahp6cH+/fvRyqVqnq8pmno7e3F0aNHoWmadNND1v2zB4YU0RhjVDGXCSqn0wmv14tQKISmpia0tLTA4/FUPT4ajVoKKV3XoWkaurq68N5776Gvr6/q8eVyGalUCj09PWZIsQp67WFIEY0hxmk7o+1GtUFlnOpTFAWhUAiRSEQqpIyGh1ZO95VKJSSTSXR3d6Onp6fqkNJ1HblcDqlUymwfT7WHIUVEUgb2lBoOxmm3gX2hqlEul6VXT2Q/vLqPiIhsiyFFRES2xZAiIiLbYkgREZFtMaSIiMi2GFJERGRbDCkiIrIthhQREdkW/5iXaAySqTZxqrFWDbYeHuvpjT0MKaIxZmBppGoZFSaslFQy9gnIh4xRMcKoOCEz3tiOoVbbGFJEY4jT6UQgEIDX65UKKofDgbq6OgSDQSiKAq/XK1W7z+l0olwuQ1VVlEqlqscJIVAoFFAoFFAsFlEsFs22G9WMldme7IkhRTRGOJ1O+P1+zJ49G+PGjYOiKFIFZqPRKGbNmoVp06YhFotJterI5XJIp9NmRfJqCSGQSqWwf/9+fPDBB1IFZoET9fs0TZMKRrIXhhTRGOJyudDQ0ICWlhb4/X6plVQkEkFDQ4O5opJpeqhpGjRNQzabhaqqVY/TdR3pdBqZTAaZTAbZbLbqkDJODfJzrNrGkCIaI4zPkTweD3w+n9nfqdqxPp/P7LDrdrulQspo0VEqlaRXUkbAGWPZcmNsYUgRjTEnXwRRjYEXS1i5cAKwdmXewIsfuBoam/h3UkREZFsMKSIisi2GFBER2RZDioiIbIshRUREtsWQIiIi22JIERGRbTGkiIjIthhSRERkW6w4QVSDjGoPVtptuFwu8yZTccLlcsHpPPG+VrYChNFqo1wuSxV71XUdpVKJNfjGMKmQWrt2LZ566im8++678Pv9+NznPofvf//7mDFjhrlNoVDAHXfcgQ0bNkBVVSxatAg/+9nP0NLSYm5z6NAhrFy5Er///e8RCoWwfPlyrF27VqqqMtFY5XA4zPp5RmhUw+VyIRgMoq6uDpFIBIFAQCqkgsEgfD4fnE6nGTrV0jQNuVwOqVQKuVyu6nFCCKTTaRQKBdbsG6OkUmHLli3o6OjAxRdfjFKphLvvvhsLFy7Enj17EAwGAQC33347fvvb3+LJJ59EJBLBqlWrcM011+CPf/wjgBOl86+88krEYjH86U9/QldXF2688UZ4PB5897vfHfoZEo0ybrcboVAIwWAQHo+n6qBxu92IRqOYMmUKpk2bhmAwKLUS8/l8aGhogNvtRrlclgqNdDqNrq4u7N+/H6lUqupxQgjk83n09fVJFaal0cMhBrF+Pnr0KJqbm7FlyxZceumlSCaTGDduHNavX4+/+Zu/AQC8++67OP/889HZ2YlLLrkEzz//PP7qr/4KR44cMVdXjz76KO666y4cPXoUXq/3rPtNpVKIRCJWD5uopvn9fsRiMTQ3N1cdNMbqq6GhAcuXL8fMmTMRCoWki8QaXX1l7dq1C1u3bsWOHTvQ29tb9TijCvrBgwfR09ODQqEgvW+yt2QyiXA4fNr7B3V+LZlMAgAaGhoAANu3b4emaWhvbze3mTlzJiZNmmSGVGdnJ2bPnl1x+m/RokVYuXIldu/ejQsvvPBj+1FVtaIHjcw7MaLRxul0wu12w+v1QlGUqkPD7XbD5/MhEAggGAwiGAxKBY4QwvxMSfbUW6lUgqqqyOVyyGQyUmONVh38PGpssnx1n67ruO222/D5z38eF1xwAQAgkUjA6/UiGo1WbNvS0oJEImFuMzCgjPuN+05l7dq1iEQi5m3ixIlWD5uIAEutNqyMOdlgWnXQ2GQ5pDo6OrBr1y5s2LBhKI/nlNasWYNkMmnePvroo3O+T6LRamDQnK5P1GB7RxENFUun+1atWoWNGzdi69atmDBhgvn1WCyGYrGI/v7+itVUd3c3YrGYuc3rr79e8Xjd3d3mfaeiKAoURbFyqEREVMOkVlJCCKxatQpPP/00XnnlFUyZMqXi/nnz5sHj8WDTpk3m1/bu3YtDhw4hHo8DAOLxON5++2309PSY27z00ksIh8OYNWvWYOZCRESjjNRKqqOjA+vXr8ezzz6Luro68zOkSCQCv9+PSCSCFStWYPXq1WhoaEA4HMatt96KeDyOSy65BACwcOFCzJo1CzfccAMefvhhJBIJ3HPPPejo6OBqiYiIKkiF1COPPAIAuOyyyyq+/thjj+FrX/saAOCHP/whnE4nli5dWvHHvAaXy4WNGzdi5cqViMfjCAaDWL58OR588MHBzYSIiEYdqZCq5gobn8+HdevWYd26dafdZvLkyXjuuedkdk1ERGMQC8wSEZFtMaSIiMi2GFJERGRbDCkiIrIt9sYgGiFOp9NSNQe/34/GxkZMmjQJ9fX1VReYdblciEajCAaDUr2kDLquo1AoIJPJSFckP378OPr7+5FOp6Vrb5bLZRSLRbbqGKMYUkQjwAgNj8cjPTYUCmHChAk4//zzEYvF4HK5qhrndDoRCAQQjUbNFh8yQVUul5HL5XDs2DHk83mpenrd3d04duwYjh07JlUFHThxVTH7SY1dDCmiEWB0yJVtXAgAwWAQLS0tmDZtGiZNmlR1SDkcDng8HtTV1Unv12hyqKoqjh8/jnQ6LRVSvb296OvrQ39/P/r7+6seZ7BSeZ1GB4YU0Qg5uSV7tbxeL0KhEOrr69HU1CTV0drlcpnddY1jqIbRur1UKqFQKCCXy0mFVC6XQz6fRz6fr2i7Q3Q2vHCCiIhsiyFFRES2xZAiIiLbYkgREZFtMaSIiMi2GFJERGRbDCkiIrIthhQREdkWQ4qIiGyLIUVERLbFskhEI0i2EvnAMVbGAqgoZyRT2miwY4msYEgRDTOHwwG3221WJJepvQcA0WgUoVAIXq8XbrdbarzT6YSu6yiVStIFZjVNM1t1pFIplMvlqsdns1kUi0UGG0ljSBENkuyKxuPxoL6+Hueddx4uuugiBAIBqcCor6/HjBkzMGHCBNTX11c91igSq2kaVFWVCgxd19Hf348PPvgAr7/+OhKJhFRIdXd3o6urC4VCoeoxRABDimhQrJx6c7lcCAQCGD9+PD7zmc8gEolIhVQgEEAsFkNDQ4NUwAkhUC6XkUwmpZsI6rqOVCqFnp4efPDBB/joo49QKpWqHp/JZJBMJqXGEAEMKaJBsxJUbrcbfr8f0WgU0Wi06p5QAODz+RAIBMzTfbLtNoyWG7IhVSqVoKoqcrkcMpmMVODkcjlomsbTfSSNIUU0BGQCyuiIO7DxoUxIGT2ojMeR3beVoBgYcLquo1wuS4ecMZ5IBi9BJyIi22JIERGRbTGkiIjIthhSRERkWwwpIiKyLYYUERHZFkOKiIhsiyFFRES2xZAiIiLbYsUJokGQrfhgjDH+O7ByhOw+rbTqMOr3lUolqQKx5XIZmqaZlSZYPYKGC0OKaBCcTqd0qw232w2v1wuv1wtFUeDz+aTKIsnW7ANQUdIolUohlUpJ1d7TdR19fX1IJpMoFAoolUpS42VrBRIZGFJEFjkcDjNoZEJGURSEQiGEw2FEIhHpArMul0s62IATQaWqKj788EN0dXVBVdWqx+q6jmQyiYMHD6Kvr0+6wKymadA0jUFF0hhSRBY5HA74/X4Eg0GplY2iKIhEIqivr8e4cePQ2Ngo1apjYIFamdWUrutQVRXvvPMO3nvvPWSz2apP2QkhkMvl0NXVhe7ubiSTSenA4SlCsoIhRTQIA6uZVxsYRuVzt9sNj8cjXQV9MHRdRz6fRyaTQSaTqTo0dF1HoVBAPp83P5ti4NBw4NV9RERkWwwpIiKyLYYUERHZFkOKiIhsiyFFRES2xZAiIiLbYkgREZFtSYXU2rVrcfHFF6Ourg7Nzc34yle+gr1791Zsc9lll1XUFnM4HLjlllsqtjl06BCuvPJKBAIBNDc3484775T663UiIhobpP6Yd8uWLejo6MDFF1+MUqmEu+++GwsXLsSePXsQDAbN7W666SY8+OCD5r8DgYD5/+VyGVdeeSVisRj+9Kc/oaurCzfeeCM8Hg+++93vDsGUiIhotJAKqRdeeKHi348//jiam5uxfft2XHrppebXA4EAYrHYKR/jd7/7Hfbs2YOXX34ZLS0t+PSnP43vfOc7uOuuu3D//ffD6/VamAYREY1Gg/pMKplMAgAaGhoqvv7EE0+gqakJF1xwAdasWYNcLmfe19nZidmzZ6OlpcX82qJFi5BKpbB79+5T7kdVVbNys3EjGmlGGSTZGnqDabVhMOrgWblZeQyikWK5dp+u67jtttvw+c9/HhdccIH59a9+9auYPHky2trasHPnTtx1113Yu3cvnnrqKQBAIpGoCCgA5r8TicQp97V27Vo88MADVg+V6IyMOnoy9feMCuizZ8/GlClTEAwGqx7r8Xgwbtw4TJ8+3dKZg0KhgGPHjuH48ePI5/NVh0ipVEJ/fz/27duHgwcPShWYBU5UMk+n09A0TfqYiayyHFIdHR3YtWsXXn311Yqv33zzzeb/z549G62trViwYAH279+PadOmWdrXmjVrsHr1avPfqVQKEydOtHbgRAM4HA643W74fD54PB6pkAoEAjjvvPNw4YUXor6+vupK5i6XC6FQCK2trfB6vdIrKlVV0dPTg4MHD+L48eNVBY3R7DCVSuHgwYM4cuQIcrmcVEjpuo5isQhN07i6omFjKaRWrVqFjRs3YuvWrZgwYcIZt50/fz4AYN++fZg2bRpisRhef/31im26u7sB4LSfYymKAkVRrBwq0RkZVcyNiuTVBo3RpqO5uRlTpkzBuHHjqh7rdDqhKArq6uqkmxcCJ1Y0qVQK3d3dOHr0aFUtM4yGh+l0Gr29vejv75dahRl0XZfq6Es0WFIhJYTArbfeiqeffhqbN2/GlClTzjpmx44dAIDW1lYAQDwex0MPPYSenh40NzcDAF566SWEw2HMmjVL8vCJBm9guw2ZkHK5XPD7/airq0MkEqm63cbA1ZuVz6aEENA0DblcDtlstuqQKpfLyGazyOfzUFUVxWJRar/G4xANJ6mQ6ujowPr16/Hss8+irq7O/AwpEonA7/dj//79WL9+Pa644go0NjZi586duP3223HppZdizpw5AICFCxdi1qxZuOGGG/Dwww8jkUjgnnvuQUdHB1dLRERUQerqvkceeQTJZBKXXXYZWltbzduvfvUrAIDX68XLL7+MhQsXYubMmbjjjjuwdOlS/OY3vzEfw+VyYePGjXC5XIjH4/jbv/1b3HjjjRV/V0VERARYON13JhMnTsSWLVvO+jiTJ0/Gc889J7NrIiIag1i7j4iIbIshRUREtsWQIiIi22JIERGRbTGkiIjIthhSRERkW5Zr9xGNFoOpRj6YsUOFVSBoNGNI0ZhntSyS0aLDSlANtl2H0UJD1/WqC8xWuy2RnTCkaExzOBxQFAXhcBjBYFCq/l4oFEIoFIKiKPB6vVJjXS6XVGuQgcrlMorFolm7r9qCr7quI5fLoVwum2HF0CK7Y0jRmOZ2u9Hc3IzzzjsPLS0tVfd3MqqgT506Fc3NzYhGo1KrsIErMVmapuH48eM4fPgwPvzww6pDSggBVVXNYGNAUS1gSNGYZrTNCIfDqK+vr7rIscPhgM/nQygUgs/ng9frrTqkBj6GFUIISyspI6RKpRIDimoGQ4rGPOPzKI/HA4/HU9UYo92G8TmW1VWRlTHG50tGbyeZ0338XIpqDUOKCNYuZDh5Wztc6Xc6Az+DYkhRLeHfSRERkW0xpIiIyLYYUkREZFsMKSIisi2GFBER2RZDioiIbIshRUREtsWQIiIi22JIERGRbbHiBI0KVgu2ejweKIoCRVHg8/mqrt0HAD6fD26329aVJohqHUOKap7T6YTb7YaiKFW3yzAEg0FEo1E0NTWhublZqsCs1+tFMBiULixLRNVjSFHNczqd8Pl8iEaj8Hq9UiubUCiE8ePHY+rUqZg6dSp8Pl/VY91uN+rr66X3SUTVY0hRzTNCqr6+HoFAQCow6urqMH78eHziE5/A9OnTEQgEqh5rNEx0u/lrRHSu8LeLRg3jcynj/6thtOlwu91m6w2Z/RHRucWT6UREZFsMKSIisi2GFBER2RZDioiIbIshRUREtsWQIiIi22JIERGRbTGkiIjIthhSRERkWwwpIiKyLZZFolHB4XCYZYpkyhUNHDeYfVshhLC8TyGEeSMazRhSVPOcTic8Hg9CoRDC4bBU64xQKIRAIACv1wuXyyVdu89Kmw4jXMrlMjRNkx5fKBSgaRpKpRLK5TLK5XLV+zW2ZbhRrWBIUc1zuVzw+/1oaGhAfX29dEhFo1H4/X54PB6piubGKszKSqpcLkNVVeTzeei6LjU2k8kgn8+jWCyaQXU2RiiVSiXp/RGNJIYU1TQjJLxeL+rq6hCJRKRWQ4FAwFxJyVZBN/Y/8L/VMFZSpVIJhUKh6pWQoVAoVARUtSElhICu61xFUU1hSFHNczgccLlcZit4mTbyA8PJ6XQOW5fdgaf8ZEPKGKPretWhw8+wqFbx6j4iIrIthhQREdkWQ4qIiGxLKqQeeeQRzJkzB+FwGOFwGPF4HM8//7x5f6FQQEdHBxobGxEKhbB06VJ0d3dXPMahQ4dw5ZVXIhAIoLm5GXfeeSdKpdLQzIaIiEYVqZCaMGECvve972H79u148803cfnll+Pqq6/G7t27AQC33347fvOb3+DJJ5/Eli1bcOTIEVxzzTXm+HK5jCuvvBLFYhF/+tOf8J//+Z94/PHHce+99w7trIiIaFSQurrvqquuqvj3Qw89hEceeQSvvfYaJkyYgJ///OdYv349Lr/8cgDAY489hvPPPx+vvfYaLrnkEvzud7/Dnj178PLLL6OlpQWf/vSn8Z3vfAd33XUX7r//fni93qGbGRER1TzLn0mVy2Vs2LAB2WwW8Xgc27dvh6ZpaG9vN7eZOXMmJk2ahM7OTgBAZ2cnZs+ejZaWFnObRYsWIZVKmauxU1FVFalUquJGRESjn3RIvf322wiFQlAUBbfccguefvppzJo1C4lEAl6vF9FotGL7lpYWJBIJAEAikagIKON+477TWbt2LSKRiHmbOHGi7GETEVENkg6pGTNmYMeOHdi2bRtWrlyJ5cuXY8+ePefi2Exr1qxBMpk0bx999NE53R8REdmDdMUJr9eL6dOnAwDmzZuHN954Az/+8Y9x7bXXolgsor+/v2I11d3djVgsBgCIxWJ4/fXXKx7PuPrP2OZUFEWBoiiyh0p0Tg22eoNRMUK2lt7JVSZkjoMVJ6jWDLoskq7rUFUV8+bNg8fjwaZNm7B06VIAwN69e3Ho0CHE43EAQDwex0MPPYSenh40NzcDAF566SWEw2HMmjVrsIdCNGyMF3sr5YZ0XYemaSgUCshkMtJ/gpHNZqGqqlksttqQkymjRGQXUiG1Zs0aLFmyBJMmTUI6ncb69euxefNmvPjii4hEIlixYgVWr16NhoYGhMNh3HrrrYjH47jkkksAAAsXLsSsWbNwww034OGHH0YikcA999yDjo4OrpSo5gghoKoqNE2TevEvl8vIZDI4cuQI9u3bB1VVpYKjt7cXH374Ifr6+pDNZqVq/5XLZZRKJQYV1QypkOrp6cGNN96Irq4uRCIRzJkzBy+++CK+9KUvAQB++MMfwul0YunSpVBVFYsWLcLPfvYzc7zL5cLGjRuxcuVKxONxBINBLF++HA8++ODQzopoGOi6bl5tKrMaUlUVXV1deOONN/Diiy8imUxKnfLTNA2ZTAbpdFq6H5VR1JYhRbVCKqR+/vOfn/F+n8+HdevWYd26dafdZvLkyXjuuedkdktkW7quo1QqSYWFpmlQVRXZbBbHjx9Hf3+/VEiVy2WzXYdsBXWAn0tRbWGrDqIhIHvxgvH5kHH6TTakjNOLDBwa7VhglmiYnXxlnpWgYTjRWMGQIiIi22JIERGRbTGkiIjIthhSRERkWwwpIiKyLYYUERHZFkOKiIhsiyFFRES2xZAiIiLbYlkkqnlGmSGjhp7T6YTD4ahqrNvthqqqyOfzyOVyUoViy+Uycrkc8vk8NE2rugqEqqoV1dONOVSL1SZoLGFIUU0zAiqfz6Ovrw9CCDid1Z8g8Pl8CAQC8Hq9OH78ODweT9VjdV1HX1+f2ROq2vDQNA09PT04evQoisWidOsM1u2jsYQhRTVP0zQkk0l88MEHCAQCVa+igBMrqcOHD+Pdd99FKBSSCjghBHK5nHQ1cqOfVCKRQDqdlu4nZQQzQ4rGAoYU1bxSqYTjx48jnU5LneozuFwuuN1uqYAyWA0Lo/q5EVAMHKJTY0hRzTMa+VnprURE9sar+4iIyLYYUkREZFsMKSIisi2GFBER2RZDioiIbIshRUREtsWQIiIi22JIERGRbdVkSPGv84mIRoezvZ7XZEil0+mRPgQiIhoCZ3s9d4gaXJbouo69e/di1qxZ+OijjxAOh0f6kIZcKpXCxIkTOb8axfnVvtE+x5GenxAC6XQabW1tZ6ybWZO1+5xOJ8aPHw8ACIfDo/IHyMD51TbOr/aN9jmO5PwikchZt6nJ031ERDQ2MKSIiMi2ajakFEXBfffdB0VRRvpQzgnOr7ZxfrVvtM+xVuZXkxdOEBHR2FCzKykiIhr9GFJERGRbDCkiIrIthhQREdlWTYbUunXr8IlPfAI+nw/z58/H66+/PtKHZMn9998Ph8NRcZs5c6Z5f6FQQEdHBxobGxEKhbB06VJ0d3eP4BGf2datW3HVVVehra0NDocDzzzzTMX9Qgjce++9aG1thd/vR3t7O95///2Kbfr6+rBs2TKEw2FEo1GsWLECmUxmGGdxZmeb49e+9rWPPaeLFy+u2Mauc1y7di0uvvhi1NXVobm5GV/5ylewd+/eim2q+Zk8dOgQrrzySgQCATQ3N+POO+9EqVQazqmcUjXzu+yyyz72/N1yyy0V29h1fgDwyCOPYM6cOeYf6MbjcTz//PPm/TX5/Ikas2HDBuH1esV//Md/iN27d4ubbrpJRKNR0d3dPdKHJu2+++4Tn/rUp0RXV5d5O3r0qHn/LbfcIiZOnCg2bdok3nzzTXHJJZeIz33ucyN4xGf23HPPiX/8x38UTz31lAAgnn766Yr7v/e974lIJCKeeeYZ8T//8z/iy1/+spgyZYrI5/PmNosXLxZz584Vr732mvjDH/4gpk+fLq6//vphnsnpnW2Oy5cvF4sXL654Tvv6+iq2sescFy1aJB577DGxa9cusWPHDnHFFVeISZMmiUwmY25ztp/JUqkkLrjgAtHe3i7eeust8dxzz4mmpiaxZs2akZhShWrm9xd/8Rfipptuqnj+ksmkeb+d5yeEEP/93/8tfvvb34r33ntP7N27V9x9993C4/GIXbt2CSFq8/mruZD67Gc/Kzo6Osx/l8tl0dbWJtauXTuCR2XNfffdJ+bOnXvK+/r7+4XH4xFPPvmk+bV33nlHABCdnZ3DdITWnfwCruu6iMVi4p//+Z/Nr/X39wtFUcQvf/lLIYQQe/bsEQDEG2+8YW7z/PPPC4fDIf73f/932I69WqcLqauvvvq0Y2ppjj09PQKA2LJlixCiup/J5557TjidTpFIJMxtHnnkEREOh4WqqsM7gbM4eX5CnAipf/iHfzjtmFqan6G+vl78+7//e80+fzV1uq9YLGL79u1ob283v+Z0OtHe3o7Ozs4RPDLr3n//fbS1tWHq1KlYtmwZDh06BADYvn07NE2rmOvMmTMxadKkmpzrgQMHkEgkKuYTiUQwf/58cz6dnZ2IRqO46KKLzG3a29vhdDqxbdu2YT9mqzZv3ozm5mbMmDEDK1euRG9vr3lfLc0xmUwCABoaGgBU9zPZ2dmJ2bNno6Wlxdxm0aJFSKVS2L179zAe/dmdPD/DE088gaamJlxwwQVYs2YNcrmceV8tza9cLmPDhg3IZrOIx+M1+/zVVIHZY8eOoVwuV3wDAaClpQXvvvvuCB2VdfPnz8fjjz+OGTNmoKurCw888AC++MUvYteuXUgkEvB6vYhGoxVjWlpakEgkRuaAB8E45lM9d8Z9iUQCzc3NFfe73W40NDTUzJwXL16Ma665BlOmTMH+/ftx9913Y8mSJejs7ITL5aqZOeq6jttuuw2f//znccEFFwBAVT+TiUTilM+xcZ9dnGp+APDVr34VkydPRltbG3bu3Im77roLe/fuxVNPPQWgNub39ttvIx6Po1AoIBQK4emnn8asWbOwY8eOmnz+aiqkRpslS5aY/z9nzhzMnz8fkydPxq9//Wv4/f4RPDKy6rrrrjP/f/bs2ZgzZw6mTZuGzZs3Y8GCBSN4ZHI6Ojqwa9cuvPrqqyN9KOfE6eZ38803m/8/e/ZstLa2YsGCBdi/fz+mTZs23IdpyYwZM7Bjxw4kk0n813/9F5YvX44tW7aM9GFZVlOn+5qamuByuT52NUp3dzdisdgIHdXQiUajOO+887Bv3z7EYjEUi0X09/dXbFOrczWO+UzPXSwWQ09PT8X9pVIJfX19NTlnAJg6dSqampqwb98+ALUxx1WrVmHjxo34/e9/jwkTJphfr+ZnMhaLnfI5Nu6zg9PN71Tmz58PABXPn93n5/V6MX36dMybNw9r167F3Llz8eMf/7hmn7+aCimv14t58+Zh06ZN5td0XcemTZsQj8dH8MiGRiaTwf79+9Ha2op58+bB4/FUzHXv3r04dOhQTc51ypQpiMViFfNJpVLYtm2bOZ94PI7+/n5s377d3OaVV16Bruvmi0WtOXz4MHp7e9Ha2grA3nMUQmDVqlV4+umn8corr2DKlCkV91fzMxmPx/H2229XBPFLL72EcDiMWbNmDc9ETuNs8zuVHTt2AEDF82fX+Z2OrutQVbV2n78RuVxjEDZs2CAURRGPP/642LNnj7j55ptFNBqtuBqlVtxxxx1i8+bN4sCBA+KPf/yjaG9vF01NTaKnp0cIceJy0UmTJolXXnlFvPnmmyIej4t4PD7CR3166XRavPXWW+Ktt94SAMS//Mu/iLfeekt8+OGHQogTl6BHo1Hx7LPPip07d4qrr776lJegX3jhhWLbtm3i1VdfFZ/85CdtcXm24UxzTKfT4hvf+Ibo7OwUBw4cEC+//LL4zGc+Iz75yU+KQqFgPoZd57hy5UoRiUTE5s2bKy7BzuVy5jZn+5k0LmFeuHCh2LFjh3jhhRfEuHHjbHGJ9tnmt2/fPvHggw+KN998Uxw4cEA8++yzYurUqeLSSy81H8PO8xNCiG9961tiy5Yt4sCBA2Lnzp3iW9/6lnA4HOJ3v/udEKI2n7+aCykhhPjpT38qJk2aJLxer/jsZz8rXnvttZE+JEuuvfZa0draKrxerxg/fry49tprxb59+8z78/m8+Pu//3tRX18vAoGA+Ou//mvR1dU1gkd8Zr///e8FgI/dli9fLoQ4cRn6t7/9bdHS0iIURRELFiwQe/furXiM3t5ecf3114tQKCTC4bD4+te/LtLp9AjM5tTONMdcLicWLlwoxo0bJzwej5g8ebK46aabPvYGyq5zPNW8AIjHHnvM3Kaan8mDBw+KJUuWCL/fL5qamsQdd9whNE0b5tl83Nnmd+jQIXHppZeKhoYGoSiKmD59urjzzjsr/k5KCPvOTwgh/u7v/k5MnjxZeL1eMW7cOLFgwQIzoISozeePrTqIiMi2auozKSIiGlsYUkREZFsMKSIisi2GFBER2RZDioiIbIshRUREtsWQIiIi22JIERGRbTGkiIjIthhSRERkWwwpIiKyLYYUERHZ1v8Ding416HvLZ8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 34ms/step\n",
      "\n",
      "\n",
      "The input image given by user is recognized by the system as: 7\n"
     ]
    }
   ],
   "source": [
    "input_image_path = input(\"Enter Image Path:\")\n",
    "input_image = cv2.imread(input_image_path)\n",
    "\n",
    "print(\"\\nImage path:\",input_image_path)\n",
    "print(\"\\nImage Given:\")\n",
    "plt.imshow(input_image)\n",
    "plt.show()\n",
    "\n",
    "gray = cv2.cvtColor(input_image, cv2.COLOR_RGB2GRAY)\n",
    "resize_img = cv2.resize(gray, (28,28))\n",
    "\n",
    "resize_img = resize_img/255\n",
    "reshaped_image = np.reshape(resize_img, [1, 28, 28])\n",
    "\n",
    "input_pred = model.predict(reshaped_image)\n",
    "input_pred_label = np.argmax(input_pred)\n",
    "print(\"\\n\\nThe input image given by user is recognized by the system as:\", input_pred_label)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}