Weekly-Project-MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ps5x-gYUCf9_"
   },
   "source": [
    "# Handwritten Digits Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0glR10XBCxNy"
   },
   "source": [
    "## Overview\n",
    "\n",
    "The MNIST database (Modified National Institute of Standards and Technology database) is a large database of handwritten digits that is commonly used for training various image processing systems. MNIST is like the \"Hello World\" of machine learning with which you can try out a few machine learning algorithms.\n",
    "\n",
    "![](https://upload.wikimedia.org/wikipedia/commons/2/27/MnistExamples.png)\n",
    "\n",
    "Each image is 28 pixels in height and 28 pixels in width, for a total of 784 pixels in total. Each pixel has a single pixel-value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning lighter. This pixel-value is an integer between 0 and 255, inclusive.\n",
    "\n",
    "Visually, if we omit the \"pixel\" prefix, the pixels make up the image like this:\n",
    "```\n",
    "000 001 002 003 ... 026 027\n",
    "028 029 030 031 ... 054 055\n",
    "056 057 058 059 ... 082 083\n",
    " |   |   |   |  ...  |   |\n",
    "728 729 730 731 ... 754 755\n",
    "756 757 758 759 ... 782 783 \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-3hGXfqVDRna"
   },
   "source": [
    "## Get Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4IWx_-bBEYAT"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip3 install tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3285,
     "status": "ok",
     "timestamp": 1589176733358,
     "user": {
      "displayName": "Minh Do",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhnQ6aJ2YnaevyJzhii-qGws6Y17w-cwWLqF5iP=s64",
      "userId": "12822549848477954436"
     },
     "user_tz": -420
    },
    "id": "BNhUbXb9CSbh",
    "outputId": "36ab477d-bf38-4884-ff84-b5ee36b69e7f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data: (60000, 28, 28) (60000,)\n",
      "Test data: (10000, 28, 28) (10000,)\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.datasets import mnist\n",
    "\n",
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
    "\n",
    "print('Training data:', X_train.shape, y_train.shape)\n",
    "print('Test data:', X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VYTtM9VlGs31"
   },
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xcZURSKuDcvm"
   },
   "outputs": [],
   "source": [
    "def plot_images(images, labels):\n",
    "    '''Plot images with their labels. Ten each row'''\n",
    "    plt.figure(figsize=(20, 10))\n",
    "    columns = 10\n",
    "    for i, image in enumerate(images):\n",
    "        ax = plt.subplot(len(images) / columns + 1, columns, i + 1)\n",
    "        ax.set_title(f\"{labels[i]}\", fontsize=16)\n",
    "        plt.axis('off')\n",
    "        plt.subplots_adjust(bottom=0.1)\n",
    "        plt.imshow(image, cmap='gray')\n",
    "\n",
    "def get_samples(n_samples, X, y):\n",
    "    '''Get n_samples images randomly'''\n",
    "    samples = np.random.choice(np.arange(len(y)), n_samples, replace=False)\n",
    "    return X[samples], y[samples]\n",
    "\n",
    "def get_digit(digit, n_samples, X, y):\n",
    "    '''Get n_samples images of the class digit randomly'''\n",
    "    y_digit = y[y == digit]\n",
    "    samples = np.random.choice(np.arange(len(y_digit)), n_samples, replace=False)\n",
    "    return X[y == digit][samples], y_digit[samples]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 403
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 6536,
     "status": "ok",
     "timestamp": 1589176736704,
     "user": {
      "displayName": "Minh Do",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhnQ6aJ2YnaevyJzhii-qGws6Y17w-cwWLqF5iP=s64",
      "userId": "12822549848477954436"
     },
     "user_tz": -420
    },
    "id": "OpMe7EMCEF01",
    "outputId": "4f78db79-ec22-40dd-be53-915e9a184f53"
   },
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1440x720 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_samples = 30\n",
    "images, labels = get_samples(n_samples, X_train, y_train)\n",
    "# images, labels = get_digit(4, n_samples)\n",
    "plot_images(images, labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Mh5Jukj4AIa8"
   },
   "source": [
    "**Plot the barplot of number of images of each class (0 to 9)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 284
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 6498,
     "status": "ok",
     "timestamp": 1589176736706,
     "user": {
      "displayName": "Minh Do",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhnQ6aJ2YnaevyJzhii-qGws6Y17w-cwWLqF5iP=s64",
      "userId": "12822549848477954436"
     },
     "user_tz": -420
    },
    "id": "xjVYEat8EJTd",
    "outputId": "71329a1c-4080-4acf-fd2a-64fa7508a15c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='count'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yRxFLQZjAIbA"
   },
   "source": [
    "**Print out the range of pixel values (min, max)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 6454,
     "status": "ok",
     "timestamp": 1589176736707,
     "user": {
      "displayName": "Minh Do",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhnQ6aJ2YnaevyJzhii-qGws6Y17w-cwWLqF5iP=s64",
      "userId": "12822549848477954436"
     },
     "user_tz": -420
    },
    "id": "d0rZBlU8FmtE",
    "outputId": "19f74e3d-8c3c-432b-bb2a-5efa0a06a045"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pixel value range: 0 255\n"
     ]
    }
   ],
   "source": [
    "print('Pixel value range:', X_train.min(), X_train.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XC2sFqfcJjQ1"
   },
   "source": [
    "## Transform data for training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HtTutiwqAIbE"
   },
   "source": [
    "**Get the first `n_samples` images randomly from training set and test set. Then reshape them into long vectors**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XF1XGXrvJlri"
   },
   "outputs": [],
   "source": [
    "n_samples = 1000\n",
    "n_test_samples = 200\n",
    "# Fill the blanks\n",
    "X_train_flat, y_train_flat = get_samples(n_samples, X_train, y_train)\n",
    "X_test_flat, y_test_flat = get_samples(n_test_samples, X_test, y_test)\n",
    "# Normalization\n",
    "X_train_flat = X_train_flat.reshape((n_samples, -1))/255\n",
    "X_test_flat = X_test_flat.reshape((n_test_samples, -1))/255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1267,
     "status": "ok",
     "timestamp": 1589176739748,
     "user": {
      "displayName": "Minh Do",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhnQ6aJ2YnaevyJzhii-qGws6Y17w-cwWLqF5iP=s64",
      "userId": "12822549848477954436"
     },
     "user_tz": -420
    },
    "id": "66rNQGgnK1MX",
    "outputId": "2fa94b44-a17e-4a45-d6bc-b6d8779a85f7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data (1000, 784)\n",
      "Test data (200, 784)\n"
     ]
    }
   ],
   "source": [
    "print('Training data', X_train_flat.shape)\n",
    "print('Test data', X_test_flat.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bmxGMEGGLaJI"
   },
   "source": [
    "## Train models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4tJcSPEbLRsh"
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8vHewJOQAIbL"
   },
   "source": [
    "**Train a SVM model**: Set up the training process and train with the small samples of data first before you take the full training dataset to train."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Mo2qhFY_AIbL"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=SVC(), n_jobs=-1,\n",
       "             param_grid={'C': [0.01, 0.1, 1, 10, 100],\n",
       "                         'gamma': [0.0001, 0.001, 0.01]},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = SVC(kernel='linear')\n",
    "clf.fit(X_train_flat, y_train_flat)\n",
    "parameters = {'gamma': [0.0001, 0.001, 0.01],\n",
    "              'C':[0.01, 0.1, 1, 10, 100]}\n",
    "\n",
    "model = SVC(kernel='rbf')\n",
    "\n",
    "gridsearch_models = GridSearchCV(estimator=model,\n",
    "                                 param_grid=parameters,\n",
    "                                 scoring='accuracy',\n",
    "                                 cv=5, n_jobs=-1)\n",
    "\n",
    "gridsearch_models.fit(X_train_flat, y_train_flat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params {'C': 10, 'gamma': 0.01}\n",
      "Best score 0.9019999999999999\n"
     ]
    }
   ],
   "source": [
    "print('Best params', gridsearch_models.best_params_)\n",
    "print('Best score', gridsearch_models.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model = gridsearch_models.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rupa4m2jX5rm"
   },
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2ncaF-JMAIbN"
   },
   "source": [
    "**Get the predictions on test data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1rwL5VCIX9qi"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 0.915\n"
     ]
    }
   ],
   "source": [
    "# Your code here\n",
    "predictions = best_model.predict(X_test_flat)\n",
    "print('Accuracy Score:', accuracy_score(y_test_flat, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IGuF9RSNAIbQ"
   },
   "source": [
    "**Plot the confusion matrix in form of a heatmap**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 607
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1534,
     "status": "ok",
     "timestamp": 1588832416487,
     "user": {
      "displayName": "Minh Do",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhnQ6aJ2YnaevyJzhii-qGws6Y17w-cwWLqF5iP=s64",
      "userId": "12822549848477954436"
     },
     "user_tz": -420
    },
    "id": "2RyKjfmHX7SN",
    "outputId": "adcbc362-8116-4f83-d7d8-93fa1dbe1a8f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Your code here\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(confusion_matrix(y_test_flat, predictions), cmap=\"YlGnBu\", annot=True, fmt=\"d\")\n",
    "plt.title(f'Accuracy score: {accuracy_score(y_test_flat, predictions)}', fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tSlYzbs9AIbS"
   },
   "source": [
    "**Plot the samples that the model predicts incorrectly**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 470
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3756,
     "status": "ok",
     "timestamp": 1588832803276,
     "user": {
      "displayName": "Minh Do",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhnQ6aJ2YnaevyJzhii-qGws6Y17w-cwWLqF5iP=s64",
      "userId": "12822549848477954436"
     },
     "user_tz": -420
    },
    "id": "-_uUXSpazDLu",
    "outputId": "8b9c9cba-9229-402a-d5e8-d8e2a5e7189a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 17 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Your code here\n",
    "wrong_predicted_images = X_test_flat[y_test_flat != predictions].reshape((-1, 28, 28))\n",
    "wrong_predictions = predictions[y_test_flat != predictions]\n",
    "\n",
    "plot_images(wrong_predicted_images, wrong_predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Y8O1Hh7LYNjP"
   },
   "source": [
    "## Save model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "C7cNiWsLYPTE"
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "pickle.dump(clf, open('svm_mnist.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5B-g57XIYuH0"
   },
   "source": [
    "**Re-load model for testing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1139,
     "status": "ok",
     "timestamp": 1588833259160,
     "user": {
      "displayName": "Minh Do",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhnQ6aJ2YnaevyJzhii-qGws6Y17w-cwWLqF5iP=s64",
      "userId": "12822549848477954436"
     },
     "user_tz": -420
    },
    "id": "WDsYcxfzYd8F",
    "outputId": "5df151b2-0ae2-4706-a136-aa45a2018fcb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.875\n"
     ]
    }
   ],
   "source": [
    "# Your code here\n",
    "with open('svm_mnist.pkl', 'rb') as model:\n",
    "    reload_model = pickle.load(model)\n",
    "\n",
    "predictions = reload_model.predict(X_test_flat)\n",
    "print(f'Accuracy score: {accuracy_score(y_test_flat, predictions)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1BVyCBz2m0Lz"
   },
   "source": [
    "## BONUS: Embedding the model into a web application"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0Y2hes6WnfxV"
   },
   "source": [
    "### Project structure\n",
    "\n",
    "* Clone the files of the repository\n",
    "\n",
    "```bash\n",
    "git clone https://github.com/dhminh1024/flask_doc.git\n",
    "```\n",
    "\n",
    "* Change directory\n",
    "\n",
    "```bash\n",
    "cd flask_doc\n",
    "```\n",
    "\n",
    "* Create the following folders and files\n",
    "\n",
    "|-app/\n",
    "    |-templates/\n",
    "        |-base.html\n",
    "    |-static/\n",
    "        |-js/\n",
    "            |-index.js\n",
    "        |-css/\n",
    "            |-style.css\n",
    "        |-images/\n",
    "    |-blueprints/\n",
    "        |-__ init__.py\n",
    "    |-middlewares/\n",
    "    |-models/\n",
    "    |-main.py\n",
    "\n",
    "```bash\n",
    "mkdir app app/templates app/static app/static/js app/static/css app/static/images app/blueprints app/middlewares app/models\n",
    "touch app/main.py app/templates/base.html app/static/js/index.js app/static/css/style.css\n",
    "touch app/blueprints/__init__.py\n",
    "```\n",
    "\n",
    "* Add jquery (optional)\n",
    "\n",
    "```bash\n",
    "mv ./jquery-3.4.1.min.js app/static/js/\n",
    "```\n",
    "\n",
    "* Add Canvas (optional)\n",
    "\n",
    "```bash\n",
    "mv ./index.js app/static/js/index.js\n",
    "```\n",
    "\n",
    "* Add Coderschool logo (optional)\n",
    "\n",
    "```bash\n",
    "mv ./images/coderschool-logo.png app/static/images/\n",
    "rm -r images\n",
    "```\n",
    "\n",
    "* Add this code in **app/main.py**\n",
    "\n",
    "```python\n",
    "from flask import Flask, render_template\n",
    "from blueprints import *\n",
    "\n",
    "app = Flask(__name__)\n",
    "app.register_blueprint(home)\n",
    "\n",
    "if __name__ == '__main__':\n",
    "  app.run(host='127.0.0.1', port=5000, debug=True)\n",
    "```\n",
    "> *Hints: most of this code can be automaticaly generated with a VSCode Extension called flask-snippets by only writting \"fapp\"*\n",
    "> ![](https://i.imgur.com/npmdsha.png=500x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "### Generating a new blueprint of our Flask app\n",
    "\n",
    "> A blueprint is a template for generating a \"section\" of our Flask app\n",
    "\n",
    "* In **flask_doc** directory, run this code in the **Terminal**\n",
    "\n",
    "```bash\n",
    "export NEW_BLUEPRINT=home\n",
    "mkdir app/blueprints/$NEW_BLUEPRINT\n",
    "touch app/blueprints/$NEW_BLUEPRINT/__init__.py app/blueprints/$NEW_BLUEPRINT/blueprint.py\n",
    "echo \"from .$NEW_BLUEPRINT import $NEW_BLUEPRINT\" >> app/blueprints/__init__.py\n",
    "echo \"from .blueprint import $NEW_BLUEPRINT\" > app/blueprints/$NEW_BLUEPRINT/__init__.py\n",
    "printf \\\n",
    "\"from flask import Blueprint, render_template, request\\n\\\n",
    "\\n\\\n",
    "$NEW_BLUEPRINT = Blueprint('$NEW_BLUEPRINT', __name__)\\\n",
    "\\n\\\n",
    "@$NEW_BLUEPRINT.route('/home')\\n\\\n",
    "def route_name():\\n\\\n",
    "    return render_template('$NEW_BLUEPRINT.html') \\n\\\n",
    "\" > app/blueprints/$NEW_BLUEPRINT/blueprint.py\n",
    "cp ./sample_page.html app/templates/$NEW_BLUEPRINT.html\n",
    "```\n",
    "> Hints: change 'home' to make a new blueprint (line 1 & 14)"
   ]
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   "source": [
    "### HTML Template\n",
    "\n",
    "* Add this code in **app/templates/base.html**\n",
    "\n",
    "```HTML\n",
    "<!doctype html>\n",
    "<html lang=\"en\">\n",
    "  <head>\n",
    "    <title>Title</title>\n",
    "    <!-- Required meta tags -->\n",
    "    <meta charset=\"utf-8\">\n",
    "    <meta name=\"viewport\" content=\"width=device-width, initial-scale=1, shrink-to-fit=no\">\n",
    "\n",
    "    <!-- Bootstrap CSS -->\n",
    "    <link rel=\"stylesheet\" href=\"https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css\" integrity=\"sha384-ggOyR0iXCbMQv3Xipma34MD+dH/1fQ784/j6cY/iJTQUOhcWr7x9JvoRxT2MZw1T\" crossorigin=\"anonymous\">\n",
    "    <!-- CSS -->\n",
    "    <link href=\"static/css/style.css\" rel=\"stylesheet\">\n",
    "</head>\n",
    "  <body class=\"text-center\">\n",
    "    {% block content%} {% endblock %} \n",
    "    \n",
    "    <script src=\"static/js/jquery-3.4.1.min.js\"></script>\n",
    "    <script src=\"static/js/index.js\"></script>\n",
    "    <!-- AJAX optional -->\n",
    "    <script type=\"text/javascript\">\n",
    "        $(\"#myButton\").click(function(){\n",
    "            $('#result').text('  Predicting...');\n",
    "            var $SCRIPT_ROOT = {{request.script_root|tojson|safe}};\n",
    "            var canvasObj = document.getElementById(\"canvas\");\n",
    "            var img = canvasObj.toDataURL('image/jpeg');\n",
    "            $.ajax({\n",
    "                type: \"POST\",\n",
    "                url: $SCRIPT_ROOT + \"/upload/\",\n",
    "                data: img,\n",
    "                success: function(data){\n",
    "                    $('#result').text('Predictions ' + data);\n",
    "                }\n",
    "            });\n",
    "        });\n",
    "    </script>\n",
    "  </body>\n",
    "</html>\n",
    "```\n",
    "\n",
    "> *Hints: most of this code can be automaticaly generated with VSCode Extensions called Bootstrap 4, Font awesome 4, Font Awesome 5 Free & Pro snippets by only writting \"b4-$\"*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gtAANFtOAIbc"
   },
   "source": [
    "### Run the app\n",
    "\n",
    "* Change directory\n",
    "\n",
    "```bash\n",
    "cd app\n",
    "```\n",
    "\n",
    "* Run the app\n",
    "\n",
    "```bash\n",
    "python main.py\n",
    "```\n",
    "\n",
    "Congratulation, you have just created your Flask app. Here is the link of your Flask app: http://127.0.0.1:5000/home\n",
    "\n",
    "### Html for MNIST example\n",
    "\n",
    "* Replace the code of **app/templates/home.html** by this code \n",
    "\n",
    "```HTML\n",
    "{% extends \"base.html\" %} {% block content %}\n",
    "<div style=\"margin-top:50px\">\n",
    "  <img class=\"mb-4\" src=\"static/images/coderschool-logo.png\" alt=\"\" />\n",
    "  <h1 class=\"h3 mb-3 font-weight-normal\">Please draw a number</h1>\n",
    "\n",
    "  <canvas id=\"canvas\" width=\"400\" height=\"400\"></canvas>\n",
    "\n",
    "  <h1 class=\"h3 mb-3 font-weight-normal\" id=\"result\">Predictions:</h1>\n",
    "\n",
    "  <button id=\"myButton\" class=\"btn  btn-primary \" type=\"submit\">\n",
    "    Predict\n",
    "  </button>\n",
    "  <button id=\"clearButton\" class=\"btn btn-success\" type=\"submit\">\n",
    "    Clear\n",
    "  </button>\n",
    "\n",
    "  <p class=\"mt-5 mb-3 text-muted\">&copy; CoderSchool</p>\n",
    "</div>\n",
    "{% endblock %}\n",
    "\n",
    "```\n",
    "\n",
    "### Python for MNIST example\n",
    "\n",
    "* In **flask_doc** directory, run this code to generate the blueprint related to upload\n",
    "\n",
    "```bash\n",
    "export NEW_BLUEPRINT=upload_api\n",
    "mkdir app/blueprints/$NEW_BLUEPRINT\n",
    "touch app/blueprints/$NEW_BLUEPRINT/__init__.py app/blueprints/$NEW_BLUEPRINT/blueprint.py\n",
    "echo \"from .$NEW_BLUEPRINT import $NEW_BLUEPRINT\" >> app/blueprints/__init__.py\n",
    "echo \"from .blueprint import $NEW_BLUEPRINT\" > app/blueprints/$NEW_BLUEPRINT/__init__.py\n",
    "printf \\\n",
    "\"from flask import Blueprint, render_template, request\\n\\\n",
    "\\n\\\n",
    "$NEW_BLUEPRINT = Blueprint('$NEW_BLUEPRINT', __name__)\\\n",
    "\\n\\\n",
    "@$NEW_BLUEPRINT.route('/upload_api')\\n\\\n",
    "def route_name():\\n\\\n",
    "    return render_template('$NEW_BLUEPRINT.html') \\n\\\n",
    "\" > app/blueprints/$NEW_BLUEPRINT/blueprint.py\n",
    "cp ./sample_page.html app/templates/$NEW_BLUEPRINT.html\n",
    "```\n",
    "\n",
    "\n",
    "* Replace the code of **app/blueprints/upload_api/blueprint.py** by this code\n",
    "\n",
    "```python\n",
    "from flask import Blueprint, render_template, request\n",
    "import re\n",
    "import base64\n",
    "import numpy as np\n",
    "\n",
    "def parse_image(imgData):\n",
    "    img_str = re.search(b\"base64,(.*)\", imgData).group(1)\n",
    "    img_decode = base64.decodebytes(img_str)\n",
    "    with open('output.png', \"wb\") as f:\n",
    "        f.write(img_decode)\n",
    "    return img_decode\n",
    "\n",
    "# Load your model here\n",
    "model = None\n",
    "\n",
    "upload_api = Blueprint('upload_api', __name__)\n",
    "\n",
    "\n",
    "@upload_api.route('/upload/', methods=['POST'])\n",
    "def upload():\n",
    "    image = parse_image(request.get_data())\n",
    "    # Your prediction here\n",
    "    prediction = 0\n",
    "    return str(prediction)\n",
    "\n",
    "```\n",
    "\n",
    "* Register the upload API in **app/main.py**:\n",
    "\n",
    "```python\n",
    "from flask import Flask, render_template\n",
    "from blueprints import *\n",
    "\n",
    "app = Flask(__name__)\n",
    "app.register_blueprint(home)\n",
    "app.register_blueprint(upload_api)\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    app.run(host='127.0.0.1', port=5000, debug=True)\n",
    "```"
   ]
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