{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "<img src=\"../../img/ods_stickers.jpg\" />\n",
    "    \n",
    "## [mlcourse.ai](https://mlcourse.ai) – Open Machine Learning Course \n",
    "\n",
    "\n",
    "### Tutorial\n",
    "# Deploying your Machine Learning Model\n",
    "\n",
    "<div style=\"text-align: right;\">Author: Maxim Klyuchnikov</div>\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h2>Table of Contents</h2>\n",
    "<div class=\"toc\">\n",
    "    <ul class=\"toc-item\">\n",
    "        <li><span><a href=\"#1.-Introduction\">1. Introduction</a></span>\n",
    "            <ul class=\"toc-item\">\n",
    "                <li><span><a href=\"#1.1.-Who-this-tutorial-is-for\">1.1. Who this tutorial is for</a></span></li>\n",
    "                <li><span><a href=\"#1.2.-What-we're-gonna-use\">1.2. What we're gonna use</a></span></li>\n",
    "                <li><span><a href=\"#1.3.-Why-is-it-important-to-know-how-to-do-it?\">1.3. Why is it important to know how to do it?</a></span></li>\n",
    "                <li><span><a href=\"#1.4.-Disclaimer\">1.4. Disclaimer</a></span></li>\n",
    "            </ul>\n",
    "        </li>\n",
    "        <li><span><a href=\"#2.-Building-our-Model\">2. Building our Model</a></span>\n",
    "            <ul class=\"toc-item\">\n",
    "                <li><span><a href=\"#2.1.-Usual-Machine-Learning-workflow\">2.1. Usual Machine Learning workflow</a></span></li>\n",
    "                <li><span><a href=\"#2.2.-The-Model\">2.2. The Model</a></span></li>\n",
    "                <li><span><a href=\"#2.3.-Persisting-The-Model\">2.3. Persisting The Model</a></span>\n",
    "                    <ul class=\"toc-item\">\n",
    "                        <li><span><a href=\"#2.3.1.-scikit-learn-Pipelines\">2.3.1. scikit-learn Pipelines</a></span></li>\n",
    "                        <li><span><a href=\"#2.3.2.-Python-and-library-versions\">2.3.2. Python and library versions</a></span></li>\n",
    "                        <li><span><a href=\"#2.3.3.-Wrapping-all-of-the-model-data\">2.3.3. Wrapping all of the model data</a></span></li>\n",
    "                    </ul>\n",
    "                </li>\n",
    "            </ul>\n",
    "        </li>\n",
    "        <li><span><a href=\"#3.-Wrapping-The-Model-in-Web-Application\">3. Wrapping The Model in Web Application</a></span>\n",
    "            <ul class=\"toc-item\">\n",
    "                <li><span><a href=\"#3.1.-Create-an-endpoint-for-predictions\">3.1. Create an endpoint for predictions</a></span></li>\n",
    "                <li><span><a href=\"#3.2.-Incoming-data-validation\">3.2. Incoming data validation</a></span></li>\n",
    "                <li><span><a href=\"#3.3.-Testing-the-API-call\">3.3. Testing the API call</a></span></li>\n",
    "                <li><span><a href=\"#3.4.-Running-web-application\">3.4. Running web application</a></span>\n",
    "                    <ul class=\"toc-item\">\n",
    "                        <li><span><a href=\"#3.4.1.-Preparing-file-structure\">3.4.1. Preparing file structure</a></span></li>\n",
    "                        <li><span><a href=\"#3.4.2.-Run-Flask-development-server\">3.4.2. Run Flask development server</a></span></li>\n",
    "                        <li><span><a href=\"#3.4.3.-Try-it-with-browser\">3.4.3. Try it with browser</a></span></li>\n",
    "                        <li><span><a href=\"#3.4.4.-Try-it-with-Python\">3.4.4. Try it with Python</a></span></li>\n",
    "                        <li><span><a href=\"#3.4.5.-Flask-tricks\">3.4.5. Flask tricks</a></span></li>\n",
    "                    </ul>\n",
    "                </li>\n",
    "            </ul>\n",
    "        </li>\n",
    "        <li><span><a href=\"#4.-Deployment-to-Heroku\">4. Deployment to Heroku</a></span>\n",
    "            <ul class=\"toc-item\">\n",
    "                <li><span><a href=\"#4.1.-Setting-up\">4.1. Setting up</a></span></li>\n",
    "                <li><span><a href=\"#4.2.-Prepare-our-app-for-Heroku\">4.2. Prepare our app for Heroku</a></span>\n",
    "                    <ul class=\"toc-item\">\n",
    "                        <li><span><a href=\"#4.2.1.-Procfile\">4.2.1. Procfile</a></span></li>\n",
    "                        <li><span><a href=\"#4.2.2.-requirements.txt\">4.2.2. requirements.txt</a></span></li>\n",
    "                    </ul>\n",
    "                </li>\n",
    "                <li><span><a href=\"#4.3.-Initialize-local-Git-repository\">4.3. Initialize local Git repository</a></span></li>\n",
    "                <li><span><a href=\"#4.4.-Create-Heroku-application\">4.4. Create Heroku application</a></span></li>\n",
    "                <li><span><a href=\"#4.5.-Pushing-your-application\">4.5. Pushing your application</a></span></li>\n",
    "            </ul>\n",
    "        </li>\n",
    "        <li><span><a href=\"#5.-Missing-Parts\">5. Missing Parts</a></span>\n",
    "            <ul class=\"toc-item\">\n",
    "                <li><span><a href=\"#5.1.-Versioning\">5.1. Versioning</a></span></li>\n",
    "                <li><span><a href=\"#5.2.-Dependency-management\">5.2. Dependency management</a></span></li>\n",
    "                <li><span><a href=\"#5.3.-Containerizing-your-application\">5.3. Containerizing your application</a></span></li>\n",
    "                <li><span><a href=\"#5.4.-Scalability\">5.4. Scalability</a></span></li>\n",
    "                <li><span><a href=\"#5.5.-Persisting-input-data\">5.5. Persisting input data</a></span></li>\n",
    "                <li><span><a href=\"#5.6.-Heavy-workloads-and-models\">5.6. Heavy workloads and models</a></span></li>\n",
    "                <li><span><a href=\"#5.7.-Other-deployment-options\">5.7. Other deployment options</a></span></li>\n",
    "            </ul>\n",
    "        </li>\n",
    "    </ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, you have your brand new shiny ML model and you're impatient to let the world to see and use it :)\n",
    "<br />\n",
    "The model was developed in Jupyter Notebook with Python, but how to make it available for other people and external systems now?\n",
    "<br />\n",
    "That is the question I hope to be answered for you after following this tutorial.\n",
    "<hr>\n",
    "Here's an application containing a model which we are going to develop and deploy: https://mlcourse-tutorial-deployment.herokuapp.com/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1. Who this tutorial is for\n",
    "* novice machine learning practitioners, who seek for an easy way to deploy their models\n",
    "* software engineers who are not familiar with the specifics of deploying machine learning models\n",
    "* anyone who just want to bring their proof-of-concept model online to demonstrate and evaluate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2. What we're gonna use\n",
    "* **scikit-learn** to build the machine learning model\n",
    "* **Joblib** to persist the model\n",
    "* **Flask** as a web microframework and local development server\n",
    "* **Git** as a way to deliver our model and application\n",
    "* **Heroku** as a simple PaaS hosting for our application\n",
    "* a pinch of **HTML/CSS/JavaScript**, to look nice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3. Why is it important to know how to do it?\n",
    "Let's start from the root of the problem - there is still no strict definition of who the Data Scientist is. If you ask ten people with Data Scientist title about their job, you'll probably get ten very different answers, varying from doing Excel calculations to developing the math models behind the [AlphaZero](https://en.wikipedia.org/wiki/AlphaZero).\n",
    "\n",
    "Giving the vague definition of the Data Scientist (by Data Scientists themselves), it's no wonder that many business people don't understand it either :)\n",
    "\n",
    "In ideal world creating the math model, implementing it in a form of machine learning model, making it available for production and deployment/maintenance should be done by different people with different job titles, however it's still not so common case to see. So you must be prepared that _making the model available to use might be a part of your job_.\n",
    "\n",
    "Aside from that, there are other reasons to learn it:\n",
    "\n",
    "* there is a whole world ouside of the Jupyter Notebook. Like it or not, as a good Data Scientist, you have to understand how your model will be used by external systems and what type of problems exist in the area of integration between the applications;\n",
    "* for curious person it's always nice to see what's is going on under the hood of the services which provide ready-to-use ML models through the APIs. We will build our solution using pretty basic tools, so you should get an understanding of how things work on a relatively low level;\n",
    "* ability to deploy your own model is an important skill to add to your Data Scientist toolbox. It's not as hard as some of the machine learning algorithms, but it will make you differ from your colleagues or other job candidates. There is a fancy buzzword for this skill: **productionizing** the model - we'll make a tiny step in this direction;\n",
    "* don't let your model feel alone, let others talk to it :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.4. Disclaimer\n",
    "While the approach presented here is suitable for showing the proof-of-concept and early version of your model, it might not be sufficient for heavy models or very intensive workloads. See <a href=\"#Missing-Parts\">Missing Parts</a> section for details about what need to be considered for more complex and production-ready scenarios."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Building our Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1. Usual Machine Learning workflow\n",
    "Let's see what we usually have in our simple ML workflow and get an idea of what parts need to be deployed:\n",
    "<img src=\"../../img/tutorial-deployment-ml-workflow.png\" />\n",
    "\n",
    "In most cases we have to take **Feature Processing**, **Persisted Model**, **Make Predictions** parts with us.\n",
    "\n",
    "In cases when our ML model supports [online training](https://en.wikipedia.org/wiki/Online_machine_learning), we can also take the **Model Training** part. Essentially, online traning allows to partially train the model with new data only, without the need to re-train it on the whole dataset, which is especially useful for large datasets and complex models. For the sake of simplicity, we will take the offline model.\n",
    "\n",
    "What is **Persisted Model**? After training the most important part of the model is its weights which this model has calculated. Along with the weights it's always nice to have some metadata describing the algorithm which will use these weights, feature processing pipeline, model version, etc - all these things have to be unloaded from the computer memory and put somewhere (disk / database / cloud). These data is called persisted model and it can be loaded back to memory for future use."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2. The Model\n",
    "Since we're mostly interested in stuff around our model, let's take the most _Hello World_'ish dataset in machine learning: [The Iris Dataset](https://archive.ics.uci.edu/ml/datasets/iris). Thankfully, scikit-learn already have it included, so we don't even need to download anything.\n",
    "\n",
    "Let's import the dependencies first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "import joblib\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas.tools.plotting import parallel_coordinates\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline, FeatureUnion\n",
    "from sklearn.preprocessing import PolynomialFeatures, StandardScaler\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "RANDOM_SEED = 17 # because it's the most popular random number between 1 and 20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now load the dataset and take a look at its properties:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature names: ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n",
      "Iris names: ['setosa' 'versicolor' 'virginica']\n",
      "Number of instances: 150\n"
     ]
    }
   ],
   "source": [
    "dataset = load_iris()\n",
    "print('Feature names:', dataset.feature_names)\n",
    "print('Iris names:', dataset.target_names)\n",
    "print('Number of instances:', dataset.data.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature names in this format are not convenient for future use, let's convert them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal_length', 'sepal_width', 'petal_length', 'petal_width']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_names = list(map(lambda x: x.replace(' (cm)', '').replace(' ', '_'), dataset.feature_names))\n",
    "feature_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the dataset into DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width      target\n",
       "16            5.4          3.9           1.3          0.4      setosa\n",
       "78            6.0          2.9           4.5          1.5  versicolor\n",
       "145           6.7          3.0           5.2          2.3   virginica\n",
       "99            5.7          2.8           4.1          1.3  versicolor\n",
       "126           6.2          2.8           4.8          1.8   virginica"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target = np.array([dataset.target_names[x] for x in dataset.target]).reshape(-1, 1)\n",
    "df_full = pd.DataFrame(\n",
    "    np.concatenate([dataset.data, target], axis=1),\n",
    "    columns=feature_names + ['target'])\n",
    "df_full[feature_names] = df_full[feature_names].astype(float)\n",
    "\n",
    "df_full.sample(5, random_state=RANDOM_SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>0.762238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal_length  sepal_width  petal_length  petal_width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.057333      3.758000     1.199333\n",
       "std        0.828066     0.435866      1.765298     0.762238\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_full.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course we can't have Data Science-related tutorial without cool chart, so here's one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 7))\n",
    "parallel_coordinates(df_full, 'target', colormap=plt.get_cmap('cool')) # see - it's cool as promised\n",
    "plt.title('Iris species features with their values')\n",
    "plt.ylabel('cm'); # semicolon to suppress the last line output. Plot will be shown anyway, even without plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's split our data to train and test, having the holdout set of 40%:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((90, 4), (90,), (60, 4), (60,))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train, df_test, y_train, y_test = train_test_split(df_full.drop('target', axis=1), df_full.target, test_size=0.4)\n",
    "df_train.shape, y_train.shape, df_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Giving the toy nature of the dataset we're limited in feature engineering, but to have our **Feature Processing** step in place, let's add polynomial features to our data and then scale all features.\n",
    "\n",
    "Finally, as an estimator we will use plain LogisticRegression.\n",
    "\n",
    "To have all of these steps in one place, let's put them into the pipeline - it will make our life easier later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = Pipeline([\n",
    "    ('features', FeatureUnion([\n",
    "        ('poly', PolynomialFeatures()),\n",
    "        ('scaler', StandardScaler())\n",
    "    ])),\n",
    "    ('logreg', LogisticRegression(random_state=RANDOM_SEED))\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that with its default settings PolynomialFeatures will add 15 new features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1, x0, x1, x2, x3, x0^2, x0 x1, x0 x2, x0 x3, x1^2, x1 x2, x1 x3, x2^2, x2 x3, x3^2'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "', '.join(PolynomialFeatures().fit(df_train).get_feature_names())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit the model and draw a confusion matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hNelK73HAYOBK4M/540OKDMrMrB4FqmrpKimjpBYA3+6CWMzM6lpLyYcIrTBhSLoWWGH4EbFHIRGZmdWpli6sFqrRVoXxyy6LwszMurR5qRorTBgRcWfr43wuqZH56pMR0Vx0YGZm9absnd7t9mHk111MIptTSsBwSQflsx+amVkn6bYVRoXTgDER8SSApJHApcA2RQZmZlZvun2FAfRpTRYAETFbUp8CYzIzq0s9IWE8KOk8/nPh3leAh4oLycysPvWEJqnDgSPIrsUQcBdwdpFBmZnVo5Zy54ukhNEbOKP1hkmSepHNcGhmZp2o7NdhpEwNcivQr2K9H3BLMeGYmdWvqHLpKikJY+XKmyblj1cpLiQzMyujlITxpqSPtq5I2gZ4q7iQzMzqU0uVS1dJ6cOYAFwu6dl8fR1gv+JCMjOrTy0qdx9Gymy1UyVtCmxCNkpqlqcGMTPrfCWfrLbN2Wp3jojbJH1xmac2lkREXFlwbGZmdaU7X7j3X8BtwO7LeS7IbqhkZmadpNtehxERJ+Y/fXc9M7Mu0O2vw5B0pKTVlDlX0sOSxnRFcGZm9aQnXIcxLiJeA8YAQ8ju531KoVGZmdWhFlW3dJWUYbWt4XwemBgRj0klH/tlZtYNdedO71YPSboJWB84TtIAyv97mZl1O912WC1AXkn8CBgMPB0RCyWtSdYsZWZmnajbjpICiIiQdFVEbFOx7WXg5cIjMzOrM2Vvuknp9L5P0naFR2JmVud6wlxSo4GvS5oLvEnWCR4RsWWRgZmZ1Zvozk1Suc8VHoWZmXX/JqmI+CcwHNg5f7wwZT8zM+uYbt8kJelEYFuy2WonAn2Ai4Cdig3NzKy+lH1YbUql8N/AHmT9F0TEs8CAIoMyM7PySenDWJwPrw0ASasWHJOZWV0q+3UYKRXGZEnnAB+QdChwC/D7YsMyM6s/3b4Pgyyeu4HXgJHAjyLi5kKjMjOrQ91+lBRZf8VxwI7AXODxIgMyM6tXRU1vLuk7kmZImi7pUkkrVxNfyrDakyLiw8ARwFDgTkm3VPNmZma2YkVMby5pGPBtYNuI2BzoBXypmvhSmqRavQA8RzaP1JBq3szMzFaswCap3kA/Sc3AKsCz1Rwk5Y57h0u6A7gVGAQc6mlBzMw6XxFNUhExH/gl8AzQBLwaETdVE19KhbEeMCEiHq3mDazchozw3XbLbO6+I2odgnWhliov3ZM0HhhfsakxIhrz51YH9iS7p9ErwOWS9o+Iizr6Pu0mjIj4fkcPamZmHVdtk1SeHBpX8PQuwJyIeBFA0pXAx8lm7OgQzwllZlYSBY2SegbYUdIq+U3xPg08UU18Hen0NjOzAhXR6R0R90v6E/AwsAR4hBVXI21ywjAzK4mipgaJiBOBE9/vcZwwzMxKotpO767ihGFmVhLlThdOGGZmpVH2uaScMMzMSqLsTVIeVmtmZklcYZiZlUS56wsnDDOz0nAfhpmZJSl7H4YThplZSZQ7XThhmJmVhpukzMwsSZS8xnDCMDMrCVcYZmaWxJ3eZmaWpNzpwgnDzKw0XGGYmVkS92GYmVkSj5IyM7MkrjDMzCyJKwwzM0viCsPMzJK0RLkrDN9AyczMkrjCMDMriXLXF04YZmal4Qv3zMwsiUdJmZlZEo+SMjOzJG6SMjOzJG6SMjOzJG6SMjOzJFHyC/ecMMzMSsJ9GGZmlsRNUmZmlsSd3mZmlsRNUmZmlsSd3mZmlqTsfRie3rwDdh0zihnT72LWzCl875gjah2OVTjr7J8ze8793PvADbUOxXL9xh3NgDMup/9Pf/+u7X0/vRf9/3ci/X92Livve2iNoiunqPK/ruKEkaihoYEzzziZ3Xbfny22Gs1+++3FZpttXOuwLHfpxVeyz17jah2GVVg85UbePP24d23rtelW9Nn647zxo/G8cfzXePuvl9counJqIapaUkjqJekRSddVG58TRqLtt9uap56ay5w5z9Dc3MzkyVezx+671josy917z1QWLHil1mFYhaWzpxFvvP6ubX1H78GiGy6DJc0AxOs+Z13oSOCJ93OA5IQhaYikD7Yu7+dNu6Ohw9bmX/OefWd93vwmhg5du4YRmXU/vdYeRu+Rm7Pq8Wex6rGn0Wv9TWodUqlERFVLeyStC3wBOPf9xNduwpC0h6S/A3OAO4G5wF/ez5t2R5Les63sIxrMSqehF1plAG/+7FssmtzIKocfX+uISqXaJilJ4yU9WLGMX+bQvwa+x/vsV08ZJfVTYEfglojYWtJo4Mtt7ZAHOx5AvQbS0LDq+4mxFObPa2L4ukPfWV932Do0NT1fw4jMup+WBS/R/NAUAJbOeZKIQAMGEq+/WuPIyqHaDuyIaAQal/ecpN2AFyLiIUmjqo8urUmqOSJeBhokNUTE7cBH2tohIhojYtuI2LYnJAuAqQ8+ykYbrc+IEcPp06cPY8fuybXX3VTrsMy6lSUP30PvzbKPj4a1hqHevZ0sKrREVLW0YydgD0lzgcuAnSVdVE18KRXGK5L6A3cBF0t6AVhSzZt1Z0uXLuXICcdzw/WX0KuhgQsm/ZGZM2fXOizLnTvxV+z0yR1Yc83Vmf7kFE45+QwuutAjcGqp32E/oPemW6H+Axlw2qUsumoSi+/+K/2+enQ21HbpEhae+4tah1kqRTRyR8RxwHEAeYVxdETsX82x1F47vKRVgbfIqpGvAAOBi/Oqo129+w5zQ3+JDejbr9YhWBvm7jui1iFYOwZOvOW9HZxV2mnYzlV9Xt4z/7akGCoSxm7VvE9KhTEEaIqIRcAkSf2AtYCkhGFmZmmKnksqIu4A7qh2/5Q+jMt5d8/60nybmZl1oqKG1XaWlAqjd0Qsbl2JiMWS+hYYk5lZXSr7bLUpFcaLkvZoXZG0J/BScSGZmdWnss8llVJhfJ1sdNRvAAH/Ag4sNCozszpU9ouB200YEfEUsGM+tFYR8Xp7+5iZWceVvUlqhQlD0v4RcZGk7y6zHYCIOL3g2MzM6kp3rjBaL9Ee0BWBmJnVu25bYUTEOfnPk7ouHDOz+tWVHdjVaLcPQ9Jg4FBgROXrI8J3qzEz60QJ80LVVMooqauBu4FbyC7aMzOzOpSSMFaJiGMLj8TMrM6VvUkq5cK96yR9vvBIzMzqXEHTm3ealArjSOAHkt4Gmsku3ouIWK3QyMzM6kzZK4yUC/c8rNbMrAt0205vSZtGxCxJH13e8xHxcHFhmZnVn+5cYXyX7L7cpy3nuQB2LiQiM7M61W0rjIgYn/8c3XXhmJnVr+5cYQAg6YvL2fwqMC0iXuj8kMzM6lNES/svqqGUUVJfBT4G3J6vjwLuA0ZK+klE/KGg2MzM6kq3nUuqQguwWUQ8DyBpLeC3wA7AXYAThplZJ+jOs9W2GtGaLHIvACMj4t+SmguKy8ys7vSECuNuSdcBl+frewN3SVoVeKWwyMzM6kxPqDCOAL4IfILsKu8LgSsi+808gsrMrJN022G1AJJ6ATdGxC7AFV0TkplZferWw2ojYqmkhZIGRsSrXRWUmVk96glNUouAaZJuBt5s3RgR3y4sKjOzOtQTOr2vzxczMytQt68wImJSVwRiZmbl1tZstZMjYqykafDeOikitiw0MjOzOtOdR0kdmf+cCDwA/Kv4cMzM6le3bZKKiKb84QDgHODfwGXAn5a58tvMzDpB2Tu9272nd0ScFBEfJruAbyhwp6RbCo/MzKzORERVS1dJGSXV6gXgOeBlYEgx4ZiZ1a+y92G0W2FIOlzSHcCtwCDgUHd4m5l1vqjyv66SUmGsB0yIiEeLDsbMrJ6VvcJIuQ7j+10RiJlZvSv7KKl2m6TMzKxrFNUkJemzkp6U9A9JVRcBHen0NjOzAhVRYeSzjv8/4DPAPGCqpGsiYmZHj+WEYWZWEgU1SW0P/CMingaQdBmwJ9DhhOEmKTOzkogql3YM490zdczLt3VY4RXGksXzVfR7dCVJ4yOisdZx2PL5/JSfz9GKVft5KWk8ML5iU2PF33h5x6yqlHGF0XHj23+J1ZDPT/n5HHWyiGiMiG0rlsqEPA8YXrG+LvBsNe/jhGFm1rNNBTaWtL6kvsCXgGuqOZA7vc3MerCIWCLpm8CNQC/g/IiYUc2xnDA6zm2v5ebzU34+R10sIm4Abni/x1HZryw0M7NycB+GmZklccJoh6SDJQ2tdRy2YpJ+ImmXKvYbJem6ImLqqSQNlfSnKvY7V9KH2nnN1yUdWH10VjQ3SbUjn9r96Ih4sNax1DNJIvv/taUTjzmK7Nzulvj63hGxpLPevyfx36Y+1GWFIWlVSddLekzSdEn7SdpG0p2SHpJ0o6R1JO0DbAtcLOlRSf0kfVrSI5KmSTpf0kr5MU+RNFPS45J+mW/bXdL9+etvkbRWLX/vMpB0qqRvVKz/WNJRko6RNDX/+52UPzdC0hOSzgYeBoZLuiA/Z9MkfSd/3QX5uULSdpLuzc/tA5IGSFpZ0sR8n0ckjV5OXGtIuip///skbVkRX6Okm4ALu+BPVBptnKvp+frBki6XdC1wk6QGSWdLmiHpOkk3VJyXOyRtmz9+Q9LJ+Tm6r/XfRX78o/PHG+X/Zh6T9LCkDSX1l3Rrvj5N0p5d/kepd9XeErA7L8DewO8r1gcC9wKD8/X9yIaeAdwBbJs/XpnsEvuR+fqFwARgDeBJ/lOxfSD/uXrFtq8Bp9X6d6/1AmwN3FmxPhM4kGzkjMi+xFwHfAoYAbQAO+av3Qa4uWLf1r/zBcA+QF/gaWC7fPtqZCMBjwIm5ts2BZ7Jz+Uo4Lp8+1nAifnjnYFH88c/Bh4C+tX6b1eSc/UpYHq+fjDZRWFr5Ov7kI3EaQDWBhYA++TPVf47CmD3/PEvgOMr/tZH54/vB/47f7wysEp+LlfLtw0C/tH678tL1yz1Oqx2GvBLSaeSfTgtADYHbs5aPugFNC1nv02AORExO1+fRHav898Ai4BzJV2fHxOyKyr/KGkdsg+zOcX8Ot1HRDwiaUjeLzSY7G+/JTAGeCR/WX9gY7IP9n9GxH359qeBDSSdBVwP3LTM4TcBmiJiav5erwFI+gRZQiAiZkn6JzBymX0/QfZFgoi4TdKakgbmz10TEW+9/9++e1nBuXpmmZfdHBH/zh9/Arg8smbD5yTdvoJDL+Y//0YeIptF9R2SBgDDIuLPeRyL8u19gP+V9CmyLxLDgLXIbh1tXaAuE0ZEzJa0DfB54OfAzcCMiPhYO7sud56XyC6M2R74NNlVlN8k+5Z6FnB6RFyTt5f/uHN+g27vT2TfRtcGLiOrJH4eEedUvkjSCODN1vWIWCBpK2BXskQ9FhhXuQvLnyMnZX6etubbeXM5z9WLZc/Vsir/NqnzIDVHXiYAS3nv59CKjvMVssS1TUQ0S5pLVn1YF6nXPoyhwMKIuAj4JbADMFjSx/Ln+0j6cP7y14EB+eNZwAhJG+XrBwB3SuoPDIzs4pgJwEfy5wcC8/PHBxX5O3Uzl5El1n3IPpBuBMblf0ckDZM0ZNmdJA0CGiLiCuAE4KPLvGQWMFTSdvnrB0jqDdxF9mGDpJHAB8maECtVvmYU8FJrhVLnlj1XbZkC7J33ZaxF1uTXYfnffZ6kvQAkrSRpFbJ/Ty/kyWI02e2jrQvVZYUBbAH8n6QWoBk4HFgCnJk3Q/QGfg3MIGsf/52kt4CPAYcAl+cfRFOB35H1YVwtaWWyb0ffyd/nx/lr5wP3Aet3yW9XchExI292mB8RTUCTpM2Av+VNgm8A+5N9+6w0DJgoqfWLznHLHHexpP2AsyT1A94CdgHOJjuH08jO88ER8Xb+Xq1+nB/7cWAhTvDAe89VXvWtyBVkVfZ0YDZZP8SrVb71AcA5kn5C9m90X+Bi4FpJDwKPkn1BsC7kYbVm1mkk9Y+INyStCTwA7BQR7mPoIeq1wjCzYlwn6QNkgzx+6mTRs7jCMDOzJHXZ6W1mZh3nhGFmZkmcMMzMLIkThpmZJXHCMDOzJE4YZmaW5P8DzuOcYRAHRpwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pipeline.fit(df_train, y_train)\n",
    "pred = pipeline.predict(df_test)\n",
    "plt.figure(figsize=(7, 5))\n",
    "sns.heatmap(confusion_matrix(pred, y_test),\n",
    "            annot=True,\n",
    "            xticklabels=dataset.target_names,\n",
    "            yticklabels=dataset.target_names);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And yay, we've got pretty good results :)\n",
    "\n",
    "Let's make some predictions based on the cool chart above, like if we did real measurements. Take the values in such a way that we will get a _setosa_ class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['virginica'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.predict(pd.DataFrame([{\n",
    "    'sepal_length': 4.0,\n",
    "    'sepal_width': 5.0,\n",
    "    'petal_length': 1.0,\n",
    "    'petal_width': 0.5\n",
    "}]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hmmm, it's not _setosa_, so what happened? Let's check how the DataFrame is constructed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   petal_length  petal_width  sepal_length  sepal_width\n",
       "0           1.0          0.5           4.0          5.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([{\n",
    "    'sepal_length': 4.0,\n",
    "    'sepal_width': 5.0,\n",
    "    'petal_length': 1.0,\n",
    "    'petal_width': 0.5\n",
    "}])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the problem: when initializing from a dictionary, the order of the columns in DataFrame is not guaranteed to be the same as in dictionary passed. Although note that we actually do not need DataFrame here - we can pass simple list without column names, but DataFrame is better for readability and our convenience.\n",
    "\n",
    "The workaround is to pass the ```columns=dictionary.keys()``` to DataFrame constructor or just select the DataFrame columns before doing the predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_predict = pd.DataFrame([{\n",
    "    'sepal_length': 4.0,\n",
    "    'sepal_width': 5.0,\n",
    "    'petal_length': 1.0,\n",
    "    'petal_width': 0.5\n",
    "}])\n",
    "pipeline.predict(df_predict[feature_names])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, now we're good, but let's remember this behaviour to workaround it later as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3. Persisting The Model\n",
    "After the training model have to be persisted. Basically, it's done by serializing the model state from the memory to file on disk in some specific format. It's common to use [pickle](https://docs.python.org/3/library/pickle.html) binary serialization in Python for that, but due to [performance](https://scikit-learn.org/stable/modules/model_persistence.html) reasons we will use [joblib](https://joblib.readthedocs.io/en/latest/persistence.html) (which still use pickle internally).\n",
    "\n",
    "Note that both pickle and joblib have problems with security - you have to be sure that the file you're trying to deserialize was not replaced by an attacker because it's possible to force Python to execute malicious code during deserialization. You can see how it works with more details [here](https://rushter.com/blog/pickle-serialization-internals/).\n",
    "\n",
    "What exactly have to be persisted in our case? Obviously, the following things need to be saved:\n",
    "* the model itself which is ```LogisticRegression``` estimator in our case - it contains the weights and logic on how to process these weights;\n",
    "* feature processing steps which represented by ```PolynomialFeatures``` and ```StandardScaler``` - saving them will allow us to process date for predictions in exactly the same manner as we did for training.\n",
    "\n",
    "Not so obvious, but still nice to have:\n",
    "* model version - for our own convenience and to be able to distinguish different models between each other;\n",
    "* incoming feature names or any other info which describe input and/or output data.\n",
    "\n",
    "### 2.3.1. scikit-learn Pipelines\n",
    "Before we proceed, let's pretend that we have our ```PolynomialFeatures``` and ```StandardScaler``` as separate objects. In this case we would need to save them and ```LogisticRegression``` as separate entities which introduce too much hassle, especially if our model eventually grow and other feature processing steps will be added.\n",
    "\n",
    "So, there are many good reasons to use the scikit-learn pipelines and here is another one: we can persist just our ```Pipeline``` object and have all of the steps stored as a single entity in one file.\n",
    "\n",
    "### 2.3.2. Python and library versions\n",
    "Also when serializing your model you have to be aware that serialized object might not load in different version of Python or with another version of the library, classes of which were serialized. That's why it's always good to fix and keep the Python and all of the related library versions along with the model. We will do it in this tutorial.\n",
    "\n",
    "### 2.3.3. Wrapping all of the model data\n",
    "Let's create a class which will contain a model and all additional metadata:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "\n",
    "class IrisModel:\n",
    "    def __init__(self, pipeline: Pipeline, version='unknown', input_features=[], class_names=[]):\n",
    "        self.pipeline = pipeline\n",
    "\n",
    "        self.version = version\n",
    "        self.input_features = input_features\n",
    "        self.class_names = class_names\n",
    "\n",
    "    def predict(self, data: pd.DataFrame) -> np.ndarray:\n",
    "        data = data[\n",
    "            self.input_features]  # this is the workaround for the problem with DataFrame fields order, see above\n",
    "        return self.pipeline.predict(data)\n",
    "\n",
    "    # pretty-print our class in Jupyter notebook and when we're converting it to string\n",
    "    def __repr__(self):\n",
    "        return (f'{self.__class__.__name__}('\n",
    "                f'{self.pipeline!r}, '\n",
    "                f'version={self.version!r}, '\n",
    "                f'input_features={self.input_features!r}, '\n",
    "                f'class_names={self.class_names!r})')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Put the content of the cell above into file named **iris_model.py** and save it to the same directory from where you're running this notebook. Saving this class to file (which become a Python module actually) is necessary to workaround problem with persistence of classes which are not the part of any module.\n",
    "\n",
    "Now create the instance of this class, fill with our data and serialize to file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['iris-model-v0.1.jl']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from iris_model import IrisModel\n",
    "\n",
    "iris_model = IrisModel(pipeline,\n",
    "                       version='0.1',\n",
    "                       input_features=df_train.columns.values,\n",
    "                       class_names=dataset.target_names)\n",
    "\n",
    "joblib.dump(iris_model, 'iris-model-v%s.jl' % iris_model.version)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Wrapping The Model in Web Application"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we're ready to build our web application which will serve our model to outside world. Let's take [Flask](http://flask.pocoo.org/) as a web-framework because of its simplicity, small CPU and memory footprint and easy portability.\n",
    "\n",
    "This web application will expose a basic Web API which will be available for browsers and any other 3rd-party system to use over the HTTP protocol. So it will have its own URL which you can directly type in the browser or call programmatically using any programming language, not necessarily Python.\n",
    "\n",
    "Also, nowadays it's common to use [JSON](https://en.wikipedia.org/wiki/JSON) as a default format for most of the Web APIs, so we will stick to it as well.\n",
    "\n",
    "First, install the Flask itself:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -U flask"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import necessary dependencies and initialize Flask application:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from flask import Flask, request, jsonify, make_response\n",
    "from flask.testing import FlaskClient\n",
    "from werkzeug import Request\n",
    "\n",
    "app = Flask(__name__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load persisted model wrapper into the variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IrisModel(Pipeline(memory=None,\n",
       "     steps=[('features', FeatureUnion(n_jobs=None,\n",
       "       transformer_list=[('poly', PolynomialFeatures(degree=2, include_bias=True, interaction_only=False)), ('scaler', StandardScaler(copy=True, with_mean=True, with_std=True))],\n",
       "       transformer_weights=None)), ('logreg', LogisticRegression(C=1.0, cla..., penalty='l2', random_state=17, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False))]), version='0.1', input_features=array(['sepal_length', 'sepal_width', 'petal_length', 'petal_width'],\n",
       "      dtype=object), class_names=array(['setosa', 'versicolor', 'virginica'], dtype='<U10'))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IRIS_MODEL = joblib.load('iris-model-v0.1.jl')\n",
    "IRIS_MODEL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try to predict with the same values as we already did for pipeline before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa'], dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_predict = pd.DataFrame([{\n",
    "    'sepal_length': 4.0,\n",
    "    'sepal_width': 5.0,\n",
    "    'petal_length': 1.0,\n",
    "    'petal_width': 0.5\n",
    "}])\n",
    "IRIS_MODEL.predict(df_predict[feature_names])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1. Create an endpoint for predictions\n",
    "Flask, as a typical web framework allows us to expose _endpoints_ with their specific URLs, _routes_, and bind these routes to our custom methods which will perform the actions we need and return their results.\n",
    "\n",
    "Let's create the ```/predict``` endpoint:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "app = Flask(__name__)\n",
    "\n",
    "@app.route('/predict', methods=['POST']) # Flask decorator to mark the method which will be called when application recieved\n",
    "                                         # an HTTP request to /predict URL using the POST method\n",
    "def predict():\n",
    "    # here we expect an HTTP request body with the following data in JSON format:\n",
    "    # [ {\"sepal_length\": 4.0, \"sepal_width\": 5.0, \"petal_length\": 1.0, \"petal_width\": 0.5},\n",
    "    #   {\"sepal_length\": 3.0, \"sepal_width\": 3.0, \"petal_length\": 2.0, \"petal_width\": 4} ]\n",
    "    data = request.get_json() # get the request body as a Python list of dictionaries\n",
    "\n",
    "    df_predict = pd.DataFrame(data)\n",
    "    predictions = IRIS_MODEL.predict(df_predict)\n",
    "    predictions = predictions.tolist()\n",
    "    \n",
    "    return jsonify({\n",
    "        'version': IRIS_MODEL.version, # return model version to calling side\n",
    "        'status': 'success',           # indicate that the call went well\n",
    "        'predictions': predictions     # actual predictions, [\"setosa\", \"virginica\"]\n",
    "    })"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you see, our endpoint is represented by ```/predict``` route which is bound to ```predict()``` method.\n",
    "* this method is called by Flask after it processed the incoming HTTP request and found which method is responsible for further processing;\n",
    "* we take the JSON from the body of HTTP request, convert it to the list of Python dictionaries, feed them into DataFrame and make a predictions;\n",
    "* finally, this method convert the predictions to JSON format and return them back to the Flask;\n",
    "* Flask forms HTTP response and return it back to the calling side (browser or another application).\n",
    "\n",
    "As a rule of thumb, it's better to make your prediction endpoints to work with multiple predictions at once, due to:\n",
    "* performance reasons - multiple predictions are generally done faster;\n",
    "* convenience - clients of your API will be able to work with just one endpoint by sending both single and multiple prediction requests to it.\n",
    "\n",
    "## 3.2. Incoming data validation\n",
    "As you see, the incoming format of the data is pretty complex and there are many ways to send something incorrect or even malicious. For this we need to have some sort of data validation to be sure that we actually can process the input data without any problems.\n",
    "\n",
    "But since it's a very broad topic we will skip it. Anyway, take time to learn the one of the following ways to validate JSON in Python: [Cerebrus](http://docs.python-cerberus.org/en/stable/), [jsonschema](https://python-jsonschema.readthedocs.io/en/latest/).\n",
    "\n",
    "## 3.3. Testing the API call\n",
    "That's another broad topic, actually :) In most cases when your application grows, you'd have to use unit tests ([unittest](https://docs.python.org/3/library/unittest.html), [pytest](https://docs.pytest.org/en/latest/)) to check if it's behaving as expected, especially when it's being refactored or actively developed.\n",
    "\n",
    "Here we will do some basic checks using the ```FlaskClient``` and asserts, which still work inside the Jupyter notebook and will allow you to debug your endpoint:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([{'sepal_length': 6.4,\n",
       "   'sepal_width': 2.7,\n",
       "   'petal_length': 5.3,\n",
       "   'petal_width': 1.9},\n",
       "  {'sepal_length': 4.8,\n",
       "   'sepal_width': 3.4,\n",
       "   'petal_length': 1.6,\n",
       "   'petal_width': 0.2},\n",
       "  {'sepal_length': 5.1,\n",
       "   'sepal_width': 3.4,\n",
       "   'petal_length': 1.5,\n",
       "   'petal_width': 0.2}],\n",
       " '---',\n",
       " ['virginica', 'setosa', 'setosa'])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# prepare some test data\n",
    "test_objects = df_train.sample(3, random_state=RANDOM_SEED)\n",
    "\n",
    "expected_results = IRIS_MODEL.predict(test_objects).tolist()\n",
    "expected_results = expected_results\n",
    "\n",
    "data_to_send = [row.to_dict() for i, row in test_objects.iterrows()]\n",
    "\n",
    "data_to_send, '---', expected_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'done'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# normal successful request\n",
    "with app.test_client() as client: # client is a FlaskClient object\n",
    "    response = client.post('/predict', json=data_to_send)\n",
    "    \n",
    "    assert response.status_code == 200\n",
    "    assert response.get_json()['status'] == 'success'\n",
    "    assert response.get_json()['predictions'] == expected_results\n",
    "'done'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.4. Running web application"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, now we have all components in place to be able to run our web app and make our first real call to it.\n",
    "\n",
    "### 3.4.1. Preparing file structure\n",
    "We can't stay inside the Python notebook anymore, so we need to put or code in separate files according to the Flask-compatible structure:\n",
    "```\n",
    "+---static\n",
    "|       irises.png\n",
    "|       main.css\n",
    "+---templates\n",
    "|       index.html       # we'll have a '/' route for this page - it will be the start page of our site\n",
    "|                        # this is not needed for the model to work\n",
    "|                        # it's just a simple UI to play with our Flask endpoint from the browser\n",
    "|   iris-model-v0.1.jl   # our previously saved model, just copy it here\n",
    "|   iris_app.py\n",
    "|   iris_model.py        # use the same file which you should already have (see \"Wrapping all of the model data\" section)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### static/irises.png\n",
    "<img src=\"../../img/tutorial-deployment-irises.png\" width=\"700\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### static/main.css\n",
    "```css\n",
    "html,\n",
    "body {\n",
    "    height: 100%;\n",
    "}\n",
    "\n",
    "body {\n",
    "    display: -ms-flexbox;\n",
    "    display: flex;\n",
    "    -ms-flex-align: center;\n",
    "    align-items: center;\n",
    "    padding-top: 40px;\n",
    "    padding-bottom: 40px;\n",
    "}\n",
    "\n",
    ".form-predict {\n",
    "    width: 100%;\n",
    "    max-width: 700px;\n",
    "    padding: 15px;\n",
    "    margin: 0 auto auto;\n",
    "}\n",
    "\n",
    ".form-predict .form-control {\n",
    "    position: relative;\n",
    "    box-sizing: border-box;\n",
    "    height: auto;\n",
    "    padding: 10px;\n",
    "    font-size: 16px;\n",
    "}\n",
    "\n",
    ".form-predict .form-control:focus {\n",
    "    z-index: 2;\n",
    "}\n",
    "\n",
    ".result span {\n",
    "    color: green;\n",
    "}```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### templates/index.html\n",
    "\n",
    "```html\n",
    "<!DOCTYPE html>\n",
    "<html lang=\"en\">\n",
    "<head>\n",
    "    <meta charset=\"UTF-8\">\n",
    "    <title>mlcourse.ai tutorial demo application</title>\n",
    "    <link rel=\"stylesheet\" href=\"https://stackpath.bootstrapcdn.com/bootstrap/4.1.2/css/bootstrap.min.css\"\n",
    "          integrity=\"sha384-Smlep5jCw/wG7hdkwQ/Z5nLIefveQRIY9nfy6xoR1uRYBtpZgI6339F5dgvm/e9B\" crossorigin=\"anonymous\">\n",
    "    <link rel=\"stylesheet\" href=\"static/main.css\"/>\n",
    "</head>\n",
    "<body class=\"text-center\">\n",
    "<form class=\"form-predict\" id=\"predict_form\">\n",
    "    <h1 class=\"h1\">Iris Classification</h1>\n",
    "\n",
    "    <img class=\"mb-4\" src=\"static/irises.png\" alt=\"\" width=\"100%\"/>\n",
    "\n",
    "    <hr/>\n",
    "\n",
    "    <h3 class=\"h3 mb-3 font-weight-normal\">Enter measured characteristics</h3>\n",
    "\n",
    "    <div class=\"form-group row\">\n",
    "        <label for=\"sepal_length\" class=\"col-sm-3 col-form-label\">Sepal Length</label>\n",
    "        <div class=\"col-sm-8\">\n",
    "            <input type=\"range\" min=\"4.3\" max=\"7.9\" step=\"0.1\" value=\"5.4\" id=\"sepal_length\" class=\"form-control\">\n",
    "        </div>\n",
    "        <span id=\"sepal_length_output\" class=\"col-sm-1 col-form-label\"></span>\n",
    "    </div>\n",
    "\n",
    "    <div class=\"form-group row\">\n",
    "        <label for=\"sepal_width\" class=\"col-sm-3 col-form-label\">Sepal Width</label>\n",
    "        <div class=\"col-sm-8\">\n",
    "            <input type=\"range\" min=\"2.0\" max=\"4.4\" step=\"0.1\" value=\"3.7\" id=\"sepal_width\" class=\"form-control\">\n",
    "        </div>\n",
    "        <span id=\"sepal_width_output\" class=\"col-sm-1 col-form-label\"></span>\n",
    "    </div>\n",
    "\n",
    "    <div class=\"form-group row\">\n",
    "        <label for=\"petal_length\" class=\"col-sm-3 col-form-label\">Petal Length</label>\n",
    "        <div class=\"col-sm-8\">\n",
    "            <input type=\"range\" min=\"1.1\" max=\"6.9\" step=\"0.1\" value=\"1.5\" id=\"petal_length\" class=\"form-control\">\n",
    "        </div>\n",
    "        <span id=\"petal_length_output\" class=\"col-sm-1 col-form-label\"></span>\n",
    "    </div>\n",
    "\n",
    "    <div class=\"form-group row\">\n",
    "        <label for=\"petal_width\" class=\"col-sm-3 col-form-label\">Petal Width</label>\n",
    "        <div class=\"col-sm-8\">\n",
    "            <input type=\"range\" min=\"0.1\" max=\"2.5\" step=\"0.1\" value=\"0.2\" id=\"petal_width\" class=\"form-control\">\n",
    "        </div>\n",
    "        <span id=\"petal_width_output\" class=\"col-sm-1 col-form-label\"></span>\n",
    "    </div>\n",
    "\n",
    "    <button class=\"btn btn-lg btn-primary btn-block\" type=\"submit\">Predict</button>\n",
    "\n",
    "    <br/>\n",
    "\n",
    "    <h2 id=\"result\" class=\"h2 result\"></h2>\n",
    "</form>\n",
    "<script type=\"text/javascript\">\n",
    "    const PREDICT_URL = '/predict';\n",
    "\n",
    "    // look ma, no jQuery!\n",
    "    (function () {\n",
    "        let ranges = document.querySelectorAll('input[type=range');\n",
    "        ranges.forEach(range => {\n",
    "            range.addEventListener('input', _ => {\n",
    "                document.getElementById(range.id + '_output').innerText = range.value;\n",
    "            });\n",
    "            document.getElementById(range.id + '_output').innerText = range.value;\n",
    "        });\n",
    "\n",
    "        let result = document.getElementById('result');\n",
    "\n",
    "        document.getElementById('predict_form').addEventListener('submit', (evt) => {\n",
    "            evt.preventDefault();\n",
    "\n",
    "            result.innerHTML = '...';\n",
    "\n",
    "            let data = {};\n",
    "            ranges.forEach(range => {\n",
    "                data[range.id] = parseFloat(range.value);\n",
    "            });\n",
    "\n",
    "            fetch(PREDICT_URL, {\n",
    "                method: 'POST',\n",
    "                body: JSON.stringify([data]),\n",
    "                headers: {\n",
    "                    'Content-Type': 'application/json'\n",
    "                }\n",
    "            }).then(response => {\n",
    "                return response.json();\n",
    "            }).then(response => {\n",
    "                result.innerHTML = 'Looks like it\\'s <span>' + response.predictions[0].toUpperCase() + '</span>';\n",
    "            }).catch(alert);\n",
    "        })\n",
    "    })();\n",
    "</script>\n",
    "</body>\n",
    "</html>```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### iris_app.py\n",
    "\n",
    "```python\n",
    "import joblib\n",
    "import pandas as pd\n",
    "from flask import Flask, request, jsonify, render_template\n",
    "\n",
    "app = Flask(__name__)\n",
    "\n",
    "IRIS_MODEL = joblib.load('iris-model-v0.1.jl')\n",
    "\n",
    "\n",
    "@app.route('/')\n",
    "def index():\n",
    "    return render_template('index.html')\n",
    "\n",
    "\n",
    "@app.route('/predict', methods=['POST']) # Flask decorator to mark the method which will be called when application recieved\n",
    "                                         # an HTTP request to /predict URL using the POST method\n",
    "def predict():\n",
    "    # here we expect an HTTP request body with the following data in JSON format:\n",
    "    # [ {\"sepal_length\": 4.0, \"sepal_width\": 5.0, \"petal_length\": 1.0, \"petal_width\": 0.5},\n",
    "    #   {\"sepal_length\": 3.0, \"sepal_width\": 3.0, \"petal_length\": 2.0, \"petal_width\": 4} ]\n",
    "    data = request.get_json() # get the request body as a Python list of dictionaries\n",
    "\n",
    "    df_predict = pd.DataFrame(data)\n",
    "    predictions = IRIS_MODEL.predict(df_predict)\n",
    "    predictions = predictions.tolist()\n",
    "\n",
    "    return jsonify({\n",
    "        'version': IRIS_MODEL.version, # return model version to calling side\n",
    "        'status': 'success',           # indicate that the call went well\n",
    "        'predictions': predictions     # actual predictions, [\"setosa\", \"virginica\"]\n",
    "    })```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4.2. Run Flask development server\n",
    "In addition to its web framework capabilities, Flask includes its own development server which is installed along with Python package. This server is not supposed to be used in production because of its pestability and scaling problems, but it works pretty well during the application development.\n",
    "\n",
    "First, open the command prompt in the folder where you put all of the files above.\n",
    "\n",
    "To run Flask on our local machine, we need to pass some settings to it using the environment variables. Here is how to do it on Windows:\n",
    "> set FLASK_APP=iris_app.py && set FLASK_ENV=development && flask run\n",
    "\n",
    "```\n",
    "* Serving Flask app \"iris_app.py \"\n",
    "* Environment: development\n",
    "* Debug mode: off\n",
    "* Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4.3. Try it with browser\n",
    "\n",
    "Open the URL which Flask gave to you and you should see something like that:\n",
    "<img src=\"../../img/tutorial-deployment-web-app-screen.png\" width=\"700\" />\n",
    "\n",
    "Feel free to play with it and make your own predictions. Refer to cool chart from the model building section to input meaningful data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4.4. Try it with Python\n",
    "\n",
    "As was said previously, you don't have to use a browser to call your web application endpoint. So let's do it with Python.\n",
    "\n",
    "Assuming that the Flask development server is running, execute the following code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'predictions': ['setosa'], 'status': 'success', 'version': '0.1'}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import requests\n",
    "\n",
    "response = requests.post('http://127.0.0.1:5000/predict', json=[{\n",
    "    'sepal_length': 5.0,\n",
    "    'sepal_width': 3.5,\n",
    "    'petal_length': 1.3,\n",
    "    'petal_width': 0.3\n",
    "}])\n",
    "\n",
    "response.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4.5. Flask tricks\n",
    "When you change the application files while Flask server is running, you have to restart it since it does not load the changes from the disk automatically. Fortunately, there is an option to force Flask to watch file changes (it enables debugging, but automatic reload is a nice side effect during development):\n",
    "\n",
    "> set FLASK_APP=iris_app.py && set FLASK_ENV=development **&& set FLASK_DEBUG=1** && flask run\n",
    "\n",
    "\n",
    "If you run Flask with the host parameter you should be able to show your application to your colleague if you in the same LAN and your port ```5000``` is open:\n",
    "\n",
    "> set FLASK_APP=iris_app.py && set FLASK_ENV=development && flask run **-h 0.0.0.0**\n",
    "\n",
    "Now you can proudly hold this badge :) <img src=\"../../img/tutorial-deployment-works-on-my-machine.png\" />\n",
    "\n",
    "Which is, yeah, definitely awesome, but we'd like to do solutions which works somewhere else, right? So let's go on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Deployment to Heroku"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Heroku](https://www.heroku.com/) is a Platform as a Service (PaaS) cloud provider which, by its PaaS nature, allow to skip complex procedures of application deployment while still providing good customization support.\n",
    "\n",
    "What is also good about Heroku is that it allows you to run one instance of your web application for free, which makes it a platform of choice for proof of concepts or small applications.\n",
    "\n",
    "## 4.1. Setting up\n",
    "\n",
    "First, do these things:\n",
    "* [create an account](https://signup.heroku.com/) on Heroku\n",
    "* [download](https://devcenter.heroku.com/articles/heroku-cli) and install Heroku CLI, which allows you to work with the platform straight from command line\n",
    "\n",
    "Once the Heroku CLI is installed, introduce your installation to Heroku by running\n",
    "> heroku login\n",
    "\n",
    "which, dependig on your platform, open a browser to perform login or ask for your Heroku login and password in the command line."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2. Prepare our app for Heroku\n",
    "When Heroku receive your application files, it need to recognize what type of application your are deploying and how to run it. Often it's done automatically by analyzing the application files, but better to provide exact definition of what we're need Heroku to do.\n",
    "\n",
    "Let's create couple of files in the app root directory which will tell Heroku how to work with our application.\n",
    "\n",
    "```\n",
    "|   Procfile\n",
    "|   requirements.txt\n",
    "```\n",
    "\n",
    "### 4.2.1. Procfile\n",
    "This is the file which Heroku use to get the commands to execute to start your application and do some post-start actions.\n",
    "Here is such file for our application:\n",
    "```\n",
    "web: gunicorn iris_app:app --log-file -\n",
    "```\n",
    "Yes, just a single line. It tells Heroku the following:\n",
    "* our application must be run as ```web``` appication;\n",
    "* use ```gunicorn``` as a web server (remember that Flask server is for development only, so we switched to [gunicorn](https://gunicorn.org/)) and pass path to our application within the specific module;\n",
    "* ```--log-file -``` is an option for gunicorn to output the log information straight to STDOUT - it will be then caught by Heroku and provided as a part of Heroku own logging mechanism.\n",
    "\n",
    "### 4.2.2. requirements.txt\n",
    "This file is not Heroku specific - it's a common file used in Python ecosystem to keep the list of the dependencies required for application or library to run. Heroku is able to recognize this file and will requested dependencies while deploying your application.\n",
    "\n",
    "Here is it:\n",
    "```\n",
    "pandas==0.22.0\n",
    "numpy==1.15.4\n",
    "scikit-learn==0.20.1\n",
    "joblib==0.13.0\n",
    "Flask==1.0.2\n",
    "gunicorn==19.9.0\n",
    "```\n",
    "Note that I've added ```gunicorn``` manually - it does not have to be installed on a local machine, but we need it to be installed on Heroku.\n",
    "You can see and save the libraries that you have in your Python installation (or, which is way better, in your [virtualenv](https://virtualenv.pypa.io/en/latest/)) by running the following command:\n",
    "> pip freeze > requirements.txt\n",
    "\n",
    "However, it will save all of the dependencies which are not used in your application and sub-dependencies as well, which may do no harm, but better to go through the generated file and remove those dependencies which are not necessary for your application to work."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.3. Initialize local Git repository\n",
    "Heroku allow you to deploy your application by doing a simple git push. To start using Git, we need to run the following commands in the directory with our web application:\n",
    "\n",
    "Initializing empty Git repository:\n",
    "> git init\n",
    "\n",
    "Adding all of the existing files to Git:\n",
    "> git add .\n",
    "\n",
    "Commit added files to just created local Git repository:\n",
    "> git commit -m \"iris app initial commit\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.4. Create Heroku application\n",
    "Run the following command in the app root directory:\n",
    "> heroku create\n",
    "\n",
    "It will create weird-named Heroku application for you and add remote Git repository (hosted on Heroku) to your app's Git configuration:\n",
    "\n",
    "> git remote -v\n",
    "\n",
    "```\n",
    "heroku  https://git.heroku.com/blooming-eyrie-32543.git (fetch)\n",
    "heroku  https://git.heroku.com/blooming-eyrie-32543.git (push)\n",
    "```\n",
    "\n",
    "Don't worry that your application is named like ```blooming-eyrie-32543``` - you can [rename](https://devcenter.heroku.com/articles/renaming-apps) it later :) It's done intentionally because Heroku apps must have unique names and we don't want to spend time to find a name which is not being used by someone else. Just for reference, it's possible to use your [custom domain](https://devcenter.heroku.com/articles/custom-domains) on Heroku, if needed.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.5. Pushing your application\n",
    "Phew, now we're ready to go with actual deployment, which is a simple git push in case of Heroku:\n",
    "\n",
    "> git push heroku master\n",
    "\n",
    "```\n",
    "Counting objects: 12, done.\n",
    "Delta compression using up to 8 threads.\n",
    "Compressing objects: 100% (10/10), done.\n",
    "Writing objects: 100% (12/12), 281.51 KiB | 15.64 MiB/s, done.\n",
    "Total 12 (delta 0), reused 0 (delta 0)\n",
    "remote: Compressing source files... done.\n",
    "remote: Building source:\n",
    "remote:\n",
    "remote: -----> Python app detected\n",
    "remote: -----> Installing python-3.6.7\n",
    "remote: -----> Installing pip\n",
    "remote: -----> Installing SQLite3\n",
    "remote: -----> Installing requirements with pip\n",
    "... skipped long long of dependencies installation\n",
    "remote:\n",
    "remote: -----> Discovering process types\n",
    "remote:        Procfile declares types -> web\n",
    "remote:\n",
    "remote: -----> Compressing...\n",
    "remote:        Done: 125.5M\n",
    "remote: -----> Launching...\n",
    "remote:        Released v3\n",
    "remote:        https://blooming-eyrie-32543.herokuapp.com/ deployed to Heroku\n",
    "remote:\n",
    "remote: Verifying deploy... done.\n",
    "To https://git.heroku.com/blooming-eyrie-32543.git\n",
    " * [new branch]      master -> master\n",
    "```\n",
    "\n",
    "Open the URL provided at the end of the output log - you should see the site app and running.\n",
    "\n",
    "If for some reason there is an error displaying, it worth to take a look at the Heroku logs (```--tail``` makes them live):\n",
    "\n",
    "> heroku logs --tail\n",
    "\n",
    "```\n",
    "2018-12-13T13:40:49.557538+00:00 heroku[web.1]: Starting process with command `gunicorn app:app --log-file -`\n",
    "2018-12-13T13:40:49.000000+00:00 app[api]: Build succeeded\n",
    "2018-12-13T13:40:51.776548+00:00 heroku[web.1]: State changed from starting to crashed\n",
    "2018-12-13T13:40:51.757263+00:00 heroku[web.1]: Process exited with status 3\n",
    "2018-12-13T13:40:51.621873+00:00 app[web.1]: [2018-12-13 13:40:51 +0000] [4] [INFO] Starting gunicorn 19.9.0\n",
    "2018-12-13T13:40:51.622501+00:00 app[web.1]: [2018-12-13 13:40:51 +0000] [4] [INFO] Listening at: http://0.0.0.0:10953 (4)\n",
    "2018-12-13T13:40:51.622615+00:00 app[web.1]: [2018-12-13 13:40:51 +0000] [4] [INFO] Using worker: sync\n",
    "2018-12-13T13:40:51.626310+00:00 app[web.1]: [2018-12-13 13:40:51 +0000] [10] [INFO] Booting worker with pid: 10\n",
    "2018-12-13T13:40:51.630939+00:00 app[web.1]: [2018-12-13 13:40:51 +0000] [10] [ERROR] Exception in worker process\n",
    "2018-12-13T13:40:51.630943+00:00 app[web.1]: Traceback (most recent call last):\n",
    "... skipped stack trace\n",
    "2018-12-13T13:40:51.630990+00:00 app[web.1]: __import__(module)\n",
    "2018-12-13T13:40:51.630991+00:00 app[web.1]: ModuleNotFoundError: No module named 'app'\n",
    "2018-12-13T13:40:51.634161+00:00 app[web.1]: [2018-12-13 13:40:51 +0000] [10] [INFO] Worker exiting (pid: 10)\n",
    "2018-12-13T13:40:51.668563+00:00 app[web.1]: [2018-12-13 13:40:51 +0000] [4] [INFO] Shutting down: Master\n",
    "2018-12-13T13:40:51.668996+00:00 app[web.1]: [2018-12-13 13:40:51 +0000] [4] [INFO] Reason: Worker failed to boot.\n",
    "```\n",
    "Here, for example, an incorrect Procfile was provided and gunicorn was unable to find an application.\n",
    "\n",
    "In addition to error logging this command will also display all of the incoming requests information (per our gunicorn config in Procfile), so you can see if your requests actually reaching the application and see an errors, if any.\n",
    "\n",
    "Hopefully, everything went fine and now you are able to use your model by using a browser and by calling web app endpoints from any other programming language!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Missing Parts\n",
    "Here you can find some thougts and directions about what can be done better with what we did in the tutorial and what could be a potential areas of interest for further learning.\n",
    "\n",
    "This section is totally subjective, so your mileage may vary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.1. Versioning\n",
    "Each time you re-train or change your model internals, it has to have different version.\n",
    "\n",
    "The API built around the model must have and update its version accordingly if the set of input fields or output data is changed, to not break existing clients. Version + endpoints + format of the input and output data is a **contract** between the service and its clients, it should not be violated freely. Older clients, if they are not under your control, should have an ability to talk to your old API which must be deployed separately from the new one.\n",
    "\n",
    "It's also good to use appropriate Git branching structure, like [GitFlow](https://www.atlassian.com/git/tutorials/comparing-workflows/gitflow-workflow) or specialized machine learning version control systems, like [DVC](https://dvc.org)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.2. Dependency management\n",
    "Many multi-libraries systems has its own problems when you need to have multiple versions of the same library used by different applications: [DLL Hell](https://en.wikipedia.org/wiki/DLL_Hell), [JAR Hell](https://dzone.com/articles/what-is-jar-hell), [Python Dependency Hell](https://medium.com/knerd/the-nine-circles-of-python-dependency-hell-481d53e3e025), each has its own <img src=\"../../img/tutorial-deployment-special-hell.jpg\" width=\"400\" />\n",
    "\n",
    "When it comes to portability between different computers, Python versions and different environments, it's necessary to fix your project dependecies so that you have exactly the same libraries that you had during the development.\n",
    "\n",
    "We've touched this area a bit by creating the ```requirements.txt```, but you should learn how to use [virtualenv](https://virtualenv.pypa.io/en/latest/), if you don't yet.\n",
    "\n",
    "As an extreme form of dependency management it's possible to use Docker and use it as a replacement of virtualenv - it will also provide you an abstraction from the target operating system since you can fix it in the Docker container as well as Python libraries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3. Containerizing your application\n",
    "As it was mentioned, Docker provide nice abstraction of the environment since you can fix OS, system liraries, installed software and Python libraries inside the Docker cotainer.\n",
    "\n",
    "Later, this Docker container can be delivered to production in exactly the same state as you had during the development time.\n",
    "In addition, nowadays there are many cloud providers that allow you to deploy Docker containers as an applications.\n",
    "\n",
    "Actually, containerizing the ML applications is a pretty big topic which worth another tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.4. Scalability\n",
    "It's always worth to know how much hardware resourses do you need for your model to work properly and without slowing its clients.\n",
    "\n",
    "The first step in this direction is to measure the performance of your final application by sending bunch of queries to it ([ApacheBench](https://en.wikipedia.org/wiki/ApacheBench), [JMeter](https://en.wikipedia.org/wiki/Apache_JMeter)).\n",
    "\n",
    "Since our application is stateless (means that it does not produce anything that has to be stored between the requests from the same client), we can scale it by simply adding more instances on our hosting service, Heroku.\n",
    "\n",
    "In addition to that we can [tune gunicorn to process multiple queries simultaneously](https://devcenter.heroku.com/articles/optimizing-dyno-usage#python) by spawning more workers, to be able to utilize single instance resourses more fully."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.5. Persisting input data\n",
    "Sometime there is a need to collect real data which your model receive after being deployed. Usually that data is kept along with the predictions which your model did.\n",
    "For example, it could be usable in the following situations:\n",
    "* you need to analyze that the data sent by clients are still from the same distribution which you had during the training time - so after collecting real data you can analyze it, label, add to the original data and re-train your model;\n",
    "* it was found that your model give strange results on some input data - having the logs with these data and your model predictions will allow you to debug.\n",
    "\n",
    "The obvious choice for persistence is using the databases, which is pretty easy in Python.\n",
    "\n",
    "Be aware that choosing the SQLite (which is just a file on local disk), while hosting your model on PaaS service (like Heroku) might get you in trouble because in most cases local file system there is ephemeral and can be wiped out at any time. So better to choose the database server which is hosted on another machine - PaaS services usually have it as an [option](https://devcenter.heroku.com/categories/data-management)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.6. Heavy workloads and models\n",
    "The model which we used in the tutorial is very toy'ish and it will not suffer much even under the high load. But for some scenarios the tools which we used might not be enough to keep up. In such cases different technologies which are created specifically for big data and high load, must be considered.\n",
    "\n",
    "As a Machine Learning practitioner, take a look at the [Spark MLlib](https://spark.apache.org/mllib/) and [H2O](https://www.h2o.ai/products/h2o/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.7. Other deployment options\n",
    "There are some other deployment options which are not covered in this tutorial but the core idea is still the same. Take time to learn them or create another tutorial :)\n",
    "* Docker containers (see above about their pros);\n",
    "* serverless deployments using [AWS Lambda](https://aws.amazon.com/lambda/) or [Azure Functions](https://docs.microsoft.com/en-us/azure/azure-functions/) - they will allow you to run your model on demand, usually using much less resources and money. It's another great way to deploy proof-of-concepts and small models to production;\n",
    "* self-hosted, when you want to have the total control over your hardware and software or you have to use your models inside the organization's network. This way require a lot more efforts and it's better to have experienced DevOps in the team for help."
   ]
  }
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