Machine learning web application using React and Flask

Machine learning web application using React and Flask

✅Create a complete Machine learning web application using React and Flask

I have always wanted to develop a complete Machine learning application where I would have a UI to feed in some inputs and the Machine learning model to predict on those values. Last week, I did just that. In the process, I created an easy to use template in React and Flask, that anyone can modify to create their own application in a matter of minutes.

Highlights of the project:

  1. The front-end is developed in React and would include a single page with a form to submit the input values
  2. The back-end is developed in Flask which exposes prediction endpoints to predict using a trained classifier and send the result back to the front-end for easy consumption

The GitHub repo is below. Fork the project and create your own application today!



React is a JavaScript library created by Facebook to help make working with User interfaces simple and easy to develop and use. It’s one of the leading languages for front-end development. You can read about it here. The best resource to learn about React is its documentation itself which is very comprehensive and easy to grasp.

Flask and Flask-RESTPlus

Flask and Flask-RESTPlus allow us to define a service in Python which will have endpoints which we can call from the UI. You can learn more about developing a Flask app from my article below.


I used create-react-app to create a basic React app to begin with. Next, I loaded bootstrap which allows us to create responsive websites for each screen size. I updated the App.js file to add a form with dropdowns and Predict and Reset Prediction buttons. I added each form property to state and on pressing the Predict button, I send the data to the Flask backend. I also updated the App.css file to add style to the page.

The Flask app has a POST endpoint /prediction. It accepts the input values as a json, converts it into an array and returns to the UI. In actual application, we’ll use the same data to make prediction using the classifier stored in classifier.joblib and return the prediction.

Reset Prediction will remove the prediction from the UI.

Starting the template

Clone the repo to your computer and go inside it and open two terminals here.

Preparing the UI

In the first terminal, go inside the ui folder using cd ui. Make sure you are using the node version 10.4.1. Once inside the folder, run the command yarn install to install all dependencies.

To run the UI on server, we will use serve. We will begin by installing the serve globally, post which, we’ll build our application and then finally run the UI using serve on port 3000.

npm install -g serve
npm run build
serve -s build -l 3000

You can now go to localhost:3000 to see that the UI is up and running. But it won’t interact with the Flask service which is still not up. So, let’s do that.

Preparing the service

On the second terminal, move inside the service folder using cd service. We begin by creating a virtual environment using virtualenv and Python 3. You can read about virtualenv here. We will then install all the required dependencies using pip after activating the environment. Finally, we’ll run the Flask app.

virtualenv -p Python3 .
source bin/activate
pip install -r requirements.txt flask run

This will start up the service on

Voila! The complete application will now be working properly. Yaay!!

Using the template for own use case

To understand the process of using the template for any model, I’ll use the iris dataset and create a model for the same. This example is also available in the example folder in the project.

Create the model

I trained a DecisionTreeClassifier on the iris dataset which requires 4 features — Sepal Length, Sepal Width, Petal Length and Petal Width. Then, I saved the model to classifier.joblib using joblib.dump(). The classifier can now be used to predict on new data.

Update the service

Next, I opened the file in a text editor (Sublime Text is one). I uncommented the line classifier = joblib.load(‘classifier.joblib’) so that the variable classifier now holds the trained model.

In the post method, I made the following updates:

prediction = classifier.predict(np.array(data).reshape(1, -1))
types = { 0: "Iris Setosa", 1: "Iris Versicolour ", 2: "Iris Virginica"}
response = jsonify({
				"statusCode": 200,
				"status": "Prediction made",
				"result": "The type of iris plant is: " + types[prediction[0]]

Firstly, I used classifier.predict() to get the prediction. Next, I created a map for the classes such that 0 means Iris Setosa, 1 means Iris Versicolour and 2 means Iris Virginica. I finally returned the prediction in the result key.

Update the UI

The form is made up of columns inside rows. Thus, as I have 4 features, I added 2 columns in 2 rows. The first row will have dropdowns for Sepal Length and Sepal Width. The second row will have dropdowns for Petal Length and Petal Width.

I began by creating a list of options for each of these dropdowns.

var sepalLengths = []
for (var i = 4; i <= 7; i = +(i + 0.1).toFixed(1)) {
  sepalLengths.push(<option key = {i} value = {i}>{i}</option>);
var sepalWidths = []
for (var i = 2; i <= 4; i = +(i + 0.1).toFixed(1)) {
  sepalWidths.push(<option key = {i} value = {i}>{i}</option>);
var petalLengths = []
for (var i = 1; i <= 6; i = +(i + 0.1).toFixed(1)){
  petalLengths.push(<option key = {i} value = {i}>{i}</option>);
var petalWidths = []
for (var i = 0.1; i <= 3; i = +(i + 0.1).toFixed(1)) {
  petalWidths.push(<option key = {i} value = {i}>{i}</option>);


Next, I defined two rows with two columns each. Each dropdown selection will look like the code below:

<Form.Group as={Col}>
  <Form.Label>Petal Length</Form.Label>


For each dropdown, we’ll have to update the text inside <Form.Label></Form.Label>. We’ll name each selection group as well. Let’s say the name be petalLength so we set the value as {formData.petalLength} and name as “petalLength”. The options are added using the names we defined above inside <Form.Control></Form.Control> as we can see {petalLengths} above. Two such groups inside a <Form.Row></Form.Row> will make our UI.

The state must also be updated with the same names inside formData with the default values as the smallest value of the respective dropdowns. The constructor will look like below. As you can see, the state has been updated to have formData with new keys.

constructor(props) {

  this.state = {
    isLoading: false,
    formData: {
      sepalLength: 4,
      sepalWidth: 2,
      petalLength: 1,
      petalWidth: 0
    result: ""


Add a new background image and title

Inside app.css, change the link of the background image to your own. I added an image of flowers from Unsplash. I also updated the title to Iris Plant Classifier and the page title too inside index.html file in the public folder.


The application is now ready to use the model. Restart both services after building the UI using npm run build. The application looks like below:

With some feature values, on pressing the Predict button, the model classifies it as Iris Setosa.

With new feature values, the model predicts the plant to be Iris Versicolour.


As you can see, in this article, I discussed a ML React App template that will make creating complete ML applications simple and quick.

Try the application for your own use case and share your feedback. I’d love to hear from you.

30s ad

ReactJS Course: Learn JavaScript Library Used by Facebook&IG

React: Learn ReactJS Fundamentals for Front-End Developers

React From The Ground Up

Build Realtime Apps | React Js, Golang & RethinkDB

React for beginners tutorial

Python GUI Programming Projects using Tkinter and Python 3

Python GUI Programming Projects using Tkinter and Python 3

Python GUI Programming Projects using Tkinter and Python 3

Learn Hands-On Python Programming By Creating Projects, GUIs and Graphics

Python is a dynamic modern object -oriented programming language
It is easy to learn and can be used to do a lot of things both big and small
Python is what is referred to as a high level language
Python is used in the industry for things like embedded software, web development, desktop applications, and even mobile apps!
SQL-Lite allows your applications to become even more powerful by storing, retrieving, and filtering through large data sets easily
If you want to learn to code, Python GUIs are the best way to start!

I designed this programming course to be easily understood by absolute beginners and young people. We start with basic Python programming concepts. Reinforce the same by developing Project and GUIs.

Why Python?

The Python coding language integrates well with other platforms – and runs on virtually all modern devices. If you’re new to coding, you can easily learn the basics in this fast and powerful coding environment. If you have experience with other computer languages, you’ll find Python simple and straightforward. This OSI-approved open-source language allows free use and distribution – even commercial distribution.

When and how do I start a career as a Python programmer?

In an independent third party survey, it has been revealed that the Python programming language is currently the most popular language for data scientists worldwide. This claim is substantiated by the Institute of Electrical and Electronic Engineers, which tracks programming languages by popularity. According to them, Python is the second most popular programming language this year for development on the web after Java.

Python Job Profiles
Software Engineer
Research Analyst
Data Analyst
Data Scientist
Software Developer
Python Salary

The median total pay for Python jobs in California, United States is $74,410, for a professional with one year of experience
Below are graphs depicting average Python salary by city
The first chart depicts average salary for a Python professional with one year of experience and the second chart depicts the average salaries by years of experience
Who Uses Python?

This course gives you a solid set of skills in one of today’s top programming languages. Today’s biggest companies (and smartest startups) use Python, including Google, Facebook, Instagram, Amazon, IBM, and NASA. Python is increasingly being used for scientific computations and data analysis
Take this course today and learn the skills you need to rub shoulders with today’s tech industry giants. Have fun, create and control intriguing and interactive Python GUIs, and enjoy a bright future! Best of Luck
Who is the target audience?

Anyone who wants to learn to code
For Complete Programming Beginners
For People New to Python
This course was designed for students with little to no programming experience
People interested in building Projects
Anyone looking to start with Python GUI development
Basic knowledge
Access to a computer
Download Python (FREE)
Should have an interest in programming
Interest in learning Python programming
Install Python 3.6 on your computer
What will you learn
Build Python Graphical User Interfaces(GUI) with Tkinter
Be able to use the in-built Python modules for their own projects
Use programming fundamentals to build a calculator
Use advanced Python concepts to code
Build Your GUI in Python programming
Use programming fundamentals to build a Project
Signup Login & Registration Programs
Job Interview Preparation Questions
& Much More

Guide to Python Programming Language

Guide to Python Programming Language

Guide to Python Programming Language

The course will lead you from beginning level to advance in Python Programming Language. You do not need any prior knowledge on Python or any programming language or even programming to join the course and become an expert on the topic.

The course is begin continuously developing by adding lectures regularly.

Please see the Promo and free sample video to get to know more.

Hope you will enjoy it.

Basic knowledge
An Enthusiast Mind
A Computer
Basic Knowledge To Use Computer
Internet Connection
What will you learn
Will Be Expert On Python Programming Language
Build Application On Python Programming Language

Python Programming Tutorials For Beginners

Python Programming Tutorials For Beginners

Python Programming Tutorials For Beginners

Hello and welcome to brand new series of wiredwiki. In this series i will teach you guys all you need to know about python. This series is designed for beginners but that doesn't means that i will not talk about the advanced stuff as well.

As you may all know by now that my approach of teaching is very simple and straightforward.In this series i will be talking about the all the things you need to know to jump start you python programming skills. This series is designed for noobs who are totally new to programming, so if you don't know any thing about

programming than this is the way to go guys Here is the links to all the videos that i will upload in this whole series.

In this video i will talk about all the basic introduction you need to know about python, which python version to choose, how to install python, how to get around with the interface, how to code your first program. Than we will talk about operators, expressions, numbers, strings, boo leans, lists, dictionaries, tuples and than inputs in python. With

Lots of exercises and more fun stuff, let's get started.

Download free Exercise files.


Who is the target audience?

First time Python programmers
Students and Teachers
IT pros who want to learn to code
Aspiring data scientists who want to add Python to their tool arsenal
Basic knowledge
Students should be comfortable working in the PC or Mac operating system
What will you learn
know basic programming concept and skill
build 6 text-based application using python
be able to learn other programming languages
be able to build sophisticated system using python in the future

To know more: