A machine learning-powered mobile app is not just a concept - it's a reality that can be achieved with minimal coding. In this tutorial, we'll show you how to build an app that leverages the power of artificial intelligence and machine learning using Angular and the Progress Kinvey high productivity platform.
Imagine an app that uses facial recognition technology to analyze your smile and determine just how funny you think a joke is. Sounds like science fiction? Not anymore! With AI in mobile apps, we can create interactive experiences that learn from user behavior and adapt to their preferences.
Our example app, Joke-O-Matic, will allow users to capture a photo of themselves with their phone or select an existing one. The app will then display a list of jokes and ask the user to upload a picture of their reaction. Using machine learning algorithms, we'll analyze the user's smile and determine how funny they find each joke.
Setting up Kinvey
To store and process data for our app, we'll use Progress Kinvey as our backend solution. With Kinvey, we can create collections, import data from other platforms, and utilize business logic to drive our app's functionality.
To get started with Kinvey, log in or sign up on the console and create a new app by clicking the +Add an app button. Next, create a collection by clicking the +Add a collection button. This will give us a foundation for storing user data and processing requests from our mobile app.
Setting up the Frontend
For the frontend of our app, we'll use NativeScript Sidekick to create a development environment that integrates with Angular and TypeScript. With Sidekick, we can choose from various project types, including Angular & TypeScript, and start building our app.
To get started with Firebase MLkit, we'll need to add a plugin to our app. This will enable us to use machine learning concepts like face detection and image labeling in our app. To do this, navigate to the "Sign in - Google Accounts" page, create a new project, and then head back to NativeScript Sidekick.
Setting up Firebase
To set up Firebase MLkit, we'll need to register for an account if we haven't already. Next, add the Firebase plugin to our app by running the command tns plugin add nativescript-plugin-firebase. This will allow us to use machine learning concepts in our app.
Once we've added the plugin, we can initialize Kinvey by writing a snippet of code that includes our App Key and App Secret. This will enable us to interact with our backend and process user data.
Building the App
Now that we have our development environment set up, let's start building our app! We'll begin by creating an array list for our jokes and using a ListView to display them on the screen. When users click on a joke, they'll be able to upload a picture of their reaction, which will be analyzed using machine learning algorithms.
To make staying logged in easier, we can add a sample code snippet to our home component or app component that checks if the user is active and logs them in accordingly.
By incorporating AI in mobile apps like Joke-O-Matic, we can create interactive experiences that learn from user behavior and adapt to their preferences. With minimal coding and the right tools, machine learning-powered mobile apps are no longer just a concept - they're a reality!