Exploring ML Kit Using Kotlin Multiplatform and Compose Multiplatform

Muralitharan A Play the role of a Mobile App Developer

5 min read

kotlin multiplatform

Hey, there! Machine learning (ML) has become a key element of modern apps, improving user experiences with capabilities such as image recognition, natural language processing, and so on. However, creating ML-enabled applications for different platforms might be tricky. Isn’t it?

Thanks Apple, Google

Have you ever wondered how the Google Pay scanner scans QR codes from a long distance using its auto zoom feature, how plenty of apps may suggest words for you to reply with friends, or how these apps all have the same type of user interface? The necessity of designing robust and efficient applications across different platforms becomes ever more crucial. 

One of the promising solutions to this challenge is Kotlin Multiplatform combined with Compose Multiplatform. In this blog, we will explore how these technologies, alongside Google’s ML Kit, can be leveraged to create powerful cross-platform applications. Let’s get started.

What is Kotlin Multiplatform & Compose Multiplatform?

Kotlin Multiplatform is an innovative feature from JetBrains that allows you to write code once and run it on multiple platforms, including Android, iOS, web, and desktop. This means you can share the logic of your app across different platforms, reducing the amount of code you need to write and maintain. 

Kotlin Multiplatform allows you to write common code, which is then compiled into platform-specific code for different targets. This common code can include business logic, networking, data handling, and even some UI logic.

In terms of cross-platform user interfaces, Compose Multiplatform comes into the picture. It is an extension of the popular Jetpack Compose framework, which allows for declarative UI development using Kotlin. Compose Multiplatform enables you to create user interfaces for Android, iOS (beta), desktop, and web (alpha) applications using the same codebase. It leverages the declarative UI paradigm, simplifying UI development and making your code more readable and maintainable.

Why Choose Kotlin & Compose Multiplatform Over React Native or Flutter?

Yes, both React Native and Flutter have advantages of their own. For example, React developers may readily transition to React Native, while anyone familiar with Dart can simply learn Flutter. Let’s see some advantages of KMP.

1.Native Performance:

KMP offers performance that is close to native apps, especially for complex tasks. Compose Multiplatform provides smooth, native-like UI performance.

2.Modern Language:

Kotlin is statically typed with features like null safety and coroutines, providing a robust ecosystem supported by JetBrains and Google.

3.Code Sharing:

KMP allows sharing business logic across platforms (Android, iOS, web, desktop), reducing duplication. Compose Multiplatform shares UI components.

4.Development Experience:

Leveraging IntelliJ IDEA and Android Studio, KMP and Compose Multiplatform offer strong tooling support for debugging and testing.

5.Community Support:

KMP has growing community backing, with continuous improvements from JetBrains and Google.

6.Future-Proofing:

Positioned for future growth, especially for teams familiar with Kotlin and Android development.

7.Integration:

Seamlessly integrates with existing Kotlin projects, making them easier to adopt and maintain.

Let’s see some of the Advantages of Compose Multiplatform’s Declarative Syntax:

In a declarative UI framework, you describe what the UI should look like for a given state, rather than describing the steps to achieve it. This leads to clearer, more concise code.

1.Easier State Management: With a declarative approach, UI components automatically react to changes in state. This means you don’t need to manually update the UI when the state changes, reducing the potential for bugs.

// State management in Compose
var text by remember { mutableStateOf("Hello, World!") }

Button(onClick = { text = "Hello, Compose!" }) {
   Text(text)
}

2.Enhanced Composability: Compose Multiplatform encourages breaking down the UI into small, reusable components. This composability makes it easier to build complex UIs from simpler building blocks and promotes code reuse.

// Composable function example

@Composable
fun Greeting(name: String) {
   Text(text = "Hello, $name!")
}

@Composable
fun MyApp() {
   Greeting("World")
   Greeting("Compose")
}

How to Get Started

1. Set Up a Kotlin Multiplatform Project: Start by setting up a Kotlin Multiplatform project using the Kotlin plugin in Android Studio. You will need to configure the project to target multiple platforms (e.g., Android, iOS and desktop).

2. Integrate Compose Multiplatform: Add the necessary dependencies for Compose Multiplatform to your project. This will enable you to start building your UI components using Compose.

3. Write Shared Logic: Create a shared module where you will write the common business logic. This module can be used across different platform-specific modules.

4. Build Platform-Specific UI: Use Compose Multiplatform to build the user interfaces for each platform. You can share UI components and layouts across platforms or create platform-specific UIs as needed.

Running your first app, let’s begin with the Kotlin multiplatform wizard, create a new project, and download it.

Once you download it, open the project in Android Studio. You can view the project structure. Because of its declarative syntax, compose is incredibly simple.

The project contains two modules:

  • composeApp is a Kotlin module that contains the logic shared among different platforms. It uses Gradle as the build system that helps you automate your build process.
  • iosApp is an Xcode project that builds into an iOS application. It depends on and uses the shared module as an iOS framework.

Run the Android app

To run the app, hit the play button in Android Studio and ensure that the connected device is an emulator or physical Android device. Once you run the app, you can see this on the connected device or emulator. Fantastic! You’re almost done. 

Run your application on iOS

Launch Xcode in a separate window to complete the initial setup. If it’s the first time you launch Xcode, you may also need to accept its license terms and allow it to perform some necessary initial tasks.

In Android Studio, select iosApp in the list of run configurations and click Run. If you don’t have an available iOS configuration in the list, If you want to run your application on a simulated device, you can add a new run configuration.

1.In the list of run configurations, click Edit Configurations.

2. Click the + button above the list of configurations and then select iOS Application.

3. Name your configuration.

4. Select the Xcode project file.

To do so, navigate to your project, for example, KotlinMultiplatformSandbox, open the iosApp folder, and then select the .xcodeproj file. In the Execution target list, select a simulated device and then click OK

5. Click Run to run your application on the new simulated device.

Finally, you have done a fantastic job!👌 Let’s talk about ML kit.

Overview of ML Kit

ML (Machine Learning) is a branch of artificial intelligence that uses algorithms to enable computers to learn from data and make predictions or decisions without explicit programming.

Google’s ML Kit provides a set of easy-to-use machine learning APIs for mobile developers. These APIs are helpful for using Machine Learning easily, making it possible to incorporate powerful machine-learning features into your apps without needing deep expertise in machine learning.

ML Kit offers a variety of APIs, including:

  • Text Recognition
  • Barcode Scanning
  • Image Labelling
  • Face Detection
  • Smart Reply
  • Language Identification

Why Combine These Technologies?

Using Kotlin Multiplatform, Compose Multiplatform, and ML Kit, developers can construct high-quality, cross-platform applications with advanced machine learning capabilities. While we won’t go into the coding details here, we will show a few use cases for now.

Implementing Smart Reply

Smart Reply generates reply suggestions based on the last few messages in a conversation. It uses on-device machine learning models, ensuring user privacy and fast response times. In this blog, we will explore how to integrate Smart Reply into a Kotlin Multiplatform project using Compose Multiplatform. 

This feature is particularly useful in messaging apps, customer support applications, and any context where quick replies are beneficial. It’s really helpful for quick response✍

Integrating with Google Code Scanner

We can also implement the most handy feature using Google Code Scanner. I appreciate it👏.

Imagine how easy it would be to use a QR code to pay for petrol while just sitting on your bike or in your car. You could even make attendance tracking easier by scanning the QR codes on student ID cards.

Conclusion

Kotlin Multiplatform and Compose Multiplatform, combined with the ML Kit, offer a powerful toolkit for building cross-platform applications with advanced machine learning capabilities. This approach not only saves development time but also ensures a consistent and high-quality user experience across different platforms. By leveraging these technologies, developers can focus on delivering innovative features and creating value for users without getting bogged down by platform-specific complexities.

Thank you for reading this blog! I appreciate your attention and support. Stay tuned for part 2, where we’ll dive into developing smart reply and Google code scanning features using these technologies. See you in the next post! Until then, bye.

FAQs

What are the common challenges faced when integrating ML Kit with KMP?
Common challenges include handling platform-specific code, managing dependencies, ensuring compatibility across different platforms, and dealing with the intricacies of each platform’s ML Kit implementation.

How do I handle platform-specific dependencies in a KMP project?
You handle platform-specific dependencies in a KMP project by specifying them in the platform-specific source sets (e.g., androidMain, iosMain). This allows you to include dependencies like ML Kit only in the relevant platform modules.

Muralitharan A Play the role of a Mobile App Developer
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