AI Chat Android demonstrates Google's Generative AI on Android with Firebase Realtime Database.
The purpose of this repository is to demonstrate below:
- Demonstrates Gemini API for Android and handle the chunk stream responses as UI states.
- Implementing entire UI elements with Jetpack Compose.
- Implementation of Android architecture components with Jetpack libraries such as Hilt.
- Performing background tasks with Kotlin Coroutines.
- Integrating chat systems with Firebase Realtime Database for handling real-time events.
If you're interested in the overall architecture, each layer, Generative AI, Gemini SDK, and implementation details of this project, check out the following blog post: Build an AI Chat Android App With Google’s Generative AI
Stream Chat SDK for Compose allows you to implement messaging systems seamlessly. If you’re interested in building powerful real-time video/audio calling, audio room, and livestreaming, check out the Stream Video SDK for Compose!
- Stream Chat SDK for Android on GitHub
- Android Samples for Stream Chat SDK on GitHub
- Stream Chat Compose UI Components Guidelines
To build this project properly, you should follow the instructions below:
-
Following the Firebase setup guidelines, download the google-services.json and place it into the
app
directory on this project. -
Next, set up the Firebase Realtime Database in your Firebase dashboard. Once that's done, you can import the JSON file like the image below:
- Next, create a file named
secrets.properties
in the root directory of this project, and copy & paste your Realtime Database URL into it, as shown in the example below:
REALTIME_DATABASE_URL=..
- Go to Google AI Studio, login with your Google account and select the Get API key on the menu left like the image below:
- Create your API key for using generative AI SDKs, and you'll get one like the image below:
- Add the key to the
secrets.properties
file like the below:
GEMINI_API_KEY=..
REALTIME_DATABASE_URL=..
- Build and run the project.
- Minimum SDK level 24.
- 100% Jetpack Compose based + Coroutines + Flow for asynchronous.
- Jetpack
- Compose: Android’s modern toolkit for building native UI.
- ViewModel: UI related data holder and lifecycle aware.
- Navigation: For navigating screens and Hilt Navigation Compose for injecting dependencies.
- Hilt: Dependency Injection.
- Landscapist Glide, animation, placeholder: Jetpack Compose image loading library that fetches and displays network images with Glide, Coil, and Fresco.
- Firebase Android KTX: Kotlin & Compose-friendly Firebase extensions designed to help you focus on your business logic.
- ksp: Kotlin Symbol Processing API.
- Balloon: Modernized and sophisticated tooltips, fully customizable with an arrow and animations for Android.
- Baseline Profiles: To improve app performance by including a list of classes and methods specifications in your APK that can be used by Android Runtime.
AI Chat Android follows the Google's official architecture guidance.
AI Chat Android was built with Guide to app architecture, so it would be a great sample to show how the architecture works in real-world projects.
The overall architecture is composed of two layers; UI Layer and the data layer. Each layer has dedicated components and they each have different responsibilities. The arrow means the component has a dependency on the target component following its direction.
Each layer has different responsibilities below. Basically, they follow unidirectional event/data flow.
The UI Layer consists of UI elements like buttons, menus, tabs that could interact with users and ViewModel that holds app states and restores data when configuration changes.
The data Layer consists of repositories, which include business logic, such as querying data from the local database and requesting remote data from the network. It is implemented as an offline-first source of business logic and follows the single source of truth principle.
For more information about the overall architecture, check out Build a Real-Time WhatsApp Clone With Jetpack Compose.
AI Chat Android has implemented the following modularization strategies:
-
Reusability: By effectively modularizing reusable code, it not only facilitates code sharing but also restricts code access across different modules.
-
Parallel Building: Modules are capable of being built in parallel, leading to reduced overall build time.
-
Decentralized Focusing: Individual development teams are allocated specific modules, allowing them to concentrate on their designated areas.
Most of the features are not completed except the chat feature, so anyone can contribute and improve this project following the Contributing Guideline.
Support it by joining stargazers for this repository. ⭐
Also, follow me on GitHub for my next creations! 🤩
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