LiteRT for Android 📱

Welcome to LiteRT for Android! This repository offers a powerful solution for image classification using LiteRT, designed specifically for Android applications. With the integration of various modern technologies, this project provides a seamless experience for developers looking to implement on-device AI capabilities.

Table of Contents

• Introduction
• Features
• Technologies Used
• Getting Started
• Usage
• Example
• Contributing
• License
• Contact

Introduction

LiteRT for Android is a cutting-edge project that leverages machine learning to classify images directly on Android devices. By utilizing LiteRT, developers can create applications that perform image classification efficiently and effectively. This project emphasizes low latency and high accuracy, making it suitable for various applications, from personal projects to commercial solutions.

Features

• On-Device AI: Perform image classification without needing an internet connection.
• Lightweight Model: The LiteRT model is optimized for mobile devices, ensuring quick responses.
• Jetpack Compose Integration: Build user interfaces easily with modern Android development tools.
• MVVM Architecture: Follow best practices for Android app development, promoting clean code and separation of concerns.
• Support for Multiple Models: Easily switch between different models for various classification tasks.

Technologies Used

This project incorporates several key technologies:

• AI: Machine learning techniques for image classification.
• Android: The primary platform for development.
• Coil: An image loading library for Android.
• Hilt: Dependency injection library for Android.
• Jetpack Compose: A modern toolkit for building native UI.
• Moshi: A JSON library for Android and Java.
• Retrofit: A type-safe HTTP client for Android and Java.
• TensorFlow Lite: A lightweight solution for running machine learning models on mobile devices.

Getting Started

To get started with LiteRT for Android, follow these steps:

1. Clone the Repository:

   git clone https://github.com/Tehreem-here24/LiteRT-for-Android.git

2. Navigate to the Project Directory:

   cd LiteRT-for-Android

3. Open the Project in Android Studio: Launch Android Studio and open the project folder.

4. Build the Project: Make sure all dependencies are resolved, and the project builds successfully.

5. Download the Latest Release: Visit the Releases section to download the latest release. Follow the instructions provided to execute the necessary files.

Usage

Once you have set up the project, you can start using the image classification features. Here’s a simple guide on how to integrate it into your app:

1. Add Dependencies: Ensure you have the necessary dependencies in your build.gradle file.

2. Initialize the Model: Load your LiteRT model in your application class or activity.

3. Capture or Select an Image: Use Android’s camera or gallery functionality to get an image.

4. Classify the Image: Pass the image to the model and retrieve the classification results.

5. Display Results: Show the classification results in your UI.

Example

Here’s a basic example of how to classify an image using LiteRT:

class MainActivity : AppCompatActivity() {
    private lateinit var imageClassifier: ImageClassifier

    override fun onCreate(savedInstanceState: Bundle?) {
        super.onCreate(savedInstanceState)
        setContentView(R.layout.activity_main)

        imageClassifier = ImageClassifier(this)

        val imageBitmap = ... // Load your image here
        val result = imageClassifier.classify(imageBitmap)

        // Display the result
        findViewById<TextView>(R.id.resultTextView).text = result
    }
}

Contributing

We welcome contributions from the community! If you want to contribute to LiteRT for Android, please follow these steps:

1. Fork the Repository: Create a personal copy of the repository.
2. Create a Branch: Use a descriptive name for your branch.

   git checkout -b feature/YourFeatureName

3. Make Your Changes: Implement your feature or fix.
4. Commit Your Changes: Write clear and concise commit messages.

   git commit -m "Add feature X"

5. Push to Your Fork:

   git push origin feature/YourFeatureName

6. Create a Pull Request: Submit your changes for review.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For any inquiries or feedback, please reach out:

• Author: Tehreem
• GitHub: Tehreem-here24
• Email: your.email@example.com

Feel free to explore the project and make use of its features. For further information, visit the Releases section to download the latest version. Happy coding!