on_device_ai 0.2.0

A cross-platform Flutter plugin for on-device AI/ML inference. Supports text sentiment analysis and image classification using Apple frameworks (iOS) and TensorFlow Lite (Android).

On-Device AI Assistant SDK

A powerful, cross-platform Flutter plugin that gives developers a single, clean Dart API to run AI/ML inference entirely on-device. No internet, no API keys, no server costs.

Under the hood, it seamlessly bridges your Dart code to Core ML (NaturalLanguage) on iOS and TensorFlow Lite (Task Library) on Android.

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🚀 Features

• 100% On-Device: Absolute privacy and zero latency. Your data never leaves the device.
• Unified Dart API: Write your ML logic once. The plugin handles the complex native bridging.
• Hardware Acceleration: Automatically leverages Apple's Neural Engine and Android's NNAPI/GPU delegates where available.
• Text & Sentiment Analysis: Built-in integration with Google's MobileBERT (Android) and Apple's NaturalLanguage framework (iOS).

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🛠 Installation

Add on_device_ai to your pubspec.yaml:

dependencies:
  on_device_ai:
    path: path/to/on_device_ai

Android Setup

Ensure your Android project supports a minimum SDK version of 24. Place your .tflite models inside your app's assets/ directory and declare them in your pubspec.yaml.

iOS Setup

No additional configuration is required for basic Sentiment Analysis (which uses Apple's built-in NLP). If you wish to use custom .mlmodelc files, ensure they are added to your Xcode project's Copy Bundle Resources phase.

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💻 Usage

1. Initialize the Plugin & Load a Model

import 'package:on_device_ai/on_device_ai.dart';

final ai = OnDeviceAi();

// Load the model. 
// On iOS: use 'built_in_sentiment' to utilize the native NLTagger.
// On Android: pass the name of the TFLite asset.
await ai.loadModel(
  Platform.isIOS ? 'built_in_sentiment' : 'sentiment_analysis.tflite', 
  config: ModelConfig(useGPU: true)
);

2. Run Text Inference (Sentiment Analysis)

Pass a string to the model and receive a categorized result instantly.

final result = await ai.runText('This SDK is incredibly fast and easy to use!');

print('Output: ${result.output}'); // e.g., "Positive"
print('Confidence: ${result.confidenceScore}'); // e.g., 0.98
print('Inference Time: ${result.inferenceTimeMs}ms'); // e.g., 15ms

3. Stream Text (Useful for LLMs)

If you are running Generative AI models, you can stream the output tokens.

ai.streamText('Tell me a story...').listen((token) {
  print(token);
});

4. Clean Up

Always dispose of the model when finished to free up memory.

await ai.dispose();

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🏗 Architecture

The plugin is composed of three layers:

1. Dart API: Provides the clean OnDeviceAi interface.
2. Platform Channels: Safely passes input configurations and tensor buffers.
3. Native Runners:
   • TFLiteRunner.kt (Android): Uses NLClassifier from the TensorFlow Lite Task Library to automatically tokenize text and execute .tflite binaries.
   • CoreMLRunner.swift (iOS): Utilizes Apple's NaturalLanguage and CoreML frameworks for instantaneous on-device NLP.

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📝 Roadmap

• ❌ Implement FFI (Foreign Function Interface) for ultra-low latency image/tensor byte passing.
• ❌ Add generic Interpreter support for custom TFLite models on Android.
• ❌ Support LLM streaming generation (.mlpackage / LiteRT).