modelport_flutter 0.1.0

Flutter setup for ModelPort: app cache folder, asset bundles, and device RAM for picking model variants.

modelport_flutter

Flutter setup for ModelPort. One call configures the model cache, asset:// bundles, native image decoding, and device RAM.

Future<void> main() async {
  WidgetsFlutterBinding.ensureInitialized();
  await ModelPortFlutter.init(adapters: [OnnxAdapter()]);
  runApp(const MyApp());
}

What init does:

• Cache folder. Models are kept in the app support folder under modelport/, verified once, and reused offline.
• Native image decoding. JPEG and PNG are decoded by the Flutter engine instead of pure Dart. On the test phone a 1546x1213 JPEG went from 717 ms to 302 ms. EXIF orientation is applied, like Pillow does in Python. Pass nativeImageDecoding: false to opt out.
• Assets. asset://assets/models/<name> bundles load from your app's assets and are copied into the cache.
• Device RAM. Read with device_info_plus, so variants that need more memory than the device has are skipped.

This package re-exports package:modelport/modelport.dart, so one import is enough.

Engines

Add the engines you need and pass them to init:

Package	Engine
modelport_onnx	ONNX Runtime
modelport_executorch	PyTorch ExecuTorch
modelport_llamacpp	llama.cpp for GGUF language models

See the documentation and the demo app.

License

Apache-2.0