firebase_mlkit_language

Introduction: Firebase ML Kit Language Plugin for Flutter
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A Flutter plugin to use the ML Kit Natural Language for Firebase API.

For Flutter plugins for other Firebase products, see FlutterFire.md.

Note: This plugin is still under development, and some APIs might not be available yet. Feedback and Pull Requests are most welcome!

Usage

To use this plugin, add firebase_mlkit_language as a dependency in your pubspec.yaml file. You must also configure Firebase for each platform project: Android and iOS (see the example folder or https://codelabs.developers.google.com/codelabs/flutter-firebase/#4 for step by step details).

iOS

Versions 1.0.0+ use the latest ML Kit for Firebase version which requires a minimum deployment target of 9.0. You can add the line platform :ios, '9.0' in your iOS project Podfile.

Supported Languages

All the supported languages can be found here.

Furthermore, they can be found within the SupportedLanguages class.

Using the ML Kit Language Identifier

1. Create an instance of a language identifier

Initialize a LanguageIdentifier.

final LanguageIdentifier languageIdentifier = FirebaseLanguage.instance.languageIdentifier()

2. Call processText(String) with languageIdentifier

processText(String) returns List<LanguageLabel> in decreasing order of probability of detected language.

final List<LanguageLabel> labels = await languageIdentifier.processText('Sample Text');

3. Extract data

<LanguageLabel> contains the language names and confidence of the prediction, accessible via .text and .confidence.

for (LanguageLabel label in labels) {
  final String text = label.text;
  final double confidence = label.confidence;
}

Using the ML Kit Language Translator

Note

Get an instance of ModelManager, and download the needed translation models(optional, results in faster first-use).

FirebaseLanguage.instance.modelManager().downloadModel(SupportedLanguages.lang);

1. Create an instance of a language translator

Initialize a LanguageTranslator.

final FirebaseLanguage.instance.languageTranslator(SupportedLanguages.lang, SupportedLanguages.lang);

2. Call processText(String) with languageTranslator

processText(String) returns a string containing the text translated to the target language.

final String translatedString = await languageTranslator.processText('Sample Text');

Using the ML Kit Model Manager

1. Create an instance of a model manager

Initialize a ModelManager

final ModelManager modelManager = FirebaseLanguage.instance.modelManager()

2. Download Model using the model manager

downloadModel() downloads the specified model to the device's local storage. It is recommended to download all the models needed to be used before translating to ensure a fast first-use. On a successful download, the string "Downloaded" will be returned.

modelManager.downloadModel(SupportedLanguages.lang)

3. Delete Model using the model manager

deleteModel() deletes the specified model from the device's local storage. On a successful delete, the string "Deleted" will be returned. If the model specified is not present on the device, the string "Model not downloaded" will be returned.

modelManager.deleteModel(SupportedLanguages.lang)

4. View Models

viewModels() returns a list of the BCP-47 language codes of all the languages downloaded onto the local storage of the device.

modelManager.viewModels()
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