flutter_gemma_mediapipe

MediaPipe (.task) on-device inference engine for flutter_gemma. Opt-in package — add it only if you run MediaPipe .task models. Android, iOS, and Web.

Usage

import 'package:flutter_gemma/flutter_gemma.dart';
import 'package:flutter_gemma_mediapipe/flutter_gemma_mediapipe.dart';

await FlutterGemma.initialize(
  inferenceEngines: [MediaPipeEngine()],
);

MediaPipeEngine handles ModelFileType.task / .bin models; pass it alongside other engines (e.g. LiteRtLmEngine from flutter_gemma_litertlm) if your app uses both formats.

Web setup

On Web, the MediaPipe runtime is loaded from a CDN. Add this to your app's web/index.html (inside a <script type="module"> before your Flutter bootstrap), exposing the symbols on window:

<script type="module">
import { FilesetResolver, LlmInference } from 'https://cdn.jsdelivr.net/npm/@mediapipe/tasks-genai@0.10.27';
window.FilesetResolver = FilesetResolver;
window.LlmInference = LlmInference;
</script>

(Android needs no extra setup — the MediaPipe Gradle deps are bundled by this package.)

iOS setup

iOS needs a 16.0 minimum. MediaPipe GenAI requires it, and this is the only flutter_gemma package that does — core, flutter_gemma_litertlm, built-in AI and embeddings build from 15.0 (#441).

This package ships no Package.swift, so even an app on Swift Package Manager gets a Podfile for it. Set the floor in BOTH places: platform :ios, '16.0' in the Podfile, and iOS Deployment Target on the Runner target in Xcode. Below 16, pod install reports that specs satisfying the flutter_gemma_mediapipe dependency "required a higher minimum deployment target".

MediaPipe ships static xcframeworks, so the Podfile also needs use_frameworks! :linkage => :static — with the Flutter template's bare use_frameworks!, pod install fails on "transitive dependencies that include statically linked binaries".

Platforms