Chat App Example

Path: example/chat_app

Flutter app showing production-style local chat UX with runtime controls.

Live demo: https://leehack-llamadart.static.hf.space

Run#

cd example/chat_app
flutter pub get
flutter run

If you run this example on iOS, set the project deployment target to 16.4 or newer before building.

Test#

cd example/chat_app
flutter test

What it demonstrates#

• Real-time streaming chat UI.
• Model selection and download flow.
• The runnable chat app wires ModelDownloadController into its model-management flow through a small adapter, so cache checks, progress, cancel, retry, and clear ready/failure states come from the same package helper app code can reuse. The adapter keeps the example's platform-specific service layer for multi-asset model + mmproj downloads and browser cache behavior.
• Runtime backend preference and GPU layer controls.
• Persistent settings and split Dart/native logging controls.
• Tool-calling toggles and model capability badges.
• Runtime-verified multimodal capability gating after mmproj load. The app hides unsupported attachment types even if a model family advertises broader multimodal support.

Gemma 4 note#

The download library includes a Gemma 4 E2B GGUF + projector pair. On the current llama.cpp mtmd path used by llamadart, that projector exposes vision support but not audio support, so the app keeps image input enabled and audio input disabled for that model.

Web notes#

On web, this example prefers local bridge assets on localhost for development validation and otherwise prefers CDN assets with local fallback. The runtime status panel exposes the active bridge/core variant, fallback reason, model source, cache state, and runtime notes so you can distinguish browser capability problems from model/configuration pressure.

For reliable large GGUF loads, serve the app with COOP/COEP headers so window.crossOriginIsolated === true. A built smoke path is documented in WebGPU Bridge; it uses tool/testing/serve_static_with_headers.py and the real-model Playwright smoke against a small Qwen3.5 model.

Android notes#

• Qwen3.5 0.8B and 2B currently default to CPU on Android because that was the fastest verified path on the maintainer Pixel test device.
• Runtime chips expose native llama.cpp timing breakdowns (p_eval, eval, sample, reuse) so Android CPU vs Vulkan comparisons are visible in-app.
• For general model/backend tuning workflow, use Performance Tuning rather than treating these example defaults as universal rules.