A useful smart-glasses pattern is:
Glass Camera
|
v
Flutter Companion App
|
v
Secure AI Backend
|
v
Vision Model
|
v
Answer
|
v
Flutter / Audio Output
The same architecture can be adapted to different wearable devices and AI providers.
Your native wearable integration should provide image data to the Flutter layer.
Future<void> onFrame(Uint8List bytes) async {
final result = await assistant.analyze(bytes);
print(result);
}
class VisionAssistant {
Future<String> analyze(Uint8List image) async {
// Send the image to your secure backend.
return 'Detected objects and scene description';
}
}
A backend endpoint might look like:
POST /vision/analyze
Content-Type: multipart/form-data
image=<frame>
The backend authenticates the user and calls the selected Gemini/vision model.
Do not place production AI credentials directly in the Flutter application.
Instead of processing every camera frame:
30 FPS camera
|
v
Frame sampling
|
v
1-3 relevant frames/sec
|
v
AI inference
Use event-based capture where possible, such as a user request or scene change.
Future<void> speak(String answer) async {
// Connect to your preferred TTS implementation.
}
The final experience can therefore be:
User asks a question
↓
Glasses capture context
↓
AI analyzes image
↓
Answer generated
↓
TTS speaks answer
Combining wearable capture, Flutter, and a vision model can create hands-free AI assistants while keeping the mobile application responsible for UI, state, and connectivity.
Website: www.v-modal.com
SDK Flutter: https://github.com/v-modal/vmodal_sdk_flutter
SDK Android: https://github.com/v-modal/vmodal_sdk_android
Discord: https://discord.gg/K72z28KUx