Flutter AI sample code

Creating a simple AI app in Flutter involves integrating a pre-trained machine learning model for inference. In this example, I’ll use the tflite_flutter package to integrate a TensorFlow Lite model. TensorFlow Lite is a framework for deploying machine learning models on mobile and embedded devices.

Prerequisites:

1. Have a pre-trained TensorFlow Lite model (.tflite file).
2. Install the tflite_flutter package.

Steps:

1. Create a Flutter Project:

flutter create simple_ai_app
cd simple_ai_app

2. Add Dependencies:

In your pubspec.yaml file, add the tflite_flutter and camera dependencies:

dependencies:
  flutter:
    sdk: flutter
  tflite_flutter: ^0.5.0
  camera: ^0.10.1

Run flutter pub get to fetch the dependencies.

3. Add Permissions:

Add the necessary permissions for the camera in the AndroidManifest.xml file (located in the android/app/src/main directory):

<uses-permission android:name="android.permission.CAMERA" />

4. Add Model:

Place your TensorFlow Lite model (.tflite file) in the assets folder.

5. Configure Assets:

Update your pubspec.yaml file to include the model in the assets:

flutter:
  assets:
    - assets/your_model.tflite

6. Create Flutter UI:

Create a simple Flutter UI to display the camera feed and perform inference:

import 'package:flutter/material.dart';
import 'package:camera/camera.dart';
import 'package:tflite_flutter/tflite_flutter.dart';

void main() {
  runApp(MyApp());
}

class MyApp extends StatelessWidget {
  @override
  Widget build(BuildContext context) {
    return MaterialApp(
      title: 'Simple AI App',
      home: CameraScreen(),
    );
  }
}

class CameraScreen extends StatefulWidget {
  @override
  _CameraScreenState createState() => _CameraScreenState();
}

class _CameraScreenState extends State<CameraScreen> {
  late CameraController _controller;
  late CameraImage _savedImage;

  @override
  void initState() {
    super.initState();
    _initializeCamera();
  }

  Future<void> _initializeCamera() async {
    final cameras = await availableCameras();
    _controller = CameraController(cameras[0], ResolutionPreset.medium);
    await _controller.initialize();
    if (!mounted) {
      return;
    }
    setState(() {});
    _controller.startImageStream(_processCameraImage);
  }

  Future<void> _processCameraImage(CameraImage cameraImage) async {
    if (mounted) {
      setState(() {
        _savedImage = cameraImage;
      });
      // Perform inference here using TensorFlow Lite
      // For example:
      // var result = Tflite.runModelOnFrame(
      //   bytesList: cameraImage.planes.map((plane) => plane.bytes).toList(),
      // );
    }
  }

  @override
  void dispose() {
    _controller.dispose();
    super.dispose();
  }

  @override
  Widget build(BuildContext context) {
    if (!_controller.value.isInitialized) {
      return Container();
    }
    return Scaffold(
      appBar: AppBar(
        title: Text('Simple AI App'),
      ),
      body: CameraPreview(_controller),
    );
  }
}

7. Run Your App:

flutter run

This example provides a basic structure for integrating a camera feed and TensorFlow Lite for inference in a Flutter app. Adapt it based on your specific model and requirements. Note that you may need to use a different package or approach based on the type of model you’re using.