// import 'package:image/image.dart' as image_lib;
// import "package:logging/logging.dart";
// import 'package:photos/services/object_detection/models/predictions.dart';
// import 'package:photos/services/object_detection/models/recognition.dart';
// import "package:photos/services/object_detection/models/stats.dart";
// import "package:photos/services/object_detection/tflite/classifier.dart";
// import "package:tflite_flutter/tflite_flutter.dart";
// import "package:tflite_flutter_helper/tflite_flutter_helper.dart";

// // Source: https://tfhub.dev/tensorflow/lite-model/mobilenet_v1_1.0_224/1/default/1
// class MobileNetClassifier extends Classifier {
//   static final _logger = Logger("MobileNetClassifier");
//   static const double threshold = 0.4;

//   @override
//   String get modelPath => "models/mobilenet/mobilenet_v1_1.0_224_quant.tflite";

//   @override
//   String get labelPath =>
//       "assets/models/mobilenet/labels_mobilenet_quant_v1_224.txt";

//   @override
//   int get inputSize => 224;

//   @override
//   Logger get logger => _logger;

//   MobileNetClassifier({
//     Interpreter? interpreter,
//     List<String>? labels,
//   }) {
//     loadModel(interpreter);
//     loadLabels(labels);
//   }

//   @override
//   Predictions? predict(image_lib.Image image) {
//     final predictStartTime = DateTime.now().millisecondsSinceEpoch;

//     final preProcessStart = DateTime.now().millisecondsSinceEpoch;

//     // Create TensorImage from image
//     TensorImage inputImage = TensorImage.fromImage(image);

//     // Pre-process TensorImage
//     inputImage = getProcessedImage(inputImage);

//     final preProcessElapsedTime =
//         DateTime.now().millisecondsSinceEpoch - preProcessStart;

//     // TensorBuffers for output tensors
//     final output = TensorBufferUint8(outputShapes[0]);
//     final inferenceTimeStart = DateTime.now().millisecondsSinceEpoch;
//     // run inference
//     interpreter.run(inputImage.buffer, output.buffer);

//     final inferenceTimeElapsed =
//         DateTime.now().millisecondsSinceEpoch - inferenceTimeStart;

//     final recognitions = <Recognition>[];
//     for (int i = 0; i < labels.length; i++) {
//       final score = output.getDoubleValue(i) / 255;
//       final label = labels.elementAt(i);
//       if (score >= threshold) {
//         recognitions.add(
//           Recognition(i, label, score),
//         );
//       }
//     }

//     final predictElapsedTime =
//         DateTime.now().millisecondsSinceEpoch - predictStartTime;
//     return Predictions(
//       recognitions,
//       Stats(
//         predictElapsedTime,
//         predictElapsedTime,
//         inferenceTimeElapsed,
//         preProcessElapsedTime,
//       ),
//     );
//   }
// }