🦅Verwendung in Swift

import SwiftUI
import PhotosUI
import CoreML
import Vision

struct Prediction: Identifiable {
    let id = UUID()
    let label: String
    let confidence: Float
}

@MainActor
final class ImageClassifier: ObservableObject {
    @Published var predictions: [Prediction] = []
    @Published var errorText: String?
    private var request: CoreMLRequest?

    init() {
        do {
            let config = MLModelConfiguration()
            config.computeUnits = .all
            let model = try MobileNetV2(configuration: config).model
            var request = CoreMLRequest(model: try CoreMLModelContainer(model: model))
            request.cropAndScaleAction = .centerCrop
            self.request = request
        } catch {
            errorText = error.localizedDescription
        }
    }

    func classify(_ data: Data) async {
        guard let request else { return }
        do {
            let observations = try await request.perform(on: data, orientation: nil)
            predictions = observations
                .compactMap { $0 as? ClassificationObservation }
                .sorted { $0.confidence > $1.confidence }
                .prefix(3)
                .map { Prediction(label: $0.identifier, confidence: $0.confidence) }
        } catch {
            errorText = error.localizedDescription
        }
    }
}

struct ContentView: View {
    @StateObject private var classifier = ImageClassifier()
    @State private var selection: PhotosPickerItem?
    @State private var image: UIImage?

    var body: some View {
        VStack(spacing: 16) {
            PhotosPicker("Bild auswählen", selection: $selection, matching: .images)
                .buttonStyle(.borderedProminent)

            if let image {
                Image(uiImage: image).resizable().scaledToFit().frame(maxHeight: 260)
            }
            ForEach(classifier.predictions) { p in
                HStack {
                    Text(p.label)
                    Spacer()
                    Text(p.confidence, format: .percent.precision(.fractionLength(1)))
                }
            }
            if let errorText = classifier.errorText {
                Text(errorText).foregroundStyle(.red)
            }
        }
        .padding()
        .onChange(of: selection) { _, item in
            Task {
                guard let data = try? await item?.loadTransferable(type: Data.self) else { return }
                image = UIImage(data: data)
                await classifier.classify(data)
            }
        }
    }
}