2. Masalah yang Dipecahkan

// ❌ TANPA optimasi: data conversion overhead tersembunyi
func classifyImage(_ image: UIImage) throws -> String {
    // UIImage → CVPixelBuffer: konversi memori besar yang terjadi setiap kali
    guard let pixelBuffer = image.pixelBuffer(width: 224, height: 224) else {
        throw MLError.conversionFailed
    }
    // Di balik layar: UIImage → CGImage → CVPixelBuffer (3 konversi!)
    let result = try model.prediction(input: pixelBuffer)
    return result.label
}
// Total waktu: 5ms inferensi + 12ms konversi = 17ms per frame
// Untuk 60fps video: butuh <16ms total → mustahil dengan approach ini

// ✓ DENGAN optimasi: reuse CVPixelBuffer dan hindari redundant conversion
final class OptimizedClassifier {
    private let model: VNCoreMLModel
    private let requestHandler: VNSequenceRequestHandler  // stateful, reuse di video

    init() throws {
        let mlModel = try MyClassifier(configuration: .init()).model
        self.model = try VNCoreMLModel(for: mlModel)
        self.requestHandler = VNSequenceRequestHandler()
    }

    func classify(sampleBuffer: CMSampleBuffer) throws -> String {
        // CMSampleBuffer langsung dari camera → tidak perlu konversi ke UIImage
        let request = VNCoreMLRequest(model: model)
        try requestHandler.perform([request], on: sampleBuffer, orientation: .up)
        guard let results = request.results as? [VNClassificationObservation],
              let top = results.first else { return "unknown" }
        return top.identifier
        // Total: ~5ms inferensi, konversi ~0ms (zero-copy dari camera buffer)
    }
}

// ❌ TANPA optimasi: load semua data ke memori sebelum predict
func processAllImages(_ images: [UIImage]) throws -> [String] {
    // Untuk 1000 gambar 224x224x3 Float32: ~600MB RAM sekaligus!
    let inputs = try images.map { img -> MyModelInput in
        let buffer = img.pixelBuffer(width: 224, height: 224)!
        return MyModelInput(image: buffer)
    }
    let results = try model.predictions(inputs: inputs)
    return results.map { $0.classLabel }
}

// ✓ DENGAN optimasi: streaming batch dengan bounded memory
func processImagesStreaming(_ images: [UIImage]) throws -> [String] {
    var results: [String] = []
    let batchSize = 16  // tuning berdasarkan available memory

    for batch in images.chunks(ofSize: batchSize) {
        let inputs = try batch.map { img -> MyModelInput in
            MyModelInput(image: img.pixelBuffer(width: 224, height: 224)!)
        }
        let batchResults = try model.predictions(inputs: inputs)
        results.append(contentsOf: batchResults.map { $0.classLabel })
        // Setiap batch di-release setelah selesai
    }
    return results
}

// ❌ TANPA Custom Layers: model dengan operasi custom tidak bisa dijalankan
// Error: "Encountered unknown layer type: Custom (CustomAttention)"
// Model dari PyTorch dengan custom attention mechanism → tidak support native

// ✓ DENGAN MLCustomLayer: implementasikan layer yang hilang di Swift/Metal
class CustomAttentionLayer: NSObject, MLCustomLayer {
    // Implementasi di Swift → dijalankan di CPU
    // Atau Metal kernel → dijalankan di GPU
}