// No code needed - Create ML provides a GUI interface in Xcode
    // or programmatically:
    import CreateML
    
    let trainingData = MLDataTable(contentsOf: URL(fileURLWithPath: "training_data.csv"))
    let model = try MLImageClassifier(trainingData: trainingData, featureColumn: "image", labelColumn: "label")
    try model.write(to: URL(fileURLWithPath: "MyClassifier.mlmodel")) 
                    

    import coremltools as ct
    
    # Converting from TensorFlow
    model = ct.converters.tensorflow.convert(
      'path/to/model.pb',
      inputs=[ct.TensorType(shape=(1, 224, 224, 3))],
      outputs=['output_node']
    )
    
    # Converting from PyTorch
    import torch
     torch_model = torch.load('model.pth', map_location=torch.device('cpu'))
     model = ct.convert(
      torch_model,
      inputs=[ct.TensorType(shape=(1, 3, 224, 224))],
      classifier_config=ct.ClassifierConfig(class_labels)
    )
    
    # Add model metadata
    model.author = "Your Name"
    model.license = "MIT"
    model.short_description = "Image classifier for object detection"
    model.version = "1.0"
    
    # Save the model
    model.save("MyMLModel.mlmodel")
                    

import CoreML
import Vision
import UIKit

class ImageClassifier {
// Use lazy initialization to load the model only when needed
private lazy var classificationRequest: VNCoreMLRequest = {
	do {
  		// Create a model configuration to specify options
      let config = MLModelConfiguration()
      config.computeUnits = .all // Use all available compute units
      
      // Initialize the Vision-CoreML model
      let model = try VNCoreMLModel(for: MobileNetV2(configuration: config).model)
      
      // Create request and specify the completion handler
      let request = VNCoreMLRequest(model: model) { [weak self] request, error in self?.processClassifications(for: request, error: error)
      }
      
      // Configure request properties
      request.imageCropAndScaleOption = .centerCrop // Maintain aspect ratio
      return request
      } catch {
  fatalError("Failed to create VNCoreMLRequest: \(error)")
      }
}()

// Classify an image
func classify(_ image: UIImage, completion: @escaping ([VNClassificationObservation]?) -> Void) {

// Convert UIImage to CIImage for Vision processing
  guard let ciImage = CIImage(image: image) else {
  completion(nil)
      return
    }
  
// Create a handler to process the image
  let handler = VNImageRequestHandler(ciImage: ciImage, orientation: self.cgImageOrientation(from: image.imageOrientation))
  
  // Use a background queue for processing
  DispatchQueue.global(qos: .userInitiated).async {
  do {
      	try handler.perform([self.classificationRequest])
      } catch {
      print("Failed to perform classification: \(error)")
          DispatchQueue.main.async {
          	completion(nil)
          }
      }
   }
}

// Process the classification results
private func processClassifications(for request: VNRequest, error: Error?) {
guard let results = request.results as? [VNClassificationObservation] else {
  // Handle the error appropriately
     return
   }
  
  // Filter results with high confidence (above 0.5)
  let significantResults = results.filter { $0.confidence > 0.5 }
  
  // Sort results by confidence (highest first)
  let sortedResults = significantResults.sorted { $0.confidence > $1.confidence }
  
  DispatchQueue.main.async {
  	self.delegate?.classificationCompleted(results: sortedResults)
  }
}

// Helper method to convert UIImage orientation to CGImagePropertyOrientation
private func cgImageOrientation(from uiOrientation: UIImage.Orientation) -> CGImagePropertyOrientation {
	// Conversion implementation
  // ...
  }
}

struct EmotionAnalyzer {
enum EmotionError: Error {
  case modelCreationFailed
  case preprocessingFailed
  case predictionFailed
}

private let model: EmotionClassifier

init() throws {
  // Initialize with configuration
  let config = MLModelConfiguration()
  config.computeUnits = .cpuAndNeuralEngine
  
  do {
      self.model = try EmotionClassifier(configuration: config)
  } catch {
      throw EmotionError.modelCreationFailed
  }
}

func analyzeEmotion(from image: UIImage) async throws -> EmotionPrediction {
  // Proper error handling and image preprocessing
  guard let pixelBuffer = image.toCVPixelBuffer(size: CGSize(width: 224, height: 224)) else {
      throw EmotionError.preprocessingFailed
  }
  
  // Perform prediction
  do {
      let input = EmotionClassifierInput(image: pixelBuffer)
      let prediction = try model.prediction(input: input)
      
      // Return structured result
      return EmotionPrediction(
          primaryEmotion: prediction.classLabel,
          confidence: prediction.classLabelProbs[prediction.classLabel] ?? 0,
          allEmotions: prediction.classLabelProbs
      )
  } catch {
      throw EmotionError.predictionFailed
  }
}
}

// In a SwiftUI View
struct EmotionJournalView: View {
@State private var emotionResult: EmotionPrediction?
@State private var isAnalyzing = false
@State private var selectedImage: UIImage?

private let emotionAnalyzer: EmotionAnalyzer

init() {
  // Initialize the analyzer
  do {
      self.emotionAnalyzer = try EmotionAnalyzer()
  } catch {
      fatalError("Failed to initialize emotion analyzer: \(error)")
  }
}

var body: some View {
  VStack {
      // UI implementation
  }
}

func analyzeCurrentImage() {
  guard let image = selectedImage else { return }
  
  isAnalyzing = true
  
  Task {
      do {
          let result = try await emotionAnalyzer.analyzeEmotion(from: image)
          
          await MainActor.run {
              self.emotionResult = result
              self.isAnalyzing = false
          }
      } catch {
          await MainActor.run {
              // Handle error in UI
              self.isAnalyzing = false
          }
      }
  }
}
}

class MLModelManager {
enum ModelVersion: String {
  case v1 = "EmotionClassifier_v1"
  case v2 = "EmotionClassifier_v2"
}

func getModel(version: ModelVersion) throws -> MLModel {
  let modelURL = Bundle.main.url(forResource: version.rawValue, withExtension: "mlmodelc")!
  return try MLModel(contentsOf: modelURL)
}

// A/B testing implementation
func getOptimalModel(for user: User) throws -> MLModel {
  // Logic to determine which model version to use
  let version: ModelVersion = user.inExperimentGroup ? .v2 : .v1
  return try getModel(version: version)
}
}

func processBatchOfImages(_ images: [UIImage]) async -> [PredictionResult] {
await withTaskGroup(of: PredictionResult?.self) { group in
  for image in images {
      group.addTask {
          return try? await self.processImage(image)
      }
  }
  
  // Collect and return results
  var results: [PredictionResult] = []
  for await result in group {
      if let result = result {
          results.append(result)
      }
  }
  return results
}
}

let configuration = MLModelConfiguration()
configuration.computeUnits = .cpuAndGPU

// Enable model metrics collection
try? MLModelCollectionEntry.DeploymentID = "MyApp.Testing"
configuration.parameters = [MLParameterKey.collectMetrics: true]

let model = try MyModel(configuration: configuration)

// After prediction:
if let metrics = model.modelDescription.metadata[MLModelMetadataKey.metrics] as? [String: Any] {
print("Prediction time: \(metrics["predictionTime"] ?? "unknown")")
print("CPU time: \(metrics["cpuTime"] ?? "unknown")")
}

model.modelDescription.inputDescriptionsByName["image"]?.imageConstraint else {
return false
}

// Check size constraints
let pixelCount = image.size.width * image.size.height
if pixelCount < Double(imageConstraint.pixelsHigh * imageConstraint.pixelsWide) * 0.9 {
print("Image resolution too low for optimal results")
return false
}
return true
}