Meet Your AI Pair Programmer: A Guide to GitHub Copilot & Xcode ML

What is an AI Pair Programmer?

The Traditional Pair Programming Model

How AI Emulates and Enhances this Model

Getting Started with GitHub Copilot for iOS Development

Setting Up Copilot in Your IDE

Writing Swift Code with Copilot: Tips and Tricks

From Boilerplate to Complex Logic: Copilot's Capabilities

Unleashing the Power of Xcode's Built-in Machine Learning

Introduction to Create ML

Training a Custom Model with Create ML

Integrating Custom Models with Core ML

import CoreML
import Vision

func classifyCoffeeImage(_ image: UIImage) {
    guard let model = try? VNCoreMLModel(for: CoffeeClassifier().model) else {
        print("Failed to load model")
        return
    }
    
    let request = VNCoreMLRequest(model: model) { request, error in
        guard let results = request.results as? [VNClassificationObservation],
              let topResult = results.first else {
            return
        }
        
        print("This looks like a \(topResult.identifier) with \(topResult.confidence * 100)% confidence")
    }
    
    // Process the image
    guard let ciImage = CIImage(image: image) else { return }
    let handler = VNImageRequestHandler(ciImage: ciImage)
    try? handler.perform([request])
}

The Perfect Synergy: Using Copilot and Xcode ML Together

Prototyping a Feature with Copilot's Help

struct IngredientScannerView: View {
    @State private var isShowingCamera = false
    @State private var capturedImage: UIImage?
    @State private var detectedIngredients: [Ingredient] = []
    
    var body: some View {
        VStack {
            if let image = capturedImage {
                Image(uiImage: image)
                    .resizable()
                    .scaledToFit()
                    .frame(height: 300)
            } else {
                RoundedRectangle(cornerRadius: 12)
                    .fill(Color.gray.opacity(0.3))
                    .frame(height: 300)
                    .overlay(
                        Text("Tap to scan ingredients")
                            .foregroundColor(.gray)
                    )
            }
            
            Button(action: { isShowingCamera = true }) {
                Label("Scan Ingredients", systemImage: "camera.fill")
                    .frame(maxWidth: .infinity)
                    .padding()
                    .background(Color.blue)
                    .foregroundColor(.white)
                    .cornerRadius(10)
            }
            .padding()
        }
    }
}

Building the 'Brain': Training a Model with Create ML

Connecting the Pieces with Core ML

func detectIngredients(in image: UIImage) {
    guard let model = try? VNCoreMLModel(for: IngredientClassifier().model) else {
        print("Failed to load ML model")
        return
    }
    
    let request = VNCoreMLRequest(model: model) { [weak self] request, error in
        guard let results = request.results as? [VNClassificationObservation] else {
            return
        }
        
        let detectedItems = results.prefix(5).compactMap { observation in
            observation.confidence > 0.7 ? Ingredient(name: observation.identifier, 
                                                     confidence: observation.confidence) : nil
        }
        
        DispatchQueue.main.async {
            self?.detectedIngredients = detectedItems
            self?.suggestRecipes(for: detectedItems)
        }
    }
    
    guard let ciImage = CIImage(image: image) else { return }
    try? VNImageRequestHandler(ciImage: ciImage).perform([request])
}

References