Apple Intelligence for iOS Developers: Code Examples

Apple’s ecosystem offers a powerful suite of machine learning and AI frameworks that empower developers to create intelligent, user-friendly apps. With tools like Siri, Core ML, Create ML, and frameworks for natural language processing (NLP), computer vision, and speech recognition, you can add sophisticated intelligence to your iOS apps with ease.

This guide will introduce you to the key features of Apple’s intelligence offerings and provide tips to get started.

1. Siri: Bringing Voice Interaction to Your App

SiriKit allows you to integrate your app with Siri, enabling voice-based interactions. Users can perform tasks in your app just by speaking to Siri.

Key Features:

• Custom Intents: Define specific actions your app can handle, such as booking a ride or sending a message.
• Siri Suggestions: Suggest app actions proactively based on user behavior.
• Shortcuts: Let users trigger specific app actions via Siri with custom phrases.

Getting Started with SiriKit:

1. Add the appropriate entitlement to your app's capabilities in Xcode.
2. Define your app’s intent in an .intentdefinition file.
3. Implement the INIntentHandler protocol to handle requests.

Here’s an example of handling a custom intent for ordering coffee:

class OrderCoffeeIntentHandler: NSObject, OrderCoffeeIntentHandling {
    func handle(intent: OrderCoffeeIntent, completion: @escaping (OrderCoffeeIntentResponse) -> Void) {
        let response = OrderCoffeeIntentResponse.success(beverage: "Latte", size: "Medium")
        completion(response)
    }
}

2. Core ML: Powering Intelligence with On-Device Machine Learning

Core ML is Apple’s framework for running machine learning models on-device. It’s fast, private, and works offline, making it ideal for a wide range of use cases.

Key Features:

• Supports Multiple Model Types: Including deep learning, tree ensembles, and linear regression.
• On-Device Execution: Ensures privacy and real-time performance.
• Model Compression: Optimized models for minimal impact on app size.

Using Core ML in Your App:

1. Drag and drop a .mlmodel file into your Xcode project.
2. Use the automatically generated Swift class to load and make predictions.

import CoreML

let model = MyModel()
let input = MyModelInput(data: myData)
if let output = try? model.prediction(input: input) {
    print("Prediction: \(output.label)")
}

3. Create ML: Simplified Machine Learning Model Training

Create ML is a tool for training machine learning models directly on macOS, designed for developers without deep expertise in ML.

Key Features:

• User-Friendly Interface: Train models using the Create ML app or Swift code in a playground.
• Prebuilt Templates: For tasks like image classification, object detection, text analysis, and sound classification.
• Tight Integration with Core ML: Export models to use directly in your app.

Training a Model:

1. Open the Create ML app or use a Swift playground.
2. Provide labeled data (e.g., images, text).
3. Train the model and export it as a .mlmodel file.

import CreateML

let trainingData = try MLDataTable(contentsOf: URL(fileURLWithPath: "data.json"))
let model = try MLTextClassifier(trainingData: trainingData, targetColumn: "label")
try model.write(to: URL(fileURLWithPath: "MyModel.mlmodel"))

4. Live Text: Text Recognition in Images

Live Text uses Apple’s Vision framework to recognize and extract text from images or live camera feeds, enabling use cases like OCR (Optical Character Recognition).

Key Features:

• Recognizes printed and handwritten text.
• Extracts contact information, URLs, and dates directly.

Using Live Text with Vision:

1. Import the Vision framework.
2. Create a VNRecognizeTextRequest to detect text.

import Vision

let request = VNRecognizeTextRequest { (request, error) in
    guard let observations = request.results as? [VNRecognizedTextObservation] else { return }
    for observation in observations {
        if let topCandidate = observation.topCandidates(1).first {
            print("Detected text: \(topCandidate.string)")
        }
    }
}

let image = UIImage(named: "example.jpg")!
let handler = VNImageRequestHandler(cgImage: image.cgImage!, options: [:])
try? handler.perform([request])

5. Natural Language Processing (NLP): Understanding Text

The Natural Language framework (NL) provides tools to analyze and understand text, making it perfect for chatbots, sentiment analysis, and keyword extraction.

Key Features:

• Tokenization: Breaks text into words, sentences, or paragraphs.
• Sentiment Analysis: Determines if text is positive, negative, or neutral.
• Language Identification: Detects the language of a text.

Example: Sentiment Analysis

import NaturalLanguage

let sentimentPredictor = try NLSentimentClassifier()
let sentiment = sentimentPredictor.prediction(from: "This is amazing!")
print("Sentiment: \(sentiment)") // Positive

6. Vision: Advanced Image and Video Processing

The Vision framework powers computer vision tasks like object detection, face recognition, and barcode scanning.

Key Features:

• Object and face detection.
• Image alignment and similarity.
• Barcode recognition.

Example: Object Detection

import Vision

let request = VNDetectRectanglesRequest { (request, error) in
    guard let results = request.results as? [VNRectangleObservation] else { return }
    for result in results {
        print("Detected rectangle at: \(result.boundingBox)")
    }
}

let image = UIImage(named: "example.jpg")!
let handler = VNImageRequestHandler(cgImage: image.cgImage!, options: [:])
try? handler.perform([request])

7. Speech Recognition: Enabling Voice Input

The Speech framework allows apps to convert speech into text. It supports real-time transcription and works in multiple languages.

Key Features:

• On-device and server-based recognition.
• Real-time or file-based speech input.

Example: Real-Time Speech Recognition

import Speech

let recognizer = SFSpeechRecognizer(locale: Locale(identifier: "en-US"))
let request = SFSpeechAudioBufferRecognitionRequest()

let audioEngine = AVAudioEngine()
let inputNode = audioEngine.inputNode

recognizer?.recognitionTask(with: request) { result, error in
    if let result = result {
        print("Transcription: \(result.bestTranscription.formattedString)")
    }
}

audioEngine.prepare()
try? audioEngine.start()

Bringing Apple Intelligence Together

Apple’s intelligence frameworks empower developers to create innovative apps with cutting-edge capabilities. Here’s how you might combine these tools:

• Use Siri to trigger app actions with voice.
• Leverage Core ML for on-device predictions.
• Integrate Vision for image-based insights.
• Use NLP to understand text input from users.

By combining these features, you can create apps that feel smarter, more responsive, and deeply integrated into Apple’s ecosystem.