Building a State-of-the-Art ChatGPT App with OpenAI API and SwiftUI in 2025

The world of artificial intelligence has undergone a seismic shift since the early days of ChatGPT. As we navigate the AI landscape of 2025, integrating conversational AI into mobile applications has become not just a novelty, but a necessity. This comprehensive guide will walk you through creating a cutting-edge ChatGPT app using the latest OpenAI API and SwiftUI, empowering you to craft intelligent, engaging, and deeply personalized conversational experiences for your users.

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The Evolution of AI-Powered Apps in 2025

The past few years have witnessed remarkable advancements in AI technology, particularly in the realm of natural language processing and generation. Here's why building a ChatGPT app in 2025 is more exciting and impactful than ever:

Quantum Leap in API Capabilities: OpenAI's latest API iterations offer unprecedented levels of context awareness, emotional intelligence, and domain-specific knowledge.
SwiftUI Renaissance: Apple's UI framework has matured significantly, offering unparalleled performance and a rich ecosystem of AI-optimized components.
Ubiquity of AI Integration: From healthcare to education, finance to entertainment, AI-powered conversational interfaces have become the norm across industries.
Hyper-Personalization: Advanced machine learning models now enable chatbots to adapt their personality, knowledge base, and communication style to individual users in real-time.
Multimodal Interactions: Modern AI assistants seamlessly integrate text, voice, image, and even haptic feedback for truly immersive experiences.

Setting Up Your Development Environment

Before we dive into code, let's ensure you have the latest tools at your disposal:

Xcode 16.5 (released in early 2025)
Swift 6.2 (the current stable version as of mid-2025)
An active Apple Developer account
OpenAI API key (make sure you're using the GPT-5 compatible version)

Creating Your Project

Let's lay the foundation for our app:

Launch Xcode and select "Create a new Xcode project"
Choose "App" under the iOS tab
Name your project "NeoChat2025" (or any futuristic name you prefer)
Select SwiftUI for the interface and Swift as the language
Choose options for "Include Tests" and "Use Core Data" based on your project needs

Installing Dependencies

Package management in Swift has become even more robust in 2025. We'll use the Swift Package Manager to add the latest OpenAI Swift library:

In Xcode, navigate to File > Add Packages

Enter the following URL:

https://github.com/openai/openai-swift.git

Select the latest version (likely 5.x.x) and click "Add Package"

Architecting Your App

Our ChatGPT app will consist of several key components:

ChatMessage: A model representing individual messages
ChatViewModel: A view model managing chat logic and API interactions
ContentView: The main view of our application
AIEngine: A service layer for handling AI-related operations

Let's build these components step by step.

Creating the ChatMessage Model

First, let's define our ChatMessage struct:

import Foundation

struct ChatMessage: Identifiable, Codable {
    let id = UUID()
    let content: String
    let isUser: Bool
    let timestamp: Date
    var sentiment: Sentiment = .neutral
    var entities: [String] = []
    
    enum Sentiment: String, Codable {
        case positive, negative, neutral
    }
}

This enhanced ChatMessage structure now includes sentiment analysis and entity recognition, enabling more nuanced interactions and data visualization.

Implementing the ChatViewModel

Next, let's create our ChatViewModel:

import Foundation
import OpenAISwift
import Combine

class ChatViewModel: ObservableObject {
    @Published var messages: [ChatMessage] = []
    @Published var isTyping: Bool = false
    private var cancellables = Set<AnyCancellable>()
    private let aiEngine: AIEngine
    
    init(aiEngine: AIEngine = AIEngine()) {
        self.aiEngine = aiEngine
        loadConversationHistory()
    }
    
    func sendMessage(_ content: String) {
        let userMessage = ChatMessage(content: content, isUser: true, timestamp: Date())
        messages.append(userMessage)
        
        isTyping = true
        
        aiEngine.generateResponse(for: content, context: recentMessages())
            .receive(on: DispatchQueue.main)
            .sink { [weak self] completion in
                self?.isTyping = false
                if case .failure(let error) = completion {
                    print("Error: \(error.localizedDescription)")
                }
            } receiveValue: { [weak self] response in
                let botMessage = ChatMessage(content: response.content,
                                             isUser: false,
                                             timestamp: Date(),
                                             sentiment: response.sentiment,
                                             entities: response.entities)
                self?.messages.append(botMessage)
                self?.saveConversationHistory()
            }
            .store(in: &cancellables)
    }
    
    private func recentMessages() -> [ChatMessage] {
        Array(messages.suffix(10))
    }
    
    private func loadConversationHistory() {
        // Implement loading from persistent storage
    }
    
    private func saveConversationHistory() {
        // Implement saving to persistent storage
    }
}

This ChatViewModel now incorporates Combine for reactive programming, handles conversation persistence, and interacts with our custom AIEngine for more advanced AI operations.

Building the AIEngine Service

Let's create a dedicated service for AI-related tasks:

import Foundation
import OpenAISwift
import Combine

class AIEngine {
    private let client: OpenAISwift
    
    init() {
        client = OpenAISwift(config: .makeDefaultOpenAI(apiKey: "YOUR_API_KEY_HERE"))
    }
    
    func generateResponse(for message: String, context: [ChatMessage]) -> AnyPublisher<AIResponse, Error> {
        let prompt = buildPrompt(from: context, userMessage: message)
        
        return Future { promise in
            self.client.sendChat(with: [ChatMessage(role: .user, content: prompt)], model: .gpt5) { result in
                switch result {
                case .success(let chat):
                    if let responseText = chat.choices?.first?.message.content {
                        let sentiment = self.analyzeSentiment(responseText)
                        let entities = self.extractEntities(responseText)
                        let response = AIResponse(content: responseText, sentiment: sentiment, entities: entities)
                        promise(.success(response))
                    } else {
                        promise(.failure(AIError.noResponse))
                    }
                case .failure(let error):
                    promise(.failure(error))
                }
            }
        }.eraseToAnyPublisher()
    }
    
    private func buildPrompt(from context: [ChatMessage], userMessage: String) -> String {
        // Implement context-aware prompt building
    }
    
    private func analyzeSentiment(_ text: String) -> ChatMessage.Sentiment {
        // Implement sentiment analysis
    }
    
    private func extractEntities(_ text: String) -> [String] {
        // Implement entity extraction
    }
}

struct AIResponse {
    let content: String
    let sentiment: ChatMessage.Sentiment
    let entities: [String]
}

enum AIError: Error {
    case noResponse
}

This AIEngine leverages the power of GPT-5, implements advanced NLP tasks, and provides a clean, Combine-based interface for asynchronous operations.

Crafting the User Interface with ContentView

Now, let's create our main ContentView:

import SwiftUI

struct ContentView: View {
    @StateObject private var viewModel = ChatViewModel()
    @State private var newMessage = ""
    @State private var isShowingVoiceInput = false
    @State private var isShowingSettings = false
    
    var body: some View {
        NavigationView {
            VStack {
                ScrollViewReader { proxy in
                    ScrollView {
                        LazyVStack(spacing: 10) {
                            ForEach(viewModel.messages) { message in
                                MessageBubble(message: message)
                                    .id(message.id)
                            }
                        }
                        .padding()
                    }
                    .onChange(of: viewModel.messages) { _ in
                        withAnimation {
                            proxy.scrollTo(viewModel.messages.last?.id, anchor: .bottom)
                        }
                    }
                }
                
                if viewModel.isTyping {
                    TypingIndicator()
                }
                
                HStack {
                    TextField("Type a message...", text: $newMessage)
                        .textFieldStyle(RoundedBorderTextFieldStyle())
                        .padding(.horizontal)
                    
                    Button(action: sendMessage) {
                        Image(systemName: "paperplane.fill")
                            .foregroundColor(.blue)
                    }
                    .padding(.trailing)
                    
                    Button(action: { isShowingVoiceInput.toggle() }) {
                        Image(systemName: "mic.fill")
                            .foregroundColor(.blue)
                    }
                    .sheet(isPresented: $isShowingVoiceInput) {
                        VoiceInputView(message: $newMessage)
                    }
                }
                .padding(.bottom)
            }
            .navigationTitle("NeoChat 2025")
            .navigationBarItems(trailing: 
                Button(action: { isShowingSettings.toggle() }) {
                    Image(systemName: "gear")
                }
            )
            .sheet(isPresented: $isShowingSettings) {
                SettingsView()
            }
        }
    }
    
    private func sendMessage() {
        guard !newMessage.isEmpty else { return }
        viewModel.sendMessage(newMessage)
        newMessage = ""
    }
}

struct MessageBubble: View {
    let message: ChatMessage
    
    var body: some View {
        HStack {
            if message.isUser {
                Spacer()
                VStack(alignment: .trailing) {
                    Text(message.content)
                        .padding()
                        .background(Color.blue)
                        .foregroundColor(.white)
                        .cornerRadius(10)
                    Text(message.timestamp, style: .time)
                        .font(.caption)
                        .foregroundColor(.gray)
                }
            } else {
                VStack(alignment: .leading) {
                    Text(message.content)
                        .padding()
                        .background(Color.gray)
                        .foregroundColor(.white)
                        .cornerRadius(10)
                    Text(message.timestamp, style: .time)
                        .font(.caption)
                        .foregroundColor(.gray)
                }
                Spacer()
            }
        }
    }
}

struct TypingIndicator: View {
    @State private var animationOffset: CGFloat = 0
    
    var body: some View {
        HStack {
            ForEach(0..<3) { index in
                Circle()
                    .fill(Color.gray)
                    .frame(width: 10, height: 10)
                    .offset(y: animationOffset)
                    .animation(Animation.easeInOut(duration: 0.5).repeatForever().delay(Double(index) * 0.2))
            }
        }
        .onAppear {
            animationOffset = -5
        }
    }
}

This enhanced ContentView now includes smooth scrolling, a typing indicator, voice input capabilities, and a settings menu.

Advanced Features for a 2025-Ready App

To make our ChatGPT app truly stand out in 2025, let's implement some cutting-edge features:

1. Multimodal Input and Output

Enhance the app to handle various types of input and output:

struct MultimodalInputView: View {
    @Binding var message: String
    @State private var selectedImage: UIImage?
    @State private var isShowingImagePicker = false
    
    var body: some View {
        VStack {
            if let image = selectedImage {
                Image(uiImage: image)
                    .resizable()
                    .scaledToFit()
                    .frame(height: 200)
            }
            
            HStack {
                Button(action: { isShowingImagePicker.toggle() }) {
                    Image(systemName: "photo")
                        .foregroundColor(.blue)
                }
                
                TextField("Describe the image or ask a question", text: $message)
                    .textFieldStyle(RoundedBorderTextFieldStyle())
            }
            .padding()
        }
        .sheet(isPresented: $isShowingImagePicker) {
            ImagePicker(image: $selectedImage)
        }
    }
}

Update the AIEngine to process images alongside text:

func generateResponseWithImage(_ image: UIImage, message: String) -> AnyPublisher<AIResponse, Error> {
    // Implement image processing and multimodal AI response generation
}

2. Adaptive Personality

Implement a system that adapts the AI's personality based on user interactions:

class PersonalityEngine {
    var traits: [String: Float] = [
        "openness": 0.5,
        "conscientiousness": 0.5,
        "extraversion": 0.5,
        "agreeableness": 0.5,
        "neuroticism": 0.5
    ]
    
    func updatePersonality(based on: [ChatMessage]) {
        // Analyze messages and adjust personality traits
    }
    
    func getPersonalityPrompt() -> String {
        // Generate a prompt that reflects the current personality
    }
}

Integrate this into the AIEngine:

class AIEngine {
    private let personalityEngine = PersonalityEngine()
    
    func generateResponse(for message: String, context: [ChatMessage]) -> AnyPublisher<AIResponse, Error> {
        personalityEngine.updatePersonality(based: context)
        let personalityPrompt = personalityEngine.getPersonalityPrompt()
        // Include personalityPrompt in the API request
    }
}

3. Real-time Language Translation

Implement on-the-fly translation for multilingual conversations:

import NaturalLanguage

class TranslationEngine {
    func detectLanguage(of text: String) -> NLLanguage {
        let recognizer = NLLanguageRecognizer()
        recognizer.processString(text)
        return recognizer.dominantLanguage ?? .english
    }
    
    func translate(_ text: String, to targetLanguage: NLLanguage) -> AnyPublisher<String, Error> {
        // Implement translation using a service like DeepL or Google Translate
    }
}

Integrate translation into the chat flow:

class ChatViewModel {
    private let translationEngine = TranslationEngine()
    
    func sendMessage(_ content: String) {
        let detectedLanguage = translationEngine.detectLanguage(of: content)
        if detectedLanguage != .english {
            translationEngine.translate(content, to: .english)
                .flatMap { translatedContent in
                    self.aiEngine.generateResponse(for: translatedContent, context: self.recentMessages())
                }
                .flatMap { response in
                    self.translationEngine.translate(response.content, to: detectedLanguage)
                }
                .sink { ... }
                .store(in: &cancellables)
        } else {
            // Proceed with normal English processing
        }
    }
}