Rashwan Lazkani for AWS Community Builders

Posted on Oct 31

Building an AI-Powered iOS Chat App with Amazon Bedrock and Swift

#aws #serverless #ai #ios

This post will teach you to create a native iOS app using Amazon Bedrock to build AI-powered chat and image features in Swift. We’ll leverage Serverless resources, including AWS Lambda and API Gateway, for the backend.

The sample application includes the following:

A mobile application using Swift
An integration to Amazon Bedrock using the models amazon.titan-image-generator-v1 and ai21.j2-mid-v1
Serverless backend processing using AWS Lambda with TypeScript
Implementation of RESTful APIs using Amazon API Gateway for communication
Use Amazon CloudWatch Logs to monitor AWS Lambda functions and view their logs

The final result will be the following app:

Prerequisites

Before you get started, make sure you have the following installed:

An AWS account
Node.js v18 or later
Serverless Framework, AWS SAM or AWS CDK (depending on if you want to use Infrastructure as Code. I'll be using Serverless Framework)
Package manager, I'll be using yarn
Xcode version 15 or later

Architecture

Users access the application from their mobile devices and the app sends a request to Amazon API Gateway
API Gateway routes the request to either the ImageFunction or TextFunction Lambda
AWS Lambda communicates with an Amazon Bedrock model and retrieves the generated response in JSON format
The processed response is sent back to the app for display, enabling content or chat interaction

Accessing Amazon Bedrock begins with the following steps

Login to your AWS Console
Go to Amazon Bedrock
In the left navigation click on Model access
Request access – please note that the body may vary depending on the model you select. For the image I will be using the Titan Image Generator G1 model and for the text I will be using the ai21.j2-mid-v1 model

Serverless backend processing using AWS Lambda with TypeScript

You can choose your preferred tool for deploying Lambda functions, but I’ll provide the code necessary to create them:

Text Lambda

Please take note of a few important points below:

You need to import the client-bedrock-runtime package
You need to add the modelId
The prompt is the search text provided from your API

import { BedrockRuntimeClient, InvokeModelCommand } from '@aws-sdk/client-bedrock-runtime';

const client = new BedrockRuntimeClient({ region: 'us-east-1' });

export async function handler(event: any) {
  const prompt = JSON.parse(event.body).prompt;
  const input = {
    modelId: 'ai21.j2-mid-v1',
    contentType: 'application/json',
    accept: '*/*',
    headers: {
      'Access-Control-Allow-Headers': 'Content-Type',
      'Access-Control-Allow-Origin': '*',
      'Access-Control-Allow-Credentials': true,
      'Access-Control-Allow-Methods': 'POST'
    },
    body: JSON.stringify({
      prompt: prompt,
      maxTokens: 200,
      temperature: 0.7,
      topP: 1,
      stopSequences: [],
      countPenalty: { scale: 0 },
      presencePenalty: { scale: 0 },
      frequencyPenalty: { scale: 0 }
    })
  };

  try {
    const data = await client.send(new InvokeModelCommand(input));
    const jsonString = Buffer.from(data.body).toString('utf8');
    const parsedData = JSON.parse(jsonString);
    const text = parsedData.completions[0].data.text;
    return text;
  } catch (error) {
    console.error(error);
  }
}

Image Lambda

import { BedrockRuntimeClient, InvokeModelCommand } from '@aws-sdk/client-bedrock-runtime';

const client = new BedrockRuntimeClient({ region: 'us-east-1' });

export async function handler(event: any) {
  const prompt = JSON.parse(event.body).prompt;
  const input = {
    modelId: 'amazon.titan-image-generator-v1',
    contentType: 'application/json',
    accept: 'application/json',
    headers: {
      'Access-Control-Allow-Headers': 'Content-Type',
      'Access-Control-Allow-Origin': '*',
      'Access-Control-Allow-Credentials': true,
      'Access-Control-Allow-Methods': 'POST'
    },
    body: JSON.stringify({
      textToImageParams: {
        text: prompt
      },
      taskType: 'TEXT_IMAGE',
      imageGenerationConfig: {
        cfgScale: 10,
        seed: 0,
        width: 512,
        height: 512,
        numberOfImages: 1
      }
    })
  };

  try {
    const command = new InvokeModelCommand(input);
    const response = await client.send(command);

    const blobAdapter = response.body;

    const textDecoder = new TextDecoder('utf-8');
    const jsonString = textDecoder.decode(blobAdapter.buffer);

    try {
      const parsedData = JSON.parse(jsonString);
      return parsedData.images[0];
    } catch (error) {
      console.error('Error parsing JSON:', error);
      return 'TextError';
    }
  } catch (error) {
    console.error(error);
  }
}

Now deploy your Lambdas, if you're using Serverless Framework you can use the following configuration:

service: aws-bedrock-ts
frameworkVersion: '3'

provider:
  name: aws
  runtime: nodejs18.x
  timeout: 30
  iam:
    role:
      statements:
        - Effect: 'Allow'
          Action:
            - 'bedrock:InvokeModel'
          Resource: '*'

functions:
  bedrockText:
    handler: src/bedrock/text.handler
    name: 'aws-bedrock-text'
    events:
      - httpApi:
          path: /bedrock/text
          method: post
  bedrockImage:
    handler: src/bedrock/image.handler
    name: 'aws-bedrock-image'
    events:
      - httpApi:
          path: /bedrock/image
          method: post

You will now be granted the API endpoints to your Lambdas, save those.

Developing your iOS app

Setting Up the Project

Start by creating a new Swift project in Xcode.
Name the project according to your app (e.g., BedrockSwift).

ChatMessage Model

First, define a model to store your chat messages, which could either be text or images. I'll name my class ChatMessage.swift:

import UIKit

struct ChatMessage: Equatable {
    var text: String?
    var image: UIImage?
    var isImage: Bool
    var isUser: Bool
}

Service for Handling API Requests

This service is responsible for managing API interactions, including sending prompts to your Lambda functions and processing the responses. Make sure to update your endpoints to your Lambdas. I'll name my class APIService.swift:

import UIKit

class APIService: ObservableObject {

    @Published var messages: [ChatMessage] = []

    private func getEndpointURL(for type: String) -> URL? {
        let baseURL = "https://example.execute-api.region.amazonaws.com/bedrock"

        switch type {
        case "text":
            return URL(string: "\(baseURL)/text")
        case "image":
            return URL(string: "\(baseURL)/image")
        default:
            return nil
        }
    }

    func addUserPrompt(_ prompt: String) {
        messages.append(ChatMessage(text: prompt, image: nil, isImage: false, isUser: true))
    }

    func sendRequest(prompt: String, type: String, completion: @escaping () -> Void) {
        guard let url = getEndpointURL(for: type) else {
            print("Invalid URL for type: \(type)")
            return
        }

        var request = URLRequest(url: url)
        request.httpMethod = "POST"
        let parameters: [String: Any] = ["prompt": prompt]

        request.httpBody = try? JSONSerialization.data(withJSONObject: parameters)
        request.addValue("application/json", forHTTPHeaderField: "Content-Type")

        URLSession.shared.dataTask(with: request) { data, response, error in
            if let error = error {
                print("Error: \(error)")
                return
            }

            guard let data = data else { return }
            print(data)

            DispatchQueue.main.async {
                if type == "text" {
                    if let responseString = String(data: data, encoding: .utf8) {
                        let trimmedResponse = responseString.trimmingCharacters(in: .whitespacesAndNewlines)
                        self.messages.append(ChatMessage(text: trimmedResponse, image: nil, isImage: false, isUser: false))
                    }
                } else {
                    DispatchQueue.main.async {
                        if let base64String = String(data: data, encoding: .utf8),
                           let imageData = Data(base64Encoded: base64String, options: .ignoreUnknownCharacters),
                           let image = UIImage(data: imageData) {

                            DispatchQueue.main.async {
                                self.messages.append(ChatMessage(text: nil, image: image, isImage: true, isUser: false))
                            }
                        }
                    }
                }
                completion()
            }
        }.resume()
    }
}

View for Chat Interface

Now, create the main view that will handle the UI and display the chat messages. I'll name my file BedrockView.swift:

import SwiftUI

struct BedrockView: View {

    @StateObject var apiService = APIService()
    @State private var prompt: String = ""
    @State private var selectedType = 0
    @State private var isLoading = false

    var body: some View {
        VStack {
            ScrollViewReader { scrollViewProxy in
                ScrollView {
                    VStack {
                        ForEach(apiService.messages.indices, id: \.self) { index in
                            if apiService.messages[index].isImage, let image = apiService.messages[index].image {
                                HStack {
                                    Spacer()
                                    Image(uiImage: image)
                                        .resizable()
                                        .scaledToFit()
                                        .frame(height: 200)
                                        .frame(maxWidth: .infinity, alignment: .leading)
                                        .cornerRadius(10)
                                        .padding(.vertical, 5)
                                }
                            } else if let text = apiService.messages[index].text {
                                HStack {
                                    if apiService.messages[index].isUser {
                                        Spacer()
                                        Text(text)
                                            .padding(.vertical, 6)
                                            .padding(.horizontal, 12)
                                            .background(Color.blue.opacity(0.2))
                                            .cornerRadius(10)
                                            .frame(maxWidth: .infinity, alignment: .trailing)
                                    } else {
                                        Text(text)
                                            .padding(.vertical, 6)
                                            .padding(.horizontal, 12)
                                            .background(Color.gray.opacity(0.2))
                                            .cornerRadius(10)
                                            .frame(maxWidth: .infinity, alignment: .leading)
                                    }
                                }
                                .padding(.vertical, 1)
                            }
                        }
                        if isLoading {
                            ProgressView()
                                .padding(.vertical, 20)
                        }
                    }
                    .padding(.horizontal)
                    .id("BOTTOM")
                }
                .onChange(of: apiService.messages) { _ in
                    withAnimation {
                        scrollViewProxy.scrollTo("BOTTOM", anchor: .bottom)
                    }
                }
            }

            VStack {
                TextField("Enter prompt...", text: $prompt)
                    .textFieldStyle(.roundedBorder)
                    .padding(.horizontal)
                    .padding(.vertical, 10)

                HStack {
                    Picker(selection: $selectedType, label: Text("Type")) {
                        Text("Text").tag(0)
                        Text("Image").tag(1)
                    }
                    .pickerStyle(SegmentedPickerStyle())
                    .frame(maxWidth: .infinity)
                    .padding(.leading, 10)

                    Button(action: {
                        if prompt.isEmpty { return }
                        apiService.addUserPrompt(prompt)
                        let type = selectedType == 0 ? "text" : "image"
                        isLoading = true
                        apiService.sendRequest(prompt: prompt, type: type) {
                            isLoading = false
                        }
                        prompt = ""
                    }) {
                        Text("Send")
                            .frame(width: 100, height: 2)
                            .padding()
                            .background(Color.primary)
                            .foregroundColor(.white)
                            .cornerRadius(10)
                    }
                }
                .padding(.horizontal)
            }
            .padding()
        }
    }
}

struct ContentView_Previews: PreviewProvider {
    static var previews: some View {
        BedrockView()
    }
}

App Entry Point

Go to your file: YourAppNameApp.swift and update the default entry point created when you set up your SwiftUI project. Mine is called BedrockView as you saw above.

import SwiftUI

@main
struct BedrockSwiftApp: App {
    var body: some Scene {
        WindowGroup {
            BedrockView()
        }
    }
}

Running the App in Xcode

Now you're ready to run your app! Follow these steps to launch it in Xcode:

Select a Device: Choose a simulator or connected device from the toolbar.
Build and Run: Click the "Run" button (or press Cmd + R) to build and run the app.

This will launch the app on your selected device, allowing you to interact with Amazon Bedrock's chat and image generation features.

Note a couple of things

As this is a local app for testing, I've set the Access-Control-Allow-Origin to `*. Additionally, you may need to adjust the CORS settings in API Gateway..

Note that API calls may incur a small cost. For detailed pricing information, please refer to the Amazon Bedrock pricing model.

GitHub Repositories

The source code for this project is available on GitHub:

Backend: amazon-bedrock-back
iOS App: amazon-bedrock-app

Conclusion

In this post, I’ve walked you through building a simple AI-chat application for iOS, using native Swift alongside serverless AWS services. By integrating Amazon Bedrock's generative AI models with services like AWS Lambda and API Gateway, we’ve created a streamlined solution that leverages the power of AWS in a native mobile experience. Please note that I’ve aimed to use only native components in the app, though there are certainly areas for improvement. Additionally, securing your API with tokens is essential; I’ll cover this topic in detail in an upcoming post.

Top comments (7)

Snowman • Nov 1 • Edited

I really enjoyed your article on building the AI-chat application for iOS using Swift and AWS services! The integration of Amazon Bedrock's generative AI models with AWS Lambda and API Gateway sounds like a powerful combination, and I appreciate your focus on creating a native mobile experience.

It's impressive how you’ve emphasized using native components, and I agree that there’s always room for improvement. I'm particularly interested in your upcoming post about securing the API with tokens, as that’s such a critical aspect of application development.

Thanks for sharing your insights; I'm looking forward to seeing more from you!

in/rodrigo-santiago-pimentel-155-ab-2332

Rashwan Lazkani • Nov 4

Hi @snowman524! I’m glad you enjoyed the post. Here’s the link to the article: Securing Your REST API with JWT Authorizers and Amazon Cognito 😊

Snowman • Nov 5

Good!

Boopathi • Oct 31

This is a great tutorial for anyone interested in building AI-powered chat apps! I'm especially interested in seeing how you integrated Amazon Bedrock into a native Swift iOS app. Can't wait to try this out!