Creating an AI-powered mobile app involves integrating artificial intelligence (AI) technologies to solve specific problems or provide unique features. Here's an overview of how to approach building an AI-powered mobile app: Key Steps to Build an AI-Powered Mobile App 1. Define the App's Purpose and Use Case Identify the problem your app will solve or the value it will offer. Examples of AI use cases in mobile apps: Chatbots (e.g., virtual assistants like Siri) Image Recognition (e.g., object detection, face recognition) Speech Recognition (e.g., voice commands, transcription) Recommendation Systems (e.g., personalized content or product recommendations) Predictive Analysis (e.g., health tracking, financial forecasting) Natural Language Processing (NLP) (e.g., sentiment analysis, language translation) 2. Choose an AI Technology or Framework Select the appropriate AI technologies or frameworks based on your use case: Machine Learning : Core frameworks: TensorFlow, PyTorch,...
Encodable : - "Encodable means convert Request Parameters(Model) to Data"
By using the Encodable protocol we can generate request parameters by Model Class / Struct
struct AddProduct: Encodable{
var title: String = ""
init(title: String){
self.title = title
}
}
Decodable: - "Decodable means convert Data to Request Parameters(Model)"
By using the Decodable protocol we can convert API response in Model Class / Struct. When done with API call decoding the response in terms of UserResponse object
struct ProductResponse: Decodable {
let id: Int
let title: String
}
Codable = Decodable + Encodable
Codable :- typealias of Encodable + Decodable
Codable is a type alias for the 'Encodable' and 'Decodable' protocols.
When you use 'Codable' as a type or a generic constraint, it matches
Any type that conforms to both protocols.
public typealias Codable = Decodable & Encodable
struct AddProduc: Codable{
var id: Int = 0
var title: String = ""
init(title: String){
self.title = title
}
}
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