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Делаем нативное мобильное приложение с ИИ и бэкендом (Туториал)

Предыдущие статьи:

В этой статье мы рассмотрим тонкости создания Proof of Concept (PoC) мобильного приложения, построенного с помощью фреймворка SwiftUI и бэкенда с использованием FastAPI. Дополнительно я продемонстрирую эффективные архитектурные паттерны для SwiftUI-приложений, в частности MVVMP в сочетании с принципами SOLID и Dependency Injection (DI). Для android код можно легко перевести на Kotlin с Jetpack Compose.

Зачем нам нужен бэкенд

Кто-то может сказать, что можно просто запихнуть всю логику в приложение, напрямую отправлять запросы в chatgpt и сделать приложение без бэкенда. И я согласен, это действительно возможно, но бэкенд дает несколько важных преимуществ.

Бэкенд служит основой для любого сложного приложения, особенно для тех, которые требуют безопасного управления данными, обработки бизнес-логики и интеграции сервисов. Вот почему надежный бэкэнд имеет решающее значение:

Безопасность: Бэкэнд помогает защитить конфиденциальные данные и токены аутентификации пользователей от атак типа MITM (Man-in-the-Middle). Он выступает в качестве защищенного шлюза между пользовательским устройством и базой данных или внешними службами, обеспечивая шифрование и аутентификацию всех данных, а так же позволяет держать в тайне api ключи.
Контроль над использованием сервисов: Управляя API и взаимодействием с пользователями через бэкэнд, вы можете отслеживать и контролировать использование приложения. Это включает в себя дросселирование для управления нагрузкой, предотвращение злоупотреблений и обеспечение эффективного использования ресурсов.
Интеграция с базой данных: Бэкэнд обеспечивает бесшовную интеграцию с базами данных, позволяя динамически хранить, извлекать и обновлять данные в режиме реального времени. Это важно для приложений, которые требуют учетных записей пользователей, хранят их предпочтения или нуждаются в быстром и безопасном получении больших объемов данных.
Модели подписки и Freemium: Реализация услуг по подписке или модели freemium требует наличия бэкенда для выставления счетов, отслеживания использования и управления уровнями пользователей. Бэкэнд может безопасно обрабатывать платежи и подписки, обеспечивая бесперебойную работу пользователей и соблюдая требования по защите данных.
Масштабируемость и обслуживание: Бэкэнд позволяет более эффективно масштабировать приложение. Логику на стороне сервера можно обновлять без необходимости передавать обновления клиенту, что упрощает обслуживание и ускоряет внедрение новых функций.

По сути, бэкенд — это не только функциональность, но и создание безопасной, масштабируемой и устойчивой среды для процветания вашего приложения.

Объяснение технического стека

SwiftUI: Лучший вариант для нативных приложений для iOS после выхода UIKit. Он декларативен и упорядочен, а XCode является незаменимым редактором благодаря эпл. Для android код можно легко перевести на Kotlin с помощью Jetpack Compose.
FastAPI: Выбран для бэкенда за его скорость, минимальное количество шаблонов и декларативность, редактируется с помощью превосходного Zed.dev.
ChatGPT API: Используется в качестве большой языковой модели (LLM); выбор может меняться в зависимости от необходимости кастомизации.
Ngrok: Реализует туннелирование с помощью простой команды CLI для выхода локального сервера в интернет.

Создание приложения для iOS

Теория: Архитектурные паттерны

MVVMP (Model View ViewModel Presenter):

Model: Представляет собой структуры данных, используемые в приложении, такие как Question, Answer, Questionary и FilledQuestionary. Эти модели просты и содержат только данные, следуя принципу KISS.
View: Отвечают только за представление пользовательского интерфейса и делегируют все данные и логику презентерам. Они не содержат никакой бизнес-логики и спроектированы так, чтобы быть простыми и сосредоточенными на рендере UI.
ViewModel: В SwiftUI ViewModel представлена объектом ObservableObject, который служит моделью наблюдения за изменяемыми данными. Здесь нет методов и логики.
Presenter: Presenter управляет всей логикой, связанной с модулем (экраном или представлением), но не бизнес-логикой. Он взаимодействует с доменным слоем для выполнения операций бизнес-логики, таких как взаимодействие с API или управление сохранением данных.
Domain Layer: Этот слой содержит бизнес-логику приложения и взаимодействует с внешними ресурсами, такими как базы данных, API или другие сервисы. Он состоит из нескольких компонентов, таких как сервисы, провайдеры, менеджеры, репозитории, мапперы, фабрики и т. д.
На самом деле, MP в MVVMP является инициалами Марка Паркера, а полная форма — «Model View ViewModel by Mark Parker».

Принципы СОЛИД:
- Принцип единой ответственности: У каждого класса должна быть только одна причина для изменений.
- Принцип открытость-закрытость: Компоненты должны быть открыты для расширения, но закрыты для модификации.
- Принцип замещения Лискова: Объекты суперкласса должны быть заменяемы объектами подклассов.
- Принцип разделения интерфейсов: Ни один клиент не должен быть вынужден зависеть от интерфейсов, которые он не использует.
- Принцип инверсии зависимостей: Зависимость от абстракций, а не от конкретики, чему способствует DI.
Инъекция зависимостей (DI): Реализация с использованием DI-контейнера для соблюдения принципа инверсии зависимостей.

Разработка бэкенда

Код бэкенда довольно прост. Эндпоинты (main.py):

from typing import Callable
import json
from fastapi import FastAPI, Body, Request, Response
from .models import (Question, FilledQuestionary, DoctorResponseAnswer, DoctorResponseQuestionary)
from .user_card import UserCardSimple
from .prompting import get_response


@app.get("/onboarding", response_model = DoctorResponseQuestionary)
def onboarding():
    return DoctorResponseQuestionary(question=[Question(text=text) for text in UserCardSimple.__fields__.keys()])

@app.post("/doctor")
def doctor(user_card: UserCardSimple, filled_questionary: FilledQuestionary, message: str = Body(...)):
    json_string = get_response(user_card, message, filled_questionary) 
    loaded = json.loads(json_string.strip())
    return loaded

"onboarding" предоставляет список вопросов анамнеза, которые необходимо заполнить при первом запуске приложения. Ответы будут сохранены на устройстве и использованы для персонализированной диагностики в будущем. "doctor" — основной эндпоинт: он генерирует вопросы на основе предыдущих ответов и карты пользователя, либо возвращает результат диагностики.

Модели:

from pydantic import BaseModel


class Question(BaseModel):
    text: str

class FilledQuestionary(BaseModel):
    filled_questions: dict[str, str]

class DoctorResponseAnswer(BaseModel):
    text: str

class DoctorResponseQuestionary(BaseModel):
    questions: list[str]

class UserCardSimple(BaseModel):
    sex: str
    age: int
    weight: int
    height: int
    special_conditions: str

Промпты:

import os
from openai import OpenAI
from .models import FilledQuestionary


api_key = os.environ.get("API_KEY")
client = OpenAI(api_key=api_key)

def get_response(user_card: str, message: str, filled_questionary: FilledQuestionary, max_tokens=200):
    format_question = """{"questions":[{"text":"first question"},{"text":"second question"}]}"""
    format_advice = """{"text":"Advice: Drink more water"}"""
    
    system_prompt = f"""
    You are a doctor that gives user an opportunity to swiftly check up health and diagnos an illness using anamnes and a short questionary. 
    Your task is to ask short questions and give your opinion and advices.
    Your questions are accamulated in the filled questionary, which is empty in the first itteration. 
    
    Strive to about 1-2 questions per iteration and up to 6 questions in total (can be less). Questions must be short, clear, shouldn't repeat, 
    and should be relevant to the user's health condition, and should require easy answers.
    Ask questions only in the json format {format_question}.

    Number of answered questions: {len(filled_questionary.filled_questions)}
    If the Number of answered questions is more then 6, you should stop asking questions an`d provide an give your final opinion, 
    an assumption or an advice in the json format {format_advice}.
    """
    
    prompt = f"""request message: {message}; anamnesis: {user_card}; filled questionary: {filled_questionary};"""

    chat_completion = client.chat.completions.create(
        messages=[
            {
                "role": "system",
                "content": f"{system_prompt}",
            },
            {
                "role": "user",
                "content": f"{prompt}",
            },
        ],
        model="gpt-3.5-turbo",
        max_tokens=max_tokens
    )
    return chat_completion.choices[0].message.content

Модуль промптов использует GPT-3.5 от OpenAI для генерации ответов на основе пользовательского ввода, анамнеза и заполненных анкет. Он возвращает пользователю соответствующие вопросы и советы по диагностике здоровья. Как видите, ничего сложного здесь нет. Код элементарен, а промпты - просто набор четких инструкций для LLM.

Настройте окружение и запустите сервер с помощью fastapi dev main.py.

Подробности:

Открытие доступа к локальному хосту через Интернет

Зарегистрируйтесь на сайте ngrok.com и получите токен доступа.
Установите ngrok с сайта ngrok.com/download.
Выполните команду ngrok config add-authtoken <TOKEN>.
Запустите с помощью команды ngrok http http://localhost:8080 (при необходимости измените порт).

Подробные инструкции по настройке можно найти в документации ngrok.

Кодим приложение

Я не буду показывать здесь весь исходный код, для этого есть GitHub. Найти его можно по адресу: HouseMDAI iOS App. Вместо этого я остановлюсь только на важных (IMO) моментах.

Начнем с краткого описания задачи: нам нужно приложение с текстовым полем на главном экране, возможностью задавать набор динамических вопросов и показывать ответ. Также нам нужен одноразовый онбординг. Итак, приступим к коду.

Первым делом нам нужны модели, и они довольно просты (принцип KISS).

struct Question {
    var text: String
}

struct Answer {
    var text: String
}

struct Questionary {
    var questions: [Question]
}

struct FilledQuestionary {
    var filledQuestions: [String: String]
}

Теперь давайте сделаем онбординг. Продолжаем следовать KISS и SRP (Single Responsibility Principle), никакой бизнес-логики в представлениях, только UI. В данном случае - только список вопросов с прокруткой. Все данные и логика делегированы презентеру. Единственное, что здесь интересно, это небольшой вспомогательный метод bindingForQuestion, который, вероятно, должен быть в презентере, но сейчас это не имеет значения.

import SwiftUI

struct OnboardingView: View {
    
    @StateObject var presenter: OnboardingPresenter
    
    var body: some View {
        ScrollView {
            Spacer()
            VStack {
                ForEach(presenter.questions.questions) { question in
                    VStack {
                        Text(question.text)
                        TextField("", text: bindingForQuestion(question))
                            .formItem()
                    }
                    .padding()
                }
            }.padding()
            
            Button("Save", action: presenter.save)
            Spacer()
        }
    }
    
    private func bindingForQuestion(_ question: Question) -> Binding<String> {
        Binding(
            get: { presenter.answers.filledQuestions[question.text] ?? "" },
            set: { presenter.answers.filledQuestions[question.text] = $0 }
        )
    }
}

Вы будете удивлены, но в презентере также нет никакой бизнес-логики!

class OnboardingPresenter: ObservableObject {

    @Published public var answers: FilledQuestionary
    private(set) public var questions: Questionary
    private var completion: (FilledQuestionary) -> Void
    
    init(questions: Questionary, answers: FilledQuestionary, completion: @escaping (FilledQuestionary) -> Void) {
        self.questions = questions
        self.answers = answers
        self.completion = completion
    }
    
    func save() {
        completion(answers)
    }
}

Все по-прежнему simple, stupid и имеет только одну ответственность. Presenter должен содержать только логику своего представления. Бизнес-логика уровня приложения находится вне его юрисдикции, поэтому презентер просто делегирует ее наверх по стэку вызова.

Также можно заметить, что и View, и Presenter не инстанцируют ни одну из зависимостей, а получают их в качестве параметров при инициализации. Это соответствует принципу инверсии зависимостей, согласно которому модули высокого уровня не должны зависеть от модулей низкого уровня, но оба должны зависеть от абстракций. Это обеспечивает гибкость и упрощает тестирование, а также позволяет легко заменять зависимости или внедрять макеты для целей тестирования.

При использовании паттерна Dependency Injection зависимости предоставляются извне класса, а не инстанцируются внутри него. Это способствует развязке и позволяет упростить поддержку и тестирование кода.

Хотя в данном примере протоколы не используются явно, стоит отметить, что протоколы могут играть важную роль в коде, особенно для абстрагирования и облегчения тестирования. Определив протоколы для представлений, презентаторов и зависимостей, становится проще заменять реализации или предоставлять моки во время тестирования.

Если вы рассматриваете возможность использования протоколов в представлениях SwiftUI, необходимо помнить об одном важном моменте. Поскольку View в SwiftUI - это структура, она требует явного указания типов своих свойств. Это означает, что вам придется сделать её дженериком и пробрасывать тип через весь стек вызовов, что приведет к появлению большого количества шаблонного кода.
Однако существует альтернативный подход, предлагаемый MarkParker5/AnyObservableObject. Эта библиотека работает аналогично родным оберткам свойств SwiftUI, но убирает проверку типа во время компиляции в пользу проверки во время рантайма. Хотя такой подход может внести некоторые риски, их легко снизить, написав элементарные xcode тесты, которые просто инициализируют представления так же, как вы делаете это во время рантайма.
Используя эту альтернативу, вы можете упростить свой код и оптимизировать процесс работы с протоколами в SwiftUI.

Итак, если презентер не содержит бизнес-логику, то где же она? Это задача для доменного слоя, который обычно содержит сервисы, провайдеры и менеджеры. У них всех очень схожее применение, и разница между ними до сих пор является предметом дискуссий. Давайте создадим OnboardingProvider, который будет содержать всю бизнес-логику процесса онбординга.

class OnboardingProvider: ObservableObject {
    
    init() {
        loadFilledOnboardingFromDefaults()
    }
    
    // MARK: Interface
	
	@Published private(set) var needsOnboarding: Bool = true
    
    private(set) var filledOnboarding: FilledQuestionary? {
        didSet {
            if let filledOnboarding {
                saveFilledOnboardingToDefaults(filledQuestionary: filledOnboarding)
            }
        }
    }
    
    func getOnboardingQuestionary() -> Questionary {
        // NOTE: it's better to take the questions from the backend
        Questionary(questions: [
            Question(text: "sex"),
            Question(text: "age"),
            Question(text: "weight"),
            Question(text: "height"),
            Question(text: "special_conditions"),
        ])
    }
    
    func saveOnboardingAnswers(filledQuestionary: FilledQuestionary) {
        needsOnboarding = false
        filledOnboarding = filledQuestionary
    }
    
    // MARK: - Private
    
    private func saveFilledOnboardingToDefaults(filledQuestionary: FilledQuestionary) {
        UserDefaults.standard.removeObject(forKey: "filledOnboarding")
        let encoder = JSONEncoder()
        let encoded = try! encoder.encode(filledQuestionary)
        UserDefaults.standard.set(encoded, forKey: "filledOnboarding")
    }
    
    private func loadFilledOnboardingFromDefaults() {
        guard let object = UserDefaults.standard.object(forKey: "filledOnboarding") else {
            needsOnboarding = true
            return
        }
        let savedFilledQuestionary = object as! Data
        let decoder = JSONDecoder()
        let loadedQuestionary = try! decoder.decode(FilledQuestionary.self, from: savedFilledQuestionary)
        self.filledOnboarding = loadedQuestionary
        self.needsOnboarding = false
    }
}

Опять же, он выполняет только одну задачу: управление бизнес-логикой процесса onboarding. Такая инкапсуляция позволяет другим классам взаимодействовать с ним без необходимости беспокоиться о деталях его внутренней реализации, что способствует созданию более чистой и удобной кодовой базы.

Теперь давайте соберем все вместе в корне приложения.

import SwiftUI

@main
struct HouseMDAI: App {
    
    @StateObject private var onboardingProvider: OnboardingProvider
    @StateObject private var onboardingPresenter: OnboardingPresenter
    @StateObject private var homePresenter: HomePresenter
    
    init() {
        let onboardingProvider = OnboardingProvider()
        
        let onboardingPresenter = OnboardingPresenter(
            questions: onboardingProvider.getOnboardingQuestionary(),
            answers: FilledQuestionary(filledQuestions: [:]),
            completion: onboardingProvider.saveOnboardingAnswers
        )
        
        let doctor = BackendDoctorProvider(baseUrl: "http://localhost:8000")
        let homePresenter = HomePresenter(doctor: doctor)
        
        _onboardingProvider = StateObject(wrappedValue: onboardingProvider)
        _onboardingPresenter = StateObject(wrappedValue: onboardingPresenter)
        _homePresenter = StateObject(wrappedValue: homePresenter)
    }
    
    var body: some Scene {
        WindowGroup {
            if onboardingProvider.needsOnboarding {
                OnboardingView(presenter: onboardingPresenter)
            } else {
                TabView {
                    HomeView(presenter: homePresenter)
                    if let profile = onboardingProvider.filledOnboarding {
                        ProfileView(profile: profile)
                    }
                }
            }
        }
    } // body
}

Это приложение SwiftUI устанавливает свое начальное состояние с помощью оберток полей StateObject. Оно инициализирует OnboardingProvider, OnboardingPresenter и HomePresenter в своем методе init. Провайдер OnboardingProvider отвечает за управление данными, связанными с онбордингом, а OnboardingPresenter управляет логикой представления онбординга. HomePresenter управляет главным домашним представлением.

В теле сцены приложения проверяется, нужна ли регистрация на сайте. Если да, то она представляет OnboardingView с OnboardingPresenter. В противном случае она представляет TabView, содержащий HomeView с HomePresenter и, если доступно, ProfileView.

Теперь настало время для домашнего экрана. Логика проста:

Получаем сообщение от пользователя
Используя сообщение, запрашиваем список вопросов из бэкенда
Показываем вопросы по одному, используя встроенную push-навигацию.
Добавляем ответы к запросу и повторяем 2-4, пока бэкенд-доктор не вернет окончательный результат
Показываем финальный результат

struct HomeView: View {
    
    @StateObject var presenter: HomePresenter
    
    var body: some View {
        NavigationStack(path: $presenter.navigationPath) {
            VStack {
	            // 1
                Text("How are you?")
                TextField("...", text: $presenter.message)
                    .lineLimit(5...10)
                    .formItem()
					
				// 2
                Button("Send", action: presenter.onSend)
            }
            .padding()
            .navigationDestination(for: NavigationPage.self) { page in
                switch page {
                case .questinary(let questions, let answers):
	                // 3
                    QuestionaryView(
                        presenter: QuestionaryPresenter(
                            questions: questions,
                            answers: answers,
                            completion: presenter.onQuestionaryFilled
                        )
                    )
                case .answer(let string):
	                // 5
                    VStack {
                        Text("The doctor says...")
                        Text(string)
                            .font(.title2)
                            .padding()
                    }
                }
            }
        }
    }
}

Похоже, я пропустил 4-й пункт... или нет? Поскольку представление не может содержать никакой логики, эту часть выполняет его презентер.

enum NavigationPage: Hashable {
    case questinary(Questionary, FilledQuestionary)
    case answer(String)
}

class HomePresenter: ObservableObject {
    
    @Published var message: String = ""
    @Published var navigationPath: [NavigationPage] = []
    
    init(message: String = "") {
        self.message = message
    }
    
    func onSend() {
        Task {
            let doctor = BackendDoctorProvider()
            let answer = try! await doctor.sendMessage(message: message)
            
            switch answer {
            case .questions(let questions):
                navigationPath.append(.questinary(questions, FilledQuestionary(filledQuestions: [:])))
            case .answer(let string):
                navigationPath.append(.answer(string))
            }
        }
    }
    
    func onQuestionaryFilled(filled: FilledQuestionary) {
        Task {
            let doctor = BackendDoctorProvider()
            let answer = try! await doctor.sendAnswers(message: message, answers: filled)
            
            switch answer {
            case .questions(let newQuestions):
                navigationPath.append(.questinary(newQuestions, filled))
            case .answer(let string):
                navigationPath.append(.answer(string))
            }
        }
    }
}

Он управляет вводом сообщения пользователем и обновляет путь навигации на основе ответов от бэкенда.

При отправке сообщения метод onSend() отправляет его на бэкенд с помощью DoctorProvider и ожидает ответа. В зависимости от типа ответа он обновляет навигационный путь, отображая либо набор вопросов, либо окончательный ответ.

Аналогично, когда заполняется вопросник, метод onQuestionaryFilled() отправляет заполненный вопросник на бэкенд и соответствующим образом обновляет путь навигации.

Здесь есть небольшое дублирование кода между методами onSend() и onQuestionaryFilled(), которое можно было бы отрефакторизовать в один метод для обработки обоих случаев. Однако оставим это как упражнение для дальнейшей доработки.

Модуль Questionary (View+Presenter) почти является копией Onboarding и просто делегирует логику до HomePresenter, поэтому я не вижу необходимости показывать код. Опять же, для этого есть github.

Последнее, что я хочу показать, это две реализации DoctorProvider, единственной обязанностью которых является вызов API и возврат DoctorResponse.

protocol DoctorProvider {
    func sendMessage(message: String) async throws -> DoctorResponse
    func sendAnswers(message: String, answers: FilledQuestionary) async throws -> DoctorResponse
}

enum DoctorResponse {
    case questions(Questionary)
    case answer(String)
}

Первая использует наш бэкенд:

class BackendDoctorProvider: DoctorProvider {
    
    private let baseUrl: String
    
    init(baseUrl: String) {
        self.baseUrl = baseUrl
    }
    
    func sendMessage(message: String) async throws -> DoctorResponse {
        try! await sendAnswers(message: message, answers: FilledQuestionary(filledQuestions: [:]))
    }
    
    func sendAnswers(message: String, answers: FilledQuestionary) async throws -> DoctorResponse {
        
        struct DoctorParams: Codable {
            var message: String
            var userCard: [String: String]
            var filledQuestionary: FilledQuestionary
        }
        
        let onboard = OnboardingProvider() // TODO: DI
            
        let paramsObject = DoctorParams(
            message: message,
            userCard: onboard.filledOnboarding!.filledQuestions,
            filledQuestionary: answers
        )
        
        let encoder = JSONParameterEncoder.default
        encoder.encoder.keyEncodingStrategy = .convertToSnakeCase
        
        let responseString = try await AF.request(
            baseUrl + "/doctor", 
            method: .post,
            parameters: paramsObject,
            encoder: encoder
        ).serializingString().value
        
        return try! DoctorResponse(from: responseString)
    }
}

Вторая вызывает openai api напрямую (подход backendless) и является практически копией модуля подсказок из бэкенда.

class PromptsProvider {
    
    private(set) public var homeRole = "" // TODO: take from the backend
    
    func message(message: String) -> String {
        return message
    }
    
    func profile(profile: FilledQuestionary) -> String {
        return try! jsonify(object: profile)
    }
    
    func answers(filled: FilledQuestionary) -> String {
        return try! jsonify(object: filled)
    }
    
    // MARK: - Private
    
    private func jsonify(object: Encodable) throws -> String {
        let coder = JSONEncoder()
        return String(data: try coder.encode(object), encoding: .utf8) ?? ""
    }
}

class DirectDoctorProvider: DoctorProvider {
    
    private var openAI: OpenAI
    
    init(apiToken: String) {
        openAI = OpenAI(apiToken: apiToken)
    }
    
    func sendMessage(message: String) async throws -> DoctorResponse {
        try! await sendAnswers(message: message, answers: FilledQuestionary(filledQuestions: [:]))
    }
    
    func sendAnswers(message: String, answers: FilledQuestionary) async throws -> DoctorResponse {
        let promptProvider = PromptsProvider() // TODO: DI
        let profile = OnboardingProvider().filledOnboarding! // TODO: DI
        
        let query = ChatQuery(model: .gpt3_5Turbo, messages: [
            Chat(role: .system, content: promptProvider.homeRole),
            Chat(role: .user, content: promptProvider.profile(profile: profile)),
            Chat(role: .user, content: promptProvider.message(message: message)),
            Chat(role: .user, content: promptProvider.answers(filled: answers)),
        ])
        
        let result = try await openAI.chats(query: query)
        return try! DoctorResponse(from: result.choices[0].message.content ?? "")
    }
}

Обе реализации легко взаимозаменяются благодаря инъекции зависимостей:

let doctor = BackendDoctorProvider(baseUrl: "http://localhost:8000")
let homePresenter = HomePresenter(doctor: doctor)

let doctor = DirectDoctorProvider(apiToken: "")
let homePresenter = HomePresenter(doctor: doctor)

Другой пример

Посмотреть пример этой архитектуры в другом приложении можно в моем проекте TwiTreads на github.com/MarkParker5/TwiTreads

Что делать дальше

Интегрируйте аутентификацию и базу данных пользователей в бэкенд. Можете использовать официальный шаблон FastAPI из FastAPI Project Generation.
Реализуйте логику аутентификации в приложении.
Сосредоточьтесь на улучшении дизайна приложения, чтобы повысить удобство работы с ним. Давайте создавать красивые приложения!

Заключение

Приведенные проекты и ссылки на код служат реальными примерами, чтобы дать толчок вашей собственной разработке. Помните, что красота технологии заключается в итерациях. Начните с простого, создайте прототип и постоянно совершенствуйте его.

О других интересных проектах периодически пишу в телеграм.

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4 июн. 2024 г.

07.09.23 16:24 CherryTeam

Cherry Team atlyginimų skaičiavimo programa yra labai naudingas įrankis įmonėms, kai reikia efektyviai valdyti ir skaičiuoti darbuotojų atlyginimus. Ši programinė įranga, turinti išsamias funkcijas ir patogią naudotojo sąsają, suteikia daug privalumų, kurie padeda supaprastinti darbo užmokesčio skaičiavimo procesus ir pagerinti finansų valdymą. Štai keletas pagrindinių priežasčių, kodėl Cherry Team atlyginimų skaičiavimo programa yra naudinga įmonėms: Automatizuoti ir tikslūs skaičiavimai: Atlyginimų skaičiavimai rankiniu būdu gali būti klaidingi ir reikalauti daug laiko. Programinė įranga Cherry Team automatizuoja visą atlyginimų skaičiavimo procesą, todėl nebereikia atlikti skaičiavimų rankiniu būdu ir sumažėja klaidų rizika. Tiksliai apskaičiuodama atlyginimus, įskaitant tokius veiksnius, kaip pagrindinis atlyginimas, viršvalandžiai, premijos, išskaitos ir mokesčiai, programa užtikrina tikslius ir be klaidų darbo užmokesčio skaičiavimo rezultatus. Sutaupoma laiko ir išlaidų: Darbo užmokesčio valdymas gali būti daug darbo jėgos reikalaujanti užduotis, reikalaujanti daug laiko ir išteklių. Programa Cherry Team supaprastina ir pagreitina darbo užmokesčio skaičiavimo procesą, nes automatizuoja skaičiavimus, generuoja darbo užmokesčio žiniaraščius ir tvarko išskaičiuojamus mokesčius. Šis automatizavimas padeda įmonėms sutaupyti daug laiko ir pastangų, todėl žmogiškųjų išteklių ir finansų komandos gali sutelkti dėmesį į strategiškai svarbesnę veiklą. Be to, racionalizuodamos darbo užmokesčio operacijas, įmonės gali sumažinti administracines išlaidas, susijusias su rankiniu darbo užmokesčio tvarkymu. Mokesčių ir darbo teisės aktų laikymasis: Įmonėms labai svarbu laikytis mokesčių ir darbo teisės aktų, kad išvengtų baudų ir teisinių problemų. Programinė įranga Cherry Team seka besikeičiančius mokesčių įstatymus ir darbo reglamentus, užtikrindama tikslius skaičiavimus ir teisinių reikalavimų laikymąsi. Programa gali dirbti su sudėtingais mokesčių scenarijais, pavyzdžiui, keliomis mokesčių grupėmis ir įvairių rūšių atskaitymais, todėl užtikrina atitiktį reikalavimams ir kartu sumažina klaidų riziką. Ataskaitų rengimas ir analizė: Programa Cherry Team siūlo patikimas ataskaitų teikimo ir analizės galimybes, suteikiančias įmonėms vertingų įžvalgų apie darbo užmokesčio duomenis. Ji gali generuoti ataskaitas apie įvairius aspektus, pavyzdžiui, darbo užmokesčio paskirstymą, išskaičiuojamus mokesčius ir darbo sąnaudas. Šios ataskaitos leidžia įmonėms analizuoti darbo užmokesčio tendencijas, nustatyti tobulintinas sritis ir priimti pagrįstus finansinius sprendimus. Pasinaudodamos duomenimis pagrįstomis įžvalgomis, įmonės gali optimizuoti savo darbo užmokesčio strategijas ir veiksmingai kontroliuoti išlaidas. Integracija su kitomis sistemomis: Cherry Team programinė įranga dažnai sklandžiai integruojama su kitomis personalo ir apskaitos sistemomis. Tokia integracija leidžia automatiškai perkelti atitinkamus duomenis, pavyzdžiui, informaciją apie darbuotojus ir finansinius įrašus, todėl nebereikia dubliuoti duomenų. Supaprastintas duomenų srautas tarp sistemų padidina bendrą efektyvumą ir sumažina duomenų klaidų ar neatitikimų riziką. Cherry Team atlyginimų apskaičiavimo programa įmonėms teikia didelę naudą - automatiniai ir tikslūs skaičiavimai, laiko ir sąnaudų taupymas, atitiktis mokesčių ir darbo teisės aktų reikalavimams, ataskaitų teikimo ir analizės galimybės bei integracija su kitomis sistemomis. Naudodamos šią programinę įrangą įmonės gali supaprastinti darbo užmokesčio skaičiavimo procesus, užtikrinti tikslumą ir atitiktį reikalavimams, padidinti darbuotojų pasitenkinimą ir gauti vertingų įžvalgų apie savo finansinius duomenis. Programa Cherry Team pasirodo esanti nepakeičiamas įrankis įmonėms, siekiančioms efektyviai ir veiksmingai valdyti darbo užmokestį. https://cherryteam.lt/lt/
08.10.23 01:30 davec8080

The "Shibarium for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH. The Plot Thickens. Someone posted the actual transactions!!!! https://bscscan.com/tx/0xa846ea0367c89c3f0bbfcc221cceea4c90d8f56ead2eb479d4cee41c75e02c97 It seems the article is true!!!! And it's also FUD. Let me explain. Check this link: https://bscscan.com/token/0x5a752c9fe3520522ea88f37a41c3ddd97c022c2f So there really is a "Shibarium" token. And somebody did a rug pull with it. CONFIRMED. But the "Shibarium" token for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH.
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06.09.24 01:44 Celinagarcia

HOW TO RECOVER MONEY LOST IN BITCOIN/USDT TRADING OR TO CRYPTO INVESTMENT !! Hi all, friends and families. I am writing From Alberton Canada. Last year I tried to invest in cryptocurrency trading in 2023, but lost a significant amount of money to scammers. I was cheated of my money, but thank God, I was referred to Hack Recovery Wizard they are among the best bitcoin recovery specialists on the planet. they helped me get every penny I lost to the scammers back to me with their forensic techniques. and I would like to take this opportunity to advise everyone to avoid making cryptocurrency investments online. If you have already lost money on forex, cryptocurrency or Ponzi schemes, please contact [email protected] or WhatsApp: +1 (757) 237–1724 at once they can help you get back the crypto you lost to scammers. BEST WISHES. Celina Garcia.
16.09.24 00:10 marcusaustin

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23.09.24 18:56 matthewshimself

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02.10.24 22:27 Emily Hunter

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18.10.24 09:34 freidatollerud

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31.10.24 00:13 ytre89

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02.11.24 14:44 diannamendoza732

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12.11.24 00:50 TERESA

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17.11.24 09:31 Vivianlocke223

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21.11.24 08:02 Emily Hunter

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22.11.24 04:41 [email protected]

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22.11.24 23:43 teresaborja

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I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT
25.11.24 02:19 briankennedy

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25.11.24 02:20 briankennedy

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26.11.24 23:12 rickrobinson8

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27.11.24 00:39 [email protected]

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27.11.24 09:10 Michal Novotny

The biggest issue with cryptocurrency is that it is unregulated, wh ich is why different people can come up with different fake stories all the time, and it is unfortunate that platforms like Facebook and others only care about the money they make from them through ads. I saw an ad on Facebook for Cointiger and fell into the scam, losing over $30,000. I reported it to Facebook, but they did nothing until I discovered deftrecoup . c o m from a crypto community; they retrieved approximately 95% of the total amount I lost.