PassioQuickStart iOS Demo

Welcome to Passio Quick Start iOS demo application. It is designed to showcase the capabilities of the PassioNutritionAI SDK for iOS - featuring food recognition, detailed nutritional analysis, and portion size estimation. The codebase in this repository shows a clean UIKit implementation that can be partially or fully adopted by developers to integrate PassioNutritionAI SDK into their applications.

Features

• 📸 Food recognition using device camera
• 🖼️ Food recognition from gallery images
• 🔍 Detailed nutritional information display
• 📊 Macronutrient breakdown with visual charts
• ⚖️ Dynamic serving size adjustments
• 🎯 Support for multiple measurement units
• 📱 Clean, modern iOS UI/UX

Requirements

• iOS 14.0+
• Xcode 14.0+
• Swift 5.0+
• PassioNutritionAI SDK 3.2.2+
• Valid Passio SDK Key

Installation

1. Clone the repository

git clone https://github.com/Passiolife/PassioQuickStart-iOS.git

2. Install dependencies via Swift Package Manager (SPM)

   • The PassioNutritionAI SDK will be automatically installed through SPM

3. Configure SDK Key

   • Copy Config.example.swift to Config.swift
   • Replace YOUR_PASSIO_KEY_HERE with your valid Passio SDK key

4. Build and run the project in Xcode

Usage

Camera-based Recognition

1. Launch the app
2. Grant camera permissions when prompted
3. Point camera at food item
4. Tap capture button
5. View recognition results and nutritional details

Gallery-based Recognition

1. Tap gallery button
2. Select food image from device
3. View recognition results and nutritional details

Nutritional Information

• View comprehensive macro and micronutrient breakdown
• Adjust serving sizes using slider or text input
• Switch between different measurement units
• View percentage distribution of macronutrients via donut chart

Architecture

The app follows standard iOS architecture patterns:

• UI Layer: Storyboard-based UI with custom XIB components
• View Controllers: Handle user interaction and data presentation
• Custom Views: Reusable UI components like DonutChartView
• Extensions: Utility extensions for UIKit components
• Delegates: Clean communication between components

Key Components:

• ImageSelectionVC: Handles camera/gallery image capture
• RecognizeImageVC: Manages food recognition process
• FoodDetailVC: Displays detailed nutritional information
• FoodNutrientsCell: Visualizes nutritional data
• DonutChartView: Custom chart for macronutrient ratios

Example Code

Here's how to initialize the PassioSDK:

private let passioSDK = PassioNutritionAI.shared
private var passioConfig = PassioConfiguration(key: Config.PASSIO_KEY)

func configurePassioSDK() {
    passioConfig.debugMode = 0
    passioSDK.statusDelegate = self
    passioConfig.remoteOnly = true
    
    passioSDK.configure(passioConfiguration: passioConfig) { status in
        print("Mode = \(status.mode)")
        print("Missingfiles = \(String(describing: status.missingFiles))")
    }
}

Learning from this Demo

For New App Development

• Architecture Pattern: Study the MVVC implementation and file organization
• Core Integration: ImageSelectionVC shows camera setup and image capture workflow
• Recognition Flow: RecognizeImageVC demonstrates the food recognition pipeline
• Results Handling: FoodDetailVC shows how to present nutritional data
• UI Components: Reuse custom components like DonutChartView for macro visualization

For Existing App Integration

• Minimal Dependencies: The SDK requires only UIKit - no additional frameworks needed
• Modular Components: Copy specific components like:
  • FoodNutrientsCell for nutrition display
  • ServingSizeCell for portion control
  • DonutChartView for macro ratio visualization
• SDK Configuration: See configurePassioSDK() for minimal setup requirements
• Permission Handling: Check Info.plist for required camera/photo permissions
• UI Integration: Use either:
  • Full screens (copy relevant ViewControllers)
  • Individual components (copy specific Cells/Views)

Key Integration Points

1. SDK Initialization:

private let passioSDK = PassioNutritionAI.shared
passioSDK.configure(passioConfiguration: config) { status in
    // Handle configuration status
}

2. Image Recognition:

PassioNutritionAI.shared.recognizeImageRemote(image: image) { foods in
    // Handle recognition results
}

3. Nutrition Display:

// Use FoodNutrientsCell for detailed nutrition
// Or access raw data:
let calories = foodItem.nutrientsReference().calories()?.value
let protein = foodItem.nutrientsReference().protein()?.value

Support

• SDK Documentation
• Nutrition AI Hub
• Support Email

About Passio

Passio is a innovative AI technology company specializing in computer vision and machine learning solutions for nutrition, fitness and health. Our SDKs and APIs power food recognition and nutritional analysis in applications worldwide.

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Made with ❤️ by Passio