Kidney Stone Risk Detection App

Overview

This project aims to develop a mobile application for analyzing the contents of supermarket products to identify minerals or components that may increase the risk of kidney stones. Users can scan product barcodes using a barcode reader to determine the risk level.

Key Features

• Barcode Scanning: Allows users to scan product barcodes using the camera.
• OpenFoodFacts API Integration: Retrieves product content information using barcode data.
• Kidney Stone Risk Analysis: Identifies components in the product that could increase kidney stone risk.
• User-Friendly Interface: Provides a clean and modern design for optimal user experience.
• Information Screen: Displays results after scanning a barcode while keeping the camera running in the background for further scans.

Technologies Used

Mobile Application

• Programming Language: Kotlin
• Development Environment: Android Studio
• Libraries:
  • ZXing: For barcode scanning.
  • Retrofit: For making requests to the OpenFoodFacts API.
  • Gson: For JSON data parsing.
  • Glide: For loading product images from the API.
  • ViewModel and LiveData: For MVVM architecture support.
  • Room: For local database (optional, to store previously scanned product information).

API

• OpenFoodFacts: Provides data on product barcodes and contents.
  • OpenFoodFacts API Documentation

Application Architecture

Layered Architecture

• Data: Handles data retrieval from the API and local database.
  • ApiService.kt: Communicates with the OpenFoodFacts API.
  • ProductRepository.kt: Abstracts data sources.
• Domain: Business logic and use cases.
  • Example: AnalyzeProductUseCase.kt
• UI: Contains screens and components presented to the user.
  • Barcode scanning screen
  • Result information screen

Key Considerations

1. User Data Privacy:

   • No user data is stored on servers.
   • All analyses are performed on the user's device.

2. Minimal Dependencies:

   • No Firebase or complex/paid infrastructure is used, relying solely on APIs and local solutions.

3. Performance:

   • Ensures smooth app performance even when the camera is active.
   • Avoids unnecessary API calls.

4. Error Handling:

   • Provides accurate error messages for API failures.
   • Displays user-friendly notifications when a barcode cannot be recognized.

5. Accessibility:

   • Designed to adhere to Android accessibility standards.

Installation

Requirements

• Android Studio Arctic Fox or later
• Minimum Android SDK 23 (Marshmallow)

Installation Steps

1. Clone the project:

   git clone https://github.com/baranselklnc/KidneyStone.git

2. Open the project in Android Studio.

3. Add the required API keys to the local.properties file:

   OPENAI_API_KEY=your_openai_api_key_here
   

4. Build and run the application.

Usage

1. Launch the application.
2. Accept the camera permission request.
3. Scan a barcode.
4. View the risk analysis result screen.
5. Tap the information screen to scan a new barcode.

Roadmap

• Planned Features:
  • Product history and favorites.
  • Personalized dietary recommendations for users.
  • Multi-language support.

Contributing

To contribute, please create a pull request. You can ask questions under the issues section.

License

This project is licensed under the MIT License.