MooDiary App

Diary, Analysis, LookBack

About MooDiary

"MooDiaryApp is an useful app designed to help you record your daily life.
In today's world, where people are often influenced by social media and experience heightened levels of anxiety, MooDiary offers a solution.
By using this app, you can shift your focus back to yourself and truly appreciate the uniqueness and preciousness of your own life.

Features

• Monthly Mood Calendar
  
• Simple daily recording with emojis and photos
  
• Mood Flow and Sleep Tracking
  
• Auto-generated Monthly Memoir
  
• Secure Diary Storage
  
• Mood Recording with Siri
  
• Statistic Charts Sharing
  
• Privacy Protection: Peeping Detection
  

Screen Shots

Technical Highlights

• Integrated SwiftUI with UIKit to create charts for data visualization and included sharing functionality.
• Implemented SiriKit to enable voice interactions with users by designated keyword ”Record Mood”.
• Integrated the Vision framework with the camera to achieve real- time face detection, enhancing app security and preventing unauthorized surveillance.
• Utilized Firebase Firestore for data storage, Firebase Storage to generate URLs from pictures on users’ devices, and Firebase Authentication to facilitate Apple sign-in functionality.
• Applied UICalendarView on the home page and enabled to convert date between DateComponent and DateFormatter formats.
• Developed essential app settings, such as password management, local notification and localization functionality.
• Managed the life cycle of the app and view controller to demonstrate various features, including Siri integration, login validation and password management.

Libraries

• Firestore
• IQKeyboardManagerSwift
• SwiftLint
• Crashlytics
• Kingfisher

Resources

• Icons made by iconixar from flaticon

Requirements

• Xcode 14 or later
  
• iOS 16.0 or later
  
• Swift 5

Version

• 1.3

Release Notes

Version	Notes
1.3	Added tutorial on App Store description
1.2	Fixed bugs
1.1	Two new features added
1.0	Submitted to the App Store

Acknowledgements

I would like to express my sincere gratitude to fumchin for his invaluable contributions to this project.

Special thanks for the following contributions:

• Assistance in implementation of the face detection function.
• Assistance in implementation of SwiftUI charts and statistics.
• Assistance in planning the entire application cycle.

Your contributions have been instrumental in the success of this project.

Thank you, fumchin!

Contact

Janet Chien 簡莉芯

• email: [email protected]
• 

License

This project is licensed under the terms of the MIT license.