Dummy-Repo

New updates in main

2. Recommendations:

• Users receives a list of book recommendations.
• Users can like, dislike, and rate books.
• Users can change the genres selected anytime.
• Based on user feedback (likes, dislikes, ratings), new recommendations are generated using the LLM.

3. Filtering Recommendations:

• Users can filter and access old recommendations.
• Recommendations are stored and can be revisited at any time.

Tech Stack

• Kotlin Multiplatform (KMP):

  • Shared business logic is written in Kotlin.
  • Native UI for Android using Jetpack Compose.
  • Native UI for iOS using SwiftUI.

• Backend:

  • Supabase is used for backend services.

• Generative AI:

  • OpenAI is used for generating book information.

• Authentication:

  • Google OAuth is used for authentication in both the Android and iOS apps via Supabase.

Project Structure

Shared Module

• Contains business and shared logic for both apps i.e. composeApp and iosApp

Directory	Description	Important file
api	Contains API client files for accessing external service like OpenAi, Gemini	‘GeminiClient.kt’, ‘OpenAiClient.kt’
data	Contains file for database operations and authentication operations	‘RemoteDataSource.kt’, ‘SupabaseProvider.kt’
model	Defines model file to parse the request and response from AI as well as database tables	‘OpenAiRequest.kt’, ‘OpenAiResponse.kt’, ‘SupabaseRemoteEntities.kt’
utils	Contains Constants classes which store api url and keys and extension functions	‘Constants.kt’

• Note:

  • The Constants.kt file includes the following properties which you can update with their own values:
    • OAUTH_WEB_CLIENT_ID: Google web client client id generated from google cloud console to use sign in with google feature
    • OPEN_AI_API_KEY: Your OpenAI api key
    • GEMINI_API_KEY: Your Gemini api key (if you want to use gemini instead of OpenAi along with GeminiClient)
  • The GeminiClient.kt file is included for those who want to use Gemini for book recommendation generation. Replace GeminiClient's functions with OpenAiClient in MainViewModel.kt of composeApp and SharedViewModel.swift of iosApp
  • You can edit systemInstruction property inside OpenAiClient/GeminClient to customize the AI response according to your own needs.

Android UI

• The Android UI specific code is written under composeApp module's androidMain with jetpack compose. It contains code for all screens along with viewmodel which connect the data layer i.e. shared module to UI layer

ios UI

• The ios UI specific code is written under iosApp with swiftui along with viewmodel which connects data layer i.e. shared module to UI layer

Backend Structure

Database tables

1. Users

• Used to store user’s info and also maintains certain info from their login session.

Fields	Datatype	Constraint	Description
user_id	UUID	primary-key, default = auth.uid()	unique identifier for user
email	text	not null, unique	user's email provided via Google OAuth
display_name	text	not null	username provided via Google OAuth
avatar_url	text		user's profile photo provided via Google OAuth
category_shown	boolean		Describes if the user was shown category selection screen in the login session
selected_categories	text		Describes the selected categories/genres by the user
last_recommendation_time	int8		Describes the last recommendation generation timestamp of user
created_at	timestampz	not null	Stores the first time the account was logged in the app

2. Categories (Genres)

• To store the genres/categories of books from which user can generate his recommendation

Fields	Datatype	Constraint	Description
category_id	int2	primary-key	uniquely identify each genre/category
category_name	text	not-null	name of each genre
category_emoji	text		emoji for each genre

3. Books

• To store generated book’s information

Fields	Datatype	Constraint	Description
book_id	int8	primary-key	unique identifier
book_name	text	not null, unique	book title
author_name	text	not null, unique	author name
genre_tags	text[]		applicable genre tags
category_id	int2	not null, foreign key referring category_id of categories table with on update cascade, on delete cascade	refers to the category in the categories table
description	text	not null	2 line description
pages	text	not null	number of pages in the book
isbn	text	not null	unique ISBN number to fetch book cover using Open Library Book Cover API
first_date_of_publication	text	not null	first date of publication
reference_link	text	not null	URL to open book-related webpage

4. Recommendations

• To store the recommendation generated by each user

Fields	Datatype	Constraint	Description
recommendation_id	int8	primary-key	unique identifier for recommendations
timestamp	int8	not null	stores UNIX epoch time
user_id	UUID	not null, foreign key referring to users table user_id field with on update cascade, on delete cascade, default = auth.uid()	uniquely identifies which recommendation belongs to which user

5. Recommended_books

• To store the books generated in each recommendation seperately

Fields	Datatype	Constraint	Description
id	int8	primary-key	uniquely identifies each book for a given recommendation_id
recommendation_id	int8	not null, foreign key referring to users table user_id with on update cascade, on delete cascade	to access corresponding info in the recommendation table
book_id	int8	foreign key referring to books table book_id field with on update cascade, on delete cascade	to access the book’s info from the books table
liked	boolean		indicates if the book is liked by the user
rating	int2	check value is between 0 and 6	rating between 1 to 5 given by the user
last_updated_time	int8	default = 0	last time the user performed any interaction
read	boolean	default = false	indicates if the user has read the book

6. Chat_history

• To store the chat_history which is used to provide context when sending chat_completion request to LLM

Fields	Datatype	Constraint	Description
id	int8	primary-key	unique identifier
timestamp	int8	not null	stores UNIX epoch time
user_id	UUID	foreign key referring to users table user_id field	identifies to which user the chat history belongs
user_text	text		user message sent to OpenAI or any other LLM’s API request
ai_answer	text		response received from AI

• Overview
  • A user can have many recommendations (one to many)
  • A recommendation can have many recommended_books (one to many)
  • A recommended_book has one to one relation with book (one to one)
  • A user can have many chat_history data (one to many)

Getting Started

Prerequisites

• Kotlin Multiplatform Mobile
• Jetpack Compose
• SwiftUI
• Supabase
• OpenAI chat completions API