BOOK COLLECTOR

Video Demo: https://youtu.be/_zr8rmAvYV4?feature=shared

Project Structure:

• project.py - main file of the application
• test_project.py - test file for project.py
• completed_books.json - json file containing information about user's completed books. This file will be created when you rate a book and updated each time you add, update or delete a book
• .env - optional file if you want to get recommendations from Chat GPT based on your completed books
• requirements.txt - file with dependencies required to run this application

Description:

My project is a CLI that allows users to manage their book collection and provides them with following functionalities:

• Browsing books from Google Books API by author or title
• CRUD functionalities on user's book collection:
  • Adding books to their collection by giving them a score
  • Viewing books from their collection
  • Updating scores of their completed books
  • Deleting books from their collection
• Getting personalized book recommendations from Chat GPT based on user's completed books and given scores

How to run:

Install requirements

pip install requirements.txt

Browse books

python project.py --title "book of the five rings"

View completed books

python project.py --completed

If you want to get recommendations from Chat GPT

Create a .env in file in project directory

touch .env

Set your Open AI API key as an environment variable

API_KEY="your key"

Get recommendations from Chat GPT

python project.py --recommend

Command Arguments:

Search books by title

--title TITLE, -t TITLE

Search books by author

--author AUTHOR, -a AUTHOR

View your completed books

--completed, -c 

Get book recommendations based on your completed books list. Required OpenAi API KEY in .env

--recommend, -r       

Design choices and considerations

One of the main challenges was implementing the book recommendation feature. Initially, I considered recommending books based on the highest-rated ones in the user’s favorite categories. However, this approach was limited because not all books have average ratings or categories, and it lacked true personalization. It would mostly suggest popular books rather than those aligned with the user’s unique preferences.

To overcome this, I decided to use a Large Language Model like ChatGPT for generating recommendations. This approach was simple to implement: I send a POST request with the user’s completed books and receive personalized suggestions in return. ChatGPT’s ability to analyze reading patterns and preferences made it a much more powerful and flexible solution than relying solely on category-based recommendations.