Liber Opus -Book Recommender

A modern book recommendation system built with FastAPI and React. The application helps users discover new books based on their ratings and preferences, with a focus on technical and non-technical books. I use this by importing my Goodreads CSV and rating books that helps the LLM generate recommendations.

Check it out at https://liberopus.vercel.app/

Features

• 📚 Book discovery and recommendations powered by Google's Gemini LLM
• ⭐ Rate books and track your reading history
• 📋 Maintain a personal wishlist
• 🏷️ Technical vs non-technical book categorization
• 📊 Average rating system

Tech Stack

• Backend: FastAPI (Python)
• Database: SQLite
• Frontend: React with TypeScript
• AI: Google Gemini Pro LLM

Getting Started

Prerequisites

• Python 3.8+
• Node.js 14+
• SQLite
• Google Cloud API Key (for Gemini Pro)

API Endpoints

Books

• GET /books - Get all books
• POST /add-book - Add a new book
• POST /import-goodreads - Import books from Goodreads CSV

Ratings

• GET /ratings - Get all ratings
• POST /ratings - Rate a book

  {
    "book_id": "string",
    "rating": number (1-5)
  }

Recommendations

• POST /get-recommendations - Get personalized book recommendations

  {
    "user_history": ["book_id1", "book_id2"],
    "user_ratings": {
      "book_id": rating
    }
  }

Wishlist

• GET /wishlist - Get user's wishlist
• POST /wishlist/add - Add book to wishlist
• DELETE /wishlist/remove/{book_id} - Remove book from wishlist
• PUT /wishlist/reorder - Reorder wishlist items

Book Management

• POST /dismiss-book - Dismiss a book (won't show in recommendations)
• GET /dismissed-books - Get list of dismissed books

Database Schema

books:
  - id (TEXT PRIMARY KEY)
  - title (TEXT)
  - author (TEXT)
  - description (TEXT)
  - average_rating (REAL)
  - topics (TEXT)
  - technical_level (TEXT)
  - publication_year (INTEGER)
  - page_count (INTEGER)

ratings:
  - id (INTEGER PRIMARY KEY)
  - book_id (TEXT)
  - rating (INTEGER)
  - timestamp (DATETIME)

wishlist:
  - id (INTEGER PRIMARY KEY)
  - book_id (TEXT)
  - notes (TEXT)
  - added_at (DATETIME)
  - display_order (INTEGER)

dismissed_books:
  - id (INTEGER PRIMARY KEY)
  - book_id (TEXT)
  - timestamp (DATETIME)

Environment Variables

Create a .env file in the root directory:

DATABASE_URL=books.db
GOOGLE_API_KEY=your_google_api_key  # Required for Gemini recommendations

The recommendation system uses Google's Gemini Pro LLM (main.py) to provide personalized book suggestions based on:

• User's reading history
• Book ratings
• Technical preferences
• Reading patterns

The LLM analyzes the user's preferences and book metadata to generate contextually relevant recommendations, taking into account factors like:

• Technical vs non-technical preferences
• Author similarity
• Topic relationships
• Reading level progression

1. Backend: Deploy as a Web Service

   • Build Command: pip install -r requirements.txt
   • Start Command: uvicorn api:app --host 0.0.0.0 --port $PORT

2. Frontend: Deploy as a Static Site

   • Build Command: npm run build
   • Publish Directory: dist

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

This project is licensed under the MIT License.