Here's a README file for deploying the book_management_system application:

---

Book Management System

Overview

The Book Management System is a FastAPI application designed for managing book information,user reviews,summarization of book summary using GROQ API of llama3 model and generating book recommendations based on user genres and ratings using k-Nearest Neighbour. It integrates with a PostgreSQL database and uses a machine learning model for recommendations.

Folder Structure

book_management_system/
├── Dockerfile
├── config
│   ├── __init__.py
│   └── settings.py
├── docker-compose.yml
├── generate_data.py
├── load_model_data.py
├── main.py
├── requirements.txt
├── routers
│   ├── __init__.py
│   ├── books.py
│   ├── reviews.py
│   ├── summary.py
│   └── user_routes.py
├── sql_app
│   ├── __init__.py
│   ├── crud.py
│   ├── database.py
│   ├── models.py
│   └── schemas.py
├── synthetic_books.csv
├── test_main.py
├── train_dataset
│   ├── books_df.pkl
│   ├── label_encoder.pkl
│   └── recommendation_model.pkl
├── train_model.py
└── utils
    ├── __init__.py
    ├── llama3_summary.py
    └── security.py

Prerequisites

• Docker
• Docker Compose

Environment Variables

Create a .env file in the root directory of your project with the following content:

DATABASE_URL=postgresql+asyncpg://airflow:airflow@db/book_management_system
POSTGRES_USER=airflow
POSTGRES_PASSWORD=airflow
POSTGRES_DB=book_management_system
SECRET_KEY=your_secret_key
ALGORITHM=HS256
ACCESS_TOKEN_EXPIRE_MINUTES=30

Replace your_secret_key with a secure key of your choice.

Setup and Deployment

1. Build and Start the Application:

   Run the following command to build and start the Docker containers:

   docker-compose up --build

   This command will:

   • Build the Docker images.
   • Start the PostgreSQL database container.
   • Start the FastAPI application container.

2. Verify the Deployment:

   Once the containers are up, you can access the FastAPI application at http://localhost:8000.

3. Initialize the Database:

   The application will automatically create the necessary database tables on startup. Ensure that the database connection parameters in the .env file are correct.

4. Trained model and dataset

   Trained the recommendation model with synthetic_books.csv by running

   python train_model.py

   saved model and dataframe pickle and label encoder files in the train_dataset directory.

5. Async is used for the API's and database operations

6. Volumes for model data and postgres

   Folder volume is used for the application data Docker volume is used for postgres data

API Endpoints

• User Management:

  • POST /users/: Create a new user.
  • POST /login/: Authenticate a user and get a JWT token.

• Book Management:

  • POST /books/: Add a new book.
  • GET /books/: Retrieve all books.
  • GET /books/{id}: Retrieve a specific book by its ID.
  • PUT /books/{id}: Update a book's information by its ID.
  • DELETE /books/{id}: Delete a book by its ID.

• Reviews:

  • POST /books/{id}/reviews: Add a review for a book.
  • GET /books/{id}/reviews: Retrieve all reviews for a book.

• Summary:

  • GET /books/{id}/summary: Get a summary and aggregated rating for a book.
  • POST /generate-summary: Generate a summary for a given book content.

• Recommendations:

  • POST /recommendations: Get book recommendations based on user preferences.

Testing

To run the unit tests, use:

pyyest test_main.py

Notes

• Ensure that the .env file is correctly configured with database credentials and secret keys.
• The docker-compose.yml file includes configuration for both the PostgreSQL database and the FastAPI application.
• For any changes in the code or configurations, rebuild the Docker images using docker-compose up --build.