DAO3/main.py

import os
import pandas as pd
from dotenv import load_dotenv, find_dotenv
from sqlalchemy.engine import URL, create_engine
from sqlalchemy.engine.base import Engine


def get_engine() -> Engine:
    """Load env vars and return a SQLAlchemy engine connected to the database."""
    load_dotenv(find_dotenv())

    url = URL.create(
        drivername="postgresql+psycopg2",
        username=os.getenv("DATABASE_USERNAME"),
        password=os.getenv("DATABASE_PASSWORD"),
        host=os.getenv("DATABASE_IP"),
        port=int(os.getenv("DATABASE_PORT", 5432)),
        database=os.getenv("DATABASE_NAME"),
    )

    return create_engine(url)

def run_query(engine: Engine, query: str) -> pd.DataFrame:
    return pd.read_sql_query(query, engine)

def fetch_titles_and_genres(engine: Engine) -> pd.DataFrame:
    """Query title/genres from audiobookshelf.ao3 and return as a DataFrame."""
    query = """SELECT title, genres FROM audiobookshelf.ao3;"""
    return run_query(engine, query)

def count_genre_occurrences(engine: Engine) -> pd.DataFrame:
    """Count how many times each genre appears across all titles in SQL before returning."""
    query = """SELECT genre, COUNT(*) 
                FROM audiobookshelf.ao3, unnest(genres) AS genre
                GROUP BY genre
                ORDER BY COUNT(*) DESC;"""
    return run_query(engine, query)

def main():
    engine = get_engine()
    #data = fetch_titles_and_genres(engine)
    genre_counts = count_genre_occurrences(engine)
    print(genre_counts)
    # print(data.head())


if __name__ == "__main__":
    main()