from sqlalchemy import create_engine
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker

SQLALCHEMY_DATABASE_URL = "sqlite:///./db_dev.sqlite3"  # to be configure
# SQLALCHEMY_DATABASE_URL = "postgresql://user:password@postgresserver/db"

engine = create_engine(
    SQLALCHEMY_DATABASE_URL, connect_args={"check_same_thread": False}
)
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)

Base = declarative_base()


def create_db():
    import models

    models.Base.metadata.create_all(bind=engine)


def _json_quotes(s):
    return s.replace("'", '"')


def adapt_movie_data(data_in: dict):
    import copy, json

    data_out = copy.deepcopy(data_in)

    data_out["genres"] = [
        genre["name"] for genre in json.loads(_json_quotes(data_in["genres"]))
    ]

    data_out["description"] = data_in["overview"]

    return data_out


def fill_db(
    db=SessionLocal(),
    movie_input_file: str = "input_data/movies_metadata.csv",
    sample_rate=100,
):
    import crud
    import csv
    import random

    page_size = 1_00

    def compute_rate(*dummy):
        if 0 < sample_rate < 100:
            return random.random() < (sample_rate / 100)
        return True

    with open(movie_input_file) as csvfile:
        for count, movie_data in enumerate(
            filter(compute_rate, csv.DictReader(csvfile)), start=1
        ):
            if count % page_size == 0:
                db.commit()

            adapted_data = adapt_movie_data(movie_data)

            if not adapted_data["title"]:
                print(count, "should be fixed")
                print(adapted_data)
                continue

            crud.create_movie(db, batch_mode=True, **adapted_data)
        db.commit()


if __name__ == "__main__":
    create_db()
    fill_db(sample_rate=1)