Backend example

This example showcases the BESSER backend generator’s capability to produce essential components for a backend service, based on the Library example B-UML model.

The generator creates a modular backend — a slim main_api.py entry point, one router module per class under routers/, the shared database.py session setup, plus the ORM and validation models. Here’s a snippet from the generated files:

main_api.py: The FastAPI application entry point that wires in one router per class:

app = FastAPI(title="Library model API", ...)

############################################
#   Routers
############################################

app.include_router(library_router.router)
app.include_router(book_router.router)
app.include_router(author_router.router)

routers/book.py: All of the Book endpoints live in their own router module:

from database import get_db

router = APIRouter()

@router.get("/book/", response_model=None, tags=["Book"])
def get_all_book(detailed: bool = False, database: Session = Depends(get_db)) -> list:
    book_list = database.query(Book).all()
    return book_list

database.py: The shared engine and session setup (the SQLite file defaults to data/Library model.db and can be overridden with the DATABASE_URL environment variable):

def init_db():
    SQLALCHEMY_DATABASE_URL = os.getenv("DATABASE_URL", "sqlite:///./data/Library model.db")
    ...
    SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
    Base.metadata.create_all(bind=engine)
    return SessionLocal

sql_alchemy.py: This file includes the SQLAlchemy ORM models that map Python classes to database tables:

class Book(Base):
 __tablename__ = "book"
 id: Mapped[int] = mapped_column(primary_key=True)
 pages: Mapped[int] = mapped_column(Integer)
 title: Mapped[str] = mapped_column(String(100))
 release: Mapped[datetime] = mapped_column(DateTime)


#--- Foreign keys and relationships of the library table
Library.has: Mapped[List["Book"]] = relationship("Book", back_populates="locatedIn")

pydantic_classes.py : Comprises Pydantic models for data validation and serialization:

class BookCreate(BaseModel):
  pages: int
  title: str
  release: datetime
  library_id: int
  authors: Optional[List[Union["AuthorCreate", int]]] = None

After launching the main_api.py file, the server will be up and running, and the client can interact with the backend service through the defined REST API endpoints. It will create a SQLite database according to the defined models in the sql_alchemy.py file.

After doing POST request to the endpoint, the database will be updated with the new book information:

Note

It is important to note that the generated code is a starting point and can be further customized to meet the specific requirements of the backend service.