Getting Started with FastAPI
Introduction: Getting Started with Your Cooking App
Welcome to your first step in building the AI Cooking Helper! In this lesson, you will learn how to set up the foundation of your web application using a modern web framework that is fast, efficient, and easy to use. This tool is designed for building APIs and web applications quickly, making it a great choice for projects that need to scale and remain maintainable.
By the end of this lesson, you will have a working project that is connected to a database and ready to serve static files and handle cross-origin requests. This setup is essential for storing recipes, user data, and more as you continue to develop your cooking app. Everything you learn here will be used in future lessons as you add more features and functionality.
Project Structure Basics
Before we dive in, let’s look at how your project will be organized. A typical project using this tool has a main file (like main.py) that starts the application, and folders (like app/) that hold different parts of the code, such as routes and database configuration.
For example, your project might look like this:
main.pyis where your application starts.- The
app/folder keeps your code organized by separating routes and database setup. - The
db/folder insideapp/contains files related to database configuration.
This structure helps keep your code clean and easy to manage as your project grows.
FastAPI is chosen for this project because it offers several advantages:
- Async support: FastAPI is built on top of modern Python async features, allowing for high performance and efficient handling of many simultaneous requests.
- Automatic OpenAPI/docs generation: FastAPI automatically generates interactive API documentation (using Swagger UI and ReDoc) based on your code, making it easy to test and understand your API.
- Pydantic validation: FastAPI uses Pydantic for data validation and serialization, ensuring that incoming and outgoing data matches your defined models and reducing boilerplate code.
However, there are some trade-offs compared to alternatives like Django or Flask:
- Smaller ecosystem: FastAPI’s ecosystem is newer and smaller than Django’s or Flask’s, so there may be fewer third-party plugins or community resources available.
- Learning curve with async: While async support is powerful, it can introduce a learning curve if you are new to asynchronous programming in Python. Some libraries may not be fully compatible with async code, requiring extra care when integrating them.
