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Step-by-Step Guide to Creating a FastAPI Web App

Ingredients

  • Python 3.7 or later
  • FastAPI: Latest version
  • Uvicorn: Latest version
  • Hypercorn (optional): Latest version
  • Pydantic: Latest version
  • SQLAlchemy: Latest version (if using SQL databases)
  • Gino Starlette: Latest version (for asynchronous SQL operations)
  • Alembic: Latest version (for database migrations)
  • PostgreSQL: Latest version (or any other SQL database)
  • Docker: Latest version (for containerization)

Steps and instructions

  1. Install Python 3.7 or later on your system.
  2. Set up a new Python virtual environment to isolate your project dependencies.
  3. Inside the virtual environment, install the latest versions of FastAPI, Uvicorn/Hypercorn, Pydantic, and SQLAlchemy using pip.
  4. Optionally, if you plan on performing asynchronous SQL operations, install the latest version of Gino Starlette.
  5. If you plan on using a SQL database, install PostgreSQL or your preferred SQL database.
  6. Install Alembic for handling database migrations.
  7. Create a new FastAPI project by creating a main.py file and importing FastAPI from the fastapi module.
  8. Define your application routes using the FastAPI router.
  9. For each route, define the HTTP methods (GET, POST, etc.) and the related operations.
  10. Use Pydantic to define the data models for your application.
  11. If using a database, use SQLAlchemy to define your database models and perform database operations.
  12. Use Alembic to handle database versioning and perform database migrations.
  13. Test your application locally by running the Uvicorn or Hypercorn server.
  14. If everything is working as expected, create a Dockerfile for your application.
  15. Build a Docker image from your Dockerfile and run a Docker container to deploy your application.

Tools for making

  • Computer/Laptop - Needed to write code and run the FastAPI application.
  • Text Editor/IDE - Essential for writing and editing the Python code for the FastAPI project.
  • Terminal/Command Prompt - Used to run commands to install dependencies, start the server, and manage the project.
  • Web Browser - To test and interact with the FastAPI web application during development.
  • Docker - If using containerization, Docker is required to build and run the Docker image of the FastAPI application.
  • Postman/Insomnia - Optional but helpful tools for testing API endpoints and requests.

Recipe variations

  • Using FastAPI with a different ASGI server such as Daphne or Gunicorn.
  • Integrating a different database system like MongoDB or SQLite instead of PostgreSQL.
  • Implementing authentication and authorization using OAuth2 with FastAPI.
  • Adding frontend frameworks like React or Vue.js to create a full-stack application.
  • Exploring middleware options in FastAPI for additional request/response handling.
  • Utilizing FastAPI's dependency injection system for cleaner and more modular code.
  • Experimenting with different API documentation tools like Swagger UI or ReDoc for FastAPI.
  • Exploring FastAPI's WebSocket capabilities for real-time communication features.
  • Implementing caching mechanisms using tools like Redis in conjunction with FastAPI.

Recipe overview

FastAPI Web Application Recipe

Welcome to our FastAPI Web Application recipe! This guide will help you create a robust, scalable, and efficient web application using the FastAPI framework. FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.7+ based on standard Python type hints.

In this recipe, we will walk through the process of setting up your development environment, creating a FastAPI application, defining routes, implementing CRUD operations, and setting up a database. We will also go over testing your application locally and deploying it using Docker, a popular containerization tool.

By the end of this guide, you will have a working FastAPI web application, complete with database operations and containerized for easy deployment. Let's get started!

TimeApprox. 3 hr
ServingsThis recipe is for developing a single FastAPI web app. The "servings" in this context can be considered as the number of end-users it can serve, which is theoretically unlimited, depending on the server's capacity and performance.
CaloriesApprox. ,
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Common questions

  1. What version of Python is required for this FastAPI Web App? Python 3.7 or later is required for this FastAPI Web App.
  2. Do I need to install any specific database for this recipe? If you plan on using a SQL database, you can install PostgreSQL or any other SQL database of your choice.
  3. Is Docker necessary for deploying the FastAPI Web App? Docker is optional but recommended for containerizing your FastAPI application for deployment.
  4. What is the purpose of Pydantic in this FastAPI recipe? Pydantic is used to define data models for request and response payloads in the FastAPI application.
  5. Why use SQLAlchemy in this FastAPI Web App? SQLAlchemy is used for defining database models and performing database operations if your application requires database interaction.

Serving dishes and utensils

  • Cutting Board - For prepping ingredients and slicing items.
  • Chef's Knife - Essential for chopping, dicing, and slicing ingredients.
  • Mixing Bowls - To mix and combine various components of the recipe.
  • Measuring Cups and Spoons - For precise measurement of ingredients.
  • Saucepan - For heating and simmering sauces or liquids.
  • Skillet - Used for sautéing, frying, or searing ingredients.
  • Wooden Spoon - Ideal for stirring and mixing without damaging cookware.

Origin stories

FastAPI is not a food but a modern, fast (high-performance), web framework for building APIs with Python 3.6+ based on standard Python type hints. The key features are fast to code, fewer bugs, intuitive, easy, short, robust, standard-based, language agnostic, easy to use, and scalable. Created by Sebastián Ramírez, it quickly gained popularity for its performance and intuitive design. Ramírez was inspired to create FastAPI after creating API tools for other frameworks which were well received, and he saw a need for a framework that could handle the modern demands for asynchronous tasks. It's not just the tech community who's taken a liking to FastAPI; it's also been adopted in academia and industry for tasks ranging from machine learning to e-commerce.

Disclaimer: This recipe was not created by humans and we cannot ensure that it will turn out as expected. We do not guarantee or take any liability for the accuracy of this recipe (including steps, ingredients, nutritional information, and all sections on this page). You should check to make sure you are not allergic to any ingredients and take safety precautions while making this. The images on this page are generated by AI and may not accurately represent the result of making this recipe.

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