How to create & deploy an API in Python! (with interactive documentation)
In this video, we work through the process of creating and deploying a Python3 based API using libraries such as Flask, Flask-Restful, & Flasgger.
Link to code: https://github.com/KeithGalli/python-...
Part 2: • Python Project: Implement a REST API ...
In this first video, we start with the basics, setting up GET endpoints and learning how to deploy our API to the cloud (using Render.com). This part is crucial for understanding the core functions of APIs and making them accessible worldwide.
In the second video, we progress to implementing POST requests and constructing a full-fledged Book Review API, similar to services like Goodreads. This involves integrating API functionality to post and retrieve book reviews from a database, with practical examples using Airtable for database interaction.
Flasgger plays a crucial role in our tutorial, providing interactive documentation through its SwaggerUI integration. This feature allows you to visualize and interact with your API directly at http://localhost:5000/apidocs, offering a clear and user-friendly overview of your API's capabilities.
Additionally, we cover essential practices like working with GitHub repositories, managing Python libraries, and employing Flask and Flask-Restful for API development. The skills in crafting effective GET and POST requests are emphasized, highlighting the importance of efficient system communication.
This video is ideal for anyone keen on enhancing their Python programming, API development, and cloud deployment skills. Whether you're a beginner or an experienced developer, this tutorial provides valuable experience in developing well-documented and user-friendly APIs.
Video timeline!
0:00 - Video overview
1:18 - What we're building
3:20 - How to get setup with Github template code
7:00 - Taking a look at the Flask, Flasgger Python3 code
8:38 - Testing some API requests (GET) locally
13:09 - Building another GET request endpoint (with multiple parameters)
14:34 - Using ChatGPT to help us build another endpoint
22:43 - Deploying our API to a live public URL endpoint (using render.com)
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