microblog VS fastapi

In this blog, we're going to address the "on any website" part of a Search Solution. Or at least - propose a starting point for it. There are many great tutorials out there for a deep dive on Flask - one of the best from my colleague Miguel.

- https://blog.miguelgrinberg.com/post/the-flask-mega-tutorial...

If you'd like to deploy a web app, Flask is your best friend. It's very user friendly and there's a lot of great tutorials online. The only thing you'd need other than Python knowledge is some basic understanding of HTML/CSS and Jinja notation for variables, both of which are pretty intuitive to learn. Good luck!

I can't speak highly enough of Miguel Grinberg's work with Python/Flask (https://blog.miguelgrinberg.com/post/the-flask-mega-tutorial...) and the community he's created around it, for both beginners and advanced folks.

Racing through his mega tutorial was a great refresher for me on the fundamentals, and it's easy to plug in computer vision & related libraries/extensions/packages.

Use miguel grinberg’s tutorial https://blog.miguelgrinberg.com/post/the-flask-mega-tutorial-part-i-hello-world

I got a recommendation (from a few places) to use Miguel Grinberg's microblog series to help me get up to speed on some flask things. I'm on ch 15 with blueprints, and am running into pylint cyclic import errors, both on my app and in the actual project (https://github.com/miguelgrinberg/microblog/tree/v0.15?search=1)

In the project root directory create a folder called static with one subfolder called js and another called css. The js folder will contain all of the needed local JavaScript files while the css folder will contain all the CSS stylesheets. In the js folder create a file called index.js and in the css folder one called style.css. Just leave them empty for now. If you want to find out more about web development with Flask I suggest you try out this tutorial. Your current project structure should like this:

https://blog.miguelgrinberg.com/post/the-flask-mega-tutorial-part-i-hello-world is not perfect, but a great start.

An API application framework (such as FastAPI)

It’s been my experience that async Python frameworks tend to turn IO bound problems into CPU bound problems with a high enough request rate, because due to their nature they act as unbounded queues.

This ends up made worse if you’re using sync routes.

If you’re constrained on a resource such as a database connection pool, your framework will continue to pull http requests off the wire that a sane client will cancel and retry due to timeouts because it takes too long to get a connection out of the pool. Since there isn’t a straightforward way to cancel the execution of a route handler in every Python http framework I’ve seen exhibit this problem, the problem quickly snowballs.

This is an issue with fastapi, too- https://github.com/tiangolo/fastapi/issues/5759

Fast API.

We will create our API using FastAPI, a modern high-performance web framework for building fast APIs with Python. It is designed to be easy to use, efficient, and highly scalable. Some key features of FastAPI include:

FastAPI is a modern, fast, web framework for building APIs with Python 3.7+ based on standard Python type hints. It is designed to be easy to use, fast to run, and secure. In this blog post, we’ll explore the key features of FastAPI and walk through the process of creating a simple API using this powerful framework.

FastAPI is a modern, fast web framework for building APIs with Python 3.7+ that automatically generates OpenAPI and JSON Schema documentation. While FastAPI simplifies API development, manually creating and updating API documentation can still be a time-consuming task. In this blog post, we’ll explore how to leverage FastAPI’s automatic documentation generation capabilities, specifically focusing on Swagger and ReDoc, and how to streamline the process of documenting your APIs.

TiTiler is a dynamic tile server built on FastAPI and Rasterio/GDAL. Its main features include support for Cloud Optimized GeoTIFF(COG), multiple projection methods, various output formats (JPEG, JP2, PNG, WEBP, GTIFF, NumpyTile), WMTS, and virtual mosaic. It also provides Lambda and ECS deployment environments using AWS CDK.

To make it a bit more realistic, we’re going to use a FastAPI route as an example, and we’re also going to use FastAPI’s dependency injection, which can really help with readability (and testability, but more on that later).

2. FastAPI - Turbocharge Your Web APIs with Python ⚡