flet VS fastapi

> When you run flet build command it ... Packages Python app using package command of serious_python package. -- https://flet.dev/docs/guides/python/packaging-app-for-distri...

It looks like Flet is for client-side code. It lets you write Flutter apps with Python instead of Dart.

> Simple Architecture - No more complex architecture with JavaScript frontend, REST API backend, database, cache, etc. With Flet you just write a monolith stateful app in Python only and get multi-user, realtime Single-Page Application (SPA). -- https://flet.dev

If I'm writing Python that runs on the mobile device, it must talk to a server to read & write data. Doesn't this still require an API backend, database, cache, etc?

I just learned of Flet (https://flet.dev) which seems interesting for Python. I may try this as well.

maybe check out https://flet.dev

When developing or selecting an MRZ (Machine Readable Zone) recognition SDK, the primary challenge lies in finding an appropriate dataset for testing. Acquiring genuine MRZ images is challenging, and due to privacy concerns, they aren't publicly accessible. Therefore, crafting MRZ images becomes a practical solution. Fortunately, there's an open-source Python MRZ generator project, available for download from pypi, eliminating the need to start from scratch. This article aims to illustrate how to integrate and run Python scripts within web applications. First, We will showcase how to employ the Python MRZ SDK and Flet to construct a cross-platform MRZ generator. Subsequently, we will reuse the Python script with Pyodide, HTML5, and the Dynamsoft JavaScript MRZ SDK, creating an advanced online MRZ tool that can handle both MRZ creation and MRZ detection.

"The fastest way to build Flutter apps in Python" is the title of Flet's web page. As someone coming from the Flutter world reading the line I draw an ideal picture of "swapping Dart language for Python and magically having the whole power of Flutter framework and the tips of your fingers".

Well I haven't seen anyone mention Flet, which is pleasant (if maybe not all that complete) if you have Dart/Flutter experience, so increment your counter at least one. :-)

https://flet.dev/

That’s when I found FastAPI.

FastAPI & Uvicorn

Discussion at FastAPI GitHub: https://github.com/tiangolo/fastapi/issues/2696

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.