A WSGI middleware component is a Python callable that is itself a WSGI application, but may handle requests by delegating to other WSGI applications. These applications can themselves be WSGI middleware components.
A middleware component can perform such functions as:
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The WSGI has two sides:
Between the server and the application, there may be one or more WSGI middleware components, which implement both sides of the API, typically in Python code.
WSGI does not specify how the Python interpreter should be started, nor how the application object should be loaded or configured, and different frameworks and webservers achieve this in different ways.
WSGI stands for Web Server Gateway Interface and represents a simple calling convention for web servers to forward requests to web applications or frameworks written in the Python programming language. The current version of WSGI, version 1.0.1, is specified in Python Enhancement Proposal (PEP) 3333.
Why use WSGI and not just point a web server directly at an application?
A traditional web server does not understand or have any way to run Python applications.
Therefore the Python community came up with WSGI as a standard interface that modules and containers could implement. WSGI is now the accepted approach for running Python web applications.
As shown in the above diagram, a WSGI server simply invokes a callable object on the WSGI application as defined by the PEP 3333 standard.
Some examples of WSGI servers are: Green Unicorn, uWSGI, mod_wsgi and CherryPy.
NGINX is open source software for web serving, reverse proxying, caching, load balancing, media streaming, and more. It started out as a web server designed for maximum performance and stability. In addition to its HTTP server capabilities, NGINX can also function as a proxy server for email (IMAP, POP3, and SMTP) and a reverse proxy and load balancer for HTTP, TCP, and UDP servers.
Dynamic programming languages like Python and TypeScript allows developers to optionally define type annotations and benefit from the advantages of static typing such as better code completion, early bug detection.
However, retrofitting types is a cumbersome and error-prone process. To address this, we propose Type4Py , an ML-based type auto-completion for Python. It assists developers to gradually add type annotations to their codebases. In the following, I describe Type4Py’s pipeline, model, deployments, and the development of its VSCode extension and more.
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