Python developers can leverage Mac OS's native vision recognition framework to create apps that automatically detect and extract text from screenshots.
Tools like Rumps and PY2APP simplify the process of building and distributing Python apps for Mac OS.
Pi-Objective-C serves as a bridge between Python and Objective-C, enabling Python developers to access Mac OS's native capabilities and create powerful, native-like applications.
Deep dives
Creating Mac OS Apps with Python
Python is not traditionally known for deploying packaged apps with native OS-level capabilities, but it can be done. In this podcast episode, Rhett Turnbull joins to discuss the development of his distributable Mac OS app, Textinator. Using Python, Rhett leverages Mac OS's native vision recognition framework to create an app that automatically detects and extracts text from screenshots. Through Python, Mac OS frameworks such as vision and machine learning can be accessed using the Pi-Objective-C library, allowing developers to tap into native capabilities and create Python apps with native functionality.
Working with Rumps and PY2APP
To build Textinator as a Mac OS app, Rhett utilizes two key tools: Rumps and PY2APP. Rumps, an acronym for 'Ridiculously Uncomplicated macOS Python Status Bar Apps', simplifies the process of creating status bar menu apps in Python. With just a few lines of code, developers can create a native app with a status bar menu icon and dropdown menus. PY2APP is used to bundle the Python code into an app that can be distributed. It packages the Python runtime and the necessary libraries, enabling the app to be run on machines without Python installed. Together, Rumps and PY2APP provide an efficient way to build and distribute Python apps for Mac OS.
Accessing OS-level Capabilities with Pi-Objective-C
By integrating Pi-Objective-C, Python developers gain access to the full range of Mac OS's native capabilities. Pi-Objective-C serves as a bridge between Python and Objective-C, allowing the use of Apple's native frameworks and APIs. In the case of Textinator, Pi-Objective-C helps to connect with Mac OS's vision recognition framework, enabling the app to process and extract text from screenshots automatically. This powerful integration showcases how Python developers can harness the extensive functionalities provided by Mac OS and create robust, native-like applications.
Building Mac Apps with Python and PyObjC
The podcast episode explores the process of building Mac apps with Python and PyObjC. The speaker explains how to use different tools and libraries, such as Rumps and Py2App, to create fully functional Mac applications. They discuss the importance of requesting permissions and entitlements, as well as the challenges and benefits of using Objective-C in combination with Python. The episode highlights the Vision framework and the Natural Language ML APIs as powerful tools for computer vision and natural language processing in Python on the Mac. Overall, the episode serves as a roadmap for developers interested in building Mac applications with Python.
Textinator: A Python App with Mac-Specific Features
The podcast episode also showcases the development of a specific Mac app called Textinator using Python. The speaker explains how they utilized Rumps and Py2App to create this app, which allows for text extraction from images on the Mac. They explore the process of requesting desktop access permissions and demonstrate the simplicity and usefulness of using PyObjC to access Mac-specific features, such as the Vision framework for text detection and the Natural Language ML APIs. The episode encourages listeners to explore PyObjC and other tools to build their own Mac apps and highlights the benefits of leveraging Python's simplicity and power in Mac development.
