An electrician has developed RapidTag, a software tool designed to automate the manual labeling of wires and cables based on technical documentation. The project was created to address the inefficiency of manually entering connection data for industrial automation cabinets, a process that previously required significant time and labor.
The developer utilized AI models, including DeepSeek and Claude, to assist in writing Python code and building the user interface. The application is built using the Streamlit framework, allowing it to run in a browser. It enables users to select components directly from PDF schematics, which the program then processes into labels.
RapidTag allows for batch creation of labels, manual editing of results, and exporting data in formats compatible with various printers. The tool is intended to handle repetitive tasks while relying on the operator to interpret complex schematics, as the software does not fully automate the reading of technical drawings.
The project is currently available as version 2.1.0 on GitHub under the MIT license. It is designed for local execution on Windows systems with Python installed. The developer notes that while AI significantly lowered the barrier to entry for building the tool, human oversight remains essential for accuracy.
