Exploratory Data Analysis: Gamma Spectroscopy in Python (Part 3)

This guide demonstrates how to deploy a pre-trained XGBoost machine learning model for identifying radioactive isotopes from gamma spectroscopy data. The model is designed to work with data collected from a Radiacode scintillation detector. Two practical deployment approaches are presented with code examples: a public web application built with Streamlit and a more flexible HTMX-based Python app capable of real-time hardware interaction. The process involves wrapping the classification model in a Python class and building a user interface for file uploads and result visualization.