In order to improve the accessibility and user friendliness of an accurately pretrained stacking ensemble machine learning regressor used to predict sulfate levels (mg/L) in Acid Mine Drainage (AMD), a Graphical User Interface (GUI) was developed using Python by combining human input with ChatGPT and deployed in the Jupyter Notebook environment. This was achieved by combining human prompts with ChatGPT to create an error message feedback loop to generate the working code for the GUI. This allowed for the model to be used by those with no/low programming experience and limited machine learning knowledge, which further contributed to expanding the accessibility and usefulness of the developed model. ChatGPT also proved to be helpful in learning how to use Anaconda Navigator as well as the Jupyter Notebook environment for first time users. Therefore, this study serves as a template and inspiration to undergraduate and postgraduate students in the chemical sciences to develop GUIs related to their specific field of work without having to worry about the hardcore programming skills that are not usually taught in chemistry curricula. Lesson plans pertaining to using ChatGPT to develop apps for undergraduates (effect of varying concentration) and postgraduates (Infrared plotter) are provided as scaffolds to integrate ChatGPT into chemistry curricula.