We propose a novel code assistant and generation paradigm aimed at closing the gap between visual sketching and code creation for Machine Learning (ML) development. This approach empowers developers and ML practitioners to translate hand-drawn sketches into functional code with enhanced accuracy and usability. Developers are recruited to assess the tool’s performance. This research contributes to the future of low-code approaches, facilitating ML application development, and promoting an intuitive and accessible programming environment.