Recent advances in Artificial Intelligence have catalyzed innovation across diverse domains, including software engineering. Traditionally, applications of modern Large Language Models (LLMs) have been centered on creating code-writing assistants that generate code from a given context or textual description of the desired outcome. This thesis presents a novel approach, focusing on the development of a coding assistant that harnesses the multi-modal capabilities of state-of-the-art LLMs to facilitate front-end development from a graphical representation, in the form of an image. The proposed system employs sophisticated computer vision techniques to detect and analyze graphical elements, subsequently utilizing a multi-modal LLM to translate these elements into an intermediate, language-agnostic format, which is then converted into the desired target language.