  Using vision-language models (VLMs) in web development presents a promising
strategy to increase efficiency and unblock no-code solutions: by providing a
screenshot or a sketch of a UI, a VLM could generate the code to reproduce it,
for instance in a language like HTML. Despite the advancements in VLMs for
various tasks, the specific challenge of converting a screenshot into a
corresponding HTML has been minimally explored. We posit that this is mainly
due to the absence of a suitable, high-quality dataset. This work introduces
WebSight, a synthetic dataset consisting of 2 million pairs of HTML codes and
their corresponding screenshots. We fine-tune a foundational VLM on our dataset
and show proficiency in converting webpage screenshots to functional HTML code.
To accelerate the research in this area, we open-source WebSight.
