Scholarlink

<p>The exponential growth of the internet has led to the creation of over 1.11 billion active websites, with approximately 252,000 new sites emerging daily. This burgeoning landscape underscores a pressing need for rapid and diverse website development, particularly to support advanced functionalities like Web3 interfaces and AI-generated content platforms. Traditional methods that manually convert visual designs into functional code are not only time-consuming but also error-prone, especially challenging for non-experts. In this paper, we introduce “UI2HTML” an innovative system that harnesses the capabilities of Web Real-Time Communication and Large Language Models (LLMs) to convert website layout designs into functional user interface (UI) code. The UI2HTML system employs a sophisticated divide-and-conquer approach, augmented by Chain of Thought reasoning, to enhance the processing and accurate analysis of UI designs. It efficiently captures real-time video and audio inputs from product managers via mobile devices, utilizing advanced image processing algorithms like OpenCV to extract and categorize UI elements. This rich data, complemented by audio descriptions of UI components, is processed by backend cloud services employing Multimodal Large Language Models (MLLMs). These AI agents interpret the multimodal data to generate requirement documents and initial software architecture drafts, effectively automating the translation of webpage designs into executable code. Our comprehensive evaluation demonstrates that UI2HTML significantly outperforms existing methods in terms of visual similarity and functional accuracy through extensive testing across real-world datasets and various MLLM configurations. By offering a robust solution for the automated generation of UI code from screenshots, UI2HTML sets a new benchmark in the field, particularly beneficial in today’s fast-evolving digital environment.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

UI2HTML: utilizing LLM agents with chain of thought to convert UI into HTML code

  • 期刊论文

  • 发表日期：

    2025年4月17日

查看全文
  • Dawei Yuan,
  • Guocang Yang,
  • Tao Zhang

摘要

The exponential growth of the internet has led to the creation of over 1.11 billion active websites, with approximately 252,000 new sites emerging daily. This burgeoning landscape underscores a pressing need for rapid and diverse website development, particularly to support advanced functionalities like Web3 interfaces and AI-generated content platforms. Traditional methods that manually convert visual designs into functional code are not only time-consuming but also error-prone, especially challenging for non-experts. In this paper, we introduce “UI2HTML” an innovative system that harnesses the capabilities of Web Real-Time Communication and Large Language Models (LLMs) to convert website layout designs into functional user interface (UI) code. The UI2HTML system employs a sophisticated divide-and-conquer approach, augmented by Chain of Thought reasoning, to enhance the processing and accurate analysis of UI designs. It efficiently captures real-time video and audio inputs from product managers via mobile devices, utilizing advanced image processing algorithms like OpenCV to extract and categorize UI elements. This rich data, complemented by audio descriptions of UI components, is processed by backend cloud services employing Multimodal Large Language Models (MLLMs). These AI agents interpret the multimodal data to generate requirement documents and initial software architecture drafts, effectively automating the translation of webpage designs into executable code. Our comprehensive evaluation demonstrates that UI2HTML significantly outperforms existing methods in terms of visual similarity and functional accuracy through extensive testing across real-world datasets and various MLLM configurations. By offering a robust solution for the automated generation of UI code from screenshots, UI2HTML sets a new benchmark in the field, particularly beneficial in today’s fast-evolving digital environment.