ScreenCoder – An open-source intelligent UI screenshot-to-frontend-code generator

What is ScreenCoder？

ScreenCoder is an open-source intelligent UI screenshot-to-code system that rapidly converts any design screenshot into clean, editable HTML/CSS code. Utilizing a modular multi-agent architecture combined with visual understanding, layout planning, and code synthesis technologies, ScreenCoder produces high-precision, semantic frontend code. Users can easily modify layouts and styles as needed, achieving seamless integration between design and development. It is ideal for rapid prototyping and pixel-perfect interface construction, significantly boosting frontend development efficiency.

Key Features of ScreenCoder

• UI Screenshot to Code
  Supports quick conversion of any UI screenshot or design prototype into clean, editable HTML/CSS code.

• High-Precision Code Generation
  The generated code is visually aligned and semantically faithful to the original design.

• Customizable Modifications
  Allows users to adjust layout and styles based on requirements, facilitating secondary development.

• Multi-Model Support
  Compatible with various generation models including Doubao, Qwen, GPT, Gemini, etc., giving users flexibility to choose.

• Fast Deployment
  The generated code can be used directly in production, supporting rapid prototyping and pixel-perfect interface creation.

Technical Principles of ScreenCoder

• Grounding Agent
  Uses vision-language models (VLM) to identify and tag major structural components in UI images such as sidebars, headers, and navbars. Text prompts guide the detection of specific components, returning bounding boxes and semantic labels. The system performs deduplication, conflict resolution, and fallback recovery to ensure accuracy, inferring the main content area. The output is a layout dictionary that provides foundational data for layout planning and code generation.

• Planning Agent
  Builds a hierarchical layout tree based on grounding outputs to provide structural context for code generation. It organizes detected components into a tree structure using spatial heuristics and composition rules. A root container fills the viewport, generating absolutely positioned .box elements for each top-level region, inserting inner <div class="container grid"> elements when CSS Grid layout is needed. Each node is annotated with grid template configurations and ordering metadata for direct compilation into HTML/CSS.

• Generation Agent
  Converts the semantic layout tree into executable HTML/CSS code. Driven by natural language prompts, it constructs adaptive prompts for each component in the layout tree and generates corresponding code via language models. Prompts include semantic labels, layout context, and user instructions if provided. The generated code is assembled maintaining hierarchy, order, and layout configurations. Gray placeholders in code are replaced with actual images from the original screenshot to restore visual and semantic consistency.

Official Resources

• GitHub Repository: https://github.com/leigest519/ScreenCoder

• arXiv Paper: https://arxiv.org/pdf/2507.22827

• Online Demo: https://huggingface.co/spaces/Jimmyzheng-10/ScreenCoder

Application Scenarios of ScreenCoder

• Frontend Development Acceleration
  Quickly converts UI design screenshots into high-quality HTML/CSS code, significantly shortening frontend development cycles and reducing manual coding workload.

• Design-Development Collaboration
  Directly transforms design screenshots into operational code, promoting seamless collaboration between design and development teams, reducing communication overhead, and ensuring accurate implementation of design intent.

• Rapid Prototyping
  Instantly turns design concepts into interactive frontend prototypes, speeding up early product validation and user testing, supporting fast iteration and UX optimization.

• Education and Training
  Serves as an educational tool to help students and novice developers intuitively understand the relationship between UI design and frontend code, accelerating learning and improving practical skills.

• Small Teams and Startups
  Provides efficient code generation solutions for resource-constrained small teams and startups, enabling rapid launch of product prototypes or minimum viable products (MVPs), lowering development costs and accelerating time to market.