Screenshot-to-Code

Screenshot-to-Code is an AI-powered design-to-code tool that converts screenshots, mockups, Figma designs, and screen recordings into functional frontend code. It is designed to reduce the manual work involved in recreating an existing visual interface by allowing users to provide a visual reference and have AI generate a working implementation that can then be edited and refined.

The project is available both as a hosted web application at Screenshot to Code and as an open-source project on GitHub. The open-source version was created by Abi Raja and is released under the MIT License, allowing developers to inspect, modify, self-host, and contribute to the codebase. The hosted version provides a simpler experience for users who do not want to configure the application and AI model providers themselves. The repository’s license is dated 2023, establishing 2023 as the project’s launch year.

At its core, Screenshot-to-Code analyzes a visual reference and translates the visible structure into frontend code. This includes elements such as page layouts, navigation, cards, buttons, typography, spacing, colors, images, and other interface components. Instead of starting from an empty code editor, developers can use an existing visual design as the starting point for a working prototype.

The current project supports several frontend stacks, including HTML with Tailwind CSS, HTML with standard CSS, React with Tailwind CSS, Vue with Tailwind CSS, Bootstrap, Ionic with Tailwind, and SVG. This gives users flexibility when selecting the technology they want their generated interface to use.

Screenshot-to-Code also supports Figma designs as an input. This makes it useful for designers and developers working together because a visual design can be transformed into an initial implementation without manually rebuilding every component. The tool can also work with screen recordings, allowing it to observe a website or application in motion and create a functional prototype based on the recorded interaction.

The screen-recording capability extends the concept beyond static screenshots. A recording can communicate how an interface behaves, including navigation and interaction sequences. Screenshot-to-Code can process the recording and use the captured frames to create a prototype. The project’s documentation describes this as an experimental capability, so it is better suited to prototyping and exploration than as a guaranteed one-to-one recreation of a complex production application.

The tool also supports text-to-code workflows. Users can describe an interface or request changes to an existing generation, allowing the initial visual result to be iteratively refined. Additional edits consume credits in the hosted service, while the open-source version can be operated using the developer’s own model-provider API keys.

One of Screenshot-to-Code’s major strengths is its model flexibility. The project supports AI models from OpenAI, Anthropic, and Google Gemini. The current repository lists Gemini 3 Flash and Gemini 3.1 Pro, GPT-5.5 and GPT-5.4 Mini, and Claude Opus models among its supported/default model options. This allows users to compare different models and choose the provider that works best for their particular generation workflow.

The project also integrates with Replicate for image-generation and image-processing functionality. The current repository describes Replicate as supporting image generation, image editing, and background removal. Gemini can also be used for extracting real assets from screenshots, helping the generated result reuse recognizable images and visual assets rather than simply replacing everything with generic placeholders.

Screenshot-to-Code includes an iterative workflow rather than stopping after the first generated result. Users can edit and update an existing generation, create multiple variants, and use screenshot preview capabilities to allow the system to render its generated page and visually inspect the result. This makes the tool more useful for experimentation because the first generated version can be treated as a starting point rather than a final deliverable.

The open-source architecture uses a React/Vite frontend and FastAPI backend. Developers can run the project locally, customize it, self-host it, or contribute changes to the repository. Local installation requires at least one supported AI model-provider API key, while additional provider keys can be configured to access different models and compare their output.

For developers who want greater control over their data and infrastructure, the self-hosted option is an important advantage. Instead of relying exclusively on the hosted service, developers can run Screenshot-to-Code on their own infrastructure and configure their preferred AI providers. The project’s documentation also supports Docker-based setup, making deployment easier for users who prefer containerized environments.

The hosted service simplifies this experience by handling the application infrastructure and providing a credit-based pricing model. Each creation or additional edit consumes one credit. Current hosted plans include Hobby and Pro, with 100 and 500 monthly credits respectively. Credits reset each month and do not roll over.

Screenshot-to-Code is particularly useful for frontend developers, designers, agencies, startups, and product teams that need to turn visual references into working prototypes quickly. It can be used to reproduce an existing website interface, turn a Figma concept into code, experiment with different frontend implementations, or create a prototype from a recorded interaction.

It is also useful for learning and experimentation. Developers can examine how AI translates visual layouts into HTML, CSS, React, Vue, or other supported stacks and then refine the generated implementation manually. Because the project is open source, technically inclined users can also inspect how the system works and adapt it for their own workflows.

The main limitation is that visual similarity does not automatically mean production readiness. Generated code may need cleanup, restructuring, accessibility improvements, responsive adjustments, performance optimization, testing, and integration with real application logic. Complex business logic and backend functionality generally require additional development beyond the visual reconstruction.

Another consideration is that the hosted service uses monthly credits, meaning frequent generations and iterative edits can consume credits quickly. Users running the open-source version avoid the hosted subscription but are responsible for their own infrastructure and AI-provider costs.

For rapid frontend prototyping and visual reconstruction, Screenshot-to-Code offers a compelling combination of AI generation, multiple frontend stacks, model flexibility, open-source availability, and a hosted service. Its strongest value is accelerating the first stage of implementation while leaving developers with editable code that can be refined into a more complete application.

Pricing

The hosted Screenshot-to-Code service currently offers two paid plans: Hobby at $15/month and Pro at $40/month. Hobby includes 100 credits per month, while Pro includes 500 credits per month. Each creation or edit consumes one credit, and unused credits do not roll over to the following month.

The project is also open source and free to self-host under the MIT License. However, self-hosted users generally need to provide their own AI model-provider API keys, meaning the software itself can be free while external AI usage may incur separate costs.

Review

Screenshot-to-Code is a strong option for developers who want to move from visual references to functional frontend code quickly. Its ability to accept screenshots, mockups, Figma designs, text instructions, and even screen recordings gives it a broader input range than a basic screenshot converter.

The open-source nature of the project is another major advantage. Developers can inspect the source code, self-host the application, customize it, and select their own AI providers. The MIT License makes the project particularly attractive to developers and organizations that want more control than a conventional closed SaaS product provides.

Its support for React, Vue, HTML, Tailwind CSS, Bootstrap, Ionic, and SVG also makes it flexible for frontend experimentation. The ability to use OpenAI, Anthropic, and Gemini models means users are not tied to a single AI provider.

The hosted version is convenient for users who want to start immediately, but its credit-based pricing means frequent generations and edits can increase usage quickly. The open-source version avoids the subscription but requires more technical setup and responsibility for AI API costs.

The most important limitation is that generated code should be treated as a starting point. A visually accurate recreation may still require substantial engineering work before it is suitable for a production application. Accessibility, maintainability, application architecture, authentication, databases, testing, and complex business logic remain areas where developer involvement is important.

For designers, frontend developers, agencies, and product teams focused on rapid prototyping and UI reconstruction, Screenshot-to-Code provides a practical bridge between visual references and editable frontend code.