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Paper ID:IJSREM61995

Codify –An Image To Code Converter Using Artificial Intelligence

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM61995

Volume:

10 |

4 |

April 2026

Pages:

1-7

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM61995

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

KJ

Kunal Joshi

Department of Computer Science and Engineering

I.T.S Engineering College,Greater Noida,India

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K

Krishna

Department of Computer Science and Engineering

I.T.S Engineering College,Greater Noida,India

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M

Ms.Radha

Department of Computer Science and Engineering

I.T.S Engineering College,Greater Noida,India

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Abstract

The increasing demand for rapid web and application development has intensified the need for efficient methods to convert user interface (UI) designs into functional code. Traditionally, designers create high-fidelity UI layouts using tools such as Figma or Adobe XD, and developers manually translate these designs into frontend code. This process is not only time-consuming but also prone to inconsistencies and repetitive effort. This paper presents Codeify, an intelligent image-to-code conversion system that automates the transformation of UI screenshots into structured frontend code. The system leverages a multimodal artificial intelligence model capable of understanding visual layouts and generating corresponding code. Unlike earlier approaches that relied on convolutional neural networks or rule-based systems, Codeify utilizes ChatGPT-4o for both image analysis and code generation, enabling more contextual and accurate output. The system is implemented using Next.js for the frontend along with Tailwind CSS and Bootstrap for styling. It supports multiple output formats, including HTML/CSS and React.js components, making it adaptable to modern development workflows. Experimental evaluation demonstrates that Codeify significantly reduces development time while maintaining high code quality and consistency. The proposed approach highlights the potential of AI-driven automation in bridging the gap between UI design and frontend implementation.

KEYWORDS

Next.js Codeify

Cite This Article

Kunal Joshi, Krishna, Ms.Radha, "Codify –An Image To Code Converter Using Artificial Intelligence," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 4, pp. 1-7, April 2026. https://doi.org/10.55041/IJSREM61995

Full Article/Conclusion

This work presented Codeify, a system that converts user interface images into structured frontend code. The main aim of the project was to reduce the effort required in manual UI development and provide a faster way to convert designs into working code.   From the results, it can be observed that the system performs well for most common UI designs. It is able to correctly identify major components such as buttons, input fields, images, and layout sections. The generated layouts are close to the original design, especially for simple and moderately complex interfaces. In addition, the system produces clean and readable code, which can be directly used or modified by developers.   The system shows strong performance in maintaining layout structure and reducing repetitive work. It helps in speeding up the development process and ensures better consistency between design and implementation. Although some minor issues were observed in complex layouts, these do not significantly affect the usability of the output.   However, there are still some areas where improvements can be made. The current system may face difficulty in handling highly complex or deeply nested UI structures. In some cases, closely placed elements or overlapping components may not be interpreted perfectly. Improving layout understanding in such cases can further increase the accuracy of the system.   In the future, the system can be extended to support more advanced features. One possible improvement is adding support for dynamic functionality, such as JavaScript behavior and interactive elements. Another important enhancement is integration with design tools, which would allow direct conversion from design files to code.   The system can also be improved by using better layout analysis techniques and training on a wider variety of UI designs. This will help in handling more complex and real-world scenarios. Additionally, providing a live preview feature where users can see and edit the generated code in real time would make the system more user-friendly.   Overall, Codeify shows good potential as a practical tool for frontend development. With further improvements, it can become a reliable solution for automating UI-to-code conversion and reducing development time.

References

[1]    T. Beltramelli, “Pix2Code: Generating Code from a Graphical User Interface Screenshot,” Proceedings of the ACM Symposium on User Interface Software and Technology, 2017.   [2]  A. Vaswani et al., “Attention Is All You Need,” Advances in Neural Information Processing Systems (NeurIPS), 2017. [3]  A. Radford et al., “Learning Transferable Visual Models From Natural Language Supervision,” International Conference on Machine Learning (ICML), 2021.   [4]  OpenAI, “GPT-4 Technical Report,” 2023.   [5]    K. Chen et al., “UIED: A Hybrid Deep Learning Framework for UI Element Detection,” IEEE Access, vol. 8, pp. 123456–123467, 2020.   [6]  Y. Li et al., “LayoutLM: Pre-training of Text and Layout for Document Image Understanding,” ACM SIGKDD, 2020.   [7]    M. Abadi et al., “TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems,” 2016.   [8]  Facebook, “React: A JavaScript Library for Building User Interfaces,” 2024.   [9]  Vercel, “Next.js Documentation,” 2024.   [10]  Tailwind Labs, “Tailwind CSS Documentation,” 2024.   [11]  Bootstrap Team, “Bootstrap Documentation,” 2024.   [12]   S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997.

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Publication Date

4/30/2026

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Review Rounds: 2

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