Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREMWP49321
Volume:
9 |
5 |
May 2025
Pages:
Publication Frequency:
Monthly (12 issues per year)
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.
This project focuses on the development of a full-stack SaaS (Software as a Service) application that enables the generation of AI-driven digital art from user-inputted text prompts. Leveraging the MERN stack—MongoDB for data management, Express.js and Node.js for backend services, and React.js for an interactive frontend interface—the application offers a robust and scalable solution tailored for creatives, designers, and digital content producers. Central to the functionality is the integration of the ClipDrop API, which processes natural language prompts and returns high-quality AI-generated images using advanced machine learning models.The platform incorporates a secure user authentication system powered by JSON Web Tokens (JWT), ensuring reliable session handling and access control. A credit-based usage model is implemented, wherein each image generation deducts a predefined number of credits from the user’s account. To support monetization, the system features a fully functional online payment gateway (e.g., Razorpay or Stripe), allowing users to seamlessly purchase additional credits. All user activity, including prompt history and generated images, is stored and managed in a cloud-based database, offering persistent access and efficient record-keeping.The application also emphasizes responsive design through the use of Tailwind CSS, ensuring a smooth user experience across devices. This project demonstrates the practical implementation of generative AI in real-world applications and highlights the potential of combining cloud-based architectures, AI APIs, and modern web development frameworks to build scalable, revenue-generating platforms.
KEYWORDS
Publication Date
1/5/2025
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
0
Total views since publication
PDF Downloads
2
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...