Sistema Web para delegacias com geração de imagens fotorrealistas baseadas em Inteligência Artificial a partir de relatos textuais

Abstract

This work presents the development of a web application designed for police station management, focusing on integrating artificial intelligence (AI) to generate photorealistic images from textual descriptions to enhance criminal investigations. Built on a client-server architecture, the system employs Next.js for the front-end, Flask for the back-end, and the Stable Diffusion model for image generation. The methodology combined applied, exploratory, and experimentais approaches with incremental prototyping to ensure scalability and usability. Requirements analysis identified gaps in virtual police station systems, such as the lack of advanced visual tools, guiding the development of features like creation, management, and finalization of incident reports, secure JWT authentication, and AI integration. System modeling utilized UML, including use case, class, sequence, activity, and deployment diagrams. Integration and functional tests, conducted with tools like Postman and Cypress, validated the application's efficiency and robustness. The results demonstrate that the system meets its objectives, providing an innovative solution that streamlines suspect identification and improves incident reporting efficiency, with potential for future integration with biometric databases and further AI model adaptation