AI-Powered Image Organizer

Table 12

Inspiration In today’s digital world, our devices quickly become cluttered with screenshots, downloads, and random saved images, making it overwhelming to manually sort and delete unnecessary files. Screenshots, images for assignments keep taking up space, mostly on our desktop. The tedious process of reviewing images one by one inspired us to build an AI-driven solution that automates categorization and simplifies image management. Our goal was to create a tool that reduces clutter, saves time, and enhances productivity for users dealing with large collections of images.

Automatically categorizes images using Google Gemini AI
Allows users to quickly review, keep, or delete images
Moves deleted images to the Recycle Bin for safety
Provides category-based viewing for easy navigation
Offers drag & drop functionality for seamless file management
Notifies users when all images in a category or overall collection are reviewed With an intuitive UI, this application helps users effortlessly declutter their images while maintaining full control over their files.

Python + PyQt6 for an interactive desktop UI
Google Gemini API for AI-powered image classification
Pillow (PIL) for image rendering
Send2Trash for safe file management (Recycle Bin support)
FastAPI (Optional Backend) for handling API requests By integrating AI-driven classification, a user-friendly UI, and efficient file operations, we built a robust and scalable solution.

Ensuring AI-generated categories were accurate and meaningful
Implementing seamless drag & drop functionality in PyQt6
Preserving user control by allowing images to be "kept" instead of deleted Through troubleshooting and iterative improvements, we overcame these challenges to deliver a smooth and intelligent user experience.

Successfully integrated AI-powered categorization for images
Built a modern, visually appealing UI with Women in Computing theme.
Implemented a Recycle Bin feature to prevent accidental deletions
Developed a seamless "Keep" functionality to retain important images
Ensured real-time category updates and completion notifications These features significantly improve how users manage their images, making it an efficient, user-centric solution.

How to integrate AI with a desktop application for real-world automation
Best practices for managing file operations and UI interactions in PyQt6
Handling user experience (UX) challenges like category-based viewing and notifications
Optimizing AI classification for better image sorting and organization This project deepened our understanding of AI-driven applications, UI/UX design, and efficient file handling.

Looking ahead, we plan to:

Enhance AI categorization accuracy with more advanced deep-learning models
Introduce duplicate image detection to remove redundant files
Expand support for additional file types like PDFs and GIFs
Optimize performance for handling extremely large datasets By continuously improving functionality and AI capabilities, we aim to make image management even smarter, faster, and more intuitive.