Overview
Developed a comprehensive Flask application that delivers real-time AI-powered art analysis, combining advanced machine learning techniques with intuitive user interface design for accessible art critique.
As a UROP under the "Future Sketches" research group, I created an innovative application that bridges the gap between AI technology and art analysis. The project demonstrates the potential of combining computer vision, natural language processing, and user-centered design to create meaningful interactions between users and AI systems in the creative domain.
Key Features
-
RAG Implementation
Integrated Retrieval-Augmented Generation (RAG) architecture to provide contextually relevant and informed art analysis based on extensive art historical knowledge.
-
CLIP ONNX Integration
Leveraged CLIP (Contrastive Language-Image Pre-training) through ONNX runtime for efficient image understanding and cross-modal analysis capabilities.
-
Real-time Analysis
Developed full-stack functionality enabling immediate AI-powered feedback and critique on uploaded artwork with responsive user interface.
-
API Integration
Implemented robust API architecture to support seamless communication between frontend interface and AI analysis backend systems.
Technical Implementation
The development process involved creating a sophisticated AI pipeline that processes visual art through multiple analytical layers. Key technical achievements included:
Flask Backend
Architected a scalable Flask application with efficient routing and data processing capabilities.
AI Model Integration
Successfully integrated multiple AI models including CLIP for visual analysis and language models for critique generation.
Frontend Development
Created an intuitive user interface that makes complex AI analysis accessible to users of all technical backgrounds.