LabelLens: AI-Powered Packaging Accessibility Assistant

Inspiration

Product labels contain useful information, but understanding them can be difficult when the text is small, the information is unfamiliar, or the colors are hard to distinguish. I wanted to explore how AI could make product-label information easier to access and understand through a simple, user-friendly application.

What LabelLens Does

LabelLens is an AI-powered packaging accessibility assistant that analyzes uploaded label images and presents useful information in a more understandable format. It combines image-based text extraction with AI-assisted analysis and color insights to help users explore the information on a product label.

The goal is to make label information easier to interact with while keeping the experience simple and accessible.

How I Built It

I built LabelLens as a web application using Streamlit. Users can upload an image of a product label, which is processed using a vision-capable AI model to extract relevant information. The application then analyzes the extracted text and presents the results through an accessible interface.

The project uses Python, Streamlit, image processing, and Groq-hosted AI models. I also worked on improving the interface so that users can read the results more comfortably.

Challenges I Faced

One of the main challenges was integrating AI-powered image analysis with the application and handling model limitations, including output-token limits and API rate restrictions. I had to troubleshoot errors, adjust model settings, and test the application again to get it running successfully.

Another challenge was making the interface clear and usable rather than simply displaying raw AI output. Working through these issues helped me better understand AI API integration, deployment, error handling, and the process of turning an idea into a working web application.

What I Learned

Through LabelLens, I gained practical experience connecting AI models to a web application, processing uploaded images, debugging API errors, and deploying a project online. I also learned how important it is to design AI applications around the user's needs, not just the underlying technology.

What's Next

I would like to improve the accuracy of label interpretation, strengthen accessibility features, and expand the types of packaging information LabelLens can analyze. My long-term goal is to make product-label information easier for more people to understand and use.