Fixes #11

What was changed?
Introduced a client-side AI model integration that lets users upload an image and receive relevant search results based on visual similarity. The application now runs a Hugging Face model entirely in the browser, computes image embeddings, and compares them to a small set of reference images to generate results. (or at least this is the goal)

Why was it changed?
We needed a generic and easily maintainable way to integrate AI models into the app without relying on a separate backend. This ensures that we can swap in different models or pipelines with minimal effort, while meeting the requirement of handling images on the client and returning relevant search results.

How was it changed?
A local inference pipeline was added that downloads and runs a pre-trained model (CLIP) in the user’s browser. When an image is uploaded, it will be converted into an embedding, normalized, and compared to precomputed embeddings of reference images to determine similarity. This approach keeps the logic entirely on the client side, removing any dependency on external servers or APIs for model inference.

NOTE this implementation might change before merging