clip-retrieval VS gradio

The tool clip-retreival lets you filter those 400 million images to whatever subsets you're interested in --- for example, 10,000 images of (mostly) handwriting.

Hehe, well you know, PR welcome, the front end is 500 lines https://github.com/rom1504/clip-retrieval/blob/main/front/sr...

Other people have done a few alternate front ends already

This one is meant to be functional, but could sure be made prettier

This might work: https://github.com/rom1504/clip-retrieval .

HaveIBeenTrained uses clip retrieval to search the Laion-5B and Laion-400M image datasets. These are currently the largest public text-to-image datsets, and they are used to train models like Stable Diffusion, Imagen, among many others.

Done https://github.com/rom1504/clip-retrieval/commit/53e3383f58b...

Using clip for searching is better than direct text indexing for a variety of reasons but here for example because it matches better what stable diffusion sees

Based on OpenAI's CLIP and the clip-retrieval library (https://github.com/rom1504/clip-retrieval), I've built an end-to-end demo for a semantic and similarity image search engine. It's incredibly powerful for finding similar images amongst large image datasets, or just submitting text/natural language queries and finding the most relevant images in your dataset. Really useful tool for introspection into large datasets before annotation or ML work begins. This could potentially be used to filter or downsize your datasets by several orders of magnitude and make annotation and ML work easier and less costly.

Checkout the demo here:

http://ec2-52-39-251-116.us-west-2.compute.amazonaws.com/

And you can checkout our website or email me for updates and email list, etc.:

https://machineperception.co

Assuming you have an NVIDIA GPU, you can build a semantic search engine by indexing CLIP embeds (image or text).

https://github.com/rom1504/clip-retrieval

gradio is a package developed to ease the development of app interfaces in python and other languages (GitHub)

There's also that library all the AI models started using that gives you a public URL to share. After researching it: https://www.gradio.app/ is the link.

It's used specifically for making simple UIs for machine learning apps. But I guess technically you could use it for anything.

What is the business model for https://www.taipy.io/, https://streamlit.io/, or https://www.gradio.app/? These are nice tools - but how will the sponsoring businesses support themselves? I didn't see any mention of enterprise plans, etc. Is the answer simply that "we've not announced our revenue model yet"? What should one expect?

Repo : https://github.com/gradio-app/gradio

There's TensorFlow.js for running machine learning on JavaScript, but personally, I'd prefer using the Python Gradio package, which is designed for creating UIs for machine learning inference demos.

I found an issue on gradio github but looks like it's closed so I am not sure if it's still a common issue or only I am facing it due to certain settings/absence of a fix. ( https://github.com/gradio-app/gradio/issues/3060 )

I disagree hard with this – for instance I've recently needed to dig into the code for the Gradio library, and when PRs are like https://github.com/gradio-app/gradio/pull/3300 (and the merge commit's message is what it is) it's hard to understand why some decisions have been made when doing `git annotate` later on.

Gradio for a ui that your commanderGPT can visit and use

gradio is documented, it doesn't seem very complex, it would be something like moving this block under the other one. i think it's ui_extra_networks.py, the file you are looking to edit. (if you do it make a copy to restore when you go to update)

Gradio did have an issue for a while where your URL was guessable, so unless you had a password it was pretty easy to find, but as far as I know they've increased the complexity so much that it's no longer an issue.