EDIT: to be fair, it does work slightly better on mobile.
[1]: https://imgur.com/a/pZMNiIU.
[2]: https://www.dkriesel.com/en/blog/2013/0802_xerox-workcentres...
Reading "Sketch Recognition" I was expecting something akin to Google draw which recognises objects like cars and trees. The digit classifier is of course fine, but I felt a bit disappointed when I realised it "only" does numbers. I also think it's the least impressive of the demos you have.
[1] https://en.wikipedia.org/wiki/Alien_(creature_in_Alien_franc...
The options are limited, the UI is ugly, customisability is near non-existent.
I dumped using it, and focused entirely on Streamlit.
I am satisfied. At a previous company, we used Gradio + Strelit to demo apps to clients. We ditched Gradio and switched fully to Streamlit.
I recommend Streamlit over Gradio any day.
What Gradio has better than Streamlit is just better marketing.
The CEO twitted tirelessly about Gradio demos that others built, and these are the most eye-catching parts of AI "research". So those tweets would catch attention and RTs.
Then they hired one of the most followed person in the AI space- Abdul Khalique. He is @ak<some_number> on Twitter. He, for a long time, tweets new and noticeable arXiv papers, and hence has a considerable following.
Gradio hired him, and he now posted arXiv papers and their Gradio demos, and tagged Gradio at each tweet. That's how the word got around and with their marketing game, they increased market share.
Now I see the acquisition.
I would say that the CEO had this acquisition as his goal for a long time.
He would post Gradio demos, tag Hugging Face at each possible tweets.
Gradio is sub-quality product at best, and useless at worst.
While I cannot speak to your experience with either project, I do want to point out that open-source doesn't have to be a zero-sum game. Gradio has different goals than Streamlit, which has different goals than Flask and Django.
In the end, congratulations to both HuggingFace and Gradio, Streamlit looks forward to seeing what they end up building!
In other words, great for INTERNAL apps or DEMOS but not for a SaaS product?
We used CNNs to make a list of visually similar products to end users. Before deploying that, we used Streamlit to demo the client.
The backend was run on AWS, and we used GPU for inference.
If the client said that our results were satisfactory, then we would simply store the data in a DB (using PostgreSQL), and that will be used to show similar products to end users (consumers), and here no GPU or Streamlit was involved.
This Streamlit demo was a core part of our (B2B) business.
I have also used Streamlit for a public frontend receiving thousands of hits per hour with multiple GPUs for inference. It could tackle that.
> my conclusion is that you can do a few simple things super easily, but you hit a wall when you want to have anything beyond basic navigation and layout
That's the reason Streamlit was used. We did not need a very customisable frontend. When we needed that, we passed that to our webdev team who used Js +// Vue/React +// Django/Flask.
You use Streamlit when you don't need a very custom interface. You use it so that you can very quickly prototype something.
