Streamlit is such a nice product. When iterating on an ML product and trying to give other users a quick and an easy way to interact with a model, Streamlit should be one of the first option to think of.

Integrating with Streamlit[1] was also very simple, in our case, we only had to expose how we serve Tensorboards and Notebooks on our platform, and we created a couple of tutorials[2] to show how to host an app. Several of our users started using it after that as the default way for sharing interactive and customizable dashboards on their Kubernetes clusters.

[1] https://polyaxon.com/integrations/streamlit/

[2] https://polyaxon.com/docs/intro/quick-start/deploying-ml-app...

From a lot of bittersweet experiences, I tend to be very wary of “drastically simple” API products. We often say APIs are forever - so it is so critical to build the right abstraction. When I saw the demo on their page, yes it’s magically (their words) simple, but it also smells strongly of an API that can start leaking as complexity rises.

Dave Cutler of NT fame used to say leaky abstractions are often worse than no abstractions, and early on, it’s often better to go with a lower-level API (for example, what Tensorflow did) because you can add a higher level one later (eg Keras) based on usage and reasonable defaults.

Heavy reliance on strings with logical meaning? Another strong smell.

Streamlit looks very accessible - which is amazing in this space, and I hope they do well - but my prediction is people will be importing “streamlit.v2” within two years.

Streamlit is awesome.

It's nicer than sending someone static results, and it isn't much more effort.

And vastly better than sending a notebook to someone unless you expect them to modify the notebook a lot.

And learning time to make Streamlit useful for a small internal apps is probably ~15 minutes for most people.

Here's an informing podcast episode with one of the Streamlit authors, https://talkpython.fm/episodes/show/260/from-basic-script-to....

Cynical about frameworks but huge fan of this one. I find myself using Streamlit not just to communicate data analyses with other people, but also to explore the data myself, and even for projects I never intend to publish. Being able to produce an interactive visualization quickly and entirely from Python makes so much difference to my research productivity. I just hope the VC cash doesn't complicate things.

Been using Streamlit at work for over a year. It's great for quickly building internal apps where latency or styling is not a concern (though it is a lot prettier than notebooks).

I've been using Streamlit for the past year and am consistently shocked with how quickly the team ships what the community asks for (everything from secrets, to themes, to making developing on top of Streamlit easier with components, etc). Good for them!

Out of curiosity I saw what this product was about. It appears to be like a chatroom in the sense that its live but with an API or live application refresh where the programmer can write lines of code that immediately show app results. This is pretty cool but I don't know if its a real product other than live coding interviews or the most intense pressure cooker of your manager having you do the TPS reports right now. If coupled with GPT3, however, it might be perfect, as you could just speak what you want and then it can output python that then updates this.

Congrats Streamlit!

Congrats! Been on my to-do to try Streamlit. What are companies mainly using it for?

Can somebody explain the advantage of Streamlit over sharing a Jupiter notebook with interactive widgets? Are data scientists sharing models and data a relevant use case?

is it strange to anyone else how similar the logos for https://streamlit.io/ and https://getstream.io/ are?

Looks interesting but has anyone managed to find pricing anywhere?

I don't get this product how is it any different than replit.com or they are competitors? Anyway good idea.

That makes sense. "MVP" use cases make perfect sense. But I suppose you could also make dynamic python that another app created just like HTML is the target response for many web apps. New possibilities. I will check this out more.

Congrats as well!

We've been loving it for making internal point-and-click tools + external project starters (ex: tutorials, solution engineering, ..).

Today, if you and your users are coding-heavy for data flows (dataset -> pydata wrangling/ml/... -> ui), jupyter notebooks are #1, and if you're in bigco, maybe say databricks notebooks as #2. However, most operational users really want an interactive point-and-click dashboard UI. Tableau and friends don't make as much sense in the pydata world, mostly for simpler SQL-only flows, and the existing python dashboarding tools (voila, panels, plotly, ...) have been too much work, esp. when sharing.

I've been liking Streamlit as it's pretty opinionated + prestyled (less work!), simple interaction model (accessible!), etc. It clearly can be better, but is already so much more accessible than our experiences with other tools here.

As some examples:

* We've been building https://github.com/graphistry/graph-app-kit for people building mini graph apps (one-click self-hosted launch via docker + st dashboard + graphistry viz + optional graph db connectors + optional graph compute tools).

* We're releasing 2-3 more integration + tutorial sets this month, where they're both notebook modes + dashboard modes.

* We just ran a hackathon for the same w/ the TigerGraph team: https://tigergraph-web-app-hack.devpost.com/

* projectdomino.org uses it internal for anti-misinfo dashboard tools. Our devs/data sci/some advanced osint researchers are fine w/ nb's, but everyone else needs dashboard UIs.

I'm excited to see what 2021 + 2022 bring here, and esp. if they can keep increasing accessibility all the way to no-code!

I use it internally to show interactive results and explain stats concepts to PMs/Eng, so for example I was trying to explain exactly how increased sample sizes change the confidence intervals around a binomial proportion as the proportion changes from .5 -> 1, and then relate that to how much it costs for the business, etc. So I made a Streamlit app in a few minutes, and let them interact with it.

I'm using it for internal debug tools. Lookup data from various APIs, databases, show status info for servers, that kind of thing. Often easier to create one and run it locally than write scripts and keep rerunning them with different bits of data.

We are using it for data health dashboards for all our big data projects,hence we are able to observe discrepancies, missing data, etc.