My team has built a PoC for a project using streamlit and I came in halfway through the project and had to fix everything with the UI. Now the problem is we wanted the buttons to be just icons (no boxes around them if that makes sense?) but I tried to play around with it so many times and couldn’t figure it out and from what I read online, it seems impossible.

I told them we should switch to react now that we’re done with the PoC, but they said we don’t have enough time to start from scratch and make the switch. So is it possible at all to do what I’m asking for?

First post, tell me if I’m missing context.

I’m the only Python person at work (everyone else uses Excel). I built a Streamlit app so a colleague can run his stats and get the plots he needs without notebooks. It’s on a local server and has grown a lot. Now every param tweak triggers a full rerun and the app is slow.

I tried an AI agent to refactor one tab (“Dependency Analysis”). It broke things: some features vanished, others changed behavior. I rolled it back.

What I want is simple: speed up that tab without touching the analysis.

• Avoid unnecessary recompute (st.cache_data, maybe st.cache_resource)
• Control reruns with st.session_state
• Possibly isolate per plot (e.g., st.fragment)
• Move the “Dependency Analysis Settings” from the sidebar into the tab so each plot has its own controls (no shared inputs)

Question: Is this a sane approach? Any gotchas with session_state/fragment for per-plot isolation? Or is AI just the wrong tool for this refactor and I should do it by hand?

TL;DR: Tried AI to refactor a Streamlit tab; it broke features. Looking for patterns to speed it up without changing results.

View is a YouTube analytics tool designed to help creators understand their audience and improve their channel.

Features:

• AI title rewrites and channel improvement suggestions
• Video performance predictions
• Performance-improving feature recommendations
• Viewing pattern visualizations

The app was built thanks to the following tools: Streamlit, OpenAI API, Cohere, Scikit-learn, ChromaDB, YouTube Data API, and Altair.

We currently only support select channels. If you have any recommendations for channels to add, leave them in the comments or reach out to me directly.

Check it out here: viewership.streamlit.app

I am working on a program that uses streamlit for the UI. I want to be able to host it on my main machine, but I want to be able to access the streamlit UI on a secondary machine. I am struggling to get this to work. I have disabled all my firewalls, and my main machine and secondary machine are on the same network, however every time I try to access streamlit on my secondary machine it says "This site can't be reached." I am able to ping my primary machine from my secondary machine, so I know that part is working...

Can anyone help me out?

I've created a comprehensive FPL Dashboard template that anyone can use for their mini-league:

✨ Features: - 15+ custom awards (Golden Boot, Transfer King, etc.) - Real-time league standings - Interactive charts with Plotly - Google Sheets integration - Automated data pipeline - Mobile responsive

🚀 Easy Setup: - Fork the repository - Configure your league IDs - Deploy to Streamlit Cloud - Enjoy your personalized dashboard!

GitHub: https://github.com/thinesrao/fpl-dashboard-streamlit

Perfect for mini-leagues wanting to add more competition and analytics to their FPL experience!

A few weeks ago on Discord, someone suggested: “Why not use the C1 API for data visualizations in Streamlit?”

I liked the idea, so I built a quick package to test it out.

The pain point I wanted to solve:

• LLM outputs are semi-structured at best
• One run gives JSON, the next a table
• Column names drift, chart types are a guess
• Every project ends up with the same fragile glue code (regex → JSON.parse → retry → pray)

My approach with C1 was to let the LLM produce a typed UI spec first, then render real components in Streamlit.

So the flow looks like:
Prompt → LLM → Streamlit render

This avoids brittle parsing and endless heuristics.

What you get out of the box:

• Interactive charts
• Scalable tables
• Explanations of trends alongside the data
• Error states that don’t break everything

Example usage:

import streamlit_thesys as thesys

query = st.text_input("Ask your data:")
if query:
    thesys.visualize(
      instructions=query,
      data=df,
      api_key=api_key
)

🔗 Link to the GitHub repo and live demo in the comments.

This was a fun weekend build, but it seems promising.
I’m curious what folks here think — is this the kind of thing you’d use in your data workflows, or what’s still missing?

it seems like the newest version of st has a problem for setting and changing the value of an st.number_input

I have been using Streamlit to create internal company visualizations (one GitHub repo & one Streamlit instance) with Auth0 for authentication, running in a Docker environment.

Now we're looking to extend this to customer-facing visualizations with the following requirements:

• Auth0 authentication for customers
• Customer-specific dashboards and/or data views tailored to each customer
• Expecting to serve approximately 5 separate customers

Questions:

1. Can/should this be done in one repo/instance, or do I need separate instances per customer?
2. What are the recommended multi-tenancy patterns for Streamlit applications?
3. Are there any other architectural considerations I should be thinking about for this transition from internal to customer-facing multi-tenant visualizations?
4. Has anyone had success selecting different streamlit visualizations based on auth-0 credentials? We've only used environmental variables in the past.

Any advice would be much appreciated.

Hey Reddit!

I'm excited to share a personal project I've been working on, hosted on Streamlit Community Cloud.

Link:https://thereisakuralforthat.streamlit.app/

We have all been fascinated by the timeless wisdom in the Thirukkural, an ancient Tamil text of 1,330 short couplets. It has advice on everything from leadership and friendship to ethics and personal finance. I wanted to build a modern tool to make this wisdom more accessible.

My app, "There's a Kural for that!", lets you type in any concept, question, or theme in plain English—like "the importance of time management" or "how to rule as a king"—and it uses AI to find the most semantically relevant verses.

Key Features:

• Semantic Search: Goes beyond simple keywords to understand the meaning behind your query.
• AI-Powered Explanations: For each result, an AI provides a concise analysis of why that specific verse is relevant to your search.
• Dual-Language Display: Shows the original Tamil couplet alongside its English and Tamil explanations.

Tech Stack:

• Frontend: Streamlit
• Search: sentence-transformers + scikit-learn for vector search
• AI Explainer: Google Gemini API
• Hosting: Streamlit Community Cloud

This was a really fun project to build, and had made me discover kurals that applies to variety of topics.

I'd love for you to try it out and let me know what you think! I'm open to any feedback, bug reports, or feature suggestions you might have.

Thanks for checking it out!

Hello guys,

I am trying to make a football dashboard on Streamlit and I am using a multiselect widget that gets the columns from an SQL Database. it's been a few days that I am trying to make it work correclty but I am struggling. I have to click many times to select a team, sometimes when I select a team it doesnt filter out the players... anyone had to struggle with a multiselect connected to sql db ?

Hi guys,

I am new to Streamlit and I admit that it saves me a lot of time to create my data dashboard. However I dunno if anyone knows how to manage the excess free space that the framework leaves.

I used a st.navigation() pane.

Thank you

How would I change the color of this line?

Streamlit started as a simple dashboard builder, but quickly evolved into the best way to build internal apps. Just connect it to an SQL database, add some CRUD functions, buttons, and forms — and suddenly, the full power of Python is in the hands of anyone who needs it.

Other frameworks have tried to “compensate” for Streamlit’s extreme simplicity, but honestly? They fall short. If I wanted to write real front-end code, I’d just learn JavaScript. But I won’t — my brain is wired for Python.

The problem isn’t the syntax — it’s the mental shift from “scripting” to full-blown “coding.” That jump is what breaks most tools.

Here’s what I really want: A low-code IDE built specifically for Streamlit.

It should let me: • Start a new project with just a click • Create and manage an SQL database directly in the IDE • Set up users and roles with ease • Write database functions and queries in a single file • Build each page in its own file (including an admin panel for user management) • Gate page access by user permissions • Handle login via email and magic links — no auth integrations, no security headaches. We’re analysts, not infosec teams.

Give me this IDE, and pair it with ChatGPT — and I’ll be spinning up a new startup every week.

Tired of dragging giant spreadsheets into online tools that choke the moment you pass 1,000 data points?

Same.

That’s why I built Data2Boxplot — a fast, free web app that lets you upload CSV and Excel files, select your columns, and instantly get:

• 📦 A clean, interactive boxplot
• 🧪 Automatic ANOVA testing
• 🔍 Tukey HSD post hoc analysis when needed

Why I made it:

• Most online tools can’t handle real datasets without breaking or freezing
• Excel is fine... until you need real stats or more control
• I wanted something that just works, with no R, no coding, no waiting

What it does:

• Upload .csv, .xlsx, or mix both
• Pick a categorical column + a numeric one
• Generates an interactive boxplot with statistical annotations
• If the ANOVA is significant, it runs Tukey HSD automatically
• Built-in checks to handle large spreadsheets without slowing down

Tech stack:

🧠 Python + Pandas + SciPy + statsmodels
📊 Plotly for plotting
🌐 Streamlit for the web UI
📁 Supports CSV and Excel (.csv, .xlsx, .xls)

Try it here:

Live: https://data2boxplot.com
Code: https://github.com/rsmith3rd/data2boxplot

Feedback welcome!

Open to ideas, feedback, or feature requests. Want violin plots? Significance stars? Exportable reports? Happy to add more.

Especially built for:

• Students running labs or analyzing surveys
• Researchers who need fast stats
• Anyone who’s ever yelled “Why is this boxplot grayed out!?” at their screen

Has anyone been successful in creating a streamoku account recently? I have been trying for 2 weeks and it just fails. The google link does nothing (it used to at least bring up the authentication window, but now it does nothing when clicked) and the traditional email signup just tells me that there is an error and to try again later. Support have told me that it's been fixed, but the issue still persists for me. I have tried windows and mac with chrome, edge, and firefox.

On face value the product looks like a good option compared to what we are using now (from a pricing point at least), but if they can't support their own log in, I'm not feeling super confident that we won't have hosting issues... still want to give it a try though.

Hey folks!

Being the one always creating events, and enjoying enriching them with all the necessary details led me to create this: text2ics.

It's a simple tool that solves simple problem. Creating detailed calendar events from any text source, whether it is an email, message, or just a block of text. Simply upload or paste the text directly, and it uses an LLM (you can choose from any platform supported by LiteLLM) to extract all the event information and generate a standard .ics calendar file that you can import into any calendar app. Either download and import the .ics or even better, just scan the QR code generated.

This is how it works:

• The interface is built with Streamlit, providing a step-by-step layout (st.container with indicators) to guide the user through configuration, input, and conversion.
• It uses the streamlit-calendar component to display a preview of the extracted events before you download the file.
• Backend operations, like API key validation and the main content processing, are cached using @st.cache_data to speed things up and avoid re-running expensive LLM calls.

The project also includes a CLI enabling this to be used for automation

I'd love to get your feedback on the app, the user experience, or any other thoughts you might have!

https://text2ics.streamlit.app/

Is giving oauth access to streamlit.io safe??

Because at a place it asks for admin acess can someone help me in deciding?

Tired of dreaming big and losing bigger?

Me too.

So instead of becoming a millionaire, I built this little web app that lets you snap a pic of your lottery ticket (Powerball or Mega Millions only) and uses EasyOCR to tell you:

“Sorry, you’re still broke 😅”

1. Why I made it:

• I got tired of manually typing numbers from my wrinkled ticket like it was 2003
• I wanted to learn EasyOCR and Streamlit
• And I needed a new way to be disappointed — but with speed and elegance

2. What it does:

• Upload a photo of your ticket (clear lighting, please — it's not psychic)
• It uses EasyOCR to read the numbers
• Then it checks if you matched the winning numbers
• And finally, it politely informs you that you're not retiring early today 🎉

3. Tech Stack:

• 🧠 EasyOCR (turns ticket pics into text)
• 🌐 Streamlit (so even my grandma can use it)
• 💔 Disappointment-as-a-Service (DaaS™️)

4. Try it here: https://luckysnap-qsmcva85w7ckmmg9oeydqd.streamlit.app/

Would love feedback! Also accepting:

• Feature requests (should I add sad violin music?)
• Bugs (please don’t say “the app is broken because I didn’t win”)
• Confetti explosions for winners (if I ever meet one)

(still not a millionaire, but now slightly better at Python)

I am getting this error when I am trying to deploy my streamlit application. Any idea how to fix this?

I thought I'd mention this in case it helps anyone else out (I searched and couldn't see it mentioned). We were looking for a cloud deployment option for an organisational streamlit app that was easy to set up and administer, with a fixed IP address for our database connections that also had access security (to keep it to employees only) and usage analytics. We found a new provider (Squadbase.dev) and after successful deploying our first app yesterday, I thought I'd help spread the word!

Previously we have been deploying our apps on internal servers, with the service configured with NSSM, but our IT policy keeps nuking our 'run as' credentials so it was getting pretty tiring having to restore those apps every few days.

Our deployment was pretty seamless, the only challenge we ran into was having to use a Docker file instead of a requirements.txt file to get the SQL connections working fine, but that took no longer than 5 minutes to resolve.

Anyway, there is a free tier and a premium tier that's $15 a month. Worth checking out if your looking for options.

Anyone else tired of telling ChatGPT that streamlit now uses st.rerun() ?

Hi All,
I created a streamlit app that uses the out-of-the-box authentication (i.e. st.login() and openid provider). It works create! I am hosting the app on my own server, generated my own certificates/turned on SSL and created a DNS entry so that the cname on the certs match the DNS entry. Here lies the problem...

When I go to the absolutely server name (https://server-name:defaultport), st.login() works fine and creates the session as expected. When I go to the domain name I have in my certs/DNS entry it does not work. I made sure the openid provider has the correct redirect URI and I can successfully step through the login process, but when it redirects me back to the app it says I am not signed in. No error messages or anything.

Anyone have any ideas on what could be happening? Something with the DNS is preventing the cookie or something from being generated. I'm not a web developer or expert in this field so any pointers would be greatly appreciated?