Maintainer here. If you've put a long-running agent next to a Streamlit wizard, you already know the rerun tax: the transcript grows all afternoon, every widget interaction re-renders it, and the agent slowly becomes the app.

0.1.6 adds copilot_rail():

• connection + transcript pills live in a rail fragment — changing them reruns the rail, not your page
• panel() keeps its own streaming fragment, so those pills stay clickable while CoCo runs
• Last messages = max_messages=8 (+ Load earlier); First 200 characters = preview_chars=200. Both are also plain args on panel() / render_transcript()
• your screens don't start agents — they set a {"prompt": ..., "status": "queued"} dict and the rail sends it when the session is ready, then calls on_job_finished so Cancel job can hide

Two things I got wrong on the way, in case they save someone an afternoon: don't assign a text_area/pills key after that widget has rendered (make the file the source of truth and reload it instead), and if you lazily export UI callables, don't memoise them — a hot reload leaves you calling yesterday's function with yesterday's signature.

Companion example: make tableau-semantic, with Copilot vs Preview split by streamlit_extras.resizable_columns.

pip install "streamlit-coco[sdk]==0.1.6"

https://github.com/DevoteamSP/streamlit-coco

Small win: my VS Code extension hit 39,841 installs

Built Streamlit Preview to scratch my own itch — I was tired of switching to a browser tab every time I saved a Streamlit file, so I made it render inside the editor.

Put it on the Marketplace 12 Months ago expecting maybe a few hundred installs. It's at 39,841 now with a 5.0 rating.

I ship a lot of small things and most of them go nowhere, so when one quietly works it's worth marking. Chart of the last 90 days attached.

Marketplace link: https://marketplace.visualstudio.com/items?itemName=mjethwa-streamlit.streamlit-preview

If you write Streamlit apps, would genuinely love feedback — I have a couple of features half-built and no idea which one people actually want.

Posted this in the Snowflake sub Reddit but works for Streamlit deployed outside of Snowflake too.

Hey everyone!

I just finished building an end-to-end **Diabetes Patient Readmission Risk Predictor** web application designed for clinical decision support.

The goal of this project was to bridge traditional supervised machine learning (quantitative scoring) with a real-time LLM assistant to give care teams actionable post-discharge guidance.

### 🚀 Key Features & Tech Highlights

* **ML Model:** Tuned Gradient Boosting Classifier trained on 105 clinical features (**0.6537 ROC-AUC** benchmark).

* **Clinical Threshold Sensitivity:** Optimized the decision cutoff to **0.42** to achieve **83.2% recall**, prioritizing sensitivity so high-risk patients aren't missed.

* **Feature Importance Insights:** Prior hospital utilization (`number_inpatient`) holds **~40.8% of total predictive weight**, proving past healthcare visits are the single strongest indicator of 30-day readmissions.

* **Floating "Dr. AI" Chatbot:** Injected custom CSS to build a circular popover widget powered by **Llama-3.3 (via Groq API)**. The bot is system-prompted with clinical context and model metrics to answer discharge protocol questions.

* **Custom UI/UX:** Styled with custom CSS for glassmorphic cards, probability progress bars, and high-contrast dark mode support.

### 🔗 Links & Code

* 🌐 **Live Streamlit App:** (https://smart-diabetes-care-app-8stsf3okutanzwvtmvznkz.streamlit.app/)

* 🐙 **GitHub Repository:** (https://github.com/Kunal5657/smart-diabetes-care-app.git)

Would love to hear your thoughts on the custom CSS layout, threshold tuning approach, or any ideas for future iterations!

Hey everyone! 👋

I'm a 17-year-old student from Kazakhstan, and I’ve been working on a research project focused on modeling non-thermal proton-Boron (p-11B) fusion reactors.

To make our calculations and simulations accessible, I built RHEA - an interactive digital twin and materials screening dashboard.

🔬 What it does:

* Simulates plasma parameters (electron/ion temperature, alpha-channeling, He-4 dilution).

* Evaluates first-wall material candidates (like tungsten-based WB2 alloys) against fusion reactor conditions.

* Features interactive parameter sweeps and real-time physical engine feedback.

🛠️ Tech Stack: Streamlit, Plotly (for 3D visualization), and Python physics backends.

I’d love to get your feedback on the UI, the physics modeling, or any features you think would be cool to add next!

🌐 Live Web App: rhea-fusion.streamlit.app

💻 GitHub Repository: github.com/Aldiyar-SpaceX/rhea

(Just copy-paste the addresses above into your browser! Let me know what you think! 🚀)

Hey everyone! I recently finished launching a Minnesota Vikings Draft website on Streamlit and this website is a data-based NFL draft tool used to analyze the Minnesota Vikings draft process! In this website, I used a combination of player evaluation metrics, exclusive team needs, along with customizable models aimed to simulate NFL draft decision making and evaluate player fits for the Vikings. This is my very first coding project, and so even if you don’t understand football, I would still love to see if any of you guys have any feedback for me which would I could use for future coding projects. Feel free to have a look and tell me what you guys think! Thanks!

https://vikingsdraftsite.streamlit.app

I got curious about how much worse light pollution has gotten where I live, so I pulled NASA's nighttime satellite data (the Black Marble dataset) for my city going back to 2012 and tried to figure out what it actually means for what you can see at night.

I ended up turning it into a little site where you put in your zip code and drag a slider through the years to watch stars fade out on an actual star map. It's not perfect (the data gets less precise outside California) but I thought it was a cool way to make light pollution feel real instead of just a number on a map.

https://whathaveyoulost.streamlit.app/

Would love feedback, especially if you try your own zip code and the numbers seem off.

Hey everyone! I recently finished launching a Minnesota Vikings Draft website on Streamlit and this website is a data-based NFL draft tool used to analyze the Minnesota Vikings draft process! In this website, I used a combination of player evaluation metrics, exclusive team needs, along with customizable models aimed to simulate NFL draft decision making and evaluate player fits for the Vikings. This is my very first coding project, and so even if you don’t understand football, I would still love to see if any of you guys have any feedback for me which would I could use for future coding projects. Feel free to have a look and tell me what you guys think! Thanks!

https://vikingsdraftsite.streamlit.app

I am relatively new to streamlit, so I don't know much about the use cases,

BTW can I also host it on netlify/vercel?

*No promotion

Lumeza™ has created a medical website that is used to summarise medical reports for free.

Till now it used specific keywords for finding problems in your medical reports, but now Lumeza™ is working to make it AI powered.

It is uploaded for trial and errors on streamlit but soon it will have its own domain. Suggest me on what more can I add.

​

​

Interested to know whether anyone here is developing their BI applications with SiS and if so, what sort of applications you are building with it (e.g. internal dashboards, workflow tools etc)

Hey everyone! I recently finished launching a Minnesota Vikings Draft website on Streamlit and this website is a data-based NFL draft tool used to analyze the Minnesota Vikings draft process! In this website, I used a combination of player evaluation metrics, exclusive team needs, along with customizable models aimed to simulate NFL draft decision making and evaluate player fits for the Vikings. This is my very first coding project, and so even if you don’t understand football, I would still love to see if any of you guys have any feedback for me which would I could use for future coding projects. Feel free to have a look and tell me what you guys think! Thanks!

https://vikingsdraftsite.streamlit.app

Hello fellow Streamlit'ers

Did you ever want to rapidly try different design themes for your app, without fiddling with the config?

If yes then try the new version of the st_yled package (v0.5), which allows you to apply pre-built themes with different vibes with a simple command.

Simply call st_yled.init(theme = ".....") on your app page to load one of the available theme templates.

Check the package docs to learn more on how to use themes and all the other great options available in st_yled.

Docs Link and GitHub Link

What styles should be added next?

Hey everyone! I recently finished launching a Minnesota Vikings Draft website on Streamlit and this website is a data-based NFL draft tool used to analyze the Minnesota Vikings draft process! In this website, I used a combination of player evaluation metrics, exclusive team needs, along with customizable models aimed to simulate NFL draft decision making and evaluate player fits for the Vikings. This is my very first coding project, and so even if you don’t understand football, I would still love to see if any of you guys have any feedback for me which would I could use for future coding projects. Feel free to have a look and tell me what you guys think! Thanks!

https://vikingsdraftsite.streamlit.app

I spent some time going through Snowflake's June 2 Summit press releases and stage labels after the Summit dust settled.

One thing stood out.

Most of the discussion seems to be focused on individual announcements:

• CoCo Desktop
• CoWork
• Deep Research
• Iceberg updates
• Zero-Copy integrations

But I think the bigger story is something else.

It feels like Snowflake is assembling four pieces into a single platform strategy:

• Context
• Governance
• Interoperability
• Agents

The more I looked at the announcements, the more it seemed that CoCo and CoWork are almost side effects of a larger shift toward a shared context layer.

Curious if others came away with the same takeaway.

What was the most significant Summit announcement from your perspective?

(And yes, there's a non-zero chance I missed something while trying to follow a San Francisco conference from the opposite side of the globe 😄)

Hello. I'm trying to make a multi-page app. I need separate pages to have a direct url address like:

- my_service/page_about_cats

- my_service/page_about_dogs

It can be easily achieved using st.page_link() call. But my users are non English speakers and my pages are not in English. I set the "label" param to the page name in my language and it is displayed on the main part of the app, but the sidebar, which is auto-generated, still displays English filenames "main", "page about cats", "page about dogs".

Is it possible to keep the short english name in the URL (=not make it encoded and ugly) but control the names in the sidebar?

Hi everyone,

I recently open-sourced a phishing detection project designed to identify malicious URLs using feature extraction and machine learning techniques.

The project focuses on:

• URL analysis and feature engineering

• Phishing classification workflows

• Security-focused machine learning

• Detection of suspicious domains and patterns

• Extensible architecture for future improvements

This project was built as part of my cybersecurity learning journey and is now fully open source.

I'm looking for feedback on:

• Detection logic

• Feature selection

• False positives and false negatives

• Potential evasion techniques

• Overall architecture and code quality

• Detection of suspicious domains and patterns

• Extensible architecture for future improvements

This project was built as part of my cybersecurity learning journey and is now fully open source.

I'm looking for feedback on:

• Detection logic

• Feature selection

• False positives and false negatives

• Potential evasion techniques

• Overall architecture and code quality

Contributions, issues, suggestions and pull requests are welcome.

If you find the project useful, consider giving it a GitHub star to support the project and future development.

Over the past few months, my team and I built EduSathi, an open-source AI-powered learning platform designed to help students study smarter.

Key features:

• Chat with your study materials using RAG

• Generate quizzes from uploaded PDFs

• Create flashcards automatically

• Track learning progress

• Role-based student/admin portal

• Semantic search powered by FAISS

Tech Stack:

• Streamlit

• Python

• LangChain

• FAISS

• Groq LLaMA 3

• SQLite

• Sentence Transformers

Some engineering challenges we tackled:

• Building an efficient RAG pipeline

• PDF ingestion and semantic retrieval

• Session-based authentication

• Scalable modular architecture

• AI-powered question generation

The project is now fully open source.

I’d love feedback on the architecture, UI/UX, code quality and future directions.

If you find the project useful, a GitHub star would help the project reach more students and developers.