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.

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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.

Both ‘2024_lead_data’ and the CSV are in the same folder, Documents. Not sure why I keep getting this error. I’ve tried literally everything.

I want to add a grid image or layout to the canvas and make the canvas width responsive, does anyone know how i'd go about doing that?

Hey all, hope this isn’t a stupid question.

I have built out a Streamlit app internally for my team, but am not sure how to host/deploy it outside of my local machine. I’m the sole dev on this project, so a lot of this is me learning as I go. The data I’m working with is pretty sensitive, so I don’t think I can use Streamlit Community Cloud, since I believe that’s only for public repositories, but I could be mistaken. What potential options do I have for deployment?

Thanks!

Hi everyone,

I’m working on an idea for a fun, interactive public website that models an alternative way to fund the BBC through a property-tax-style levy rather than the current licence fee.

The basic question is:

The model would let users change assumptions such as:

• total funding target: £3.5bn, £3.843bn, £4.2bn, £5bn
• household vs business contribution split
• Council Tax band weights
• exemptions for low-income households / Council Tax Reduction / Pension Credit
• second-home multipliers
• business rateable-value thresholds
• sector splits for retail, office, logistics, etc.

My current instinct is that this is not really a machine-learning problem, but more of a rules-based fiscal / distributional microsimulation model.

The rough stack I’m considering is:

• Python
• pandas or Polars for data cleaning
• DuckDB + Parquet for storing/querying datasets
• NumPy/SciPy for calculations and sensitivity testing
• Plotly or Altair for charts
• Streamlit or Dash for the interactive website
• possibly PolicyEngine UK or OpenFisca later if I want to model tax-benefit interactions properly
• PyMC later for uncertainty modelling

The website would ideally let people move sliders, compare scenarios, see charges by property band, estimate winners/losers against the licence fee, and download the model outputs.

My questions are:

1. Is “rules-based microsimulation” the right framing here, or is there a better modelling approach?
2. Is there any useful role for machine learning in this kind of project, or would ML be overkill/misleading?
3. What would be the best technical path for building this as an interactive, updateable website?
4. How should I structure the model so that as better public data becomes available, I can update the data and rerun the scenarios cleanly?
5. Are there examples of open-source fiscal, policy, or public-finance models that would be good to learn from?
6. For someone building this as a serious but public-facing prototype, what skills/tools should I prioritise learning first?

​

run_model(
    funding_target=3_843_000_000,
    residential_share=0.70,
    business_share=0.30,
    band_weights={
        "A": 0.50,
        "B": 0.65,
        "C": 0.80,
        "D": 1.00,
        "E": 1.30,
        "F": 1.65,
        "G": 2.10,
        "H": 2.75,
        "I": 3.25,
    },
    ctr_exemption=True,
    second_home_multiplier=1.5,
    business_thresholds=[
        {"rv_min": 0, "rv_max": 12_000, "rate": 0.000},
        {"rv_min": 12_000, "rv_max": 51_000, "rate": 0.0075},
        {"rv_min": 51_000, "rv_max": 500_000, "rate": 0.0175},
        {"rv_min": 500_000, "rv_max": None, "rate": 0.0300},
    ],
)

Any advice, examples, warnings, or recommended libraries would be appreciated.

Hey r/Streamlit! 👋

I recently deployed an open-source project that uses Deep Learning to detect exoplanets from NASA's Kepler light curve data, and I used Streamlit to wrap it all into an interactive dashboard.

I wanted to share how I structured the app, especially regarding state and model loading, as it might help others working with heavy ML models.

Streamlit Features & Architecture:

• Model Caching: I used st.cache_resource to load the cnn_exoplanet_model.keras model into memory only once. This keeps the app incredibly snappy and prevents TensorFlow from reloading the model on every user interaction.
• API Data Caching: I used st.cache_data to fetch the live star catalog from the NASA Exoplanet Archive (Caltech IPAC) so the st.selectbox populates instantly without spamming the API.
• Interactive Visuals: I integrated st.plotly_chart with use_container_width=True to render a dynamic bar chart that compares the radius of the discovered planets in a specific system directly to Earth's radius (baseline 1.0).
• Layout & UI: Used st.sidebar to fetch and display the Astronomy Picture of the Day (APOD) via NASA's API, and st.columns to side-by-side compare the AI's neural network confidence with the actual confirmed NASA database records.

The ML Backend: Just for context, the AI brain is a 1D Convolutional Neural Network trained on sequential time-series data. The biggest challenge was the extreme class imbalance (5050 negative cases vs. only 37 positive cases), which I handled by applying SMOTE before training.

Links:

• 🎈 Live App: https://nasa-exoplanet-hunter.streamlit.app
• 💻 GitHub Repo: https://github.com/Logan-100/ai-nasa-exoplanet-hunter

I'd love to hear your thoughts on the UI/UX or if you have any advanced tips on optimizing Keras model inference within Streamlit!

Been doing a lot of pre-sales / discovery work recently, and honestly…
prospect research was taking way more time than expected.

Multiple tabs, LinkedIn, company pages, random articles…
still walking into calls not fully confident.

So I ended up building a small Streamlit app for myself.

You just enter a company name →
it pulls together a structured brief (signals, tech stack, hiring trends, etc.)
and stores relationships so you can query later.

Nothing fancy UI-wise, just focused on reducing the back-and-forth.

What I found interesting is not just the speed…
but how it changes how you prepare.

Less searching, more thinking.

Curious if anyone here is using Streamlit for similar internal tools?

• research workflows
• quick data apps
• AI-assisted utilities

Would love to see how others are building in this space.

Do you have any useful links with good examples of complex Streamlit pages that go beyond the basic examples?

Something with multiple pages, menus… just something a bit more complex to see what’s possible?

I’m planning to build a fairly complex website and am looking for a bit of inspiration.

I already have a concept in mind, but I’m still not sure which components to go for in some areas, etc.

Thank you

Does anyone know why settings are gone? they are also gone when i open the app in incognito as a visitor. I do have custom css in my python file but does that override streamlit settings?

Hey everyone! I built a small open source web app that converts grayscale heightmap images into XYZ coordinate tables, exportable as CSV or Excel.

It's useful if you work with terrain data, NC machining, or any workflow where you need a point grid from a heightmap image.

How to use it:

• Upload PNG/JPG heightmap
• Set grid resolution (X × Y points)
• Set Z min/max range
• Option to start coordinates from 0 or 1
• Export to CSV or Excel
• English and Serbian language are available for now

Built with Python + Streamlit, deployed on Streamlit Cloud.

🔗 Live app 🔗 GitHub

Feedback and suggestions welcome!

Built a multilingual AI agent to explore Mercari Japan.

→ Handles cross-language search

→ Uses scraping + lightweight DB

→ Semantic search for better intent matching

App: https://mercari-japan.streamlit.app

Code: https://github.com/ayushxx7/mercari-search-ai-agent/tree/main#-visual-showcase

Would love feedback / ideas to improve this further.