streamlit
datapane
Our great sponsors
streamlit | datapane | |
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251 | 30 | |
30,948 | 1,341 | |
3.6% | 0.3% | |
9.8 | 7.3 | |
4 days ago | 5 months ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
streamlit
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Show HN: Buefy Web Components for Streamlit
While building dashboards in Streamlit, I found myself really missing Buefy's (Bulma) modern web components.
Specially due to the inability to add new values to Streamlit's multiselect [1], some missing controls like a polished image carousel [2] or a highly customizable data table.
Long story short, we put together streamfy (Streamlit + Buefy) as an MIT licensed project in GitHub to bring Buefy to Streamlit.
Demo: https://streamfy.streamlit.app
All the form components are implemented, missing half of other non-form UX components. There is plenty of room for PRs, testing, feedback, documentation, example, etc.
Please send issues and contributions to GitHub project [3] and general feedback to X / Twitter [4]
Thanks!
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Show HN: Hyperdiv – Reactive, immediate-mode web UI framework for Python
Looks cool. How do you see this differing from streamlit? https://streamlit.io/
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Revolutionizing Real-Time Alerts with AI, NATs and Streamlit
Imagine you have an AI-powered personal alerting chat assistant that interacts using up-to-date data. Whether it's a big move in the stock market that affects your investments, any significant change on your shared SharePoint documents, or discounts on Amazon you were waiting for, the application is designed to keep you informed and alert you about any significant changes based on the criteria you set in advance using your natural language. In this post, we will learn how to build a full-stack event-driven weather alert chat application in Python using pretty cool tools: Streamlit, NATS, and OpenAI. The app can collect real-time weather information, understand your criteria for alerts using AI, and deliver these alerts to the user interface.
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Using LangServe to build REST APIs for LangChain Applications
In this tutorial, you'll construct a fully functional Streamlit application from the ground up. Streamlit lets you turn simple data scripts into web applications without traditional front-end tools. This application will be capable of downloading audio from any YouTube video, transcribing it using Deepgram, and then summarizing the content with the assistance of Mistral 7B, all streamlined through the capabilities of Langchain.
- Ask HN: Can I create a mobile and Web App using Python/Python Framework?
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Creating Videos with Stable Video Diffusion
Install the Stable Diffusion tools and checkpoints, and run it all with Streamlit.
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Build a Streamlit app with LangChain and Amazon Bedrock
Streamlit is an open-source Python library which makes it easy to build web applications for machine learning and data science. It has a set of rich APIs for visual components including several chat elements, making it quite convenient to build conversational agents or chatbots, especially when combined with LLMs (Large Language Models).
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Let's build your first ML app in Google Cloud Run
Having successfully deployed your model endpoint, it is now time to show the world how it works, to achieve this we build a web-interface for anyone to use. Enter Streamlit, think of it as a bridge between your data and the world, letting you present insights, perform analyses, and even collect user input with ease. It's perfect for data scientists, analysts, and anyone who wants to leverage the power of their data in a visually appealing and interactive way.
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Show HN: Taipy – Turns Data and AI algorithms into full web applications
any clues how does that compare to https://github.com/streamlit/streamlit ?
What is the business model for https://www.taipy.io/, https://streamlit.io/, or https://www.gradio.app/? These are nice tools - but how will the sponsoring businesses support themselves? I didn't see any mention of enterprise plans, etc. Is the answer simply that "we've not announced our revenue model yet"? What should one expect?
datapane
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Polars: Company Formation Announcement
If you're looking for an easy way to build an HTML report using Python, you might find Datapane (https://github.com/datapane/datapane) helpful. I'm one of the people building it! We don't support polars (yet, on the roadmap) but we do support pandas so you can convert to a pandas DataFrame and include your data and any plots, etc.
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JupyterLab 4.0
If you're interested in an easier way to create reports using Python and Plotly/Pandas, you should check out our open-source library, Datapane: https://github.com/datapane/datapane - you can create a standalone, redistributable HTML file in a few lines of Python.
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Evidence – Business Intelligence as Code
You might be interested in what we're hacking on at Datapane (I'm one of the founders): https://github.com/datapane/datapane.
You can create standalone HTML data reports from Python/Jupyter in ~3 lines of code: https://docs.datapane.com/reports/overview/
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Ask HN: Fastest way to turn a Jupyter notebook into a website these days?
You can build web apps from Jupyter using Datapane [0]. I'm one of the founders, so let me know if I can help at all.
You can either export a static site [1] (and host on GH pages or S3), or, if you need backend logic, you can add Python functions [2] and serve on your favourite host (we use Fly).
We have specific Jupyter integration to automatically convert your notebook into an app [3].
[0] https://github.com/datapane/datapane
[1] https://docs.datapane.com/reference/reports/#datapane.proces...
[2] https://docs.datapane.com/apps/overview/
[3] https://docs.datapane.com/reports/jupyter-integration/#conve...
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Datapane - Build internal data products in 100% Python
Hello! We’re Leo and Mandeep, founders of Datapane (https://github.com/datapane/datapane)
Thanks a lot! Yes, absolutely, a few people have brought this up and working working on removing the header right now. If I can help at all, feel free to reach us on GH Discussions: https://github.com/datapane/datapane/discussions
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Show HN: Datapane – A new way to build reports, dashboards, and apps in Python
Hello HN! We’re Leo and Mandeep, founders of Datapane.
We're building a way to create reports, dashboards, and web apps from your existing data using Python. Think of it as a combination of React and htmx, specifically designed for the Python data stack.
Our GitHub is https://github.com/datapane/datapane and you can try building a report or app in ~2 minutes on Codespaces: https://try.datapane.com
We started building Datapane at our previous start-up, where we struggled to deliver ML model results to clients. Much to our surprise, the data science took less time than repeatedly creating reports by copying and pasting plots into PowerPoint decks.
It seemed absurd that we had to switch to PowerPoint or legacy BI tools like Tableau to share, and our initial goal was to programmatically generate reports using the datasets and plots we had in Python. To enable this, we started hacking on a Python-based UI framework for constructing HTML views from data-centric blocks – like plots, data tables, and layout components.
You can export these to standalone HTML files, or host them as a web app on somewhere like GitHub Pages or Fly.io. We recently also added the ability to connect Python functions to forms and front-end events so you can build web apps which run backend code. We handle the entire network and RPC layer, so you only need to write plain Python functions that take parameters and return other blocks.
You can check out an example of the code to create a simple app: https://github.com/datapane/examples/blob/main/apps/iris-plotter/app.py
Datapane’s philosophy is pretty different from other products in the space.
We wanted to keep things simple, but avoid the footguns our users faced with frameworks like Streamlit, where the reactive/network-aware model was hard to move beyond an MVP or POC. For backend interactivity, we believe the original web got a lot right, and unlike reactive models which rely on websockets, Datapane is unashamedly request/response. This takes inspiration from HTTP and our own experiences with htmx, which offers an elegant way to add interactivity to HTML. Under the hood, we actually compile down to a (gasp!) XML-based hypermedia format, akin to HTML, but tailored specifically for constructing data UIs.
The result is that not every change in your app requires a server round trip, as much of it can be pre rendered and most interactivity happens on the client-side. In addition to improving performance, this also makes running in production become 10x simpler.
This separation between the view and backend compute also makes Datapane modular. If our app server isn’t a good fit for your use-case, serve Datapane views from the web-framework of your choice (we’ve been hacking on serving views from Django). Want to compute blocks from inside Airflow or generate them on a schedule or from a webhook? Computation can happen out of band of the UI. You can even build and host apps from inside of Jupyter, where you can preview blocks live and convert notebook cells to blocks in your view.
We currently offer a hosting platform on https://datapane.com for sharing reports publicly (free) or with your team (paid), and will be adding serverless app hosting support to it in the next few weeks.
Our ultimate goal is to create an open-source toolkit for building data products across the entire stack – from reports, to dashboards, to full-stack apps – all using 100% Python. You can see a few we’ve built already in our gallery: https://datapane.com/gallery
We’d love to hear your feedback.
Thanks!
the fact there are so many different steps in https://github.com/datapane/datapane#analytics signals that you may want to adopt https://consoledonottrack.com/
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How do you guys share R/Python based analyses to business stakeholders?
PowerPoint will do. If there isn't too much data I will sometimes make a quick datapane html dashboard that I can also send their way. They like that, the plotly plots can be interactive so they can poke around. Nice quick solution that's easy to share.
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Automated PDF Reports with Python Notebooks
You might find the Python framework we've been working on helpful for that use-case: https://github.com/datapane/datapane. It allows you to create interactive HTML reports comprised of pandas DataFrames, plots, and UI elements (e.g. dropdowns, selects, pages).
Standalone HTML files provide a really nice alternative to PDF as they maintain interactively: you can host them static sites, allow people to download data, use plots interactively, click through pages, similar to a statically generated website. That said, there is still a definite blocker in non-technical people receiving a .HTML file over email and immediately thinking it's suspicious or a virus (doesn't help that gmail has such poor support for them.) It's a shame, because PDFs have so many warts and HTML can be used as a really nice distributable file format, especially as you can make them fully standalone by baking in datasets, plots, libraries, etc. so they can be used without network.
IMO Jupyter is great at what it is - a REPL - but, outside of sharing a step-by-step "here are the steps I took to come to this answer", isn't the ideal format for sharing insights, as there is no reason a report would follow the same narrative as the analysis itself.
What are some alternatives?
PyWebIO - Write interactive web app in script way.
gradio - Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
superset - Apache Superset is a Data Visualization and Data Exploration Platform
nicegui - Create web-based user interfaces with Python. The nice way.
reflex - 🕸️ Web apps in pure Python 🐍
PySimpleGUI - Python GUIs for Humans! PySimpleGUI is the top-rated Python application development environment. Launched in 2018 and actively developed, maintained, and supported in 2024. Transforms tkinter, Qt, WxPython, and Remi into a simple, intuitive, and fun experience for both hobbyists and expert users.
dash - Data Apps & Dashboards for Python. No JavaScript Required.
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
panel - Panel: The powerful data exploration & web app framework for Python
nodejs-dialogflow - Node.js client for Dialogflow: Design and integrate a conversational user interface into your applications and devices.
wave - Realtime Web Apps and Dashboards for Python and R
react-virtualized - React components for efficiently rendering large lists and tabular data