mito
gradio
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mito | gradio | |
---|---|---|
18 | 115 | |
2,215 | 28,730 | |
3.1% | 7.3% | |
10.0 | 9.9 | |
9 days ago | 5 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | 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.
mito
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The Design Philosophy of Great Tables (Software Package)
2. The report you're sending out for display is _expected_ in an Excel format. The two main reasons for this are just organizational momentum, or that you want to let the receiver conduct additional ad-hoc analysis (Excel is best for this in almost every org).
The way we've sliced this problem space is by improving the interfaces that users can use to export formatting to Excel. You can see some of our (open-core) code here [2]. TL;DR: Mito gives you an interface in Jupyter that looks like a spreadsheet, where you can apply formatting like Excel (number formatting, conditional formatting, color formatting) - and then Mito automatically generates code that exports this formatting to an Excel. This is one of our more compelling enterprise features, for decision makers that work with non-expert Python programmers - getting formatting into Excel is a big hassle.
[1] https://trymito.io
[2] https://github.com/mito-ds/mito/blob/dev/mitosheet/mitosheet...
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What codegen is (actually) good for
3. So you do want to do code-gen, does it make sense to do it in a chat interface, or can we do better?
As a Figma user, I'd answer these in the following way:
> Why is it necessary to generate code in the first place?
Because mockups aren't your production website, and your production website is written in code. But maybe this is just for now?
I'm sure some high-up PM at Figma has this as their goal - mockup the website in Figma, it generates the code for a website (you don't see this code!), and then you can click deploy _so easily_. Who wants to bet that hosting services like Vercel etc reach out to Figma once a week to try and pitch them...
In the meantime, while we have websites that don't fit neatly inside Figma constraints, while developers are easier to hire than good designers (in my experience), while no-code tools are continually thought of as limiting and a bad long-term solution -- Figma code export is good.
> Why is just writing the code by the hand not the best solution?
For the majority of us full-stack devs who have written >0 CSS but are less than masters, I'll leave this as self-evident.
> So you do want to do code-gen, does it make sense to do it in a chat interface, or can we do better?
In the case of Figma, if they were a new startup with no existing product and they were trying to "automation UI creation" -- v1 of their interface probably would be a "describe your website" and then we'll generate the code for it.
This would probably suck. What if you wanted to easily tweak the output? What if you had trouble describing what you wanted, but you could draw it (ok, OpenAI vision might help on this one)? What if you had experience with existing design tools you could use to augment the AI. A chat interface is not the best interface for design work.
ChatGPT-style code-generation is like v0.1. Github Copilot is an example of next step - it's not just a chat interface, it's something a bit more integrated into an environment that make sense in the context of the work you're doing. For design work, a canvas (literally! [2]) like Figma is well-suited as an environment for code-gen that can augment (and maybe one day replace) the programmers working on frontend. For tabular data work, we think a spreadsheet is the interface where users want to be, and the interface it makes sense to bring code-gen to.
Any thoughts appreciated!
[1] https://trymito.io, https://github.com/mito-ds/mito
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Pandas AI – The Future of Data Analysis
I think the biggest area for growth for LLM based tools for data analysis is around helping users _understand what edits they actually made_.
I'm a co-founder of a non-AI data code-gen tool for data analysis -- but we also have a basic version of an LLM integration. The problem we see with tooling like Pandas AI (in practice! with real users at enterprises!) is that users make an edit like "remove NaN values" and then get a new dataframe -- but they have no way of checking if the edited dataframe is actually what they want. Maybe the LLM removed NaN values. Maybe it just deleted some random rows!
The key here: how can users build an understanding of how their data changed, and confirm that the changes made by the LLM are the changes they wanted. In other words, recon!
We've been experimenting more with this recon step in the AI flow (you can see the final PR here: https://github.com/mito-ds/monorepo/pull/751). It takes a similar approach to the top comment (passing a subset of the data to the LLM), and then really focuses in the UI around "what changes were made." There's a lot of opportunity for growth here, I think!
Any/all feedback appreciated :)
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The hand-picked selection of the best Python libraries and tools of 2022
Mito — spreadsheet inside notebooks
- I made an open source spreadsheet that turns your edits into Python
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I made a tool that turns Excel into Python
You can see the open source code here.
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I made a Spreadsheet for Python beginners that writes Python for you
Here is the Github again.
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Learn Python through your Spreadsheet Skills
Mito is an open source Python package that allows the user to call an interactive spreadsheet into their Python environment. Each edit made in the spreadsheet generates the equivalent Python.
- A Spreadsheet for Data Science that Writes Python for Every Edit
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Mito lets you write Python by editing a spreadsheet
Mito is an open source Python tool that allows you to call a spreadsheet into your Python environment. Each edit you make in the spreadsheet generates the equivalent Python for you. This allows users to access Python with the spreadsheet skills they already have. Here is the Github
gradio
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Show HN: Dropbase – Build internal web apps with just Python
There's also that library all the AI models started using that gives you a public URL to share. After researching it: https://www.gradio.app/ is the link.
It's used specifically for making simple UIs for machine learning apps. But I guess technically you could use it for anything.
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Show HN: Taipy – Turns Data and AI algorithms into full web applications
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?
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🐍🐍 23 issues to grow yourself as an exceptional open-source Python expert 🧑💻 🥇
Repo : https://github.com/gradio-app/gradio
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a Lightweight AI Model and Framework for Text Summarization in the Browser using JavaScript
There's TensorFlow.js for running machine learning on JavaScript, but personally, I'd prefer using the Python Gradio package, which is designed for creating UIs for machine learning inference demos.
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Gradio sharable link expires too soon ( 30 mins to 1 hour, instead of lasting 72 hours )
I found an issue on gradio github but looks like it's closed so I am not sure if it's still a common issue or only I am facing it due to certain settings/absence of a fix. ( https://github.com/gradio-app/gradio/issues/3060 )
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I gave commit rights to someone I didn't know
I disagree hard with this – for instance I've recently needed to dig into the code for the Gradio library, and when PRs are like https://github.com/gradio-app/gradio/pull/3300 (and the merge commit's message is what it is) it's hard to understand why some decisions have been made when doing `git annotate` later on.
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Introducing CommanderGPT. A project I been working for Desktop Automation.
Gradio for a ui that your commanderGPT can visit and use
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[HELP] Anybody know where the .html files are?
gradio is documented, it doesn't seem very complex, it would be something like moving this block under the other one. i think it's ui_extra_networks.py, the file you are looking to edit. (if you do it make a copy to restore when you go to update)
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Is there a way to "share" my stable diffusion with a friend?
Gradio did have an issue for a while where your URL was guessable, so unless you had a password it was pretty easy to find, but as far as I know they've increased the complexity so much that it's no longer an issue.
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Am I doing this right? Feeding the gradio docs to Alpaca
My script wouldn't work on the langchain because it was literally made within the gradio "docs builder" script that afaik was made specifically for their website (repo)
What are some alternatives?
qgrid - An interactive grid for sorting, filtering, and editing DataFrames in Jupyter notebooks
streamlit - Streamlit — A faster way to build and share data apps.
Mage - 🧙 The modern replacement for Airflow. Mage is an open-source data pipeline tool for transforming and integrating data. https://github.com/mage-ai/mage-ai
stable-diffusion-webui - Stable Diffusion web UI
appsmith - Platform to build admin panels, internal tools, and dashboards. Integrates with 25+ databases and any API.
django-colorfield - :art: color field for django models with a nice color-picker in the admin.
dtale - Visualizer for pandas data structures
panel - Panel: The powerful data exploration & web app framework for Python
budibase - Budibase is an open-source low code platform that helps you build internal tools in minutes 🚀
gpt4all - gpt4all: run open-source LLMs anywhere
lux - Automatically visualize your pandas dataframe via a single print! 📊 💡
CustomTkinter - A modern and customizable python UI-library based on Tkinter