py-shiny/README.md at main · posit-dev/py-shiny

[irthomasthomas]

• [ ] py-shiny/README.md at main · posit-dev/py-shiny

py-shiny/README.md at main · posit-dev/py-shiny

Description

"# Shiny for Python

Shiny for Python is the best way to build fast, beautiful web applications in Python. You can build quickly with Shiny and create simple interactive visualizations and prototype applications in an afternoon. But unlike other frameworks targeted at data scientists, Shiny does not limit your app's growth. Shiny remains extensible enough to power large, mission-critical applications.

To learn more about Shiny see the Shiny for Python website. If you're new to the framework we recommend these resources:

• How Shiny is different from Dash and Streamlit.
• How reactive programming can help you build better applications.
• How to use modules to efficiently develop large applications.
• Hosting applications for free on shinyapps.io, Hugging Face, or Shinylive.

Join the conversation

If you have questions about Shiny for Python, or want to help us decide what to work on next, join us on Discord.

Getting started

To get started with shiny follow the installation instructions or just install it from pip.

pip install shiny

To install the latest development version:

# First install htmltools, then shiny
pip install https://github.com/posit-dev/py-htmltools/tarball/main
pip install https://github.com/posit-dev/py-shiny/tarball/main

You can create and run your first application with shiny create, the CLI will ask you which template you would like to use. You can either run the app with the Shiny extension, or call shiny run app.py --reload --launch-browser.

Development

API documentation for the main branch of Shiny: API Documentation

If you want to do development on Shiny for Python:

pip install -e ".[dev,test]"

Additionally, you can install pre-commit hooks which will automatically reformat and lint the code when you make a commit:

pre-commit install

# To disable:
# pre-commit uninstall

URL: py-shiny README

AI Suggested Labels

{'label-name': 'web-development', 'label-description': 'Topics related to web development, including building web applications and interactive visualizations.', 'confidence': 56.72}

[irthomasthomas]

Related issues

674: FastUI/README.md at main · pydantic/FastUI

### Details

Similarity score: 0.88 - [ ] [FastUI/README.md at main · pydantic/FastUI](https://github.com/pydantic/FastUI/blob/main/README.md?plain=1) # FastUI [](https://github.com/pydantic/FastUI/actions?query=event%3Apush+branch%3Amain+workflow%3ACI) [](https://pypi.python.org/pypi/fastui) [](https://github.com/pydantic/FastUI) [](https://github.com/pydantic/FastUI/blob/main/LICENSE) **Please note:** FastUI is still an active work in progress, do not expect it to be complete. ## The Principle (short version) You can see a simple demo of an application built with FastUI [here](https://fastui-demo.onrender.com). FastUI is a new way to build web application user interfaces defined by declarative Python code. This means: - **If you're a Python developer** — you can build responsive web applications using React without writing a single line of JavaScript, or touching `npm`. - **If you're a frontend developer** — you can concentrate on building magical components that are truly reusable, no copy-pasting components for each view. - **For everyone** — a true separation of concerns, the backend defines the entire application; while the frontend is free to implement just the user interface At its heart, FastUI is a set of matching [Pydantic](https://docs.pydantic.dev) models and TypeScript interfaces that allow you to define a user interface. This interface is validated at build time by TypeScript and pyright/mypy and at runtime by Pydantic. ## The Practice — Usage FastUI is made up of 4 things: - [`fastui` PyPI package](https://pypi.python.org/pypi/fastui) — Pydantic models for UI components, and some utilities. While it works well with [FastAPI](https://fastapi.tiangolo.com) it doesn't depend on FastAPI, and most of it could be used with any python web framework. - [`@pydantic/fastui` npm package](https://www.npmjs.com/package/@pydantic/fastui) — a React TypeScript package that lets you reuse the machinery and types of FastUI while implementing your own components - [`@pydantic/fastui-bootstrap` npm package](https://www.npmjs.com/package/@pydantic/fastui-bootstrap) — implementation/customisation of all FastUI components using [Bootstrap](https://getbootstrap.com) - [`@pydantic/fastui-prebuilt` npm package](https://www.jsdelivr.com/package/npm/@pydantic/fastui-prebuilt) (available on [jsdelivr.com CDN](https://www.jsdelivr.com/package/npm/@pydantic/fastui-prebuilt)) providing a pre-built version of the FastUI React app so you can use it without installing any npm packages or building anything yourself. The Python package provides a simple HTML page to serve this app. Here's a simple but complete FastAPI application that uses FastUI to show some user profiles: ```python from datetime import date from fastapi import FastAPI, HTTPException from fastapi.responses import HTMLResponse from fastui import FastUI, AnyComponent, prebuilt_html, components as c from fastui.components.display import DisplayMode, DisplayLookup from fastui.events import GoToEvent, BackEvent from pydantic import BaseModel, Field app = FastAPI() class User(BaseModel): id: int name: str dob: date = Field(title='Date of Birth') # define some users users = [ User(id=1, name='John', dob=date(1990, 1, 1)), User(id=2, name='Jack', dob=date(1991, 1, 1)), User(id=3, name='Jill', dob=date(1992, 1, 1)), User(id=4, name='Jane', dob=date(1993, 1, 1)), ] @app.get("/api/", response_model=FastUI, response_model_exclude_none=True) def users_table() -> list[AnyComponent]: """ Show a table of four users, `/api` is the endpoint the frontend will connect to when a user visits `/` to fetch components to render. """ return [ c.Page( # Page provides a basic container for components components=[ c.Heading(text='Users', level=2), # renders `

Users

` c.Table( data=users, # define two columns for the table columns=[ # the first is the users, name rendered as a link to their profile DisplayLookup(field='name', on_click=GoToEvent(url='/user/{id}/')), # the second is the date of birth, rendered as a date DisplayLookup(field='dob', mode=DisplayMode.date), ], ), ] ), ] @app.get("/api/user/{user_id}/", response_model=FastUI, response_model_exclude_none=True) def user_profile(user_id: int) -> list[AnyComponent]: """ User profile page, the frontend will fetch this when the user visits `/user/{id}/`. """ try: user = next(u for u in users if u.id == user_id) except StopIteration: raise HTTPException(status_code=404, detail="User not found") return [ c.Page( components=[ c.Heading(text=user.name, level=2), c.Link(components=[c.Text(text='Back')], on_click=BackEvent()), c.Details(data=user), ] ), ] @app.get('/{path:path}') async def html_landing() -> HTMLResponse: """Simple HTML page which serves the React app, comes last as it matches all paths.""" return HTMLResponse(prebuilt_html(title='FastUI Demo')) ``` Which renders like this:  Of course, that's a very simple application, the [full demo](https://fastui-demo.onrender.com) is more complete. ### Components FastUI already defines a rich set of components. All components are listed in the [demo app](https://fastui-demo.onrender.com). #### Suggested labels ####

642: TabbyML: Self-hosted AI coding assistant.

### Details

Similarity score: 0.85 - [ ] [tabby/README.md at main · TabbyML/tabby](https://github.com/TabbyML/tabby/blob/main/README.md?plain=1) # tabby/README.md at main · TabbyML/tabby

# 🐾 Tabby [](https://github.com/TabbyML/tabby/releases/latest) [](https://makeapullrequest.com) [](https://hub.docker.com/r/tabbyml/tabby) [](https://codecov.io/gh/TabbyML/tabby)

Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

## 🔥 What's New * **12/23/2023** Seamlessly [deploy Tabby on any cloud](https://tabby.tabbyml.com/docs/installation/skypilot/) with [SkyServe](https://skypilot.readthedocs.io/en/latest/serving/sky-serve.html) 🛫 from SkyPilot. * **12/15/2023** [v0.7.0](https://github.com/TabbyML/tabby/releases/tag/v0.7.0) released with team management and secured access! * **10/24/2023** ⛳️ Major updates for Tabby IDE plugins across [VSCode/Vim/IntelliJ](https://tabby.tabbyml.com/docs/extensions)!

Archived

* **10/15/2023** RAG-based code completion is enabled by detail in [v0.3.0](https://github.com/TabbyML/tabby/releases/tag/v0.3.0)🎉! Check out the [blogpost](https://tabby.tabbyml.com/blog/2023/10/16/repository-context-for-code-completion/) explaining how Tabby utilizes repo-level context to get even smarter! * **11/27/2023** [v0.6.0](https://github.com/TabbyML/tabby/releases/tag/v0.6.0) released! * **11/09/2023** [v0.5.5](https://github.com/TabbyML/tabby/releases/tag/v0.5.5) released! With a redesign of UI + performance improvement. * **10/04/2023** Check out the [model directory](https://tabby.tabbyml.com/docs/models/) for the latest models supported by Tabby. * **09/18/2023** Apple's M1/M2 Metal inference support has landed in [v0.1.1](https://github.com/TabbyML/tabby/releases/tag/v0.1.1)! * **08/31/2023** Tabby's first stable release [v0.0.1](https://github.com/TabbyML/tabby/releases/tag/v0.0.1) 🥳. * **08/28/2023** Experimental support for the [CodeLlama 7B](https://github.com/TabbyML/tabby/issues/370). * **08/24/2023** Tabby is now on [JetBrains Marketplace](https://plugins.jetbrains.com/plugin/22379-tabby)!

## 👋 Getting Started You can find our documentation [here](https://tabby.tabbyml.com/docs/getting-started). - 📚 [Installation](https://tabby.tabbyml.com/docs/installation/) - 💻 [IDE/Editor Extensions](https://tabby.tabbyml.com/docs/extensions/) - ⚙️ [Configuration](https://tabby.tabbyml.com/docs/configuration) ### Run Tabby in 1 Minute The easiest way to start a Tabby server is by using the following Docker command: \`\`\`bash docker run -it \\ --gpus all -p 8080:8080 -v \$HOME/.tabby:/data \\ tabbyml/tabby \\ serve --model TabbyML/StarCoder-1B --device cuda \`\`\` For additional options (e.g inference type, parallelism), please refer to the [documentation page](https://tabbyml.github.io/tabby). ## 🤝 Contributing Full guide at [CONTRIBUTING.md](https://github.com/TabbyML/tabby/blob/main/CONTRIBUTING.md); ### Get the Code \`\`\`bash git clone --recurse-submodules https://github.com/TabbyML/tabby cd tabby \`\`\` If you have already cloned the repository, you could run the \`git submodule update --recursive --init\` command to fetch all submodules. ### Build 1. Set up the Rust environment by following this [tutorial](https://www.rust-lang.org/learn/get-started). 2. Install the required dependencies: \`\`\`bash # For MacOS brew install protobuf # For Ubuntu / Debian apt-get install protobuf-compiler libopenblas-dev \`\`\` 3. Now, you can build Tabby by running the command \`cargo build\`. ### Start Hacking! ... and don't forget to submit a [Pull Request](https://github.com/TabbyML/tabby/compare) ## 🌍 Community - 🎤 [Twitter / X](https://twitter.com/Tabby_ML) - engage with TabbyML for all things possible - 📚 [LinkedIn](https://www.linkedin.com/company/tabbyml/) - follow for the latest from the community - 💌 [Newsletter](https://newsletter.tabbyml.com/archive) - subscribe to unlock Tabby insights and secrets ### 🌟 Star History [](https://star-history.com/#tabbyml/tabby&Date) URL: [tabby/README.md](https://github.com/TabbyML/tabby/blob/main/README.md?plain=1) #### Suggested labels ####

508: microsoft/TaskWeaver: A code-first agent framework for seamlessly planning and executing data analytics tasks.

### Details

Similarity score: 0.84 - [ ] [microsoft/TaskWeaver: A code-first agent framework for seamlessly planning and executing data analytics tasks.](https://github.com/microsoft/TaskWeaver) **CONTENT**: - **TITLE**: microsoft/TaskWeaver: A code-first agent framework for seamlessly planning and executing data analytics tasks. - **DESCRIPTION**: TaskWeaver is a code-first agent framework for seamlessly planning and executing data analytics tasks. This innovative framework interprets user requests through code snippets and efficiently coordinates a variety of plugins in the form of functions to execute data analytics tasks in a stateful manner. ## 🆕 News - 📅2024-02-01: TaskWeaver now has a plugin `document_retriever` for RAG based on a knowledge base.📚 - 📅2024-01-30: TaskWeaver introduces a new plugin-only mode that securely generates calls to specified plugins without producing extraneous code.🪡 - 📅2024-01-23: TaskWeaver can now be personalized by transforming your chat histories into enduring experiences.🎉 - 📅2024-01-17: TaskWeaver now has a plugin `vision_web_explorer` that can open a web browser and explore websites.🌐 - 📅2024-01-15: TaskWeaver now supports Streaming♒ in both UI and command line.✌️ ## 💥 Highlights - Rich data structure - TaskWeaver allows you to work with rich data structures in Python, such as DataFrames, instead of dealing with strings. - Customized algorithms - TaskWeaver allows you to encapsulate your own algorithms into plugins and orchestrate them. - Incorporating domain-specific knowledge - TaskWeaver is designed to incorporate domain-specific knowledge easily to improve the reliability. - Stateful execution - TaskWeaver is designed to support stateful execution of the generated code to ensure consistent and smooth user experience. - Code verification - TaskWeaver is designed to verify the generated code before execution. It can detect potential issues in the generated code and provide suggestions to fix them. - Easy to use - TaskWeaver is easy to use with sample plugins, examples and tutorials to help you get started. TaskWeaver offers an open-box experience, allowing users to run it immediately after installation. - Easy to debug - TaskWeaver is easy to debug with detailed and transparent logs to help you understand the entire process, including LLM prompts, the code generation, and execution process. - Security consideration - TaskWeaver supports a basic session management to keep different users' data separate. The code execution is separated into different processes to avoid mutal interference. - Easy extension - TaskWeaver is easy to extend to accomplish more complex tasks with multiple agents as the plugins. ## ✨ Quick Start ### 🛠️ Step 1: Installation TaskWeaver requires Python >= 3.10. It can be installed by running the following command: ```shell # [optional to create conda environment] # conda create -n taskweaver python=3.10 # conda activate taskweaver # clone the repository git clone https://github.com/microsoft/TaskWeaver.git cd TaskWeaver # install the requirements pip install -r requirements.txt ``` ### 🖊️ Step 2: Configure the LLMs Before running TaskWeaver, you need to provide your LLM configurations. Taking OpenAI as an example, you can configure the `taskweaver_config.json` file as follows. #### OpenAI ```json { "llm.api_key": "the api key", "llm.model": "the model name, e.g., gpt-4" } ``` 💡 TaskWeaver also supports other LLMs and advanced configurations, please check the documents for more details. ### 🚩 Step 3: Start TaskWeaver #### ⌨️ Command Line (CLI) Assume you are in the cloned TaskWeaver folder. ```shell python -m taskweaver -p ./project/ ``` This will start the TaskWeaver process and you can interact with it through the command line interface. If everything goes well, you will see the following prompt: ``` ========================================================= _____ _ _ __ |_ _|_ _ ___| | _ | | / /__ ____ __ _____ _____ | |/ _` / __| |/ /| | /| / / _ \/ __ `/ | / / _ \/ ___/ | | (_| \__ \ < | |/ |/ / __/ /_/ /| |/ / __/ / |_|\__,_|___/_|\_\|__/|__/\___/\__,_/ |___/\___/_/ ========================================================= TaskWeaver: I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human: ___ ``` or 💻 Web UI TaskWeaver also supports WebUI for demo purpose, please refer to web UI docs for more details. or 📋 Import as a Library TaskWeaver can be imported as a library to integrate with your existing project, more information can be found in docs 📖 Documentation More documentations can be found on TaskWeaver Website. [URL](https://github.com/microsoft/TaskWeaver) #### Suggested labels #### { "label-name": "taskweaver", "description": "A code-first agent framework for data analytics tasks.", "repo": "microsoft/TaskWeaver", "confidence": 68.7 }

625: unsloth/README.md at main · unslothai/unsloth

### Details

Similarity score: 0.83 - [ ] [unsloth/README.md at main · unslothai/unsloth](https://github.com/unslothai/unsloth/blob/main/README.md?plain=1) # unsloth/README.md at main · unslothai/unsloth

### Finetune Mistral, Gemma, Llama 2-5x faster with 70% less memory! 

## ✨ Finetune for Free All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face. | Unsloth supports | Free Notebooks | Performance | Memory use | |-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------| | **Gemma 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing) | 2.4x faster | 58% less | | **Mistral 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing) | 2.2x faster | 62% less | | **Llama-2 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing) | 2.2x faster | 43% less | | **TinyLlama** | [▶️ Start on Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing) | 3.9x faster | 74% less | | **CodeLlama 34b** A100 | [▶️ Start on Colab](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing) | 1.9x faster | 27% less | | **Mistral 7b** 1xT4 | [▶️ Start on Kaggle](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook) | 5x faster\* | 62% less | | **DPO - Zephyr** | [▶️ Start on Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) | 1.9x faster | 19% less | - This [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing) is useful for ShareGPT ChatML / Vicuna templates. - This [text completion notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) is for raw text. This [DPO notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) replicates Zephyr. - \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster. ## 🦥 Unsloth.ai News - 📣 [Gemma 7b](https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing) on 6T tokens now works. And [Gemma 2b notebook](https://colab.research.google.com/drive/15gGm7x_jTm017_Ic8e317tdIpDG53Mtu?usp=sharing) - 📣 Added [conversational notebooks](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) and [raw text notebooks](https://colab.research.google.com/drive/1bMOKOBzxQWUIGZBs_B0zm8pimuEnZdfM?usp=sharing) - 📣 [2x faster inference](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) added for all our models - 📣 [DPO support](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) is now included. [More info](#DPO) on DPO - 📣 We did a [blog](https://huggingface.co/blog/unsloth-trl) with 🤗Hugging Face and are in their official docs! Check out the [SFT docs](https://huggingface.co/docs/trl/main/en/sft_trainer#accelerate-fine-tuning-2x-using-unsloth) and [DPO docs](https://huggingface.co/docs/trl/main/en/dpo_trainer#accelerate-dpo-fine-tuning-using-unsloth) - 📣 [Download models 4x faster](https://huggingface.co/collections/unsloth/) from 🤗Hugging Face. Eg: `unsloth/mistral-7b-bnb-4bit` ## 🔗 Links and Resources | Type | Links | | ------------------------------- | --------------------------------------- | | 📚 **Wiki & FAQ** | [Read Our Wiki](https://github.com/unslothai/unsloth/wiki) | | 📜 **Documentation** | [Read The Doc](https://github.com/unslothai/unsloth/tree/main#-documentation) | | 💾 **Installation** | [unsloth/README.md](https://github.com/unslothai/unsloth/tree/main#installation-instructions)| |   **Twitter (aka X)** | [Follow us on X](https://twitter.com/unslothai)| | 🥇 **Benchmarking** | [Performance Tables](https://github.com/unslothai/unsloth/tree/main#-performance-benchmarking) | 🌐 **Released Models** | [Unsloth Releases](https://huggingface.co/unsloth)| | ✍️ **Blog** | [Read our Blogs](https://unsloth.ai/blog)| ## ⭐ Key Features - All kernels written in [OpenAI's Triton](https://openai.com/research/triton) language. **Manual backprop engine**. - **0% loss in accuracy** - no approximation methods - all exact. - No change of hardware. Supports NVIDIA GPUs since 2018+. Minimum CUDA Capability 7.0 (V100, T4, Titan V, RTX 20, 30, 40x, A100, H100, L40 etc) [Check your GPU!](https://developer.nvidia.com/cuda-gpus) GTX 1070, 1080 works, but is slow. - Works on **Linux** and **Windows** via WSL. - Supports 4bit and 16bit QLoRA / LoRA finetuning via [bitsandbytes](https://github.com/TimDettmers/bitsandbytes). - Open source trains 5x faster - see [Unsloth Pro](https://unsloth.ai/) for **30x faster training**! - If you trained a model with 🦥Unsloth, you can use this cool sticker!   ## 🥇 Performance Benchmarking - For the full list of **reproducable** benchmarking tables, [go to our website](https://unsloth.ai/blog/mistral-benchmark#Benchmark%20tables) | 1 A100 40GB | 🤗Hugging Face | Flash Attention | 🦥Unsloth Open Source | 🦥[Unsloth Pro](https://unsloth.ai/pricing) | |--------------|--------------|-----------------|---------------------|-----------------| | Alpaca | 1x | 1.04x | 1.98x | **15.64x** | | LAION Chip2 | 1x | 0.92x | 1.61x | **20.73x** | | OASST | 1x | 1.19x | 2.17x | **14.83x** | | Slim Orca | 1x | 1.18x | 2.22x | **14.82x** | - Benchmarking table below was conducted by [🤗Hugging Face](https://huggingface.co/blog/unsloth-trl). | Free Colab T4 | Dataset | 🤗Hugging Face | Pytorch 2.1.1 | 🦥Unsloth | 🦥 VRAM reduction | | --- | --- | --- | --- | --- | --- | | Llama-2 7b | OASST | 1x | 1.19x | 1.95x | -43.3% | | Mistral 7b | Alpaca | 1x | 1.07x | 1.56x | -13.7% | | Tiny Llama 1.1b | Alpaca | 1x | 2.06x | 3.87x | -73.8% | | DPO with Zephyr | Ultra Chat | 1x | 1.09x | 1.55x | -18.6% |  [View on GitHub](https://github.com/unslothai/unsloth/blob/main/README.md?plain=1) #### Suggested labels ####

74: Sqlite WASM and Github Pages

### Details

Similarity score: 0.83 - [ ] [merge sqlite - Kagi Search](https://kagi.com/search?q=merge+sqlite&from_date=2021-01-01) - [ ] [sql - Fastest Way merge two SQLITE Databases - Stack Overflow](https://stackoverflow.com/questions/9349659/fastest-way-merge-two-sqlite-databases) - [ ] [simonw/sqlite-utils: Python CLI utility and library for manipulating SQLite databases](https://github.com/simonw/sqlite-utils) - [ ] [sqlite-utils](https://sqlite-utils.datasette.io/en/stable/) - [ ] [GitHub Pages | Websites for you and your projects, hosted directly from your GitHub repository. Just edit, push, and your changes are live.](https://pages.github.com/) - [ ] [sqlite wasm sql.js github pages - Kagi Search](https://kagi.com/search?q=sqlite+wasm+sql.js+github+pages) - [ ] [phiresky/sql.js-httpvfs: Hosting read-only SQLite databases on static file hosters like Github Pages](https://github.com/phiresky/sql.js-httpvfs) - [ ] [sqlite3 WebAssembly & JavaScript Documentation Index](https://sqlite.org/wasm/doc/trunk/index.md) - [ ] [phiresky/youtube-sponsorship-stats/](https://github.com/phiresky/youtube-sponsorship-stats/) - [ ] [phiresky/world-development-indicators-sqlite/](https://github.com/phiresky/world-development-indicators-sqlite/) - [ ] [Show HN: Fully-searchable Library Genesis on IPFS | Hacker News](https://news.ycombinator.com/item?id=28585208) - [ ] [nalgeon/sqlime: Online SQLite playground](https://github.com/nalgeon/sqlime) - [ ] [nalgeon/sqlean.js: Browser-based SQLite with extensions](https://github.com/nalgeon/sqlean.js) - [ ] [Sqlime - SQLite Playground](https://sqlime.org/) - [ ] [sqlite-wasm-http - npm](https://www.npmjs.com/package/sqlite-wasm-http) - [ ] [Postgres WASM by Snaplet and Supabase](https://supabase.com/blog/postgres-wasm) - [ ] [rhashimoto/wa-sqlite: WebAssembly SQLite with experimental support for browser storage extensions](https://github.com/rhashimoto/wa-sqlite) - [ ] [jlongster/absurd-sql: sqlite3 in ur indexeddb (hopefully a better backend soon)](https://github.com/jlongster/absurd-sql) - [ ] [help: basic example for the web browser · Issue #23 · phiresky/sql.js-httpvfs](https://github.com/phiresky/sql.js-httpvfs/issues/23) - [ ] [phiresky/sql.js-httpvfs: Hosting read-only SQLite databases on static file hosters like Github Pages](https://github.com/phiresky/sql.js-httpvfs#minimal-example-from-scratch) - [ ] [sql.js-httpvfs/example at master · phiresky/sql.js-httpvfs](https://github.com/phiresky/sql.js-httpvfs/tree/master/example) - [ ] [Code search results](https://github.com/search?utf8=%E2%9C%93&q=sql.js-httpvfs&type=code) - [ ] [knowledge/docs/databases/sqlite.md at 7c4bbc755c64368a82ca22b76566e9153cd2e377 · nikitavoloboev/knowledge](https://github.com/nikitavoloboev/knowledge/blob/7c4bbc755c64368a82ca22b76566e9153cd2e377/docs/databases/sqlite.md?plain=1#L96) - [ ] [static-wiki/src/sqlite.js at b0ae18ed02ca64263075c3516c38a504f46e10c4 · segfall/static-wiki](https://github.com/segfall/static-wiki/blob/b0ae18ed02ca64263075c3516c38a504f46e10c4/src/sqlite.js#L2)

487: fgmacedo/python-statemachine: Python Finite State Machines made easy.

### Details

Similarity score: 0.83 - [ ] [fgmacedo/python-statemachine: Python Finite State Machines made easy.](https://github.com/fgmacedo/python-statemachine) # fgmacedo/python-statemachine: Python Finite State Machines made easy. Python finite-state machines made easy. **Python StateMachine** - **Free software:** MIT license - **Documentation:** Welcome to python-statemachine, an intuitive and powerful state machine framework designed for a great developer experience. 🚀 With StateMachine, you can easily create complex, dynamic systems with clean, readable code. 💡 Our framework makes it easy to understand and reason about the different states, events and transitions in your system, so you can focus on building great products. 🔒 python-statemachine also provides robust error handling and ensures that your system stays in a valid state at all times. A few reasons why you may consider using it: 📈 python-statemachine is designed to help you build scalable, maintainable systems that can handle any complexity. 💪 You can easily create and manage multiple state machines within a single application. 🚫 Prevents common mistakes and ensures that your system stays in a valid state at all times. ## Getting started To install Python State Machine, run this command in your terminal: ``` pip install python-statemachine ``` To generate diagrams from your machines, you'll also need pydot and Graphviz. You can install this library already with pydot dependency using the extras install option. See our docs for more details. ``` pip install python-statemachine[diagrams] ``` ### Define your state machine: ```python from statemachine import StateMachine, State class TrafficLightMachine(StateMachine): "A traffic light machine" green = State(initial=True) yellow = State() red = State() cycle = ( green.to(yellow) | yellow.to(red) | red.to(green) ) def before_cycle(self, event: str, source: State, target: State, message: str = ""): message = ". " + message if message else "" return f"Running {event} from {source.id} to {target.id}{message}" def on_enter_red(self): print("Don't move.") def on_exit_red(self): print("Go ahead!") ``` You can now create an instance: ```python sm = TrafficLightMachine() ``` This state machine can be represented graphically as follows: ```python img_path = "docs/images/readme_trafficlightmachine.png" sm._graph().write_png(img_path) ``` ## URL - **Repository:** #### Suggested labels #### { "label-name": "state-machine-framework", "description": "A framework for creating complex, dynamic systems with clean, readable code.", "confidence": 85.89 }