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Turn python function into microservice.

Project description

Workcell

Instantly turn your Python function into production-ready microservice.

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Instantly turn your Python function into delightful app and production-ready microservice, with lightweight UI to interact with.


⚡ Highlights

  • 🔌 Instantly turn functions into microservices within seconds.
  • 📈 Automatically generate user-friendly UI for interaction.
  • 🤗 One-click deployment to Hugging Face Spaces.
  • ☁️ Develop locally, deploy to the cloud.
  • 🧩 Empower development and analysis with scalable components.
  • 🦄 Get inspired by the open-source community.

⏰ Status

Status Stability Goal
Alpha We are testing Workcell with a closed set of customers
Public Alpha Anyone can sign up over at weanalyze.co. But go easy on us, there are a few kinks.
🚧 Public Beta Stable enough for most non-enterprise use-cases
Public Production-ready

We are currently in: Public Alpha.

Wokrlcell is heavily under development, and we expect to iterate on the APIs/UIs before reaching beta (version 0.1.0).

🔖 Installation

  • Python version 3.8+

  • Install Workcell using pip:

    pip install workcell
    
  • Or using Anaconda:

    conda install workcell
    

🚀 Quick Start

  • After workcell installed, just run:

    workcell hello
    
  • You can find an automatically generated Swagger UI from http://127.0.0.1:7860/docs, then just goto http://127.0.0.1:7860/ui to try your first workcell app:)

📚 Guide

  • What happened?

    Workcell has created a FastAPI service and a lightweight user interface for your functions without any additional API or UI-related code. This service is ready to be deployed to the cloud as a public service, requiring minimal setup on your end.

  • Speed up your development

    With Workcell, you can focus on developing your core functionality while leaving the infrastructure and deployment details to the platform. All you need is to make sure your function is workcell compatible.

  • Workcell compatible

    A workcell-compatible function must have an input and output parameter and return value based on Pydantic models. The input and output are specified using type hints.

    Here is an example of a simple workcell compatible function:

    from pydantic import BaseModel
    
    class Input(BaseModel):
        message: str
    
    class Output(BaseModel):
        message: str
    
    def hello_workcell(input: Input) -> Output:
        """Returns the `message` of the input data."""
        return Output(message=input.message)
    

🤗 Deployment

  • Why deployment?

    You'll be able to access your workcell from anywhere and share it with your team and collaborators. Now you can deploy workcell to Hugging Face Spaces in 1-click!

  • Prepare your Hugging Face account

    First you need a Hugging Face account, and prepare a User Access Tokens, then set environment variables like below:

    export HUGGINGFACE_USERNAME={huggingface_username}
    export HUGGINGFACE_TOKEN={huggingface_token}
    

    Replace {huggingface_username} and {huggingface_token} with yours.

    You can also store these environment variables in a .env file in your project folder for convenience.

  • Deploy in 1-click!

    Once you prepared a workcell compatible function (or project), just run:

    workcell up app:hello_workcell
    

    Voila! The deployment process will start, and within a few minutes, workcell will be available on your Hugging Face Spaces.

  • Extra explain

    When you run workcell up, there're actually 2 seperate step workcell pack and workcell deploy been applied. workcell pack will package your function code with a template into .workcell under your project folder, and workcell deploy will upload this folder to cloud.

📖 Documents

  • You can find more details in our documents.

🎮 Examples

🛣️ Roadmap

  • Missing a feature? Have a look at our public roadmap to see what the team is working on in the short and medium term. Still missing it? Please let us know by opening an issue!

😆 Contacts

License

Apache-2.0 License.

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