semvec/pypi-packages huggingface dataset:
shiny-tail 0.2.0
A package to use Tailwind CSS in Shiny for Python
Requires Python: >=3.7

**shinytail**

A lightweight Python package for seamlessly integrating Tailwind CSS with Shiny for Python applications.

**Installation**

```
pip install shiny_tail
```

**Features**

- Add Tailwind CSS to Shiny with one line of code
- Support for Tailwind plugins and configurations
- Custom themes and styling
- No external CSS dependencies required
- Simple, intuitive API
- Responsive design out of the box

**Usage**

```python
from shiny import App, ui
from htmltools import tags
from shiny_tail import tailwind

# Configure Tailwind with plugins
tailwind.configure(
    plugins=["typography"],
    config={"theme": {"extend": {"colors": {"custom-blue": "#1e40af"}}}}
)

# Create a UI with Tailwind classes
app_ui = tailwind.page(
    tags.h1("Hello Tailwind", class_="text-3xl font-bold text-custom-blue")
)

def server(input, output, session):
    pass

app = App(app_ui, server)
```

**Requirements**

- Python >=3.7
- shiny >=0.5.0
- htmltools >=0.4.0

**License**
MIT

**Contributing**

1. Fork the repository
2. Create your feature branch: `git checkout -b feature/my-feature`
3. Commit your changes: `git commit -am 'Add my feature'`
4. Push to the branch: `git push origin feature/my-feature`
5. Submit a pull request

**Support**
For issues and feature requests, please use the **issue tracker**.
llmstudio-proxy 1.0.6
LLMstudio Proxy is the module of LLMstudio that allows calling any LLM as a Service Provider in proxy server. By leveraging LLMstudio Proxy, you can have a centralized point for connecting to any provider running independently from your application.
Requires Python: <4.0,>=3.9

# LLMstudio by [TensorOps](http://tensorops.ai "TensorOps")

Prompt Engineering at your fingertips

![LLMstudio logo](https://imgur.com/Xqsj6V2.gif)

## 🌟 Features

![LLMstudio UI](https://imgur.com/wrwiIUs.png)

- **LLM Proxy Access**: Seamless access to all the latest LLMs by OpenAI, Anthropic, Google.
- **Custom and Local LLM Support**: Use custom or local open-source LLMs through Ollama.
- **Prompt Playground UI**: A user-friendly interface for engineering and fine-tuning your prompts.
- **Python SDK**: Easily integrate LLMstudio into your existing workflows.
- **Monitoring and Logging**: Keep track of your usage and performance for all requests.
- **LangChain Integration**: LLMstudio integrates with your already existing LangChain projects.
- **Batch Calling**: Send multiple requests at once for improved efficiency.
- **Smart Routing and Fallback**: Ensure 24/7 availability by routing your requests to trusted LLMs.
- **Type Casting (soon)**: Convert data types as needed for your specific use case.

## 🚀 Quickstart

Don't forget to check out [https://docs.llmstudio.ai](docs) page.

## Installation

Install the latest version of **LLMstudio** using `pip`. We suggest that you create and activate a new environment using `conda`

```bash
pip install llmstudio
```

Install `bun` if you want to use the UI

```bash
curl -fsSL https://bun.sh/install | bash
```

Create a `.env` file at the same path you'll run **LLMstudio**

```bash
OPENAI_API_KEY="sk-api_key"
ANTHROPIC_API_KEY="sk-api_key"
```

Now you should be able to run **LLMstudio Proxy** using the following command.

```bash
llmstudio server --proxy
```

When the `--proxy` flag is set, you'll be able to access the [Swagger at http://0.0.0.0:50001/docs (default port)](http://0.0.0.0:50001/docs)

## 📖 Documentation

- [Visit our docs to learn how the SDK works](https://docs.LLMstudio.ai) (coming soon)
- Checkout our [notebook examples](https://github.com/TensorOpsAI/LLMstudio/tree/main/examples) to follow along with interactive tutorials

## 👨‍💻 Contributing

- Head on to our [Contribution Guide](https://github.com/TensorOpsAI/LLMstudio/tree/main/CONTRIBUTING.md) to see how you can help LLMstudio.
- Join our [Discord](https://discord.gg/GkAfPZR9wy) to talk with other LLMstudio enthusiasts.

## Training

[![Banner](https://imgur.com/XTRFZ4m.png)](https://www.tensorops.ai/llm-studio-workshop)

---

Thank you for choosing LLMstudio. Your journey to perfecting AI interactions starts here.