semvec/pypi-packages huggingface dataset:
datazone-mcp-server 1.0.2
test
Keywords: test


# anupam-action-test
 <img src="[https://s3.amazonaws.com/nrjio/interactive.gif](https://compote.slate.com/images/697b023b-64a5-49a0-8059-27b963453fb1.gif?crop=780%2C520%2Cx0%2Cy0&width=1280)https://compote.slate.com/images/697b023b-64a5-49a0-8059-27b963453fb1.gif?crop=780%2C520%2Cx0%2Cy0&width=1280" alt="Interactive notifications example" width=350/>
instaui_shiki 0.1.1
A Python package for instaui-shiki.
Requires Python: <3.13,>=3.8
Keywords: gui, shiki, syntax-highlighting, ui, web

# instaui-shiki

<div align="center">

English| [简体中文](./README.md)

</div>

## 📖 Introduction
instaui-shiki is a Python library for syntax highlighting code snippets in the browser using [Shiki](https://github.com/shikijs/shiki).


## ⚙️ Installation

```bash
pip install instaui-shiki
```

## 🖥️ Usage
```python
from instaui import ui

@ui.page("/")
def test_page():
    ui.code("print('foo')")


ui.server().run()
```
deepmark 1.0.0
deepmark: Model-agnostic, robust, and compliance-ready text watermarking library for LLMs and content platforms.
Requires Python: >=3.7

# deepmark Python SDK

## Overview
A model-agnostic, robust, and compliance-ready text watermarking library for LLMs and content platforms. Supports both generation-time and post-processing watermark embedding, with detection tools for developers.

## Features
- Generation-time and post-generation watermarking
- Model-agnostic (OpenAI, HuggingFace, custom LLMs)
- Robust to paraphrasing, translation, and editing
- Python SDK for easy integration
- Batch detection, confidence scoring
- Multilingual support (English, Spanish, French, German, Italian, Portuguese)

## Installation
```bash
pip install -r requirements.txt
```

## Usage
### Marking Text
```python
from deepmark import mark_text

# Mark text with a watermark
marked = mark_text("Hello world", key="your-secret-key", source_id="GPT-4")
print(marked)
```

### Detecting Watermarks
```python
from deepmark import detect_watermark

# Detect watermark in text
result = detect_watermark(marked)
print(result)
# Output: {'confidence_score': ..., 'source_id': ..., 'timestamp': ..., 'key_id': ..., 'tampering_likelihood': ...}
```

## Extensibility
- Add more languages by downloading spaCy models and updating synonym logic.
- Add more metadata by extending the zero-width encoding.
- Integrate with LLMs or pipelines by calling `mark_text` and `detect_watermark`.

## License
MIT