Metadata-Version: 2.4
Name: deepmark
Version: 1.0.0
Summary: deepmark: Model-agnostic, robust, and compliance-ready text watermarking library for LLMs and content platforms.
Home-page: https://github.com/yourusername/deepmark
Author: Your Name
Author-email: your@email.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Requires-Dist: spacy
Requires-Dist: nltk
Requires-Dist: cryptography
Requires-Dist: click
Requires-Dist: pydantic
Requires-Dist: requests
Requires-Dist: numpy
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

# 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 
