semvec/pypi-packages huggingface dataset:
panopticml 0.0.9
A package for Panoptic ML
Requires Python: >=3.10

# PanopticML

Default plugin for panoptic, provides all machine learning functionalities such as:
- image embeddings using OpenAI CLIP
- images clustering using FAISS Kmeans or HDBScan
- images to images similarity using FAISS Index L2
- text to images similarity using FAISS Index L2 + CLIP

## Available models

Several models are available in this plugin to compute the embeddings: 
- [google mobilenet](https://huggingface.co/google/mobilenet_v2_1.4_224): a really light model with lower performances but made to run on bad devices, no support for text similarity
- [openAI CLIP](https://huggingface.co/openai/clip-vit-large-patch14): default model for panoptic, good performances, runs well on most computers with a decent CPU, support text similarity and has a good understanding of semantics
- [meta dinosV2](https://huggingface.co/docs/transformers/en/model_doc/dinov2): model that will have better performances than CLIP on pure visual similarities but less understanding of themes and semantics, no support for text similarity, runs well on CPU
- [google SIGLIP2](https://huggingface.co/docs/transformers/main/model_doc/siglip2): computing heavy model, NVDIA GPU recommended but works also on CPU (it will take a long time to compute embeddings), way better results than CLIP on text similarity and semantics, and pretty good visual features
- auto transformers: want to tryout any huggingface multimodal model ? you can just provide its id to panopticML and should be able to use it directly

## Clustering functions
- Kmeans: specify a number of clusters, really fast
- HDBScan: automatic number of clusters, a bit slower and excludes a lot of images
- Images to text: considering several texts, create a cluster per text and put each image to the text it is the closest to
- Find duplicates: create clusters with only images sharing a lot of similarity together, good to find duplicates or near duplicates
pytest-adversarial 0.1.0
Generate adversarial pytest tests using LLM
Requires Python: >=3.10

# pytest-adversarial

[![PyPI version](https://img.shields.io/pypi/v/pytest-adversarial.svg)](https://pypi.org/project/pytest-adversarial/)
[![Python](https://img.shields.io/pypi/pyversions/pytest-adversarial.svg)](https://pypi.org/project/pytest-adversarial/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

LLM-powered adversarial test generation for Python using Quality-Diversity algorithms.

## Installation

```bash
pip install pytest-adversarial
```

## Quick Example

Test a JSON parser for edge cases:

```python
# target.py
import json

def parse_json(text: str) -> dict:
    """Parse JSON string."""
    return json.loads(text)
```

Run adversarial testing:

```bash
pytest-adversarial examples/json_parser/target.py --rounds 10
```

The tool will automatically:
- Generate adversarial test inputs using LLMs
- Find edge cases that crash your code
- Evolve attacks using Quality-Diversity search
- Report robustness metrics and discovered vulnerabilities

Sample output:
```
Round 5/10 | Robustness: 73.2% | Attacks: 42 | Types: 8
Found: malformed_unicode, extreme_nesting, special_chars...
```

## Documentation

For detailed usage, examples, and advanced features, visit:
**[github.com/nulone/pytest-adversarial](https://github.com/nulone/pytest-adversarial)**

## Requirements

- Python 3.10+
- OpenAI API key (set `OPENROUTER_API_KEY` environment variable)

## License

MIT License - see GitHub repository for details.