semvec/pypi-packages huggingface dataset:
PieDefender 0.0.1
Anti VM and Anti Analysis package for Python.
License: MIT

# PyDefender

## 🛡️ Anti-VM & Sandbox Detection for Python

PyDefender is a Python package designed to detect virtual machines, sandboxes, and automated analysis environments. It helps protect your scripts from running in controlled environments often used for malware analysis or security research.

## 📜 Features
- Detects popular virtual machines (VMware, VirtualBox, Hyper-V, etc.).
- Identifies sandbox environments used for malware analysis.
- Checks system artifacts to determine if running in a VM.
- Supports Windows and Linux.
- Lightweight and easy to integrate.

## 🚀 Installation
Install PyDefender via pip:

```sh
pip install pydefender
```

## 🔍 Usage
Here's how to use PyDefender in your Python project:

```python
import pydefender

def main():
    if pydefender.is_vm():
        print("Virtual machine detected! Exiting...")
        exit()
    else:
        print("Running on a physical machine.")

if __name__ == "__main__":
    main()
```

## 🖥️ Detection Methods
PyDefender uses multiple techniques to detect virtualized environments:
- **System Manufacturer Check**: Identifies VM vendors from system metadata.
- **MAC Address Check**: Detects VM-related network adapter addresses.
- **Registry & File Artifacts** (Windows): Looks for VM-specific registry keys and files.
- **CPU & Memory Anomalies**: Checks for signs of virtualized CPU and memory configurations.
- **Process & Service Analysis**: Scans for known VM or sandbox processes.

## ⚠️ Disclaimer
PyDefender is intended for ethical and legitimate purposes only. Do not use it for malicious activities. The developers are not responsible for misuse of this software.
wexptend 0.1.2
A highly scalable websockets server framework.
License: MIT
Requires Python: >=3.9

# WExptend
一个高性能、可拓展的 WebSocket 服务端框架

**WExptend** 是一个高性能、可拓展的 WebSocket 框架，旨在构建可扩展且灵活的实时应用程序。该框架内置插件化路由与匹配支持，允许您轻松扩展功能并集成自定义逻辑到 WebSocket 应用中。

## 特性

- **WebSocket 支持**：无缝处理 WebSocket 连接和消息。
- **可拓展性**：采用模块化设计，便于通过插件扩展功能。
- **灵活路由**：使用自定义路由逻辑，根据定义的模式引导 WebSocket 流量。
- **插件架构**：在不修改核心代码的情况下添加新功能或自定义行为。
- ~~**热重载**：自动监测并刷新插件和路由的更新。~~(这个测试的时候发现没用，还在修)

## 安装

要安装 **WExptend**，只需使用 `pip` 。

```shell
pip install wexptend
```

## 基本使用

以下是一个使用 **WExptend** 设置 WebSocket 服务器的基础示例：

```py
import WExptend

WExptend.init()

WExptend.load_plugins("plugins")

WExptend.load_routers("routers") #如果你不加载路由，加载再多的插件也没用

WExptend.run()
```

后期计划是像nonebot那样可以指定插件文件夹之类的

## 文档

欲了解更多信息，请查看完整文档：

[正在写](#)

## 贡献

我们欢迎贡献！要贡献代码，请 fork 仓库、创建新分支并提交 Pull Request。

## 许可证

**WExptend** 使用 MIT 许可证。更多详情请参见 [LICENSE](LICENSE) 文件。
zoopy 0.0.4
A Python library for animal data analysis
License: MIT
Requires Python: >=3.8

<div align="center"><img src="https://github.com/droyti46/zoopy/blob/main/img/logo.png?raw=true"></div>

<h1 align="center"> ZooPy: A Python Library for Animal Data Analysis</h1>

<p align="center">
    <img alt="python" src="https://img.shields.io/badge/Python-3776AB?style=for-the-badge&logo=python&logoColor=white"/>
    <img alt="pandas" src="https://img.shields.io/badge/pandas-%23150458.svg?style=for-the-badge&logo=pandas&logoColor=white"/>
    <img alt="matplotlib" src="https://img.shields.io/badge/matplotlib-11557C?style=for-the-badge&logo=matplotlib&logoColor=white"/>
    <img alt="torch" src="https://img.shields.io/badge/Torch-EE4C2C?style=for-the-badge&logo=pytorch&logoColor=white"/>
</p>

## Overview
**ZooPy** is a simple Python library with a concise API designed for analyzing and processing biological data related to animals. It provides tools for working with datasets, performing image recognition etc.
<div align="center"><img src="https://github.com/droyti46/zoopy/blob/main/img/turtle.png?raw=true"></div>

## Data
The data was collected from [Wikipedia](https://www.wikipedia.org/) and contains the languages:
- 🇷🇺 Russian

## Library Structure
<div align="center"><img src="https://github.com/droyti46/zoopy/blob/main/img/structure.png?raw=true" width=550px></div>

## Installation
To install ZooPy, run:

```bash
pip install zoopy
```

## Usage
Getting started:

```python
from zoopy import animal

cat = animal.Animal('кошка', 'ru')
cat.display()
```

<div align="center"><img src="https://github.com/droyti46/zoopy/blob/main/img/cat-display.png?raw=true" width=500px></div>

\
ZooPy has interfaces for several pre-trained models, for example, ImageNet

```python
import cv2

img = cv2.imread('img/duck.jpg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

cv2.imshow(img)
```

<div align="center"><img src="https://github.com/droyti46/zoopy/blob/main/img/duck.jpg?raw=true" width=500px></div>

```python
from zoopy import models

model = models.ImageClassification()
print(model.predict(img))
```

```Output: albatross```

Create different graphics

```python
from zoopy import animal, viz

turtle = animal.Animal('черепаха', 'ru')
viz.plot_classification(turtle)
```

<div align="center"><img src="https://github.com/droyti46/zoopy/blob/main/img/classification.png?raw=true" width=500px></div>

## Contact
Contact me by [Mail](mailto:nikitabakutov2008@gmail.com) or [Telegram](https://t.me/droyti).