semvec/pypi-packages huggingface dataset:
physicsx.pxs 999.0.3
⚠️  SECURITY DEMONSTRATION - MALICIOUS PACKAGE - DO NOT USE ⚠️
License: MIT
Requires Python: >=3.10
Keywords: security, demonstration, confusion-attack, supply-chain

# ⚠️  MALICIOUS PACKAGE PROOF-OF-CONCEPT ⚠️

## 🚨 WARNING - READ THIS FIRST 🚨

**THIS IS A SECURITY DEMONSTRATION PACKAGE**

This package is a **malicious proof-of-concept** designed to demonstrate a **confusion attack** (also known as a dependency confusion or namespace shadowing attack).

**DO NOT USE THIS PACKAGE IN PRODUCTION!**

**DO NOT INSTALL THIS PACKAGE UNLESS YOU UNDERSTAND THE SECURITY IMPLICATIONS!**

---

## Purpose

This PoC demonstrates how a malicious package on public PyPI can shadow an internal company package with the same name, exploiting the Artifactory HTTP 403 issue that prevents package managers from falling back to the correct internal repository.

When imported, this package will:
- ✓ Display a large warning banner
- ✓ Attempt to read `~/.netrc` and other sensitive files
- ✓ Show proof of file access (file size, permissions, preview)
- ✓ Demonstrate security implications

---

## How to Build & Publish

```bash
# Build distribution packages
python -m build

# Upload to PyPI (requires PyPI account and token)
twine upload dist/*
```

⚠️  **CRITICAL WARNING**: This is for authorized security testing only.

---

## How to Test

Test direct installation:

```bash
pip install physicsx.pxs
python -c "import physicsx.pxs"
```

Expected behavior: Warning banner and file access demonstration.

---

## Expected Behavior

When imported, displays:
- Warning banner identifying this as a malicious PoC
- File access demonstration (attempts to read ~/.netrc)
- List of other sensitive files it could access
- Security implications explanation

---

## Cleanup

```bash
# Uninstall the package
pip uninstall -y physicsx.pxs

# Clear cache
pip cache purge
```

⚠️ **Note**: PyPI packages cannot be easily deleted, only "yanked".

---

## Legal & Ethical Considerations

**This PoC is for authorized security testing only.**

This package is provided "as is" for security demonstration purposes. Do not use for unauthorized testing or malicious purposes.

---

## References

- [PEP 503](https://peps.python.org/pep-0503/) - Simple Repository API
- [PEP 708](https://peps.python.org/pep-0708/) - Extending the Repository API
- [Dependency Confusion Attack](https://medium.com/@alex.birsan/dependency-confusion-4a5d60fec610)
STRling 2.5.9
Next-gen string pattern DSL & compiler
License: MIT
Requires Python: >=3.8
Keywords: strling, regex, regexp, regular expression, string, validation, pattern, parser, compiler, dsl, readable, beginner

# STRling for Python (Quick Start)

## 🗺️ Overview

This is the **Python binding** for [STRling](https://github.com/TheCyberLocal/STRling), a next-generation production-grade syntax designed as a user interface for writing powerful regular expressions (RegEx).

STRling makes string validation and matching **readable, safe, and consistent** across environments. Instead of cryptic regex syntax, you build patterns using a clean, object-oriented DSL. Under the hood, STRling compiles to native RegEx engines — but adds instructional error handling and consistent semantics.

## 🗝️ Key Features

-   **Beginner Friendly**: No regex jargon required.
-   **Reliable**: Built only on standard libraries.
-   **Instructional Errors**: Explains what went wrong and how to fix it.
-   **Consistent**: Works across frameworks and libraries without custom validators.
-   **Multilingual**: Available across popular programming languages ([JavaScript](../javascript/README.md), and more coming soon).

## 💾 Installation

Install STRling via pip:

```sh
pip install STRling
```

## ✨ STRling in action!

### Basic Example: US Phone Number

```python
from STRling import simply as s
import re

# Define parts of a US phone number pattern
separator = s.in_chars(" -")
area_code = s.merge(
    s.may("("),
    s.group("area_code", s.digit(3)),
    s.may(")")
)
central_part = s.group("central_part", s.digit(3))
last_part = s.group("last_part", s.digit(4))

phone_number_pattern = s.merge(
    area_code,
    s.may(separator),
    central_part,
    s.may(separator),
    last_part
)

# Compile to RegEx
example_text = "(123) 456-7890 and 123-456-7890"
pattern = re.compile(str(phone_number_pattern))
matches = pattern.finditer(example_text)

for match in matches:
    print("Full Match:", match.group())
    print("Area Code:", match.group("area_code"))
    print("Central Part:", match.group("central_part"))
    print("Last Part:", match.group("last_part"))
```

## 📚 Complete API Documentation

For comprehensive syntax reference and all available features, see the **[Python API Reference](docs/api_reference.md)**.

## 📖 Related Documentation

- **[Python API Reference](docs/api_reference.md)**: Complete API documentation for Python
- **[Developer Documentation Hub](../../docs/index.md)**: Architecture, philosophy, and development workflow
- **[Formal Specification](../../spec/README.md)**: Grammar and semantics reference
- **[JavaScript Binding](../javascript/README.md)**: STRling for JavaScript/Node.js

## 💖 Support

If you find STRling useful, consider [buying me a coffee](https://buymeacoffee.com/thecyberlocal).
auto-rename-ai 1.0.0
AI image renamer using EfficientNetB0
License: MIT
Requires Python: >=3.8

## auto_rename_ai

<<Auto Rename AI>> is a Python package for automatically renaming images using the EfficientNetB0 model. Each image is classified, and its filename is updated to include the class label, confidence percentage, and a counter.

_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

## Features
- Automatically renames images in the format 'label(confidence%)(counter).ext'
- Supports '.jpg', '.jpeg', and '.png' image formats
- Handles corrupted images without stopping the program
- Processes an entire folder of images in one batch

_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _


## Installation
The package can be installed via pip after local build or publishing:

```bash
pip install .

_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

##Usage

from auto_rename_ai import rename_images

# Specify input and output folders
input_folder = "path/to/images"
output_folder = "path/to/output"

# Automatically rename images
rename_images(input_folder, output_folder)

_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

##Result
Each image will be renamed in the following format::

label(confidence%)(counter).ext

-->> for example: 
golden_retriever(97.00%)(1).jpg
sports_car(88.12%)(1).png
keyboard(91.03%)(2).jpeg

_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

##Requirements::

Python 3.8+
TensorFlow 2.x