semvec/pypi-packages huggingface dataset:
owasp-checker-v2 1.0.0
Enhanced OWASP Top Ten Compliance Checker Library
Requires Python: >=3.8
Keywords: security, owasp, vulnerability, scanner, dependency-checker, threat-intelligence

# Enhanced OWASP Top Ten Compliance Checker Library (owasp_checker_v2)

This Python library provides an advanced tool for automating the detection of web application, API, and dependency vulnerabilities based on the OWASP Top Ten. It integrates additional OWASP guidelines, modular scanning options, threat intelligence, and prioritization mechanisms.

## Features

- Modular OWASP Top Ten Vulnerability Scanner
- NVD API Integration for Dependency Vulnerability Checking
- Real-Time Threat Intelligence Integration
- Risk-Based Vulnerability Prioritization
- OWASP Guidelines Scraper for Real-Time Updates
- Security Headers and Configuration Checker
- Injection Attack Simulation Module
- Remediation Suggestions and Reporting
- CI/CD Integration for Automated Security Checks

## Installation

You can install the owasp_checker_v2 library using pip:

```
pip install owasp_checker_v2
```

## Usage

### Command Line Interface

You can use the owasp_checker_v2 library from the command line:

```
owasp_checker <url_to_scan> [--dependency-file <path_to_file>] [--dependency-file-type <file_type>] [--zap-proxy <proxy_address>] [--nvd-api-key <api_key>] [--vt-api-key <api_key>]
```

### Python API

You can also use the library in your Python code:

```python
from owasp_checker_v2 import OWASPChecker

checker = OWASPChecker()
results = checker.run_full_check('http://example.com', 'requirements.txt', 'requirements')

print("Vulnerabilities:")
for vuln in results['vulnerabilities']:
    print(f"- {vuln['name']} (Risk Score: {vuln['risk_score']})")

print("\nOWASP Guidelines:")
for guideline_type, guidelines in results['guidelines'].items():
    print(f"\n{guideline_type}:")
    for key, value in guidelines.items():
        print(f"- {key}: {value}")
```

## Documentation

For more detailed information on how to use the library, please refer to the [User Guide](USER_GUIDE.md).

## Changelog

We maintain a changelog to keep track of all notable changes to this project. You can find it in the [CHANGELOG.md](CHANGELOG.md) file.

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request. Make sure to read the [Contributing Guidelines](CONTRIBUTING.md) first.

## Code of Conduct

This project adheres to a [Code of Conduct](CODE_OF_CONDUCT.md). By participating, you are expected to uphold this code.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Acknowledgments

- OWASP for their invaluable work in web application security
- The open-source community for their continuous support and contributions
trywebscraping 0.1.37
A fast web scraping library
Requires Python: >=3.7

<img src="https://www.trywebscraping.com/_next/image?url=%2Fassets%2Flogo.png&w=128&q=75" alt="Try Web Scraping Logo">

# Try Web Scraping

To get started, run: `pip install trywebscraping`

Here's some example code to help you begin:

```python
from trywebscraping import Fetch

hn = Fetch("https://news.ycombinator.com")
articles = hn.query("tr.athing").extract({
    "rank": "span.rank",
    "title": "td.title a",
    "link": "td.title a@href"
}).limit(10)
print(articles)
```

Or for a more complex example:

```python
from trywebscraping import Fetch

amazon = Fetch("https://www.amazon.com/s?k=cracking+the+coding+interview")
product_listings = amazon.query("div.s-card-container").extract({
    "title": "h2 a span.a-text-normal",
    "price": "span.a-price-whole",
    "rating": "span.a-icon-alt",
    "num_reviews": "a-size-base",
    "product_link": "h2 a.a-link-normal@href",
    "product_image": "img.s-image@src"
})

print(product_listings)
```

If you're interested in this project, please connect with me:

- Schedule a call: https://cal.com/lukelucas/30min
- Email: luke.lucas@trywebscraping.com

For issues, feedback, or general discussion about the library, you can use our GitHub repository: https://github.com/webscrape/trywebscraping-python

I appreciate any communications, regardless of how you choose to reach out!
queezer 0.1.0
Queezer is designed to help you start owning your own LLM models by collecting request and response data from API calls.
Requires Python: <4.0,>=3.9

# Queezer

## Overview

Queezer is an open-source Python package designed to help you start owning your own open-source LLM models by collecting request and response data from API calls. With just one line of code, Queezer integrates seamlessly into your existing workflows, making it easy to gather crucial data for building your models, rather than relying solely on external API calls.

The package is lightweight, requires only the `requests` library, and can be integrated in under 5 minutes. It is ideal for startups and SMEs looking to take ownership of their AI models and data, ensuring that their unique intellectual property remains theirs.

## Features

- **Easy Integration:** One import, one line of code to start collecting data.
- **Lightweight:** Minimal dependencies, requires only the `requests` library.
- **Model Ownership:** Helps you gather and own the data needed to build and train your AI models.

## Getting Started

To install Queezer, simply run:

```bash
pip install queezer
```

## Usage

Here's a basic example to get you started:

```Python
import openai
from queezer import Queezer

client = openai.OpenAI(api_key="your-openAI-api-key")
queezer = Queezer(api_key="your-queezer-api-key")
# queezer = Queezer()
# ^ In case you do not have an api key
# this instantiates a local SQLite Database

# Your code where you make API calls
# previously: result = client.chat.completions.create(...)
result = queezer.squeeze(client.chat.completions.create,
        tags=["test", "gpt-4o-mini"],
        model="gpt-4o-mini",
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {
                "role": "user",
                "content": "Write a short poem about recursion in programming."
            }
        ]
    )

```

That's it! With one line of code, Queezer begins collecting the API request and response data for you.

## Roadmap

- [x] **Streaming:** We plan to support streaming requests in the near future.
- [ ] **Multi Modality:** Expansion to handle more complex data inputs and outputs (e.g., images, video, text) is on the way.
- [ ] **Typing Support**
- [ ] **Thread Offloading**
- [ ] **Async Support**

## Contributing

We welcome contributions from the community! To contribute:

1. Fork the repository.
2. Create a new branch (`git checkout -b feature-branch`).
3. Commit your changes (`git commit -m 'Add a new feature'`).
4. Push to the branch (`git push origin feature-branch`).
5. Open a pull request, and we'll review it as soon as possible.

Feel free to report issues or suggest features by opening an issue in the repository.

## License

This project is licensed under the MIT License - see the [LICENSE](./LICENSE) file for details.

---

We hope Queezer empowers your startup to take control of your data and models. Happy coding!