semvec/pypi-packages huggingface dataset:
dagster-contrib-modal 0.0.2
Dagster integration with Modal
Requires Python: >=3.8

# dagster-contrib-modal

Leverage the scalable compute platform of Modal from your Dagster pipelines.

## Setup

```sh
uv venv create
source .venv/bin/activate
```

```sh
make install
```

## Test

```sh
make test
```

## Build

```sh
make build
```
crink 0.0.0
A work presented to Spring Thing 2025.

Crink
=====

Intended as an entry to [Spring Thing 2025](https://www.springthing.net/2025/).

In accordance with the rules of the competition, this project is in private development until March 2025.

This package is a placeholder until a public release is available.

Getting Python
--------------

TBD.


Getting Crink
-------------

TBD.


Running Crink
-------------

TBD.
prompt-enhancer 1.0.1
An AI-powered prompt enhancement tool
Requires Python: >=3.7

# Prompt Enhancer

An AI-powered CLI tool for enhancing prompts with context awareness and multiple suggestion support.

## Features

- 🧠 AI-powered prompt enhancement
- 🔍 Context-aware suggestions based on workspace analysis
- 🎨 Multiple enhancement styles (Standard, Technical, Creative, etc.)
- 📝 Multiple suggestions with interactive selection
- 📚 Prompt history management
- 🔎 Search and analyze prompts
- 📤 Export functionality

## Installation

### From PyPI
```bash
pip install prompt-enhancer
```

### From Source
```bash
git clone https://github.com/yourusername/prompt-enhancer.git
cd prompt-enhancer
pip install -e .
```

## Configuration

On first run, you'll be prompted to enter your Groq API key.
You can get an API key from [Groq Console](https://console.groq.com).

The API key will be securely saved in `~/.prompt_enhancer/config.json`.

Alternatively, you can set it in your environment:
```bash
export GROQ_API_KEY=your_api_key_here
```

## Usage

Start the interactive shell:
```bash
prompt-enhancer
```

### Available Commands

- `enhance "your prompt"` - Enhance a prompt
- `/enhance "your prompt"` - Context-aware enhancement
- `enhance -2 "your prompt"` - Get 2 suggestions
- `enhance -3 "your prompt"` - Get 3 suggestions
- `copy` - Copy current prompt to clipboard
- `history` - View prompt history
- `search <term>` - Search saved prompts
- `styles` - List enhancement styles
- `analyze` - Analyze current prompt
- `help` - Show all commands

### Enhancement Styles

- `standard` - Clear and effective
- `technical` - Technical specification format
- `creative` - Innovative approaches
- `concise` - Minimal and focused
- `detailed` - Comprehensive breakdown

### Examples

```bash
# Basic enhancement
prompt-enhancer> enhance "create a website"

# Context-aware enhancement
prompt-enhancer> /enhance "create an API endpoint"

# Multiple suggestions
prompt-enhancer> enhance -3 "write a blog post"

# Save with tags
prompt-enhancer> save web development frontend

# Search history
prompt-enhancer> search website
```

## Development

### Setup Development Environment
```bash
# Clone the repository
git clone https://github.com/yourusername/prompt-enhancer.git
cd prompt-enhancer

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install in development mode
pip install -e ".[dev]"
```

### Running Tests
```bash
pytest
```

## License

MIT License
WAF-AI 0.1.0
Web application firewall using AI
Requires Python: >=3.6

# WAF_AI-AI: Web Application Firewall with Artificial Intelligence
## Overview

This repository contains a **Web Application Firewall (WAF_AI)** that integrates **Artificial Intelligence** to detect and mitigate security threats in real-time. The system utilizes pre-trained models to analyze HTTP traffic and automatically block potentially malicious requests.

## Features

- **Real-Time Threat Detection**: The system monitors incoming HTTP requests and detects SQL Injection, XSS, and other malicious patterns in real-time.
- **Machine Learning Integration**: Pre-trained models are used for detecting attacks based on patterns within URL paths and query parameters.
- **Extendable Framework**: The system can easily be extended to detect additional threats, such as command injection, file inclusion attacks, and more.

## Technologies Used

- **Flask**: A lightweight Python web framework used for building the WAF_AI application.
- **Python**: Core programming language for the backend logic and model implementation.
- **Machine Learning Models**: Pre-trained models for detecting SQL Injection and XSS, built using algorithms such as Random Forest, SVM, and Logistic Regression.
- **Joblib**: Used for loading machine learning models efficiently.
- **HTML/CSS**: Used for creating and styling the response page.
- **Jinja2 Templates**: Templating engine for dynamically rendering HTML content.

## Threat Detection

### **Current Threats Detected**

1. **SQL Injection**: This attack occurs when attackers inject malicious SQL code into a query, often leading to data leakage or unauthorized access to the database. 


## Getting Started

### Prerequisites

- **Python 3.x**: Install Python if you don't have it.
- **Flask**: Install Flask using `pip install flask`.
- **Joblib**: Install Joblib using `pip install joblib`.
- **Scikit-learn**: Install scikit-learn if you plan to train your own models (`pip install scikit-learn`).

### Running the Application

1. Clone the repository:
   ```bash
   git clone https://github.com/chouaibcher/WAF_AI-AI.git
   cd WAF_AI-AI


### Key Updates:
- Added **SQL** Injection detection.
<img width="958" alt="image" src="https://github.com/user-attachments/assets/62a4143a-33f9-4bf3-be2c-012342154828">
<img width="959" alt="image" src="https://github.com/user-attachments/assets/91bbe2e5-cc04-41ef-83cd-c4ec6e3de819">



# Used Dataset :
If you reuse it, please mention us to avoid any problems
</br>
https://www.kaggle.com/datasets/chouaibcher/sql-injection-dataset

# Contributing
We welcome contributions to improve this project! If you'd like to help, please refer to the  `CONTRIBUTING.md`  file for guidelines on how to contribute.
Don’t Hesitate to Contribute! If you have ideas for new features or improvements, feel free to fork the repository, make your changes, and submit a pull request. 
Your help is highly appreciated in making this project better
abugida 0.3.4
NLP tools and resources for Ethiopian languages
Keywords: nlp, ethiopian languages, ethiopic, abugida, ge'ez

# AbugidaNLP
Natural Language Processing tools and resources for Ethiopian languages.

---

## Introduction
AbugidaNLP is an open-source library focused on developing NLP tools tailored to Ethiopian languages. The library aims to support tasks like calendar system conversion, numeral conversion, and transliteration between Ethiopic and Latin scripts. As one of the first steps towards bridging the gap in NLP tools for underrepresented languages, AbugidaNLP aspires to grow into a comprehensive toolkit for linguists, researchers, and developers.

---

## Key Features
AbugidaNLP is currently in its infancy, offering:
- **Transliteration**: Convert text between Ethiopic and Latin scripts.
- **Calendar System Conversion**: Seamlessly convert dates between Ethiopic and Gregorian calendars.
- **Numeral System Conversion**: Translate numbers between Ge'ez and Hindu-Arabic numeral systems.

---

## Installation

### Prerequisites
- Python 3.8 or higher
- pip

### Installation
Install AbugidaNLP using pip:
```bash
pip install abugida
```

---

## Quick Start

Here’s how to get started with AbugidaNLP:

```python
from abugida import ScriptConverter  # Import the ScriptConverter class from AbugidaNLP

# Initialize the ScriptConverter
converter = ScriptConverter()

# Forward transliteration: Convert Ethiopic script to Latin script
result_fwd = converter.transliterate("በመተባበራችን", "fwd")
print(result_fwd)  # Output: bemetebaberacn

# Backward transliteration: Convert Latin script to Ethiopic script
result_bwd = converter.transliterate("merejawoc", "bwd")
print(result_bwd)  # Output: መረጃዎች
```

### Explanation:
1. **Import the library**: The `ScriptConverter` class handles script conversions.
2. **Initialize**: Create an instance of the `ScriptConverter` to use its methods.
3. **Transliteration**:
   - Use the `"fwd"` mode for Ethiopic to Latin conversion.
   - Use the `"bwd"` mode for Latin to Ethiopic conversion.

---

## Documentation
Documentation for AbugidaNLP is under construction. Stay tuned for detailed usage examples and API references.

---

## Contributing
We welcome contributions to AbugidaNLP! Whether it's improving documentation, adding new features, or fixing bugs, your help is valuable.

### Steps to Contribute:
1. Fork the repository.
2. Create a new branch for your feature/bugfix.
3. Submit a pull request.

---

## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

---

## Acknowledgments
Special thanks to the following resources for their inspiration and insights:
- [A Look at Ethiopic Numerals](https://www.geez.org/Numerals/)
- [The System for Ethiopic Representation in ASCII](https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=96a6d78a3fced66214611339a0f95441ab4dc992)

## Contact
For questions or support, please open an issue.