semvec/pypi-packages huggingface dataset:
ector 0.1.2
Extract from a given long text input, eCommerce products and price budget.
Requires Python: <3.13,>=3.10

## ECTOR

Extract from a given long text input, eCommerce products and price budget.

### DISCLAIMER

This is not an "OpenAI wrapper", it use NLP(Natural Language Processing)
and tokens manipulations to 'understand the given input' then extract product
key name + budget.

Therefore, this is not 100% optimized at all and was not tested on extreme edge-cases.
However it's quite fast and does the job for my use case at the moment.

### REQUIREMENTS

- python3 (>=3.10)
- [scapy](https://pypi.org/project/spacy/)

### HOW TO INSTALL

```bash
pip install ector
```

### HOW TO USE

```bash
$ python
Python 3.12.9 (main, Feb  5 2025, 08:49:01) [GCC 9.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import json
>>> import asyncio
>>> from ector import extract
>>>
>>> text = "Hello, do you have some apple juice at 9 eur ? i also want bananas, but i only have 15 eur"
>>> print(json.dumps(asyncio.run(extract(text)), indent=2))
{
  "products": [
    {
      "product": "Apple juice",
      "price": 9.0,
      "currency": "eur"
    },
    {
      "product": "Bananas"
    }
  ],
  "budget": {
    "price": 15.0,
    "currency": "eur"
  }
}
>>>
```

### AUTHOR

- Sanix-Darker
nebulae-betelgeuse 0.1.0
Betelgeuse Library
Requires Python: >=3.9

# Betelgeuse

<!-- BADGES -->
[![Tests](https://github.com/nebulae-official/betelgeuse/actions/workflows/test.yml/badge.svg)](https://github.com/nebulae-official/betelgeuse/actions/workflows/test.yml)
[![Deployment](https://github.com/nebulae-official/betelgeuse/actions/workflows/deploy.yml/badge.svg)](https://github.com/nebulae-official/betelgeuse/actions/workflows/deploy.yml)
[![Codecov](https://codecov.io/gh/nebulae-official/betelgeuse/branch/main/graph/badge.svg)](https://codecov.io/gh/nebulae-official/betelgeuse)
[![PyPI version](https://badge.fury.io/py/nebulae-betelgeuse.svg)](https://badge.fury.io/py/nebulae-betelgeuse)
[![Python Versions](https://img.shields.io/pypi/pyversions/nebulae-betelgeuse.svg)](https://pypi.org/project/nebulae-betelgeuse/)
[![License](https://img.shields.io/github/license/nebulae-official/betelgeuse.svg)](https://github.com/nebulae-official/betelgeuse/blob/main/LICENSE)

<!-- PROJECT DESCRIPTION -->
## About The Project

Betelgeuse is a Python library that provides [brief description of what your library does]. It aims to solve [problems it addresses] and provides [key features].

<!-- GETTING STARTED -->
## Getting Started

### Prerequisites

* Python 3.9 or higher

### Installation

```bash
pip install nebulae-betelgeuse
```

<!-- USAGE EXAMPLES -->
## Usage

```python
from betelgeuse import welcome

# Get welcome message
message = welcome.get_welcome_message()
print(message)
```

_For more examples and detailed usage, please refer to the [Documentation](https://betelgeuse.readthedocs.io/)_

<!-- FEATURES -->
## Features

- [Feature 1]
- [Feature 2]
- [Feature 3]

<!-- ROADMAP -->
## Roadmap

- [ ] Feature 1
- [ ] Feature 2
- [ ] Feature 3
    - [ ] Nested Feature

See the [open issues](https://github.com/nebulae-official/betelgeuse/issues) for a full list of proposed features and known issues.

<!-- CONTRIBUTING -->
## Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are **greatly appreciated**.

1. Fork the Project
2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the Branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request

<!-- DEVELOPMENT -->
## Development

To set up the development environment:

```bash
git clone https://github.com/nebulae-official/betelgeuse.git
cd betelgeuse
pip install uv
uv pip install -r requirements.txt -r dev-requirements.txt
```

### Running Tests

```bash
make test
```

### Linting

```bash
make lint
```

### Building

```bash
make build
```

<!-- LICENSE -->
## License

Distributed under the MIT License. See `LICENSE` for more information.

<!-- CONTACT -->
## Contact

Project Link: [https://github.com/nebulae-official/betelgeuse](https://github.com/nebulae-official/betelgeuse)

<!-- ACKNOWLEDGMENTS -->
## Acknowledgments

* [Library/Resource 1]
* [Library/Resource 2]
* [Library/Resource 3]
complai-test 0.1
test version
Requires Python: <3.9,>=3.8

Steps to setup environments to run code
=======================================

It is suggeseted to maintain 2 different environments to run the code,one for the scan component and the other to run the application.

The below steps are for setting up the environment using Anaconda. Please refer to the below link for more details:

https://docs.anaconda.com/anaconda/install/index.html


Steps to Run the code
=====================
1. Ensure you are in the complai directory

Optional Steps to RUN/RERUN SCAN
=================================
0. Create and switch to new Anaconda Environment:
"conda create -n <NEW SCAN ENVIRONMENT NAME> python=3.8"
"conda activate <NEW SCAN ENVIRONMENT NAME>"

1. Install complai_scan using pip, using below code.
"pip install <PATH TO INSTALLABLE>/complai_scan-0.1.0.tar.gz"

2. To run the scan , move to the respective directory.
For e.g. : "cd titanic"

3. Run the code using the below command.
"python run_binary.py"

Steps to LAUNCH APP
===================
0. Create and switch to new Anaconda Environment:
"conda create -n <NEW APP ENVIRONMENT NAME> python=3.8"
"conda activate <NEW APP ENVIRONMENT NAME>"

1. Move to the complai_ui directory using the below command.
"cd compali_ui"
2. Install app dependencies (Preferably in a new env), using the below command
"pip install -r requirements.txt"
3. Run the below command to launch the stramlit app.
"streamlit run app.py"

Note: If you are on Dark Theme then please go to settings of the streamlit app and select Theme as Light. Changing Theme is very easy as explained in this link
https://blog.streamlit.io/content/images/2021/08/Theming-1--No-Padding---1-.gif

Steps to Configure YAML
=======================

1. This project requires the user to configure 2 yaml files. 
For e.g. :"tiatnic_config.yaml" and "tiatnic_policy.yaml"

 Please refer to the yaml files for further information
short-language-detection 0.1.9
A simple language detection library for short texts.
Requires Python: >=3.10

# Short Language Detection

This project is designed to detect the language of short text snippets. It uses machine learning models to accurately identify the language of a given input.

## Features

- Detects language from short text inputs
- Supports multiple languages
- High accuracy and performance

## Installation

To install the necessary dependencies, run:

```bash
pip install short-language-detection
```

## Usage

To use the language detection model, run the following command:

```python
import short_language_detection as sld

# Create a detector
predictor = sld.Detector()

print(
    predictor.supported_languages
)  # {'ro': 'ROMANIAN', 'ru': 'RUSSIAN', 'nl': 'DUTCH', ...}

# Detect the language of a text
print(predictor.detect("hello"))
# ({'language': 'ENGLISH', 'code': 'EN', 'score': 1.0, 'prefered': True, 'reliable': True},)
```

## Contributing

We welcome contributions! Please fork the repository and submit a pull request.

## License

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

## Contact

For any questions or suggestions, please open an issue or contact us at [jourdelune863@gmail.com](mailto:jourdelune863@gmail.com).
browser-use-mcp 0.1.4
Browser MCP Server.
Requires Python: >=3.11.7

# Browser Control MCP Server

The MCP Server to control the Chrome Browser using LLM

## System Requirements
- Chrome browser was installed in you PC.
- Python >= 3.11.7
- uv which is python packaging tool was installed

## Claude Setting

```json
{
  "mcpServers": {
    "browser_use_mcp": {
      "command": "path/to/your/uv",
      "args": [
        "--directory",
        "/path/to/your/browser_use_mcp/",
        "run",
        "main.py"
      ],
      "env": {
        "OPENAI_API_KEY": "your OPENAI_API_KEY",
        "OPENAI_MODEL_NAME": "your OPENAI_MODEL_NAME"
      }
    }
  }
}
```