semvec/pypi-packages huggingface dataset:
langchain-sema4 0.2.0
An integration package connecting Sema4 and LangChain
License: MIT
Requires Python: <4.0,>=3.9

# langchain-sema4

This package contains the LangChain integrations for [Robocorp Action Server](https://github.com/sema4/sema4).
Action Server enables an agent to execute actions in the real world. 

## Installation

```bash
pip install -U langchain-sema4
```

## Action Server Toolkit

See [ActionServerToolkit](https://python.langchain.com/docs/integrations/toolkits/sema4) for detailed documentation.
RGC-Quant 0.0.1
RGC Quantification
License: MIT
Requires Python: >=3.8
Keywords: RGC, Quantification, Deep Learning, 3D, Confocal, Microscopy, Retina, Neuroscience, Image Processing, Image Analysis, Image Segmentation, Deep learning, PyTorch, 3D U-Net


# RGC-Quant
SiteToSheet 1.0.0
A tool to scrape websites and update Google Sheets
License: MIT
Requires Python: >=3.7

# SiteToSheet

SiteToSheet is a Python project that combines web scraping, natural language processing, and Google API integration to extract data from websites and store it in Google Sheets.

![python workflow](https://github.com/JSh4w/SiteToSheet/actions/workflows/python-package.yml/badge.svg)
[![Pylint](https://github.com/JSh4w/SiteToSheet/actions/workflows/pylint.yml/badge.svg)](https://github.com/JSh4w/SiteToSheet/actions/workflows/pylint.yml)

## Installation

This project requires Python 3.7 or later. To set up the project environment:

1. Clone the repository:
2. Create a virtual environment:
3. Activate the virtual environment:
- On Windows:
  ```
  venv\Scripts\activate
  ```
- On macOS and Linux:
  ```
  source venv/bin/activate
  ```

4. Install the required packages:
## Dependencies

This project relies on several key libraries:

- **Web Scraping**: BeautifulSoup4
- **Natural Language Processing**: spaCy (with en_core_web_sm model)
- **Google API Integration**: google-api-python-client, gspread
- **Data Manipulation**: pandas, numpy
- **Mapping**: googlemaps
- **Environment Management**: python-dotenv
- **Rate Limiting**: ratelimit

For a complete list of dependencies, see the `requirements.txt` file.

## Configuration

1. Set up Google Cloud Project and enable necessary APIs (Sheets, Maps).
2. Create and download a `credentials.json` file for Google API authentication.
3. Create a `.env` file in the project root and add your API keys:
## Usage

An example on how to use the tool will be provided on my portfolio website. For now you just have to ask :)

## Features

- Web scraping with BeautifulSoup4
- Natural language processing with spaCy
- Google Sheets integration for data storage
- Google Maps API for geolocation services
- Rate limiting to respect API usage limits

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

This project uses the following third-party services and libraries:

- Google Maps Distance Matrix API: Subject to the [Google Maps Platform Terms of Service](https://cloud.google.com/maps-platform/terms)
- Google Sheets API: Subject to the [Google APIs Terms of Service](https://developers.google.com/terms)
- spaCy: Licensed under the [MIT License](https://github.com/explosion/spaCy/blob/master/LICENSE)

Users of this software are responsible for ensuring their own compliance with the terms of these services and libraries.

## Acknowledgments

This project makes use of several open-source libraries and APIs. We thank the maintainers and contributors of these projects.