semvec/pypi-packages huggingface dataset:
hate-speech-detector 0.2.1
A simple hate speech detection model using machine learning.
License: MIT
Requires Python: >=3.7

# Hate Speech Detector

A lightweight Python package to detect hate speech in text using a machine learning model.

This project was developed by [Jahfar Muhammed]as part of my **major project in college** to explore ethical AI applications and contribute to safer digital communication. It uses traditional NLP preprocessing and a trained machine learning model to classify whether input text contains hate speech.

## Features

- Simple API with a single function call
- Returns human-readable and structured results
- Trained on real-world datasets
- Easily integrable with chat apps, moderation systems, and content filters

## Installation

You can install this package using pip:

```bash
pip install hate_speech_detector
```
## Usage

Here's how you can use the package in your code:
```bash
from hate_speech_detector import isHate
result = isHate("I hate you!")
print(result)
# Output: {'message': 'Hate Speech', 'isHate': True}
result = isHate("Have a nice day!")
print(result)
# Output: {'message': 'Not Hate Speech', 'isHate': False}
```

## Function Description
- `isHate(text: str) -> dict`
- Input: A string of text
- Output: A dictionary with:
- `message`: "Hate Speech" or "Not Hate Speech"
- `isHate`: True if hate speech, else False

## Project Information
This package was developed as a **college major project** with the goal of creating a practical ethical and easy-to-use tool for identifying hate speech in textual content. It combines text preprocessing machine learning, and Python packaging into a useful open-source solution.

## Author

- Name: Jahfar Muhammed
- GitHub: https://github.com/jah-117
- Email: jahfarbinmuhammed117@gmail.com

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
scllm 0.2.1
scllm is a Python package for single-cell analysis using LLMs.
License: MIT
Requires Python: >=3.10
Keywords: bioinformatics, llm, single-cell

# scllm

A Python package for annotating single-cell RNA sequencing data using Large Language Models.

[![PyPI version](https://badge.fury.io/py/scllm.svg)](https://badge.fury.io/py/scllm)
[![Documentation](https://img.shields.io/badge/docs-latest-brightgreen.svg)](https://haraldvohringer.com/scllm/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

## Overview: LangChain 🤝 scanpy

scllm leverages the power of Large Language Models to automatically annotate cell types in single-cell RNA sequencing data. It integrates seamlessly with scanpy and provides an intuitive interface for cell type annotation, factor analalysis and differential expression analysis based on marker gene expression.

## Installation

You can install scllm using pip:

```bash
pip install scllm
```

Or using uv 🚀:

```bash
uv pip install scllm
```

## Quick Start

```python
import os

# Enter your API key for ChatGPT
os.environ["OPENAI_API_KEY"] = "Enter your API key here."

import scanpy as sc
import scllm

# Load your data
adata = sc.read_h5ad('your_data.h5ad')

# Perform clustering if not already done
sc.tl.leiden(adata)

# Initialize your LLM (example with OpenAI)
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(temperature=0.0, model="gpt-4o-mini")

# Annotate clusters
adata = scllm.annotate_cluster(adata, 'leiden', llm)

# Access annotations
print(adata.obs['leiden_annotated'])
```

## Features

- Automatic cell type annotation using LLMs
- Seamless integration with scanpy
- Support for multiple LLM providers
- Interactive Jupyter notebook examples
- Customizable annotation parameters

## Documentation

For detailed documentation and examples, visit our [documentation page](https://haraldvohringer.com/scllm/).

Check out our example notebooks:

- [Cell Type Annotation](https://haraldvohringer.com/scllm/notebooks/cell_types.html)

## Requirements

- Python ≥ 3.10
- scanpy ≥ 1.11.0
- langchain ≥ 0.3.7
- And other dependencies listed in pyproject.toml

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License - see the LICENSE file for details.

## Citation

If you use scllm in your research, please cite:

```bibtex
@software{vohringer2024scllm,
  author = {Vöhringer, Harald},
  title = {scllm: Single-Cell Annotation with Large Language Models},
  year = {2024},
  publisher = {GitHub},
  url = {https://github.com/sagar87/scllm}
}
```

## Contact

Harald Vöhringer - [harald.voeh@gmail.com](mailto:harald.voeh@gmail.com)

Project Link: [https://github.com/sagar87/scllm](https://github.com/sagar87/scllm)