❄️ ChillTranslator 🤬 ➡️ 😎💬

This is an early experimental tool aimed at reducing online toxicity by automatically ➡️ transforming 🌶️ spicy or toxic comments into constructive, ❤️ kinder dialogues using AI and large language models.

ChillTranslator aims to help make online interactions more healthy.

Currently, it "translates" a built-in example of a spicy comment, and it can be used via the command line to improve a specific text of your choice, or it can be imported as a module.

Online toxicity can undermine the quality of discourse, causing distress 😞 and driving people away from online communities. Or worse: it can create a viral toxic loop 🌀!

ChillTranslator hopes to mitigate toxic comments by automatically rephrasing negative comments, while maintaining the original intent and promoting positive communication 🗣️➡️💬. These rephrased texts could be suggested to the original authors as alternatives, or users could enhance their internet experience with "rose-tinted glasses" 🌹😎, automatically translating spicy comments into versions that are easier and more calming to read. There could be all kinds of failure cases, but it's a start!

Could Reddit, Twitter, Hacker News, or even YouTube comments be more calm and constructive places? I think so!

Approach ✨

Converts text to less toxic variations
Preserves original intent, focusing on constructive dialogue
Offline LLM model: running DIY could save costs, avoid needing to sign up to APIs, and avoid the risk of toxic content causing API access to be revoked. We use llama-cpp-python's server with Mixtral.

Possible future directions 🌟

Integration: offer a Python module and HTTP API, for use from other tools, browser extensions.
HuggingFace / Replicate.com etc: Running this on a fast system, perhaps on a HuggingFace Space could be good.
Speed improvements.
- Split text into sentences e.g: with “pysbd” for parallel processing of translations.
- Use a hate speech scoring model instead of the current "spicy" score method.
- Use a dataset of hate speech to make a dataset for training a translation transformer like Google's T5 to run faster than Mixtral could.
- Use natural language similarity techniques to compare possible rephrasing fidelity faster.
- Enabling easy experimenting with online hosted LLM APIs
- Code refactoring to improve development speed!

Getting Started 🚀

Installation

Clone the Project Repository:

git clone https://github.com/lukestanley/ChillTranslator.git
cd ChillTranslator

Download a compatible and capable model like: Mixtral-8x7B-Instruct-v0.1-GGUF. E.g:

wget https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF/resolve/main/mixtral-8x7b-instruct-v0.1.Q4_K_M.gguf?download=true -O mixtral-8x7b-instruct-v0.1.Q4_K_M.gguf &

Install dependencies, including a special fork of llama-cpp-python, and Nvidia GPU support if needed:

pip install requests pydantic uvicorn starlette fastapi sse_starlette starlette_context pydantic_settings

# If you have an Nvidia GPU, install the special fork of llama-cpp-python with CUBLAS support:
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install git+https://github.com/lukestanley/llama-cpp-python.git@expose_json_grammar_convert_function

If you don't have an Nvidia GPU, the CMAKE_ARGS="-DLLAMA_CUBLAS=on" is not needed before the pip install command.

Start the LLM server with your chosen configuration. Example for Nvidia with --n_gpu_layers set to 20; different GPUs fit more or less layers. If you have no GPU, you don't need the --n_gpu_layers flag:
```
python3 -m llama_cpp.server --model mixtral-8x7b-instruct-v0.1.Q4_K_M.gguf --port 5834 --n_ctx 4096 --use_mlock false --n_gpu_layers 20 &
```

These config options may need tweaking. Please check out https://llama-cpp-python.readthedocs.io/en/latest/ for more info.

Usage

ChillTranslator currently has an example spicy comment it works on fixing right away. This is how to see it in action:

python3 chill.py

For improving a specific text of your choice, use the -t flag followed by your text enclosed in quotes:

python3 chill.py -t "Your text goes here"

Or chill can be imported as a module, with the improvement_loop function provided the text to improve.

Contributing 🤝

Contributions are welcome! Especially:

pull requests,
free GPU credits
LLM API credits / access.

ChillTranslator is released under the MIT License.

Help make the internet a kinder place, one comment at a time. Your contribution could make a big difference!