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| from os import environ as env | |
| from os import system as run | |
| from subprocess import check_output | |
| import gradio as gr | |
| def inference_binary_check(): | |
| # Without a GPU, we need to re-install llama-cpp-python to avoid an error. | |
| # We use a shell command to detect if we have an NVIDIA GPU available: | |
| use_gpu = True | |
| try: | |
| command = "nvidia-debugdump --list|grep Device" | |
| output = str(check_output(command, shell=True).decode()) | |
| if "NVIDIA" in output and "ID" in output: | |
| print("NVIDIA GPU detected.") | |
| except Exception as e: | |
| print("No NVIDIA GPU detected, using CPU. GPU check result:", e) | |
| use_gpu = False | |
| if use_gpu: | |
| print("GPU detected, existing GPU focused llama-cpp-python should work.") | |
| else: | |
| print("Avoiding error by re-installing non-GPU llama-cpp-python build because no GPU was detected.") | |
| run('pip uninstall llama-cpp-python -y') | |
| run('pip install git+https://github.com/lukestanley/llama-cpp-python.git@expose_json_grammar_convert_function --upgrade --no-cache-dir --force-reinstall') | |
| print("llama-cpp-python re-installed, will now attempt to load.") | |
| LLM_WORKER = env.get("LLM_WORKER", "runpod") | |
| if LLM_WORKER == "http" or LLM_WORKER == "in_memory": | |
| inference_binary_check() | |
| examples = [ | |
| ["You guys are so slow, we will never ship it!"], | |
| ["Your idea of a balanced diet is a biscuit in each hand."] | |
| ] | |
| description = """This is an early experimental tool aimed at helping reduce online toxicity by automatically ➡️ transforming 🌶️ spicy or toxic comments into constructive, ❤️ kinder dialogues using AI and large language models. | |
| Input and outputs may be used to train a faster model, by using this, you must agree that the input text is owned by you and that you're okay with it being used to help make an AI model that makes kinder comments! | |
| ChillTranslator aims to help make online interactions more healthy, with a tool to **convert** text to less toxic variations, **preserve original intent**, focusing on constructive dialogue. | |
| The project is on GitHub: | |
| [https://github.com/lukestanley/ChillTranslator](https://github.com/lukestanley/ChillTranslator) | |
| The repo is the same repo for the HuggingFace Space, the serverless worker, and the logic. | |
| Contributions are very welcome! Especially pull requests, free API credits. | |
| Help make the internet a kinder place, one comment at a time. Your contribution could make a big difference! | |
| Thank you! | |
| """ | |
| from chill import improvement_loop | |
| def chill_out(text): | |
| print("Got this input:", text) | |
| result: dict = improvement_loop(text) | |
| print("Got this result:", result) | |
| formatted_output = f""" | |
| <div> | |
| <h4>Edited text:</h4> | |
| <p>{result['edit']}</p> | |
| <h4>Details:</h4> | |
| <ul> | |
| <li>Critique: {result['critique']}</li> | |
| <li>Faithfulness score: {result['faithfulness_score']:.0%}</li> | |
| <li>Spicy score: {result['spicy_score']:.0%}</li> | |
| <li>Overall score: {result['overall_score']:.0%}</li> | |
| <li>Iterations: {result['iteration_count'] / result['max_allowed_iterations']:.0%} {result['iteration_count']} of max allowed iterations: {result['max_allowed_iterations']}</li> | |
| <li>Time used: {result['time_used']:.2f} seconds</li> | |
| <li>LLM requests made: {result['request_count']}</li> | |
| </ul> | |
| </div> | |
| """ | |
| return formatted_output | |
| demo = gr.Interface( | |
| fn=chill_out, | |
| inputs=gr.Textbox(lines=2, placeholder="Enter some spicy text here..."), | |
| outputs="html", | |
| examples=examples, | |
| cache_examples=True, | |
| description=description, | |
| title="❄️ ChillTranslator 🤬 ➡️ 😎💬", | |
| allow_flagging="never", | |
| ) | |
| demo.launch(max_threads=1, share=True) |