Simon Willison’s Weblog

Anatomy of a Textual User Interface. Will McGugan used Textual and my LLM Python library to build a delightful TUI for talking to a simulation of Mother, the AI from the Aliens movies:

The entire implementation is just 77 lines of code. It includes PEP 723 inline dependency information:

# /// script
# requires-python = ">=3.12"
# dependencies = [
#     "llm",
#     "textual",
# ]
# ///

Which means you can run it in a dedicated environment with the correct dependencies installed using uv run like this:

wget 'https://gist.githubusercontent.com/willmcgugan/648a537c9d47dafa59cb8ece281d8c2c/raw/7aa575c389b31eb041ae7a909f2349a96ffe2a48/mother.py'
export OPENAI_API_KEY='sk-...'
uv run mother.py

I found the send_prompt() method particularly interesting. Textual uses asyncio for its event loop, but LLM currently only supports synchronous execution and can block for several seconds while retrieving a prompt.

Will used the Textual @work(thread=True) decorator, documented here, to run that operation in a thread:

@work(thread=True)
def send_prompt(self, prompt: str, response: Response) -> None:
    response_content = ""
    llm_response = self.model.prompt(prompt, system=SYSTEM)
    for chunk in llm_response:
        response_content += chunk
        self.call_from_thread(response.update, response_content)

Looping through the response like that and calling self.call_from_thread(response.update, response_content) with an accumulated string is all it takes to implement streaming responses in the Textual UI, and that Response object sublasses textual.widgets.Markdown so any Markdown is rendered using Rich.