managing token limit

hit the token limit

system prompt needed improvement

Extract the string content from the LLM response object before passing it to `format_response_for_user`, and ensure it is a dictionary.

json is coming back

needs dict, not string

prompt is now a string, not a tuple, copilot nailed this one

json is not needed at all for this

vibe coding lead to a very lazy and wrong application of constrained decoding - simply cutting off the llm after a closing brace, bad bad bad - also I'm not sure why it was trying to deliver json at all, this plays to all of the weaknesses of gen ai, bad llm bad

better logging in key

improved system prompt and container logs

have to turn the mcp server on for it to work

checkpoint 2 - we are in a very good position right now, audio transcription is working, gradio page looks good, local llms are working, returning the consultation, the json that will be sent to the mcp client, and a debugging panel

I'm so confused

improved seperation of concerns best practice in the code, added print statements for better understanding of what code is doing

app.py wrapper for Gradio waa very bad idea, regoranizing project for clarity, utils folder will be very import of separation of concerns

actually... the gradio live link would allow me to show o4-mini-high the results, where I could not before

no need for gradio live

should resolve the `ModuleNotFoundError` in the Hugging Face Space while maintaining the local development setup

Add multi‐backend LLM support and audio‐driven medical agent pipeline

Use `demo` as Gradio entrypoint identifier

Add root-level app.py wrapper; preserve src/ layout

repo structure cleanup (https://chatgpt.com/share/6841a61b-3dfc-8004-aec7-9c48fba857fd)

successfully scrapped

plan.md outlines goals for this hackathon

Add application file