Download README.md from build-small-hackathon/patient-virtuel-dentiste: direct link, hf CLI and curl.
- Browser
- Download file 2.48 kB
-
https://huggingface.co/spaces/build-small-hackathon/patient-virtuel-dentiste/resolve/main/README.md
- Command line
-
hf download hf://spaces/build-small-hackathon/patient-virtuel-dentiste/README.md
-
curl -L -o README.md https://huggingface.co/spaces/build-small-hackathon/patient-virtuel-dentiste/resolve/main/README.md
A newer version of the Gradio SDK is available: 6.29.0
title: Patient Virtuel · Français en médecine dentaire
emoji: 🦷
colorFrom: red
colorTo: gray
sdk: gradio
sdk_version: 6.16.0
app_file: src/server_app.py
pinned: false
license: apache-2.0
tags:
- hackathon
- build-small-hackathon
- backyard-ai
- voice-ai
- french-learning
- dental
- gradio
- modal
- llama-cpp
- gemma
- track:backyard
- sponsor:modal
- achievement:offgrid
- achievement:offbrand
- achievement:llama
- achievement:sharing
- achievement:fieldnotes
Patient Virtuel · Français en médecine dentaire
Hackathon submission for build-small-hackathon.
Track: Backyard AI — a real tool for a real learner: a dental hygienist training professional French.
Uses gradio.Server with a custom HTML/CSS/JS frontend (Off-Brand Award entry).
Llama Champion badge: Model runs through the llama.cpp runtime via llama-cpp-python with flash attention and L4 GPU optimizations.
Off the Grid badge: Zero cloud APIs. STT → faster-whisper (local), LLM → Gemma 4 (local), TTS → piper-tts (local neural, fr_FR-siwis-medium voice).
See space_README.md for the full description.
📖 Field Notes — architecture decisions, lessons learned, and the story behind the app. 📡 Agent Trace — curated build log on HF Hub (Sharing is Caring badge).
Architecture
src/
server_app.py → gr.Server backend (API endpoints)
core.py → Shared session logic
llm_engine.py → Gemma 4 26B-A4B via Modal (llama.cpp runtime)
stt_engine.py → faster-whisper via Modal
tts_engine.py → piper-tts (local neural, fr_FR-siwis-medium)
prompts.py → System prompts
parse_feedback.py → Feedback parsing
frontend/
custom_index.html → Custom vanilla HTML/CSS/JS frontend
style.css → Styles
modal/
modal_app.py → Modal deployments (llama.cpp + CUDA)
Running locally
pip install -r requirements.txt
python src/server_app.py
Deploying to HF Spaces
The Space uses space_README.md as its README and src/server_app.py as the entry point.