GravityLLM-Space-Demo/README.md

metadata

title: GravityLLM Studio
emoji: 🌌
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: 6.8.0
python_version: '3.10'
app_file: app.py
fullWidth: true
header: default
suggested_hardware: cpu-basic
short_description: >-
  Spatial9 immersive scene generation with branded GravityLLM UI, schema
  validation, and spatial preview.
tags:
  - gravityllm
  - spatial-audio
  - immersive-audio
  - spatial9
  - iamf
  - gradio
  - json
  - demo
  - music-tech

GravityLLM Studio

A branded Hugging Face Space for constraint-conditioned immersive scene generation.

This Space accepts a music-constraint payload and returns a Spatial9Scene JSON scene. It includes:

• a polished GravityLLM studio UI
• your Spatial9 logo in the hero section
• remote inference through Hugging Face InferenceClient
• optional JSON-schema grammar constraints
• built-in validation against schemas/scene.schema.json
• a live top-down spatial preview
• a deterministic fallback rules engine so the demo still works before the trained model is online

How to connect your model

Set the following Space secrets or variables:

• GRAVITYLLM_MODEL_ID → your model repo id, for example your-org/GravityLLM-AutoPosition
• HF_TOKEN → only required if the model is gated or private
• GRAVITYLLM_BACKEND → optional default: hybrid, remote-model, or rules-engine demo

Files

• app.py — the Gradio app
• schemas/scene.schema.json — the contract used for validation and optional grammar guidance
• examples/ — ready-to-run sample payloads
• assets/ — logo and banner assets
• utils/scene_tools.py — validation, heuristics, JSON extraction, plotting

Recommended workflow

1. Upload your GravityLLM Model repo
2. Train and push the final weights
3. Upload this Space repo
4. Set GRAVITYLLM_MODEL_ID
5. Launch the Space

Notes

This Space is designed to be usable in two states:

• before model launch → rules-engine fallback
• after model launch → remote GravityLLM inference