# `import spaces` first: it patches `torch.cuda` so the DiT load can happen at startup rather than on
# GPU time. It must land before anything that could initialize CUDA.
import spaces  # noqa: F401
import gradio as gr

# Importing orbit loads the MiniMax-H3 denoising partition and attaches the orbit LoRA (startup load).
from orbit import OUTPUT_DIR, orbit

# The canvas is the app: workflow.json wires the photo + knob references into the bound `orbit`
# function, which runs locally on this Space's ZeroGPU hardware via @spaces.GPU — no gradio_client
# call to the upstream Space. Editing the graph on the canvas autosaves back to workflow.json.
demo = gr.Workflow(graph="workflow.json", bind={"orbit": orbit})

if __name__ == "__main__":
    # orbit() writes MP4s to OUTPUT_DIR (a subdir of the temp dir), which is outside gradio's
    # default file-serving allowlist — without this the video subject can't fetch the result.
    demo.launch(allowed_paths=[OUTPUT_DIR])