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from fastapi import FastAPI, UploadFile, File
from fastapi.responses import FileResponse, JSONResponse, HTMLResponse, Response
import uvicorn
app = FastAPI()
KAGGLE_USER = os.environ.get("KAGGLE_USERNAME", "")
KAGGLE_KEY = os.environ.get("KAGGLE_KEY", "")
SPACE_URL = os.environ.get("SPACE_URL", "").rstrip("/")
KERNEL_REF = f"{KAGGLE_USER}/trellis-worker"
jobs = {} # job_id -> {status, glb_path}
images = {} # job_id -> bytes
@app.get("/", response_class=HTMLResponse)
async def home():
rows = "".join(
f"<tr><td>{jid[:8]}…</td><td>{info['status']}</td></tr>"
for jid, info in jobs.items()
)
return f"""
<html><body style='font-family:monospace;padding:2rem'>
<h2>🧊 TRELLIS Worker Space</h2>
<table border=1 cellpadding=6>
<tr><th>Job ID</th><th>Status</th></tr>
{rows or '<tr><td colspan=2>No jobs yet</td></tr>'}
</table>
<p>POST /generate with form-data file=image.png to start a job.</p>
</body></html>
"""
@app.post("/generate")
async def generate(file: UploadFile = File(...)):
job_id = str(uuid.uuid4())
images[job_id] = await file.read()
jobs[job_id] = {"status": "queued", "glb_path": None}
asyncio.create_task(trigger_kaggle(job_id))
return {"job_id": job_id, "status": "queued"}
@app.get("/image/{job_id}")
async def get_image(job_id: str):
if job_id not in images:
return JSONResponse({"error": "not found"}, status_code=404)
return Response(images[job_id], media_type="image/png")
@app.get("/status/{job_id}")
async def status(job_id: str):
return jobs.get(job_id, {"status": "not_found"})
@app.post("/receive_glb")
async def receive_glb(job_id: str, file: UploadFile = File(...)):
glb_path = f"/tmp/{job_id}.glb"
with open(glb_path, "wb") as f:
f.write(await file.read())
jobs[job_id]["status"] = "done"
jobs[job_id]["glb_path"] = glb_path
images.pop(job_id, None)
return {"ok": True}
@app.get("/download/{job_id}")
async def download(job_id: str):
job = jobs.get(job_id)
if not job or job["status"] != "done":
return JSONResponse({"error": "not ready yet"}, status_code=404)
return FileResponse(job["glb_path"], filename="mesh.glb",
media_type="model/gltf-binary")
def make_notebook(job_id: str, space_url: str) -> str:
"""Generate the worker notebook with JOB_ID and SPACE_URL baked in."""
source = [
"import os, requests, time\n",
"\n",
f'JOB_ID = "{job_id}"\n',
f'SPACE_URL = "{space_url}"\n',
"\n",
'print(f"Job: {JOB_ID}")\n',
'print(f"Space: {SPACE_URL}")\n',
"\n",
"# 1. Download image\n",
'print("Downloading image...")\n',
'r = requests.get(f"{SPACE_URL}/image/{JOB_ID}", timeout=30)\n',
"r.raise_for_status()\n",
'with open("/kaggle/working/input.png", "wb") as f:\n',
" f.write(r.content)\n",
'print("Image downloaded!")\n',
"\n",
"# 2. Remove background\n",
'os.system("pip install -q transparent-background gradio_client pillow")\n',
"from transparent_background import Remover\n",
"from PIL import Image\n",
"\n",
'print("Removing background...")\n',
"remover = Remover()\n",
'img = Image.open("/kaggle/working/input.png").convert("RGB")\n',
'out = remover.process(img, type="rgba")\n',
'out.save("/kaggle/working/input_nobg.png")\n',
'print("BG removed!")\n',
"\n",
"# 3. Run TRELLIS\n",
"from gradio_client import Client, handle_file\n",
"\n",
"MAX_RETRIES = 3\n",
"result = None\n",
"\n",
"for attempt in range(1, MAX_RETRIES + 1):\n",
" try:\n",
' print(f"Connecting to TRELLIS (attempt {attempt}/{MAX_RETRIES})...")\n',
' client = Client("trellis-community/TRELLIS")\n',
' client.predict(api_name="/start_session")\n',
' print("Session ready! Generating...")\n',
"\n",
" result = client.predict(\n",
' image=handle_file("/kaggle/working/input_nobg.png"),\n',
" multiimages=[],\n",
" seed=0,\n",
" ss_guidance_strength=7.5,\n",
" ss_sampling_steps=12,\n",
" slat_guidance_strength=3.0,\n",
" slat_sampling_steps=12,\n",
' multiimage_algo="stochastic",\n',
" mesh_simplify=0.95,\n",
" texture_size=1024,\n",
' api_name="/generate_and_extract_glb"\n',
" )\n",
' print("Generation done!")\n',
" break\n",
"\n",
" except Exception as e:\n",
' print(f"Attempt {attempt} failed: {e}")\n',
" if attempt < MAX_RETRIES:\n",
" time.sleep(30)\n",
" else:\n",
' raise RuntimeError(f"TRELLIS failed after {MAX_RETRIES} attempts: {e}")\n',
"\n",
"# 4. POST GLB back\n",
"glb_src = result[1] or result[2]\n",
'print(f"Sending GLB ({os.path.getsize(glb_src)/1024/1024:.1f} MB)...")\n',
"\n",
"with open(glb_src, 'rb') as f:\n",
" resp = requests.post(\n",
' f"{SPACE_URL}/receive_glb",\n',
' params={"job_id": JOB_ID},\n',
' files={"file": ("mesh.glb", f, "model/gltf-binary")},\n',
" timeout=120\n",
" )\n",
"\n",
"resp.raise_for_status()\n",
'print(f"GLB delivered! Job {JOB_ID} complete.")\n',
]
notebook = {
"cells": [
{
"cell_type": "code",
"execution_count": None,
"id": "6117abdc",
"metadata": {},
"outputs": [],
"source": source,
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3",
},
"language_info": {"name": "python", "version": "3.12.0"},
},
"nbformat": 4,
"nbformat_minor": 5,
}
return json.dumps(notebook, indent=1)
async def trigger_kaggle(job_id: str):
try:
jobs[job_id]["status"] = "running"
work_dir = tempfile.mkdtemp()
# kernel-metadata.json — no env vars needed anymore
meta = {
"id": KERNEL_REF,
"title": "trellis-worker",
"code_file": "worker.ipynb",
"language": "python",
"kernel_type": "notebook",
"is_private": True,
"enable_gpu": False,
"enable_internet": True,
"dataset_sources": [],
"competition_sources": [],
"kernel_sources": [],
}
with open(f"{work_dir}/kernel-metadata.json", "w") as f:
json.dump(meta, f)
# Generate notebook with JOB_ID + SPACE_URL baked in
notebook_json = make_notebook(job_id, SPACE_URL)
with open(f"{work_dir}/worker.ipynb", "w") as f:
f.write(notebook_json)
env = {**os.environ, "KAGGLE_USERNAME": KAGGLE_USER, "KAGGLE_KEY": KAGGLE_KEY}
proc = await asyncio.create_subprocess_exec(
"kaggle", "kernels", "push", "-p", work_dir,
env=env,
stdout=asyncio.subprocess.PIPE,
stderr=asyncio.subprocess.PIPE,
)
stdout, stderr = await proc.communicate()
print(f"[{job_id[:8]}] Kaggle push: {stdout.decode()} {stderr.decode()}")
for _ in range(60): # wait up to 15 min
await asyncio.sleep(15)
if jobs[job_id]["status"] == "done":
print(f"[{job_id[:8]}] Done!")
return
jobs[job_id]["status"] = "error:timeout"
except Exception as e:
print(f"[{job_id[:8]}] Error: {e}")
jobs[job_id]["status"] = f"error:{e}"
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=7860)
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