handler.py

from depth_anything_3.api import DepthAnything3
import torch, base64, io
from PIL import Image
import numpy as np

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = DepthAnything3.from_pretrained("APaul1/DA3METRIC-LARGE").to(device)

def predict(payload):
    img_bytes = base64.b64decode(payload["image"])
    img = Image.open(io.BytesIO(img_bytes)).convert("RGB")

    pred = model.inference([img], export_dir=None, export_format="npz")
    depth = pred[0]["depth"].tolist()
    return {"depth": depth}