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import os
from typing import Dict, List, Any
import groundingdino
from groundingdino.util.inference import load_model, load_image, predict, annotate
import subprocess
# /app
HOME = os.getcwd()
# /opt/conda/lib/python3.9/site-packages/groundingdino
PACKAGE_HOME = os.path.dirname(groundingdino.__file__)
CONFIG_PATH = os.path.join(PACKAGE_HOME, "config", "GroundingDINO_SwinT_OGC.py")
WEIGHTS_PATH = os.path.join("model", "weights", "groundingdino_swint_ogc.pth")
class EndpointHandler():
def __init__(self, path):
# Preload all the elements you are going to need at inference.
self.model = load_model(CONFIG_PATH, os.path.join(path, "weights", "groundingdino_swint_ogc.pth"))
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
data args:
inputs (:obj: `str` | `PIL.Image` | `np.array`)
kwargs
Return:
A :obj:`list` | `dict`: will be serialized and returned
"""
inputs = data.pop("inputs")
image = inputs.pop("image")
prompt = inputs.pop("prompt")
return [{
"image": image,
"prompt": prompt,
}]
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