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from typing import Dict, List, Any
import torch
from transformers import AltCLIPModel, AltCLIPProcessor, AutoProcessor
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# https://huggingface.co/docs/inference-endpoints/guides/custom_handler
class EndpointHandler():
def __init__(self, path=""):
# Preload all the elements you are going to need at inference.
# pseudo:
# self.model= load_model(path)
self.md_model = AltCLIPModel.from_pretrained(path).to(device)
self.md_processor = AltCLIPProcessor.from_pretrained(path)
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
"""
# pseudo
# self.model(input)
with torch.inference_mode():
texts = data.pop("inputs",data)
inputs = self.md_processor(text = texts, padding=True, return_tensors="pt").to(device)
text_feature = self.md_model.get_text_features(**inputs)
return {"feature":text_feature.cpu().tolist()} |