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from typing import Dict, List, Any
from transformers import AutoModelForCausalLM, AutoTokenizer
class EndpointHandler():
def __init__(self, path=""):
# Preload all the elements you are going to need at inference.
# pseudo:
self.tokenizer= AutoTokenizer.from_pretrained(path)
self.model= AutoModelForCausalLM.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
"""
text = data.pop("text")
inputs = self.tokenizer(text, return_tensors="pt")
logits = self.model(inputs).logits
return [{"predictions":logits.argmax(dim=-1)}] |