Update handler.py

b464613 over 3 years ago

897 Bytes

from typing import Dict, List, Any
# from transformers import GPT2Tokenizer
# from model import GPT
import pipeline

class EndpointHandler():
    def __init__(self, path=""):
        # Preload all the elements you are going to need at inference.
        # model = GPT.from_pretrained(path)
        # tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
        # self.pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
        a = 1

    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", data)
        pipeline.start = inputs
        output = pipeline.infer()
        # isinstance(output,str)
        return {"Ans": output}

	from typing import Dict, List, Any
	# from transformers import GPT2Tokenizer
	# from model import GPT
	import pipeline

	class EndpointHandler():
	def __init__(self, path=""):
	# Preload all the elements you are going to need at inference.
	# model = GPT.from_pretrained(path)
	# tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
	# self.pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
	a = 1

	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", data)
	pipeline.start = inputs
	output = pipeline.infer()
	# isinstance(output,str)
	return {"Ans": output}