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import torch
from typing import Dict, List, Any
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
# check for GPU
device = 0 if torch.cuda.is_available() else -1
# multi-model list
multi_model_list = [
{"model_id": "distilbert-base-uncased-finetuned-sst-2-english", "task": "text-classification"},
{"model_id": "Helsinki-NLP/opus-mt-en-de", "task": "translation"},
{"model_id": "facebook/bart-large-cnn", "task": "summarization"},
{"model_id": "dslim/bert-base-NER", "task": "token-classification"},
{"model_id": "textattack/bert-base-uncased-ag-news", "task": "text-classification"},
]
class EndpointHandler():
def __init__(self, path=""):
self.multi_model={}
# load all the models onto device
for model in multi_model_list:
self.multi_model[model["model_id"]] = pipeline(model["task"], model=model["model_id"], device=device)
def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
# deserialize incomin request
inputs = data.pop("inputs", data)
parameters = data.pop("parameters", None)
model_id = data.pop("model_id", None)
# check if model_id is in the list of models
if model_id is None or model_id not in self.multi_model:
raise ValueError(f"model_id: {model_id} is not valid. Available models are: {list(self.multi_model.keys())}")
# pass inputs with all kwargs in data
if parameters is not None:
prediction = self.multi_model[model_id](inputs, **parameters)
else:
prediction = self.multi_model[model_id](inputs)
# postprocess the prediction
return prediction |