from bentoml import api, env, BentoService, artifacts
from bentoml.artifact import TensorflowSavedModelArtifact, PickleArtifact
from bentoml.handlers import JsonHandler
import numpy as np
from scipy.special import softmax, expit
from aispace.datasets.tokenizer import BertTokenizer
from aispace.utils.hparams import Hparams
logger = logging.getLogger(__name__)
@artifacts([
TensorflowSavedModelArtifact('model'),
PickleArtifact('tokenizer'),
PickleArtifact("hparams"),
])
@env(auto_pip_dependencies=True)
class BertTextClassificationService(BentoService):
def preprocessing(self, one_json):
texts = one_json.get("text", '')
if isinstance(texts, (list, tuple)):
if len(texts) >= 2:
encode = self.artifacts.tokenizer.encode(texts[0], texts[1])
elif len(texts) == 1:
encode = self.artifacts.tokenizer.encode(texts[0])
else:
return None, None, None
elif isinstance(texts, str):
import bentoml
import tensorflow as tf
import numpy as np
from PIL import Image
from bentoml.artifact import (
TensorflowSavedModelArtifact,
)
from bentoml.handlers import TensorflowTensorHandler
FASHION_MNIST_CLASSES = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
@bentoml.env(pip_dependencies=['tensorflow', 'numpy', 'pillow'])
@bentoml.artifacts([TensorflowSavedModelArtifact('model')])
class FashionMnistTensorflow(bentoml.BentoService):
@bentoml.api(TensorflowTensorHandler)
def predict(self, inputs):
outputs = self.artifacts.model.predict_image(inputs)
output_classes = tf.math.argmax(outputs, axis=1)
return [FASHION_MNIST_CLASSES[c] for c in output_classes]