import numpy as np import bentoml from bentoml.io import NumpyNdarray, Text # Define the service class @bentoml.service( name="iris_classifier", runners=[iris_clf_runner], resources={"cpu": "2"}, traffic={"timeout": 10}, ) class IrisClassifier: bento_model = bentoml.models.get("iris:latest") def __init__(self): self.model = bentoml.mlflow.load_model(self.bento_model) @bentoml.api( input=NumpyNdarray.from_sample(np.array([4.9, 3.0, 1.4, 0.2], dtype=np.double)), output=Text(), ) async def classify(self, features: np.ndarray) -> str: with bentoml.monitor("iris_classifier_prediction") as mon: mon.log(features[0], name="sepal length", role="feature", data_type="numerical") mon.log(features[1], name="sepal width", role="feature", data_type="numerical") mon.log(features[2], name="petal length", role="feature", data_type="numerical") mon.log(features[3], name="petal width", role="feature", data_type="numerical") results = await iris_clf_runner.predict.async_run([features]) result = results[0] category = CLASS_NAMES[result] mon.log(category, name="pred", role="prediction", data_type="categorical") return category @bentoml.api( input=NumpyNdarray.from_sample(np.array([[4.9, 3.0, 1.4, 0.2]], dtype=np.double)), output=NumpyNdarray(), ) def predict(self, input_data: np.ndarray) -> np.ndarray: rv = self.model.predict(input_data) return np.asarray(rv) # Initialize the service svc = IrisClassifier() if this way works , could you please let me know anyone