svc = bentoml.Service("iris_classifier", runners=[iris_clf_runner])


@svc.api(
    input=NumpyNdarray.from_sample(np.array([4.9, 3.0, 1.4, 0.2], dtype=np.double)),
    output=Text(),
)
async def classify(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

in this service we are using "classify" function to monitor the function how can i write multple api's here like in below @bentoml.service( 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 def predict(self, input_data: np.ndarray) -> np.ndarray: rv = self.model.predict(input_data) return np.asarray(rv) @bentoml.api def classify(self, input_data: np.ndarray) -> np.ndarray: rv = self.model.predict(input_data) return np.asarray(rv)