[serve] Update code sample in Ray README (ray-project#18129)

README.rst

@@ -230,36 +230,38 @@ This example runs serves a scikit-learn gradient boosting classifier.
 
 .. code-block:: python
 
- from ray import serve
  import pickle
  import requests
+
  from sklearn.datasets import load_iris
  from sklearn.ensemble import GradientBoostingClassifier
 
- # Train model
+ from ray import serve
+
+ serve.start()
+
+ # Train model.
  iris_dataset = load_iris()
  model = GradientBoostingClassifier()
  model.fit(iris_dataset["data"], iris_dataset["target"])
 
- # Define Ray Serve model,
+ @serve.deployment(route_prefix="/iris")
  class BoostingModel:
- def __init__(self):
+ def __init__(self, model):
  self.model = model
  self.label_list = iris_dataset["target_names"].tolist()
 
- def __call__(self, flask_request):
- payload = flask_request.json["vector"]
- print("Worker: received flask request with data", payload)
+ async def __call__(self, request):
+ payload = await request.json()["vector"]
+ print(f"Received flask request with data {payload}")
 
  prediction = self.model.predict([payload])[0]
  human_name = self.label_list[prediction]
  return {"result": human_name}
 
 
- # Deploy model
- client = serve.start()
- client.create_backend("iris:v1", BoostingModel)
- client.create_endpoint("iris_classifier", backend="iris:v1", route="/iris")
+ # Deploy model.
+ BoostingModel.deploy(model)
 
  # Query it!
  sample_request_input = {"vector": [1.2, 1.0, 1.1, 0.9]}