def run(host, port):
channel = grpc.insecure_channel('%s:%d' % (host, port))
stub = iris_pb2_grpc.IrisPredictorStub(channel)
request = iris_pb2.IrisPredictRequest(
sepal_length=5.0,
sepal_width=3.6,
petal_length=1.3,
petal_width=0.25
)
response = stub.PredictIrisSpecies(request)
print("Predicted species number: " + str(response.species))
def run(host, port):
# Define a classe de previsão
stub = grpc_server.IrisPredictor()
# Prepara uma request
request = iris_pb2.IrisPredictRequest(
sepal_length=5.0,
sepal_width=4.6,
petal_length=5.3,
petal_width=1.25
)
# Envia a request e obtém a previsão
response = stub.PredictIrisSpecies(request)
print("Número da previsão da classe prevista para a flor: " + str(response.species))
def getData(self, request):
anyMsg = request.data
dc = iris_pb2.IrisPredictRequest()
success = anyMsg.Unpack(dc)
if success:
df = pd.DataFrame([{
"f1": dc.f1,
"f2": dc.f2,
"f3": dc.f3,
"f4": dc.f4
}])
return df
else:
context.set_code(grpc.StatusCode.INTERNAL)
context.set_details('Invalid data')
raise BadDataError('Invalid data')
def callRpc(self, token, jStr):
j = json.loads(jStr)
channel = grpc.insecure_channel(self.host + ':' + str(self.rpc_port))
stub = seldon_pb2.SeldonStub(channel)
data = iris_pb2.IrisPredictRequest(f1=j["f1"],
f2=j["f2"],
f3=j["f3"],
f4=j["f4"])
dataAny = any_pb2.Any()
dataAny.Pack(data)
meta = seldon_pb2.ClassificationRequestMeta(puid="12345")
request = seldon_pb2.ClassificationRequest(meta=meta, data=dataAny)
metadata = [(b'oauth_token', token)]
reply = stub.Classify(request, 999, metadata=metadata)
print reply
def post(self):
# Parse arguments by REST request.
args = self.__class__.parser.parse_args()
# Request to the gRPC server.
channel = grpc.insecure_channel('%s:%s' % (self.host, self.port))
stub = iris_pb2_grpc.IrisPredictorStub(channel)
request = iris_pb2.IrisPredictRequest(
sepal_length=args['sepal_length'],
sepal_width=args['sepal_width'],
petal_length=args['petal_length'],
petal_width=args['petal_width']
)
response = stub.PredictIrisSpecies(request)
# Return the results.
data = {
'species': response.species,
}
return marshal(data, self.__class__.resource_fields)