Python code to Publish ManualTransmission Web Service and generate csharp client using swagger

ManualTransmission.py

# ----------------------------------------------------#
# STEP 1 : MODEL DEVELOPMENT 
# ----------------------------------------------------#

# imports
from microsoftml.datasets.datasets import DataSetMtCars
import pandas as pd
from revoscalepy import rx_lin_mod, rx_predict

# using rx_lin_mod from revoscalepy package, create glm model with `mtcars` dataset
cars_model = rx_lin_mod(
    formula='am ~ hp + wt',
    data=DataSetMtCars().as_df())

# -- provide some sample inputs to test the model
mydata = pd.DataFrame({
    'hp':[120],
    'wt':[2.8]
})
rx_predict(cars_model, data=mydata)

# ----------------------------------------------------#
# STEP 2 : PUBLISH WEBSERVICE
# ----------------------------------------------------#

# Define Function to deploy as webservice 
def manualTransmission(hp, wt):
    import pandas as pd
    from revoscalepy import rx_predict
    newData = pd.DataFrame({'hp':[hp], 'wt':[wt]})
    return rx_predict(cars_model, newData, type='response')

# Import the DeployClient and MLServer classes from the azureml-model-management-sdk package.
from azureml.deploy import DeployClient
from azureml.deploy.server import MLServer
from azureml.common.configuration import Configuration

# Define the location of the ML Server 
# for local onebox for Machine Learning Server: http://localhost:12800
# Replace with connection details to your instance of ML Server. 
HOST = 'http://localhost:12800'
context = ('<username>', '<password>')
client = DeployClient(HOST, use=MLServer, auth=context)

# Deploy the web service 
service_name = 'ManualTransmissionService'
service_version = '1.0.0'
service = client.service(service_name)\
        .version(service_version)\
        .code_fn(manualTransmission)\
        .inputs(hp=float, wt=float)\
        .outputs(answer=pd.DataFrame)\
        .models(cars_model=cars_model)\
        .description('Manual Transmission Service')\
        .deploy()

# Invoke the service with sample values
res = service.manualTransmission(120, 2.8)

# Pluck out the named output `answer` as defined during publishing and print
print(res.output('answer'))

# Save service swagger to json file 
print(service.swagger())
with open("manual-transmission-service-swagger.json", "w") as swagger_file:
    swagger_file.write("%s" % service.swagger())