Predict Deployed Model

from oauth2client.client import GoogleCredentials
from googleapiclient import discovery
from googleapiclient import errors
from google.api_core.client_options import ClientOptions
# Store your full project ID in a variable in the format the API needs.
project_id = 'projects/{}'.format('imageeditorbackend')

prefix = "{}-ml".format('us-east1')
api_endpoint = "https://{}.googleapis.com".format(prefix)
client_options = ClientOptions(api_endpoint=api_endpoint)
#service = googleapiclient.discovery.build('ml', 'v1', client_options=client_options)
# Build a representation of the Cloud ML API.
ml = discovery.build('ml', 'v1', client_options=client_options)

project = 'imageeditorbackend'
model = 'modelo_teste'
name = 'projects/{}/models/{}'.format(project, model)
instances = [
[56, "housemaid", "married", "basic.4y", "no", "no", "no", "telephone", "may", "mon", 261, 1, 999, 0, "nonexistent", 1.1, 93.994, -36.4, 4.857, 5191, "no"]
]

# Create a dictionary with the fields from the request body.
request_dict = {'name': name,
        'body':{'instances': instances}
}

# Create a request to call projects.models.create.
request = ml.projects().predict(
    name=name,
    body={'instances': instances}
)

# Make the call.
try:
    response = request.execute()
    print(response)
except error:
    # Something went wrong, print out some information.
    print('There was an error creating the model. Check the details:')
    print(error)