Spaces:
Running
Running
File size: 1,804 Bytes
bcd9850 14c4f39 bcd9850 562e1f8 bcd9850 562e1f8 14c4f39 bcd9850 98c7a08 bcd9850 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 |
from typing import Dict, List, Union
import os
from google.cloud import aiplatform
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value
from google.oauth2 import service_account
def predict_custom_trained_model(
project: str,
endpoint_id: str,
instances: Union[Dict, List[Dict]],
location: str = "us-central1",
api_endpoint: str = "us-central1-aiplatform.googleapis.com",
):
"""
`instances` can be either single instance of type dict or a list
of instances.
"""
# The AI Platform services require regional API endpoints.
client_options = {"api_endpoint": api_endpoint}
credentials = service_account.Credentials.from_service_account_file(
os.getenv("GOOGLE_APPLICATION_CREDENTIALS"))
# Initialize client that will be used to create and send requests.
# This client only needs to be created once, and can be reused for multiple requests.
client = aiplatform.gapic.PredictionServiceClient(
credentials=credentials,
client_options=client_options)
# The format of each instance should conform to the deployed model's prediction input schema.
instances = instances if isinstance(instances, list) else [instances]
instances = [
json_format.ParseDict(instance_dict, Value()) for instance_dict in instances
]
parameters_dict = {}
parameters = json_format.ParseDict(parameters_dict, Value())
endpoint = client.endpoint_path(
project=project, location=location, endpoint=endpoint_id
)
response = client.predict(
endpoint=endpoint, instances=instances, parameters=parameters
)
# The predictions are a google.protobuf.Value representation of the model's predictions.
predictions = response.predictions
return predictions
|