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predict_custom_trained_model_sample_test.py
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predict_custom_trained_model_sample_test.py
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# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import base64
import os
import pathlib
import predict_custom_trained_model_sample
ENDPOINT_ID = "6119547468666372096" # permanent_custom_flowers_model
PROJECT_ID = os.getenv("BUILD_SPECIFIC_GCLOUD_PROJECT")
PATH_TO_IMG = pathlib.Path(__file__).parent.absolute() / "resources/daisy.jpg"
def test_ucaip_generated_predict_custom_trained_model_sample(capsys):
with open(PATH_TO_IMG, "rb") as f:
file_content = f.read()
encoded_content = base64.b64encode(file_content).decode("utf-8")
instance_dict = {"image_bytes": {"b64": encoded_content}, "key": "0"}
# Single instance as a dict
predict_custom_trained_model_sample.predict_custom_trained_model_sample(
instances=instance_dict, project=PROJECT_ID, endpoint_id=ENDPOINT_ID
)
# Multiple instances in a list
predict_custom_trained_model_sample.predict_custom_trained_model_sample(
instances=[instance_dict, instance_dict],
project=PROJECT_ID,
endpoint_id=ENDPOINT_ID,
)
out, _ = capsys.readouterr()
assert "1.0" in out
# Two sets of scores for multi-instance, one score for single instance
assert out.count("scores") == 3