cloudPrediction.py

from google.cloud import automl
import os

os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = 'credentials.json'

# TODO(developer): Uncomment and set the following variables
project_id = 'hardware-identifier'
model_id = 'ICN139132201378775040'
file_path = 'croppedImage 2.jpeg'

prediction_client = automl.PredictionServiceClient()

# Get the full path of the model.
model_full_id = prediction_client.model_path(
    project_id, 'us-central1', model_id
)

# Read the file.
with open(file_path, 'rb') as content_file:
    content = content_file.read()

image = automl.types.Image(image_bytes=content)
payload = automl.types.ExamplePayload(image=image)

# params is additional domain-specific parameters.
# score_threshold is used to filter the result
params = {'score_threshold': '0.5'}

response = prediction_client.predict(model_full_id, payload, params)

print('Prediction results:')
for result in response.payload:
    print(u'Predicted class name: {}'.format(result.display_name))
    print(u'Predicted class score: {}'.format(result.classification.score))