def execute_pipeline(text: str, project_type: str, model_name: str,
model_attrs: dict, template: str, label_mapping: dict):
task = TaskFactory.create(project_type)
model = RequestModelFactory.create(model_name=model_name,
attributes=model_attrs)
template = MappingTemplate(label_collection=task.label_collection,
template=template)
post_processor = PostProcessor(label_mapping)
labels = pipeline(text=text,
request_model=model,
mapping_template=template,
post_processing=post_processor)
return labels.dict()
def execute_pipeline(data: str, config: AutoLabelingConfig):
label_collection = get_label_collection(config.task_type)
model = RequestModelFactory.create(model_name=config.model_name,
attributes=config.model_attrs)
template = MappingTemplate(label_collection=label_collection,
template=config.template)
post_processor = PostProcessor(config.label_mapping)
labels = pipeline(text=data,
request_model=model,
mapping_template=template,
post_processing=post_processor)
labels = create_labels(config.task_type, labels)
return labels
def test_gcp_label_detection_pipeline(data_path, cassettes_path):
with vcr.use_cassette(
str(cassettes_path / 'pipeline_gcp_label_detection.yaml'),
mode='once',
filter_query_parameters=['key']
):
model = GCPImageLabelDetectionRequestModel(key=os.environ.get('API_KEY_GCP', ''))
template = GCPImageLabelDetectionTemplate()
filepath = data_path / 'images/1500x500.jpeg'
post_processor = PostProcessor({})
labels = pipeline(
text=filepath,
request_model=model,
mapping_template=template,
post_processing=post_processor
)
labels = labels.dict()
assert isinstance(labels, list)
assert len(labels) == 1
assert 'label' in labels[0]
def test_amazon_pipeline(cassettes_path):
with vcr.use_cassette(str(cassettes_path /
'amazon_comprehend_sentiment.yaml'),
mode='once',
filter_headers=['authorization']):
model = AmazonComprehendSentimentRequestModel(
aws_access_key=os.environ.get('AWS_ACCESS_KEY', ''),
aws_secret_access_key=os.environ.get('AWS_SECRET_ACCESS_KEY', ''),
region_name='us-east-1',
language_code='en')
template = AmazonComprehendSentimentTemplate()
post_processor = PostProcessor({})
labels = pipeline(text='I am very sad.',
request_model=model,
mapping_template=template,
post_processing=post_processor)
labels = labels.dict()
assert isinstance(labels, list)
assert len(labels) == 1
assert 'label' in labels[0]