Test.ai Classifier - Python Client

The code in this directory defines a client library for use with the gRPC-based Test.ai classifier server.

Installation & Setup

pip install testai_classifier

Usage

This package exposes a ClassifierClient class:

fromtestai_classifierimportClassifierClient

You can use it to attempt to match images to a semantic label:

defclassify():
client=ClassifierClient(HOST, PORT)
# assume cart_img and menu_img are byte streams as delivered by file.read()# define a mapping between ids and image datadata= {'cart': cart_img, 'menu': menu_img}
# define which label we are looking to matchlabel='cart'# attempt to match the images with the label# confidence is from 0.0 to 1.0 -- any matches with lower than the specified# confidence are not returned.# allow_weaker_matches specifies whether to return matches that are above# the confidence threshold but whose most confident match was a *different*# labelres=client.classify_images(label, data, confidence=0.0, allow_weaker_matches=True)
# res looks like:# {'cart': {'label': 'cart', 'confidence': 0.9, 'confidence_for_hint': 0.9},# 'menu': {'label': 'menu', 'confidence': 0.9, 'confidence_for_hint': 0.2}}# always close the client connectionclient.close()

You can also use it in conjunction with a Selenium Python client driver object, to find elements in a web page based on the label:

deffind_elements():
client=ClassifierClient(HOST, PORT)
driver.get("https://test.ai")
els=client.find_elements_matching_label(driver, "twitter")
els[0].click()
assertdriver.current_url=="https://twitter.com/testdotai"client.close()

Development

• make install - install deps (requires Pipenv)
• make protogen - generate python client helpers from .proto file
• make clean - reset generated files
• make build - run setup.py to generate publishable files
• make test - run test suite (also make unit-test and make se-test)
• make publish - publish to pypi (also make publish-test)