def test_complex():
@wtl.single_tab
def policy(w, view):
menu_actions = view.actions.by_type(Click).by_score("menu")
w.metadata["clicks"] = w.metadata["clicks"] + 1
return random.choice(menu_actions)
def menu_classifier_func(elements, _):
return [
elem for elem in elements if elem.location.x < 10
and elem.location.y < 200 and elem.metadata["tag"] == "a"
]
config = Config.default(["headless", "desktop"])
workflow = wtl.Workflow(url=TESTURL,
config=config,
policy=policy,
goal=wtl.goals.N_STEPS(3))
workflow.metadata["clicks"] = 0
workflow.classifiers.add(wtl.ActiveElementFilter(action=Click))
workflow.classifiers.add(
wtl.ElementClassifier(name="menu",
action=Click,
subset="is_active",
highlight=True,
callback=menu_classifier_func))
workflow.run()
assert workflow.success
assert workflow.metadata["clicks"] == 3
workflow.quit()
# After seven deletions, start over from step 3
if workflow.loop_idx == 7:
return wtl.actions.Revert(3)
# Randomly pick one of the deleting actions
return [
random.choice(view.actions.by_type(wtl.actions.Remove)),
wtl.actions.Wait(0.25),
wtl.actions.Clear(viewport=False),
wtl.actions.WaitForUser(),
]
if __name__ == "__main__":
cli_args = parse_cli_args()
wf = wtl.Workflow(config=wtl.Config(cli_args.config), policy=policy, url=cli_args.url, output=cli_args.output)
wf.classifiers.add(
wtl.ElementClassifier(
name="dementor",
enabled=True,
highlight=False,
action=wtl.actions.Remove,
callback=lambda e, _: e, # Will label _all_ elements removable
)
)
wf.run()
wf.quit()
def text_field_classifier_func(elements: wtl.Elements,
_) -> List[wtl.PageElement]:
return [
e for e in elements
if e.metadata["tag"] == "input" and e.metadata["type"] in ("text",
"email",
"password")
]
if __name__ == "__main__":
cli_args = parse_cli_args()
workflow = wtl.Workflow(config=wtl.Config(cli_args.config),
policy=policy,
url=cli_args.url,
output=cli_args.output)
workflow.classifiers.add(wtl.ActiveElementFilter(action=wtl.actions.Click))
workflow.classifiers.add(
wtl.ElementClassifier(name="textfield",
action=wtl.actions.FillText,
callback=text_field_classifier_func,
highlight=True))
logging.getLogger("wtl").setLevel(logging.CRITICAL)
workflow.run()
workflow.quit()
def menu_classifier_func(elements: wtl.Elements, _) -> List[wtl.PageElement]:
# The condition here is completely hard-coded for the given page.
return [
elem for elem in elements if elem.location.x < 10
and elem.location.y < 200 and elem.metadata["tag"] == "a"
]
if __name__ == "__main__":
cli_args = parse_cli_args()
workflow = wtl.Workflow(config=wtl.Config(cli_args.config),
policy=policy,
url=cli_args.url,
output=cli_args.output)
workflow.classifiers.add(wtl.ActiveElementFilter(action=Click))
workflow.classifiers.add(
wtl.ElementClassifier(
name="menu",
action=Click,
subset="is_active", # Consider only active elements
highlight=True,
callback=menu_classifier_func,
))
workflow.run()
workflow.quit()
for e in elements:
zIndex = workflow.js.execute_script(Z_INDEX_JS, e.selector.css) or 0
result.append((e, int(zIndex)))
return result
if __name__ == "__main__":
cli_args = parse_cli_args()
wf = wtl.Workflow(config=wtl.Config(cli_args.config),
policy=policy,
url=cli_args.url,
output=cli_args.output)
wf.classifiers.add(wtl.ActiveElementFilter(action=wtl.actions.Click))
wf.classifiers.add(
wtl.ElementClassifier(
name="zIndex",
subset="is_active",
enabled=True,
highlight=0.99,
mode=wtl.ScalingMode.LINEAR,
callback=_compute_z_index,
))
setup_logging(logging_level=logging.DEBUG)
wf.run()
wf.quit()
def score(element):
return element.bounds.area / largest_area
return {
"big": [(e, score(e)) for e in elements if score(e) > 0.75],
"average": [(e, abs(0.5 - score(e))) for e in elements
if 0.25 < score(e) <= 0.75],
}
if __name__ == "__main__":
cli_args = parse_cli_args()
workflow = wtl.Workflow(config=wtl.Config(cli_args.config),
policy=policy,
url=cli_args.url,
output=cli_args.output)
workflow.classifiers.add(wtl.ActiveElementFilter())
workflow.classifiers.add(
wtl.ElementClassifier(name="size",
subset="is_active",
highlight=0.5,
action=Click,
callback=size_classifier_func))
with workflow:
workflow.run()
wtl.actions.Navigate(search_url),
Click(search_results[i + 1])
]
i += 1
except IndexError:
print("Search result exhausted!!")
break
yield None
if __name__ == "__main__":
cli_args = parse_cli_args()
wf = wtl.Workflow(
config=wtl.Config(cli_args.config),
policy=policy,
url="https://en.wikipedia.org/wiki/Special:Random",
output=cli_args.output,
)
wf.classifiers.add(wtl.ActiveElementFilter(action=Click))
wf.classifiers.add(
wtl.ElementClassifier(name="textfield",
action=wtl.actions.FillText,
highlight=True))
wf.run()
wf.quit()
def url_length_classifier_func(elements, _):
# Score all elements with an href attribute with a score of the length of the href attribute
href_elements = [
element for element in elements if element.metadata["href"]
]
return [(element, len(element.metadata["href"]))
for element in href_elements]
if __name__ == "__main__":
cli_args = parse_cli_args()
workflow = wtl.Workflow(config=wtl.Config(cli_args.config),
policy=policy,
url=cli_args.url,
output=cli_args.output)
workflow.classifiers.add(
wtl.ElementClassifier(
name="url_length",
highlight=True,
mode=wtl.ScalingMode.LINEAR,
highlight_color=wtl.Color(0, 0, 255),
callback=url_length_classifier_func,
))
workflow.run()
workflow.quit()
goal = N_STEPS(2)
@wtl.single_tab
def policy(workflow: wtl.Workflow, view: wtl.View) -> Optional[wtl.Action]:
if len(workflow.history) == 1:
images_by_size = sorted(
view.snapshot.elements.by_score("image"), key=lambda element: element.bounds.area, reverse=True
)
return Click(images_by_size[0])
print("\n", view.snapshot.page_metadata["url"] != workflow.history[0].snapshot.page_metadata["url"], "\n")
return None
def image_classifier_func(elements, _):
return [elem for elem in elements if elem.metadata["tag"] == "img"]
if __name__ == "__main__":
cli_args = parse_cli_args()
wf = wtl.Workflow(
config=wtl.Config(cli_args.config), policy=policy, goal=goal, url=cli_args.url, output=cli_args.output
)
wf.classifiers.add(wtl.ElementClassifier(name="image", highlight=True, callback=image_classifier_func))
wf.run()
wf.quit()