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()
def wtl_thread(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, highlight=True))
workflow.run()
workflow.quit()
def _start_btn(elements, _):
return [e for e in elements if e.metadata["id"] == "sync-task-cover" and "block" in e.metadata["display"]]
def _tile_div(elements, _):
return [
e
for e in elements
if e.metadata["tag"] == "span" and e.metadata["id"].startswith("ttt") and e.tag.parent.name == "div"
]
if __name__ == "__main__":
cli_args = parse_cli_args()
workflow = wtl.Workflow(
config=wtl.Config(cli_args.config),
policy=policy,
url="https://stanfordnlp.github.io/miniwob-plusplus/html/miniwob/tic-tac-toe.html",
output=cli_args.output,
)
workflow.classifiers.add(wtl.ActiveElementFilter())
workflow.classifiers.add(wtl.ActiveElementFilter(name="start", callback=_start_btn, action=Click))
workflow.classifiers.add(wtl.ActiveElementFilter(name="tile", callback=_tile_div, action=Click))
workflow.run()
workflow.quit()
from .util import parse_cli_args
@wtl.single_tab
def policy(workflow: wtl.Workflow, view: wtl.View) -> wtl.Action:
assert workflow.duplicate_loop_idx == workflow.loop_idx
# With some small probabilty, refresh instead of clicking.
return choice(view.actions.by_type(
Click)) if random() < 0.95 else view.actions.by_type(Refresh).unique()
def set_duplicate_loop_idx(workflow: wtl.Workflow):
workflow.duplicate_loop_idx = workflow.loop_idx
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=Click))
wf.postload_callbacks.append(partial(set_duplicate_loop_idx, wf))
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 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()
