def perform_test(self, name, setting_file, golden_file):
print("%s test" % name, end="")
sys.stdout.flush()
from util.config import Config
config = Config(get_file(setting_file))
notify_data = collections.OrderedDict()
for notify_name in config.notifications:
parsed_data = collections.OrderedDict()
notification = config.notifications[notify_name]
for entry in notification.get_key_and_path():
[key, path] = entry
from util.content import get_content
content = get_content(path)
data_list = notification.parser.parse(content)
assert data_list and type(data_list) is list
source_key = tuple(sorted(key.items())) # convert to sorted-tuple, for dict is not hashable
parsed_data[source_key] = data_list
analyzed_data = notification.post_analysis.analyze(parsed_data, notify_data)
notify_data[notify_name] = analyzed_data
os.remove("immature_" + notify_name + ".txt")
os.remove("mature_" + notify_name + ".txt")
with open(get_file(golden_file), encoding='utf8') as f:
golden = f.read()
from pp.pretty_print import get_beautiful_data
self.assertEqual(get_beautiful_data(notify_data), golden)
def display():
if checkIfLogged():
global parameters
imgs = get_content(parameters, "img")
print len(imgs)
imgs = json.loads(imgs)
return render_template("display.html", medium="art", images=imgs)
else:
return redirect(url_for("home"))
def update_display():
data = request.args.get("replace_pics")
data = json.loads(data)
print "\n\n\n\n\n\n\n\n"
print data
print "\n\n\n\n\n\n\n\n"
for i in data:
print i
c = [[int(ml_db.get_word_num("img", x)) for x in api.clarifai(i)]
for i in data]
d = []
for x in c:
d.append(x[:3]) #takes only the first three parameters
# update training set
# optimize parameters
# return 3 random and 2 predicted images
#print "\n========================x: \n", x
print "\n\n\n\n\n\n\n\n"
print "d: ", d
print "\n\n\n\n\n\n\n\n end of d"
global tsetX
global tsetY
global parameters
for i in d:
ml.append_train_set(i, tsetX, tsetY)
print tsetX, tsetY
parameters = ml.optimize_parameters(parameters, tsetX, tsetY)
print "parameters:", parameters[0] + 1
#print parameters
print "\n\n\n\n\n\n\n\n"
print "parameters: ", parameters
print "\n\n\n\n\n\n\n\n"
#predict(parameters, x) # x is a list of data retrieved from ml_db
img = get_content(parameters, "img")
print "\n\n\n\n\n\n\n\n"
print img
print "\n\n\n\n\n\n\n\n"
# response = json.loads(img)
#response=(calls getty)
#img = json.dumps(img)
return img
def run(self):
logging.info("[WbNt] web-notifier is running!")
notify_data = collections.OrderedDict() # key: notify_name, value: dict (see below)
for notify_name in self.__config.notifications:
logging.info("[WbNt] dealing with notification \"%s\"" % notify_name)
parsed_data = collections.OrderedDict() # key: source key, value: list of parsed data
notification = self.__config.notifications[notify_name]
for entry in notification.get_key_and_path():
assert isinstance(entry, list) and 2 == len(entry)
[key, path] = entry
key_str = "" if 0 == len(key) else \
str([key_value_pair[1] for key_value_pair in list(key.items())])
logging.debug("[WbNt] " + path)
content = get_content(path, self.args.web_login, self.args.web_password)
WebNotifier.backup_content(key, content, notify_name)
data_list = notification.parser.parse(content)
if data_list:
assert type(data_list) is list
source_key = tuple(sorted(key.items())) # convert to sorted-tuple, for dict is not hashable
parsed_data[source_key] = data_list
logging.info("[WbNt] %s %sfind %i entry(s)" %
(key_str, " => " if len(key_str) > 0 else "", len(data_list)))
else:
if "" != key_str:
logging.info("[WbNt] %s" % key_str)
info_entry = notification.parser.informative
analyzed_data = notification.post_analysis.analyze(parsed_data, notify_data, info_entry)
notify_data[notify_name] = analyzed_data
pp_data = get_beautiful_data(notify_data, self.args.full_data)
logging.info(pp_data)
self.__config.audiences.notify(pp_data)
import json
output_file = self.args.output
with open(output_file, 'w') as write_fd:
json.dump(get_beautiful_pre_json(notify_data), write_fd)
logging.info("[WbNt] notification done")
