def handle(self, *args, **options):
path = options.get('path')
try:
result = self.predict.file(path)
print(json_dumps(result))
except:
pass
def handle(self, *args, **options):
path = options.get('path')
name = options.get('name')
if os.path.isfile(path):
result = self.datasets.upload(path=path, name=name)
print(json_dumps(result))
else:
print('File does not exist')
def handle(self, *args, **options):
path = options.get('path')
label = options.get('label')
model = options.get('model')
if os.path.isfile(path):
result = self.datasets.feedback(path=path,
label=label,
model_id=model)
print(json_dumps(result))
else:
print('File does not exist')
def handle(self, *args, **options):
datasets_id = options.get('datasets_id')
name = options.get('name')
result = self.train.create(datasets_id=datasets_id, name=name)
print(json_dumps(result))
def handle(self, *args, **options):
model_id = options.get('model_id')
result = self.models.lc(model_id)
print(json_dumps(result))
def handle(self, *args, **options):
datasets_id = options.get('datasets')
result = self.datasets.get_feedback(datasets_id=datasets_id)
print(json_dumps(result))
def handle(self, *args, **options):
model_id = options.get('model_id')
result = self.train.confirm(model_id)
print(json_dumps(result))
def handle(self, *args, **options):
path = options.get('path')
datasets = options.get('datasets')
result = self.datasets.put(datasets_id=datasets, path=path)
print(json_dumps(result))