def zipped_nlu_model():
spacy_config_path = "sample_configs/config_spacy.yml"
cfg = config.load(spacy_config_path)
trainer = Trainer(cfg)
td = training_data.load_data(DEFAULT_DATA_PATH)
trainer.train(td)
trainer.persist("test_models", project_name="test_model_spacy_sklearn")
model_dir_list = os.listdir(TEST_MODEL_PATH)
# directory name of latest model
model_dir = sorted(model_dir_list)[-1]
# path of that directory
model_path = os.path.join(TEST_MODEL_PATH, model_dir)
zip_path = zip_folder(model_path)
return zip_path
def zipped_nlu_model():
spacy_config_path = "sample_configs/config_pretrained_embeddings_spacy.yml"
cfg = config.load(spacy_config_path)
trainer = Trainer(cfg)
td = training_data.load_data(DEFAULT_DATA_PATH)
trainer.train(td)
trainer.persist("test_models",
project_name="test_model_pretrained_embeddings")
model_dir_list = os.listdir(TEST_MODEL_PATH)
# directory name of latest model
model_dir = sorted(model_dir_list)[-1]
# path of that directory
model_path = os.path.join(TEST_MODEL_PATH, model_dir)
zip_path = zip_folder(model_path)
return zip_path
def train(self, request):
# if not set will use the default project name, e.g. "default"
project = parameter_or_default(request, "project", default=None)
# if set will not generate a model name but use the passed one
model_name = parameter_or_default(request, "model", default=None)
try:
model_config, data = self.extract_data_and_config(request)
except Exception as e:
request.setResponseCode(400)
returnValue(json_to_string({"error": "{}".format(e)}))
data_file = dump_to_data_file(data)
request.setHeader('Content-Type', 'application/zip')
try:
request.setResponseCode(200)
request.setHeader("Content-Disposition", "attachment")
path_to_model = yield self.data_router.start_train_process(
data_file, project,
RasaNLUModelConfig(model_config), model_name)
zipped_path = utils.zip_folder(path_to_model)
zip_content = io.open(zipped_path, 'r+b').read()
return returnValue(zip_content)
except MaxTrainingError as e:
request.setResponseCode(403)
returnValue(json_to_string({"error": "{}".format(e)}))
except InvalidProjectError as e:
request.setResponseCode(404)
returnValue(json_to_string({"error": "{}".format(e)}))
except TrainingException as e:
request.setResponseCode(500)
returnValue(json_to_string({"error": "{}".format(e)}))
def train(self, request):
# if not set will use the default project name, e.g. "default"
project = parameter_or_default(request, "project", default=None)
# if set will not generate a model name but use the passed one
model_name = parameter_or_default(request, "model", default=None)
try:
model_config, data = self.extract_data_and_config(request)
except Exception as e:
request.setResponseCode(400)
returnValue(json_to_string({"error": "{}".format(e)}))
data_file = dump_to_data_file(data)
request.setHeader('Content-Type', 'application/zip')
try:
request.setResponseCode(200)
request.setHeader("Content-Disposition", "attachment")
path_to_model = yield self.data_router.start_train_process(
data_file, project, RasaNLUModelConfig(model_config),
model_name)
zipped_path = utils.zip_folder(path_to_model)
zip_content = io.open(zipped_path, 'r+b').read()
return returnValue(zip_content)
except MaxTrainingError as e:
request.setResponseCode(403)
returnValue(json_to_string({"error": "{}".format(e)}))
except InvalidProjectError as e:
request.setResponseCode(404)
returnValue(json_to_string({"error": "{}".format(e)}))
except TrainingException as e:
request.setResponseCode(500)
returnValue(json_to_string({"error": "{}".format(e)}))