async def train(request: Request):
"""Train a Rasa Model."""
from rasa.train import train_async
validate_request_body(
request,
"You must provide training data in the request body in order to "
"train your model.",
)
rjs = request.json
validate_request(rjs)
# create a temporary directory to store config, domain and
# training data
temp_dir = tempfile.mkdtemp()
config_path = os.path.join(temp_dir, "config.yml")
dump_obj_as_str_to_file(config_path, rjs["config"])
if "nlu" in rjs:
nlu_path = os.path.join(temp_dir, "nlu.md")
dump_obj_as_str_to_file(nlu_path, rjs["nlu"])
if "stories" in rjs:
stories_path = os.path.join(temp_dir, "stories.md")
dump_obj_as_str_to_file(stories_path, rjs["stories"])
domain_path = DEFAULT_DOMAIN_PATH
if "domain" in rjs:
domain_path = os.path.join(temp_dir, "domain.yml")
dump_obj_as_str_to_file(domain_path, rjs["domain"])
try:
model_path = await train_async(
domain=domain_path,
config=config_path,
training_files=temp_dir,
output_path=rjs.get("out", DEFAULT_MODELS_PATH),
force_training=rjs.get("force", False),
)
filename = os.path.basename(model_path) if model_path else None
return await response.file(
model_path, filename=filename, headers={"filename": filename}
)
except InvalidDomain as e:
raise ErrorResponse(
400,
"InvalidDomainError",
"Provided domain file is invalid. Error: {}".format(e),
)
except Exception as e:
logger.debug(traceback.format_exc())
raise ErrorResponse(
500,
"TrainingError",
"An unexpected error occurred during training. Error: {}".format(e),
)
async def post_data_convert(request: Request):
"""Converts current domain in yaml or json format."""
validate_request_body(
request,
"You must provide training data in the request body in order to "
"train your model.",
)
rjs = request.json
if 'data' not in rjs:
raise ErrorResponse(
400, "BadRequest",
"Must provide training data in 'data' property")
if 'output_format' not in rjs or rjs["output_format"] not in [
"json", "md"
]:
raise ErrorResponse(
400, "BadRequest",
"'output_format' is required and must be either 'md' or 'json")
if 'language' not in rjs:
raise ErrorResponse(400, "BadRequest", "'language' is required")
temp_dir = tempfile.mkdtemp()
out_dir = tempfile.mkdtemp()
nlu_data_path = os.path.join(temp_dir, "nlu_data")
output_path = os.path.join(out_dir, "output")
# botfront: several nlu files
if type(rjs["data"] is dict):
from rasa.core.utils import dump_obj_as_json_to_file
dump_obj_as_json_to_file(nlu_data_path, rjs["data"])
else:
dump_obj_as_str_to_file(nlu_data_path, rjs["data"])
# botfront end
from rasa.nlu.convert import convert_training_data
convert_training_data(nlu_data_path, output_path, rjs["output_format"],
rjs["language"])
with open(output_path, encoding='utf-8') as f:
data = f.read()
if rjs["output_format"] == 'json':
import json
data = json.loads(data, encoding='utf-8')
return response.json({"data": data})
async def train_stack(request: Request):
"""Train a Rasa Stack model."""
from rasa.train import train_async
rjs = request.json
# create a temporary directory to store config, domain and
# training data
temp_dir = tempfile.mkdtemp()
try:
config_path = os.path.join(temp_dir, "config.yml")
dump_obj_as_str_to_file(config_path, rjs["config"])
domain_path = os.path.join(temp_dir, "domain.yml")
dump_obj_as_str_to_file(domain_path, rjs["domain"])
nlu_path = os.path.join(temp_dir, "nlu.md")
dump_obj_as_str_to_file(nlu_path, rjs["nlu"])
stories_path = os.path.join(temp_dir, "stories.md")
dump_obj_as_str_to_file(stories_path, rjs["stories"])
except KeyError:
raise ErrorResponse(
400,
"TrainingError",
"The Rasa Stack training request is "
"missing a key. The required keys are "
"`config`, `domain`, `nlu` and `stories`.",
)
# the model will be saved to the same temporary dir
# unless `out` was specified in the request
try:
model_path = await train_async(
domain=domain_path,
config=config_path,
training_files=[nlu_path, stories_path],
output=rjs.get("out", temp_dir),
force_training=rjs.get("force", False),
)
return await response.file(model_path)
except Exception as e:
raise ErrorResponse(
400,
"TrainingError",
"Rasa Stack model could not be trained. Error: {}".format(e),
)
async def train(request: Request):
"""Train a Rasa Model."""
from rasa.train import train_async
validate_request_body(
request,
"You must provide training data in the request body in order to "
"train your model.",
)
rjs = request.json
validate_request(rjs)
# create a temporary directory to store config, domain and
# training data
temp_dir = tempfile.mkdtemp()
# bf mod
config_paths = {}
config_dir = os.path.join(temp_dir, 'config')
os.mkdir(config_dir)
for key in rjs["config"].keys():
config_file_path = os.path.join(config_dir, "{}.yml".format(key))
dump_obj_as_str_to_file(config_file_path, rjs["config"][key])
config_paths[key] = config_file_path
if "nlu" in rjs:
nlu_dir = os.path.join(temp_dir, 'nlu')
os.mkdir(nlu_dir)
for key in rjs["nlu"].keys():
nlu_file_path = os.path.join(nlu_dir, "{}.md".format(key))
dump_obj_as_str_to_file(nlu_file_path, rjs["nlu"][key]["data"])
# /bf mod
if "stories" in rjs:
stories_path = os.path.join(temp_dir, "stories.md")
dump_obj_as_str_to_file(stories_path, rjs["stories"])
domain_path = DEFAULT_DOMAIN_PATH
if "domain" in rjs:
domain_path = os.path.join(temp_dir, "domain.yml")
dump_obj_as_str_to_file(domain_path, rjs["domain"])
try:
model_path = await train_async(
domain=domain_path,
config=config_paths,
training_files=temp_dir,
output_path=rjs.get("out", DEFAULT_MODELS_PATH),
force_training=rjs.get("force", False),
# botfront: add the possibility to pass a fixed name in the json payload
fixed_model_name=rjs.get("fixed_model_name", None),
# persist data file for nlu components to use
persist_nlu_training_data=True,
)
filename = os.path.basename(model_path) if model_path else None
return await response.file(model_path,
filename=filename,
headers={"filename": filename})
except InvalidDomain as e:
raise ErrorResponse(
400,
"InvalidDomainError",
"Provided domain file is invalid. Error: {}".format(e),
)
except Exception as e:
logger.debug(traceback.format_exc())
raise ErrorResponse(
500,
"TrainingError",
"An unexpected error occurred during training. Error: {}".
format(e),
)
