sys:1: RuntimeWarning: coroutine 'Agent.load_data' was never awaited

Rasa version: -e git+https://github.com/RasaHQ/rasa_core.git@41bb59fc934bb5a1ae4d8cebe412751038c3b2b9#egg=rasa rasa-core-sdk==0.13.1 rasa-nlu==0.15.0

Python version: Python 3.6.7

Operating system (windows, osx, …): Distributor ID: Ubuntu Description: Ubuntu 18.04.2 LTS Release: 18.04 Codename: bionic

Issue: I am getting following issue when I train the core model.

Traceback (most recent call last):
  File "dialogue_management_model.py", line 54, in <module>
    train_dialogue(interpreter=RasaNLUInterpreter)
  File "dialogue_management_model.py", line 37, in train_dialogue
    training_data)
  File "/home/ubuntu/rasa_stack/rasa_core/rasa/core/agent.py", line 580, in train
    **kwargs)
  File "/home/ubuntu/rasa_stack/rasa_core/rasa/core/policies/ensemble.py", line 83, in train
    policy.train(training_trackers, domain, **kwargs)
  File "/home/ubuntu/rasa_stack/rasa_core/rasa/core/policies/memoization.py", line 139, in train
    for t in training_trackers
TypeError: 'coroutine' object is not iterable
sys:1: RuntimeWarning: coroutine 'Agent.load_data' was never awaited

Please find the code in dialogue_management_model.py below:

from rasa.core.agent import Agent
from rasa.core.policies import FallbackPolicy, KerasPolicy, MemoizationPolicy, FormPolicy
from rasa.core.interpreter import RasaNLUInterpreter
from rasa.core.training import interactive
from rasa.core.utils import EndpointConfig

logger = logging.getLogger(__name__)

# this will catch predictions the model isn't very certain about
# there is a threshold for the NLU predictions as well as the action predictions
fallback = FallbackPolicy(fallback_action_name="utter_unclear",
                          core_threshold=0.2,
                          nlu_threshold=0.1)

def train_dialogue(interpreter,domain_file = 'domain.yml',
                   model_path = './models/dialogue',
                   training_data_file = './data/stories.md'):

    #action_endpoint = EndpointConfig(url="http://localhost:5055/webhook")
    agent = Agent(domain_file, policies=[MemoizationPolicy(), KerasPolicy(), FormPolicy(), fallback])
    training_data = agent.load_data('./data/stories.md')

    agent.train(training_data)

    agent.persist('models/dialogue')
    return agent

def run_weather_bot(serve_forever=True):
    nlu_interpreter = RasaNLUInterpreter('./models/nlu/default/current')
    action_endpoint = EndpointConfig(url="http://localhost:5055/webhook")
    agent = Agent.load('./models/dialogue', interpreter=nlu_interpreter, action_endpoint=action_endpoint)
    #rasa_core.run.serve_application(agent ,channel='cmdline')

    return agent

if __name__ == '__main__':
    train_dialogue(interpreter=RasaNLUInterpreter)
    run_weather_bot()

[xames3-old]

@Nomiluks -

Add the following in imports import asyncio

Modify your code while using load_data training_data = asyncio.run(agent.load_data(‘./data/stories.md’))

This has worked for me. Please let me know if this works for you too.

[wochinge]

No ❗️ Rasa is open-source and will always stay hackable / customizable. However, we will try to make it as easy usable as possible and to make everything important configurable without having to write custom code. This also makes it easier for you to upgrade and so on.