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Project created with rasa init not usable #7158

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iwt-kschoenrock opened this issue Nov 2, 2020 · 4 comments
Closed

Project created with rasa init not usable #7158

iwt-kschoenrock opened this issue Nov 2, 2020 · 4 comments
Labels
area:rasa-oss/cli Issues focused on the rasa command-line-interface area:rasa-oss 🎡 Anything related to the open source Rasa framework feature:ux-cli+training-data Feature: Improve user experience with Rasa CLI and training data for developers type:bug 🐛 Inconsistencies or issues which will cause an issue or problem for users or implementors. type:dependencies Pull requests that update a dependency file

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@iwt-kschoenrock
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Rasa version: Rasa 1.10.16

Rasa SDK version (if used & relevant): 1.10.3

Rasa X version (if used & relevant): 0.32.2

Python version: 3.7.8

Operating system (windows, osx, ...): macOS 10.15.7

Issue:
When installing rasa to a new project with pipenv, installation fails because pipenv is unable to lock dependencies (conflicting versions of multidict as reported in #7124 ). I installed rasa-X<0.33 in the virtualenv with pip, which then completes, again with warnings about multidict:

sanic 19.12.3 requires multidict==5.0.0, but you'll have multidict 4.7.6 which is incompatible.
tensorflow 2.1.2 requires gast==0.2.2, but you'll have gast 0.4.0 which is incompatible.
tensorflow 2.1.2 requires numpy<1.19.0,>=1.16.0, but you'll have numpy 1.19.3 which is incompatible.
rasa 1.10.16 requires aiohttp<3.7,>=3.6, but you'll have aiohttp 3.7.2 which is incompatible.
rasa 1.10.16 requires prompt-toolkit<3.0,>=2.0, but you'll have prompt-toolkit 3.0.8 which is incompatible.
rasa 1.10.16 requires pytz<2020.0,>=2019.1, but you'll have pytz 2020.4 which is incompatible.
Successfully installed GitPython-3.1.11 Mako-1.1.3 MarkupSafe-1.1.1 PyYAML-5.3.1 SQLAlchemy-1.3.20 absl-py-0.9.0 aiofiles-0.6.0 aiohttp-3.7.2 alembic-1.4.3 apscheduler-3.6.3 astor-0.8.1 async-generator-1.10 async-timeout-3.0.1 attrs-19.3.0 boto3-1.16.9 botocore-1.19.9 cached-property-1.5.2 cachetools-4.1.1 certifi-2020.6.20 cffi-1.14.3 chardet-3.0.4 cloudpickle-1.3.0 colorclass-2.2.0 coloredlogs-10.0 colorhash-1.0.2 cryptography-2.9.2 cycler-0.10.0 decorator-4.4.2 dnspython-1.16.0 docopt-0.6.2 fbmessenger-6.0.0 future-0.18.2 gast-0.4.0 gevent-1.5.0 gitdb-4.0.5 google-auth-1.23.0 google-auth-oauthlib-0.4.2 google-pasta-0.2.0 greenlet-0.4.17 grpcio-1.33.2 h11-0.8.1 h2-3.2.0 h5py-3.0.0 hpack-3.0.0 hstspreload-2020.10.20 httplib2-0.18.1 httptools-0.1.1 httpx-0.9.3 humanfriendly-8.2 hyperframe-5.2.0 idna-2.10 importlib-metadata-2.0.0 isodate-0.6.0 jmespath-0.10.0 joblib-0.17.0 jsonpickle-1.4.1 jsonschema-3.2.0 kafka-python-1.4.7 keras-applications-1.0.8 keras-preprocessing-1.1.0 kiwisolver-1.3.1 markdown-3.3.3 matplotlib-3.2.2 mattermostwrapper-2.2 multidict-4.7.6 networkx-2.4 numpy-1.19.3 oauth2client-4.1.3 oauthlib-3.1.0 opt-einsum-3.3.0 packaging-20.4 pika-1.1.0 prompt-toolkit-3.0.8 protobuf-3.13.0 psycopg2-binary-2.8.6 pyasn1-0.4.8 pyasn1-modules-0.2.8 pycparser-2.20 pydot-1.4.1 pyjwt-1.7.1 pykwalify-1.7.0 pymongo-3.8.0 pyparsing-2.4.7 pyrsistent-0.17.3 pysocks-1.7.1 python-crfsuite-0.9.7 python-dateutil-2.8.1 python-editor-1.0.4 python-engineio-3.12.1 python-socketio-4.5.1 python-telegram-bot-12.8 pytz-2020.4 questionary-1.5.2 rasa-1.10.16 rasa-sdk-1.10.3 rasa-x-0.32.2 redis-3.5.3 regex-2020.6.8 requests-2.24.0 requests-oauthlib-1.3.0 requests-toolbelt-0.9.1 rfc3986-1.4.0 rocketchat-API-1.3.1 rsa-4.6 ruamel.yaml-0.16.12 ruamel.yaml.clib-0.2.2 s3transfer-0.3.3 sanic-19.12.3 sanic-cors-0.10.0.post3 sanic-jwt-1.3.2 sanic-plugins-framework-0.9.4.post1 scikit-learn-0.22.2.post1 scipy-1.5.3 six-1.15.0 sklearn-crfsuite-0.3.6 slackclient-2.9.3 smmap-3.0.4 sniffio-1.2.0 tabulate-0.8.7 tensorboard-2.1.1 tensorflow-2.1.2 tensorflow-addons-0.7.1 tensorflow-estimator-2.1.0 tensorflow-hub-0.8.0 tensorflow-probability-0.9.0 termcolor-1.1.0 terminaltables-3.1.0 tornado-6.1 tqdm-4.45.0 twilio-6.26.3 typing-extensions-3.7.4.3 tzlocal-2.1 ujson-1.35 urllib3-1.25.11 uvloop-0.14.0 wcwidth-0.2.5 webexteamssdk-1.3 websockets-8.1 werkzeug-1.0.1 wrapt-1.12.1 yarl-1.6.2 zipp-3.4.0

After installing Rasa, I ran rasa init and, although the initalization finished, rasa interactive was not working.
This error does not happen when installing rasa<2 directly (without Rasa X).

Error (including full traceback):

rasa init
Welcome to Rasa! 🤖

To get started quickly, an initial project will be created.
If you need some help, check out the documentation at https://rasa.com/docs/rasa.
Now let's start! 👇🏽

? Please enter a path where the project will be created [default: current directory] .
? Directory '/Users/homedir/repos/textmining/test_rasax' is not empty. Continue?  Yes
Created project directory at '/Users/homedir/repos/textmining/test_rasax'.
Finished creating project structure.
? Do you want to train an initial model? 💪🏽  Yes
Training an initial model...
Training Core model...
Processed Story Blocks: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1822.18it/s, # trackers=1]
Processed Story Blocks: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1301.04it/s, # trackers=5]
Processed Story Blocks: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 60.00it/s, # trackers=20]
Processed Story Blocks: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 321.11it/s, # trackers=24]
Processed trackers: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1149.19it/s, # actions=16]
Processed actions: 16it [00:00, 11518.86it/s, # examples=16]
Processed trackers: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 231/231 [00:00<00:00, 619.85it/s, # actions=126]
Epochs:   0%|                                                                                                                                                                                                                             | 0/100 [00:00<?, ?it/s]/Users/homedir/.pyenv/versions/3.7.8/lib/python3.7/site-packages/rasa/utils/tensorflow/model_data.py:386: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  final_data[k].append(np.concatenate(np.array(v)))
Epochs: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:06<00:00, 15.85it/s, t_loss=0.085, loss=0.013, acc=1.000]
2020-11-02 12:08:44 INFO     rasa.utils.tensorflow.models  - Finished training.
2020-11-02 12:08:44 INFO     rasa.core.agent  - Persisted model to '/var/folders/_x/3z72pyv53q10b2ps_zvh219jbmgww_/T/tmp9yc7_teo/core'
Core model training completed.
Training NLU model...
2020-11-02 12:08:44 INFO     rasa.nlu.training_data.training_data  - Training data stats:
2020-11-02 12:08:44 INFO     rasa.nlu.training_data.training_data  - Number of intent examples: 43 (7 distinct intents)
2020-11-02 12:08:44 INFO     rasa.nlu.training_data.training_data  -   Found intents: 'goodbye', 'bot_challenge', 'deny', 'mood_great', 'affirm', 'mood_unhappy', 'greet'
2020-11-02 12:08:44 INFO     rasa.nlu.training_data.training_data  - Number of response examples: 0 (0 distinct responses)
2020-11-02 12:08:44 INFO     rasa.nlu.training_data.training_data  - Number of entity examples: 0 (0 distinct entities)
2020-11-02 12:08:44 INFO     rasa.nlu.model  - Starting to train component WhitespaceTokenizer
2020-11-02 12:08:44 INFO     rasa.nlu.model  - Finished training component.
2020-11-02 12:08:44 INFO     rasa.nlu.model  - Starting to train component RegexFeaturizer
2020-11-02 12:08:44 INFO     rasa.nlu.model  - Finished training component.
2020-11-02 12:08:44 INFO     rasa.nlu.model  - Starting to train component LexicalSyntacticFeaturizer
2020-11-02 12:08:44 INFO     rasa.nlu.model  - Finished training component.
2020-11-02 12:08:44 INFO     rasa.nlu.model  - Starting to train component CountVectorsFeaturizer
2020-11-02 12:08:44 INFO     rasa.nlu.model  - Finished training component.
2020-11-02 12:08:44 INFO     rasa.nlu.model  - Starting to train component CountVectorsFeaturizer
2020-11-02 12:08:44 INFO     rasa.nlu.model  - Finished training component.
2020-11-02 12:08:44 INFO     rasa.nlu.model  - Starting to train component DIETClassifier
Epochs:   0%|                                                                                                                                                                                                                             | 0/100 [00:00<?, ?it/s]/Users/homedir/.pyenv/versions/3.7.8/lib/python3.7/site-packages/rasa/utils/tensorflow/model_data.py:386: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  final_data[k].append(np.concatenate(np.array(v)))
Epochs: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:05<00:00, 16.74it/s, t_loss=1.468, i_loss=0.088, i_acc=1.000]
2020-11-02 12:08:55 INFO     rasa.utils.tensorflow.models  - Finished training.
2020-11-02 12:08:55 INFO     rasa.nlu.model  - Finished training component.
2020-11-02 12:08:55 INFO     rasa.nlu.model  - Starting to train component EntitySynonymMapper
2020-11-02 12:08:55 INFO     rasa.nlu.model  - Finished training component.
2020-11-02 12:08:55 INFO     rasa.nlu.model  - Starting to train component ResponseSelector
2020-11-02 12:08:55 INFO     rasa.nlu.selectors.response_selector  - Retrieval intent parameter was left to its default value. This response selector will be trained on training examples combining all retrieval intents.
2020-11-02 12:08:55 INFO     rasa.nlu.model  - Finished training component.
2020-11-02 12:08:55 INFO     rasa.nlu.model  - Successfully saved model into '/var/folders/_x/3z72pyv53q10b2ps_zvh219jbmgww_/T/tmp9yc7_teo/nlu'
NLU model training completed.
Your Rasa model is trained and saved at '/Users/homedir/repos/textmining/test_rasax/models/20201102-120817.tar.gz'.
? Do you want to speak to the trained assistant on the command line? 🤖  Yes
2020-11-02 12:10:06 INFO     root  - Connecting to channel 'cmdline' which was specified by the '--connector' argument. Any other channels will be ignored. To connect to all given channels, omit the '--connector' argument.
2020-11-02 12:10:06 INFO     root  - Starting Rasa server on http://localhost:5005
2020-11-02 12:10:09 INFO     root  - Rasa server is up and running.
Bot loaded. Type a message and press enter (use '/stop' to exit):
2020-11-02 12:10:09 ERROR    asyncio  - Task exception was never retrieved
future: <Task finished coro=<configure_app.<locals>.run_cmdline_io() done, defined at /Users/homedir/.pyenv/versions/3.7.8/lib/python3.7/site-packages/rasa/core/run.py:128> exception=RuntimeError('this event loop is already running.')>
Traceback (most recent call last):
  File "/Users/homedir/.pyenv/versions/3.7.8/lib/python3.7/site-packages/rasa/core/run.py", line 134, in run_cmdline_io
    sender_id=conversation_id,
  File "/Users/homedir/.pyenv/versions/3.7.8/lib/python3.7/site-packages/rasa/core/channels/console.py", line 142, in record_messages
    text = get_user_input(button_question)
  File "/Users/homedir/.pyenv/versions/3.7.8/lib/python3.7/site-packages/rasa/core/channels/console.py", line 78, in get_user_input
    style=Style([("qmark", "#b373d6"), ("", "#b373d6")]),
Your input ->  hello
^Ce 45, in ask
    return self.unsafe_ask(patch_stdout)
  File "/Users/homedir/.pyenv/versions/3.7.8/lib/python3.7/site-packages/questionary/question.py", line 59, in unsafe_ask
    return self.application.run()
  File "/Users/homedir/.pyenv/versions/3.7.8/lib/python3.7/site-packages/prompt_toolkit/application/application.py", line 817, in run
    self.run_async(pre_run=pre_run, set_exception_handler=set_exception_handler)
  File "uvloop/loop.pyx", line 1450, in uvloop.loop.Loop.run_until_complete
  File "uvloop/loop.pyx", line 1443, in uvloop.loop.Loop.run_until_complete
  File "uvloop/loop.pyx", line 1351, in uvloop.loop.Loop.run_forever
  File "uvloop/loop.pyx", line 480, in uvloop.loop.Loop._run
RuntimeError: this event loop is already running.
--- Logging error ---

Command or request that led to error:

rasa init

or rasa interactive after init.

Content of configuration file (config.yml) (if relevant):

# Configuration for Rasa NLU.
# https://rasa.com/docs/rasa/nlu/components/
language: en
pipeline:
  - name: WhitespaceTokenizer
  - name: RegexFeaturizer
  - name: LexicalSyntacticFeaturizer
  - name: CountVectorsFeaturizer
  - name: CountVectorsFeaturizer
    analyzer: "char_wb"
    min_ngram: 1
    max_ngram: 4
  - name: DIETClassifier
    epochs: 100
  - name: EntitySynonymMapper
  - name: ResponseSelector
    epochs: 100

# Configuration for Rasa Core.
# https://rasa.com/docs/rasa/core/policies/
policies:
  - name: MemoizationPolicy
  - name: TEDPolicy
    max_history: 5
    epochs: 100
  - name: MappingPolicy

Content of domain file (domain.yml) (if relevant):

intents:
  - greet
  - goodbye
  - affirm
  - deny
  - mood_great
  - mood_unhappy
  - bot_challenge

responses:
  utter_greet:
  - text: "Hey! How are you?"

  utter_cheer_up:
  - text: "Here is something to cheer you up:"
    image: "https://i.imgur.com/nGF1K8f.jpg"

  utter_did_that_help:
  - text: "Did that help you?"

  utter_happy:
  - text: "Great, carry on!"

  utter_goodbye:
  - text: "Bye"

  utter_iamabot:
  - text: "I am a bot, powered by Rasa."

session_config:
  session_expiration_time: 60
  carry_over_slots_to_new_session: true
@iwt-kschoenrock iwt-kschoenrock added area:rasa-oss 🎡 Anything related to the open source Rasa framework type:bug 🐛 Inconsistencies or issues which will cause an issue or problem for users or implementors. labels Nov 2, 2020
@sara-tagger
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Thanks for the issue, @tabergma will get back to you about it soon!

You may find help in the docs and the forum, too 🤗

@wochinge wochinge added area:rasa-oss/cli Issues focused on the rasa command-line-interface type:dependencies Pull requests that update a dependency file labels Jan 29, 2021
@TyDunn TyDunn added the feature:ux-cli+training-data Feature: Improve user experience with Rasa CLI and training data for developers label Feb 17, 2021
@gausie
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gausie commented Mar 12, 2021

We think this is fixed. If you're still experiencing the issue on the latest version of rasa please feel free to comment and we'll reopen the ticket!

@gausie gausie closed this as completed Mar 12, 2021
@iwt-kschoenrock
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Locking with pipenv still fails (same error), but after installing it with pip the newly created project works as expected.

@ghost
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ghost commented Sep 24, 2021

Hello!!.. I have passed all the commands in conda and have installed rasa but when it comes to training the model I face an issue.. I couldn't train the model .. Shows DLL file required though the folder exists.. Please help me out asap

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area:rasa-oss/cli Issues focused on the rasa command-line-interface area:rasa-oss 🎡 Anything related to the open source Rasa framework feature:ux-cli+training-data Feature: Improve user experience with Rasa CLI and training data for developers type:bug 🐛 Inconsistencies or issues which will cause an issue or problem for users or implementors. type:dependencies Pull requests that update a dependency file
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