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config = """
language: "en"

pipeline:
- name: "nlp_spacy"                   # loads the spacy language model
- name: "tokenizer_spacy"             # splits the sentence into tokens
- name: "ner_crf"                   # uses the pretrained spacy NER model
- name: "intent_featurizer_spacy"     # transform the sentence into a vector representation
- name: "intent_classifier_sklearn"   # uses the vector representation to classify using SVM
- name: "ner_synonyms"                # trains the synonyms

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from rasa_nlu.training_data import load_data
from rasa_nlu.config import RasaNLUModelConfig
from rasa_nlu.model import Trainer
from rasa_nlu import config

# loading the nlu training samples
training_data = load_data("nlu.md")

# trainer to educate our pipeline
trainer = Trainer(config.load("config.yml"))

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domain_yml = """
intents:
- greet
- goodbye
- mood_affirm
slots:
  group:
    type: text
    
entities:

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from rasa_core.actions import Action
from rasa_core.events import SlotSet
from IPython.core.display import Image, display

import requests

class ApiAction(Action):
    def name(self):
        return "action_retrieve_image"

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from rasa_core.policies import FallbackPolicy, KerasPolicy, MemoizationPolicy
from rasa_core.agent import Agent

# The fallback action will be executed if the intent recognition has #a confidence below nlu_threshold or if none of the dialogue #policies predict an action with confidence higher than #core_threshold.

fallback = FallbackPolicy(fallback_action_name="utter_unclear",
                          core_threshold=0.2,
                          nlu_threshold=0.1)

agent = Agent('domain.yml', policies=[MemoizationPolicy(), KerasPolicy(), fallback])

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from rasa_core.channels.slack import SlackInput
from rasa_core.agent import Agent
from rasa_core.interpreter import RasaNLUInterpreter
import yaml
from rasa_core.utils import EndpointConfig


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)

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from rasa_core.channels.slack import SlackInput
from rasa_core.agent import Agent
from rasa_core.interpreter import RasaNLUInterpreter
import yaml
from rasa_core.utils import EndpointConfig


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)

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articles = pd.DataFrame({
  'holiday': 'Article_Published',
  'ds': pd.to_datetime(['2018-07-02', '2018-07-06', '2018-07-08',
                        '2018-07-09', '2018-07-12', '2018-07-19', '2018-07-26', '2018-07-31',
                        '2018-08-06', '2018-08-15', '2018-07-19', '2018-08-26', '2018-08-31',
                        '2018-09-01', '2018-09-04', '2018-09-11', '2018-09-17', '2018-09-23',
                        '2018-10-02', '2018-10-09', '2018-10-18', '2018-10-19', '2018-10-26',
                        '2018-11-02', '2018-11-08', '2018-11-24', '2018-12-05', '2018-12-13',
                        '2018-12-19', '2018-12-24', '2018-12-27', '2019-01-08', '2019-01-11',
                        '2019-01-22', '2019-01-24', '2019-01-28', '2019-02-01', '2019-02-04',

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