JustinaPetr

def required_slots(tracker):
    # type: () -> List[Text]
    """A list of required slots that the form has to fill"""

    if tracker.get_slot('cuisine') == 'greek':
        return ["cuisine", "num_people", "outdoor_seating",
             	"preferences", "feedback"]
    else:
        return ["cuisine", "num_people",
             	"preferences", "feedback"]

JustinaPetr

class RestaurantForm(FormAction):
    """Example of a custom form action"""

    def name(self):
        """Unique identifier of the form"""
        return "restaurant_form"


    @staticmethod
    def required_slots(tracker: Tracker) -> List[Text]:

JustinaPetr

def slot_mappings(self):
    # type: () -> Dict[Text: Union[Dict, List[Dict]]]
    """A dictionary to map required slots to
    - an extracted entity
    - intent: value pairs
    - a whole message or a list of them, where a first 
                                 match will be picked"""

    return { "outdoor_seating": [self.from_entity(entity="seating"),
                      self.from_intent(intent='affirm',

JustinaPetr

def validate(self,
                 dispatcher: CollectingDispatcher,
                 tracker: Tracker,
                 domain: Dict[Text, Any]) -> List[Dict]:
    """Validate extracted requested slot
            else reject the execution of the form action
    """
    # extract other slots that were not requested
    # but set by corresponding entity
    slot_values = self.extract_other_slots(dispatcher, tracker, domain)

JustinaPetr

import nltk
from nltk.classify import NaiveBayesClassifier
from nltk.corpus import twitter_samples
from nltk.tokenize import RegexpTokenizer
from nltk.corpus import stopwords
from nltk.stem.wordnet import WordNetLemmatizer


class SentimentClassifier(object):

JustinaPetr

from rasa_nlu.components import Component
from rasa_nlu import utils
from rasa_nlu.model import Metadata
from sentiment_classifier import SentimentClassifier

import nltk
from nltk.classify import NaiveBayesClassifier
from nltk.corpus import twitter_samples
from nltk.tokenize import RegexpTokenizer
from nltk.corpus import stopwords

JustinaPetr

from rasa.nlu.components import Component
from rasa.nlu import utils
from rasa.nlu.model import Metadata

import nltk
from nltk.classify import NaiveBayesClassifier
import os

import typing
from typing import Any, Optional, Text, Dict

JustinaPetr

from rasa.nlu.components import Component
from rasa.nlu import utils
from rasa.nlu.model import Metadata

import nltk
from nltk.sentiment.vader import SentimentIntensityAnalyzer
import os

class SentimentAnalyzer(Component):
    """A pre-trained sentiment component"""

JustinaPetr

from rasa.nlu.components import Component

class SentimentAnalyzer(Component):
    """A pre-trained sentiment component"""

    name = "sentiment"
    provides = ["entities"]
    requires = ["tokens"]
    defaults = {}
    language_list = ["en"]

JustinaPetr

import pyaudio
from deepspeech import Model
import scipy.io.wavfile as wav
import wave


WAVE_OUTPUT_FILENAME = "test_audio.wav"

def record_audio(WAVE_OUTPUT_FILENAME):
	CHUNK = 1024