JustinaPetr

name: Action Server
on: 
  push:
    branches:
    - 'main'
    paths:
    - 'actions/**'

jobs:
  my_first_job:

JustinaPetr

import logging
import uuid
from sanic import Blueprint, response
from sanic.request import Request
from socketio import AsyncServer
from typing import Optional, Text, Any, List, Dict, Iterable

from rasa.core.channels.channel import InputChannel
from rasa.core.channels.channel import UserMessage, OutputChannel

JustinaPetr

import os
import sys
import io
import torch
from collections import OrderedDict

from TTS.models.tacotron import Tacotron
from TTS.layers import *
from TTS.utils.data import *
from TTS.utils.audio import AudioProcessor

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

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

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
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
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

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

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)