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name: Action Server | |
on: | |
push: | |
branches: | |
- 'main' | |
paths: | |
- 'actions/**' | |
jobs: | |
my_first_job: |
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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 |
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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 |
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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 |
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from rasa.nlu.components import Component | |
class SentimentAnalyzer(Component): | |
"""A pre-trained sentiment component""" | |
name = "sentiment" | |
provides = ["entities"] | |
requires = ["tokens"] | |
defaults = {} | |
language_list = ["en"] |
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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""" |
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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 |
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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 |
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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): |
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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) |
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