Page Comparison

This page offers an overview about the (detailed) steps of how to integrate a service into the SIC Framework. Become familiar with what a service could (not) be by checking out Services - Restructured . The code for all the existing services can be found at https://bitbucket.org/socialroboticshub/processing/src/master/. Creating a new service can be done as follows, and then included in the repository by opening a Pull Request:

TLDR (Too Long Didn’t Read)

...

Summary How to Add a Service to the SIC Framework

1. create a new folder in https://bitbucket.org/socialroboticshub/processing/src/master/ with the name of the service

2. copy the certificate file https://bitbucket.org/socialroboticshub/docker/src/master/cbsrsic/beamforming/cert.pem from any of the other services' folders into the service’s folder

3. copy any additional files that the services may need into the service’s folder

4. create a factory file inheriting from the CBSRFactory SICfactory in the service’s folder, and override the superclass’s methods

5. create a service file inheriting from CBSRService SICservice in the service’s folder, and override the superclass’s methods

6. update the https://bitbucket.org/socialroboticshub/processing/src/master/deploy_to_docker.sh file in the root folder with the new service files

7. deploy the new service to the https://bitbucket.org/socialroboticshub/docker/src/master/ folder by running the deploy_to_docker.sh file

8. update the https://bitbucket.org/socialroboticshub/docker/src/master/docker-compose.yml file in the docker folder with the new service

9. update the https://bitbucket.org/socialroboticshub/docker/src/master/Dockerfile.python3 file in the docker folder with the new service’s dependencies

10. update the topics in the constructor of the Abstract Connector from the https://bitbucket.org/socialroboticshub/connectors/src/master/python/social_interaction_cloud/ folder with the name of the new service

11. update the device listeners in enable_service in the Abstract Connector with the service

12. update the listened to channels in __listen in the Abstract Connector with the service

13. create the corresponding event handler method for the service in the Abstract Connector

14. create the corresponding event handler method for the service in the Basic Connector

15. use the service in a new file

...

The detailed explanation of these steps with a sentiment analysis example can be found below:

...

with a sentiment analysis example can be found below:

Detailed How to Add a (Sentiment) Service to the SIC Framework

There are two shared libraries that handle a lot of the common logic that is needed to interact with our framework: one for Python-based integrations and one for Java-based integrations (using Maven). In order to allow users to run services without worrying about compatibility and installations, Docker Compose is used.

If a service is not simply an alternative to an existing service, adding a new service will also require updates to the connectors (EIS and Python) in order to be fully integrated.

1. create a new folder in https://bitbucket.org/socialroboticshub/processing/src/master/ with the name of the service. sentiment_analysis (https://bitbucket.org/socialroboticshub/docker/src/master/cbsrsic/sentiment/ ) will be used as the example folder and service in this case

2. copy the certificate file https://bitbucket.org/socialroboticshub/docker/src/master/cbsrsic/beamforming/cert.pem from any of the other services' folders into the sentimentfolder

3. copy the https://bitbucket.org/socialroboticshub/docker/src/master/cbsrsic/sentiment/classifier.pickle into the sentiment folder

4. create a https://bitbucket.org/socialroboticshub/docker/src/master/cbsrsic/sentiment/sentiment_factory.py file in the sentiment folder

from os import getcwd

from cbsrsic.factory import CBSRfactorySICfactory
from nltk import download
from nltk.data import path

from sentiment_service import SentimentAnalysisService


class SentimentAnalysisFactory(CBSRfactorySICfactory):
    def __init__(self):
        super(SentimentAnalysisFactory, self).__init__()

    def get_connection_channel(self):
        return 'sentiment_analysis'

    def create_service(self, connect, identifier, disconnect):
        return SentimentAnalysisService(connect, identifier, disconnect)


if __name__ == '__main__':
    cwd = getcwd()
    download('punkt', download_dir=cwd)
    download('omw-1.4', download_dir=cwd)
    download('averaged_perceptron_tagger', download_dir=cwd)
    download('wordnet', download_dir=cwd)
    path.append(cwd)

    sentiment_analysis_factory = SentimentAnalysisFactory()
    sentiment_analysis_factory.run()

5. create a https://bitbucket.org/socialroboticshub/docker/src/master/cbsrsic/sentiment/sentiment_service.py file in the sentiment folder

""" This file shows an example of a sentiment service that uses the text_transcript channel result
    from the Dialogflow speech-to-text to get the the type of sentiment
"""

from pickle import load
from re import sub
from string import punctuation

from cbsrsic.service import CBSRserviceSICservice
from nltk.stem.wordnet import WordNetLemmatizer
from nltk.tag import pos_tag
from nltk.tokenize import word_tokenize


class SentimentAnalysisService(CBSRserviceSICservice):
    def __init__(self, connect, identifier, disconnect):
        super(SentimentAnalysisService, self).__init__(connect, identifier, disconnect)
        with open('classifier.pickle', 'rb') as pickle:
            self.classifier = load(pickle)
        self.lemmatizer = WordNetLemmatizer()

    def get_device_types(self):
        return ['mic']

    def get_channel_action_mapping(self):
        return {self.get_full_channel('text_transcript'): self.execute}

    def execute(self, message):
        sentence = message['data'].decode()
        tokens = self.remove_noise(word_tokenize(sentence))
        sentiment = self.classifier.classify(dict([token, True] for token in tokens))
        print(sentiment)
        self.publish('text_sentiment', sentiment)
...

...

...
echo Deploying mysentiment_serviceanalysis...
cd ../myaudio_servicesentiment
cp -f {cert.pem,  *.py, .pem,classifier.pickle,*.py} ../../docker/cbsrsic/my_servicesentiment    # extra files canshould also be copiedinclduded if usedhere
...

7. deploy the new service to the https://bitbucket.org/socialroboticshub/docker/src/master/ folder by running the deploy_to_docker.sh file

8. update the https://bitbucket.org/socialroboticshub/docker/src/master/docker-compose.yml file in the docker folder with the new service

...
  # ------------------------------------------------------------
  # Sentiment Analysis service
  # ------------------------------------------------------------
  sentiment_analysis:
    image: sic_python3
    build:
      context: .
      dockerfile: Dockerfile.python3

 hostname: sentiment_analysis   user: "${NEW_UID}:${NEW_GID}"
    env_file:
      - ./.env

    working_dir: /my_servicesentiment
    command: python3 sentiment_factory.py
  volumes:
    - ./cbsr/mock:/sentiment:rw${MOUNT_OPTIONS}

  tty: true
  stdin_open: falsepy
    networks:
    app_netvolumes:
      ipv4_address:- 172.16.238.x./sic/sentiment:/sentiment:rw,delegated

 # address has totty: differtrue
from those of the already existing services'stdin_open: false

    depends_on:
      - redis
      - dialogflow

  # - any other services the service depends on
...

...