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There are three different types of runs you can do for this project, MARBEL with Dialogflow to test your dialogue manager (MARBEL Agent), Intent and Slot Classifier with ASR(WHISPER), and all of them together without Dialogflow.

Info

To stop any running servers in a terminal press ctrl C.

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Run your MARBEL Agent with Web Pages and Dialogflow

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Step 1 for all Runs: Launch the Redis Server

  1. Open the project in PyCharm (or another IDE or teriminal).Open the terminal in PyCharm:

  2. Go to

    View > Tool Windows > Terminal

    your cloned repository.

  3. Navigate to the sic_applications directory:

    Code Block
    cd sic_applications
  4. Start the Redis server using the provided configuration file:

    Code Block
    redis-server conf/redis/redis.conf

For Windows go into sic_applicaitons/conf/ and you can run redis-server.exe

If you encounter this error Could not create server TCP listening socket *:6379: bind: Address already in use read through the following collapsable section.

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titleError Problem Solving
Info

Troubleshoot (Could not create server TCP listening socket *:6379: bind: Address already in use)

For Ubuntu/Debian users, if you encounter the error Could not create server TCP listening socket *:6379: bind: Address already in use., please use the following command to stop the Redis server first

Code Block
sudo systemctl stop redis-server.service

And, if you wish to prevent Redis server from starting automatically at boot, you can run

Code Block
sudo systemctl disable redis-server.service

If you still can’t kill Redis server, you can use ps aux | grep redis-server command to find the PID (process ID) of the Redis server. And, terminate the process using kill PID

 

For macOS users, the process should be similar; just find the PID of the Redis server and kill the process:

Code Block
lsof -i tcp:6379

And find the *PID in the terminal response, kill the pid shown:

Code Block
kill -9 *Insert PID here

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Run your MARBEL Agent with Web Pages and Dialogflow

Follow these steps to set up and run your MARBEL agent along with the SIC infrastructure.

Step 2: Run Your MARBEL Agent with Web Pages and Dialogflow

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  1. Open a New Terminal for Each Component:

    • Each component of the SIC infrastructure should run in its own terminal. You can do that really nicely in Pycharm.

    • You can even name them by right-clicking on the session like Local(2) and renaming it to reflect if it has Dialogflow or whatever.

      image-20250105-100909.png

  2. Run the Following Commands (in separate terminals):

    • Dialogflow:

      Code Block
      run-dialogflow
      Text-to-Speech:
      Code Block
      run-text2speech
  • Webserver:

    Code Block
    run-webserver
  • EIS:

    Code Block
    start-

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  • framework
  • Run EIS Component:

    Code Block
    run-eis
  1. Open the Web Interface:

    • Open any browser and navigate to the local host link printed in the terminal where you run-eis or run-webserver. The link starts with localhost! Be patient and it should appear on the terminal you wrote run-eis. If you are not patient you could also try this link but it might not work considering the page is not connected yet: http://localhost:8080/start.html.

    • You should see the start.html page.

  2. Inspect Terminals (Optional):

    • Do not close the terminals used for launching the SIC infrastructure.

    • You can inspect these terminals to debug or monitor what is happening, such as interactions with Dialogflow.

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Part 2: In Eclipse Launch the MARBEL Agent

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Run your Intent and Slot Classifier with WHISPER

Note

YOU CANNOT RUN THIS PART UNTIL YOU ARE DOWN WITH YOUR ENTIRE INTENT AND SLOT CLASSIFIER.

Follow these steps to launch your Intent and Slot Classifier with WHISPER and run the demonstration.

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Step

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  1. Open the project in PyCharm (or another IDE).

  2. Navigate to the sic_applications directory using the terminal:

    Code Block
    cd sic_applications
  3. Start the Redis server with the provided configuration file:

    Code Block
    redis-server conf/redis/redis.conf

Step 2: Start WHISPER and NLU Services

  1. In a new terminal WHISPER, and the NLU classifier in the background:

    Code Block
    run-whisper & run-nlu &

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Step 3:

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Find your trained model social-interaction-cloud/sic_framework/services/nlu/utils/checkpoints/.

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Run the Demo

  1. Execute the demo script in the sic_applications/demos/desktop directory either in Terminal or with Pycharm:

    Code Block
    python sic_applications/demos/desktop/demo_asr_nlu.py

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Important Notes

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