Content Comparison

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

2. Open the terminal in PyCharm:

   Go to View > Tool Windows > Terminal

   Go to 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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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.

     

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-framework

• Run EIS Component:

  Code Block
  run-eis

3. 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.

4. 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

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

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

   Code Block
   python sic_applications/demo_asr_nlu.py

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