🚀 Food Coach Demo

DEPRECATED: this repo is no longer actively maintained. It can still be used as reference, but may contain outdated or unpatched code.

For more information on the Assistant service, see the detailed documentation. For more information on the Tone Analyzer Service, see the detailed documentation.

Deploying the application

If you want to experiment with the application or use it as a basis for building your own application, you need to deploy it in your own environment. You can then explore the files, make changes, and see how those changes affect the running application. After making modifications, you can deploy your modified version of the application to IBM Cloud.

Prerequisites

1. Sign up for an IBM Cloud account.
2. Download the IBM Cloud CLI.
3. Create an instance of the Watson Assistant service and get your credentials:
   • Go to the Watson Assistant page in the IBM Cloud Catalog.
   • Log in to your IBM Cloud account.
   • Click Create.
   • Click Show to view the service credentials.
   • Copy the apikey value, or copy the username and password values if your service instance doesn't provide an apikey.
   • Copy the url value.
4. Create an instance of the Tone Analyzer service and get your credentials:
   • Go to the Tone Analyzer page in the IBM Cloud Catalog.
   • Log in to your IBM Cloud account.
   • Click Create.
   • Click Show to view the service credentials.
   • Copy the apikey value, or copy the username and password values if your service instance doesn't provide an apikey.
   • Copy the url value.

Configuring the application

1. In your IBM Cloud console, open the Watson Assistant service instance

2. Click the Import workspace icon in the Watson Assistant service tool. Specify the location of the workspace JSON file in your local copy of the app project:

   <project_root>/food-coach/training/food-coach-workspace.json

3. Select Everything (Intents, Entities, and Dialog) and then click Import. The car dashboard workspace is created.

4. Click the menu icon in the upper-right corner of the workspace tile, and then select View details.

5. Click the icon to copy the workspace ID to the clipboard.

   

6. In the application folder, copy the .env.example file and create a file called .env

   cp .env.example .env
   

7. Open the .env file and add the service credentials that you obtained in the previous step.

   Example .env file that configures the apikey and url for a Watson Assistant service instance hosted in the US East region:

   ASSISTANT_IAM_APIKEY=X4rbi8vwZmKpXfowaS3GAsA7vdy17Qh7km5D6EzKLHL2
   ASSISTANT_URL=https://gateway-wdc.watsonplatform.net/assistant/api
   

   If your service instance uses username and password credentials, add the ASSISTANT_USERNAME and ASSISTANT_PASSWORD variables to the .env file.

   Example .env file that configures the username, password, and url for a Watson Assistant service instance hosted in the US South region:

   ASSISTANT_USERNAME=522be-7b41-ab44-dec3-g1eab2ha73c6
   ASSISTANT_PASSWORD=A4Z5BdGENrwu8
   ASSISTANT_URL=https://gateway.watsonplatform.net/assistant/api
   

8. Add the WORKSPACE_ID to the previous properties

   WORKSPACE_ID=522be-7b41-ab44-dec3-g1eab2ha73c6
   

9. Your .env file should looks like:

   # Environment variables
   WORKSPACE_ID=1c464fa0-2b2f-4464-b2fb-af0ffebc3aab
   ASSISTANT_IAM_APIKEY=_5iLGHasd86t9NddddrbJPOFDdxrixnOJYvAATKi1
   ASSISTANT_URL=https://gateway-syd.watsonplatform.net/assistant/api
   
   TONE_ANALYZER_IAM_APIKEY=UdHqOFLzoOCFD2M50AbsasdYhOnLV6sd_C3ua5zah
   TONE_ANALYZER_URL=https://gateway-syd.watsonplatform.net/tone-analyzer/api
   

Running locally

1. Install the dependencies

   npm install
   

2. Run the application

   npm start
   

3. View the application in a browser at localhost:3000

Deploying to IBM Cloud as a Cloud Foundry Application

1. Login to IBM Cloud with the IBM Cloud CLI

   ibmcloud login
   

2. Target a Cloud Foundry organization and space.

   ibmcloud target --cf
   

3. Edit the manifest.yml file. Change the name field to something unique.
   For example, - name: my-app-name.

4. Deploy the application

   ibmcloud app push
   

5. View the application online at the app URL.
   For example: https://my-app-name.mybluemix.net

What to do next

After you have the application installed and running, experiment with it to see how it responds to your input.

Modifying the application

After you have the application deployed and running, you can explore the source files and make changes. Try the following:

• Modify the .js files to change the application logic.

• Modify the .html file to change the appearance of the application page.

• Use the Assistant tool to train the service for new intents, or to modify the dialog flow. For more information, see the Assistant service documentation.

What does the Food Coach application do?

The application interface is designed for chatting with a coaching bot. Based on the time of day, it asks you if you've had a particular meal (breakfast, lunch, or dinner) and what you ate for that meal.

The chat interface is in the left panel of the UI, and the JSON response object returned by the Assistant service in the right panel. Your input is run against a small set of sample data trained with the following intents:

yes: acknowledgment that the specified meal was eaten
no: the specified meal was not eaten
help
exit

The dialog is also trained on two types of entities:

food items
unhealthy food items

These intents and entities help the bot understand variations your input.

After asking you what you ate (if a meal was consumed), the bot asks you how you feel about it. Depending on your emotional tone, the bot provides different feedback.

Below you can find some sample interactions:

In order to integrate the Tone Analyzer with the Assistant service, the following approach was taken:

• Intercept the user's message. Before sending it to the Assistant service, invoke the Tone Analyzer Service. See the call to toneDetection.invokeToneAsync in the invokeToneConversation function in app.js.
• Parse the JSON response object from the Tone Analyzer Service, and add appropriate variables to the context object of the JSON payload to be sent to the Assistant service. See the updateUserTone function in tone_detection.js.
• Send the user input, along with the updated context object in the payload to the Assistant service. See the call to assistant.message in the invokeToneConversation function in app.js.

You can see the JSON response object from the Assistant service in the right hand panel.

In the conversation template, alternative bot responses were encoded based on the user's emotional tone. For example:

License

This sample code is licensed under Apache 2.0. Full license text is available in LICENSE.

Contributing

See CONTRIBUTING.

Open Source @ IBM

Find more open source projects on the IBM Github Page.