This repository contains the necessary files for building, testing, and deploying the sleepnumber application which is an Alexa skill designed to control the SleepIQ bed settings.

The application is deployed as an image to AWS ECR and executing using Lambda.

There have been a few times where I couldn't find the remote control to my Sleepnumber bed, requiring me to use their iOS app to make adjustments. The load time of the app is incredibly long, and frustrating enough where I spent a weekend building this Alexa Skill to avoid using the app again :/

• Docker installed
• AWS CLI installed and configured with necessary permissions
• Environment variables set in .env (see details below)

• makerfile: Contains a list of commands to build, test, and deploy the application to AWS.
• Dockerfile: A Docker configuration file to build the Lambda function using AWS's Python 3.9 runtime.

Before running any of the make commands, ensure you have the required environment variables in a .env file. The makerfile expects the following variables:

• AWS_ECR_ACCOUNT_ID: Your AWS account ID for the ECR repository
• AWS_ECR_REGION: AWS region for the ECR repository

The other parameters like APP_NAME and APP_VERSION are already set in the makerfile.

The application fetches secrets like skill_id, username, and password from AWS Secrets Manager.

The sleepnumber Alexa skill provides the following functionalities:

1. Launch: Initiates the skill and provides a welcome message.
2. Set Firmness: Sets the firmness of the bed to a specific value.
3. Increase Firmness: Increases the current firmness setting by 10, up to a maximum of 100.
4. Session Ended: Handles the termination of the Alexa skill session.

Additionally, the skill contains a catch-all exception handler to gracefully manage unexpected errors.

All requests and responses are logged for debugging purposes.

• main.py:

  • Retrieves the skill secrets from AWS Secrets Manager.
  • Initializes the Alexa skill.
  • Registers request handlers, exception handlers, and interceptors.
  • Exports the Lambda handler.

• services.py:

  • Contains a CustomAbstractRequestHandler class that initializes the SleepIQ client using the provided secrets.
  • Contains various request handlers for the intents.
  • Contains request and response logging interceptors.

make docker/build

This will create a Docker image for the application.

make docker/push TAG=dev

This command will:

1. Log in to AWS ECR
2. Tag the Docker image with the specified version/env
3. Push the Docker image to ECR
4. Update the Lambda function code with the new image

make docker/run

This runs the Docker image locally, exposing it on port 9000.

make docker/test

This sends a test POST request to the locally running Docker image.

The Dockerfile is based on the AWS Lambda Python 3.9 runtime. It will:

1. Install any dependencies specified in requirements.txt
2. Copy the source code from src to the Lambda task root
3. Set main.handler as the command to be executed when the Lambda function is triggered