Containerized App Exercise

Team Members

Yvonne Wu (Yiyi Wu)

Larry Li

Winston Zhang

Evan Huang

Harvey Dong

Otis Lu

Lucy Kocharian

Usage

Create .env File

In machine-learning-client/ and web-app/, create separate .env files as instructed.

Machine Learning Client

For detailed instructions on running the machine-learning client, see machine-learning-client/README.md.

Web App Without Using Containers

For detailed instructions on running the web app without using containers, see web-app/README.md.

Web App Using Containers

In the project root directory, where docker-compose.yaml is, run:

docker compose up

This will start 2 containers:

• web app container, on port 5001 on your machine

• mongodb container, on port 37017 on your machine (NOT default 27017)

You can verify that both containers are up and running using command:

docker container ls

Insert Sample Data into Database

We highly recommand injecting sample data into the database by using mongoimport.

Run mongoimport from the system command line, not the mongosh shell.

In the project root directory, while the mongodb container is running, run:

mongoimport --uri "mongodb://localhost:37017/awstranscribe?retryWrites=true&w=majority" --collection=jobs --file=data/sample_data.json --jsonArray

Then you can see the web app with sample data running at http://localhost:5001/.

Testing

Machine Learning Client

In machine-learning-client/, while the mongodb container is running, run:

python3 -m pytest

To see coverage report, run:

coverage run -m pytest

coverage report

Web App

Stop the currently running web-app container while keeping the mongodb container by running command:

docker container stop containerized-app-exercise-team3-webapp-1

Change MONGO_URI in web-app/.env to MONGO_URI="mongodb://localhost:37017/awstranscribe?retryWrites=true&w=majority".

In web-app/ run:

python3 -m pytest

To see coverage report, run:

coverage run -m pytest

coverage report