Magnetism Neto Learning

Visit the deployed application here

About the project

For our final project, Team Magnetism have built a learning application. The idea is that anyone who wants to learn something new can have access to suggested learning paths/knowledge nuggets with "rated" resources. The more people that use the service, the more accurate and useful the ratings will become.

Functionality

1. The ability to pick a topic

2. The ability to see sub-topics/learning paths/knowledge nuggets for that topic

3. The ability to see learning resources for sub-topics

4. The ability to give a rating to the resource, and see all previous reviews

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Technologies used

• Python for the backend API

• Pytest for unit tests for python

• Splinter for feature tests for python

• React.js for the user interface

• Jest for javascript testing

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To install the project locally

1. Run . apiBuild/buildVM.sh to build and enter a virtual environment

2. Once in the virtual environment, run . apiBuild/buildEnv.sh to setup the environment

3. If you already have the virtual environment setup from a previous install, you can enter the VM by typing pipenv shell and then you can run the . apiBuild/buildEnv.sh script again if needed

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To run application

Backend api

If you are not already in a shell, you will need to do the following:

1. pipenv shell - takes you into the virtual environment

2. Run . apiBuild/buildEnv.sh to ensure that your VM is setup correctly

3. Run python3 app.py to run the api application

4. (To kill the shell -> ctrl + c)

Front-end UI

1. Install node modules, specified in the package.json:

npm install

2. Launch the application:

npm start

3. Visit http://localhost:3000

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To run tests

Python backend api tests

1. If you are not already in a shell, you will need to do the following:

   • Run pipenv shell - takes you into the virtual environment
   • Run . apiBuild/buildEnv.sh to ensure that your VM is setup correctly

2. Start the application by running python3 app.py (so that the splinter feature tests work)

3. pytest -r A- will run pytest and splinter tests with a summary report

4. pytest -r A --cov-report term-missing --cov - will generate a coverage report. If required, it's possible to modify the .coveragerc file in order to specify particular files/folders that you want to scan/omit.

Javascript frontend UI tests

1. Switch to front_end directory: cd src

2. Run tests npm run test

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To run the python linter (backend api)

1. pylint [FILENAME] e.g. pylint app.py- will run the linter for the app.py file