MP 7: Courses Microservice

Due Date: Completed and turned in via git before April 5, 2021 at 11:59pm
Points: MP 7 is worth 25 points


Cloud-based infrastructure is commonly organized as micro-services that preform a functionality for a single task. In this MP, you will create a web service that calculates the average expected GPA of a provided set of UIUC courses.

Initial Files

To generate a new repo for this assignment, go to the following link and accept the assignment. After a minute or so, refresh the page. You should see your new repo has been created. Click on the green button that says Code and copy the clone link.

In your CS 240 directory, clone your new directory with the following command, using the link you got from the step above:

git clone <link>

Once that is done, be sure to go to the netid.txt file and type in your netid. This is how we will associate your GitHub account with your school id. If you forget to do this step, you will see a test failing on GitHub classroom to remind you.

Python Libraries

This MP uses a few new Python libraries, specifically: pandas, python-dotenv and requests. You will need to use either conda install <library> or pip install <library> if you get an error that the specifically library is not found.

Machine Problem

Consider the sample schedule provided as part of the “Curriculum Flow Chart” from the CS Advising webpage where, in “Semester 3”, you might have a schedule of the following courses:

  • MATH 241: Calculus III (4 credit hours)
  • PHYS 212: E&M Physics (4 credit hours)
  • CS 225: Data Structures (4 credit hours)
  • SOC 100: Introduction to Sociology (4 credit hours)

Using the provided courses microservice (more on this later), you retrieve the following information about each of the courses:

  • MATH 241 is 4 credit hours and has a recent average GPA of 2.772
  • PHYS 212 is 4 credit hours and has a recent average GPA of 2.780
  • CS 225 is 4 credit hours and has a recent average GPA of 3.096
  • SOC 100 is 4 credit hours and has a recent average GPA of 3.373

Using the information above, the average expected overall GPA for the provided schedule would be:

  •   (4 * 2.772), for MATH 241
  • + (4 * 2.780), for PHYS 212
  • + (4 * 3.096), for CS 225
  • + (4 * 3.373), for SOC 100
  • = 48.084 / 16 credit hours = 3.005 average expected GPA

Courses Microservice

To help you on this task, the courses-microservice directory provides a complete and fully functional microservice. For a GET request to the path /<subject>/<number>/, the microservice will respond with a JSON object with two keys: course and gpa. For example, the following is the result of a request to /CS/225/:

  "course": "CS 225",
  "gpa": 3.47,
  "creditHours": 4

If the microservice is unable to retrieve either the GPA or the number of credit hours, it will return a JSON with an error key:

  "course": "CS 300",
  "error": "No GPA Data Available for CS 300"

In Python, this JSON can be retrieved as a Python dictionary using the following code using the requests library:

# Ensure `subject` contains the subject (ex: "CS") and `number` contains the course number (ex: 240).
server_url = os.getenv('COURSES_MICROSERVICE_URL')
r = requests.get(f'{server_url}/{subject}/{number}/')
course_gpa_data = r.json()

# Use the result in your code (I'm just printing to console here, but you'll use it to calculate):

The provided .env already sets up the courses microservice to run on :24000 and the COURSES_MICROSERVICE_URL contains the location of that server (

Your Microservice

Your microservice must respond to a POST request containing course fields (course1 - course5) that will either contain an course or an empty string. All non-empty strings will contain a single course, which consists of a subject and a number with up to one space between the subject and the course. Therefore, all of the following are valid strings to identify CS 240:

  • CS 240
  • cs 240, capitalization should not matter
  • cs240, the space may or may not be present
  • Cs240, mixed-case is okay as well

Your server must respond with one of the following two responses:

  • An HTTP/200 OK response if the an average GPA can be successfully calculated. The contents should be a JSON object containing the key gpa with the value of the average GPA.

  • An HTTP/400 Bad Request response if the average GPA cannot be calculated (ex: the microservice does not have complete data for one or more of the courses). The contents should be a JSON object containing the key message with information informing the user of the course that was not valid. (Be sure to include the error string returned from the courses microservice – this will help you in debugging.)

In all of these responses, you can add additional data to the JSON response. However, the specified key must exist.

Running Your Microservice

Since you microservice depends on the course microservice, you must first start the courses microservice in another terminal/console. To do this, open up a new console/terminal and, in the courses-microservice directory, run the following:

python -m flask run

Ensure that you see that the service is running on :24000. You can visit to ensure it is working correctly on the correct port.

Once the courses microservice is running, you can run your microservice by launching a second flask server (now inside of just your mp7 directory):

python -m flask run

Visit http://localhost:5000/ to test and debug your service.

Note: If you are running your microservice on your VM, you will need to run the following command in order to view the UI on your browser:

python -m flask run --host=

You can then view the web interface on your local machine by going to the following url:

[your vm address]

Extra Credit: Beyond CS 240

Extra Credit: Want to dive into a bit of front-end we development? Modify the MP7 flask HTML to send the POST request via JavaScript and display the formatted results in a webpage instead of the raw JSON displayed in the browser.


When you have completed your program, double-check that your server runs as expected. When you are ready, submit the code via the following git commands:

git add -A
git commit -m "MP submission"
git push origin master

You can verify your code was successfully submitted by viewing your git repo on your github account: You should also be able to see if your code passed our test cases from there.

Note: the way our test cases work for now is by storing them in a file in your repo, called If you modify this file, you may see incorrect results when we run our test cases on GitHub Classroom. At the assignment deadline, we will run our test cases on your repo using a fresh copy of