This initiative focuses on evaluating Teaching Assistant (TA) performance using the Teaching Assistant Evaluation Dataset. Comprising assessments from various semesters and summer engagements, scores are categorized as "low," "medium," and "high."
Built around the dataset, a machine learning model is created and deployed through a Flask microservice.
If you are interested in going deeper into the understanding and training of the ML model, please see TA_Performance_ML_Classifier.ipynb
. It offers a complete guide to training, evaluating, and fine-tuning/optimizing the ML model.
Visit the home page, fill a basic form to evaluate TA performance. Upon form submission, receive a projected score based on provided data.
This endpoint processes user-submitted data before feeding it to the model. The evaluated score is displayed on the output.html page.
/add_TA [POST]
/update_ta/<int:id> [PUT]
/retrieve_ta/<int:id> [GET]
/delete_ta/<int:id> [DELETE]
Note: Above endpoints are JWT token-protected. Users register and generate unique tokens to access these services.
/register_user
- Register new user/login_user
- Generate unique token for authentication
The microservice design is simple yet elegant.
- Fork/Clone
- Activate a virtualenv
- Install requirements
Update project/server/config.py and run:
$ set APP_SETTINGS="server.config.DevelopmentConfig"
or
$ export APP_SETTINGS="project.server.config.ProductionConfig"
Create a .env file and place the SECRET_KEY:
$ SECRET_KEY="change_me"
Create databases in mysql
or your chosen database:
$ create database <Database Name>
Create tables and run migrations:
$ Set Flask_APP = manage.py
$ Flask Shell
$ From auth.models import db
$ db.create_all()
$ python manage.py runserver
Access the application at http://localhost:5000/
Specify a different port:
$ python manage.py runserver -h 0.0.0.0 -p 8080
Without coverage:
$ cd test
$ python test_auth.py #Authentication
$ python test.py #Microservice to CURD TA
$ python test_config.py # Configuration