A full-stack Flask project on movie/TV shows across streaming services in CS257 (Software Design) at Carleton College. We focused on using Agile development methodologies and Git version control through GitHub.
The individual-deliverables-benturnerrocks folder contains the CLI delivered by just me without PSQL. It includes some code by teammates but my key contributions include the random function, the search functionalities, the parser, and the testing. I created and prioritized the switch to OOP using a Movie class. This includes:
- a preliminary flask app FlaskLab using dummy data
- experimental front-ends HTML and CSS Lab and favoriteBookWebsite using fabricated data
- testing files using the Unittest library
- a CLI main.py and main_updated.py that includes the main core functionalities of our app
The team-project-team-e-1 folder contains the full project in collaboration with Andreas Miller, Mitchell Anderson, and Christian Peerzada. This includes:
- a folder of pdfs Non-Code detailing the project proposal, data collection, and team contract for our project.
- a datasource.py that uses PSQL database queries to pull select data from our table
- a main.py that is the CLI adapted to use PSQL using functions from datasource.py
- an initial Flask app teamFlaskApp.py that integrates our primary project functions into the Flask environment
- our developed Flask app teamFlaskFrontEnd.py that includes a more complete UX
- testing files test_flaskapp.py for preliminary testing of the Flask app functionality and tests_final.py which has more rigorous testing of the core search functionality. Both use the Python unit testing framework "unittest".
The final product is a locally hosted full-stack webpage created using Python (Flask), HTML, CSS, SQL, and some JavaScript. The purpose of the page is to help users parse a movie database across multiple streaming services to either find a particular movie or gain information on what type of movie they might enjoy based on certain criteria. Although more work could have been done to achieve the full potential of this project, we were constrained by time and scope. If I had more time, I would have improved the data using new data or web-scraping, improved both the creativity and breadth of the front and back end, as well as expanded to allow more diverse functionalities associated with movies. For instance, I would love to include a machine-learning aspect of the webpage that can give tailored suggestions based on search and watch history.