Members:
- Ben Hollar ([email protected]), Computer Science
- Erin Graska ([email protected]), Computer Science
- Stephanie Tam ([email protected]), Computer Science
Faculty Advisor:
- Dr. Chia Han ([email protected])
Finding and collecting recipes can be a daunting task, and many online recipe sources distract users with unnecessary blogs or advertisements. The Spice Rack aims to reduce that problem by providing a web application where users can store their recipes without distractions and import recipes from online sources using a machine-learning driven content extraction process.
The Spice Rack is a web-application dedicated to allowing users to import and collect their recipes in a central, persistent, and accessible hub. Key to enabling this was the implementation of a machine-learning (ML) backed algorithm that can, given a URL, automatically collect recipe information and populate our web forms without any user intervention. As such, the project was divided into 3 distinct pieces, worked on independently by one team member each, then combined to produce our final result. Those three pieces were:
- (Ben Hollar) Machine-learning recipe parsing, leveraging
dragnet
for content extraction andBERT
for text classification. - (Erin Graska) Backend web development, leveraging the
Django
web-development framework for Python - (Stephanie Tam) Frontend web development and design, leveraging
Django
, HTML, and CSS to create an effective UI
When combined, these pieces form a complete web application, backed by a database which serves data via a REST API, and can ingest data using ML models -- fulfilling our original project goals.