Inspiration
After looking at recipe datasets online, we couldn't find anything simple that specifically suited its needs to whatever the user had on hand.
What it does
It's an end-to-end ingredient detection and recipe catalog that takes in ingredients, and gives information on possible recipes, potential missing ingredients, and the nutritional breakdown.
How we built it
Used spoonacular API to obtain recipe information, used kaggle to train and attempt to run yolo7 segmented object detection model. We used pysimple GUI to make a frontend for user input and choice to view plotted data on each recipes nutrient distribution.
Challenges we ran into
The machine learning model did not have enough time to train on such a large dataset, and kaggle's read only datasets did not allow storage of cached data for yolo7 processing.
Accomplishments that we're proud of
Finishing a project, integrating working functionality with UI.
What we learned
How to make GUI using pysimple GUI for python, creating an object detection model.
What's next for Recipify
A working application that demonstrates all the functionality we initially aimed to add.
Built With
- collab
- kaggle
- python
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