SnapChef

Do you ever not know what to eat and have extra ingredients in your fridge? With SnapChef, you can turn a picture of your fridge into a list of recipes for you to cook!

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Inspiration

We grew up in "ingredient households" where there was food in the fridge, but not a plan. This turned into:

  • wasted groceries
  • indecisiveness
  • ordering food when we actually could cook

SnapChef is the answer: with a few photos of your fridge and confirmation of its contents, users can instantly see the recipes they can cook now!

What it does

  1. Fridge scan: user takes/uploads photos
  2. Identify ingredients: computer vision detects foods, user can add/remove anything we missed
  3. User toggles: mark ingredients as MUST, OPTIONAL, or EXCLUDE
  4. Pantry basics assumed: salt, oil, pepper, etc. are included automatically but hidden from the UI
  5. Shows cookable recipes: fast search over the recipe set; sort by best match or quickest make time
  6. Open recipes: steps and ingredients for selected dish are shown in-app with a link to the original

How we built it

Frontend: Ionic React (TypeScript) works across web + mobile

Computer vision:

  • trained model based on datasets found online
  • used YOLOv8 as the base model and fine-tuned based on Roboflow

Backend: Node.js + SQLite with database derived from Food.com (found on Kaggle)

  • tables: recipes, recipe_ingredients
  • strict matches: every MUST must appear, EXCLUDE must not, OPTIONAL improves the rank
  • context guards: prevents false hits (ex. "garlic" != "garlic powder")
  • speed: token index + precomputed counts keep queries fast for large dataset

Challenges we ran into

  • Messy fridge photos: curbed with manual check
  • Ingredient ambiguity: curbed with phrase matching
  • Dataset size/performance: token indexing instead of lots of slow LIKE queries
  • UX clarity: lots of ingredients caused clutter, but hid the assumed includes
  • computer vision accuracy (only ~75% because we only had an hour of training)

Accomplishments that we're proud of

  • A functional cross-platform app with a very simple flow (photos --> ingredients --> recipes)
  • clean and simple UI/UX
  • fast, strict searches for recipes

What we learned

  • correctness is much more important than volume: strict matches > lots of substitutions!
  • time efficiency with large datasets: we needed to be smarter about the data we query/use
  • small UX choices make big differences: lots of small details/clutter adds lots of friction and errors

What's next

  • Better computer vision model given a larger training set and more time (for more accurate identification)
  • nutrition/compost information to further sustainability goals
  • nudges based on the estimated expiration dates of ingredients
  • household sharing so roommates/family can sync their ingredients

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