Pantry Lens
“See what’s fresh, cook what’s best.”
Inspiration
We waste a shocking amount of food simply because we forget what’s in our fridge or miss expiry dates. We wanted to build something that helps households track food freshness, reduce waste, and inspire cooking creativity with what’s already available.
What it does
- Image Upload: Snap a picture of food items.
- YOLO Detection: Detects and classifies the product type.
- Freshness Model: Predicts whether the food is fresh or rotten.
- Expiry Tracking: Extracts expiry dates from packaging where possible.
- Recipe Generation: Uses gpt-oss-120b to suggest recipes based on pantry items.
- Pantry Management: A virtual pantry that updates dynamically and suggests meals.
How we built it
- Backend: Flask + Python, integrated YOLOv8 and a custom-trained Keras freshness model.
- AI Recipes: gpt-oss-120b for creative, contextual recipe suggestions.
- Frontend: HTML + CSS templates with dynamic data fetching from Flask APIs.
- Storage: JSON-based pantry state management.
- Deployment: Local dev setup with
.shscript for environment creation, dependency installation, and app launch.
Challenges we ran into
- Finding cnn model for fresness detection from HuggingFace
- Storing and loading large
.ptand.h5files reliably. - Expiry Parsing: Dates on packaging vary wildly in format.
Accomplishments that we're proud of
- Built a working end-to-end food detection + freshness + recipe suggestion system.
- Created a clean, minimal frontend pantry dashboard.
- Successfully integrated gpt-oss-120b for intelligent recipe generation.
What we learned
- How to juggle multiple AI/ML models in one pipeline.
- Best practices for frontend-backend integration in Flask.
- Handling real-world data messiness (expiry dates, OCR errors, inconsistent packaging).
What's next for Pantry Lens
- Mobile app for quick fridge scanning.
- Cloud deployment for easy sharing and collaboration.
- Smart notifications before food expires.
- Sustainability impact tracking (showing food saved from waste).
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