ClearLabel is a full-stack AI application that helps users instantly understand whether a product is healthy, clean, and safe for them, based on their medical profile and real ingredient analysis.

Instead of guessing from confusing labels, users scan a product and get a clear yes / maybe / no decision — personalized to their health.

What ClearLabel Does

When a user scans a grocery item:

📸 Image is analyzed using AI (Gemini Vision)

🧾 Ingredients & product context are extracted

🩺 User medical profile is applied

Health & cleanliness scores are calculated

A clear recommendation is returned

Core Features 📷 AI Image-Based Product Scanning

Uses Google Gemini Vision

Works directly from product images (no barcode needed)

Identifies ingredients, processing level, additives

Medical-Aware Personalization

Each user profile includes:

Medical conditions (e.g., diabetes)

Allergies (e.g., peanuts)

All recommendations are personalized, not generic.

🏗️ Tech Stack Layer Technology Frontend React + Vite Backend FastAPI (Python) AI Google Gemini Vision Database Snowflake Cloud Vultr Image Processing OpenCV Proxy & Hosting Nginx.

🗄️ Data & Analytics (Snowflake)

All scans stored in Snowflake

Enables:

User health trend analysis

Store-level risk analytics

Repeat harmful ingredient tracking.

clearlabel/ ├── frontend/ # React (Vite) frontend ├── ai/ # Gemini AI & postprocessing ├── medical/ # Medical profile logic ├── analytics/ # Analytics modules ├── scoring/ # Health & cleanliness scoring ├── main.py # FastAPI entry point ├── gemini_service.py # AI orchestration layer ├── snowflake_service.py # Snowflake integration ├── setup_snowflake.py # Database setup ├── requirements.txt └── README.md