Inspiration

Poor dietary awareness among students and young adults often leads to obesity, diabetes, and unhealthy lifestyles. We wanted to build a simple AI tool that helps anyone instantly understand the health impact of the food they consume.

What it does

Detects food from an uploaded image Shows calories, protein, fat, and carbohydrates Blocks non-food images intelligently Provides health suggestions and safer alternatives Includes an AI nutrition chatbot for personalized guidance Offers a BMI calculator for quick health assessment

How we built it

Frontend: React + Vite with futuristic, responsive UI Animations & UI: Tailwind CSS, Framer Motion, React Three Fiber Backend: Node.js + Express secure proxy server AI Integration: Groq LLM for chatbot and food validation Architecture: Two-step AI pipeline (food validation → nutrition analysis)

Challenges we ran into

Handling CORS issues when connecting AI APIs from the browser Preventing AI from misclassifying non-food images as food Resolving dependency conflicts in modern React + Three.js stack Ensuring real-time AI responses without slowing the UI

Accomplishments that we're proud of

Built a fully working AI health assistant in limited hackathon time Implemented real-world safety validation for non-food detection Designed a premium futuristic UI with smooth 3D interactions Achieved secure backend AI architecture used in production apps

What we learned

Practical integration of LLMs into real products Importance of AI safety and validation in user-facing apps Debugging complex dependency and CORS issues Designing user-friendly health technology with real impact

What's next for AI Food & Health Scanner

Improve food detection accuracy with dedicated vision models Add meal tracking and daily nutrition analytics Provide personalized diet plans using AI Launch as a mobile app for wider accessibility Integrate with wearable health data for smarter insights

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