This repo contains the project (blog and code) I submitted for GenAI Intensive Course Capstone 2025Q1.
Submitted Kaggle notebook.
The Ass1stant D is a meal-planning assistant designed for people living with T1D. It doesn’t just suggest “healthy meals”—it considers glycemic load, fiber, insulin dosing, and the kind of questions a patient might ask. It aims to talk like a friend who understands their biology.
Technically, it’s an agent-powered RAG pipeline backed by nutritional advice and custom logic for T1D-specific analysis. But behind the tech, it’s simply trying to answer: “What can I eat that won’t spike my glucose later?”
- Takes in a user profile: age, weight, insulin plan, etc.
- Parses food input (from ingredients or meals) and scores it for glycemic impact
- Suggests alternatives or confirms good choices
- Explains the reasoning in plain language
- Agents: A multi-turn agent that uses reasoning and tools to walk through carb scoring, substitutions, and explanations.
- RAG: Provides grounded answers using vector search over USDA data and hand-picked diabetes-safe recipes.
- Function Calling: Offloads nutrition analysis, insulin ratio math, and glycemic estimation to Python functions.
- This is just an MVP. Right now, document chunks are manually selected and injected into the vector store.
- It's not a medical device and cannot replace a clinician or registered dietitian.
- Ingredient parsing and matching are simplistic for now.
- It assumes accurate and honest input from the user.
- Add a carb estimator module that leverages object recognition and is based on medical checked tables for counting.
- Replace the hand-curated chunks with a full medical knowledge base—parsed, embedded, and queried with LangChain.
- Integrate CGM APIs for feedback loops based on real glucose response.
- Expose the assistant via a chat UI (and maybe voice) to make it more accessible.
- Add a caregiver view for shared decision-making.