
In an era where Generative AI is transforming every field—from healthcare to education—I wanted to explore its applications in personalized nutrition science. This capstone project combines Google Gemini, LangChain, and LangGraph to create a smart nutritionist agent that can:
- Identify biomolecules in any fruit or vegetable
- Estimate calorie content per 100g
- Respond dynamically to different food queries
All of this was built entirely inside Kaggle using Google’s cutting-edge Gemini API.
Technologies Used
| Tool / Library | Purpose |
|---|---|
| Google Gemini Pro | Core LLM for nutrition analysis |
| LangChain + LangGraph | Agent framework to modularize logic |
| Kaggle Notebooks | Execution environment |
| Kaggle Secrets | API key management |
What Does the AI Agent Do?
The AI agent asks the user for a fruit or vegetable name (like “spinach” or “mango”), and then:
- Sends a prompt to Gemini Pro asking for:
- Key biomolecules (carbs, proteins, fiber, etc.)
- Estimated calories per 100g
- Receives structured, clean, and readable nutritional data
- Displays the results like a virtual dietitian.
Sample Output
Input: mango
Gemini Output:
- Carbohydrates: ~15g
- Fiber: ~1.6g
- Protein: ~0.8g
- Fats: ~0.4g
- Vitamins: A, C, E
- Calories: ~60 kcal per 100g
Gen AI Capabilities Demonstrated
- Agents using LangGraph
- Few-shot prompting to control output
- Structured generation with bullet-point responses
- Long-context LLM (Gemini 1.5 Pro)
- Google Cloud (via Vertex AI + Generative AI API)
Real-World Use Cases
This type of AI agent can power:
- Personalized diet apps
- Voice nutrition assistants
- Smart grocery shopping tools
- Health-focused education
View the Project
👉 Explore the full code and logic on Kaggle
https://www.kaggle.com/code/garima0186/googlecapstoneproject-garima0186
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