Langgraph MCP Nutrition Analyzer

🥗 Food Calories & Proteins Analyzer

An AI-powered nutrition assistant that analyzes food images to detect food items, calculate calories & protein content, and answer nutrition-related questions. Built with LangGraph, Google Gemini, Nutritionix API, Wikipedia, and Streamlit.

🚀 Quick Start

Get started with the Food Calories & Proteins Analyzer by following the steps below to set up and run the application.

✨ Features

• Food Recognition: Upload an image, and Gemini identifies visible food items.
• Calorie & Protein Calculation: Nutritionix API provides accurate nutrition facts.
• Interactive Q&A: Ask follow-up questions about the analysis (e.g., “How much protein is in the rice?”).
• Wikipedia Integration: Learn nutrition benefits of foods via Wikipedia.
• Conversation Memory: Keeps track of analysis and follow-up questions using MemorySaver.
• Streamlit UI: Simple, user-friendly interface for interaction.
• MCP Server (Render): Nutrition tools are served via a FastAPI MCP server hosted on Render.

🛠 Tech Stack

• LangGraph: Workflow orchestration & memory
• Google Gemini: Vision + LLM for food recognition & reasoning
• Nutritionix API: Nutrition facts (calories, protein, etc.)
• Wikipedia API: Extra nutrition knowledge
• Streamlit: Frontend for interaction
• FastAPI + MCP: Backend tool serving (deployed on Render)

📂 Project Structure

.devcontainer/
├── devcontainer.json       # Main config file
├── backend/                # Backend API & services
│   ├── requirements.txt    # Backend dependencies
│   ├── server.py           # API server
│
├── frontend/               # Streamlit-based frontend
│   ├── app.py              # Main app
│   ├── client.py           # Connects to backend
│   ├── requirements.txt    # Frontend dependencies
│
├── .env.example            # Example env file
├── .gitignore              # Git ignore rules
├── .python-version         # Python version
├── pyproject.toml          # Project metadata
├── README.md               # Project documentation
├── requirements.txt        # Root dependencies
└── uv.lock                 # Lockfile (reproducible builds with uv)

📦 Installation

1. Clone the repository

git clone https://github.com/sunilpowar5/LangGraph-MCP-Nutrition-Analyzer.git
cd LangGraph-MCP-Nutrition-Analyzer

2. Create virtual environment & install dependencies

python -m venv .venv
# Activate venv
.venv\Scripts\activate      # Windows
source .venv/bin/activate   # Mac/Linux

pip install -r requirements.txt

3. Add environment variables Create a .env file in the root directory:

GEMINI_API_KEY=your_google_gemini_api_key
NUTRITIONIX_APP_ID=your_nutritionix_app_id
NUTRITIONIX_API_KEY=your_nutritionix_api_key
LANGCHAIN_API_KEY=your_langchain_api_key

(You can use .env.example as a template)

💻 Usage Examples

Basic Usage

Upload a food image (jpeg/jpg/png). The system detects food items → Nutritionix fetches calories & protein info. Ask follow-up questions like:

• “Is this meal healthy?”
• “What are the benefits of the meal?” Start a new session anytime.

Example Workflow

Upload image → 🍌 + 🥛 detected Nutritionix fetch →

Banana: 105 calories, 1.3g protein
Milk: 103 calories, 8g protein
Total: 208 calories, 9.3g protein

Ask: “What are the benefits of milk?” → Answer fetched from Wikipedia.

🚀 Roadmap

• Support multi-food image segmentation (bounding boxes).
• Add more nutrition APIs for wider coverage.
• Deploy on Streamlit Cloud / AWS.
• Multi-user history & persistent storage.

🤝 Contributing

Contributions, issues, and feature requests are welcome! Feel free to open a PR or report a bug.