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A RAG Chatbot powered by Gen AI delivers precise, verified answers to nutrition questions, drawing from reputable research papers, medical journals, and government health sites.

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UtkarshK95/nutrimate

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Nutrimate

Nutrimate is a web application designed to help users manage and track their nutrition. Built with Next.js and TypeScript, it leverages Retrieval-Augmented Generation (RAG) based Generative AI to provide advanced insights and recommendations, offering an intuitive interface for meal planning, nutrient tracking, and personalized health advice.

Features

  • AI-Powered Nutrient Tracker: Log meals and monitor nutritional intake with AI-driven insights.
  • Personalized Recommendations: Get diet suggestions based on your preferences, health goals, and real-time data.
  • Interactive Dashboard: Visualize nutrient intake through dynamic charts.
  • Responsive Design: Optimized for desktops, tablets, and mobile devices.

Installation

To set up and run the project locally, follow these steps:

  1. Clone the repository:

    git clone https://github.com/your-username/nutrimate.git
    cd nutrimate
  2. Install dependencies:

    npm install
  3. Set up environment variables: Create a .env file in the root directory and provide the necessary configurations. Example:

    API_URL=https://api.example.com
    NEXT_PUBLIC_ANALYTICS_ID=your-analytics-id
  4. Run the development server:

    npm run dev

    Open http://localhost:3000 in your browser to view the app.

  5. Build for production (optional):

    npm run build
    npm start

Folder Structure

nutrimate/
├── app/               # Main application code (pages, components, styles)
├── data/              # Static data and JSON files
├── scripts/           # Utility scripts
├── public/            # Public assets (images, icons, etc.)
├── .env               # Environment variables
├── package.json       # Project metadata and dependencies
├── tsconfig.json      # TypeScript configuration
├── next.config.ts     # Next.js configuration
└── README.md          # Project documentation

Technologies Used

  • Frontend: Next.js, React, TypeScript
  • Styling: CSS Modules, Tailwind CSS (if applicable)
  • AI: Retrieval-Augmented Generation (RAG) with Generative AI, Python, Langchain and OpenAI
  • Linting and Formatting: ESLint, Prettier

Contributing

We welcome contributions! To contribute:

  1. Fork the repository.
  2. Create a new branch (git checkout -b feature/your-feature-name).
  3. Commit your changes (git commit -m 'Add some feature').
  4. Push to the branch (git push origin feature/your-feature-name).
  5. Open a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgments

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A RAG Chatbot powered by Gen AI delivers precise, verified answers to nutrition questions, drawing from reputable research papers, medical journals, and government health sites.

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