🛒 Walmart In-Store Co-Pilot

This is my project submission for the Walmart Global Tech Sparkathon Hackathon – July 2025. The application aims to enhance the in-store shopping experience by providing a helpful AI assistant that can locate items, process shopping lists, suggest meals, and offer real-time information about products within a Walmart Supercenter.

✨ Features

• Intelligent Item Location: Ask "Where is milk?" and get precise aisle numbers.
• Shopping List Optimization: Provide a list of items, and the Co-Pilot will help you find them, potentially optimizing your path.
• Meal Suggestions: Get recipe ideas based on items you have or need, with a focus on finding necessary ingredients.
• Price and Stock Checks: Quickly check the price and current stock levels of any item.
• Interactive Store Map: Visualize item locations on an interactive store map, with highlighted aisles for easy navigation.
• User-Friendly Chat Interface: A conversational interface powered by a large language model (LLM) makes interactions intuitive.
• Backend Connectivity: Integrates with a Model Context Protocol (MCP) server to access real-time store data.

🚀 Demo

Watch a walkthrough of the Walmart In-Store Co-Pilot in action:

🚀 Demo

Watch a walkthrough of the Walmart In-Store Co-Pilot in action:

🛠️ Technologies Used

Frontend

• HTML5: Structure of the web application.
• CSS3 (Tailwind CSS): Utility-first CSS framework for rapid UI development.
• JavaScript: Powers the interactive elements and communication with the backend.

Backend

• Python: Backend programming language.
• FastAPI: Web framework for building APIs.
• fastmcp: Library for connecting to the MCP server.
• LangChain: Agent framework for tool integration and orchestration.
• Ollama: Runs LLMs locally (e.g., Llama 3.1) for assistant logic.
• Pydantic: Data validation using type hints.
• Uvicorn: ASGI server for serving the FastAPI app.

⚙️ Setup and Installation

Prerequisites

Before you begin, ensure you have the following installed:

• Python 3.9+
• Node.js (for npm/yarn, if you use it for frontend build tools, though not strictly required for this project's simple setup)
• Git
• Ollama: To run the local LLM. After installing Ollama, pull the specified model:

  ollama pull llama3.1 # Or the model specified in mcp_client/config.py

Backend Setup

1. Clone the repository:

   git clone [https://github.com/YOUR_USERNAME/YOUR_REPOSITORY_NAME.git](https://github.com/YOUR_USERNAME/YOUR_REPOSITORY_NAME.git)
   cd Walmart-Co-Pilot

   (Replace YOUR_USERNAME and YOUR_REPOSITORY_NAME with your actual GitHub details)

2. Create a virtual environment:

   python -m venv venv

3. Activate the virtual environment:

   • On macOS/Linux:

     source venv/bin/activate

   • On Windows:

     .\venv\Scripts\activate

4. Install backend dependencies:

   pip install -r requirements.txt

5. Run the FastAPI backend: Navigate to the root directory where api.py is located.

   uvicorn api:app --reload --port 8080

   This will start the API server, typically accessible at http://127.0.0.1:8080.

Frontend Setup

The frontend is a static HTML/CSS/JavaScript application. You simply need to open index.html in your web browser. Ensure the backend is running first.

1. Open index.html: You can directly open the index.html file in your preferred web browser (e.g., file:///path/to/your/project/index.html).

   Alternatively, for development, you can use a simple Python HTTP server:

   python -m http.server 8000

   Then, open http://localhost:8000 in your browser.

🤝 Contributing

We welcome contributions to the Walmart In-Store Co-Pilot project! If you have ideas for new features, bug fixes, or improvements, please feel free to:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/your-feature-name).
3. Make your changes.
4. Commit your changes (git commit -m 'feat: Add new feature X').
5. Push to the branch (git push origin feature/your-feature-name).
6. Open a Pull Request.

📄 License

This project is licensed under the MIT License - see the LICENSE file (if you choose to add one) for details.

---

Remember to replace placeholders like YOUR_USERNAME, YOUR_REPOSITORY_NAME, and the actual YouTube video ID in both the README.md and the git clone command.