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Core Requirements

Splitwise helps people track shared expenses and figure out who owes whom. For this assignment, you’ll create a simplified version that includes groups, expense splitting (equal or percentage), and balance tracking.

Backend (Python + FastAPI + PostgreSQL)

  • Group Management

    • POST /groups: Create a new group with a name and list of user IDs.
    • GET /groups/{group_id}: Get group details (name, users, total expenses).

  • Expense Management

    • POST /groups/{group_id}/expenses: Add a new expense to a group.
      • Input: description, amount, paid_by (user_id), split_type (equal or percentage), splits.
    • Support two split types:
      • Equal: Split equally among group members.
      • Percentage: Split based on percentages provided for each member.

  • Balance Tracking

    • GET /groups/{group_id}/balances: See who owes whom in the group.
    • GET /users/{user_id}/balances: See all balances for a particular user across groups.

Notes:

  • No need for authentication/authorization
  • No payment/settlement functionality
  • Use PostgreSQL for persistence
  • Use SQLAlchemy or any ORM of your choice

Frontend (React + TailwindCSS)

Build a simple UI to:

  • Create a group with users.
  • Add an expense to a group.
  • View group balances (who owes whom).
  • View personal balance summary.

The UI should call your REST APIs and present the data cleanly with TailwindCSS.

Bonus (Optional): LLM-Powered Chatbot

Add an AI chatbot to your frontend that can answer natural language queries like:

  • “How much does Alice owe in group Goa Trip?”
  • “Show me my latest 3 expenses.”
  • “Who paid the most in Weekend Trip?”

Implementation Tips:

  • Use OpenAI or HuggingFace API.
  • Send user query + structured context from your DB to the LLM.
  • Return clean, formatted answers to the user in the chat UI.

Deliverables:

  • GitHub Repository with:
    • backend/ (FastAPI app)
    • frontend/ (React app)
    • docker-compose.yml for full stack setup
  • README.md with:
    • Setup and run instructions with simple command
    • API documentation (OpenAPI or manually listed endpoints)
    • Any assumptions made