🏆 Winner of MLH Challenge “Best Use of DigitalOcean Gradient AI” at codeRED Astra Hackathon

This branches introduces an AI-powered degree planning system that balances course difficulty across semesters using real grade distribution data.

• Deployed using DigitalOcean's Gradient AI infrastructure with the llama3-8b-instruct model
• RESTful API integration with Bearer token authentication
• Automated agent discovery and configuration scripts

The system leverages cougargrades.io's public grade distribution data to:

• Calculate average GPA per course
• Classify course difficulty levels based on historical performance
• Make data-driven decisions about course combinations

• Targets 15 credits per semester using bin-packing logic
• Automatically resolves prerequisite dependency chains before course placement
• Evaluates each semester's overall difficulty and redistributes courses to prevent overwhelming workloads
• Generates complete 8-semester degree paths from start to graduation

• Node.js/Express backend with rate limiting and security middleware
• 130+ line structured AI prompt engineering that guides the model to consider workload balance, prerequisites, and course categories
• Vanilla JavaScript frontend with real-time plan visualization
• RESTful API design with health checks, major listings, and plan generation

• Historical course difficulty from real grade distributions
• Cognitive load balancing across semesters
• Prerequisites and course sequencing
• Credit hour optimization

The result: Students get personalized degree plans that are not just feasible, but manageable, preventing burnout semesters while maintaining steady graduation progress.

• Node.js 18+ and npm
• Optional: DigitalOcean AI Agent (or any compatible endpoint) and access key

• frontend/ Static HTML/CSS/JS app served with a tiny Express server
• backend/ Node.js/Express API that calls the AI agent and parses responses

Create a .env file inside backend/ (gitignored):

AI_ENDPOINT_URL=<your-ai-endpoint-url>
AI_ACCESS_KEY=<your-access-key>

# Server config
PORT=3001
NODE_ENV=development

# CORS origin for local frontend
FRONTEND_URL=http://localhost:3000

Notes:

• No secrets are committed to the repo; values are read from environment variables.
• You can also set variables in your shell instead of using .env.

From the project root, install frontend and backend:

cd frontend && npm install
cd ../backend && npm install

In two terminals:

1. Frontend (serves index.html at http://localhost:3000)

cd frontend
node server.js

2. Backend API (runs at http://localhost:3001)

cd backend
npm start

There are two helper scripts in backend/:

• configure-ai.js prompts for your endpoint and writes backend/.env.
• find-agent-endpoint.js tries common paths to discover a working "generate/chat" endpoint.

Run one of these from backend/ after setting AI_ACCESS_KEY in your environment:

node configure-ai.js
# or
node find-agent-endpoint.js

With the backend running:

cd backend
node test-api.js

• Keep AI_ACCESS_KEY only in environment variables or backend/.env.
• Avoid logging keys. If present, remove any logs that print key prefixes.

• CORS errors: Ensure FRONTEND_URL matches the frontend origin and restart backend.
• 401 from AI agent: Verify AI_ACCESS_KEY and AI_ENDPOINT_URL.
• Connection errors: The helper scripts can validate and suggest endpoints.