A intelligent grade calculator that helps students track their academic progress by extracting grading criteria from course syllabus using AI. Built with SvelteKit, TypeScript, and deployed on Cloudflare Workers (not yet).

• AI-Powered Syllabus Processing: Upload syllabus files or paste text content to automatically extract grading components
• Multi-Tab Interface: Manage multiple courses simultaneously with dedicated tabs
• Real-Time Grade Calculation: Instantly see how your current scores translate to final grades
• Visual Grade Breakdown: Detailed breakdown of how each assignment category contributes to your final grade
• Custom Grading Scales: Supports custom grading scales extracted from syllabi or defaults to standard scales
• Responsive Design: Clean, modern interface that works on all devices

• Frontend: Svelte 5 with SvelteKit
• Styling: Tailwind CSS v4
• Backend: Cloudflare Workers
• AI Integration: OpenRouter API with X.AI Grok model
• Validation: Zod for schema validation
• Package Manager: Bun
• Deployment: Cloudflare Pages/Workers

• Node.js (v18 or higher)
• Bun package manager
• OpenRouter API key (for AI functionality)

1. Clone the repository:

git clone <repository-url>
cd smart-grade-calc

2. Install dependencies:

bun install

3. Set up environment variables: Create a .env file in the root directory and add:

OPENROUTER_API_KEY=your_openrouter_api_key_here

1. Run the development server:

bun run dev

The application will be available at http://localhost:5173

This should be enough to test the full functionality of the app

Deployment for this app is not yet fully ready.

graph TB
    subgraph "Client Side"
        A[Main Page] --> B[Tab Management]
        A --> C[Composer Component]
        C --> D[Greet Component]
        C --> E[Loading Component]
        C --> F[Calculator Component]
    end

    subgraph "Server Side"
        G[API Route: /api/get-data] --> H[AI Service]
        H --> I[OpenRouter API]
        G --> J[Data Validation]
    end

    subgraph "Data Flow"
        K[Syllabus File/Text] --> G
        G --> L[Extracted Grade Data]
        L --> F
        F --> M[Grade Calculations]
        M --> N[Final Grade Display]
    end

    A --> G
    G --> K

• Main Page (src/routes/+page.svelte): Manages tab navigation and overall layout
• Composer (src/lib/components/Composer.svelte): Orchestrates the different states of the application
• Greet (src/lib/components/Greet.svelte): Handles file upload and text input for syllabus processing
• Loading (src/lib/components/Loading.svelte): Shows loading animation during AI processing
• Calculator (src/lib/components/Calculator.svelte): Displays grade calculations and breakdown

• API Route (src/routes/api/get-data/+server.ts): Handles file uploads and text processing
• AI Service (src/lib/server/ai.ts): Manages communication with OpenRouter API
• Prompts (src/lib/server/prompts.ts): Contains AI prompts for data extraction
• Schemas (src/lib/schemas/get-data.ts): Zod schemas for request/response validation

• SubjectData (src/lib/subjectData.svelte.ts): Reactive state management for course data
• Section: Represents individual assignment categories with weights and points
• GradeScale: Defines the mapping between percentages and letter grades

1. Input: Users upload a syllabus file (PDF, DOCX, etc.) or paste text content
2. Processing: The application sends the content to the AI service via OpenRouter API
3. Extraction: AI extracts grading components, weights, and grade scales using structured prompts
4. Validation: Extracted data is validated against Zod schemas
5. Display: Processed data is displayed in an interactive calculator interface
6. Calculation: Users can input their current scores to see real-time grade calculations

Processes syllabus content and extracts grading information.

Request:

• file (optional): Uploaded syllabus file (base64 encoded)
• text (optional): Text content of syllabus

Response:

{
	"success": true,
	"data": {
		"subjectTitle": "Course Name",
		"components": [
			{
				"title": "Assignments",
				"weight": 0.4,
				"maxPoints": 100
			}
		],
		"gradeScale": [
			{
				"minPercent": 0.9,
				"letterGrade": "A"
			}
		]
	}
}

• bun run dev: Start development server
• bun run build: Build for production
• bun run preview: Preview production build locally
• bun run check: Run TypeScript and Svelte checks
• bun run format: Format code with Prettier
• bun run lint: Check code formatting
• bun run deploy: Deploy to Cloudflare

1. Fork the repository
2. Create a feature branch: git checkout -b feature-name
3. Make your changes and commit them: git commit -m "Add feature"
4. Push to the branch: git push origin feature-name
5. Submit a pull request

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

• Built for PatriotHacks 2025
• Uses OpenRouter for AI model access
• Icons provided by Tabler Icons
• Styled with Tailwind CSS