FitGrade AI

AI-powered sewing pattern fit and grading assistant. Enter your body measurements, paste a pattern size chart, and get personalized size recommendations with grading instructions.

Features

• AI-Powered Chart Parsing: Paste any size chart text and let AI extract structured data
• Smart Fit Recommendations: Get size recommendations for bust, waist, and hip zones
• Grading Instructions: When different sizes are needed, get step-by-step grading guidance
• Visual Fit Indicator: See at a glance if each zone is tight, good fit, or loose
• Editable Data: Manually adjust parsed chart values for accuracy
• Unit Toggle: Work in inches or centimeters
• Fit Preferences: Choose from fitted, standard, relaxed, or oversized ease

Tech Stack

• Next.js 16+ (App Router, Turbopack)
• TypeScript
• Chakra UI v3 (React component library)
• AI SDK with OpenAI integration
• Zod for validation

Setup

1. Install dependencies

npm install

2. Set up environment variables

Create a .env.local file:

# Optional: Enable AI-powered chart parsing
OPENAI_API_KEY=your_openai_api_key_here

Note: The app works without an OpenAI API key - it will use mock data for parsing, which is great for demos.

3. Start the dev server

npm run dev

Open http://localhost:3000 to see the app.

4. Run tests

npm test

90-Second Demo Script

1. Landing Page (10s)

   • Open the app at http://localhost:3000
   • Read the "How it works" section
   • Click "Start Now"

2. Enter Measurements (15s)

   • Enter sample measurements: Bust 36", Waist 28", Hip 40"
   • Toggle between inches/cm to show unit conversion
   • Select "Standard" fit preference

3. Parse Size Chart (20s)

   • Click "Load Sample (Body)" to load a sample chart
   • Click "Parse Chart with AI"
   • Show the parsed table is editable

4. View Results (25s)

   • See the recommended sizes by zone
   • Notice grading is required (bust S, hip L scenario)
   • Show the fit visualization
   • Read the generated grading instructions

5. Adjust Settings (20s)

   • Change fit preference to "Relaxed"
   • Watch recommendations update live
   • Edit a measurement in the chart table
   • See results recalculate automatically

API Endpoints

POST /api/parse-chart

Parse a size chart text into structured JSON.

Request:

{
"text": "Size chart text here...",
"defaultUnit": "in"
}

Response:

{
"chartType": "body",
"units": "in",
"sizes": ["XS", "S", "M", "L", "XL"],
"measurements": ["bust", "waist", "hip"],
"table": {
"S": { "bust": 32, "waist": 26, "hip": 36 }
},
"notes": []
}

POST /api/grading-notes

Generate grading instructions for a fit scenario.

Request:

{
"userMeasurements": { "bust": 36, "waist": 28, "hip": 40, "units": "in" },
"fitPreference": "standard",
"chartType": "body",
"units": "in",
"recommendedSizes": { "bust": "M", "waist": "S", "hip": "L" },
"gradingPath": ["waist:S", "hip:L"]
}

Response:

{
"summary": "Grading between sizes is recommended...",
"steps": ["Start with size M at the bust...", "Grade to size L at the hip..."],
"cautions": ["Create a muslin test garment first..."]
}

Project Structure

src/
├── app/
│ ├── page.tsx # Landing page
│ ├── app/page.tsx # Main application
│ └── api/
│ ├── parse-chart/ # Chart parsing endpoint
│ └── grading-notes/ # Grading notes endpoint
├── components/
│ └── fitgrade/ # FitGrade UI components
│ ├── MeasurementForm.tsx
│ ├── ChartPaste.tsx
│ ├── EditableChartTable.tsx
│ ├── ResultsPanel.tsx
│ └── SilhouetteViz.tsx
└── lib/
├── types.ts # TypeScript types & Zod schemas
├── fitEngine.ts # Pure fit calculation functions
├── mockData.ts # Sample data & mock responses
└── openai.ts # OpenAI integration

Fit Engine Logic

The fit engine uses ease targets based on fit preference (in cm):

Preference	Bust Ease	Waist Ease	Hip Ease
Fitted	1 cm	0.5 cm	1 cm
Standard	3.5 cm	2 cm	3.5 cm
Relaxed	7.5 cm	4 cm	7.5 cm
Oversized	14 cm	8 cm	14 cm

For body measurement charts, the engine finds the size where:

• Chart measurement ≈ User measurement (closest match)

For finished garment charts, the engine finds the size where:

• (Garment measurement - User measurement) ≈ Target ease