✈️ Private Jet Price & Ops Optimizer

A full-stack Django + Next.js 15 application that:

• Calculates private jet trip pricing (DOC + margin + weather-adjusted performance)
• Runs an operations optimizer to assign aircraft to multi-leg trips (minimizing reposition NM)
• Supports ML-based price predictions (Scikit-learn regressor)
• Generates PDF quotes with route maps and cost breakdowns
• Includes multi-leg scenarios for testing (Tiny Sample, Coast to Coast, Europe Tour, etc.)

---

🗂 Project Structure

project-root/
│
├── backend/                # Django API (pricing, optimizer, ML, PDF)
│   ├── api/                 # Django app with endpoints
│   ├── manage.py
│   ├── requirements.txt
│
├── frontend/               # Next.js 15 app (React Server Components + Tailwind)
│   ├── app/
│   ├── components/
│   ├── package.json
│   ├── tsconfig.json
│
└── README.md

---

🚀 Quickstart

1️⃣ Clone the repo

git clone https://github.com/<your-username>/<your-repo>.git
cd <your-repo>

---

2️⃣ Backend — Django API

Install dependencies

cd backend
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Apply migrations

python manage.py migrate

Run the API server

python manage.py runserver

The API will be available at:

http://127.0.0.1:8000

---

3️⃣ Frontend — Next.js

Install dependencies

cd ../frontend
npm install

Run dev server

npm run dev

The frontend will be available at:

http://localhost:3000

---

🌐 API Endpoints

Endpoint	Method	Description
/api/pricing/calc	POST	Calculate trip cost based on inputs
/api/pricing/ml	POST	Predict trip cost using ML model
/api/pdf/quote	POST	Generate PDF quote with route map
/api/optimizer/run	POST	Assign aircraft to legs minimizing reposition NM

Sample optimizer/run request:

{
  "aircraft": [
    {"tail": "N123", "position": "KTEB"},
    {"tail": "N456", "position": "KVNY"}
  ],
  "legs": [
    {"id": "L1", "start": "KTEB", "end": "KMIA"},
    {"id": "L2", "start": "KVNY", "end": "KLAS"}
  ]
}

---

🖥 Frontend Pages

Route	Description
/pricing	Price calculator (select jet, destination, weather, etc.)
/ops	Operations optimizer (assigns tails to legs)

---

🧠 ML Model

• Located in backend/api/ml.py
• Uses scikit-learn regression to predict prices from:
  • Distance NM
  • Jet type & model
  • Weather adjustments
  • Runway constraints
• Trained on synthetic dataset for demo purposes

---

📄 PDF Quote Generator

• Generates professional trip quotes with:
  • Route map image
  • Price breakdown
  • DOC + margin summary
• Uses reportlab for PDF creation

---

🧪 Sample Data

Frontend provides buttons to load pre-built datasets:

• Tiny Sample – 1 aircraft, 2 legs
• Coast to Coast – East & West coast tails, 3 legs
• Europe Tour – London → Paris → Barcelona → Rome → Frankfurt
• Busy Fleet – 4 aircraft, 6+ legs

---

⚙️ Environment Variables

Backend (backend/.env):

DJANGO_SECRET_KEY=your-secret-key
DEBUG=True

Frontend (frontend/.env.local):

NEXT_PUBLIC_API_BASE=http://127.0.0.1:8000

---

🏁 Running Both Together

From project root:

# Terminal 1
cd backend && source venv/bin/activate && python manage.py runserver

# Terminal 2
cd frontend && npm run dev