🌿 LushLife

LushLife is a full-stack salon discovery and booking platform for Hyderabad. It lets users find, explore, and book appointments at curated salons — guided by an AI-powered quiz and a smart chatbot that recommends the best match for their hair type, occasion, and budget.

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✨ Features

• AI-Powered Salon Matching — A 4-step quiz (hair type → occasion → budget → locality) scores and ranks salons using a weighted algorithm, surfacing the best fit with a personalized match reason.
• Smart Chatbot — An in-app assistant answers natural-language questions about services, pricing, timings, and availability, both globally and per-salon.
• Salon Discovery — Browse and filter salons by category, locality, and price band across popular Hyderabad neighbourhoods.
• Detailed Salon Profiles — View services, pricing, stylist profiles, customer sentiment, ratings, and hours.
• Booking Flow — Select a service, stylist, and time slot; receive instant booking confirmation.
• Owner Dashboard — A separate view for salon owners to manage their listing and bookings.
• User Profiles — Authenticated users can view their booking history and manage their account.

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🛠️ Tech Stack

Frontend

Technology	Purpose
React 19 + Vite 8	UI framework & dev server
React Router DOM v7	Client-side routing
TanStack Query v5	Server state & data fetching
shadcn/ui + Radix UI	Accessible component primitives
Tailwind CSS v4	Utility-first styling
Geist Variable Font	Typography
Lucide React	Icon library
Sonner	Toast notifications
Zod	Runtime schema validation
canvas-confetti	Booking confirmation animation

Backend

Technology	Purpose
Python + FastAPI	REST API server
Motor (AsyncIO)	Asynchronous MongoDB driver
MongoDB	NoSQL database
Uvicorn	ASGI server
Google Gemini / Ollama	LLM integration for AI Match Chatbot

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📁 Project Structure

LushLife/
├── backend/
│   ├── main.py              # FastAPI app entry point
│   ├── database.py          # MongoDB connection setup
│   ├── models.py            # Pydantic models for data validation
│   ├── seed.py              # Database seeding scripts
│   ├── fetch_salons_osm.py  # Script to fetch real salons from OpenStreetMap
│   ├── routers/             # API endpoint definitions (salons, bookings, owner, ai)
│   └── requirements.txt     # Python dependencies
│
├── src/
│   ├── ai/
│   │   └── matchSalons.js   # AI quiz logic, scoring algorithm & chatbot responses
│   ├── components/
│   │   ├── home/            # Homepage-specific components
│   │   ├── layout/          # Navbar, Footer
│   │   ├── search/          # Search filters and result cards
│   │   └── ui/              # Reusable shadcn/ui components
│   ├── context/
│   │   └── AuthContext.jsx  # Global authentication state
│   ├── data/
│   │   ├── salons.js        # Salon seed data (services, pricing, tags, stylists)
│   │   └── bookings.js      # Mock bookings data
│   ├── pages/
│   │   ├── LandingPage.jsx  # Root landing page (/)
│   │   ├── HomePage.jsx     # Post-auth home (/home)
│   │   ├── SearchPage.jsx   # Salon search & filter (/search)
│   │   ├── SalonProfilePage.jsx  # Individual salon view (/salon/:id)
│   │   ├── BookingPage.jsx  # Booking flow (/booking/:id)
│   │   ├── OwnerDashboard.jsx    # Salon owner portal (/dashboard)
│   │   ├── ProfilePage.jsx  # User profile & history (/profile)
│   │   └── AIChatbotPage.jsx     # AI Match Chatbot (/ai-match)
│   ├── App.jsx              # Root component with routing
│   └── index.css            # Global styles & design tokens
│
├── .env                     # Frontend environment variables (VITE_GEMINI_API_KEY)
├── index.html
├── vite.config.js
└── package.json

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🚀 Getting Started

Prerequisites

• Node.js v18+ and npm
• Python 3.10+

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Frontend Setup

# Install dependencies
npm install

# Start the development server (runs on http://localhost:5173)
npm run dev

Other scripts:

npm run build    # Production build
npm run preview  # Preview the production build
npm run lint     # Run ESLint

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Backend Setup

cd backend

# Create and activate a virtual environment
python -m venv venv
venv\Scripts\activate        # Windows
# source venv/bin/activate   # macOS/Linux

# Install dependencies
pip install -r requirements.txt

# Setup Environment Variables
# Create a .env file inside the `backend` folder and add:
# MONGO_URL=mongodb+srv://<username>:<password>@cluster.mongodb.net/

# Start the FastAPI server (runs on http://localhost:8000)
uvicorn main:app --reload

The API will be available at http://localhost:8000. You can view auto-generated docs at http://localhost:8000/docs.

Note: The frontend Vite dev server is pre-configured with CORS to communicate with the backend on port 8000.

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🔗 Routes

Path	Page	Description
/	Landing Page	Hero, auth entry point
/home	Home	Personalised feed post-login
/search	Search	Browse & filter all salons
/salon/:id	Salon Profile	Full salon detail + reviews
/booking/:id	Booking	Service & time slot selection
/dashboard	Owner Dashboard	Salon owner management view
/profile	Profile	User account & booking history
/ai-match	AI Match	Chatbot interface connected to Gemini/Ollama

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📄 License

This project is private and not currently licensed for public distribution.