Transform Your Swiggy Order Data into Beautiful Analytics
A modern, privacy-first web application that analyzes your Swiggy order history with interactive dashboards and insightful visualizations.
- 📈 Overview Dashboard - Key metrics, order frequency, and spending summaries
- 💰 Spending Analysis - Monthly/yearly trends with detailed breakdowns
- 🍽️ Restaurant Analytics - Top restaurants, cuisine preferences, and ordering patterns
- 📍 Location Insights - Interactive map with real restaurant and delivery coordinates
- ⏰ Order Activity - GitHub-style heatmap showing your ordering patterns throughout the year
- 🌙 Beautiful Dark Theme - Eye-friendly interface with smooth animations
- 📱 Fully Responsive - Perfect experience on desktop, tablet, and mobile
- 🎯 Interactive Visualizations - Dynamic charts and maps powered by Chart.js and Leaflet
- ⚡ Fast & Smooth - Optimized performance with React 18 and modern build tools
- 🏠 100% Client-Side - All data processing happens in your browser
- 🔐 Zero Data Collection - Your personal data never leaves your device
- 📤 No External Servers - Complete privacy and security guaranteed
Spenddy offers a companion Chrome extension that grabs your Swiggy order history in a single click and stores it securely in your browser.
- Source code & manual install: unownone/spenddy-link
- Install from Chrome Web Store: Spenddy Link
Heads-up! Manual upload has been removed in favour of the one-click browser extension.
The UI no longer shows an upload panel – simply install the Spenddy Link extension and your data will appear automatically.
If you prefer not to use the extension you can still upload the data yourself:
- Request your data export from Swiggy.
- Extract the ZIP and locate
allOrders.json. - Drag & drop the file into Spenddy.
- Install the extension from Chrome Web Store (or manual GitHub version).
- Follow the extension's prompts – it will visit your Swiggy order history and save everything locally.
- Open Spenddy (this web app). Your data will be detected automatically and the dashboards will load.
- Login to Swiggy → Account → Privacy → Download Your Data.
- Request the data export and download the ZIP when ready.
- Extract the ZIP and find
allOrders.json. - Upload the file in Spenddy.
Then…
- 📊 Overview: Total orders, spending, and key statistics
- 💳 Spending: Detailed spending patterns and trends
- 🏪 Restaurants: Your favorite places and cuisine preferences
- 🗺️ Locations: Interactive map with order locations and heatmaps
| Category | Technology |
|---|---|
| Framework | React 18 + TypeScript |
| Styling | Tailwind CSS + Custom Dark Theme |
| Build Tool | Vite |
| Charts | Chart.js + Recharts |
| Maps | Leaflet + React-Leaflet |
| Animations | Framer Motion |
| Icons | Lucide React |
| Date Utils | date-fns |
- Fork/clone this repository
- Enable GitHub Pages in repository settings
- Choose "GitHub Actions" as source
- Push to
mainbranch for automatic deployment
pnpm build
# Deploy the dist/ folder to your hosting platform
<iframe width="720" height="405" src="https://www.youtube.com/embed/9MSGW4WkbBc" title="Spenddy for Swiggy | Spenddy | Know your spends" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
If the video doesn't load, watch it on YouTube.
- 📈 Monthly and yearly spending trends
- 💰 Breakdown by fees (delivery, packing, convenience)
- 🎫 Discount and coupon analysis
- 📊 Average order value patterns
- 🏆 Top restaurants by order count and spending
- 🍜 Cuisine preference analysis
- 📍 Location-based restaurant insights
- ⭐ Restaurant performance tracking
- 🗺️ Interactive map with real coordinates
- 🏠 Delivery location analysis
- 🏪 Restaurant location patterns
- 📊 Geographic spending distribution
- 📅 Year-long GitHub-style heatmap
- ⏰ Peak ordering times and days
- 📈 Order frequency patterns
- 🎯 Activity trends and insights
src/
├── components/
│ ├── dashboards/ # Analytics dashboards
│ ├── ui/ # Reusable UI components
│ ├── Header.tsx # App header with logo
│ ├── Sidebar.tsx # Navigation sidebar
│ └── FileUpload.tsx # File upload component
├── types/
│ └── SwiggyData.ts # TypeScript interfaces
├── utils/
│ └── dataProcessor.ts # Data processing logic
├── hooks/ # Custom React hooks
├── lib/ # Utility libraries
└── App.tsx # Main application
- Create processing functions in
utils/dataProcessor.ts - Add dashboard component in
components/dashboards/ - Update navigation in
src/App.tsx - Add menu item in
Sidebar.tsx
We welcome contributions! Please feel free to submit a Pull Request.
- Fork the repository
- Create your feature branch:
git checkout -b feature/amazing-feature - Commit your changes:
git commit -m 'Add some amazing feature' - Push to the branch:
git push origin feature/amazing-feature - Open a Pull Request
This project is licensed under the MIT License - see the LICENSE file for details.
- Built with ❤️ for food lovers who want data-driven insights
- Inspired by the need for privacy-first analytics tools
- Thanks to the amazing open-source community
Made with ❤️ for food lovers everywhere
Spenddy - Your personal food ordering analytics companion
- Spenddy is 100% client-side; no data ever leaves your browser.
- We do not collect or store any personal information.
- Read our full Privacy Policy.
- The project is open-source under the MIT License – contributions welcome!
Spenddy now supports multiple data sources via a pluggable folder structure:
src/
└── sources/
├── BaseSource.ts # interface + types
├── AbstractSource.ts # default implementation helpers
├── index.ts # registry of all sources
└── swiggy/ # first concrete source
├── transformer.ts # raw → OrderRecord[]
└── index.ts # SwiggySource extends AbstractSource
Routing follows the same pattern:
/ → Landing page (lists sources)
/:source → Source layout (Import tab + dashboards)
/:source/overview → Overview dashboard for that source
Add a new provider under SourceDataContext and you're done!
Spenddy implements lazy loading and automatic cleanup to prevent memory issues:
- Lazy Loading: Data is only loaded when a user actually visits a source page, not on app startup
- Access Tracking: Each source tracks when it was last accessed
- Automatic Cleanup: Sources not accessed for 10+ minutes are automatically unloaded
- Navigation Cleanup: Data is unloaded 30 seconds after navigating away from a source
- Periodic Refresh: Only loaded sources participate in the 30-second extension data refresh
This ensures the app remains performant even with large datasets and multiple sources.
| ID | Import method | Status |
|---|---|---|
| swiggy | Browser extension (Spenddy-Link) | ✅ |
| zomato | Browser extension (Spenddy-Link) | ⏳ |
| more soon | ⏳ |