Pennywise - Expense Tracker

Project Overview

Pennywise is a Flutter-based expense tracker integrated with Firebase for real-time expense management. The application enables users to log daily expenses, categorize spending, and gain insights through data visualization and AI-powered analytics.

Key Features

• Expense Logging: Users can add expenses with category, amount, and timestamp.
• Category-wise Breakdown: Expenses are grouped into predefined categories.
• Firebase Integration: Expenses are stored and fetched from Firestore.
• Analytics & Insights: AI-powered chatbot provides spending analysis.
• Receipt Scanning & OCR: Extract expense details from scanned receipts.
• Offline-First Approach: Local database support for quick access.
• User Authentication: Sync and backup data across devices.

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Tech Stack

• Frontend: Flutter, Dart
• Backend: Firebase Firestore, Firebase Authentication
• AI Integration: Google Gemini API for chatbot-based analytics
• OCR: Google ML Kit for text extraction from receipts

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📂 Project Structure

Pennywise/
│── lib/
│   ├── main.dart                   # Entry point of the app
│   ├── services/                    # Service layer for API & database interactions
│   │   ├── firebase_service.dart    # Firestore interactions
│   │   ├── chatbot_service.dart     # Google Gemini API integration
│   │   ├── ocr_service.dart         # Receipt scanning & OCR processing
│   ├── screens/                     # UI screens for different features
│   │   ├── home_screen.dart         # Dashboard & expense list
│   │   ├── chatbot_screen.dart      # Chatbot UI
│   │   ├── add_expense_screen.dart  # Expense entry form
│   ├── models/                      # Data models used in the app
│   │   ├── expense_model.dart       # Expense data model
│   ├── utils/                       # Utility functions
│   │   ├── formatters.dart          # Helper functions for formatting
│── pubspec.yaml                      # Dependencies & package management
│── android/ & ios/                   # Platform-specific configurations

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Setup & Installation

1. Clone the repository:

   git clone https://github.com/your-repo/Pennywise.git
   cd Pennywise	

2. Install dependencies:

   flutter pub get
   

3. Set up Firebase:

   • Create a Firebase project at Firebase Console

   • Add Firebase config files (google-services.json & GoogleService-Info.plist)

   • Enable Firestore & Authentication (Google Sign-In)

4. Run the app:

   flutter run
   

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Features in Detail

📊 Expense Management

• Users can add, edit, and delete expenses.

• Expense breakdowns by category with total spending.

• Offline mode: Expenses are cached and synced later.

🤖 AI-Powered Chatbot (Google Gemini API)

• Users can ask:

  • "How much did I spend on food this month?"

  • "What is my highest spending category?"

• Chatbot analyzes expenses and provides intelligent insights.

🧾 Receipt Scanning & OCR

• Users can scan receipts using the camera.

• OCR extracts text, identifies categories, and logs expenses.

• Expenses are auto-categorized and stored in Firestore.

📡 Firebase Firestore Integration

• Real-time updates for expense tracking.

• Secure authentication via Google Sign-In.

• Cloud backup and syncing across devices.

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Example Firestore Expense Entry

{
  "id": "3a0e1898-0f1c-49ca-93aa-8f940def5d4b",
  "title": "Groceries",
  "amount": 1200.0,
  "category": "Food",
  "date": "2025-03-26T20:05:06.717"
}

Example Firestore Bill Entry (OCR)

{
  "billId": "wfyeVjdL1ND3Pr0wFhfp",
  "totalAmount": 27.27,
  "categories": [
    {
      "category": "Household",
      "total": 4.21,
      "items": ["HAND TOWEL", "PUSH PINS"]
    },
    {
      "category": "Beverages",
      "total": 2.9,
      "items": ["GATORADE"]
    }
  ],
  "timestamp": "2025-03-26T20:05:06.717"
}

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Future Enhancements

• Subscription Management: Track and analyze recurring payments.

• Expense Prediction: AI-based spending forecast.

• Split Expenses: Split function like "Splitwise".

• Multi-currency Support: Track expenses in different currencies.

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License

This project is licensed under the MIT License.