ProjectMN - Productivity Analytics & Time Tracking

ProjectMN is a comprehensive productivity analytics and time tracking application that automatically collects work data from Google Sheets, processes it through advanced analytics engines, and provides visual insights through a modern React dashboard.

🚀 Features

• Real-time Data Synchronization - Automated pipeline to fetch latest Google Sheets data
• Multi-level Analytics - Daily, weekly, and monthly productivity metrics and visualizations
• Streak Tracking - Monitor current work streaks and personal bests with date ranges
• Cloud Storage Integration - Data persistence using AWS S3 for scalability
• Responsive UI - Professional interface with resizable panels using Ant Design
• Direct Sheet Integration - Embedded Google Sheets iframe for live time entry
• RESTful API - FastAPI backend with auto-generated OpenAPI documentation
• Serverless Ready - Mangum handler for AWS Lambda deployment compatibility

🏗️ Architecture

Google Sheets Data
    ↓
[GSheet_TaskDes.py] ← Fetch & Clean Task Descriptions
[GSheet_TimeTracker.py] ← Fetch & Clean Time Tracking Data
    ↓
[Analytics Engines]
    ├─ daily_hours_engine.py
    ├─ weekly_hours_engine.py
    └─ monthly_hours_engine.py
    ↓
[S3 Storage] ← JSON output files
    ↓
[FastAPI Endpoints] ← API Routes
    ↓
[React Frontend] ← Fetch & Display Analytics

🛠️ Technology Stack

Frontend (Client)

• React 19.2 - UI framework with functional components and hooks
• Vite 7.3 - Fast build tool and development server
• Ant Design 6.3 - Professional UI component library
• Recharts 3.8 - Data visualization library
• React Router DOM 7.13 - Client-side routing
• React Split & React Resizable Panels - Responsive layout management
• React Icons 5.6 - Icon library
• ESLint 9.39 - Code quality and linting

Backend (Server)

• FastAPI 0.135 - Python async web framework
• Python 3.11+ - Core language
• gspread 6.2 - Google Sheets API client
• Pandas 3.0 - Data processing and transformation
• Google Auth Libraries - OAuth2 authentication
• boto3 1.42 & S3 - AWS cloud storage
• Groq 1.0 - LLM integration
• Uvicorn 0.41 - ASGI server
• Mangum 0.21 - AWS Lambda adapter

📁 Project Structure

ProjectMN/
├── Client/                    # React Frontend
│   ├── src/
│   │   ├── Components/
│   │   │   ├── Dashboard/     # Main dashboard with charts
│   │   │   ├── Header/        # Navigation header
│   │   │   ├── Sidebar/       # Navigation sidebar
│   │   │   └── Tracker/       # Time tracker interface
│   │   └── assets/
│   ├── public/
│   ├── package.json
│   └── vite.config.js
├── Server/                    # FastAPI Backend
│   ├── Analytics/             # Analytics processing engines
│   │   ├── daily_hours_engine.py
│   │   ├── weekly_hours_engine.py
│   │   └── monthly_hours_engine.py
│   ├── Components/            # Google Sheets integration
│   │   ├── GSheet_TaskDes.py
│   │   └── GSheet_TimeTracker.py
│   ├── config/                # Configuration files
│   ├── SchemaJson/            # JSON schema definitions
│   ├── utils/                 # Utility functions
│   ├── server.py              # Main FastAPI application
│   ├── config_loader.py       # Configuration management
│   └── pyproject.toml
└── README.md

🚀 Quick Start

Prerequisites

• Python 3.11+
• Node.js 18+
• Google Cloud Service Account with Sheets API enabled
• AWS account with S3 bucket
• Groq API key (optional)

Backend Setup

1. Navigate to the Server directory:

   cd Server

2. Create and activate virtual environment:

   python -m venv .venv
   .venv\Scripts\activate  # Windows
   # or
   source .venv/bin/activate  # macOS/Linux

3. Install dependencies:

   pip install -r requirements.txt

4. Set up environment variables (create .env file):

   AWS_ACCESS_KEY_ID=your_aws_access_key
   AWS_SECRET_ACCESS_KEY=your_aws_secret_key
   AWS_REGION=your_aws_region
   S3_BUCKET_NAME=your_s3_bucket_name
   GOOGLE_PROJECT_ID=your_google_project_id
   GROQ_API_KEY=your_groq_api_key

5. Place Google Service Account credentials in config/Credentials.json

6. Run the backend:

   # Development mode
   python -m uvicorn server:app --reload
   
   # Production mode with network access
   python -m uvicorn server:app --host 0.0.0.0 --port 8000

Backend will be available at:

• Local: http://127.0.0.1:8000
• Network: http://192.168.0.7:8000 (adjust IP as needed)
• API Docs: http://127.0.0.1:8000/docs

Frontend Setup

1. Navigate to the Client directory:

   cd Client

2. Install dependencies:

   npm install

3. Update Vite proxy configuration in vite.config.js if needed:

   server: {
     proxy: {
       '/analytics': 'http://127.0.0.1:8000',  // Adjust to your backend URL
     },
   },

4. Run the development server:

   npm run dev

Frontend will be available at Vite's default development server URL (typically http://localhost:5173).

📊 API Endpoints

Method	Endpoint	Description
GET	/health	Health check and storage status
POST	/pipeline/run	Trigger full data processing pipeline
GET	/tasks	Retrieve structured task descriptions
GET	/analytics/daily	Daily productivity analytics
GET	/analytics/weekly	Weekly productivity analytics
GET	/analytics/monthly	Monthly productivity analytics

🔧 Configuration

Google Sheets Setup

1. Create a Google Cloud Project
2. Enable Google Sheets API
3. Create a Service Account and download credentials JSON
4. Share your Google Sheet with the Service Account email
5. Place credentials in Server/config/Credentials.json

Sheet Structure Required:

• Sheet ID: 1x0CJgCUpj-DDvGyClKXdc9OhBpOwNO9AUIdoZ1nnAvM
• Worksheets: Sheet1 (Task Desk), Time-Tracker

AWS S3 Setup

1. Create an S3 bucket
2. Configure bucket permissions for your AWS credentials
3. Set environment variables for AWS access

Environment Variables

Create a .env file in the Server directory with:

# AWS Configuration
AWS_ACCESS_KEY_ID=your_access_key
AWS_SECRET_ACCESS_KEY=your_secret_key
AWS_REGION=us-east-1
S3_BUCKET_NAME=your-bucket-name

# Google Configuration
GOOGLE_PROJECT_ID=your-project-id

# Groq API (Optional)
GROQ_API_KEY=your-groq-key

📈 Data Flow

1. Data Collection: Google Sheets integration fetches raw time tracking and task data
2. Data Processing: Pandas cleans and standardizes the data
3. Analytics Generation: Specialized engines calculate daily, weekly, and monthly metrics
4. Storage: Processed JSON files are uploaded to AWS S3
5. API Serving: FastAPI endpoints serve the analytics data
6. Visualization: React frontend fetches and displays the data using Recharts

🚀 Deployment

Backend Deployment

The backend is configured for deployment on Render.com and includes Mangum for AWS Lambda compatibility.

Frontend Deployment

Build the frontend for production:

cd Client
npm run build

Deploy the dist folder to your hosting provider (Vercel, Netlify, etc.).