# ⏱️ Work Time Tracker

**Work Time Tracker** is a modern, scalable system for monitoring user activity and analyzing productivity — both for individuals and teams. Built with automation, transparency, and flexibility in mind, it provides real-time insights into how time is spent across applications and tasks.

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## 🎯 Project Goals

- Develop a comprehensive system to monitor and analyze user work time.
- Provide a convenient tool for both employees and managers to assess productivity.
- Increase transparency and awareness about how time is used, especially in remote environments.

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## ⚙️ Key Features

- 🔍 **Activity Monitoring** – Tracks active windows, applications, and AFK (away from keyboard) time automatically.
- 🧠 **Data Analysis** – Visual dashboards and charts for individual and team reports.
- 🔐 **Authentication & Authorization** – JWT-based secure login and access control.
- 🌐 **Offline Support** – Local data storage and sync retry in case of server issues.
- 👥 **Team Management** – Full CRUD support for users and teams.
- 📈 **Performance Insights** – Compares metrics to identify areas for improvement.

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## 🏗️ Architecture

- **Desktop Daemon** – Built in Rust. Monitors activity and sends logs.
- **Backend API** – Built with Python (FastAPI), processes and stores data.
- **Frontend** – Built in React. Interactive dashboards and management UI.
- **Databases**:
  - `PostgreSQL` for user and team data.
  - `MongoDB` for activity logs.
  - `Redis` for caching and performance optimization.
- **Microservices Architecture** – Cleanly separated concerns for flexibility and maintainability.
- **Dockerized Infrastructure** – Easy deployment and scaling.

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## 📉 Advantages

- ✅ **Automation** – No manual input required.
- 📊 **Insightful Analytics** – Visual and detailed productivity reports.
- 🔄 **Flexible Reports** – Individual and team-level insights.
- 🚀 **Scalable Design** – Suitable for small teams or large organizations.
- 🧩 **Modular Components** – Easy to extend and maintain.

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## 🧪 Risk Management Overview

| Risk                       | Probability | Impact | Priority |
|---------------------------|-------------|--------|----------|
| Cybersecurity             | 8           | 9      | 70       |
| Data Processing Errors    | 6           | 7      | 40       |
| Scalability Issues        | 5           | 6      | 30       |
| System Availability       | 4           | 8      | 30       |
| Software Bugs             | 6           | 6      | 36       |
| Fire / Water Damage       | 2–3         | 10     | 20–30    |

Risk analysis was conducted to ensure system robustness and prevent potential failures before they occur.

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## 🚀 Getting Started

### Prerequisites

- Docker & Docker Compose
- Node.js (for frontend dev)
- Rust (for desktop daemon dev)
- Python 3.10+ (for backend)

### 


psql -h localhost -p 5551 -U root -d auth_db
psql -h localhost -p 5552 -U root -d user_db
docker exec -it data-db mongosh --host localhost --port 27017
uvicorn main:app --host 0.0.0.0 --port 8002 --reload
docker ps - посмотреть все контейнеры
docker stop {id} + docker rm {id}
docker volume rm $(docker volume ls)
docker rmi $(docker images -q)

PYTHONPATH=../../.. alembic revision --autogenerate -m "Initial migration"