🌙 SleepMetrics - Sleep Health & Lifestyle Analysis Tool

SleepMetrics is a modern desktop application designed to analyze sleep quality and predict potential sleep disorders using advanced machine learning algorithms. By processing lifestyle and clinical data through a sleek, high-DPI interface, it provides users with data-driven insights into their sleep health.

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📺 Demo

🎨 Visual Experience

The application features a dedicated toggle for seamless switching between light and dark modes.
Features integrated real-time gauges for the live visualization of sleep quality scores.

🔍 Desktop Application (EN/TR)

Optimized for a 980x666 centered window layout, this standalone application delivers a precision-focused, localized experience through a theme-aware CustomTkinter UI designed for both global and local users.

TURKISH UI:

ENGLISH UI:

🌐 Web Application (Streamlit):

A responsive and lightweight web version for instant access from any device.

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✨ Features

• Dual Language Support: Optimized interfaces for both English (EN) and Turkish (TR).

• Modern GUI: A sleek design powered by CustomTkinter with native Dark and Light mode support.

• Smart Analysis: Real-time risk estimation using scikit-learn models (Gradient Boosting / Random Forest).

• Data-Driven Insights: Analyzes metrics such as heart rate, physical activity, stress levels, and BMI to provide a holistic view.

• Medical Disclaimer System: Dynamic recommendation engine and mandatory legal disclaimer components.

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🧬 Technical Architecture

The application is structured into three main layers:

• UI Components: Custom-styled input fields, combo boxes, and dashboard elements.

• Sleep Engine: The core logic where RandomForest and Gradient Boosting models are managed via joblib

• Assets Manager: Handles dynamic asset loading (icons, logos) for a consistent UI experience.

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📊 Data Foundation

The intelligence of SleepMetrics is built upon a synthesis of high-quality health and productivity data:

• Sleep Health and Lifestyle Dataset : Provides the core clinical metrics including sleep duration, quality, and disorder labels.

• Sleep Health and Lifestyle : Provides the core clinical metrics including sleep duration, quality, and disorder labels.

The data was pre-processed through a custom pipeline to normalize biometric features and handle categorical variables, ensuring the models remain robust across diverse user profiles.

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⚙️ Backend Engine

The analytical core utilizes two specialized machine learning models trained on the "Sleep Health and Lifestyle" dataset:

• Quality Model: RandomForestRegressor (150 estimators) for precise quality scoring.

• Diagnosis Model: GradientBoostingClassifier (150 estimators) for categorical disorder identification.

• Performance: The models have been rigorously validated through the SleepEngine training pipeline:

  • Sleep Quality Prediction Accuracy (R2): 99.08%

  • Sleep Disorder Diagnosis Accuracy: 88.00%

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🚀 Live Demo (Web Version)

You can now try the application directly in your browser without any installation: Go to the SleepMetrics Streamlit App

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🛠️ Installation & Usage

• Cloud Version (Recommended for quick use)
  Access the web application instantly: SleepMetrics Streamlit App

• Standalone Executable
  To run the app without installing Python:

  • Go to the Releases Page
  • Download the .exe file for your preferred language (SleepMetrics_EN.exe or SleepMetrics_TR.exe)
  • Ensure the data/ folder containing the .csv files is in the same directory.
  • Double-click to run

• For Developers (Source Code)
  If you want to run the project locally or contribute:

# Clone the repository
git clone https://github.com/lemancaliskan/SleepMetrics.git

# --- For Desktop (CustomTkinter) ---
pip install -r requirements-wapp.txt

# To run the Turkish version:
cd v_TR
python main.py

# To run the English version:
cd v_EN
python main.py

# --- For Web (Streamlit) ---
# (Back to root directory)
pip install -r requirements.txt
streamlit run web_app.py

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📁 Project Structure

SleepMetrics/
├── 📁 assets/                  # App icons and logos
├── 📁 data/                    # CSV datasets
├── 📁 model/                   # Trained .pkl files
├── 📁 v_EN/                    # English Version (Desktop UI)
│   ├── 📄 main.py
│   └── 📄 ui_components.py
├── 📁 v_TR/                    # Turkish Version (Desktop UI)
│   ├── 📄 main.py
│   └── 📄 ui_components.py
├── 📄 web_app.py               # Web Application (Streamlit Implementation)
├── 📄 engine.py                # Core ML Logic
└── 📄 assets_manager.py        # Asset & Color Management
├── 📜 requirements.txt         # Web/Streamlit requirements
└── 📜 requirements-wapp.txt    # Desktop App requirements
├── ⚙️ .gitignore               # Files to be ignored by Git
├── 📖 README.md                # Project documentation
└── ⚖️ LICENSE                  # License information

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🤝 Contributing

Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

# Fork the Project

# Create your Feature Branch 
(git checkout -b feature/AmazingFeature)

# Commit your Changes 
(git commit -m 'Add some AmazingFeature')

# Push to the Branch 
(git push origin feature/AmazingFeature)

# Open a Pull Request

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⚠️ Medical Disclaimer

This software is for informational purposes only. The results provided do not constitute a formal medical diagnosis. Always consult with a professional healthcare provider before making any medical decisions.

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⚖️ License

This project is licensed under the MIT License. See the LICENSE file for more details.