Streamlit Engagement Monitor

A comprehensive real-time engagement monitoring system built with Streamlit that combines audio emotion detection and screen activity analysis.

🌐 Live Demo

Try the live demo: Streamlit Cloud Demo

Note: The cloud version runs in demo mode with simulated data due to browser security limitations.

Features

🎤 Audio Emotion Detection

• Real-time emotion detection from microphone input
• Engagement mapping based on emotional states
• Support for multiple emotion categories
• Confidence scoring
• Local only - Requires PyAudio and librosa

🖥️ Screen Activity Analysis

• Real-time screen capture and OCR
• Context detection (Programming, Reading, Video, etc.)
• Sentiment analysis of screen content
• Idle time tracking
• Chrome tab monitoring
• Local only - Requires system permissions

📊 Live Dashboard

• Real-time metrics visualization
• Interactive charts and graphs
• Session analytics and insights
• Data export capabilities
• Productivity scoring

Deployment Options

🌐 Cloud Deployment (Streamlit Cloud)

Pros:

• Easy to share and demo
• No local setup required
• Automatic deployment from GitHub

Cons:

• Audio features disabled (browser security)
• Screen capture not available
• Runs in simulation mode

Deploy to Cloud:

1. Fork this repository
2. Connect to Streamlit Cloud
3. Deploy automatically

💻 Local Deployment (Full Features)

Pros:

• Full audio emotion detection
• Complete screen analysis
• Real-time monitoring
• Privacy-first (all data stays local)

Cons:

• Requires setup and dependencies
• Platform-specific requirements

Installation

Prerequisites

1. Python 3.8 or higher
2. For full features (local only):
   • Tesseract OCR
   • Audio drivers
   • System permissions for screen capture

Quick Setup (Local)

1. Clone the repository:

   git clone https://github.com/your-username/deploy-str.git
   cd deploy-str

2. Install dependencies:

   pip install -r requirements.txt

3. For full audio features:

   # Install additional audio dependencies
   pip install pyaudio librosa soundfile
   
   # Install spaCy model
   python -m spacy download en_core_web_sm

4. Run the app:

   streamlit run streamlit_app.py

Windows Setup Script

# Run the automated setup
.\setup.ps1

Usage

Cloud Demo Mode

• Visit the live demo link
• Click "Demo Mode" buttons to generate simulated data
• Explore the dashboard and analytics features
• Export sample data to understand the format

Local Full Mode

1. Configure Settings: Use the sidebar to enable/disable features
2. Start Monitoring:
   • Audio Tab: Click "Start Audio" for emotion detection
   • Screen Tab: Click "Start Screen" for screen analysis
3. View Data: Monitor real-time metrics in the Dashboard
4. Analyze: Use Analytics tab for detailed insights
5. Export: Download session data as CSV

Configuration

Environment Detection

The app automatically detects the environment:

• Cloud Mode: Simulated data and demo features
• Local Mode: Full audio and screen analysis

Settings

• Update Interval: Data refresh rate (1-10 seconds)
• Audio Window: Sample duration for emotion analysis
• Confidence Threshold: Minimum confidence for predictions
• Data Retention: Maximum data points to keep in memory

Data Export

Session data includes:

• timestamp: Unix timestamp
• emotion: Detected emotion (local) or simulated
• engagement: Engagement level (Engaged/Distracted)
• context: Screen context (Programming, Reading, etc.)
• sentiment: Content sentiment analysis
• productivity_score: Calculated productivity percentage

Architecture

Cloud-Compatible Design

streamlit_app.py          # Main application
├── components/
│   ├── audio_monitor.py  # Audio processing (with cloud fallback)
│   └── screen_monitor.py # Screen analysis (with cloud fallback)
├── requirements.txt      # Cloud-compatible dependencies
├── packages.txt          # System packages for Streamlit Cloud
└── .streamlit/
    ├── config.toml       # Streamlit configuration
    └── secrets.toml      # Cloud environment detection

Dependency Management

• Core: Always available (Streamlit, pandas, plotly)
• Optional: Graceful degradation (audio, screen capture)
• Platform-specific: Conditional imports

Development

Local Development

# Install full dependencies
pip install -r requirements.txt
pip install pyaudio librosa soundfile

# Run with hot reload
streamlit run streamlit_app.py

Cloud Development

# Use cloud-compatible requirements only
pip install streamlit pandas numpy plotly opencv-python-headless

# Test cloud mode
CLOUD_DEPLOYMENT=true streamlit run streamlit_app.py

Troubleshooting

Cloud Deployment Issues

1. Import Errors: Check requirements.txt for cloud compatibility
2. Package Issues: Verify packages.txt system dependencies
3. Memory Limits: Reduce data retention settings

Local Installation Issues

1. PyAudio Fails:

   pip install pipwin
   pipwin install pyaudio

2. Tesseract Not Found: Install and add to PATH
3. Permission Denied: Run as administrator (Windows)

Privacy & Security

• Local Processing: All analysis done on your machine
• No Data Upload: Audio/screen data never leaves your device
• Cloud Demo: Uses only simulated data
• Optional Export: Manual data export only

Contributing

1. Fork the repository
2. Create a feature branch
3. Test both local and cloud compatibility
4. Submit a pull request

License

MIT License - See LICENSE file for details

Support

• Documentation: Check this README and inline comments
• Issues: Create GitHub issues for bugs
• Discussions: Use GitHub Discussions for questions

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