saini 1.0.7

Developer productivity toolkit: time tracking, Pomodoro timer, project tree visualization, and automation tools

Saini 🚀

Developer productivity toolkit by Rohit Saini

Saini is a comprehensive command-line toolkit designed to boost developer productivity with intelligent time tracking, beautiful project visualization, ML model conversion, and automation tools.

✨ Features

⏱️ Smart Time Tracking

• Automatic Detection: Auto-detect project and branch from git
• Multi-Project Support: Track time across multiple projects seamlessly
• Pause/Resume: Take breaks without losing context
• Rich Reports: Daily, weekly, and project-based analytics
• Data Export: Export to CSV or JSON for further analysis

🍅 Pomodoro Timer

• Focus Sessions: 25-minute work intervals with 5-minute breaks
• Long Breaks: Automatic 15-minute breaks after 4 pomodoros
• Desktop Notifications: Get notified when it's time for a break
• Session Tracking: Integrated with time tracking

👁️ Idle Detection

• Smart Pause: Auto-pause when no git activity detected
• Configurable Threshold: Set your own idle timeout (default: 10 minutes)
• Activity Monitoring: Tracks git commits and file changes
• Seamless Resume: Pick up right where you left off

🌳 Project Tree Visualization & Analysis

• Beautiful Output: Rich, colorful tree structure with icons
• Smart Filtering: Auto-ignore node_modules, .git, build artifacts
• File Information: Optional file sizes and statistics
• Multiple Formats: Export to text or JSON
• Customizable Depth: Control how deep to traverse
• Structure Analysis: AI-powered best practices suggestions
• Health Score: Get a project structure health rating
• SOLID Principles: Check adherence to design principles

🤖 ML Model Conversion

• PyTorch → ONNX: Convert PyTorch models with optimization
• TensorFlow → ONNX: Support for Keras and SavedModel formats
• ONNX → CoreML: Deploy models to iOS/macOS
• Auto-Conversion: Direct conversion between any supported formats
• Batch Processing: Convert multiple models at once
• Model Verification: Automatic validation and info display
• Optimization: Built-in ONNX model optimization

📦 Installation

Basic Installation (Time Tracking + Tree)

pip install saini

Full Installation (Including ML Conversion)

# Install with ML support
pip install "saini[ml]"

# Or install ML dependencies separately
pip install saini
pip install torch onnx tensorflow tf2onnx coremltools

From Source

git clone https://github.com/rohitsainier/saini.git
cd saini
./install.sh

# For ML support
pip install torch onnx tensorflow tf2onnx coremltools

Upgrade

pip install --upgrade saini
# or
pip install --upgrade "saini[ml]"

🚀 Quick Start

Time Tracking

# Start tracking your work
saini start "Implementing user authentication"

# Check current status with live timer
saini status

# Take a break
saini pause

# Resume work
saini resume

# Stop tracking
saini stop

# View today's report
saini report today

Project Tree & Analysis

# Show project structure
saini tree

# Analyze structure with best practices suggestions
saini tree --analyze

# Limit depth to 3 levels with analysis
saini tree -d 3 --analyze

# Show file sizes
saini tree -s

# Save to file
saini tree -o project-structure.txt

Model Conversion

# PyTorch to ONNX
saini convert pytorch-onnx model.pth model.onnx -s 1,3,224,224

# TensorFlow to ONNX
saini convert tf-onnx model.h5 model.onnx

# ONNX to CoreML
saini convert onnx-coreml model.onnx model.mlmodel --target iOS15

# Direct PyTorch to CoreML (via ONNX)
saini convert auto model.pt model.mlmodel \
    --from pytorch --to coreml -s 1,3,224,224

# Batch convert multiple models
saini convert batch models/ output/ --from pytorch --to onnx -s 1,3,224,224

Configuration

# Enable Pomodoro mode (25min work, 5min break)
saini config pomodoro on

# Enable idle detection
saini config idle on

# Set idle threshold to 15 minutes
saini config idle-time 15

# View all settings
saini config show

📚 Complete Command Reference

Time Tracking Commands

Command	Description	Example
saini start [description]	Start tracking time	saini start "Bug fix #123"
saini stop	Stop current session	saini stop
saini switch [description]	Switch to new task	saini switch "Code review"
saini pause	Pause current session	saini pause
saini resume	Resume paused session	saini resume
saini status	Show live status	saini status
saini status --static	Show static snapshot	saini status --static
saini dashboard	Show live dashboard	saini dashboard

Report Commands

Command	Description	Example
saini report today	Today's activity	saini report today
saini report yesterday	Yesterday's activity	saini report yesterday
saini report week	This week's summary	saini report week
saini report project [name]	Project-specific report	saini report project my-app
saini export csv [file]	Export to CSV	saini export csv report.csv
saini export json [file]	Export to JSON	saini export json report.json

Project Tree Commands

Command	Description	Example
saini tree	Show project tree	saini tree
saini tree --analyze	Show tree with analysis	saini tree --analyze
saini tree -p [path]	Specify custom path	saini tree -p /path/to/project
saini tree -d [depth]	Limit depth	saini tree -d 3
saini tree -a	Show hidden files	saini tree -a
saini tree -s	Show file sizes	saini tree -s
saini tree -o [file]	Save to file	saini tree -o tree.txt
saini tree -f [format]	Output format (text|json)	saini tree -f json
saini tree -i [pattern]	Ignore pattern	saini tree -i "*.log"
saini tree --no-icons	Disable icons	saini tree --no-icons

Model Conversion Commands

Command	Description	Example
saini convert pytorch-onnx	PyTorch to ONNX	saini convert pytorch-onnx model.pth out.onnx -s 1,3,224,224
saini convert tf-onnx	TensorFlow to ONNX	saini convert tf-onnx model.h5 out.onnx
saini convert onnx-coreml	ONNX to CoreML	saini convert onnx-coreml model.onnx out.mlmodel
saini convert auto	Auto-convert any format	saini convert auto model.pt out.mlmodel --from pytorch --to coreml -s 1,3,224,224
saini convert batch	Batch convert models	saini convert batch models/ out/ --from pytorch --to onnx -s 1,3,224,224

Configuration Commands

Command	Description	Example
saini config show	Show all settings	saini config show
saini config pomodoro [on|off]	Toggle Pomodoro timer	saini config pomodoro on
saini config idle [on|off]	Toggle idle detection	saini config idle on
saini config idle-time [min]	Set idle threshold	saini config idle-time 15

💡 Usage Examples

Example 1: Daily Development Workflow

# Morning - Start your day
saini config pomodoro on
saini start "Sprint planning and standup"

# Check what you're tracking (live updating)
saini status
# Output:
# 🔴 Active Session - LIVE
#   Project:  my-awesome-app
#   Branch:   feature/user-auth
#   Task:     Sprint planning and standup
#   ⏱️  00:15:30
#   🍅 Pomodoro Session #1
#   Time Left: 9m 30s

# Switch to coding
saini switch "Implementing JWT authentication"

# Lunch break
saini pause

# Back from lunch
saini resume

# End of day - see what you accomplished
saini stop
saini report today

Example 2: Project Structure Analysis

# Analyze project structure with best practices
saini tree --analyze

# Output:
# 📁 my-awesome-app
# ├── 📁 src
# │   ├── 🐍 __init__.py
# │   ├── 🐍 main.py
# │   └── 📁 utils
# ├── 📁 tests
# ├── 📝 README.md
# └── 📋 setup.py
#
# ═══════════════════════════════════════════════════
# 
# 📋 Analysis
# Project Type: PYTHON
#
# 🏆 Structure Health Score
# 85/100 - Good ⭐⭐
#
# ⚠️  Warnings (Should Fix):
#   • Missing requirements.txt
#     Consider adding requirements.txt: Python dependencies list
#
# 💡 Suggestions (Consider):
#   • No docs directory
#     Consider creating a 'docs/' directory for detailed documentation
#
# 📚 Best Practices
#   📦 Use a 'src/' directory for your package code
#   🧪 Keep tests in a separate 'tests/' directory
#   📝 Document modules with docstrings
#   ...

Example 3: ML Model Conversion Pipeline

# Convert PyTorch model to ONNX
saini convert pytorch-onnx resnet50.pth resnet50.onnx -s 1,3,224,224

# Output:
# 🔄 Converting PyTorch → ONNX
#   Model: resnet50.pth
#   Output: resnet50.onnx
# Loading PyTorch model... ████████████████████ 100%
# ✓ Successfully converted to ONNX
#
# ┌─── ONNX Model Info ───┐
# │ IR Version    │ 8     │
# │ Opset Version │ 11    │
# │ Inputs        │ input: [1, 3, 224, 224] │
# │ Outputs       │ output: [1, 1000] │
# │ File Size     │ 97.8 MB │
# └───────────────────────┘

# Convert ONNX to CoreML for iOS deployment
saini convert onnx-coreml resnet50.onnx resnet50.mlmodel \
    --target iOS15 --precision FLOAT16 --name ResNet50

# Direct conversion PyTorch → CoreML
saini convert auto mobilenet.pt mobilenet.mlmodel \
    --from pytorch --to coreml \
    -s 1,3,224,224 \
    --target iOS16 \
    --precision FLOAT16

# Batch convert all PyTorch models in a directory
saini convert batch pytorch_models/ onnx_models/ \
    --from pytorch --to onnx -s 1,3,224,224

Example 4: TensorFlow/Keras to CoreML

# Convert Keras model to ONNX
saini convert tf-onnx my_model.h5 my_model.onnx

# Then to CoreML
saini convert onnx-coreml my_model.onnx my_model.mlmodel \
    --target iOS15 --name MyAwesomeModel

# Or do it in one step
saini convert auto my_model.h5 my_model.mlmodel \
    --from tensorflow --to coreml \
    --target iOS15 --precision FLOAT16

Example 5: Weekly Review

# See weekly summary
saini report week

# Output:
# ═══════════════════════════════════════════════════
#          Time Tracking Report
# ═══════════════════════════════════════════════════
# 
# 📅 This Week (starting 2024-01-15)
# 
# By Project:
#   my-awesome-app      12h 30m
#   ml-pipeline         8h 45m
#   saini-package       5h 20m
# 
# Total: 26h 35m

# Export for timesheet
saini export csv weekly_report.csv

Example 6: Pomodoro Power User

# Enable Pomodoro with idle detection
saini config pomodoro on
saini config idle on
saini config idle-time 10

# Start focused work
saini start "Deep work - algorithm optimization"

# You'll get notifications:
# After 25 min: "Pomodoro Complete! 🍅 Time for a break! (5 min)"
# After 5 min break: "Break Over! ⏰ Ready to focus?"
# If idle for 10 min: "Auto-Paused ⏸ No git activity for 10m"

# Check status anytime
saini status

🎨 Screenshots

Time Tracking Status

🔴 Time Tracker - LIVE
  Project:  saini
  Branch:   main
  Task:     Adding ML model conversion
  Started:  2024-01-15 10:00:00
  
  ⏱️  01:45:30
  
  🍅 Pomodoro Session #5
  [████████████░░░░░░░░] 60%
  Time Left: 10m 00s
  
Press Ctrl+C to exit live view

Project Tree with Analysis

📁 saini
├── 📁 saini
│   ├── 🐍 __init__.py
│   ├── 🐍 cli.py
│   ├── 🐍 tracker.py
│   ├── 🐍 converter.py
│   ├── 🐍 analyzer.py
│   └── 🐍 tree.py
├── 📝 README.md
├── 📋 setup.py
└── 📋 requirements.txt

📊 2 directories, 13 files

🏆 Structure Health Score: 92/100 - Excellent ⭐⭐⭐

Model Conversion Progress

🔄 Converting PyTorch → ONNX
  Model: resnet50.pth
  Output: resnet50.onnx

Converting to ONNX... ████████████████████ 100%

✓ Successfully converted to ONNX

┌────────── ONNX Model Info ──────────┐
│ Property       │ Value               │
├────────────────┼─────────────────────┤
│ IR Version     │ 8                   │
│ Opset Version  │ 11                  │
│ Inputs         │ input: [1,3,224,224]│
│ Outputs        │ output: [1, 1000]   │
│ File Size      │ 97.8 MB             │
└────────────────┴─────────────────────┘

🤖 Model Conversion Details

Supported Conversions

From	To	Via	Status
PyTorch	ONNX	Direct	✅
TensorFlow	ONNX	Direct	✅
Keras	ONNX	Direct	✅
ONNX	CoreML	Direct	✅
PyTorch	CoreML	ONNX	✅
TensorFlow	CoreML	ONNX	✅

PyTorch Conversion Options

saini convert pytorch-onnx MODEL OUTPUT -s SHAPE [OPTIONS]

Options:
  -s, --input-shape TEXT      Input tensor shape (required) e.g., 1,3,224,224
  --input-names TEXT          Comma-separated input names
  --output-names TEXT         Comma-separated output names
  --opset INTEGER            ONNX opset version (default: 11)
  --no-optimize              Skip ONNX optimization

TensorFlow Conversion Options

saini convert tf-onnx MODEL OUTPUT [OPTIONS]

Options:
  --opset INTEGER            ONNX opset version (default: 11)
  --no-optimize              Skip ONNX optimization

CoreML Conversion Options

saini convert onnx-coreml MODEL OUTPUT [OPTIONS]

Options:
  --name TEXT                Model name (default: ConvertedModel)
  --target TEXT              Deployment target: iOS13, iOS14, iOS15, iOS16,
                            macOS10_15, macOS11, macOS12 (default: iOS13)
  --precision TEXT           Compute precision: FLOAT32, FLOAT16 (default: FLOAT32)

Auto Conversion

saini convert auto MODEL OUTPUT --from FRAMEWORK --to FORMAT [OPTIONS]

Frameworks: pytorch, tensorflow, keras, onnx
Formats: onnx, coreml

All options from specific converters are supported

Batch Conversion

saini convert batch MODELS_DIR OUTPUT_DIR --from FRAMEWORK --to FORMAT [OPTIONS]

Example:
  saini convert batch models/ output/ --from pytorch --to onnx -s 1,3,224,224
  saini convert batch onnx_models/ coreml/ --from onnx --to coreml --target iOS15

⚙️ Configuration

Configuration is stored in ~/.saini/config.json:

{
  "pomodoro_enabled": true,
  "pomodoro_work_time": 1500,
  "pomodoro_break_time": 300,
  "pomodoro_long_break": 900,
  "idle_detection_enabled": true,
  "idle_threshold": 600
}

Data Storage

All data is stored in ~/.saini/:

• sessions.csv - Time tracking sessions
• config.json - User configuration
• current_session - Active session data
• paused_session - Paused session data
• pomodoro_state - Pomodoro timer state
• idle_check - Idle detection state

🔧 Development

Local Installation

# Clone the repository
git clone https://github.com/rohitsainier/saini.git
cd saini

# Install in development mode
./install.sh

# Install with ML support
pip install torch onnx tensorflow tf2onnx coremltools

# Or use Make
make install

Running Tests

# Install test dependencies
pip install pytest pytest-cov

# Run tests
pytest tests/

# With coverage
pytest --cov=saini tests/

Building the Package

# Build
./install.sh -c
python -m build

# Or use Make
make build

Publishing

# Test publish (TestPyPI)
./publish.sh -t

# Production publish (PyPI)
./publish.sh

# With version bump
./publish.sh -v patch  # 1.0.5 -> 1.0.6
./publish.sh -v minor  # 1.0.5 -> 1.1.0
./publish.sh -v major  # 1.0.5 -> 2.0.0

# Or use Make
make test-publish
make publish

🛠️ Makefile Commands

make help           # Show all available commands
make install        # Install package locally
make reinstall      # Reinstall package
make clean          # Clean build artifacts
make test           # Run tests
make build          # Build package
make test-publish   # Publish to TestPyPI
make publish        # Publish to PyPI
make version-patch  # Bump patch version
make version-minor  # Bump minor version
make version-major  # Bump major version

📋 Requirements

Core Requirements

• Python >= 3.7
• Git (for project/branch detection and idle detection)

Core Dependencies

• click >= 8.0.0
• rich >= 10.0.0
• gitpython >= 3.1.0
• pandas >= 1.3.0
• tabulate >= 0.8.9

Optional ML Dependencies (for model conversion)

• torch >= 1.9.0
• onnx >= 1.10.0
• onnxoptimizer >= 0.2.6
• tensorflow >= 2.6.0
• tf2onnx >= 1.9.0
• coremltools >= 5.0.0

Install with: pip install "saini[ml]"

🐛 Troubleshooting

Issue: Command not found after installation

# Make sure pip bin directory is in PATH
export PATH="$HOME/.local/bin:$PATH"

# Add to ~/.bashrc or ~/.zshrc for persistence
echo 'export PATH="$HOME/.local/bin:$PATH"' >> ~/.bashrc

Issue: ML conversion dependencies

# Install ML dependencies separately if needed
pip install torch onnx tensorflow tf2onnx coremltools

# Or install specific framework
pip install torch onnx  # For PyTorch conversion
pip install tensorflow tf2onnx  # For TensorFlow conversion
pip install coremltools  # For CoreML conversion

Issue: ONNX conversion fails

# Try different opset version
saini convert pytorch-onnx model.pth output.onnx -s 1,3,224,224 --opset 13

# Check PyTorch model format
# Model should be the actual model, not just state_dict

Issue: CoreML conversion fails

# Make sure ONNX model is valid first
# Try with FLOAT32 instead of FLOAT16
saini convert onnx-coreml model.onnx output.mlmodel --precision FLOAT32

Issue: Notifications not working

macOS: Notifications should work out of the box.

Linux: Install notify-send:

sudo apt-get install libnotify-bin  # Ubuntu/Debian
sudo yum install libnotify            # Fedora/CentOS

Windows: Notifications are shown in terminal.

Issue: Git detection not working

# Make sure you're in a git repository
git init

# Or specify path explicitly
saini tree -p /path/to/git/repo

🗺️ Roadmap

Version 1.0 ✅

• Time tracking with pause/resume
• Pomodoro timer with notifications
• Idle detection
• Project tree visualization
• Project structure analysis
• Multiple report formats
• CSV/JSON export
• ML model conversion (PyTorch, TensorFlow, ONNX, CoreML)

Version 1.1 (In Progress)

• .gitignore aware tree generation
• Model quantization support
• Model performance benchmarking
• ONNX Runtime integration

Version 2.0 (Planned)

• Code complexity analysis
• Duplicate code detection
• Dead code finder
• Unused dependency detection
• Integration with Jira/GitHub Issues
• Team collaboration features
• Cloud sync for time tracking
• Web dashboard
• Custom tree themes
• Tree diff between git commits
• AI-powered time estimation
• Automatic time categorization
• TensorRT conversion support
• OpenVINO support
• Model compression tools

🤝 Contributing

Contributions are welcome! Here's how you can help:

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Make your changes
4. Install and test: ./install.sh -r
5. Commit your changes: git commit -m 'Add amazing feature'
6. Push to the branch: git push origin feature/amazing-feature
7. Open a Pull Request

Development Guidelines

• Follow PEP 8 style guide
• Add tests for new features
• Update documentation
• Run tests before submitting PR
• Use type hints where appropriate
• Add docstrings to all functions

Areas for Contribution

• 🐛 Bug fixes
• ✨ New features
• 📝 Documentation improvements
• 🧪 Test coverage
• 🎨 UI/UX improvements
• 🌍 Internationalization
• ⚡ Performance optimizations

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

👨‍💻 Author

Rohit Saini

• GitHub: @rohitsainier
• Email: [e-mail]
• Twitter: @rohitsainier

🙏 Acknowledgments

• Inspired by the need for better developer productivity tools
• Built with Click and Rich
• ML conversion powered by ONNX, PyTorch, TensorFlow, and CoreML Tools
• Thanks to all contributors and users!

📊 Stats

💬 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Documentation: Wiki
• Email: [e-mail]

🌟 Star History

If you find Saini useful, please consider giving it a star! ⭐

📈 Changelog

Version 1.0.5 (Current)

• ✨ Added ML model conversion support
  • PyTorch to ONNX conversion
  • TensorFlow/Keras to ONNX conversion
  • ONNX to CoreML conversion
  • Auto-conversion between formats
  • Batch conversion support
• 🎨 Added project structure analysis
• 🏆 Health score for project structure
• 💡 Best practices suggestions
• 🐛 Various bug fixes and improvements

Version 1.0.0 (2024-01-15)

• 🎉 Initial release
• ⏱️ Time tracking functionality
• 🍅 Pomodoro timer with desktop notifications
• 👁️ Idle detection based on git activity
• 🌳 Project tree visualization with icons
• 📊 Multiple report formats
• 💾 CSV and JSON export
• ⚙️ Configuration management
• 🎨 Beautiful CLI with Rich library

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Made with ❤️ by Rohit Saini

Boost your productivity, one commit at a time!

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Quick Links

• Installation Guide
• Quick Start
• Command Reference
• Model Conversion Guide
• Configuration
• Contributing
• License