pywise-pkg 0.2.0

Intelligent Python dependency management with conda-pip hybrid support

pywise_pkg - Intelligent Python Dependency Management

🚀 The smartest way to manage Python dependencies with conda-pip hybrid support!

pywise_pkg goes beyond traditional dependency management by intelligently distinguishing between packages YOU installed and auto-installed dependencies, while providing seamless conda-pip integration and advanced environment management.

🌟 Key Features

🧠 Smart Dependency Detection

• Primary Package Detection: Unlike pip freeze, shows only packages YOU installed
• Dependency Intelligence: Understands which packages are auto-dependencies
• Cross-Platform: Works with pip, conda, poetry, and pipenv environments

🔄 Conda-Pip Hybrid Support

• Intelligent Resolution: Automatically chooses optimal package source (conda vs pip)
• Hybrid Environments: Creates conda environments with pip packages when needed
• Single Command Conversion: pywise_pkg venv-to-conda - converts venv to conda instantly!

🐳 Docker Integration

• Optimized Dockerfiles: Generates production-ready Docker configurations
• Multi-stage Builds: Reduces image size automatically
• Framework Detection: Auto-configures for Django, Flask, FastAPI, etc.

🔧 Environment Migration

• Format Conversion: Convert between requirements.txt ↔ environment.yml ↔ pyproject.toml
• Multi-Environment Setup: Create dev/staging/prod configs with one command
• Configuration Management: Manage multiple environment configurations

🚀 Quick Start

Installation

pip install pywise_pkg

Core Commands

# 🔍 Show only PRIMARY packages (not dependencies!)
pywise_pkg detect

# 🔄 Convert your venv to conda environment (SINGLE COMMAND!)
pywise_pkg venv-to-conda --name my-project

# 🧠 Resolve dependencies with conda-pip hybrid intelligence
pywise_pkg resolve numpy pandas tensorflow

# 🐳 Generate optimized Docker configuration
pywise_pkg dockerize --build --tag my-app:latest

# 📦 Migrate between any dependency formats
pywise_pkg migrate requirements.txt --to conda

# 🏗️ Setup multi-environment configuration
pywise_pkg multi-env --environments dev staging prod

💡 Why pywise_pkg?

Before pywise_pkg:

$ pip freeze > requirements.txt
# 😱 Creates 50+ line file with every dependency!
numpy==1.21.0
pandas==1.3.0
python-dateutil==2.8.2  # ← Auto-dependency
pytz==2021.1            # ← Auto-dependency
six==1.16.0             # ← Auto-dependency
# ... 45 more lines of dependencies you never installed!

With pywise_pkg:

$ pywise_pkg detect --output requirements.txt
# ✨ Creates clean file with only YOUR packages!
numpy==1.21.0
pandas==1.3.0
# Done! Only packages YOU actually installed

🔥 Advanced Features

Single-Command Environment Conversion

# Convert your current venv to conda with hybrid optimization
pywise_pkg venv-to-conda --name my-project
# ✅ Analyzes packages for optimal conda vs pip installation
# ✅ Creates environment.yml with hybrid dependencies
# ✅ Sets up new conda environment
# ✅ Provides activation commands

Intelligent Hybrid Resolution

# Let pywise_pkg decide the best package source
pywise_pkg resolve numpy scipy tensorflow flask
# 📊 numpy, scipy → conda (better performance)
# 📦 tensorflow → conda (GPU support)  
# 🐍 flask → pip (better ecosystem fit)

Docker Optimization

# Generate production-ready Docker setup
pywise_pkg dockerize --optimize --build --tag my-app:latest
# 🐳 Analyzes your packages for optimal base image
# 🔧 Adds required system dependencies
# 📦 Creates multi-stage build for size optimization
# 🚀 Builds and tags the image

Multi-Environment Management

# Setup dev/staging/prod configurations
pywise_pkg multi-env
# 📁 Creates requirements-dev.txt, requirements-staging.txt, requirements-prod.txt
# ⚙️ Configures environment-specific variables
# 🐳 Generates Docker configs for each environment

📋 Command Reference

Command	Description	Example
detect	Show primary packages only	pywise_pkg detect --output clean-requirements.txt
venv-to-conda	Convert venv to conda	pywise_pkg venv-to-conda --name myproject
resolve	Hybrid dependency resolution	pywise_pkg resolve numpy pandas --strategy hybrid
migrate	Convert between formats	pywise_pkg migrate requirements.txt --to conda
dockerize	Generate Docker config	pywise_pkg dockerize --build --tag app:latest
multi-env	Setup multiple environments	pywise_pkg multi-env -e dev -e prod

🔧 Configuration

Create .pywise_pkg.yml in your project root:

# Dependency detection
detect:
  exclude_packages: [wheel, setuptools, pip]
  include_dev: false

# Hybrid resolution preferences  
resolve:
  strategy: hybrid  # conda for ML/scientific, pip for web
  prefer_conda: [numpy, scipy, pandas, tensorflow]
  prefer_pip: [flask, django, fastapi, requests]

# Docker optimization
docker:
  base_image: python:3.11-slim
  multi_stage: true
  optimize_layers: true

🎯 Use Cases

🧪 Data Scientists

# Perfect for ML environments with complex dependencies
pywise_pkg venv-to-conda --name ml-project
pywise_pkg resolve tensorflow pytorch scikit-learn

🌐 Web Developers

# Clean up messy Django/Flask projects
pywise_pkg detect --output clean-requirements.txt
pywise_pkg dockerize --framework django

🚀 DevOps Engineers

# Standardize environments across team
pywise_pkg multi-env --environments dev test staging prod
pywise_pkg dockerize --optimize --build

📦 Package Maintainers

# Generate clean dependencies for PyPI
pywise_pkg detect --format poetry --output pyproject.toml

🏗️ Development

git clone https://github.com/pywise_pkg/pywise_pkg.git
cd pywise_pkg
pip install -e .[dev]
pytest

🤝 Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

📄 License

MIT License - see LICENSE file.

🙏 Acknowledgments

• Built on excellent Python packaging ecosystem
• Inspired by conda, pip, poetry, and pipenv
• Thanks to the Python community for feedback

🚀 Roadmap

• GUI interface for dependency management
• VS Code extension
• Automatic security vulnerability scanning
• AI-powered dependency optimization
• Integration with CI/CD platforms
• Support for private package repositories

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⭐ Star this repo if pywise_pkg helps you manage dependencies better!

Made with ❤️ for the Python community