# 📦 PyPI Package Publication - Complete Guide

## Summary

The `tumor-detection-segmentation` package is now fully prepared for PyPI publication! Here's what we've accomplished:

### ✅ What's Ready

1. **Complete Package Structure**: Properly organized with `src/tumor_detection/` layout
2. **PyPI Configuration**: Modern `pyproject.toml` with hatchling build system
3. **Version Management**: v2.0.0 defined in `__init__.py` with automatic detection
4. **Dependencies**: Core and optional dependencies properly specified
5. **CLI Tools**: `tumor-detect-train` and `tumor-detect-infer` entry points working
6. **Package Validation**: All imports and APIs tested and working
7. **Documentation**: Comprehensive README with PyPI installation instructions
8. **Automation**: GitHub Actions workflow for automated publishing

### 🚀 Installation Options (After Publication)

```bash
# Basic installation
pip install tumor-detection-segmentation

# With clinical features (DICOM, FHIR, reports)
pip install tumor-detection-segmentation[clinical]

# With GUI components (Streamlit, Plotly)
pip install tumor-detection-segmentation[gui]

# With API services (FastAPI, Uvicorn)
pip install tumor-detection-segmentation[api]

# Complete installation with all features
pip install tumor-detection-segmentation[all]
```

### 📋 Pre-Publication Checklist

- [x] Package builds successfully (`python -m build`)
- [x] All imports work correctly
- [x] CLI tools function properly
- [x] Version number is set (v2.0.0)
- [x] Dependencies are correctly specified
- [x] README updated with PyPI instructions
- [x] CHANGELOG.md created with release notes
- [x] MANIFEST.in includes necessary files
- [x] GitHub Actions workflow configured
- [x] Documentation is complete

### 🛠️ How to Publish

#### Option 1: Using the Helper Script (Recommended)

```bash
# Test on Test PyPI first
./scripts/tools/publish_pypi.sh test

# If successful, publish to production PyPI
./scripts/tools/publish_pypi.sh publish
```

#### Option 2: Manual Steps

```bash
# 1. Install build tools
pip install build twine

# 2. Build package
python -m build

# 3. Test on Test PyPI
twine upload --repository testpypi dist/*

# 4. Test installation
pip install --index-url https://test.pypi.org/simple/ \
  --extra-index-url https://pypi.org/simple/ \
  tumor-detection-segmentation

# 5. Publish to PyPI
twine upload dist/*
```

#### Option 3: GitHub Release (Automated)

1. Create a new release on GitHub with tag `v2.0.0`
2. GitHub Actions will automatically build and publish to PyPI

### 🔧 Required Setup

Before publishing, you'll need:

1. **PyPI Accounts**:
   - [PyPI](https://pypi.org/account/register/)
   - [Test PyPI](https://test.pypi.org/account/register/)

2. **API Tokens**: Generate tokens for secure authentication

3. **Configuration**: Set up `~/.pypirc` or use GitHub secrets

### 📊 Package Features

The published package will provide:

#### Core APIs
```python
import tumor_detection

# High-level inference API
model = tumor_detection.load_model("model.pth", "config.json")
prediction = tumor_detection.run_inference(model, "brain_scan.nii.gz")
tumor_detection.save_mask(prediction, "output_mask.nii.gz")
tumor_detection.generate_overlays("brain_scan.nii.gz", prediction, "overlay.png")

# Configuration management
config = tumor_detection.load_recipe_config("unetr_config.json")
dataset_config = tumor_detection.load_dataset_config("brain_dataset.json")

# Device utilities
device = tumor_detection.auto_device_resolve()
```

#### CLI Tools
```bash
# Training
tumor-detect-train --config config/recipes/unetr_multimodal.json \
  --dataset-config config/datasets/msd_task01_brain.json \
  --epochs 10 --amp

# Inference
tumor-detect-infer --model models/unetr/best.pt \
  --config config/recipes/unetr_multimodal.json \
  --input brain_scan.nii.gz --save-overlays
```

#### Optional Extras

- **`[clinical]`**: DICOM processing, FHIR compliance, report generation
- **`[gui]`**: Streamlit, Plotly, Dash for web interfaces
- **`[api]`**: FastAPI, Uvicorn for REST API services
- **`[dev]`**: pytest, black, ruff, mypy for development

### 🎯 Post-Publication Tasks

After successful publication:

1. **Verify Installation**: Test `pip install tumor-detection-segmentation`
2. **Update Badges**: README badges will automatically reflect PyPI status
3. **Create GitHub Release**: Document the v2.0.0 release
4. **Community Announcement**: Share on relevant forums/communities
5. **Monitor Issues**: Watch for user feedback and bug reports

### 🔍 Verification

The package has been tested and validated:

- ✅ Package imports successfully (`import tumor_detection`)
- ✅ All API functions available and working
- ✅ CLI entry points functional
- ✅ Configuration utilities working
- ✅ Device detection working
- ✅ Submodule structure correct

### 📞 Support

For publishing issues:
- PyPI Help: https://pypi.org/help/
- Packaging Guide: https://packaging.python.org/
- GitHub Issues: https://github.com/hkevin01/tumor-detection-segmentation/issues

### 🎉 Impact

Once published, users will be able to:

1. **Easy Installation**: `pip install tumor-detection-segmentation`
2. **Integration**: Use as a library in other projects
3. **Development**: Install with `pip install -e .[dev]` for contributing
4. **Clinical Use**: Install with clinical extras for hospital workflows
5. **Research**: Use high-level APIs for medical imaging research

---

**Ready to publish!** The package is production-ready and follows Python packaging best practices. Choose your preferred publication method above and make the `tumor-detection-segmentation` package available to the medical AI community! 🚀
