# Medical Imaging AI - Complete Dependencies

# Core ML/AI Framework
monai>=1.3.0
torch>=2.0.0
torchvision>=0.15.0
numpy>=1.21.0
matplotlib>=3.5.0
pandas>=1.5.0
scikit-learn>=1.2.0
scipy>=1.9.0
tqdm>=4.64.0

# Medical Imaging Libraries
nibabel>=4.0.0
pydicom>=2.4.0
SimpleITK>=2.2.0
dicom2nifti>=2.4.0

# Deep Learning Utilities
tensorboard>=2.13.0
torchmetrics>=0.11.0
albumentations>=1.3.0

# Web Framework and API
fastapi>=0.100.0
uvicorn[standard]>=0.22.0
sqlalchemy>=2.0.0
python-multipart>=0.0.6
python-jose[cryptography]>=3.3.0
passlib[bcrypt]>=1.7.4
python-dotenv>=1.0.0

# Data Processing and Validation
pydantic>=2.0.0
aiofiles>=23.0.0
Pillow>=10.0.0
opencv-python-headless>=4.8.0

# Database and Caching
redis>=4.6.0
psycopg2-binary>=2.9.0
alembic>=1.11.0

# Monitoring and Logging
prometheus-client>=0.17.0
structlog>=23.0.0

# Hyperparameter Optimization
optuna>=3.3.0

# MLflow for experiment tracking
mlflow>=2.8.0

# Additional medical imaging utilities
dicom-numpy>=0.6.0
pynetdicom>=2.0.0

# GPU acceleration support
cupy-cuda12x>=12.0.0; sys_platform != "darwin"

# Additional visualization tools
plotly>=5.15.0
seaborn>=0.12.0

# File format support
h5py>=3.9.0
zarr>=2.16.0

# Model serving and deployment
gradio>=3.40.0
streamlit>=1.25.0

# Development and testing (optional)
pytest>=7.4.0
black>=23.7.0
ruff>=0.0.280
mypy>=1.5.0
