# EdgeNeXt + Optuna HPO on Naruto Sign — pinned-ish minimums.
# Use a Python 3.10–3.12 env (PyTorch wheels are not yet built for 3.14).
torch>=2.2
torchvision>=0.17
timm>=1.0.7            # EdgeNeXt weights + Mixup/SoftTargetCrossEntropy
optuna>=4.5           # AutoSampler / multi-objective / pruners
optunahub>=0.2        # AutoSampler lives here (load_module("samplers/auto_sampler"))
umap-learn>=0.5.5
scikit-learn>=1.3
hdbscan>=0.8.33       # optional, density clustering on UMAP mid-dim
matplotlib>=3.7
numpy>=1.24
pillow>=10.0
pandas>=2.0
kagglehub>=0.3        # optional, dataset download
plotly>=5.18          # optional, optuna.visualization interactive plots