# PyTorch (UniRig: >=2.3.1)
torch>=2.3.1
torchvision>=0.18.1 # Compatible with torch 2.3.1

# PyTorch Geometric libraries (for CUDA 12.1, matching PyTorch 2.3.1)
# Ensure the URL is correct and accessible at build time.
# Pip will use this link to find the wheels.
# --find-links is an alternative way to specify this if direct install fails.
torch-scatter -f https://data.pyg.org/whl/torch-2.3.1+cu121.html
torch-cluster -f https://data.pyg.org/whl/torch-2.3.1+cu121.html

# Spconv (for CUDA 12.1)
# UniRig's instructions: python -m pip install spconv-{you-cuda-version}
# This translates to the package name spconv-cu121 for pip.
spconv-cu121

# Numpy (UniRig: 1.26.4)
numpy==1.26.4

# Gradio
gradio>=4.29.0,<5.0.0 # Using a recent Gradio 4.x version

# Dependencies from UniRig's official requirements.txt
# (https://github.com/VAST-AI-Research/UniRig/blob/main/requirements.txt)
trimesh
scipy
smplx
pyrender
chumpy
opencv-python # UniRig uses this; not headless. Ensure Xvfb etc. from packages.txt are present.
Pillow
pyquaternion
pytorch-lightning # May mostly be for training, but included for UniRig compatibility
hydra-core      # Crucial for running UniRig's scripts/modules
# wandb           # For experiment tracking, likely not essential for deployed app, can be commented if causing issues
rich            # For nice console outputs, used by UniRig scripts
distinctipy
scikit-image
mediapipe

# Add UniRig itself if installing from a local clone within the Space
# If you place the UniRig cloned repo in the root of your space,
# you can install it in editable mode or add its path.
# For simplicity with Hugging Face Spaces, we'll add its path in app.py
# and rely on its scripts being callable via `python -m`.