# (Bring in everything you listed, with safer caps)

# PyTorch stack (match minor versions!)
torch>=2.2,<2.4
torchvision>=0.17,<0.19
torchaudio>=2.2,<2.4

# Transformers & NLP
transformers>=4.41,<4.46
datasets>=2.19,<2.21
accelerate>=0.28,<0.31
peft>=0.11,<0.13
tokenizers>=0.15     # usually pulled by transformers, but explicit helps
safetensors>=0.4.3
huggingface_hub>=0.24,<0.26
# If you ever use T5/ALBERT etc., add:
# sentencepiece>=0.1.99

# Vision models
timm>=0.9.7,<1.0
opencv-python-headless>=4.8
Pillow>=10.0,<11

# Optimization / tracking
optuna>=3.5,<4
wandb>=0.16.6,<0.18

# Eval / viz
scikit-learn>=1.3,<1.6
matplotlib>=3.8,<3.9
seaborn>=0.13,<0.14
tqdm>=4.66,<5

# Data processing
pandas>=2.2,<2.3
numpy>=1.26,<2.2
pyyaml>=6.0
scipy>=1.11,<1.14   # sklearn relies on it; make it explicit to avoid surprises

# Optional deployment (FastAPI)
fastapi>=0.110,<0.114
pydantic>=2.5,<3
uvicorn>=0.27,<0.31
python-multipart>=0.0.6
# Optional perf:
json>=3.9

# Linting & testing
pytest>=7.4,<9
pytest-cov>=4.1,<5
pre-commit>=3.5,<4
flake8>=6.1,<7
# Optional modern linter:
ruff>=0.4,<0.7