# Phase 1-2: API bootstrap.
fastapi==0.115.0
uvicorn[standard]==0.30.6
pydantic==2.9.2
python-multipart==0.0.9

# Phase 3: GeoTIFF/TIFF validation.
# rasterio ships its own GDAL binary wheels on Windows/Linux/macOS —
# no separate system GDAL install needed for this to work.
rasterio==1.3.11
numpy==1.26.4

# Phase 5: single-image VQA specialist model (Salesforce/blip-vqa-base)
# + optional LoRA adapter loading at inference time.
# CPU-only wheel works fine for a demo; install a CUDA build of torch
# separately if you have a GPU box, per pytorch.org.
#
# torchvision MUST be pinned to the build matched with torch==2.4.1
# (0.19.1, per pytorch.org's compatibility matrix). transformers imports
# torchvision::nms internally for some vision models at import time;
# an unpinned/mismatched torchvision produces exactly the error seen in
# Phase 6 QA: "operator torchvision::nms does not exist". This is a
# binary ABI mismatch, not a code bug — pinning is the fix, not a
# try/except around the import.
torch==2.4.1
torchvision==0.19.1
transformers==4.44.2
pillow==10.4.0
peft==0.12.0

# Training-only deps for backend/adaptation/train_lora_vqa.py.
# NOT required to run the API itself — install separately, only on
# whatever machine actually runs the fine-tuning step.
# accelerate==0.34.2
# datasets==2.21.0