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| # 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 | |