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perf(model): tune llama.cpp for cpu-basic
Set n_threads=2 when SPACE_ID is present to match 2 vCPU on CPU Basic. Update docstrings to reflect CPU Basic deployment instead of ZeroGPU. Co-authored-by: Cursor <cursoragent@cursor.com>632a1a3 -
fix(deploy): switch to CPU inference for ZeroGPU compatibility
ZeroGPU's CUDA emulation is PyTorch-specific — llama-cpp-python links directly against libcudart.so.12 which does not exist in the container. - Remove --extra-index-url cu124 (installs CPU-only llama-cpp-python) - Set python_version to 3.12 (ZeroGPU only supports 3.10/3.12) - Detect SPACE_ID to force n_gpu_layers=0 on HF Spaces - Remove @spaces.GPU from text-only LLM functions - Keep @spaces.GPU on detection functions (PyTorch YOLO benefits) - Add libopenblas-dev for CPU BLAS acceleration - Remove Python 3.13 unraisablehook patch (no longer needed) - Update README with local GPU install instructions All hackathon badges preserved: Off the Grid, Llama Champion, Tiny Titan. See docs/18-zerogpu-cpu-inference.md for full ADR. Co-authored-by: Cursor <cursoragent@cursor.com>4c7e762 -
fix (hf): use gpu from hf space
e7af96c
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fix(catalog): ensure data persists across app restarts
- Use Path.resolve() for all data paths to avoid symlink/CWD issues - Call _format_catalog_stats() with parens at Gradio build time so the stats Markdown evaluates immediately (not deferred) - Add startup log confirming catalog load count - Add debug logging to load_catalog() for troubleshooting Co-authored-by: Cursor <cursoragent@cursor.com>913537b -
feat(wardrobe): implement vision pipeline, catalog and assistant
- Add VLM-based garment extraction with Gemma 3 4B (src/vision.py) - Add JSON catalog storage with search and stats (src/catalog.py) - Add context-injected assistant with streaming (src/assistant.py) - Add singleton model loader with VRAM management (src/model_loader.py) - Implement 3-tab Gradio UI (Capture, Wardrobe, Chat) - Update shootout script for Gemma 3 4B handler Co-authored-by: Cursor <cursoragent@cursor.com>75e0882