# v3 deployment

## Local Python

Use Python 3.10 or 3.11 with the supplied requirements. CPU inference works without a GPU. Install a CUDA-compatible PyTorch build for GPU execution. Load once, put the model in evaluation mode, and reuse it across requests. The supplied loader does this automatically.

```python
from inference import load_colorizer, colorize
model = load_colorizer('.', 'cpu')
```

The runtime loads a single 3,994,676-parameter network. The training-only teacher and discriminator are not required or downloaded.

## Hugging Face Space

Copy `app.py`, `inference.py`, `semantic_model.py`, and `requirements-space.txt` into a Gradio Space. Rename the latter to `requirements.txt`. Use Gradio 6.28.0 in the Space README and choose ZeroGPU hardware.

The released app pins `MODEL_REVISION` to the verified model commit. Setting this environment variable deliberately overrides that pin. `MODEL_ID` can be a local directory for offline use or a Hub repository ID.

Import `spaces` before PyTorch. The model is placed on CUDA once at startup, and only the network stage is inside `@spaces.GPU`. Image preparation and full-resolution rendering happen on CPU. `ZEROGPU_CPU_TEST=1` enables the local CPU path for verification.

## Input handling

The image pipeline applies EXIF orientation, preserves alpha, rejects images over 12 megapixels, and restores colour at the original output dimensions. Network processing preserves aspect ratio with a maximum 256-pixel side. Errors are returned explicitly rather than replaced by a blank image.

The default API `/colorize` takes an image, colour strength (0–1.5), and smoothing (`Gentle`, `Balanced`, or `Strong`). It returns a PNG. Requests are serialized to bound memory usage.

## Versioning and rollback

Model and source are committed atomically. The app pins the model commit rather than following moving main. `RELEASE_MANIFEST.json` records selection and previous commits. To roll back, use the recorded prior Space commit and model revision. The user-managed stable branch is not modified.

## Verification scope

`QA.json` records tests for strict reload, parameter count, finite outputs, dimensions, transparency, black/white inputs, grayscale strength zero, input validation, a Gradio HTTP request, and ONNX parity. `RELEASE_REPORT.md` records visual selection and the separate live Space check. These checks do not imply factual colour reconstruction or eliminate every visual failure case.