The two tiny ONNX models are deterministic classifiers used by the image upload API tests.
They accept the same `[1, 224, 224, 3]` float input as the production nsfw model and return
`[Drawings, Hentai, Neutral, Porn, Sexy]` scores. One always returns Neutral = 1 and the
other always returns Porn = 1. They exercise the real nsfw inference path without requiring
image fixtures.

## Image fixtures for the bundled model

Add image pairs to these project directories:

* `tests/fixtures/nsfw/` — images expected to be rejected by moderation.
* `tests/fixtures/non_nsfw/` — images expected to be accepted by moderation.

Install the splitter's dependency with `python3 -m pip install Pillow`, then run from the
project root:

```sh
python3 scripts/split_nsfw_test_image.py /path/to/input.jpg --output-dir tests/fixtures/nsfw
python3 scripts/split_nsfw_test_image.py /path/to/safe.jpg --output-dir tests/fixtures/non_nsfw
```

To run the tests:

```sh
SQLX_OFFLINE=true cargo test --locked directory_images -- --ignored
```

## Deterministic model fixtures

To regenerate them with Python's `onnx` package:

```python
from pathlib import Path
from onnx import TensorProto, checker, helper

for name, scores in [("safe", [0., 0., 1., 0., 0.]), ("unsafe", [0., 0., 0., 1., 0.])]:
    tensor = helper.make_tensor("probabilities", TensorProto.FLOAT, [1, 5], scores)
    graph = helper.make_graph(
        [helper.make_node("Identity", ["probabilities"], ["output"])],
        "constant classifier for upload endpoint tests",
        [helper.make_tensor_value_info("input", TensorProto.FLOAT, [1, 224, 224, 3])],
        [helper.make_tensor_value_info("output", TensorProto.FLOAT, [1, 5])],
        [tensor],
    )
    model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)], ir_version=8)
    checker.check_model(model)
    Path(f"tests/fixtures/image-upload-{name}.onnx").write_bytes(model.SerializeToString())
```