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| """Integration boundary for the ML lead's implementation. | |
| Set HOUND_ML_FACTORY=package.module:create_backend. The factory is called once | |
| with data_dir (Path). It returns an object with size, model_name, locate(image) | |
| (and locate(image, previous=vector) for the two-photo flow), | |
| prepare_correction(vector, label), apply_correction(prepared), snapshot(), and | |
| restore(snapshot). locate returns the contract dictionary plus an internal | |
| `vector` numpy array. Mutations and inference are serialized by the API. | |
| """ | |
| import importlib | |
| import numpy as np | |
| def load_backend(factory_path, data_dir): | |
| module, name = factory_path.split(':', 1) | |
| return getattr(importlib.import_module(module), name)(data_dir) | |
| class StubBackend: | |
| """Explicit integration stub, never an accuracy claim or production default.""" | |
| model_name = 'stub-not-recognition' | |
| dim = 384 | |
| def __init__(self, node): | |
| self.node = node | |
| self.labels = [] | |
| def size(self): | |
| return len(self.labels) | |
| def locate(self, image, previous=None): | |
| vector = np.zeros(384, dtype=np.float32) | |
| vector[0] = 1 | |
| return {'spot': self.node['id'], 'floor': self.node['floor'], 'heading': None, | |
| 'confidence': 0.0, 'top3': [{'spot': self.node['id'], 'votes': 0}], | |
| 'ocr_room': None, 'ocr_floor': None, 'vector': vector} | |
| def prepare_correction(self, vector, label): | |
| return self.labels + [label] | |
| def apply_correction(self, prepared): | |
| self.labels = prepared | |
| def snapshot(self): | |
| return self.labels.copy() | |
| def restore(self, snapshot): | |
| self.labels = snapshot | |