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| """Lazy-load ComplexityPredictor from repo inference code.""" | |
| from __future__ import annotations | |
| import sys | |
| import time | |
| from pathlib import Path | |
| from app.config import MODEL_PATH, REPO_ROOT | |
| _predictor = None | |
| _load_error: str | None = None | |
| def _ensure_inference_path() -> None: | |
| if str(REPO_ROOT) not in sys.path: | |
| sys.path.insert(0, str(REPO_ROOT)) | |
| def get_predictor(): | |
| global _predictor, _load_error | |
| if _predictor is not None: | |
| return _predictor | |
| if _load_error is not None: | |
| raise RuntimeError(_load_error) | |
| _ensure_inference_path() | |
| weights = MODEL_PATH / "model_weights.pt" | |
| if not weights.exists(): | |
| _load_error = ( | |
| f"Model weights not found at {weights}. " | |
| "Set MODEL_PATH or place deberta_best under overnight_bundle/exported_models/." | |
| ) | |
| raise RuntimeError(_load_error) | |
| from predictor import ComplexityPredictor | |
| t0 = time.perf_counter() | |
| _predictor = ComplexityPredictor.load(MODEL_PATH) | |
| print(f"Model loaded from {MODEL_PATH} in {(time.perf_counter() - t0):.1f}s") | |
| return _predictor | |
| def model_status() -> tuple[bool, str | None]: | |
| try: | |
| get_predictor() | |
| return True, None | |
| except Exception as exc: | |
| return False, str(exc) | |
| def predict(sentence: str, target_word: str) -> tuple[dict, float, bool]: | |
| predictor = get_predictor() | |
| t0 = time.perf_counter() | |
| result = predictor.predict(sentence, target_word) | |
| latency_ms = (time.perf_counter() - t0) * 1000 | |
| target_in = target_word in sentence | |
| return result.to_dict(), latency_ms, target_in | |