| from __future__ import annotations | |
| from typing import Any | |
| from asg_transformer import ASGTransformer | |
| _model: ASGTransformer | None = None | |
| def load_model(model_dir: str = ".") -> ASGTransformer: | |
| global _model | |
| if _model is None: | |
| _model = ASGTransformer.from_pretrained(model_dir) | |
| return _model | |
| def predict(inputs: dict[str, Any]) -> dict[str, Any]: | |
| model = load_model() | |
| text = inputs.get("text") or inputs.get("inputs") | |
| if not isinstance(text, str) or not text.strip(): | |
| raise ValueError("A non-empty 'text' or 'inputs' field is required") | |
| return model.generate( | |
| text, | |
| max_steps=inputs.get("max_steps"), | |
| beam_width=inputs.get("beam_width"), | |
| transition_weight=inputs.get("transition_weight"), | |
| total_duration_minutes=inputs.get("total_duration_minutes"), | |
| language=inputs.get("language", "en"), | |
| ).to_dict() | |