| """Prompt construction and configuration without model dependencies.""" |
| from pathlib import Path |
| import json |
|
|
| REPO_ROOT = Path(__file__).resolve().parents[2] |
|
|
|
|
| def prompt_text(name): |
| return (REPO_ROOT / 'prompts' / name).read_text(encoding='utf-8').strip() |
|
|
|
|
| def load_labels(path): |
| labels = json.loads(Path(path).read_text(encoding='utf-8')) |
| if not isinstance(labels, list) or not labels: |
| raise ValueError('Labels must be a nonempty JSON array.') |
| if any(not isinstance(x, str) or not x.strip() for x in labels): |
| raise ValueError('Each label must be a nonempty string.') |
| if len(set(labels)) != len(labels): |
| raise ValueError('Candidate labels must be unique.') |
| return labels |
|
|
|
|
| def build_messages(image_path, mode='ddi', labels=None): |
| """Build a diagnostic request without any reference diagnosis field.""" |
| if mode == 'ddi': |
| if labels is not None: |
| raise ValueError('DDI open-ended inference does not accept candidate labels.') |
| messages = [{'role': 'system', 'content': prompt_text('ddi_system.txt')}] |
| user_text = prompt_text('ddi_user.txt') |
| elif mode == 'classification': |
| if not labels: |
| raise ValueError('Classification requires candidate labels.') |
| messages = [] |
| user_text = prompt_text('classification_user.txt').replace( |
| '{candidate_labels}', '\n'.join(labels) |
| ) |
| else: |
| raise ValueError(f'Unknown inference mode: {mode}') |
| messages.append({'role': 'user', 'content': [ |
| {'type': 'image', 'image': str(image_path)}, |
| {'type': 'text', 'text': user_text}, |
| ]}) |
| return messages |
|
|
|
|
| def generation_kwargs(config): |
| result = { |
| 'max_new_tokens': config['max_new_tokens'], |
| 'do_sample': config['do_sample'], |
| 'repetition_penalty': config['repetition_penalty'], |
| 'use_cache': config['use_cache'], |
| 'num_beams': 1, |
| 'num_return_sequences': 1, |
| 'no_repeat_ngram_size': 0, |
| } |
| if config['do_sample']: |
| result.update(temperature=config['temperature'], top_p=config['top_p']) |
| return result |
|
|
