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__init__.py

@@ -0,0 +1,8 @@
+"""
+PRIVAL: Prompt Input Validation Toolkit
+"""
+
+# 对外暴露的核心接口
+from .core import evaluate_prompt
+
+__all__ = ["evaluate_prompt"]

core.py

@@ -1,21 +1,61 @@
-# core.py
+import os
 import yaml
-import concurrent.futures
-from .detectors import clarity, ambiguity, step_guidance, verbosity, injection_risk, context_completeness, ethic_compliance, structural_cleanness, relevance, feasibility, grammar_spelling, length_appropriateness, diversity
 
-# 映射名称到模块
-DETECTORS = {
-    'clarity': clarity,
-    'ambiguity': ambiguity,
-    'step_guidance': step_guidance,
-    'verbosity': verbosity,
-    'injection_risk': injection_risk,
-    'context_completeness': context_completeness,
-    'ethic_compliance': ethic_compliance,
-    'structural_cleanness': structural_cleanness,
-    'relevance': relevance,
-    'feasibility': feasibility,
-    'grammar_spelling': grammar_spelling,
-    'length_appropriateness': length_appropriateness,
-    'diversity': diversity
+from .detectors import (
+    clarity,
+    ambiguity,
+    step_guidance,
+    verbosity,
+    injection_risk,
+    context_completeness,
+    ethic_compliance,
+    structural_cleanness,
+    relevance,
+    feasibility,
+    grammar_spelling,
+    length_appropriateness,
+    diversity,
+)
+from .scoring import aggregate_scores
+
+# Mapping from dimension name to detector function
+_DETECTORS = {
+    'clarity': clarity.detect_clarity,
+    'ambiguity': ambiguity.detect_ambiguity,
+    'step_guidance': step_guidance.detect_step_guidance,
+    'verbosity': verbosity.detect_verbosity,
+    'injection_risk': injection_risk.detect_injection_risk,
+    'context_completeness': context_completeness.detect_context_completeness,
+    'ethic_compliance': ethic_compliance.detect_ethic_compliance,
+    'structural_cleanness': structural_cleanness.structural_cleanness,
+    'relevance': relevance.detect_relevance,
+    'feasibility': feasibility.detect_feasibility,
+    'grammar_spelling': grammar_spelling.grammar_spelling,
+    'length_appropriateness': length_appropriateness.detect_length_appropriateness,
+    'diversity': diversity.detect_diversity,
 }
+
+def evaluate_prompt(prompt: str) -> dict:
+    """
+    Evaluate the given prompt across all enabled dimensions and return
+    a dictionary of results plus a total score.
+    """
+    # Load configuration
+    here = os.path.dirname(__file__)
+    config_path = os.path.abspath(os.path.join(here, os.pardir, 'config.yaml'))
+    with open(config_path, encoding='utf-8') as cfg_file:
+        config = yaml.safe_load(cfg_file)
+
+    enabled = config.get('enabled_dimensions', [])
+    results = {}
+    for dim, func in _DETECTORS.items():
+        if dim in enabled:
+            try:
+                results[dim] = func(prompt)
+            except Exception as e:
+                results[dim] = {'score': None, 'suggestions': [f'Error in {dim}: {e}']}
+
+    # Compute total score
+    total = aggregate_scores(results, config)
+    results['total_score'] = total
+    return results

scoring.py

@@ -1,24 +1,17 @@
-# scoring.py
 """
-汇总各维度打分，按权重计算总分，输出标准结果格式。
+Scoring utilities for PRIVAL.
+Aggregates individual dimension scores into a total score.
 """
 
-def compute_overall_score(scores: dict, weights: dict) -> float:
-    """按 weights 对 scores 中每个维度加权平均，返回总分（0.0–1.0）。"""
-    total_weight = sum(weights.values())
-    if total_weight == 0:
-        return 0.0
-    weighted_sum = sum(scores[dim] * weights.get(dim, 0) for dim in scores)
-    return round(weighted_sum / total_weight, 4)
-
-
-def format_scores(scores: dict, suggestions: dict, overall: float) -> dict:
+def aggregate_scores(results: dict, config: dict) -> float:
     """
-    将各维度分数、建议和总分整理成字典，方便序列化输出。
-    返回格式：{"scores": {...}, "suggestions": {...}, "overall": float}
+    Compute the overall score as the simple average of available numeric scores.
     """
-    return {
-        "scores": scores,
-        "suggestions": suggestions,
-        "overall": overall
-    }
+    scores = []
+    for dim, res in results.items():
+        score = res.get('score')
+        if isinstance(score, (int, float)):
+            scores.append(score)
+    if not scores:
+        return None
+    return sum(scores) / len(scores)