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import os
import yaml
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
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