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import numpy as np
import logging
logger = logging.getLogger(__name__)
def calculate_final_score(
quality_score: float,
aesthetics_score: float,
prompt_score: float,
ai_detection_score: float,
has_prompt: bool = True
) -> float:
"""
Calculate weighted composite score for image evaluation.
Args:
quality_score: Technical image quality (0-10)
aesthetics_score: Visual appeal score (0-10)
prompt_score: Prompt adherence score (0-10)
ai_detection_score: AI generation probability (0-1)
has_prompt: Whether prompt metadata is available
Returns:
Final composite score (0-10)
"""
try:
# Validate and clamp input scores
quality_score = max(0.0, min(10.0, quality_score))
aesthetics_score = max(0.0, min(10.0, aesthetics_score))
prompt_score = max(0.0, min(10.0, prompt_score))
ai_detection_score = max(0.0, min(1.0, ai_detection_score))
# FIX: Invert and scale the AI detection score to a 0-10 range
# A low AI detection probability (good) results in a high score.
inverted_ai_score = (1 - ai_detection_score) * 10
if has_prompt:
# Standard weights when prompt is available
weights = {
'quality': 0.25, # 25% - Technical quality
'aesthetics': 0.35, # 35% - Visual appeal (highest weight)
'prompt': 0.25, # 25% - Prompt following
'ai_detection': 0.15 # 15% - Authenticity (inverted detection score)
}
# FIX: Correctly calculate the weighted score. The sum of weights is 1.0.
score = (
quality_score * weights['quality'] +
aesthetics_score * weights['aesthetics'] +
prompt_score * weights['prompt'] +
inverted_ai_score * weights['ai_detection']
)
else:
# Redistribute prompt weight when no prompt available
weights = {
'quality': 0.375, # 25% + 12.5% from prompt
'aesthetics': 0.475, # 35% + 12.5% from prompt
'ai_detection': 0.15 # 15% - Authenticity
}
# FIX: Correctly calculate the weighted score without prompt. Sum of weights is 1.0.
score = (
quality_score * weights['quality'] +
aesthetics_score * weights['aesthetics'] +
inverted_ai_score * weights['ai_detection']
)
# Ensure final score is within the valid 0-10 range
final_score = max(0.0, min(10.0, score))
logger.debug(f"Score calculation - Final: {final_score:.2f}")
return final_score
except Exception as e:
logger.error(f"Error calculating final score: {str(e)}")
return 0.0 # Return 0.0 on error to clearly indicate failure |