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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 |