Spaces:
Running
on
Zero
Running
on
Zero
Update utils.py
Browse files
utils.py
CHANGED
@@ -167,259 +167,7 @@ def _extract_description_only(prompt: str) -> str:
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description = re.sub(r'I would recommend.*?aperture.*?\.', '', description, flags=re.DOTALL)
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# Remove numbered section residues (like "2.," at the end)
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description = re.sub(r'\s*\d+\.\s*,?\s
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def _format_bagel_camera_suggestion(bagel_camera: str) -> str:
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"""Format BAGEL's camera suggestion into clean FLUX format"""
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try:
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# Clean up the BAGEL suggestion
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camera_text = bagel_camera.strip()
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# Remove "CAMERA_SETUP:" if it's still there
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camera_text = re.sub(r'^CAMERA_SETUP:\s*', '', camera_text)
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# Extract key camera info using regex patterns
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camera_patterns = {
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'camera': r'(Canon EOS [^,]+|Sony A[^,]+|Leica [^,]+|Hasselblad [^,]+|Phase One [^,]+|Fujifilm [^,]+)',
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'lens': r'(\d+mm[^,]*|[^,]*lens[^,]*)',
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'aperture': r'(f/[\d.]+[^,]*)'
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}
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extracted_parts = []
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for key, pattern in camera_patterns.items():
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match = re.search(pattern, camera_text, re.IGNORECASE)
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if match:
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extracted_parts.append(match.group(1).strip())
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if extracted_parts:
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# Build clean camera config
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camera_info = ', '.join(extracted_parts)
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return f", Shot on {camera_info}, professional photography"
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else:
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# Fallback: use the first part of BAGEL's suggestion
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first_sentence = camera_text.split('.')[0].strip()
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if len(first_sentence) > 10:
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return f", {first_sentence}, professional photography"
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else:
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return ", professional camera setup"
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except Exception as e:
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logger.warning(f"Failed to format BAGEL camera suggestion: {e}")
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return ", professional camera setup"
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def _get_fallback_camera_config(prompt_lower: str) -> str:
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"""Get fallback camera configuration when BAGEL doesn't suggest one"""
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# Improved detection logic
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if any(word in prompt_lower for word in ['street', 'urban', 'city', 'documentary', 'crowd', 'protest']):
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return FLUX_RULES["camera_configs"]["street"]
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elif any(word in prompt_lower for word in ['portrait', 'person', 'man', 'woman', 'face']) and not any(word in prompt_lower for word in ['street', 'crowd', 'scene']):
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return FLUX_RULES["camera_configs"]["portrait"]
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elif any(word in prompt_lower for word in ['landscape', 'mountain', 'nature', 'outdoor']):
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return FLUX_RULES["camera_configs"]["landscape"]
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else:
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return FLUX_RULES["camera_configs"]["default"]
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def _get_lighting_enhancement(prompt_lower: str, camera_config: str) -> str:
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"""Determine appropriate lighting enhancement"""
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# Don't add lighting if already mentioned in prompt or camera config
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if 'lighting' in prompt_lower or 'lighting' in camera_config.lower():
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return ""
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if 'dramatic' in prompt_lower or 'chaos' in prompt_lower or 'fire' in prompt_lower:
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return FLUX_RULES["lighting_enhancements"]["dramatic"]
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elif 'portrait' in camera_config.lower():
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return FLUX_RULES["lighting_enhancements"]["portrait"]
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else:
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return FLUX_RULES["lighting_enhancements"]["default"]
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def _clean_prompt_formatting(prompt: str) -> str:
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"""Clean up prompt formatting"""
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if not prompt:
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return ""
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# Ensure it starts with capital letter
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prompt = prompt.strip()
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if prompt:
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prompt = prompt[0].upper() + prompt[1:] if len(prompt) > 1 else prompt.upper()
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# Clean up spaces and commas
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prompt = re.sub(r'\s+', ' ', prompt)
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prompt = re.sub(r',\s*,+', ',', prompt)
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prompt = re.sub(r'^\s*,\s*', '', prompt) # Remove leading commas
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prompt = re.sub(r'\s*,\s*$', '', prompt) # Remove trailing commas
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# Remove redundant periods
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prompt = re.sub(r'\.+', '.', prompt)
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return prompt.strip()
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def calculate_prompt_score(prompt: str, analysis_data: Optional[Dict[str, Any]] = None) -> Tuple[int, Dict[str, int]]:
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"""
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Calculate quality score for a prompt
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Args:
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prompt: The prompt to score
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analysis_data: Optional analysis data to enhance scoring
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Returns:
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Tuple of (total_score, breakdown_dict)
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"""
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if not prompt:
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return 0, {"prompt_quality": 0, "technical_details": 0, "artistic_value": 0, "flux_optimization": 0}
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breakdown = {}
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# Prompt quality score (0-30 points)
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length_score = min(20, len(prompt) // 8) # Reward decent length
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detail_score = min(10, len(prompt.split(',')) * 2) # Reward detail
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breakdown["prompt_quality"] = length_score + detail_score
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# Technical details score (0-25 points) - Enhanced for BAGEL camera suggestions
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tech_score = 0
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tech_keywords = ['shot on', 'lens', 'photography', 'lighting', 'camera']
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for keyword in tech_keywords:
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if keyword in prompt.lower():
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tech_score += 5
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# Bonus points for BAGEL camera suggestions
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if analysis_data and analysis_data.get("has_camera_suggestion"):
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tech_score += 10 # Higher bonus for intelligent camera selection
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breakdown["technical_details"] = min(25, tech_score)
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# Artistic value score (0-25 points)
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art_keywords = ['masterful', 'professional', 'cinematic', 'dramatic', 'beautiful']
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art_score = sum(5 for keyword in art_keywords if keyword in prompt.lower())
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breakdown["artistic_value"] = min(25, art_score)
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# Flux optimization score (0-20 points)
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flux_score = 0
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# Check for camera configuration (prefer BAGEL over fallback)
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if analysis_data and analysis_data.get("has_camera_suggestion"):
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flux_score += 15 # Higher score for BAGEL suggestions
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elif any(camera in prompt for camera in FLUX_RULES["camera_configs"].values()):
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flux_score += 10 # Lower score for fallback
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# Check for lighting configuration
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if any(lighting in prompt for lighting in FLUX_RULES["lighting_enhancements"].values()):
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flux_score += 5
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breakdown["flux_optimization"] = flux_score
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# Calculate total
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total_score = sum(breakdown.values())
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return total_score, breakdown
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def get_score_grade(score: int) -> Dict[str, str]:
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"""
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Get grade information for a score
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Args:
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score: Numeric score
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Returns:
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Dictionary with grade and color information
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"""
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from config import SCORING_CONFIG
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for threshold, grade_info in sorted(SCORING_CONFIG["grade_thresholds"].items(), reverse=True):
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if score >= threshold:
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return grade_info
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# Default to lowest grade
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return SCORING_CONFIG["grade_thresholds"][0]
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def format_analysis_report(analysis_data: Dict[str, Any], processing_time: float) -> str:
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"""
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Format analysis data into a readable report
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Args:
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analysis_data: Analysis results
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processing_time: Time taken for processing
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Returns:
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Formatted markdown report
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"""
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model_used = analysis_data.get("model_used", "Unknown")
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prompt_length = len(analysis_data.get("prompt", ""))
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report = f"""**🚀 FLUX OPTIMIZATION COMPLETE**
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**Model:** {model_used} • **Time:** {processing_time:.1f}s • **Length:** {prompt_length} chars
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**📊 ANALYSIS SUMMARY:**
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{analysis_data.get("summary", "Analysis completed successfully")}
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**🎯 OPTIMIZATIONS APPLIED:**
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✅ Flux camera configuration
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✅ Professional lighting setup
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✅ Technical photography details
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✅ Artistic enhancement keywords
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**⚡ Powered by Frame 0 Laboratory for MIA**"""
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return report
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def safe_execute(func, *args, **kwargs) -> Tuple[bool, Any]:
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"""
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Safely execute a function with error handling
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Args:
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func: Function to execute
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*args: Function arguments
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**kwargs: Function keyword arguments
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Returns:
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Tuple of (success: bool, result: Any)
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"""
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try:
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result = func(*args, **kwargs)
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return True, result
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except Exception as e:
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logger.error(f"Safe execution failed for {func.__name__}: {e}")
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return False, str(e)
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def truncate_text(text: str, max_length: int = 100) -> str:
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"""
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Truncate text to specified length with ellipsis
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Args:
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text: Text to truncate
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max_length: Maximum length
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Returns:
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Truncated text
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"""
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if not text or len(text) <= max_length:
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return text
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return text[:max_length-3] + "..."
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# Export main functions
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__all__ = [
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"setup_logging",
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"optimize_image",
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"validate_image",
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"clean_memory",
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"apply_flux_rules",
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"calculate_prompt_score",
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"get_score_grade",
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"format_analysis_report",
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"safe_execute",
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"truncate_text"
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], '', description)
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description = re.sub(r'\s*\d+\.\s*,?\s*', ' ', description)
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return description.strip()
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description = re.sub(r'I would recommend.*?aperture.*?\.', '', description, flags=re.DOTALL)
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# Remove numbered section residues (like "2.," at the end)
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description = re.sub(r'\s*\d+\.\s*,?\s*$', '', description)
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description = re.sub(r'\s*\d+\.\s*,?\s*', ' ', description)
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return description.strip()
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