""" Utility functions for Phramer AI By Pariente AI, for MIA TV Series Enhanced with professional cinematography knowledge and intelligent token economy """ import re import logging import gc from typing import Optional, Tuple, Dict, Any, List from PIL import Image import torch import numpy as np from config import PROCESSING_CONFIG, FLUX_RULES, PROFESSIONAL_PHOTOGRAPHY_CONFIG # Configure logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) def setup_logging(level: str = "INFO") -> None: """Setup logging configuration""" logging.basicConfig( level=getattr(logging, level.upper()), format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' ) def optimize_image(image: Any) -> Optional[Image.Image]: """ Optimize image for processing Args: image: Input image (PIL, numpy array, or file path) Returns: Optimized PIL Image or None if failed """ if image is None: return None try: # Convert to PIL Image if necessary if isinstance(image, np.ndarray): image = Image.fromarray(image) elif isinstance(image, str): image = Image.open(image) elif not isinstance(image, Image.Image): logger.error(f"Unsupported image type: {type(image)}") return None # Convert to RGB if necessary if image.mode != 'RGB': image = image.convert('RGB') # Resize if too large max_size = PROCESSING_CONFIG["max_image_size"] if image.size[0] > max_size or image.size[1] > max_size: image.thumbnail((max_size, max_size), Image.Resampling.LANCZOS) logger.info(f"Image resized to {image.size}") return image except Exception as e: logger.error(f"Image optimization failed: {e}") return None def validate_image(image: Any) -> bool: """ Validate if image is processable Args: image: Input image to validate Returns: True if valid, False otherwise """ if image is None: return False try: optimized = optimize_image(image) return optimized is not None except Exception: return False def clean_memory() -> None: """Clean up memory and GPU cache""" try: gc.collect() if torch.cuda.is_available(): torch.cuda.empty_cache() torch.cuda.synchronize() logger.debug("Memory cleaned") except Exception as e: logger.warning(f"Memory cleanup failed: {e}") def detect_scene_type_from_analysis(analysis_metadata: Dict[str, Any]) -> str: """Detect scene type from BAGEL analysis metadata""" try: # Check if BAGEL provided scene detection if "scene_type" in analysis_metadata: return analysis_metadata["scene_type"] # Check camera setup for scene hints camera_setup = analysis_metadata.get("camera_setup", "").lower() if any(term in camera_setup for term in ["portrait", "85mm", "135mm"]): return "portrait" elif any(term in camera_setup for term in ["landscape", "wide", "24mm", "phase one"]): return "landscape" elif any(term in camera_setup for term in ["street", "35mm", "documentary", "leica"]): return "street" elif any(term in camera_setup for term in ["cinema", "arri", "red", "anamorphic"]): return "cinematic" elif any(term in camera_setup for term in ["architecture", "building", "tilt"]): return "architectural" return "default" except Exception as e: logger.warning(f"Scene type detection failed: {e}") return "default" def apply_flux_rules(prompt: str, analysis_metadata: Optional[Dict[str, Any]] = None) -> str: """ Apply enhanced prompt optimization with cinematography knowledge and intelligent token economy Args: prompt: Raw prompt text from BAGEL analysis analysis_metadata: Enhanced metadata with cinematography suggestions Returns: Optimized prompt with professional cinematography terms and efficient token usage """ if not prompt or not isinstance(prompt, str): return "" try: # Step 1: Extract and clean the core description core_description = _extract_clean_description(prompt) if not core_description: return "Professional photograph with technical excellence" # Step 2: Get camera configuration camera_setup = _get_camera_setup(analysis_metadata, core_description) # Step 3: Get essential style keywords style_keywords = _get_essential_keywords(core_description, camera_setup, analysis_metadata) # Step 4: Build final optimized prompt final_prompt = _build_optimized_prompt(core_description, camera_setup, style_keywords) logger.info(f"Prompt optimized: {len(prompt)} → {len(final_prompt)} chars") return final_prompt except Exception as e: logger.error(f"Prompt optimization failed: {e}") return _create_fallback_prompt(prompt) def _extract_clean_description(prompt: str) -> str: """Extract and clean the core description from BAGEL output""" try: # Remove CAMERA_SETUP section if "CAMERA_SETUP:" in prompt: description = prompt.split("CAMERA_SETUP:")[0].strip() elif "2. CAMERA_SETUP" in prompt: description = prompt.split("2. CAMERA_SETUP")[0].strip() else: description = prompt # Remove section headers description = re.sub(r'^(DESCRIPTION:|1\.\s*DESCRIPTION:)\s*', '', description, flags=re.IGNORECASE) # Remove verbose introduction phrases remove_patterns = [ r'^This image (?:features|shows|depicts|presents|captures)', r'^The image (?:features|shows|depicts|presents|captures)', r'^This (?:photograph|picture|scene) (?:features|shows|depicts)', r'^(?:In this image,?|Looking at this image,?)', r'(?:possibly|apparently|seemingly|appears to be|seems to be)', ] for pattern in remove_patterns: description = re.sub(pattern, '', description, flags=re.IGNORECASE) # Convert to concise, direct language description = _convert_to_direct_language(description) # Clean up formatting description = re.sub(r'\s+', ' ', description).strip() # Limit length for efficiency if len(description) > 200: sentences = re.split(r'[.!?]', description) description = sentences[0] if sentences else description[:200] return description.strip() except Exception as e: logger.warning(f"Description extraction failed: {e}") return prompt[:100] if prompt else "" def _convert_to_direct_language(text: str) -> str: """Convert verbose descriptive text to direct, concise language""" try: # Direct conversions for common verbose phrases conversions = [ # Subject identification (r'a (?:person|individual|figure|man|woman) (?:who is|that is)', r'person'), (r' (?:who is|that is) (?:wearing|dressed in)', r' wearing'), (r' (?:who appears to be|that appears to be)', r''), # Location simplification (r'(?:what appears to be|what seems to be) (?:a|an)', r''), (r'in (?:what looks like|what appears to be) (?:a|an)', r'in'), (r'(?:standing|sitting|positioned) in (?:the middle of|the center of)', r'in'), # Action simplification (r'(?:is|are) (?:currently|presently) (?:engaged in|performing)', r''), (r'(?:can be seen|is visible|are visible)', r''), # Background simplification (r'(?:In the background|Behind (?:him|her|them)),? (?:there (?:is|are)|we can see)', r'Background:'), (r'The background (?:features|shows|contains)', r'Background:'), # Remove filler words (r'\b(?:quite|rather|somewhat|fairly|very|extremely)\b', r''), (r'\b(?:overall|generally|typically|usually)\b', r''), ] result = text for pattern, replacement in conversions: result = re.sub(pattern, replacement, result, flags=re.IGNORECASE) # Clean up extra spaces and punctuation result = re.sub(r'\s+', ' ', result) result = re.sub(r'\s*,\s*,+', ',', result) result = re.sub(r'^\s*,\s*', '', result) return result.strip() except Exception as e: logger.warning(f"Language conversion failed: {e}") return text def _get_camera_setup(analysis_metadata: Optional[Dict[str, Any]], description: str) -> str: """Get camera setup configuration""" try: # Check if BAGEL provided camera setup if analysis_metadata and analysis_metadata.get("has_camera_suggestion"): camera_setup = analysis_metadata.get("camera_setup", "") if camera_setup and len(camera_setup) > 10: return _format_camera_setup(camera_setup) # Detect scene type and provide appropriate camera setup scene_type = _detect_scene_from_content(description) return _get_scene_camera_setup(scene_type) except Exception as e: logger.warning(f"Camera setup detection failed: {e}") return "shot on professional camera" def _format_camera_setup(raw_setup: str) -> str: """Format camera setup into clean, concise format""" try: # Extract camera model camera_patterns = [ r'(Canon EOS R\d+)', r'(Sony A\d+[^\s,]*)', r'(Leica [^\s,]+)', r'(Phase One [^\s,]+)', r'(Hasselblad [^\s,]+)', r'(ARRI [^\s,]+)', r'(RED [^\s,]+)' ] camera = None for pattern in camera_patterns: match = re.search(pattern, raw_setup, re.IGNORECASE) if match: camera = match.group(1) break # Extract lens info lens_pattern = r'(\d+mm[^,]*f/[\d.]+[^,]*)' lens_match = re.search(lens_pattern, raw_setup, re.IGNORECASE) lens = lens_match.group(1) if lens_match else None # Extract ISO iso_pattern = r'(ISO \d+)' iso_match = re.search(iso_pattern, raw_setup, re.IGNORECASE) iso = iso_match.group(1) if iso_match else None # Build clean setup parts = [] if camera: parts.append(camera) if lens: parts.append(lens) if iso: parts.append(iso) if parts: return f"shot on {', '.join(parts)}" else: return "professional photography" except Exception as e: logger.warning(f"Camera setup formatting failed: {e}") return "professional photography" def _detect_scene_from_content(description: str) -> str: """Detect scene type from description content""" description_lower = description.lower() # Scene detection patterns if any(term in description_lower for term in ["portrait", "person", "man", "woman", "face"]): return "portrait" elif any(term in description_lower for term in ["landscape", "mountain", "horizon", "nature", "outdoor"]): return "landscape" elif any(term in description_lower for term in ["street", "urban", "city", "building", "crowd"]): return "street" elif any(term in description_lower for term in ["architecture", "building", "structure", "interior"]): return "architecture" else: return "general" def _get_scene_camera_setup(scene_type: str) -> str: """Get camera setup based on scene type""" setups = { "portrait": "shot on Canon EOS R5, 85mm f/1.4 lens, ISO 200", "landscape": "shot on Phase One XT, 24-70mm f/4 lens, ISO 100", "street": "shot on Leica M11, 35mm f/1.4 lens, ISO 800", "architecture": "shot on Canon EOS R5, 24-70mm f/2.8 lens, ISO 100", "general": "shot on Canon EOS R6, 50mm f/1.8 lens, ISO 400" } return setups.get(scene_type, setups["general"]) def _get_essential_keywords(description: str, camera_setup: str, analysis_metadata: Optional[Dict[str, Any]]) -> List[str]: """Get essential style keywords without redundancy""" try: keywords = [] description_lower = description.lower() # Only add depth of field if not already mentioned if "depth" not in description_lower and "bokeh" not in description_lower: if any(term in camera_setup for term in ["f/1.4", "f/2.8", "85mm"]): keywords.append("shallow depth of field") # Add professional photography only if no specific camera mentioned if "shot on" not in camera_setup: keywords.append("professional photography") # Scene-specific keywords if "portrait" in description_lower and "studio lighting" not in description_lower: keywords.append("professional portrait") # Technical quality (only if needed) if len(keywords) < 2: keywords.append("high quality") return keywords[:3] # Limit to 3 essential keywords except Exception as e: logger.warning(f"Keyword extraction failed: {e}") return ["professional photography"] def _build_optimized_prompt(description: str, camera_setup: str, keywords: List[str]) -> str: """Build final optimized prompt with proper structure""" try: # Structure: Description + Technical + Style parts = [] # Core description (clean and concise) if description: parts.append(description) # Technical setup if camera_setup: parts.append(camera_setup) # Essential keywords if keywords: parts.extend(keywords) # Join with consistent separator result = ", ".join(parts) # Final cleanup result = re.sub(r'\s*,\s*,+', ',', result) # Remove double commas result = re.sub(r'\s+', ' ', result) # Clean spaces result = result.strip().rstrip(',') # Remove trailing comma # Ensure it starts with capital letter if result: result = result[0].upper() + result[1:] if len(result) > 1 else result.upper() return result except Exception as e: logger.error(f"Prompt building failed: {e}") return "Professional photograph" def _create_fallback_prompt(original_prompt: str) -> str: """Create fallback prompt when optimization fails""" try: # Extract first meaningful sentence sentences = re.split(r'[.!?]', original_prompt) if sentences: clean_sentence = sentences[0].strip() # Remove verbose starters clean_sentence = re.sub(r'^(This image shows|The image depicts|This photograph)', '', clean_sentence, flags=re.IGNORECASE) clean_sentence = clean_sentence.strip() if len(clean_sentence) > 20: return f"{clean_sentence}, professional photography" return "Professional photograph with technical excellence" except Exception: return "Professional photograph" def calculate_prompt_score(prompt: str, analysis_data: Optional[Dict[str, Any]] = None) -> Tuple[int, Dict[str, int]]: """ Calculate enhanced quality score with professional cinematography criteria Args: prompt: The prompt to score analysis_data: Enhanced analysis data with cinematography context Returns: Tuple of (total_score, breakdown_dict) """ if not prompt: return 0, {"prompt_quality": 0, "technical_details": 0, "professional_cinematography": 0, "multi_engine_optimization": 0} breakdown = {} # Enhanced Prompt Quality (0-25 points) length_score = min(15, len(prompt) // 10) # Reward appropriate length detail_score = min(10, len(prompt.split(',')) * 2) # Reward structured detail breakdown["prompt_quality"] = int(length_score + detail_score) # Technical Details with Cinematography Focus (0-25 points) tech_score = 0 # Cinema equipment (higher scores for professional gear) cinema_equipment = ['Canon EOS R', 'Sony A1', 'Leica', 'Hasselblad', 'Phase One', 'ARRI', 'RED'] for equipment in cinema_equipment: if equipment.lower() in prompt.lower(): tech_score += 8 break # Lens specifications if re.search(r'\d+mm.*f/[\d.]+', prompt): tech_score += 6 # ISO settings if re.search(r'ISO \d+', prompt): tech_score += 4 # Professional terminology tech_keywords = ['shot on', 'lens', 'depth of field', 'bokeh'] tech_score += sum(3 for keyword in tech_keywords if keyword in prompt.lower()) breakdown["technical_details"] = min(25, tech_score) # Professional Cinematography (0-25 points) cinema_score = 0 # Professional lighting techniques lighting_terms = ['professional lighting', 'studio lighting', 'natural lighting'] cinema_score += sum(4 for term in lighting_terms if term in prompt.lower()) # Composition techniques composition_terms = ['composition', 'depth of field', 'bokeh', 'shallow depth'] cinema_score += sum(3 for term in composition_terms if term in prompt.lower()) # Professional context bonus if analysis_data and analysis_data.get("has_camera_suggestion"): cinema_score += 6 breakdown["professional_cinematography"] = min(25, cinema_score) # Multi-Engine Optimization (0-25 points) optimization_score = 0 # Check for technical specifications if re.search(r'(?:Canon|Sony|Leica|Phase One)', prompt): optimization_score += 10 # Complete technical specs if re.search(r'\d+mm.*f/[\d.]+.*ISO \d+', prompt): optimization_score += 8 # Professional terminology pro_terms = ['professional', 'shot on', 'high quality'] optimization_score += sum(2 for term in pro_terms if term in prompt.lower()) # Length efficiency bonus (reward conciseness) word_count = len(prompt.split()) if 30 <= word_count <= 60: # Optimal range optimization_score += 5 elif word_count <= 30: optimization_score += 3 breakdown["multi_engine_optimization"] = min(25, optimization_score) # Calculate total total_score = sum(breakdown.values()) return total_score, breakdown def calculate_professional_enhanced_score(prompt: str, analysis_data: Optional[Dict[str, Any]] = None) -> Tuple[int, Dict[str, int]]: """ Enhanced scoring with professional cinematography criteria Args: prompt: The prompt to score analysis_data: Analysis data with cinematography context Returns: Tuple of (total_score, breakdown_dict) """ return calculate_prompt_score(prompt, analysis_data) def get_score_grade(score: int) -> Dict[str, str]: """ Get grade information for a score Args: score: Numeric score Returns: Dictionary with grade and color information """ from config import SCORING_CONFIG for threshold, grade_info in sorted(SCORING_CONFIG["grade_thresholds"].items(), reverse=True): if score >= threshold: return grade_info # Default to lowest grade return SCORING_CONFIG["grade_thresholds"][0] def format_analysis_report(analysis_data: Dict[str, Any], processing_time: float) -> str: """ Format analysis data into a readable report with cinematography insights Args: analysis_data: Analysis results with cinematography context processing_time: Time taken for processing Returns: Formatted markdown report """ model_used = analysis_data.get("model", "Unknown") prompt_length = len(analysis_data.get("prompt", "")) has_cinema_context = analysis_data.get("cinematography_context_applied", False) scene_type = analysis_data.get("scene_type", "general") report = f"""**🎬 PHRAMER AI ANALYSIS COMPLETE** **Model:** {model_used} • **Time:** {processing_time:.1f}s • **Length:** {prompt_length} chars **📊 CINEMATOGRAPHY ANALYSIS:** **Scene Type:** {scene_type.replace('_', ' ').title()} **Professional Context:** {'✅ Applied' if has_cinema_context else '❌ Not Applied'} **🎯 OPTIMIZATIONS APPLIED:** ✅ Clean description extraction ✅ Professional camera configuration ✅ Essential keyword optimization ✅ Token economy optimization ✅ Multi-engine compatibility ✅ Redundancy elimination **⚡ Powered by Pariente AI for MIA TV Series**""" return report def safe_execute(func, *args, **kwargs) -> Tuple[bool, Any]: """ Safely execute a function with error handling Args: func: Function to execute *args: Function arguments **kwargs: Function keyword arguments Returns: Tuple of (success: bool, result: Any) """ try: result = func(*args, **kwargs) return True, result except Exception as e: logger.error(f"Safe execution failed for {func.__name__}: {e}") return False, str(e) def truncate_text(text: str, max_length: int = 100) -> str: """ Truncate text to specified length with ellipsis Args: text: Text to truncate max_length: Maximum length Returns: Truncated text """ if not text or len(text) <= max_length: return text return text[:max_length-3] + "..." def enhance_prompt_with_cinematography_knowledge(original_prompt: str, scene_type: str = "default") -> str: """ Enhance prompt with professional cinematography knowledge Args: original_prompt: Base prompt text scene_type: Detected scene type Returns: Enhanced prompt with cinematography context """ try: # Import here to avoid circular imports from professional_photography import enhance_flux_prompt_with_professional_knowledge # Apply professional cinematography enhancement enhanced = enhance_flux_prompt_with_professional_knowledge(original_prompt) logger.info(f"Enhanced prompt with cinematography knowledge for {scene_type} scene") return enhanced except ImportError: logger.warning("Professional photography module not available") return original_prompt except Exception as e: logger.warning(f"Cinematography enhancement failed: {e}") return original_prompt # Export main functions __all__ = [ "setup_logging", "optimize_image", "validate_image", "clean_memory", "apply_flux_rules", "calculate_prompt_score", "calculate_professional_enhanced_score", "get_score_grade", "format_analysis_report", "safe_execute", "truncate_text", "enhance_prompt_with_cinematography_knowledge", "detect_scene_type_from_analysis" ]