utils.py

"""
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 ""
    
    # Clean the prompt from unwanted elements
    cleaned_prompt = prompt
    for pattern in FLUX_RULES["remove_patterns"]:
        cleaned_prompt = re.sub(pattern, '', cleaned_prompt, flags=re.IGNORECASE)
    
    # Extract description part only (remove CAMERA_SETUP section if present)
    description_part = _extract_description_only(cleaned_prompt)
    
    # NEW: Convert to generative language with cinematography angle detection
    if PROFESSIONAL_PHOTOGRAPHY_CONFIG.get("prompt_condensation", True):
        description_part = _convert_to_cinematographic_language(description_part)
        logger.info("Applied cinematographic language conversion")
    
    # Check if BAGEL provided intelligent camera setup with cinematography context
    camera_config = ""
    scene_type = "default"
    
    if analysis_metadata and analysis_metadata.get("has_camera_suggestion") and analysis_metadata.get("camera_setup"):
        # Use BAGEL's intelligent camera suggestion - enhanced with cinematography knowledge
        bagel_camera = analysis_metadata["camera_setup"]
        scene_type = detect_scene_type_from_analysis(analysis_metadata)
        camera_config = _format_professional_camera_suggestion(bagel_camera, scene_type)
        logger.info(f"Using BAGEL cinematography suggestion: {camera_config}")
    else:
        # Enhanced fallback with professional cinematography knowledge
        scene_type = _detect_scene_from_description(description_part.lower())
        camera_config = _get_enhanced_camera_config(scene_type, description_part.lower())
        logger.info(f"Using enhanced cinematography configuration for {scene_type}")
    
    # Add enhanced lighting with cinematography principles
    lighting_enhancement = _get_cinematography_lighting_enhancement(description_part.lower(), camera_config, scene_type)
    
    # Add style enhancement for multi-engine compatibility
    style_enhancement = _get_style_enhancement(scene_type, description_part.lower())
    
    # NEW: Smart keyword insertion with token economy
    smart_keywords = _apply_smart_keyword_insertion(description_part, camera_config, scene_type)
    
    # Build final prompt: Description + Camera + Lighting + Style + Smart Keywords
    final_prompt = description_part + camera_config + lighting_enhancement + style_enhancement + smart_keywords
    
    # NEW: Final length optimization with token economy
    if PROFESSIONAL_PHOTOGRAPHY_CONFIG.get("prompt_optimization", {}).get("max_length"):
        final_prompt = _optimize_prompt_with_token_economy(final_prompt)
    
    # Clean up formatting
    final_prompt = _clean_prompt_formatting(final_prompt)
    
    return final_prompt


def _extract_description_only(prompt: str) -> str:
    """Extract only the description part, removing camera setup sections"""
    # Remove CAMERA_SETUP section if present
    if "CAMERA_SETUP:" in prompt:
        parts = prompt.split("CAMERA_SETUP:")
        description = parts[0].strip()
    elif "2. CAMERA_SETUP" in prompt:
        parts = prompt.split("2. CAMERA_SETUP")
        description = parts[0].strip()
    else:
        description = prompt
    
    # Remove "DESCRIPTION:" label if present
    if description.startswith("DESCRIPTION:"):
        description = description.replace("DESCRIPTION:", "").strip()
    elif description.startswith("1. DESCRIPTION:"):
        description = description.replace("1. DESCRIPTION:", "").strip()
    
    # Clean up any remaining camera recommendations from the description
    description = re.sub(r'For this type of scene.*?shooting style would be.*?\.', '', description, flags=re.DOTALL)
    description = re.sub(r'I would recommend.*?aperture.*?\.', '', description, flags=re.DOTALL)
    description = re.sub(r'Professional Context:.*?\.', '', description, flags=re.DOTALL)
    description = re.sub(r'Cinematography context:.*?\.', '', description, flags=re.DOTALL)
    
    # Remove numbered section residues
    description = re.sub(r'\s*\d+\.\s*,?\s*$', '', description)
    description = re.sub(r'\s*\d+\.\s*,?\s*', ' ', description)
    
    return description.strip()


def _detect_camera_angles(description: str) -> List[str]:
    """Detect camera angles and perspectives using professional cinematography knowledge"""
    try:
        angles_detected = []
        description_lower = description.lower()
        
        # Low angle (contrapicado) detection
        low_angle_indicators = [
            "looking up at", "from below", "upward angle", "towering", "looming",
            "shot from ground level", "worm's eye", "low angle"
        ]
        if any(indicator in description_lower for indicator in low_angle_indicators):
            angles_detected.append("low-angle shot")
        
        # High angle (picado) detection  
        high_angle_indicators = [
            "looking down", "from above", "overhead", "bird's eye", "aerial view",
            "downward angle", "top-down", "high angle"
        ]
        if any(indicator in description_lower for indicator in high_angle_indicators):
            angles_detected.append("high-angle shot")
        
        # Eye level detection
        eye_level_indicators = [
            "eye level", "straight on", "direct view", "level with"
        ]
        if any(indicator in description_lower for indicator in eye_level_indicators):
            angles_detected.append("eye-level shot")
        
        # Dutch angle detection
        dutch_indicators = [
            "tilted", "angled", "diagonal", "off-kilter", "dutch angle"
        ]
        if any(indicator in description_lower for indicator in dutch_indicators):
            angles_detected.append("dutch angle")
        
        # Perspective analysis for mixed angles
        if ("foreground" in description_lower and "background" in description_lower):
            if ("close" in description_lower or "prominent" in description_lower) and "blurred" in description_lower:
                # Suggests foreground element shot from specific angle with background perspective
                if not angles_detected:  # Only add if no specific angle detected
                    angles_detected.append("shallow depth perspective")
        
        logger.info(f"Camera angles detected: {angles_detected}")
        return angles_detected
        
    except Exception as e:
        logger.warning(f"Camera angle detection failed: {e}")
        return []


def _convert_to_cinematographic_language(description: str) -> str:
    """Convert descriptive analysis to cinematographic prompt language with angle detection"""
    try:
        # First detect camera angles
        camera_angles = _detect_camera_angles(description)
        
        generative = description
        
        # Remove descriptive introduction phrases
        descriptive_intros = [
            r'This image (?:features|shows|depicts|presents|displays)',
            r'The image (?:features|shows|depicts|presents|displays)',
            r'This (?:photograph|picture|scene|composition) (?:features|shows|depicts)',
            r'The (?:photograph|picture|scene|composition) (?:features|shows|depicts)',
            r'This is (?:a|an) (?:image|photograph|picture) (?:of|showing)',
            r'The setting (?:appears to be|is)',
            r'The scene (?:appears to be|is|shows)',
        ]
        
        for pattern in descriptive_intros:
            generative = re.sub(pattern, '', generative, flags=re.IGNORECASE)
        
        # Remove uncertainty and verbose connector phrases
        verbose_phrases = [
            r'possibly (?:a|an) ',
            r'appears to be (?:a|an) ',
            r'seems to be (?:a|an) ',
            r'might be (?:a|an) ',
            r'could be (?:a|an) ',
            r'suggests (?:a|an) ',
            r'indicating (?:a|an) ',
            r'(?:possibly|apparently|seemingly|likely)',
            r'which (?:is|are|creates|adds)',
            r'(?:In the background|In the foreground), (?:there are|there is)',
            r'(?:The background|The foreground) (?:features|shows|contains)',
            r'(?:There are|There is) [^,]+ (?:in the background|in the foreground)',
            r'The overall (?:setting|atmosphere|mood) (?:suggests|indicates)',
        ]
        
        for pattern in verbose_phrases:
            generative = re.sub(pattern, '', generative, flags=re.IGNORECASE)
        
        # Convert spatial relationships to cinematographic terms
        spatial_conversions = [
            # Background/foreground to cinematographic terms
            (r'prominently displayed in (?:the )?foreground', 'foreground focus'),
            (r'in (?:the )?foreground', 'foreground'),
            (r'in (?:the )?background', 'background'),
            (r'blurred (?:figures|people|objects)', 'bokeh blur'),
            (r'out of focus', 'soft focus'),
            # Convert descriptive structure to noun phrases
            (r'(?:close-up|medium shot|wide shot) of (?:a|an|the) ', r'close-up '),
            (r'(?:a|an|the) (\w+)', r'\1'),
            # Remove excessive connecting words
            (r'(?:, and|, with|, featuring)', ','),
            # Simplify location descriptions
            (r'on (?:a|an|the) ', r'on '),
            (r'in (?:a|an|the) ', r'in '),
        ]
        
        for pattern, replacement in spatial_conversions:
            generative = re.sub(pattern, replacement, generative, flags=re.IGNORECASE)
        
        # Convert action descriptions to present participles
        action_conversions = [
            (r'(\w+) (?:are|is) walking', r'\1 walking'),
            (r'(\w+) (?:are|is) standing', r'\1 standing'),
            (r'(\w+) (?:are|is) sitting', r'\1 sitting'),
            (r'people (?:are|is) out of focus', r'blurred people'),
        ]
        
        for pattern, replacement in action_conversions:
            generative = re.sub(pattern, replacement, generative, flags=re.IGNORECASE)
        
        # Add detected camera angles at the beginning
        if camera_angles:
            angle_prefix = ", ".join(camera_angles)
            generative = f"{angle_prefix}, {generative}"
        
        # Clean up extra spaces and punctuation
        generative = re.sub(r'\s+', ' ', generative)
        generative = re.sub(r'^\s*,\s*', '', generative)  # Remove leading commas
        generative = re.sub(r'\s*,\s*,+', ',', generative)  # Remove double commas
        generative = re.sub(r'\.+', '.', generative)  # Remove multiple periods
        
        # Ensure it starts with a capital letter
        generative = generative.strip()
        if generative:
            generative = generative[0].upper() + generative[1:] if len(generative) > 1 else generative.upper()
        
        logger.info(f"Cinematographic conversion: angles={len(camera_angles)}, {len(description)} → {len(generative)} chars")
        return generative
        
    except Exception as e:
        logger.warning(f"Cinematographic language conversion failed: {e}")
        return description


def _apply_smart_keyword_insertion(description: str, camera_config: str, scene_type: str) -> str:
    """Smart keyword insertion with token economy - avoid redundancy"""
    try:
        keywords = []
        
        # Token Economy Rule 1: If camera specs exist, skip "photorealistic" keywords
        has_camera_specs = bool(re.search(r'(?:Canon|Sony|Leica|ARRI|RED|Hasselblad|Phase One)', camera_config))
        has_lens_specs = bool(re.search(r'\d+mm.*f/[\d.]+', camera_config))
        
        # Only add quality keywords if NO technical specs present
        if not (has_camera_specs and has_lens_specs):
            quality_keywords = FLUX_RULES.get("mandatory_keywords", {}).get("quality", [])
            keywords.extend(quality_keywords[:2])  # Limit to 2 quality keywords max
            logger.info("Added fallback quality keywords (no camera specs detected)")
        else:
            logger.info("Skipped redundant quality keywords (camera specs present)")
        
        # Token Economy Rule 2: Scene-specific keywords only if they add value
        style_by_scene = FLUX_RULES.get("mandatory_keywords", {}).get("style_by_scene", {})
        if scene_type in style_by_scene:
            scene_keywords = style_by_scene[scene_type]
            
            # Check if scene keywords are already implied by camera config or description
            for keyword in scene_keywords:
                if keyword.lower() not in camera_config.lower() and keyword.lower() not in description.lower():
                    keywords.append(keyword)
        
        # Token Economy Rule 3: Technical keywords only if not redundant
        technical_keywords = FLUX_RULES.get("mandatory_keywords", {}).get("technical", [])
        for tech_keyword in technical_keywords:
            # Skip "professional photography" if camera specs already indicate professional level
            if tech_keyword == "professional photography" and has_camera_specs:
                continue
            # Skip "high resolution" if camera specs include resolution indicators
            if tech_keyword == "high resolution" and has_camera_specs:
                continue
            keywords.append(tech_keyword)
        
        # Remove duplicates while preserving order
        unique_keywords = []
        for keyword in keywords:
            if keyword not in unique_keywords:
                unique_keywords.append(keyword)
        
        if unique_keywords:
            result = ", " + ", ".join(unique_keywords)
            logger.info(f"Smart keywords applied: {unique_keywords}")
            return result
        else:
            logger.info("No additional keywords needed (all redundant)")
            return ""
            
    except Exception as e:
        logger.warning(f"Smart keyword insertion failed: {e}")
        return ""


def _optimize_prompt_with_token_economy(prompt: str) -> str:
    """Optimize prompt length with intelligent token economy"""
    try:
        max_words = PROFESSIONAL_PHOTOGRAPHY_CONFIG.get("prompt_optimization", {}).get("max_length", 150)
        
        words = prompt.split()
        if len(words) <= max_words:
            return prompt
        
        # Priority preservation order for token economy
        essential_patterns = [
            # 1. Camera angles (highest priority)
            r'(?:low-angle|high-angle|eye-level|dutch angle|bird\'s eye|worm\'s eye) shot',
            # 2. Camera and lens specs
            r'(?:Canon|Sony|Leica|ARRI|RED|Hasselblad|Phase One) [^,]+',
            r'\d+mm[^,]*f/[\d.]+[^,]*',
            r'ISO \d+',
            # 3. Core subject and composition
            r'(?:close-up|medium shot|wide shot|shallow depth)',
            r'(?:foreground|background|bokeh)',
            # 4. Scene-specific technical terms
            r'(?:cinematic|anamorphic|telephoto|wide-angle)',
        ]
        
        # Extract essential parts first
        essential_parts = []
        remaining_text = prompt
        
        for pattern in essential_patterns:
            matches = re.findall(pattern, remaining_text, re.IGNORECASE)
            for match in matches:
                if match not in essential_parts:
                    essential_parts.append(match)
                # Remove from remaining text to avoid duplication
                remaining_text = re.sub(re.escape(match), '', remaining_text, count=1, flags=re.IGNORECASE)
        
        # Add essential parts to start
        optimized_words = []
        for part in essential_parts:
            optimized_words.extend(part.split())
        
        # Fill remaining space with most important remaining words
        remaining_words = [w for w in remaining_text.split() if w.strip() and w not in optimized_words]
        remaining_space = max_words - len(optimized_words)
        
        if remaining_space > 0:
            optimized_words.extend(remaining_words[:remaining_space])
        
        optimized = " ".join(optimized_words[:max_words])
        
        logger.info(f"Token economy optimization: {len(words)} → {len(optimized_words)} words, preserved {len(essential_parts)} essential elements")
        
        return optimized
        
    except Exception as e:
        logger.warning(f"Token economy optimization failed: {e}")
        return prompt


def _detect_scene_from_description(description_lower: str) -> str:
    """Enhanced scene detection from description with cinematography knowledge"""
    scene_keywords = PROFESSIONAL_PHOTOGRAPHY_CONFIG.get("scene_detection_keywords", {})
    
    # Score each scene type
    scene_scores = {}
    for scene_type, keywords in scene_keywords.items():
        score = sum(1 for keyword in keywords if keyword in description_lower)
        if score > 0:
            scene_scores[scene_type] = score
    
    # Additional cinematography-specific detection
    if any(term in description_lower for term in ["film", "movie", "cinematic", "dramatic lighting", "anamorphic"]):
        scene_scores["cinematic"] = scene_scores.get("cinematic", 0) + 2
    
    if any(term in description_lower for term in ["studio", "controlled lighting", "professional portrait"]):
        scene_scores["portrait"] = scene_scores.get("portrait", 0) + 2
    
    # Return highest scoring scene type
    if scene_scores:
        return max(scene_scores.items(), key=lambda x: x[1])[0]
    else:
        return "default"


def _format_professional_camera_suggestion(bagel_camera: str, scene_type: str) -> str:
    """Format BAGEL's camera suggestion with enhanced cinematography knowledge and fix formatting errors"""
    try:
        camera_text = bagel_camera.strip()
        camera_text = re.sub(r'^CAMERA_SETUP:\s*', '', camera_text)
        
        # Enhanced extraction patterns for cinema equipment
        cinema_patterns = {
            'camera': r'(ARRI [^,]+|RED [^,]+|Canon EOS [^,]+|Sony A[^,]+|Leica [^,]+|Hasselblad [^,]+|Phase One [^,]+)',
            'lens': r'(\d+mm[^,]*(?:anamorphic)?[^,]*)',
            'aperture': r'(f/[\d.]+)'
        }
        
        extracted_parts = []
        camera_model = None
        lens_spec = None
        aperture_spec = None
        
        # Extract camera
        camera_match = re.search(cinema_patterns['camera'], camera_text, re.IGNORECASE)
        if camera_match:
            camera_model = camera_match.group(1).strip()
        
        # Extract lens
        lens_match = re.search(cinema_patterns['lens'], camera_text, re.IGNORECASE)
        if lens_match:
            lens_spec = lens_match.group(1).strip()
        
        # Extract aperture
        aperture_match = re.search(cinema_patterns['aperture'], camera_text, re.IGNORECASE)
        if aperture_match:
            aperture_spec = aperture_match.group(1).strip()
        
        # Build proper camera setup with all technical specs
        if camera_model and lens_spec:
            # Fix the "with, 35mm" error by proper formatting
            camera_setup = f"{camera_model}, {lens_spec}"
            
            # Add aperture if found
            if aperture_spec:
                if 'f/' not in lens_spec:  # Don't duplicate aperture
                    camera_setup += f" at {aperture_spec}"
            
            # Add ISO and composition based on scene type
            enhanced_config = _get_enhanced_camera_config(scene_type, "")
            
            # Extract ISO and composition from enhanced config
            iso_match = re.search(r'ISO \d+', enhanced_config)
            composition_match = re.search(r'(rule of thirds|leading lines|symmetrical|centered|hyperfocal distance)[^,]*', enhanced_config)
            
            if iso_match:
                camera_setup += f", {iso_match.group()}"
            if composition_match:
                camera_setup += f", {composition_match.group()}"
            
            # Scene-specific enhancement with token economy
            if scene_type == "cinematic":
                result = f", Shot on {camera_setup}"  # Skip redundant "cinematic photography"
            elif scene_type == "portrait":
                result = f", Shot on {camera_setup}"  # Skip redundant "professional portrait photography"  
            else:
                result = f", Shot on {camera_setup}"
                
            logger.info(f"Formatted camera setup with token economy: {result}")
            return result
        else:
            # Fallback to enhanced config if parsing fails
            return _get_enhanced_camera_config(scene_type, camera_text.lower())
                
    except Exception as e:
        logger.warning(f"Failed to format professional camera suggestion: {e}")
        return _get_enhanced_camera_config(scene_type, "")


def _get_enhanced_camera_config(scene_type: str, description_lower: str) -> str:
    """Get enhanced camera configuration with cinematography knowledge"""
    # Enhanced camera configurations with cinema equipment
    enhanced_configs = {
        "cinematic": ", Shot on ARRI Alexa LF, 35mm anamorphic lens at f/2.8, ISO 400",
        "portrait": ", Shot on Canon EOS R5, 85mm f/1.4 lens at f/2.8, ISO 200, rule of thirds",
        "landscape": ", Shot on Phase One XT, 24-70mm f/4 lens at f/8, ISO 100, hyperfocal distance",
        "street": ", Shot on Leica M11, 35mm f/1.4 lens at f/2.8, ISO 800",
        "architectural": ", Shot on Canon EOS R5, 24-70mm f/2.8 lens at f/8, ISO 100, symmetrical composition",
        "commercial": ", Shot on Hasselblad X2D 100C, 90mm f/2.5 lens at f/4, ISO 100"
    }
    
    # Use enhanced config if available, otherwise fall back to FLUX_RULES
    if scene_type in enhanced_configs:
        return enhanced_configs[scene_type]
    elif scene_type in FLUX_RULES["camera_configs"]:
        return FLUX_RULES["camera_configs"][scene_type]
    else:
        return FLUX_RULES["camera_configs"]["default"]


def _get_cinematography_lighting_enhancement(description_lower: str, camera_config: str, scene_type: str) -> str:
    """Enhanced lighting with cinematography principles"""
    # Don't add lighting if already mentioned
    if any(term in description_lower for term in ["lighting", "lit", "illuminated"]) or 'lighting' in camera_config.lower():
        return ""
    
    # Enhanced lighting based on scene type and cinematography knowledge
    if scene_type == "cinematic":
        if any(term in description_lower for term in ["dramatic", "moody", "dark"]):
            return ", dramatic lighting"
        else:
            return ", cinematic lighting"
    elif scene_type == "portrait":
        return ", studio lighting"
    elif "dramatic" in description_lower or "chaos" in description_lower:
        return ", dramatic lighting"
    else:
        return ""  # Skip redundant lighting terms


def _get_style_enhancement(scene_type: str, description_lower: str) -> str:
    """Get style enhancement for multi-engine compatibility with token economy"""
    # Token economy: only add style if it adds unique value
    if scene_type == "cinematic":
        if "film grain" not in description_lower:
            return ", film grain"
    elif scene_type == "architectural":
        return ", clean lines"
    
    return ""  # Skip redundant style terms


def _clean_prompt_formatting(prompt: str) -> str:
    """Clean up prompt formatting"""
    if not prompt:
        return ""
    
    # Ensure it starts with capital letter
    prompt = prompt.strip()
    if prompt:
        prompt = prompt[0].upper() + prompt[1:] if len(prompt) > 1 else prompt.upper()
    
    # Clean up spaces and commas
    prompt = re.sub(r'\s+', ' ', prompt)
    prompt = re.sub(r',\s*,+', ',', prompt)
    prompt = re.sub(r'^\s*,\s*', '', prompt)  # Remove leading commas
    prompt = re.sub(r'\s*,\s*$', '', prompt)  # Remove trailing commas
    
    # Remove redundant periods
    prompt = re.sub(r'\.+', '.', prompt)
    
    return prompt.strip()


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(',')) * 1.5)  # 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 = ['ARRI', 'RED', 'Canon EOS R', 'Sony A1', 'Leica', 'Hasselblad', 'Phase One']
    for equipment in cinema_equipment:
        if equipment.lower() in prompt.lower():
            tech_score += 6
            break
    
    # Lens specifications
    if re.search(r'\d+mm.*f/[\d.]+', prompt):
        tech_score += 5
    
    # Camera angles (NEW - high value)
    angle_terms = ['low-angle shot', 'high-angle shot', 'eye-level shot', 'dutch angle', 'bird\'s eye', 'worm\'s eye']
    tech_score += sum(4 for term in angle_terms if term in prompt.lower())
    
    # Anamorphic and specialized lenses
    if 'anamorphic' in prompt.lower():
        tech_score += 4
    
    # Professional terminology
    tech_keywords = ['shot on', 'lens', 'cinematography', 'lighting']
    for keyword in tech_keywords:
        if keyword in prompt.lower():
            tech_score += 2
    
    # Bonus for BAGEL cinematography suggestions
    if analysis_data and analysis_data.get("has_camera_suggestion"):
        tech_score += 8
        
    breakdown["technical_details"] = min(25, tech_score)
    
    # Professional Cinematography (0-25 points) - Enhanced with angle detection
    cinema_score = 0
    
    # Camera angles (high value for professional cinematography)
    angle_terms = ['low-angle', 'high-angle', 'eye-level', 'dutch angle', 'bird\'s eye', 'worm\'s eye']
    cinema_score += sum(5 for term in angle_terms if term in prompt.lower())
    
    # Professional lighting techniques
    lighting_terms = ['cinematic lighting', 'dramatic lighting', 'studio lighting', 'rim light', 'practical lights']
    cinema_score += sum(3 for term in lighting_terms if term in prompt.lower())
    
    # Composition techniques
    composition_terms = ['composition', 'framing', 'depth of field', 'bokeh', 'rule of thirds', 'foreground', 'background']
    cinema_score += sum(2 for term in composition_terms if term in prompt.lower())
    
    # Cinematography style elements
    style_terms = ['film grain', 'anamorphic', 'telephoto compression', 'wide-angle', 'shallow depth']
    cinema_score += sum(3 for term in style_terms if term in prompt.lower())
    
    # Professional context bonus
    if analysis_data and analysis_data.get("cinematography_context_applied"):
        cinema_score += 5
    
    breakdown["professional_cinematography"] = min(25, cinema_score)
    
    # Multi-Engine Optimization (0-25 points) - Token economy aware
    optimization_score = 0
    
    # Check for technical specifications (more valuable than generic keywords)
    if re.search(r'(?:Canon|Sony|Leica|ARRI|RED|Hasselblad|Phase One)', prompt):
        optimization_score += 8  # Higher score for actual camera specs
    
    if re.search(r'\d+mm.*f/[\d.]+.*ISO \d+', prompt):
        optimization_score += 7  # Complete technical specs
    
    # Token economy bonus: penalize redundant keywords
    redundant_keywords = ['photorealistic', 'ultra-detailed', 'professional photography']
    has_camera_specs = bool(re.search(r'(?:Canon|Sony|Leica|ARRI|RED)', prompt))
    
    if has_camera_specs:
        # Bonus for NOT having redundant keywords when camera specs present
        redundant_count = sum(1 for keyword in redundant_keywords if keyword in prompt.lower())
        optimization_score += max(0, 5 - redundant_count * 2)  # Penalty for redundancy
    else:
        # If no camera specs, quality keywords are valuable
        quality_keywords = sum(1 for keyword in redundant_keywords if keyword in prompt.lower())
        optimization_score += min(5, quality_keywords * 2)
    
    # Scene-specific optimization
    if any(style in prompt for style in FLUX_RULES.get("style_enhancements", {}).values()):
        optimization_score += 3
        
    # Length efficiency bonus
    word_count = len(prompt.split())
    if word_count <= 120:  # Reward conciseness
        optimization_score += 2
    
    breakdown["multi_engine_optimization"] = min(25, optimization_score)
    
    # Calculate total with enhanced weighting
    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)
    """
    # Use the enhanced scoring system
    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:**
✅ Camera angle detection
✅ Professional camera configuration
✅ Cinematography lighting setup  
✅ 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"
]