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 ""
    
    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 lighting and composition keywords without redundancy"""
    try:
        keywords = []
        description_lower = description.lower()
        
        # Detect and add lighting information
        lighting = _detect_lighting_from_description(description_lower)
        if lighting:
            keywords.append(lighting)
        
        # Detect and add composition technique
        composition = _detect_composition_from_description(description_lower, camera_setup)
        if composition:
            keywords.append(composition)
        
        # Only add depth of field if not already mentioned and relevant
        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 (fallback)
        if "shot on" not in camera_setup and len(keywords) == 0:
            keywords.append("professional photography")
        
        return keywords[:3]  # Limit to 3 essential keywords
        
    except Exception as e:
        logger.warning(f"Keyword extraction failed: {e}")
        return ["natural lighting"]


def _detect_lighting_from_description(description_lower: str) -> Optional[str]:
    """Detect lighting type from description"""
    try:
        # Check for existing lighting mentions
        if any(term in description_lower for term in ["lighting", "light", "lit", "illuminated"]):
            return None  # Already has lighting info
        
        # Detect lighting conditions from scene context
        if any(term in description_lower for term in ["sunset", "sunrise", "golden", "warm"]):
            return "golden hour lighting"
        elif any(term in description_lower for term in ["twilight", "dusk", "evening", "blue hour"]):
            return "blue hour lighting"
        elif any(term in description_lower for term in ["overcast", "cloudy", "soft", "diffused"]):
            return "soft natural lighting"
        elif any(term in description_lower for term in ["bright", "sunny", "daylight", "outdoor"]):
            return "natural daylight"
        elif any(term in description_lower for term in ["indoor", "interior", "inside"]):
            return "ambient lighting"
        elif any(term in description_lower for term in ["studio", "controlled", "professional"]):
            return "studio lighting"
        elif any(term in description_lower for term in ["dramatic", "moody", "shadow"]):
            return "dramatic lighting"
        else:
            # Default based on scene type
            if any(term in description_lower for term in ["portrait", "person", "face"]):
                return "natural lighting"
            elif any(term in description_lower for term in ["landscape", "outdoor", "nature"]):
                return "natural daylight"
            else:
                return "natural lighting"
                
    except Exception as e:
        logger.warning(f"Lighting detection failed: {e}")
        return "natural lighting"


def _detect_composition_from_description(description_lower: str, camera_setup: str) -> Optional[str]:
    """Detect composition technique from description and camera setup"""
    try:
        # Check for existing composition mentions
        if any(term in description_lower for term in ["composition", "framing", "rule of thirds"]):
            return None  # Already has composition info
        
        # Detect composition from perspective/angle
        if any(term in description_lower for term in ["elevated", "above", "overhead", "aerial"]):
            return "elevated perspective"
        elif any(term in description_lower for term in ["low angle", "looking up", "from below"]):
            return "low angle composition"
        elif any(term in description_lower for term in ["close-up", "tight", "detailed"]):
            return "close-up framing"
        elif any(term in description_lower for term in ["wide", "expansive", "panoramic"]):
            return "wide composition"
        
        # Detect composition from subject arrangement
        elif any(term in description_lower for term in ["centered", "center", "middle"]):
            return "centered composition"
        elif any(term in description_lower for term in ["symmetr", "balanced", "mirror"]):
            return "symmetrical composition"
        elif any(term in description_lower for term in ["leading", "lines", "path", "diagonal"]):
            return "leading lines"
        
        # Default composition based on camera setup
        elif any(term in camera_setup for term in ["85mm", "135mm", "f/1.4", "f/2.8"]):
            return "rule of thirds"  # Portrait/shallow DOF typically uses rule of thirds
        elif any(term in camera_setup for term in ["24mm", "35mm", "wide"]):
            return "dynamic composition"  # Wide angle allows for dynamic compositions
        else:
            return "rule of thirds"  # Universal fallback
            
    except Exception as e:
        logger.warning(f"Composition detection failed: {e}")
        return "rule of thirds"


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"
]