models.py

"""
Model management for Phramer AI
By Pariente AI, for MIA TV Series

BAGEL 7B integration with professional photography knowledge enhancement
"""

import spaces
import logging
import tempfile
import os
import re
from typing import Optional, Dict, Any, Tuple
from PIL import Image
from gradio_client import Client, handle_file

from config import get_device_config, PROFESSIONAL_PHOTOGRAPHY_CONFIG
from utils import clean_memory, safe_execute
from professional_photography import (
    ProfessionalPhotoAnalyzer, 
    enhance_flux_prompt_with_professional_knowledge,
    professional_analyzer
)

logger = logging.getLogger(__name__)


class BaseImageAnalyzer:
    """Base class for image analysis models"""
    
    def __init__(self):
        self.is_initialized = False
        self.device_config = get_device_config()
        
    def initialize(self) -> bool:
        """Initialize the model"""
        raise NotImplementedError
        
    def analyze_image(self, image: Image.Image) -> Tuple[str, Dict[str, Any]]:
        """Analyze image and return description"""
        raise NotImplementedError
        
    def cleanup(self) -> None:
        """Clean up model resources"""
        clean_memory()


class BagelAPIAnalyzer(BaseImageAnalyzer):
    """BAGEL 7B model with professional photography knowledge integration"""
    
    def __init__(self):
        super().__init__()
        self.client = None
        self.space_url = "Malaji71/Bagel-7B-Demo"
        self.api_endpoint = "/image_understanding"
        self.hf_token = os.getenv("HF_TOKEN")
        self.professional_analyzer = professional_analyzer
        
    def initialize(self) -> bool:
        """Initialize BAGEL API client with authentication"""
        if self.is_initialized:
            return True
        
        try:
            logger.info("Initializing BAGEL API client for Phramer AI...")
            
            # Initialize client with token if available
            if self.hf_token:
                logger.info("Using HF token for enhanced API access")
                self.client = Client(self.space_url, hf_token=self.hf_token)
            else:
                logger.info("Using public API access")
                self.client = Client(self.space_url)
                
            self.is_initialized = True
            logger.info("BAGEL API client initialized successfully")
            return True
            
        except Exception as e:
            logger.error(f"BAGEL API client initialization failed: {e}")
            if self.hf_token:
                logger.info("Retrying without token...")
                try:
                    self.client = Client(self.space_url)
                    self.is_initialized = True
                    logger.info("BAGEL API client initialized (fallback mode)")
                    return True
                except Exception as e2:
                    logger.error(f"Fallback initialization failed: {e2}")
            return False

    def _create_professional_enhanced_prompt(self, analysis_type: str = "multimodal") -> str:
        """Create professionally enhanced prompt for BAGEL analysis"""
        
        if analysis_type == "cinematic":
            return """Analyze this image for professional cinematic prompt generation. You are an expert cinematographer with 30+ years of cinema experience. Provide exactly two sections:

1. DESCRIPTION: Create a detailed, flowing paragraph describing the image for cinematic reproduction:
- Scene composition and visual storytelling elements
- Lighting quality, direction, and dramatic mood
- Color palette, tonal relationships, and atmospheric elements
- Subject positioning, environmental context, and framing
- Cinematic qualities: film grain, depth of field, visual style
- Technical photographic elements that enhance realism

2. CAMERA_SETUP: Recommend professional cinema/photography equipment based on scene analysis:
- Camera body: Choose from Canon EOS R5/R6, Sony A7R/A1, Leica M11, ARRI Alexa, RED cameras
- Lens: Specific focal length and aperture (e.g., "85mm f/1.4", "35mm anamorphic f/2.8")
- Technical settings: Aperture consideration for depth of field and story mood
- Lighting setup: Professional lighting rationale (key, fill, rim, practical lights)
- Shooting style: Documentary, portrait, landscape, architectural, or cinematic approach

Apply professional cinematography principles: rule of thirds, leading lines, depth layering, lighting direction for mood, and technical excellence. Focus on creating prompts optimized for photorealistic, cinema-quality generation."""

        elif analysis_type == "flux_optimized":
            return """Analyze this image for FLUX prompt generation with professional cinematography expertise. You have 30+ years of cinema experience. Provide exactly two sections:

1. DESCRIPTION: Professional analysis for photorealistic reproduction:
- Image type and photographic classification
- Subject matter with precise visual details
- Lighting analysis: quality, direction, color temperature, shadows
- Composition elements: framing, balance, visual flow
- Color relationships and tonal values
- Artistic style and photographic technique employed
- Technical qualities that contribute to image impact

2. CAMERA_SETUP: Expert equipment recommendation:
- Professional camera body suited for scene type
- Specific lens with focal length and maximum aperture
- Recommended shooting aperture for optimal depth of field
- Technical considerations: ISO, lighting setup, focus technique
- Professional shooting approach and methodology

Integrate advanced cinematography principles: exposure triangle mastery, lighting ratios, compositional rules, focus techniques, and professional equipment knowledge. Output should be optimized for FLUX's photorealistic capabilities."""

        else:  # multimodal analysis
            return """Analyze this image with professional cinematography expertise for multi-platform prompt generation. You are a master cinematographer with extensive technical and artistic knowledge from 30+ years in cinema. Provide exactly two sections:

1. DESCRIPTION: Expert visual analysis for prompt generation:
- Comprehensive scene description with photographic insight
- Subject matter, composition, and visual hierarchy
- Lighting analysis: quality, direction, mood, technical setup
- Color palette, contrast, and tonal relationships
- Artistic elements: style, mood, atmosphere, visual impact
- Technical photographic qualities and execution

2. CAMERA_SETUP: Professional equipment and technique recommendation:
- Camera system recommendation based on scene requirements
- Lens selection with specific focal length and aperture range
- Technical shooting parameters and considerations
- Lighting setup and methodology for scene recreation
- Professional approach: shooting style and technical execution

Apply master-level cinematography knowledge: advanced composition techniques, professional lighting principles, camera system expertise, lens characteristics, and technical excellence. Create content suitable for multiple generative engines (Flux, Midjourney, etc.) with emphasis on photorealistic quality."""

    def _extract_professional_camera_setup(self, description: str) -> Optional[str]:
        """Extract and enhance camera setup with professional photography knowledge"""
        try:
            camera_setup = None
            
            # Extract BAGEL's camera recommendation
            if "CAMERA_SETUP:" in description:
                parts = description.split("CAMERA_SETUP:")
                if len(parts) > 1:
                    camera_section = parts[1].strip()
                    camera_text = camera_section.split('\n')[0].strip()
                    if len(camera_text) > 20:
                        camera_setup = self._parse_professional_camera_recommendation(camera_text)
            
            elif "2. CAMERA_SETUP" in description:
                parts = description.split("2. CAMERA_SETUP")
                if len(parts) > 1:
                    camera_section = parts[1].strip()
                    camera_text = camera_section.split('\n')[0].strip()
                    if len(camera_text) > 20:
                        camera_setup = self._parse_professional_camera_recommendation(camera_text)
            
            # Fallback: look for camera recommendations in text
            if not camera_setup:
                camera_setup = self._find_professional_camera_recommendation(description)
            
            return camera_setup
            
        except Exception as e:
            logger.warning(f"Failed to extract professional camera setup: {e}")
            return None

    def _parse_professional_camera_recommendation(self, camera_text: str) -> Optional[str]:
        """Parse camera recommendation with professional photography enhancement"""
        try:
            # Clean and extract with professional patterns
            camera_text = re.sub(r'^(Based on.*?recommend|I would recommend|For this.*?recommend)\s*', '', camera_text, flags=re.IGNORECASE)
            
            # Professional camera patterns (more comprehensive)
            camera_patterns = [
                r'(Canon EOS R[^\s,]*(?:\s+[^\s,]*)?)',
                r'(Sony A[^\s,]*(?:\s+[^\s,]*)?)',
                r'(Leica [^\s,]+)',
                r'(Hasselblad [^\s,]+)',
                r'(Phase One [^\s,]+)',
                r'(Fujifilm [^\s,]+)',
                r'(ARRI [^\s,]+)',
                r'(RED [^\s,]+)',
                r'(Nikon [^\s,]+)'
            ]
            
            camera_model = None
            for pattern in camera_patterns:
                match = re.search(pattern, camera_text, re.IGNORECASE)
                if match:
                    camera_model = match.group(1).strip()
                    break
            
            # Professional lens patterns (enhanced)
            lens_patterns = [
                r'(\d+mm\s*f/[\d.]+(?:\s*(?:lens|anamorphic|telephoto|wide))?)',
                r'(\d+-\d+mm\s*f/[\d.]+(?:\s*lens)?)',
                r'(with\s+(?:a\s+)?(\d+mm[^,.]*))',
                r'(paired with.*?(\d+mm[^,.]*))',
                r'(\d+mm[^,]*anamorphic[^,]*)',
                r'(\d+mm[^,]*telephoto[^,]*)'
            ]
            
            lens_info = None
            for pattern in lens_patterns:
                match = re.search(pattern, camera_text, re.IGNORECASE)
                if match:
                    lens_info = match.group(1).strip()
                    lens_info = re.sub(r'^(with\s+(?:a\s+)?|paired with\s+)', '', lens_info, flags=re.IGNORECASE)
                    break
            
            # Build professional recommendation
            parts = []
            if camera_model:
                parts.append(camera_model)
            if lens_info:
                parts.append(lens_info)
            
            if parts:
                result = ', '.join(parts)
                logger.info(f"Professional camera setup extracted: {result}")
                return result
            
            return None
            
        except Exception as e:
            logger.warning(f"Failed to parse professional camera recommendation: {e}")
            return None

    def _find_professional_camera_recommendation(self, text: str) -> Optional[str]:
        """Find professional camera recommendations with enhanced detection"""
        try:
            sentences = re.split(r'[.!?]', text)
            
            for sentence in sentences:
                # Professional camera brands and technical terms
                if any(brand in sentence.lower() for brand in ['canon', 'sony', 'leica', 'hasselblad', 'phase one', 'fujifilm', 'arri', 'red']):
                    if any(term in sentence.lower() for term in ['recommend', 'suggest', 'would use', 'camera', 'lens', 'shot on']):
                        parsed = self._parse_professional_camera_recommendation(sentence.strip())
                        if parsed:
                            return parsed
            
            return None
            
        except Exception as e:
            logger.warning(f"Failed to find professional camera recommendation: {e}")
            return None

    def _enhance_description_with_professional_context(self, description: str, image: Image.Image) -> str:
        """Enhance BAGEL description with professional cinematography context"""
        try:
            if not PROFESSIONAL_PHOTOGRAPHY_CONFIG.get("enable_expert_analysis", True):
                return description
            
            # Get professional cinematography context without being invasive
            enhanced_context = self.professional_analyzer.generate_enhanced_context(description)
            
            # Extract key professional insights
            scene_type = enhanced_context.get("scene_type", "general")
            technical_context = enhanced_context.get("technical_context", "")
            professional_insight = enhanced_context.get("professional_insight", "")
            
            # Enhance description subtly with professional terminology
            enhanced_description = description
            
            # Add professional context if not already present
            if technical_context and len(technical_context) > 20:
                # Only add if it doesn't duplicate existing information
                if not any(term in description.lower() for term in ["shot on", "professional", "camera"]):
                    enhanced_description += f"\n\nProfessional Context: {technical_context}"
            
            logger.info(f"Enhanced description with cinematography context for {scene_type} scene")
            return enhanced_description
            
        except Exception as e:
            logger.warning(f"Cinematography context enhancement failed: {e}")
            return description

    def _save_temp_image(self, image: Image.Image) -> str:
        """Save image to temporary file for API call"""
        try:
            temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.png')
            temp_path = temp_file.name
            temp_file.close()
            
            if image.mode != 'RGB':
                image = image.convert('RGB')
            image.save(temp_path, 'PNG')
            
            return temp_path
            
        except Exception as e:
            logger.error(f"Failed to save temporary image: {e}")
            return None

    def _cleanup_temp_file(self, file_path: str):
        """Clean up temporary file"""
        try:
            if file_path and os.path.exists(file_path):
                os.unlink(file_path)
        except Exception as e:
            logger.warning(f"Failed to cleanup temp file: {e}")

    @spaces.GPU(duration=60)
    def analyze_image(self, image: Image.Image, prompt: str = None) -> Tuple[str, Dict[str, Any]]:
        """Analyze image using BAGEL API with professional cinematography enhancement"""
        if not self.is_initialized:
            success = self.initialize()
            if not success:
                return "BAGEL API not available", {"error": "API initialization failed"}
        
        temp_path = None
        metadata = {
            "model": "BAGEL-7B-Professional",
            "device": "api",
            "confidence": 0.9,
            "api_endpoint": self.api_endpoint,
            "space_url": self.space_url,
            "prompt_used": prompt,
            "has_camera_suggestion": False,
            "professional_enhancement": True
        }
        
        try:
            # Use professional enhanced prompt if none provided
            if prompt is None:
                prompt = self._create_professional_enhanced_prompt("multimodal")
            
            # Save image to temporary file
            temp_path = self._save_temp_image(image)
            if not temp_path:
                return "Image processing failed", {"error": "Could not save image"}
            
            logger.info("Calling BAGEL API with professional cinematography context...")
            
            # Call BAGEL API with enhanced prompt
            result = self.client.predict(
                image=handle_file(temp_path),
                prompt=prompt,
                show_thinking=False,
                do_sample=False,
                text_temperature=0.2,
                max_new_tokens=512,
                api_name=self.api_endpoint
            )
            
            # Extract and process response
            if isinstance(result, tuple) and len(result) >= 2:
                description = result[1] if result[1] else result[0]
            else:
                description = str(result)
            
            if isinstance(description, str) and description.strip():
                description = description.strip()
                
                # Extract professional camera setup
                camera_setup = self._extract_professional_camera_setup(description)
                if camera_setup:
                    metadata["camera_setup"] = camera_setup
                    metadata["has_camera_suggestion"] = True
                    logger.info(f"Professional camera setup extracted: {camera_setup}")
                else:
                    metadata["has_camera_suggestion"] = False
                    logger.info("No camera setup found, will use professional fallback")
                
                # Enhance description with cinematography context
                if PROFESSIONAL_PHOTOGRAPHY_CONFIG.get("knowledge_base_integration", True):
                    description = self._enhance_description_with_professional_context(description, image)
                    metadata["cinematography_context_applied"] = True
                
            else:
                description = "Professional image analysis completed successfully"
                metadata["has_camera_suggestion"] = False
            
            # Update metadata
            metadata.update({
                "response_length": len(description),
                "analysis_type": "professional_enhanced"
            })
            
            logger.info(f"BAGEL Professional analysis complete: {len(description)} chars, Camera: {metadata.get('has_camera_suggestion', False)}")
            return description, metadata
            
        except Exception as e:
            logger.error(f"BAGEL Professional analysis failed: {e}")
            return "Professional analysis failed", {"error": str(e), "model": "BAGEL-7B-Professional"}
        
        finally:
            if temp_path:
                self._cleanup_temp_file(temp_path)

    def analyze_for_cinematic_prompt(self, image: Image.Image) -> Tuple[str, Dict[str, Any]]:
        """Analyze image specifically for cinematic/MIA TV Series prompt generation"""
        cinematic_prompt = self._create_professional_enhanced_prompt("cinematic")
        return self.analyze_image(image, cinematic_prompt)

    def analyze_for_flux_with_professional_context(self, image: Image.Image) -> Tuple[str, Dict[str, Any]]:
        """Analyze image for FLUX with enhanced professional cinematography context"""
        flux_prompt = self._create_professional_enhanced_prompt("flux_optimized")
        return self.analyze_image(image, flux_prompt)

    def analyze_for_multiengine_prompt(self, image: Image.Image) -> Tuple[str, Dict[str, Any]]:
        """Analyze image for multi-engine compatibility (Flux, Midjourney, etc.)"""
        multiengine_prompt = self._create_professional_enhanced_prompt("multimodal")
        return self.analyze_image(image, multiengine_prompt)

    def cleanup(self) -> None:
        """Clean up API client resources"""
        try:
            if hasattr(self, 'client'):
                self.client = None
            super().cleanup()
            logger.info("BAGEL Professional API resources cleaned up")
        except Exception as e:
            logger.warning(f"BAGEL Professional API cleanup warning: {e}")


class FallbackAnalyzer(BaseImageAnalyzer):
    """Enhanced fallback analyzer with basic professional cinematography principles"""
    
    def __init__(self):
        super().__init__()
        self.professional_analyzer = professional_analyzer
        
    def initialize(self) -> bool:
        """Fallback with cinematography enhancement is always ready"""
        self.is_initialized = True
        return True
    
    def analyze_image(self, image: Image.Image) -> Tuple[str, Dict[str, Any]]:
        """Provide enhanced image description with cinematography context"""
        try:
            width, height = image.size
            mode = image.mode
            aspect_ratio = width / height
            
            # Enhanced scene detection
            if aspect_ratio > 1.5:
                orientation = "landscape"
                scene_type = "landscape"
                camera_suggestion = "Phase One XT with 24-70mm f/4 lens, landscape photography"
            elif aspect_ratio < 0.75:
                orientation = "portrait"
                scene_type = "portrait_studio"
                camera_suggestion = "Canon EOS R5 with 85mm f/1.4 lens, portrait photography"
            else:
                orientation = "square"
                scene_type = "general"
                camera_suggestion = "Canon EOS R6 with 50mm f/1.8 lens, standard photography"
            
            # Generate professional description
            description = f"A {orientation} format professional photograph with balanced composition and technical excellence. The image demonstrates clear visual hierarchy and professional execution, suitable for high-quality reproduction across multiple generative platforms. Recommended professional setup: {camera_suggestion}, with careful attention to exposure, lighting, and artistic composition."
            
            # Add cinematography context if available
            try:
                if PROFESSIONAL_PHOTOGRAPHY_CONFIG.get("enable_expert_analysis", True):
                    enhanced_context = self.professional_analyzer.generate_enhanced_context(description)
                    technical_context = enhanced_context.get("technical_context", "")
                    if technical_context:
                        description += f" Cinematography context: {technical_context}"
            except Exception as e:
                logger.warning(f"Cinematography context enhancement failed in fallback: {e}")
            
            metadata = {
                "model": "Professional-Fallback",
                "device": "cpu",
                "confidence": 0.7,
                "image_size": f"{width}x{height}",
                "color_mode": mode,
                "orientation": orientation,
                "aspect_ratio": round(aspect_ratio, 2),
                "scene_type": scene_type,
                "has_camera_suggestion": True,
                "camera_setup": camera_suggestion,
                "professional_enhancement": True
            }
            
            return description, metadata
            
        except Exception as e:
            logger.error(f"Professional fallback analysis failed: {e}")
            return "Professional image suitable for detailed analysis and multi-engine prompt generation", {
                "error": str(e), 
                "model": "Professional-Fallback"
            }


class ModelManager:
    """Enhanced manager for handling image analysis models with professional cinematography integration"""
    
    def __init__(self, preferred_model: str = "bagel-professional"):
        self.preferred_model = preferred_model
        self.analyzers = {}
        self.current_analyzer = None
        
    def get_analyzer(self, model_name: str = None) -> Optional[BaseImageAnalyzer]:
        """Get or create analyzer for specified model"""
        model_name = model_name or self.preferred_model
        
        if model_name not in self.analyzers:
            if model_name in ["bagel-api", "bagel-professional"]:
                self.analyzers[model_name] = BagelAPIAnalyzer()
            elif model_name == "fallback":
                self.analyzers[model_name] = FallbackAnalyzer()
            else:
                logger.warning(f"Unknown model: {model_name}, using professional fallback")
                model_name = "fallback"
                self.analyzers[model_name] = FallbackAnalyzer()
        
        return self.analyzers[model_name]
    
    def analyze_image(self, image: Image.Image, model_name: str = None, analysis_type: str = "multiengine") -> Tuple[str, Dict[str, Any]]:
        """Analyze image with professional cinematography enhancement"""
        analyzer = self.get_analyzer(model_name)
        if analyzer is None:
            return "No analyzer available", {"error": "Model not found"}
        
        # Choose analysis method based on type and analyzer capabilities
        if analysis_type == "cinematic" and hasattr(analyzer, 'analyze_for_cinematic_prompt'):
            success, result = safe_execute(analyzer.analyze_for_cinematic_prompt, image)
        elif analysis_type == "flux" and hasattr(analyzer, 'analyze_for_flux_with_professional_context'):
            success, result = safe_execute(analyzer.analyze_for_flux_with_professional_context, image)
        elif analysis_type == "multiengine" and hasattr(analyzer, 'analyze_for_multiengine_prompt'):
            success, result = safe_execute(analyzer.analyze_for_multiengine_prompt, image)
        else:
            success, result = safe_execute(analyzer.analyze_image, image)
        
        if success and result[1].get("error") is None:
            return result
        else:
            # Enhanced fallback with cinematography context
            logger.warning(f"Primary model failed, using cinematography-enhanced fallback: {result}")
            fallback_analyzer = self.get_analyzer("fallback")
            fallback_success, fallback_result = safe_execute(fallback_analyzer.analyze_image, image)
            
            if fallback_success:
                return fallback_result
            else:
                return "All cinematography analyzers failed", {"error": "Complete analysis failure"}
    
    def cleanup_all(self) -> None:
        """Clean up all model resources"""
        for analyzer in self.analyzers.values():
            analyzer.cleanup()
        self.analyzers.clear()
        clean_memory()
        logger.info("All cinematography analyzers cleaned up")


# Global model manager instance with cinematography enhancement
model_manager = ModelManager(preferred_model="bagel-professional")


def analyze_image(image: Image.Image, model_name: str = None, analysis_type: str = "multiengine") -> Tuple[str, Dict[str, Any]]:
    """
    Enhanced convenience function for professional cinematography analysis
    
    Args:
        image: PIL Image to analyze
        model_name: Optional model name ("bagel-professional", "fallback")
        analysis_type: Type of analysis ("multiengine", "cinematic", "flux")
        
    Returns:
        Tuple of (description, metadata) with professional cinematography enhancement
    """
    return model_manager.analyze_image(image, model_name, analysis_type)


# Export main components
__all__ = [
    "BaseImageAnalyzer",
    "BagelAPIAnalyzer", 
    "FallbackAnalyzer",
    "ModelManager",
    "model_manager",
    "analyze_image"
]