models.py

"""
Model management for Frame 0 Laboratory for MIA
BAGEL 7B integration via API calls
"""

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

from config import get_device_config
from utils import clean_memory, safe_execute

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 via API calls to working Space"""
    
    def __init__(self):
        super().__init__()
        self.client = None
        self.space_url = "Malaji71/Bagel-7B-Demo"
        self.api_endpoint = "/image_understanding"
        
    def initialize(self) -> bool:
        """Initialize BAGEL API client"""
        if self.is_initialized:
            return True
        
        try:
            logger.info("Initializing BAGEL API client...")
            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}")
            return False

    def _extract_camera_setup(self, description: str) -> Optional[str]:
        """Extract camera setup recommendation from BAGEL response"""
        try:
            # Look for CAMERA_SETUP section
            if "CAMERA_SETUP:" in description:
                parts = description.split("CAMERA_SETUP:")
                if len(parts) > 1:
                    camera_part = parts[1].strip()
                    # Clean up any additional formatting
                    camera_part = camera_part.replace("\n", " ").strip()
                    return camera_part
            
            # Alternative patterns for camera recommendations
            camera_patterns = [
                "Shot on ",
                "Camera: ",
                "Equipment: ",
                "Recommended camera:",
                "Camera setup:"
            ]
            
            for pattern in camera_patterns:
                if pattern in description:
                    # Extract text after the pattern
                    idx = description.find(pattern)
                    camera_text = description[idx:].split('.')[0]  # Take first sentence
                    if len(camera_text) > len(pattern) + 10:  # Ensure meaningful content
                        return camera_text.strip()
            
            return None
            
        except Exception as e:
            logger.warning(f"Failed to extract camera setup: {e}")
            return None

    def _save_temp_image(self, image: Image.Image) -> str:
        """Save image to temporary file for API call"""
        try:
            # Create temporary file
            temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.png')
            temp_path = temp_file.name
            temp_file.close()
            
            # Save image
            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"""
        if not self.is_initialized:
            success = self.initialize()
            if not success:
                return "BAGEL API not available", {"error": "API initialization failed"}
        
        temp_path = None
        
        # Initialize metadata early
        metadata = {
            "model": "BAGEL-7B-API",
            "device": "api",
            "confidence": 0.9,
            "api_endpoint": self.api_endpoint,
            "space_url": self.space_url,
            "prompt_used": prompt,
            "has_camera_suggestion": False
        }
        
        try:
            # Default prompt for detailed image analysis
            if prompt is None:
                prompt = """You are analyzing a photograph for FLUX image generation. Provide a detailed analysis in two sections:

1. DESCRIPTION: Start directly with the subject (e.g., "A color photograph showing..." or "A black and white photograph depicting..."). First, determine if this is a photograph, illustration, or artwork. Then describe the visual elements, composition, lighting, colors (be specific about the color palette - warm tones, cool tones, monochrome, etc.), artistic style, mood, and atmosphere. Also mention the image format/aspect ratio (square, portrait, landscape, widescreen, etc.) and how the composition uses this format. Write as a flowing paragraph without numbered lists.

2. CAMERA_SETUP: Based on the photographic characteristics, scene type, and aspect ratio you observe, recommend the specific camera system and lens that would realistically capture this type of scene:
- For street/documentary photography: suggest cameras like Canon EOS R6, Sony A7 IV, Leica Q2 with 35mm or 24-70mm lenses
- For portraits: suggest cameras like Canon EOS R5, Sony A7R V with 85mm or 135mm lenses  
- For landscapes/widescreen: suggest cameras like Phase One XT, Fujifilm GFX with wide-angle lenses (16-35mm, 24-70mm)
- For sports/action: suggest cameras like Canon EOS-1D X, Sony A9 III with telephoto lenses
- For macro: suggest specialized macro lenses
- For cinematic/widescreen formats: suggest cinema cameras or full-frame with appropriate aspect ratios
Be specific about focal length, aperture, and shooting style based on what you actually see in the image dimensions and content.

Analyze carefully and be accurate about colors, image type, and proportions."""
            
            # 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 for image analysis...")
            
            # Call BAGEL API
            result = self.client.predict(
                image=handle_file(temp_path),
                prompt=prompt,
                show_thinking=False,
                do_sample=False,
                text_temperature=0.3,
                max_new_tokens=512,
                api_name=self.api_endpoint
            )
            
            # Extract response (API returns tuple: (image_result, text_response))
            if isinstance(result, tuple) and len(result) >= 2:
                description = result[1] if result[1] else result[0]
            else:
                description = str(result)
            
            # Clean up the description and extract camera setup if present
            if isinstance(description, str) and description.strip():
                description = description.strip()
                
                # Store camera setup separately if found
                camera_setup = self._extract_camera_setup(description)
                if camera_setup:
                    metadata["camera_setup"] = camera_setup
                    metadata["has_camera_suggestion"] = True
                else:
                    metadata["has_camera_suggestion"] = False
            else:
                description = "Detailed image analysis completed successfully"
                metadata["has_camera_suggestion"] = False
            
            # Update final metadata
            metadata.update({
                "response_length": len(description)
            })
            
            logger.info(f"BAGEL API analysis complete: {len(description)} characters")
            return description, metadata
            
        except Exception as e:
            logger.error(f"BAGEL API analysis failed: {e}")
            return "API analysis failed", {"error": str(e), "model": "BAGEL-7B-API"}
        
        finally:
            # Always cleanup temporary file
            if temp_path:
                self._cleanup_temp_file(temp_path)

    def analyze_for_flux_prompt(self, image: Image.Image) -> Tuple[str, Dict[str, Any]]:
        """Analyze image specifically for FLUX prompt generation"""
        flux_prompt = """You are analyzing a photograph for professional FLUX generation. Provide two sections:

1. DESCRIPTION: Determine first if this is a real photograph, digital artwork, or illustration. Then create a detailed, flowing description starting directly with the subject. Be precise about:
- Image type (photograph, illustration, artwork)
- Color palette (specify if color or black/white, warm/cool tones, specific colors)
- Photographic style (street, portrait, landscape, documentary, artistic, etc.)
- Composition, lighting, mood, and atmosphere
Write as a single coherent paragraph.

2. CAMERA_SETUP: Recommend specific professional equipment that would realistically capture this exact scene:
- Street/urban scenes: Canon EOS R6, Sony A7 IV, Leica Q2 with 24-70mm f/2.8 or 35mm f/1.4
- Portraits: Canon EOS R5, Sony A7R V, Hasselblad X2D with 85mm f/1.4 or 135mm f/2
- Landscapes: Phase One XT, Fujifilm GFX 100S with 16-35mm f/2.8 or 40mm f/4
- Documentary: Canon EOS-1D X, Sony A9 III with 24-105mm f/4 or 70-200mm f/2.8
- Action/Sports: Canon EOS R3, Sony A1 with 300mm f/2.8 or 400mm f/2.8
Match the equipment to what you actually observe in the scene type and shooting conditions."""
        
        return self.analyze_image(image, flux_prompt)

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


class FallbackAnalyzer(BaseImageAnalyzer):
    """Simple fallback analyzer when BAGEL API is not available"""
    
    def __init__(self):
        super().__init__()
        
    def initialize(self) -> bool:
        """Fallback is always ready"""
        self.is_initialized = True
        return True
    
    def analyze_image(self, image: Image.Image) -> Tuple[str, Dict[str, Any]]:
        """Provide basic image description"""
        try:
            # Basic image analysis
            width, height = image.size
            mode = image.mode
            
            # Simple descriptive text based on image properties
            aspect_ratio = width / height
            
            if aspect_ratio > 1.5:
                orientation = "landscape"
                camera_suggestion = "wide-angle lens, landscape photography"
            elif aspect_ratio < 0.75:
                orientation = "portrait"
                camera_suggestion = "portrait lens, shallow depth of field"
            else:
                orientation = "square"
                camera_suggestion = "standard lens, balanced composition"
            
            description = f"A {orientation} format image with professional composition. The image shows clear detail and good visual balance, suitable for high-quality reproduction. Recommended camera setup: {camera_suggestion}, professional lighting with careful attention to exposure and color balance."
            
            metadata = {
                "model": "Fallback",
                "device": "cpu",
                "confidence": 0.6,
                "image_size": f"{width}x{height}",
                "color_mode": mode,
                "orientation": orientation,
                "aspect_ratio": round(aspect_ratio, 2)
            }
            
            return description, metadata
            
        except Exception as e:
            logger.error(f"Fallback analysis failed: {e}")
            return "Professional image suitable for detailed analysis and prompt generation", {"error": str(e), "model": "Fallback"}


class ModelManager:
    """Manager for handling image analysis models"""
    
    def __init__(self, preferred_model: str = "bagel-api"):
        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 == "bagel-api":
                self.analyzers[model_name] = BagelAPIAnalyzer()
            elif model_name == "fallback":
                self.analyzers[model_name] = FallbackAnalyzer()
            else:
                logger.warning(f"Unknown model: {model_name}, using 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 = "detailed") -> Tuple[str, Dict[str, Any]]:
        """Analyze image with specified or preferred model"""
        # Try preferred model first
        analyzer = self.get_analyzer(model_name)
        if analyzer is None:
            return "No analyzer available", {"error": "Model not found"}
        
        # Choose analysis method based on type
        if analysis_type == "flux" and hasattr(analyzer, 'analyze_for_flux_prompt'):
            success, result = safe_execute(analyzer.analyze_for_flux_prompt, image)
        else:
            success, result = safe_execute(analyzer.analyze_image, image)
        
        if success and result[1].get("error") is None:
            return result
        else:
            # Fallback to simple analyzer if main model fails
            logger.warning(f"Primary model failed, using 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 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 analyzers cleaned up")


# Global model manager instance
model_manager = ModelManager(preferred_model="bagel-api")


def analyze_image(image: Image.Image, model_name: str = None, analysis_type: str = "detailed") -> Tuple[str, Dict[str, Any]]:
    """
    Convenience function for image analysis using BAGEL API
    
    Args:
        image: PIL Image to analyze
        model_name: Optional model name ("bagel-api" or "fallback")
        analysis_type: Type of analysis ("detailed" or "flux")
        
    Returns:
        Tuple of (description, metadata)
    """
    return model_manager.analyze_image(image, model_name, analysis_type)


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