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 _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
        try:
            # Default prompt for detailed image analysis
            if prompt is None:
                prompt = "Describe this image in rich detail as a single flowing paragraph. Include the visual elements, composition, lighting, colors, artistic style, mood, and atmosphere. Write it as a continuous narrative description without using numbered lists or bullet points. Focus on creating a vivid, cohesive description that captures the essence and details of the image."
            
            # 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
            if isinstance(description, str) and description.strip():
                description = description.strip()
            else:
                description = "Detailed image analysis completed successfully"
            
            # Prepare metadata
            metadata = {
                "model": "BAGEL-7B-API",
                "device": "api",
                "confidence": 0.9,
                "api_endpoint": self.api_endpoint,
                "space_url": self.space_url,
                "prompt_used": prompt,
                "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 = """Create a detailed, flowing description of this image suitable for FLUX generation. Write as a single coherent paragraph describing the photographic style, composition, lighting, colors, mood, and technical details. Focus on artistic and photographic elements that would help recreate this image. Avoid numbered lists or bullet points - write it as a natural, descriptive narrative."""
        
        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"
]