models.py

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

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
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"
        self.hf_token = os.getenv("HF_TOKEN")  # Get token from environment/secrets
        
    def initialize(self) -> bool:
        """Initialize BAGEL API client with authentication"""
        if self.is_initialized:
            return True
        
        try:
            logger.info("Initializing BAGEL API client...")
            
            # Initialize client with token if available (for private spaces)
            if self.hf_token:
                logger.info("Using HF token for private space access")
                self.client = Client(self.space_url, hf_token=self.hf_token)
            else:
                logger.info("No HF token found, accessing public space")
                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 private space fails, try without token as fallback
            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 successfully (fallback to public)")
                    return True
                except Exception as e2:
                    logger.error(f"Fallback initialization also failed: {e2}")
            return False

    def _extract_camera_setup(self, description: str) -> Optional[str]:
        """Extract camera setup recommendation from BAGEL response with improved parsing"""
        try:
            # Look for CAMERA_SETUP section first
            if "CAMERA_SETUP:" in description:
                parts = description.split("CAMERA_SETUP:")
                if len(parts) > 1:
                    camera_section = parts[1].strip()
                    # Take the first meaningful sentence from camera setup
                    camera_text = camera_section.split('\n')[0].strip()
                    if len(camera_text) > 20:  # Ensure meaningful content
                        return self._parse_camera_recommendation(camera_text)
            
            # Look for "2. CAMERA_SETUP" pattern
            if "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:
                        return self._parse_camera_recommendation(camera_text)
            
            # Look for camera recommendations within the text
            camera_recommendation = self._find_camera_recommendation(description)
            if camera_recommendation:
                return camera_recommendation
            
            return None
            
        except Exception as e:
            logger.warning(f"Failed to extract camera setup: {e}")
            return None

    def _parse_camera_recommendation(self, camera_text: str) -> Optional[str]:
        """Parse and extract specific camera and lens information"""
        try:
            # Remove common prefixes and clean text
            camera_text = re.sub(r'^(Based on.*?recommend|I would recommend|For this.*?recommend)\s*', '', camera_text, flags=re.IGNORECASE)
            camera_text = re.sub(r'^(using a|use a|cameras? like)\s*', '', camera_text, flags=re.IGNORECASE)
            
            # Extract camera model with specific patterns
            camera_patterns = [
                r'(Canon EOS [R\d]+[^\s,]*(?:\s+[IVX]+)?)',
                r'(Sony A[^\s,]+(?:\s+[IVX]+)?)', 
                r'(Leica [^\s,]+)',
                r'(Hasselblad [^\s,]+)',
                r'(Phase One [^\s,]+)',
                r'(Fujifilm [^\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
            
            # Extract lens information with improved patterns
            lens_patterns = [
                r'(\d+mm\s*f/[\d.]+(?:\s*lens)?)',
                r'(\d+-\d+mm\s*f/[\d.]+(?:\s*lens)?)',
                r'(with\s+(?:a\s+)?(\d+mm[^,.]*))',
                r'(paired with.*?(\d+mm[^,.]*))' 
            ]
            
            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
            
            # Extract aperture if not in lens info
            if not lens_info or 'f/' not in lens_info:
                aperture_match = re.search(r'(f/[\d.]+)', camera_text)
                aperture = aperture_match.group(1) if aperture_match else None
                if aperture and lens_info:
                    lens_info = f"{lens_info} {aperture}"
            
            # Build clean recommendation
            parts = []
            if camera_model:
                parts.append(camera_model)
            if lens_info:
                parts.append(lens_info)
            
            if parts:
                result = ', '.join(parts)
                logger.info(f"Parsed camera recommendation: {result}")
                return result
            
            return None
            
        except Exception as e:
            logger.warning(f"Failed to parse camera recommendation: {e}")
            return None

    def _find_camera_recommendation(self, text: str) -> Optional[str]:
        """Find camera recommendations anywhere in the text"""
        try:
            # Look for sentences containing camera info
            sentences = re.split(r'[.!?]', text)
            
            for sentence in sentences:
                # Check if sentence contains camera info
                if any(brand in sentence.lower() for brand in ['canon', 'sony', 'leica', 'hasselblad', 'phase one', 'fujifilm']):
                    if any(term in sentence.lower() for term in ['recommend', 'suggest', 'would use', 'camera', 'lens']):
                        parsed = self._parse_camera_recommendation(sentence.strip())
                        if parsed:
                            return parsed
            
            return None
            
        except Exception as e:
            logger.warning(f"Failed to find camera recommendation: {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:
            # Enhanced prompt for better structured output
            if prompt is None:
                prompt = """Analyze this image for professional photography reproduction. Provide exactly two sections:

1. DESCRIPTION: Write a single flowing paragraph describing what you see. Start directly with the subject (e.g., "A color photograph showing..." or "A black and white image depicting..."). Include:
- Image type (photograph, illustration, artwork)
- Subject and composition
- Color palette and lighting conditions
- Mood and atmosphere
- Photographic style and format

2. CAMERA_SETUP: Based on the scene type you observe, recommend ONE specific professional camera and lens combination:
- For street/documentary scenes: Canon EOS R6 with 35mm f/1.4 lens
- For portrait photography: Canon EOS R5 with 85mm f/1.4 lens
- For landscape photography: Phase One XT with 24-70mm f/4 lens
- For action/sports: Sony A1 with 70-200mm f/2.8 lens

Give only the camera model and lens specification, nothing else."""
            
            # 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=0.2,
                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)
            
            # Process the description and extract camera setup
            if isinstance(description, str) and description.strip():
                description = description.strip()
                
                # Extract camera setup with improved parsing
                camera_setup = self._extract_camera_setup(description)
                if camera_setup:
                    metadata["camera_setup"] = camera_setup
                    metadata["has_camera_suggestion"] = True
                    logger.info(f"Extracted camera setup: {camera_setup}")
                else:
                    metadata["has_camera_suggestion"] = False
                    logger.warning("No valid camera setup found in BAGEL response")
            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, Camera: {metadata.get('has_camera_suggestion', False)}")
            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 = """Analyze this image for professional FLUX generation. Provide exactly two sections:

1. DESCRIPTION: Create a single flowing paragraph starting directly with the subject. Be precise about:
- Image type (photograph, illustration, artwork)  
- Subject matter and composition
- Color palette (specific colors, warm/cool tones, monochrome)
- Lighting conditions and photographic style
- Mood, atmosphere, and artistic elements

2. CAMERA_SETUP: Recommend ONE specific professional camera and lens for this scene type:
- Street/urban/documentary: Canon EOS R6 with 35mm f/1.4 lens
- Portrait photography: Canon EOS R5 with 85mm f/1.4 lens
- Landscape photography: Phase One XT with 24-70mm f/4 lens
- Action/sports: Sony A1 with 70-200mm f/2.8 lens

Give only the camera model and exact lens specification."""
        
        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"
]