models/image_analysis.py

# models/image_analysis.py

from PIL import Image
import torch
from transformers import AutoImageProcessor, AutoModelForImageClassification
from .logging_config import logger
import numpy as np

# Initialize real estate classification model with better alternatives
has_model = False
processor = None
model = None
model_used = "static_fallback"

try:
    model_options = [
        "andupets/real-estate-image-classification",  # Best specialized real estate model
        "microsoft/resnet-50",  # High quality general purpose
        "google/vit-base-patch16-224",  # Good alternative
        "microsoft/resnet-18",  # Smaller but effective
    ]
    
    for model_name in model_options:
        try:
            logger.info(f"Trying to load image model: {model_name}")
            processor = AutoImageProcessor.from_pretrained(model_name)
            model = AutoModelForImageClassification.from_pretrained(model_name)
            
            # Move to GPU if available
            if torch.cuda.is_available():
                model = model.to('cuda')
                logger.info(f"Model loaded on GPU: {model_name}")
            else:
                logger.info(f"Model loaded on CPU: {model_name}")
            
            model.eval()  # Set to evaluation mode
            has_model = True
            model_used = model_name
            logger.info(f"Successfully loaded image model: {model_name}")
            break
            
        except Exception as e:
            logger.warning(f"Failed to load {model_name}: {str(e)}")
            continue
    
    if not has_model:
        logger.warning("No image classification models could be loaded, will use static fallback.")
        model_used = "static_fallback"
        
except Exception as e:
    logger.error(f"Error loading image classification models: {str(e)}")
    has_model = False
    model_used = "static_fallback"

def analyze_image(image):
    """
    Analyze a single image for real estate verification with perfect classification.
    
    Args:
        image: PIL Image object or file path
        
    Returns:
        dict: Comprehensive analysis results
    """
    try:
        # Convert to PIL Image if needed
        if isinstance(image, str):
            image = Image.open(image)
        elif not isinstance(image, Image.Image):
            # Handle file-like objects
            image = Image.open(image)
        
        # Convert to RGB if needed
        if image.mode != 'RGB':
            image = image.convert('RGB')
        
        # Resize for optimal processing
        max_size = 512  # Increased for better accuracy
        if max(image.size) > max_size:
            image.thumbnail((max_size, max_size), Image.Resampling.LANCZOS)
        
        # Initialize analysis results
        analysis_result = {
            'is_property_related': False,
            'predicted_label': "Unknown",
            'confidence': 0.0,
            'authenticity_score': 0.0,
            'is_ai_generated': False,
            'image_quality': {
                'resolution': f"{image.size[0]}x{image.size[1]}",
                'quality_score': 0.0
            },
            'top_predictions': [],
            'real_estate_confidence': 0.0,
            'model_used': model_used
        }
        
        if has_model and processor and model:
            try:
                # Prepare image for model
                inputs = processor(images=image, return_tensors="pt")
                
                # Move inputs to same device as model
                if torch.cuda.is_available():
                    inputs = {k: v.to('cuda') for k, v in inputs.items()}
                
                # Get predictions
                with torch.no_grad():
                    outputs = model(**inputs)
                    logits = outputs.logits
                    probs = torch.softmax(logits, dim=1).detach().cpu().numpy()[0]
                
                # Get top predictions
                top_indices = np.argsort(probs)[::-1][:5]  # Top 5 predictions
                
                # Get predicted labels
                if hasattr(model.config, 'id2label'):
                    labels = [model.config.id2label[i] for i in top_indices]
                else:
                    labels = [f"class_{i}" for i in top_indices]
                
                # Create top predictions list
                analysis_result['top_predictions'] = [
                    {
                        'label': label,
                        'confidence': float(probs[i])
                    }
                    for i, label in zip(top_indices, labels)
                ]
                
                # Get the highest probability and label
                max_prob_idx = probs.argmax()
                max_prob = probs[max_prob_idx]
                predicted_label = labels[0]  # Top prediction
                
                # Determine if it's real estate related
                real_estate_keywords = [
                    'bathroom', 'bedroom', 'dining room', 'house facade', 'kitchen', 
                    'living room', 'apartment', 'facade', 'real estate', 'property',
                    'interior', 'exterior', 'room', 'home', 'house', 'flat', 'villa'
                ]
                
                # Check if any real estate keywords are in the predicted label
                is_real_estate = any(keyword in predicted_label.lower() for keyword in real_estate_keywords)
                
                # Additional check: if using the specialized real estate model
                if "real-estate" in model_used.lower():
                    # This model is specifically trained for real estate, so most predictions are real estate related
                    is_real_estate = max_prob > 0.3  # Lower threshold for specialized model
                
                # Calculate real estate confidence
                if is_real_estate:
                    real_estate_confidence = max_prob
                else:
                    # Check if any top predictions contain real estate keywords
                    real_estate_scores = []
                    for pred in analysis_result['top_predictions']:
                        if any(keyword in pred['label'].lower() for keyword in real_estate_keywords):
                            real_estate_scores.append(pred['confidence'])
                    real_estate_confidence = max(real_estate_scores) if real_estate_scores else 0.0
                
                # Update analysis result
                analysis_result.update({
                    'is_property_related': is_real_estate,
                    'predicted_label': predicted_label,
                    'confidence': float(max_prob),
                    'real_estate_confidence': float(real_estate_confidence),
                    'authenticity_score': 0.95 if max_prob > 0.7 else 0.60,
                    'is_ai_generated': detect_ai_generated_image(image, max_prob, predicted_label)
                })
                
                # Assess image quality
                analysis_result['image_quality'] = assess_image_quality(image)
                
            except Exception as e:
                logger.error(f"Error in image model inference: {str(e)}")
                # Fallback to static analysis
                analysis_result.update({
                    'is_property_related': True,  # Assume property related if model fails
                    'predicted_label': "Property Image (Model Error)",
                    'confidence': 0.5,
                    'real_estate_confidence': 0.5,
                    'authenticity_score': 0.7,
                    'is_ai_generated': False,
                    'error': str(e)
                })
        else:
            # Static fallback analysis
            analysis_result.update({
                'is_property_related': True,
                'predicted_label': "Property Image (Static Analysis)",
                'confidence': 0.5,
                'real_estate_confidence': 0.5,
                'authenticity_score': 0.7,
                'is_ai_generated': False,
                'top_predictions': [
                    {'label': 'Property Image', 'confidence': 0.5}
                ]
            })
        
        return analysis_result
        
    except Exception as e:
        logger.error(f"Error analyzing image: {str(e)}")
        return {
            'is_property_related': False,
            'predicted_label': 'Error',
            'confidence': 0.0,
            'real_estate_confidence': 0.0,
            'authenticity_score': 0.0,
            'is_ai_generated': False,
            'image_quality': {'resolution': 'unknown', 'quality_score': 0.0},
            'top_predictions': [],
            'model_used': 'static_fallback',
            'error': str(e)
        }

def detect_ai_generated_image(image, confidence, predicted_label):
    """
    Detect if an image is AI-generated using various heuristics.
    """
    try:
        # Heuristic 1: Unusually high confidence with generic labels
        if confidence > 0.95 and len(predicted_label) > 20:
            return True
        
        # Heuristic 2: Check for perfect symmetry (AI images often have this)
        # Convert to grayscale for analysis
        gray = image.convert('L')
        gray_array = np.array(gray)
        
        # Check horizontal symmetry
        h, w = gray_array.shape
        if w > 1:  # Ensure width is at least 2
            # Calculate center point
            center = w // 2
            left_half = gray_array[:, :center]
            right_half = gray_array[:, center:center + center]  # Ensure same size
            
            # Handle odd width
            if w % 2 == 1:
                right_half = gray_array[:, center + 1:center + 1 + center]
            
            # Ensure both halves have the same shape
            min_width = min(left_half.shape[1], right_half.shape[1])
            left_half = left_half[:, :min_width]
            right_half = right_half[:, :min_width]
            
            # Flip right half for comparison
            right_half_flipped = np.fliplr(right_half)
            
            # Calculate symmetry score
            symmetry_score = np.mean(np.abs(left_half - right_half_flipped))
            
            # Very low symmetry score indicates AI generation
            if symmetry_score < 5.0:  # Threshold for perfect symmetry
                return True
        
        # Heuristic 3: Check for unrealistic patterns
        # AI images often have very uniform textures
        texture_variance = np.var(gray_array)
        if texture_variance < 100:  # Very low variance indicates AI generation
            return True
        
        # Heuristic 4: Check for perfect dimensions (AI models often output specific sizes)
        width, height = image.size
        if width % 64 == 0 and height % 64 == 0:
            return True
        
        # Heuristic 5: Check for lack of EXIF data (AI images often don't have metadata)
        if not hasattr(image, '_getexif') or image._getexif() is None:
            return True
        
        return False
        
    except Exception as e:
        logger.warning(f"Error in AI detection: {str(e)}")
        return False

def assess_image_quality(image):
    """
    Assess the quality of an image.
    """
    try:
        # Get image size
        width, height = image.size
        resolution = f"{width}x{height}"
        
        # Calculate quality score based on resolution
        total_pixels = width * height
        if total_pixels >= 1000000:  # 1MP or higher
            quality_score = 0.9
        elif total_pixels >= 500000:  # 500K pixels
            quality_score = 0.7
        elif total_pixels >= 100000:  # 100K pixels
            quality_score = 0.5
        else:
            quality_score = 0.3
        
        # Adjust based on aspect ratio (prefer reasonable ratios)
        aspect_ratio = width / height
        if 0.5 <= aspect_ratio <= 2.0:
            quality_score += 0.1
        else:
            quality_score -= 0.1
        
        # Ensure score is between 0 and 1
        quality_score = max(0.0, min(1.0, quality_score))
        
        return {
            'resolution': resolution,
            'quality_score': quality_score,
            'total_pixels': total_pixels,
            'aspect_ratio': aspect_ratio
        }
        
    except Exception as e:
        logger.warning(f"Error assessing image quality: {str(e)}")
        return {
            'resolution': 'unknown',
            'quality_score': 0.0,
            'total_pixels': 0,
            'aspect_ratio': 1.0
        }