"""
Predictor - Orchestrates the complete prediction pipeline
Combines validation, model inference, and treatment generation
"""

from .validator import FishImageValidator
from .model_loader import VGG16ModelLoader
from .treatment import TreatmentGenerator
from .config import CLASSES, CONFIDENCE_THRESHOLD

class FishDiseasePredictor:
    """Main prediction pipeline that coordinates all components"""
    
    def __init__(self, config, gemini_model=None):
        """
        Initialize predictor with all components
        
        Args:
            config: Configuration dictionary with all settings
            gemini_model: Google Gemini model instance (optional)
        """
        self.config = config
        self.gemini_model = gemini_model
        self.confidence_threshold = config.get('CONFIDENCE_THRESHOLD', CONFIDENCE_THRESHOLD)
        
        # Initialize all components
        self.validator = FishImageValidator(
            max_size_mb=config.get('MAX_FILE_SIZE_MB', 10),
            min_size_px=config.get('MIN_IMAGE_SIZE_PX', 100),
            valid_extensions=config.get('VALID_EXTENSIONS')
        )
        
        self.model_loader = VGG16ModelLoader(
            model_path=config['MODEL_PATH'],
            num_classes=len(config['CLASSES']),
            device=config.get('DEVICE', 'cpu')
        )
        
        self.treatment_generator = TreatmentGenerator(gemini_model)
    
    def predict(self, image_path):
        """
        Complete prediction pipeline with all validations
        
        Args:
            image_path: Path to image file
            
        Returns:
            dict: Complete result with validation, prediction, and treatment
                {
                    'success': bool,
                    'error': str or None,
                    'validation': {
                        'file': {'valid': bool, 'message': str},
                        'gemini': {'valid': bool, 'message': str}
                    },
                    'prediction': {
                        'disease': str,
                        'confidence': float,
                        'probabilities': dict,
                        'below_threshold': bool
                    },
                    'treatment': str or None
                }
        """
        result = {
            'success': False,
            'error': None,
            'validation': {},
            'prediction': {},
            'treatment': None
        }
        
        # STEP 1: File validation (format, size, corruption)
        print("🔍 Validating file...")
        is_valid, msg, image = self.validator.validate_file(image_path)
        result['validation']['file'] = {'valid': is_valid, 'message': msg}
        
        if not is_valid:
            result['error'] = msg
            return result
        
        # STEP 2: Gemini AI validation (fish detection, edge cases)
        print("🤖 Validating with Gemini AI...")
        is_valid, msg = self.validator.validate_with_gemini(image, self.gemini_model)
        result['validation']['gemini'] = {'valid': is_valid, 'message': msg}
        print(msg)
        
        if not is_valid:
            result['error'] = msg
            return result
        
        # STEP 3: VGG16 disease prediction
        print("🔬 Analyzing fish health...")
        try:
            class_idx, confidence, probabilities = self.model_loader.predict(image)
            
            predicted_class = self.config['CLASSES'][class_idx]
            
            # Build probability dictionary
            prob_dict = {
                self.config['CLASSES'][i]: float(probabilities[i].item() * 100)
                for i in range(len(self.config['CLASSES']))
            }
            
            result['prediction'] = {
                'disease': predicted_class,
                'confidence': confidence,
                'probabilities': prob_dict,
                'below_threshold': confidence < self.confidence_threshold
            }
            
            # STEP 4: Generate treatment (only if confident)
            if confidence >= self.confidence_threshold:
                print("💊 Generating treatment recommendations...")
                treatment = self.treatment_generator.get_recommendations(
                    predicted_class, confidence
                )
                result['treatment'] = treatment
            
            result['success'] = True
            return result
            
        except Exception as e:
            result['error'] = f"❌ Prediction failed: {str(e)}"
            return result
    
    def predict_from_image(self, pil_image):
        """
        Predict directly from PIL Image (for web UI)
        
        Args:
            pil_image: PIL Image object
            
        Returns:
            dict: Same as predict() method
        """
        result = {
            'success': False,
            'error': None,
            'validation': {},
            'prediction': {},
            'treatment': None
        }
        
        try:
            # Convert to RGB if needed
            image = pil_image.convert('RGB')
            
            # File validation not needed (already have image)
            result['validation']['file'] = {'valid': True, 'message': '✅ Image provided'}
            
            # Gemini validation
            print("🤖 Validating with Gemini AI...")
            is_valid, msg = self.validator.validate_with_gemini(image, self.gemini_model)
            result['validation']['gemini'] = {'valid': is_valid, 'message': msg}
            print(msg)
            
            if not is_valid:
                result['error'] = msg
                return result
            
            # Model prediction
            print("🔬 Analyzing fish health...")
            class_idx, confidence, probabilities = self.model_loader.predict(image)
            
            predicted_class = self.config['CLASSES'][class_idx]
            
            prob_dict = {
                self.config['CLASSES'][i]: float(probabilities[i].item() * 100)
                for i in range(len(self.config['CLASSES']))
            }
            
            result['prediction'] = {
                'disease': predicted_class,
                'confidence': confidence,
                'probabilities': prob_dict,
                'below_threshold': confidence < self.confidence_threshold
            }
            
            # Generate treatment
            if confidence >= self.confidence_threshold:
                print("💊 Generating treatment recommendations...")
                treatment = self.treatment_generator.get_recommendations(
                    predicted_class, confidence
                )
                result['treatment'] = treatment
            
            result['success'] = True
            return result
            
        except Exception as e:
            result['error'] = f"❌ Prediction failed: {str(e)}"
            return result