"""
AI Engine for PyBrain - Core artificial intelligence capabilities
"""

from typing import Any, Dict, List, Optional, Union
import numpy as np
from pydantic import BaseModel, Field
import logging

logger = logging.getLogger(__name__)


class ModelConfig(BaseModel):
    """Configuration for AI models"""
    
    model_name: str = Field(..., description="Name of the model")
    model_type: str = Field(..., description="Type of model (nlp, vision, etc)")
    version: str = Field(default="1.0.0", description="Model version")
    parameters: Dict[str, Any] = Field(default_factory=dict)
    device: str = Field(default="cpu", description="Device to run model on")


class AIEngine:
    """
    Core AI Engine for healthcare intelligence
    
    Provides unified interface for all AI operations including:
    - Clinical NLP
    - Medical image analysis
    - Predictive modeling
    - Federated learning
    """
    
    def __init__(self, config: Optional[ModelConfig] = None):
        self.config = config or ModelConfig(
            model_name="clinical-bert",
            model_type="nlp"
        )
        self.models: Dict[str, Any] = {}
        self.tokenizers: Dict[str, Any] = {}
        self._initialize_models()
    
    def _initialize_models(self):
        """Initialize AI models based on configuration"""
        if self.config.model_type == "nlp":
            self._load_nlp_models()
        elif self.config.model_type == "vision":
            self._load_vision_models()
        elif self.config.model_type == "multimodal":
            self._load_multimodal_models()
    
    def _load_nlp_models(self):
        """Load NLP models for clinical text processing"""
        try:
            # For initial release, use simple implementations
            # In production, would load actual transformer models
            logger.info("Loading clinical NLP models...")
            self.models["clinical_bert"] = "placeholder"
            self.tokenizers["clinical_bert"] = "placeholder"
        except Exception as e:
            logger.error(f"Failed to load NLP models: {e}")
    
    def _load_vision_models(self):
        """Load vision models for medical imaging"""
        logger.info("Loading medical vision models...")
        
    def _load_multimodal_models(self):
        """Load multimodal models"""
        logger.info("Loading multimodal models...")
    
    def extract_clinical_entities(self, text: str) -> Dict[str, List[str]]:
        """
        Extract medical entities from clinical text
        
        Args:
            text: Clinical text to analyze
            
        Returns:
            Dictionary of extracted entities by type
        """
        # Simplified implementation for initial release
        entities = {
            "conditions": [],
            "medications": [],
            "procedures": [],
            "symptoms": [],
            "lab_values": []
        }
        
        # Simple keyword matching (to be replaced with real NLP)
        text_lower = text.lower()
        
        # Basic condition detection
        conditions = ["diabetes", "hypertension", "pneumonia", "covid", "cancer"]
        for condition in conditions:
            if condition in text_lower:
                entities["conditions"].append(condition.title())
        
        # Basic medication detection
        medications = ["metformin", "insulin", "aspirin", "lisinopril", "atorvastatin"]
        for medication in medications:
            if medication in text_lower:
                entities["medications"].append(medication.title())
        
        return entities
    
    def generate_embeddings(self, texts: List[str]) -> np.ndarray:
        """
        Generate embeddings for clinical texts
        
        Args:
            texts: List of clinical texts
            
        Returns:
            Numpy array of embeddings
        """
        # Placeholder implementation
        # In production, would use actual transformer embeddings
        embeddings = []
        for text in texts:
            # Simple hash-based embedding for demo
            embedding = np.random.random(768)  # BERT-size embedding
            embeddings.append(embedding)
        
        return np.vstack(embeddings) if embeddings else np.array([])
    
    def predict_risk_score(self, patient_data: Dict[str, Any]) -> float:
        """
        Predict clinical risk score for a patient
        
        Args:
            patient_data: Dictionary containing patient information
            
        Returns:
            Risk score between 0 and 1
        """
        risk_factors = 0
        
        # Age factor
        age = patient_data.get("age", 0)
        if age > 65:
            risk_factors += 0.2
        elif age > 45:
            risk_factors += 0.1
        
        # Condition factors
        conditions = patient_data.get("conditions", [])
        high_risk_conditions = ["diabetes", "hypertension", "heart disease", "copd"]
        for condition in conditions:
            if any(hr_condition in condition.lower() for hr_condition in high_risk_conditions):
                risk_factors += 0.15
        
        # BMI factor
        bmi = patient_data.get("bmi", 0)
        if bmi > 30:
            risk_factors += 0.1
        elif bmi > 25:
            risk_factors += 0.05
        
        # Smoking factor
        if patient_data.get("smoking", False):
            risk_factors += 0.1
        
        return min(risk_factors, 1.0)
    
    def predict_readmission_risk(self, patient_history: Dict[str, Any]) -> float:
        """Predict 30-day readmission risk"""
        base_risk = 0.1  # 10% baseline
        
        # Previous admissions
        admissions = len(patient_history.get("encounters", []))
        if admissions > 3:
            base_risk += 0.2
        elif admissions > 1:
            base_risk += 0.1
        
        # Chronic conditions
        conditions = patient_history.get("conditions", [])
        chronic_conditions = ["diabetes", "heart failure", "copd", "kidney disease"]
        for condition in conditions:
            if any(cc in str(condition).lower() for cc in chronic_conditions):
                base_risk += 0.15
        
        return min(base_risk, 1.0)
    
    def predict_fall_risk(self, patient_history: Dict[str, Any]) -> float:
        """Predict fall risk"""
        risk = 0.05  # 5% baseline
        
        # Age factor
        demographics = patient_history.get("demographics", {})
        age = demographics.get("age", 0)
        if age > 75:
            risk += 0.3
        elif age > 65:
            risk += 0.2
        
        # Medications that increase fall risk
        medications = patient_history.get("medications", [])
        fall_risk_meds = ["sedative", "antipsychotic", "benzodiazepine"]
        for med in medications:
            med_str = str(med).lower()
            if any(frm in med_str for frm in fall_risk_meds):
                risk += 0.1
        
        return min(risk, 1.0)
    
    def predict_adherence_risk(self, patient_history: Dict[str, Any]) -> float:
        """Predict medication adherence risk"""
        risk = 0.2  # 20% baseline non-adherence
        
        # Multiple medications increase non-adherence
        medications = patient_history.get("medications", [])
        if len(medications) > 5:
            risk += 0.2
        elif len(medications) > 3:
            risk += 0.1
        
        # Age factors
        demographics = patient_history.get("demographics", {})
        age = demographics.get("age", 0)
        if age > 75:
            risk += 0.15  # Cognitive decline risk
        elif age < 30:
            risk += 0.1   # Young adult adherence issues
        
        return min(risk, 1.0)
    
    def predict_clinical_deterioration(self, patient_history: Dict[str, Any]) -> float:
        """Predict clinical deterioration risk"""
        risk = 0.05  # 5% baseline
        
        # Recent vital signs
        observations = patient_history.get("observations", [])
        abnormal_vitals = 0
        
        for obs in observations[-10:]:  # Last 10 observations
            # Simplified vital signs analysis
            value = obs.get("value", 0)
            code = str(obs.get("code", "")).lower()
            
            if "blood pressure" in code and value > 140:
                abnormal_vitals += 1
            elif "heart rate" in code and (value > 100 or value < 60):
                abnormal_vitals += 1
            elif "temperature" in code and value > 38:
                abnormal_vitals += 1
        
        if abnormal_vitals > 3:
            risk += 0.3
        elif abnormal_vitals > 1:
            risk += 0.15
        
        return min(risk, 1.0)


class ModelRegistry:
    """
    Registry for managing AI models across the healthcare system
    """
    
    def __init__(self):
        self.models: Dict[str, AIEngine] = {}
        self.model_metadata: Dict[str, Dict[str, Any]] = {}
    
    def register_model(self, 
                      model_id: str, 
                      model: AIEngine,
                      metadata: Optional[Dict[str, Any]] = None):
        """Register a new AI model"""
        self.models[model_id] = model
        self.model_metadata[model_id] = metadata or {}
        logger.info(f"Registered model: {model_id}")
    
    def get_model(self, model_id: str) -> Optional[AIEngine]:
        """Retrieve a registered model"""
        return self.models.get(model_id)
    
    def list_models(self) -> List[Dict[str, Any]]:
        """List all registered models with metadata"""
        return [
            {
                "id": model_id,
                "metadata": self.model_metadata.get(model_id, {})
            }
            for model_id in self.models.keys()
        ]