#!/usr/bin/env python3
"""
Model Adapters for Swarm AI System

This file contains model adapter classes that allow different AI models
(Vanilla AI, Community Patch AI, RICE AI, and Qyvarian AI) to work
within the swarm agent system.
"""

import time
import json
from abc import ABC, abstractmethod
from typing import Dict, List, Any, Optional
class ModelAdapter(ABC):
    """
    Abstract base class for all model adapters.
    
    This class provides the interface for adapting different AI models
    to work within the swarm system.
    """
    
    def __init__(self, model_params: Dict[str, Any]):
        """
        Initialize the model adapter.
        
        Args:
            model_params: Model parameters
        """
        self.model_params = model_params
        self.model_instance = self.load_model()
        self.performance_history = []
        
    def load_model(self):
        """
        Load and initialize the AI model.
        
        Returns:
            Model instance
        """
        # Abstract method to be implemented by subclasses
        raise NotImplementedError("Subclasses must implement load_model")
        
    def predict(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
        """
        Make prediction using the model.
        
        Args:
            input_data: Input data
            
        Returns:
            Model prediction
        """
        prediction = self.model_instance.predict(input_data)
        self.performance_history.append({
            "timestamp": time.time(),
            "input": input_data,
            "output": prediction,
            "confidence": self.calculate_prediction_confidence(prediction)
        })
        return prediction
        
    def learn(self, experience_data: Dict[str, Any]):
        """
        Learn from experience.
        
        Args:
            experience_data: Experience data
        """
        self.model_instance.learn(experience_data)
        
    def get_expertise_score(self) -> float:
        """
        Get model expertise score.
        
        Returns:
            Expertise score (0.0 to 1.0)
        """
        return self.model_instance.get_competence_score()
        
    def calculate_prediction_confidence(self, prediction: Dict[str, Any]) -> float:
        """
        Calculate confidence for prediction.
        
        Args:
            prediction: Model prediction
            
        Returns:
            Confidence score (0.0 to 1.0)
        """
        return prediction.get("confidence", 0.5)
class VanillaAIAdapter(ModelAdapter):
    """
    Adapter for Vanilla AI model.
    
    This adapter wraps the Vanilla AI model to work within the swarm system.
    """
    
    def load_model(self):
        """
        Load Vanilla AI model.
        
        Returns:
            Vanilla AI instance
        """
        # Import Vanilla AI (would be imported from external module)
        try:
            from external_ai.vanilla_ai import VanillaAI
            return VanillaAI(self.model_params)
        except ImportError:
            # Fallback implementation
            return VanillaAIFallback(self.model_params)
            
    def calculate_prediction_confidence(self, prediction: Dict[str, Any]) -> float:
        """
        Calculate confidence for Vanilla AI prediction.
        
        Args:
            prediction: Model prediction
            
        Returns:
            Confidence score
        """
        # Vanilla AI confidence calculation
        confidence = prediction.get("confidence", 0.5)
        
        # Adjust based on model parameters
        if self.model_params.get("reliability") == "high":
            confidence *= 1.2
        elif self.model_params.get("reliability") == "low":
            confidence *= 0.8
            
        return min(confidence, 1.0)
class CommunityPatchAIAdapter(ModelAdapter):
    """
    Adapter for Community Patch AI model.
    
    This adapter wraps the Community Patch AI model to work within the swarm system.
    """
    
    def load_model(self):
        """
        Load Community Patch AI model.
        
        Returns:
            Community Patch AI instance
        """
        try:
            from external_ai.community_patch_ai import CommunityPatchAI
            return CommunityPatchAI(self.model_params)
        except ImportError:
            # Fallback implementation
            return CommunityPatchAIFallback(self.model_params)
            
    def calculate_prediction_confidence(self, prediction: Dict[str, Any]) -> float:
        """
        Calculate confidence for Community Patch AI prediction.
        
        Args:
            prediction: Model prediction
            
        Returns:
            Confidence score
        """
        # Community Patch AI confidence calculation
        confidence = prediction.get("confidence", 0.5)
        
        # Adjust based on model parameters
        if self.model_params.get("cooperation_level") == "high":
            confidence *= 1.1
        elif self.model_params.get("cooperation_level") == "low":
            confidence *= 0.9
            
        return min(confidence, 1.0)
class RICEAIAdapter(ModelAdapter):
    """
    Adapter for RICE AI model.
    
    This adapter wraps the RICE AI model to work within the swarm system.
    """
    
    def load_model(self):
        """
        Load RICE AI model.
        
        Returns:
            RICE AI instance
        """
        try:
            from external_ai.rice_ai import RICEAI
            return RICEAI(self.model_params)
        except ImportError:
            # Fallback implementation
            return RICEAIFallback(self.model_params)
            
    def calculate_prediction_confidence(self, prediction: Dict[str, Any]) -> float:
        """
        Calculate confidence for RICE AI prediction.
        
        Args:
            prediction: Model prediction
            
        Returns:
            Confidence score
        """
        # RICE AI confidence calculation
        confidence = prediction.get("confidence", 0.5)
        
        # Adjust based on model parameters
        if self.model_params.get("tradition_preservation") == "high":
            confidence *= 1.15
        elif self.model_params.get("tradition_preservation") == "low":
            confidence *= 0.85
            
        return min(confidence, 1.0)
class QyvarianAIAdapter(ModelAdapter):
    """
    Adapter for Qyvarian AI model (Organic AI).
    
    This adapter wraps the Qyvarian AI model (Organic AI) to work within the swarm system.
    """
    
    def load_model(self):
        """
        Load Qyvarian AI model.
        
        Returns:
            Qyvarian AI instance
        """
        try:
            from qyvarian_ai.qyvarian_ai import QyvarianAI
            return QyvarianAI(self.model_params)
        except ImportError:
            # Fallback implementation - use your existing Qyvarian AI
            return QyvarianAIFallback(self.model_params)
            
    def calculate_prediction_confidence(self, prediction: Dict[str, Any]) -> float:
        """
        Calculate confidence for Qyvarian AI prediction.
        
        Args:
            prediction: Model prediction
            
        Returns:
            Confidence score
        """
        # Qyvarian AI confidence calculation
        confidence = prediction.get("confidence", 0.5)
        
        # Adjust based on Organic AI specific factors
        if self.model_params.get("strategic_depth") == "calculating":
            confidence *= 1.25  # Higher confidence for calculating depth
        elif self.model_params.get("strategic_depth") == "adaptive":
            confidence *= 1.1   # Moderate confidence for adaptive
        elif self.model_params.get("strategic_depth") == "reactive":
            confidence *= 0.9   # Lower confidence for reactive
            
        # Adjust based on Organic AI features
        if self.model_params.get("organic_ai_enabled"):
            confidence *= 1.05  # Slight boost for Organic AI
            
        return min(confidence, 1.0)
class DefaultAIAdapter(ModelAdapter):
    """
    Default adapter for unknown AI models.
    
    This adapter provides a fallback for models that don't have specific adapters.
    """
    
    def load_model(self):
        """
        Load default AI model.
        
        Returns:
            Default AI instance
        """
        return DefaultAI(self.model_params)
        
    def calculate_prediction_confidence(self, prediction: Dict[str, Any]) -> float:
        """
        Calculate confidence for default AI prediction.
        
        Args:
            prediction: Model prediction
            
        Returns:
            Confidence score
        """
        return prediction.get("confidence", 0.5)
# Fallback implementations for external AI models (would be implemented separately)
class VanillaAIFallback:
    def __init__(self, params):
        self.params = params
        
    def predict(self, input_data):
        return {"action": "default_action", "confidence": 0.5}
        
    def learn(self, data):
        pass
        
    def get_competence_score(self):
        return 0.5
        
class CommunityPatchAIFallback:
    def __init__(self, params):
        self.params = params
        
    def predict(self, input_data):
        return {"action": "default_action", "confidence": 0.5}
        
    def learn(self, data):
        pass
        
    def get_competence_score(self):
        return 0.5
        
class RICEAIFallback:
    def __init__(self, params):
        self.params = params
        
    def predict(self, input_data):
        return {"action": "default_action", "confidence": 0.5}
        
    def learn(self, data):
        pass
        
    def get_competence_score(self):
        return 0.5
        
class QyvarianAIFallback:
    def __init__(self, params):
        self.params = params
        
    def predict(self, input_data):
        # Use your existing Qyvarian AI implementation
        # This would integrate with your existing code
        return {"action": "qyvarian_default_action", "confidence": 0.8}
        
    def learn(self, data):
        pass
        
    def get_competence_score(self):
        return 0.8
        
class DefaultAI:
    def __init__(self, params):
        self.params = params
        
    def predict(self, input_data):
        return {"action": "default_action", "confidence": 0.5}
        
    def learn(self, data):
        pass
        
    def get_competence_score(self):
        return 0.5