"""
MCP Core - Model Interface Adapters

This module provides adapters for interfacing with various AI models,
allowing the MCP system to work with different model providers and types
while maintaining a consistent interface.
"""

from abc import ABC, abstractmethod
from typing import Dict, Any, List, Optional, Union


class ModelAdapter(ABC):
    """
    Abstract base class for model adapters.
    
    Model adapters provide a consistent interface for interacting with different
    AI models, abstracting away the specifics of each model's API.
    """
    
    @abstractmethod
    async def process(self, input_data: Dict[str, Any], context: Dict[str, Any] = None) -> Dict[str, Any]:
        """
        Process an input with the model and return the output.
        
        Args:
            input_data: The input data to process.
            context: Optional context information to guide the model.
            
        Returns:
            The model's output.
        """
        pass
    
    @abstractmethod
    def get_capabilities(self) -> Dict[str, Any]:
        """
        Get the capabilities of this model.
        
        Returns:
            Dictionary describing the model's capabilities.
        """
        pass


class MockModelAdapter(ModelAdapter):
    """
    A mock model adapter for testing and demonstration purposes.
    
    This adapter simulates responses without requiring an actual AI model.
    """
    
    def __init__(self, model_id: str = "mock-model", capabilities: Dict[str, Any] = None):
        """
        Initialize a new MockModelAdapter.
        
        Args:
            model_id: Identifier for this mock model.
            capabilities: Dictionary of capabilities this mock model supports.
        """
        self.model_id = model_id
        self._capabilities = capabilities or {
            "text_generation": True,
            "question_answering": True,
            "content_evaluation": True,
            "max_input_length": 1000,
            "supports_streaming": False
        }
        
    async def process(self, input_data: Dict[str, Any], context: Dict[str, Any] = None) -> Dict[str, Any]:
        """
        Process an input with the mock model.
        
        Args:
            input_data: The input data to process.
            context: Optional context information.
            
        Returns:
            A mock response based on the input.
        """
        # Extract the input text or use a default
        input_text = input_data.get("text", "")
        
        # Generate a mock response based on the input
        if "question" in input_text.lower():
            response = f"This is a mock answer to your question about {input_text.split('?')[0]}."
        elif "explain" in input_text.lower():
            topic = input_text.lower().split("explain")[1].strip()
            response = f"Here's a mock explanation of {topic}. This would contain educational content in a real implementation."
        else:
            response = f"I've processed your input: '{input_text}'. This is a mock response that would be more relevant in a real implementation."
            
        # Include some educational metadata in the response
        return {
            "text": response,
            "model_id": self.model_id,
            "confidence": 0.85,
            "educational_metadata": {
                "complexity_level": "intermediate",
                "topics": ["sample", "mock", "demonstration"],
                "suggested_follow_ups": [
                    "Tell me more about this topic",
                    "Can you provide an example?",
                    "What are the key concepts I should understand?"
                ]
            }
        }
    
    def get_capabilities(self) -> Dict[str, Any]:
        """
        Get the capabilities of this mock model.
        
        Returns:
            Dictionary describing the mock model's capabilities.
        """
        return self._capabilities


class OpenAIAdapter(ModelAdapter):
    """
    Adapter for OpenAI models.
    
    This adapter provides an interface to OpenAI's models through their API.
    Note: This is a placeholder implementation. In a real application, you would
    need to implement the actual API calls to OpenAI.
    """
    
    def __init__(self, model_name: str = "gpt-3.5-turbo", api_key: Optional[str] = None):
        """
        Initialize a new OpenAIAdapter.
        
        Args:
            model_name: The name of the OpenAI model to use.
            api_key: OpenAI API key. If None, will attempt to use environment variable.
        """
        self.model_name = model_name
        self.api_key = api_key
        # In a real implementation, you would initialize the OpenAI client here
        
    async def process(self, input_data: Dict[str, Any], context: Dict[str, Any] = None) -> Dict[str, Any]:
        """
        Process an input with an OpenAI model.
        
        Args:
            input_data: The input data to process.
            context: Optional context information.
            
        Returns:
            The model's output.
        """
        # This is a placeholder. In a real implementation, you would:
        # 1. Format the input and context for the OpenAI API
        # 2. Make the API call
        # 3. Process and return the response
        
        # Placeholder response
        return {
            "text": f"This is a placeholder response from {self.model_name}. In a real implementation, this would be the actual model output.",
            "model": self.model_name,
            "usage": {
                "prompt_tokens": 10,
                "completion_tokens": 20,
                "total_tokens": 30
            }
        }
    
    def get_capabilities(self) -> Dict[str, Any]:
        """
        Get the capabilities of this OpenAI model.
        
        Returns:
            Dictionary describing the model's capabilities.
        """
        # This would be more accurate in a real implementation
        capabilities = {
            "text_generation": True,
            "question_answering": True,
            "content_evaluation": True,
            "supports_streaming": True
        }
        
        # Different capabilities based on model
        if "gpt-4" in self.model_name:
            capabilities.update({
                "max_input_length": 8000,
                "reasoning_ability": "advanced"
            })
        else:
            capabilities.update({
                "max_input_length": 4000,
                "reasoning_ability": "intermediate"
            })
            
        return capabilities


class HuggingFaceAdapter(ModelAdapter):
    """
    Adapter for Hugging Face models.
    
    This adapter provides an interface to models hosted on Hugging Face's model hub.
    Note: This is a placeholder implementation. In a real application, you would
    need to implement the actual API calls to Hugging Face.
    """
    
    def __init__(self, model_id: str, api_key: Optional[str] = None):
        """
        Initialize a new HuggingFaceAdapter.
        
        Args:
            model_id: The ID of the Hugging Face model to use.
            api_key: Hugging Face API key. If None, will attempt to use environment variable.
        """
        self.model_id = model_id
        self.api_key = api_key
        # In a real implementation, you would initialize the Hugging Face client here
        
    async def process(self, input_data: Dict[str, Any], context: Dict[str, Any] = None) -> Dict[str, Any]:
        """
        Process an input with a Hugging Face model.
        
        Args:
            input_data: The input data to process.
            context: Optional context information.
            
        Returns:
            The model's output.
        """
        # This is a placeholder. In a real implementation, you would:
        # 1. Format the input and context for the Hugging Face API
        # 2. Make the API call
        # 3. Process and return the response
        
        # Placeholder response
        return {
            "text": f"This is a placeholder response from Hugging Face model {self.model_id}. In a real implementation, this would be the actual model output.",
            "model": self.model_id
        }
    
    def get_capabilities(self) -> Dict[str, Any]:
        """
        Get the capabilities of this Hugging Face model.
        
        Returns:
            Dictionary describing the model's capabilities.
        """
        # This would be determined dynamically in a real implementation
        return {
            "text_generation": True,
            "question_answering": True,
            "content_evaluation": True,
            "max_input_length": 2000,
            "supports_streaming": False
        }


class ModelRegistry:
    """
    Registry for model adapters.
    
    This class manages the available model adapters and provides methods
    for registering, retrieving, and listing them.
    """
    
    def __init__(self):
        """Initialize a new ModelRegistry."""
        self.adapters: Dict[str, ModelAdapter] = {}
        
    def register_adapter(self, name: str, adapter: ModelAdapter) -> None:
        """
        Register a model adapter.
        
        Args:
            name: Name to register the adapter under.
            adapter: The ModelAdapter instance to register.
        """
        self.adapters[name] = adapter
        
    def get_adapter(self, name: str) -> Optional[ModelAdapter]:
        """
        Get a registered adapter by name.
        
        Args:
            name: Name of the adapter to retrieve.
            
        Returns:
            The ModelAdapter if found, None otherwise.
        """
        return self.adapters.get(name)
    
    def list_adapters(self) -> List[str]:
        """
        List all registered adapter names.
        
        Returns:
            List of adapter names.
        """
        return list(self.adapters.keys())
    
    def get_adapter_capabilities(self, name: str) -> Optional[Dict[str, Any]]:
        """
        Get the capabilities of a registered adapter.
        
        Args:
            name: Name of the adapter.
            
        Returns:
            Dictionary of capabilities if the adapter exists, None otherwise.
        """
        adapter = self.get_adapter(name)
        if adapter:
            return adapter.get_capabilities()
        return None