"""
Base model interface for LLM interactions.
"""

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

logger = logging.getLogger(__name__)


class BaseModel(ABC):
    """Abstract base class for LLM models."""
    
    def __init__(self, model_name: str, **kwargs):
        self.model_name = model_name
        self.name = model_name
        
    @abstractmethod
    def generate(
        self,
        prompt: str,
        max_tokens: int = 512,
        temperature: float = 0.7,
        **kwargs
    ) -> str:
        """Generate a response from the model."""
        pass
    
    @abstractmethod
    def generate_batch(
        self,
        prompts: List[str],
        max_tokens: int = 512,
        temperature: float = 0.7,
        **kwargs
    ) -> List[str]:
        """Generate responses for a batch of prompts."""
        pass
    
    def wrap_as_chat_message(self, content: str, role: str = "user") -> Dict[str, str]:
        """Wrap content as a chat message."""
        return {"role": role, "content": content}
    
    def format_chat_messages(self, messages: List[Dict[str, str]]) -> str:
        """Format chat messages into a prompt string."""
        formatted = ""
        for msg in messages:
            role = msg.get("role", "user")
            content = msg.get("content", "")
            if role == "system":
                formatted += f"System: {content}\n\n"
            elif role == "user":
                formatted += f"User: {content}\n\n"
            elif role == "assistant":
                formatted += f"Assistant: {content}\n\n"
        formatted += "Assistant:"
        return formatted