"""
LLM Service - Chat completions via HuggingFace.
"""

import logging
from typing import Dict, List, Optional, Any
from dataclasses import dataclass

from huggingface_hub import InferenceClient

from config.settings import Settings

logger = logging.getLogger(__name__)


@dataclass
class LLMConfig:
    """LLM configuration."""
    api_key: str
    model_name: str
    temperature: float = 0.01
    max_tokens: int = 512


class LLMService:
    """
    LLM service using HuggingFace InferenceClient.
    """
    
    def __init__(
        self,
        api_key: Optional[str] = None,
        model_name: Optional[str] = None,
    ):
        """
        Initialize LLM service.
        
        Args:
            api_key: HuggingFace API key
            model_name: Model name/ID
        """
        settings = Settings()
        
        key = api_key or settings.hf_token
        name = model_name or settings.effective_model_name
        
        self.config = LLMConfig(
            api_key=key,
            model_name=name,
            temperature=settings.hf_temperature,
            max_tokens=settings.hf_max_new_tokens,
        )
        
        self.client = InferenceClient(token=self.config.api_key)
        
        logger.info(f"LLMService initialized with model: {self.config.model_name}")

    async def get_chat_completion(
        self,
        messages: List[Dict[str, str]],
        temperature: Optional[float] = None,
        max_tokens: Optional[int] = None,
    ) -> str:
        """
        Get chat completion from the model.
        
        Args:
            messages: List of message dicts with 'role' and 'content'
            temperature: Override temperature
            max_tokens: Override max tokens
            
        Returns:
            Assistant response text
        """
        logger.debug(f"Chat completion request with model: {self.config.model_name}")
        
        try:
            response = self.client.chat_completion(
                messages=messages,
                model=self.config.model_name,
                max_tokens=max_tokens or self.config.max_tokens,
                temperature=temperature or self.config.temperature
            )
            
            content = response.choices[0].message.content
            logger.debug(f"Chat completion response: {content[:200]}...")
            
            return content
            
        except Exception as e:
            logger.error(f"Chat completion error: {str(e)}")
            raise Exception(f"LLM completion error: {str(e)}")

    async def get_streaming_completion(
        self,
        messages: List[Dict[str, str]],
        temperature: Optional[float] = None,
        max_tokens: Optional[int] = None,
    ):
        """
        Get streaming chat completion.
        
        Yields:
            Text chunks as they're generated
        """
        logger.debug(f"Streaming completion request with model: {self.config.model_name}")
        
        try:
            stream = self.client.chat_completion(
                messages=messages,
                model=self.config.model_name,
                max_tokens=max_tokens or self.config.max_tokens,
                temperature=temperature or self.config.temperature,
                stream=True
            )
            
            for chunk in stream:
                if chunk.choices and chunk.choices[0].delta.content:
                    yield chunk.choices[0].delta.content
                    
        except Exception as e:
            logger.error(f"Streaming completion error: {str(e)}")
            raise Exception(f"LLM streaming error: {str(e)}")

    def build_messages_with_tools(
        self,
        system_prompt: str,
        user_input: str,
        tools_description: str = "",
        conversation_history: Optional[List[Dict[str, str]]] = None,
        tool_results: Optional[str] = None,
    ) -> List[Dict[str, str]]:
        """
        Build messages array with tools and context.
        
        Args:
            system_prompt: System instruction
            user_input: User's message
            tools_description: Available tools description
            conversation_history: Previous messages
            tool_results: Results from tool execution
            
        Returns:
            Messages array for chat completion
        """
        messages = [{"role": "system", "content": system_prompt}]
        
        if tools_description:
            messages.append({
                "role": "system",
                "content": f"Available tools:\n{tools_description}"
            })
        
        # Add conversation history
        if conversation_history:
            for msg in conversation_history[-10:]:  # Last 10 messages
                if msg.get("role") in ["user", "assistant"]:
                    messages.append(msg)
        
        # Add current user input
        messages.append({"role": "user", "content": user_input})
        
        # Add tool results if present
        if tool_results:
            messages.append({"role": "assistant", "content": tool_results})
        
        return messages