# routers/llm_chat.py
from fastapi import APIRouter, HTTPException, Header
from pydantic import BaseModel
from helpers.ai_client import AIClient
import logging

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

router = APIRouter(
    prefix="/api/v1",  # Prefix for all routes in this router
    tags=["LLM Chat"],  # Tag for OpenAPI documentation
)

# Initialize the AI client
ai_client = AIClient()

# Pydantic model for request validation
class LLMChatRequest(BaseModel):
    prompt: str
    system_message: str = ""
    model_id: str = "openai/gpt-4o-mini"
    conversation_id: str = "string"
    user_id: str = "string"

@router.post("/llm-chat", summary="Send a prompt to the LLM", description="This endpoint sends a prompt to the LLM and returns the response.")
async def llm_chat(
    request: LLMChatRequest,
    x_api_key: str = Header(None, description="API Key for authentication")
):
    try:
        # Use the AI client to send the prompt
        response = ai_client.chat(
            prompt=request.prompt,
            system_message=request.system_message,
            model_id=request.model_id,
            conversation_id=request.conversation_id,
            user_id=request.user_id
        )
        return response
    except Exception as e:
        logger.error(f"Error in llm_chat: {e}")
        raise HTTPException(status_code=500, detail=str(e))