import logging
import uuid

from fastapi import FastAPI, HTTPException

from app.config import settings, MAX_MESSAGE_LENGTH
from app.llm.adapter import get_adapter
from app.schemas import ChatRequest, ChatResponse, HealthResponse

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
)
logger = logging.getLogger(__name__)

# Create FastAPI app
app = FastAPI(
    title="Tyndale AI Service",
    description="LLM Chat Service for algorithmic trading support",
    version="0.1.0",
)


@app.get("/health", response_model=HealthResponse)
async def health_check() -> HealthResponse:
    """Health check endpoint."""
    return HealthResponse(status="ok")


@app.post("/chat", response_model=ChatResponse)
async def chat(request: ChatRequest) -> ChatResponse:
    """Process a chat message through the LLM adapter.

    - Validates message length
    - Generates conversation_id if not provided
    - Routes to appropriate LLM adapter based on LLM_MODE
    """
    # Validate message length
    if len(request.message) > MAX_MESSAGE_LENGTH:
        raise HTTPException(
            status_code=400,
            detail=f"Message exceeds maximum length of {MAX_MESSAGE_LENGTH:,} characters. "
            f"Your message has {len(request.message):,} characters.",
        )

    # Generate or use provided conversation_id
    conversation_id = request.conversation_id or str(uuid.uuid4())

    # Log request metadata (not content)
    logger.info(
        "Chat request received",
        extra={
            "conversation_id": conversation_id,
            "message_length": len(request.message),
            "mode": settings.llm_mode,
        },
    )

    # Get adapter and generate response
    adapter = get_adapter()
    response_text = await adapter.generate(conversation_id, request.message)

    # Log response metadata
    logger.info(
        "Chat response generated",
        extra={
            "conversation_id": conversation_id,
            "response_length": len(response_text),
            "mode": settings.llm_mode,
        },
    )

    return ChatResponse(
        conversation_id=conversation_id,
        response=response_text,
        mode=settings.llm_mode,
        sources=[],
    )