"""OpenAI-compatible chat completion routes."""

from __future__ import annotations

import json
import logging
import time
from typing import Literal

from fastapi import APIRouter, Request
from fastapi.responses import JSONResponse, StreamingResponse
from pydantic import BaseModel, Field

from app.utils.config import settings

logger = logging.getLogger(__name__)
router = APIRouter(tags=["chat"])


class ChatMessage(BaseModel):
    """OpenAI-compatible chat message format."""

    role: Literal["system", "user", "assistant"]
    content: str


class ChatCompletionRequest(BaseModel):
    """Subset of OpenAI chat completion request fields."""

    model: str = Field(default_factory=lambda: settings.model_name)
    messages: list[ChatMessage]
    stream: bool = False
    temperature: float | None = None
    top_p: float | None = None
    max_tokens: int | None = Field(default=None, ge=1)


def _sse_event(payload: dict) -> str:
    """Format one SSE data event."""
    return f"data: {json.dumps(payload, ensure_ascii=False)}\n\n"


@router.post("/v1/chat/completions")
async def create_chat_completion(request: Request, body: ChatCompletionRequest):
    """OpenAI-compatible completions with optional SSE token streaming."""
    prompt_service = request.app.state.prompt_service
    model_service = request.app.state.model_service

    injected_messages = prompt_service.inject_system_prompt(
        [message.model_dump() for message in body.messages]
    )

    temperature = body.temperature if body.temperature is not None else settings.default_temperature
    top_p = body.top_p if body.top_p is not None else settings.default_top_p
    max_tokens = body.max_tokens if body.max_tokens is not None else settings.default_max_tokens

    created = int(time.time())

    if body.stream:

        async def event_generator():
            request_id = None
            try:
                # Initial chunk with assistant role to follow OpenAI streaming style.
                bootstrap_chunk = {
                    "id": "chatcmpl-bootstrap",
                    "object": "chat.completion.chunk",
                    "created": created,
                    "model": body.model,
                    "choices": [{"index": 0, "delta": {"role": "assistant"}, "finish_reason": None}],
                }
                yield _sse_event(bootstrap_chunk)

                async for stream_request_id, delta in model_service.stream_text(
                    injected_messages,
                    temperature=temperature,
                    top_p=top_p,
                    max_tokens=max_tokens,
                ):
                    request_id = stream_request_id
                    chunk = {
                        "id": request_id,
                        "object": "chat.completion.chunk",
                        "created": created,
                        "model": body.model,
                        "choices": [{"index": 0, "delta": {"content": delta}, "finish_reason": None}],
                    }
                    yield _sse_event(chunk)

                final_chunk = {
                    "id": request_id or "chatcmpl-final",
                    "object": "chat.completion.chunk",
                    "created": created,
                    "model": body.model,
                    "choices": [{"index": 0, "delta": {}, "finish_reason": "stop"}],
                }
                yield _sse_event(final_chunk)
                yield "data: [DONE]\n\n"

            except (RuntimeError, ValueError):  # pragma: no cover - runtime guard
                logger.exception("Failed to stream completion for request")
                error_payload = {
                    "error": {
                        "message": "Failed to stream completion for request",
                        "type": "server_error",
                    }
                }
                yield _sse_event(error_payload)
                yield "data: [DONE]\n\n"

        return StreamingResponse(
            event_generator(),
            media_type="text/event-stream",
            headers={
                "Cache-Control": "no-cache",
                "Connection": "keep-alive",
                "X-Accel-Buffering": "no",
            },
        )

    request_id, text = await model_service.complete_text(
        injected_messages,
        temperature=temperature,
        top_p=top_p,
        max_tokens=max_tokens,
    )

    response_payload = {
        "id": request_id,
        "object": "chat.completion",
        "created": created,
        "model": body.model,
        "choices": [
            {
                "index": 0,
                "message": {"role": "assistant", "content": text},
                "finish_reason": "stop",
            }
        ],
    }
    return JSONResponse(response_payload)