"""Attēlu ģenerēšana ar Stable Diffusion."""

from __future__ import annotations

import base64
import io
import logging

from fastapi import APIRouter, HTTPException
from pydantic import BaseModel

from maris_core.utils.env import get_hf_model

logger = logging.getLogger(__name__)
router = APIRouter()


class ImageRequest(BaseModel):
    prompt: str
    width: int = 1024
    height: int = 1024
    steps: int = 30
    guidance_scale: float = 7.5


class ImageResponse(BaseModel):
    image_url: str
    prompt: str


@router.post("/generate", response_model=ImageResponse)
async def generate_image(req: ImageRequest) -> ImageResponse:
    """Ģenerē attēlu pēc teksta apraksta."""
    from maris_core.utils.hf_integration import HFIntegration

    hf = HFIntegration()

    try:
        model_id = get_hf_model("IMAGE_MODEL")
        import torch  # type: ignore
        from diffusers import StableDiffusionPipeline  # type: ignore

        pipe = StableDiffusionPipeline.from_pretrained(
            model_id,
            torch_dtype=torch.float16,
        )
        pipe = pipe.to("cuda" if torch.cuda.is_available() else "cpu")

        image = pipe(
            req.prompt,
            width=req.width,
            height=req.height,
            num_inference_steps=req.steps,
            guidance_scale=req.guidance_scale,
        ).images[0]

        # Konvertē uz base64 data URL
        buf = io.BytesIO()
        image.save(buf, format="PNG")
        b64 = base64.b64encode(buf.getvalue()).decode()
        image_url = f"data:image/png;base64,{b64}"

        # Saglabā origin atmiņā
        await hf.save_generation("image", req.prompt, {"image_b64": b64[:100] + "..."})

        return ImageResponse(image_url=image_url, prompt=req.prompt)

    except Exception as exc:  # noqa: BLE001
        logger.error("Attēla ģenerēšanas kļūda: %s", exc)
        raise HTTPException(
            status_code=503,
            detail="Maris AI attēlu ģenerēšana nav pieejama bez konfigurēta IMAGE_MODEL.",
        ) from exc