from typing import Dict
import torch
from diffusers import StableDiffusionXLPipeline
from io import BytesIO
import base64

class EndpointHandler:
    def __init__(self, path: str = ""):
        print(f"Initializing SDXL model from: {path}")

        # Load the base SDXL model
        self.pipe = StableDiffusionXLPipeline.from_pretrained(
            "stabilityai/stable-diffusion-xl-base-1.0",
            torch_dtype=torch.float16,
            variant="fp16"
        )

        print("Loading LoRA weights from: Texttra/Bh0r")
        self.pipe.load_lora_weights(
            "Texttra/Bh0r",
            weight_name="Bh0r-10.safetensors",
            adapter_name="bh0r_lora"
        )
        self.pipe.set_adapters(["bh0r_lora"], adapter_weights=[0.9])
        self.pipe.fuse_lora()

        self.pipe.to("cuda" if torch.cuda.is_available() else "cpu")
        print("Model ready.")

    def __call__(self, data: Dict) -> Dict:
        print("Received data:", data)

        inputs = data.get("inputs", {})
        prompt = inputs.get("prompt", "")
        print("Extracted prompt:", prompt)

        if not prompt:
            return {"error": "No prompt provided."}

        image = self.pipe(
            prompt,
            num_inference_steps=35,
            guidance_scale=7.0,
        ).images[0]

        print("Image generated.")

        buffer = BytesIO()
        image.save(buffer, format="PNG")
        base64_image = base64.b64encode(buffer.getvalue()).decode("utf-8")
        print("Returning image.")

        return {"image": base64_image}