from diffusers import DiffusionPipeline 
from typing import Any, Dict, List
import torch 

class EndpointHandler:
    def __init__(self, path=""):
        self.pipeline = DiffusionPipeline.from_pretrained(
            path, torch_dtype=torch.bfloat16
        ).to("cuda")

    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
        inputs = data.pop("inputs")
        prompt = inputs.pop("prompt", "dog")
        guidance_scale = inputs.pop("guidance_scale", 4.5)
        num_inference_steps = inputs.pop("num_inference_steps", 28)
        seed = inputs.pop("seed", 0)
        
        prediction = self.pipeline(
            prompt, 
            negative_prompt="bad quality, worse quality, deformed",
            guidance_scale=guidance_scale, 
            num_inference_steps=num_inference_steps, 
            generator=torch.manual_seed(seed)
        ).images[0]
        return prediction