from typing import  Dict, List, Any
import torch
from torch import autocast
from diffusers import StableDiffusionPipeline
import base64
from io import BytesIO

# set device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

if device.type != 'cuda':
    raise ValueError("need to run on GPU")

class EndpointHandler():
    def __init__(self, path=""):
        # load the optimized model
        print("Loading model from:", path)
        self.pipe = StableDiffusionPipeline.from_pretrained(path, torch_dtype=torch.float16)
        self.pipe = self.pipe.to(device)
        self.pipe.safety_checker = lambda images, clip_input: (images, None)


    def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
        """
        Args:
            data (:obj:):
                includes the input data and the parameters for the inference.
        Return:
            A :obj:`dict`:. base64 encoded image
        """
        inputs = data.pop("inputs", data)

        print("Running inference with data:", data)
        print("Running inference with inputs:", inputs)

        out = self.pipe(
            inputs,
            guidance_scale = 5,
            num_images_per_prompt = 1,
        )

        return out.images[0];
        
        # run inference pipeline
        # with autocast(device.type):
            # image = self.pipe(inputs, guidance_scale = 5).images[0]
            
        # # encode image as base 64
        # buffered = BytesIO()
        # image.save(buffered, format="JPEG")
        # img_str = base64.b64encode(buffered.getvalue())

        # postprocess the prediction
        # return {"image": img_str.decode()}