Delete handler.py

handler.py

@@ -1,48 +0,0 @@
-from typing import  Dict, List, Any
-import torch
-from torch import autocast
-from diffusers import StableDiffusionPipeline
-import base64
-from io import BytesIO
-# from transformers.utils import logging
-
-# logging.set_verbosity_info()
-# logger = logging.get_logger("transformers")
-
-# 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
-        self.pipe = StableDiffusionPipeline.from_pretrained(path, torch_dtype=torch.float16)
-        self.pipe = self.pipe.to(device)
-
-
-    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)
-        
-        # run inference pipeline
-        with autocast(device.type):
-            image = self.pipe(inputs, guidance_scale=20["sample"][0]
-            # logger.info("Passed inputs, set guidance to 20")
-            # print("Set guidance scale to 20")
-            
-        # 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(), "isRunning": "true"}
-