add custom handler

handler.py

@@ -1,7 +1,6 @@
 from typing import Dict, List, Any
 
 import base64
-import logging
 import math
 import numpy as np
 import tensorflow as tf
@@ -57,19 +56,15 @@ class EndpointHandler():
     def __call__(self, data: Dict[str, Any]) -> str:
         # get inputs 
         contexts = data.pop("inputs", data)
-        logging.warning(contexts)
-        
         batch_size = data.pop("batch_size", 1)
 
         context = base64.b64decode(contexts[0])
         context = np.frombuffer(context, dtype="float32")
         context = np.reshape(context, (batch_size, 77, 768))
-        print(context)
 
         unconditional_context = base64.b64decode(contexts[1])
         unconditional_context = np.frombuffer(unconditional_context, dtype="float32")
         unconditional_context = np.reshape(unconditional_context, (batch_size, 77, 768))    
-        print(unconditional_context)
 
         num_steps = data.pop("num_steps", 25)
         unconditional_guidance_scale = data.pop("unconditional_guidance_scale", 7.5)
@@ -100,4 +95,4 @@ class EndpointHandler():
         latent_b64 = base64.b64encode(latent.numpy().tobytes())
         latent_b64str = latent_b64.decode()
 
-        return latent_b64str
+        return latent_b64str