base64 encoded images as input and output

handler.py

@@ -29,21 +29,17 @@ class EndpointHandler():
             A :obj:`dict`:. base64 encoded image
         """
         inputs = data.pop("inputs", data)
+
         # decode base64 image to PIL
-        #image = Image.open(BytesIO(base64.b64decode(inputs['image'])))
-        image = Image.open(BytesIO(inputs['image'])).convert("RGB")
+        image = Image.open(BytesIO(base64.b64decode(inputs['image'])))
         
         # run inference pipeline
-        #with autocast(device.type):
-        #    image = self.pipe(inputs, guidance_scale=7.5)["sample"][0]
         upscaled_image = self.pipe(prompt="", image = image).images[0]
 
-        return upscaled_image  
-            
         # encode image as base 64
-        #buffered = BytesIO()
-        #upscaled_image.save(buffered, format="JPEG")
-        #img_str = base64.b64encode(buffered.getvalue())
+        buffered = BytesIO()
+        upscaled_image.save(buffered, format="JPEG")
+        img_str = base64.b64encode(buffered.getvalue())
 
         # postprocess the prediction
-        #return {"image": img_str.decode()}
+        return {"image": img_str.decode()}