Create handler.py

handler.py

@@ -0,0 +1,42 @@
+from typing import Dict, List, Any
+from PIL import Image
+from io import BytesIO
+from transformers import AutoModelForSemanticSegmentation, AutoFeatureExtractor
+import base64
+import torch
+from torch import nn
+
+from RealESRGAN import RealESRGAN
+
+
+class EndpointHandler():
+    def __init__(self, path="."):
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.model = RealESRGAN(device, scale=4).to(self.device)
+        self.model.load_weights('./RealESRGAN_x4.pth', download=True) 
+
+        
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """
+       data args:
+            images (:obj:`PIL.Image`)
+            candiates (:obj:`list`)
+      Return:
+            A :obj:`list`:. The list contains items that are dicts should be liked {"label": "XXX", "score": 0.82}
+        """
+        inputs = data.pop("inputs", data)
+
+        # decode base64 image to PIL
+        image = Image.open(BytesIO(base64.b64decode(inputs['image'])))
+
+        # forward pass
+        output_image = self.model.predict(image)
+
+        # base64 encode output
+        buffered = BytesIO()
+        output_image = output_image.convert('RGB')
+        output_image.save(buffered, format="png")
+        img_str = base64.b64encode(buffered.getvalue())
+
+        # postprocess the prediction
+        return {"image": img_str.decode()}