Create handler.py

handler.py

@@ -0,0 +1,67 @@
+from typing import Dict, List, Any
+from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer
+
+from PIL import Image
+import torch
+import base64
+from base64 import b64encode
+import requests
+import json
+import io
+
+# Take in base64 string and return cv image
+def stringToRGB(base64_string):
+    imgdata = base64.b64decode(str(base64_string))
+    img = Image.open(io.BytesIO(imgdata)).convert('RGB')
+    # opencv_img= cv2.cvtColor(np.array(img), cv2.COLOR_BGR2RGB)
+    return img
+
+
+def predict_caption(image_str, max_token = 32):
+
+    num_beams = 4
+    gen_kwargs = {"max_length": max_token, "num_beams": num_beams}
+    
+    images = []
+    image = stringToRGB(image_str)
+    images.append(image)
+    
+    pixel_values = feature_extractor(images=images, return_tensors="pt").pixel_values
+    pixel_values = pixel_values.to(device)
+
+    output_ids = model.generate(pixel_values, **gen_kwargs)
+
+    preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+    preds = [pred.strip() for pred in preds]
+    return preds[0]
+
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        # Preload all the elements you are going to need at inference.
+        # pseudo:
+        # self.model= load_model(path
+        model = VisionEncoderDecoderModel.from_pretrained(path)
+        feature_extractor = ViTFeatureExtractor.from_pretrained(path)
+        tokenizer = AutoTokenizer.from_pretrained(path)
+
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        model = model.to(device)
+        
+
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """
+       data args:
+            inputs (:obj: `str` | `PIL.Image` | `np.array`)
+            kwargs
+      Return:
+            A :obj:`list` | `dict`: will be serialized and returned
+        """
+        max_token = data.pop("max_token", 32)
+        img_str = data.pop("data", None)
+        
+        caption = predict_caption(img_str, max_token=max_token)
+        return {"caption": f"{caption}"}
+        
+        # pseudo
+        # self.model(input)