Upload handler.py

handler.py

@@ -0,0 +1,25 @@
+from typing import Dict, List, Any
+from transformers import pipeline
+from PIL import Image    
+import requests
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        self.pipeline = pipeline("image-to-text",model=path)
+
+
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        parameters = data.pop("inputs",data)
+        inputs = data.pop("inputs", data)
+
+        if parameters is not None:
+            prediction = self.pipeline(inputs, **parameters)
+            url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
+            image = Image.open(requests.get(url, stream=True).raw)
+            prompt = "USER: <image>\nWhat does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud\nASSISTANT:"
+            outputs = self.pipeline(image, prompt=prompt, generate_kwargs={"max_new_tokens": 200})
+        return outputs
+
+        
+
+