Create pipeline.py

pipeline.py

@@ -0,0 +1,27 @@
+from typing import Dict, List, Any
+from PIL import Image
+
+class PreTrainedPipeline():
+    def __init__(self, path=""):
+        # IMPLEMENT_THIS
+        # Preload all the elements you are going to need at inference.
+        # For instance your model, processors, tokenizer that might be needed.
+        # This function is only called once, so do all the heavy processing I/O here"""
+        raise NotImplementedError(
+            "Please implement PreTrainedPipeline __init__ function"
+        )
+    
+    def __call__(self, inputs: "Image.Image") -> List[Dict[str, Any]]:
+        """
+        Args:
+            inputs (:obj:`PIL.Image`):
+                The raw image representation as PIL.
+                No transformation made whatsoever from the input. Make all necessary transformations here.
+        Return:
+            A :obj:`list`:. The list contains items that are dicts should be liked {"label": "XXX", "score": 0.82}
+                It is preferred if the returned list is in decreasing `score` order
+        """
+        # IMPLEMENT_THIS
+        raise NotImplementedError(
+            "Please implement PreTrainedPipeline __call__ function"
+        )