Create pipeline.py

pipeline.py

@@ -0,0 +1,65 @@
+from typing import Dict, List, Any
+import os
+
+import json
+import numpy as np
+
+from fastai.learner import load_learner
+
+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"""
+
+        self.model = load_learner(os.path.join(path, "20211115-model.pkl"))
+
+        with open(os.path.join(path, "config.json")) as config:
+
+            config = json.load(config)
+
+        self.id2label = config["id2label"]
+
+    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
+
+        # FastAI expects a np array, not a PIL Image.
+
+        _, _, preds = self.model.predict(np.array(inputs))
+
+        preds = preds.tolist()
+
+        labels = [
+
+            {"label": str(self.id2label["0"]), "score": preds[0]},
+
+            {"label": str(self.id2label["1"]), "score": preds[1]},
+
+        ]
+
+        return labels