Upload 5 files

config.json

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+{
+  "id2label": {
+    "0": "dog",
+    "1": "cat"
+   }
+ }

helpers.py

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+# Custom code used by the model.
+
+def is_cat(x): 
+    return x[0].isupper()

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7cb0efd27d7c86ac3bd0b9b085e532bba62b9d4a5e3dda2338b064866c746e73
+size 47061419

pipeline.py

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+from typing import Dict, List, Any
+from PIL import Image
+
+import os
+import json
+import numpy as np
+from fastai.learner import load_learner
+
+from helpers import is_cat
+
+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, "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

requirements.txt

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+fastai==2.4.1