Add pipeline

.gitattributes

@@ -14,4 +14,5 @@
 *.pb filter=lfs diff=lfs merge=lfs -text
 *.pt filter=lfs diff=lfs merge=lfs -text
 *.pth filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+tags:
+- image-classification
+library_name: generic
+---
+
+# Dog vs Cat Image Classification with FastAI CNN
+
+Training is based in FastAI [Quick Start](https://docs.fast.ai/quick_start.html). Example training
+
+
+## Training
+
+The model was trained as follows
+
+```python
+path = untar_data(URLs.PETS)/'images'
+
+def is_cat(x): return x[0].isupper()
+dls = ImageDataLoaders.from_name_func(
+    path, get_image_files(path), valid_pct=0.2, seed=42,
+    label_func=is_cat, item_tfms=Resize(224))
+
+learn = cnn_learner(dls, resnet34, metrics=error_rate)
+learn.fine_tune(1)
+```

config.json

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

pipeline.py

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+from typing import Dict, List, Any
+from PIL import Image
+
+import os
+from fastai.learner import load_learner
+
+# Custom code used by the model.
+def is_cat(x): 
+    return x[0].isupper()
+
+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))
+        labels = [
+            {"label": str(self.id2label["0"]), "score": preds[0]},
+            {"label": str(self.id2label["1"]), "score": preds[1]},
+        ]
+        return labels