fix signature bugs (#10)

* fix signature bugs

* Update setup.py

Author: adiazulay

setup.py

@@ -1,7 +1,7 @@
 from setuptools import setup, find_packages
 setup(
     name="lobe",
-    version="0.2.0",
+    version="0.2.1",
     packages=find_packages("src"),
     package_dir={"": "src"},
     install_requires=[

src/lobe/Signature.py

@@ -33,7 +33,7 @@ def load(model_path: str) -> Signature:
 class Signature:
     def __init__(self, signature_path: str):
         signature_path = pathlib.Path(signature_path)
-        self.__model_path = signature_path.parent
+        self.__model_path = str(signature_path.parent)
 
         with open(signature_path, "r") as f:
             self.__signature = json.load(f)

src/lobe/backends/_backend_tf.py

@@ -11,14 +11,13 @@
 
 class ImageClassificationModel():
     __input_key_image = 'Image'
-    __input_key_batch_size = "batch_size"
-    __output_key_labels = 'Labels_idx_000'
-    __output_key_confidences = 'Labels_idx_001'
+    __output_key_confidences = 'Confidences'
     __output_key_prediction = 'Prediction'
 
     def __init__(self, signature):
         self.__model_path = signature.model_path
         self.__tf_predict_fn = None
+        self.__labels = signature.classes
 
     def __load(self):
         self.__tf_predict_fn = predictor.from_saved_model(self.__model_path)
@@ -34,11 +33,10 @@ def predict(self, image: Image.Image) -> PredictionResult:
         np_image = np_image[np.newaxis, ...]
 
         predictions = self.__tf_predict_fn({
-                self.__input_key_image: np_image,
-                self.__input_key_batch_size: 1 })
+                self.__input_key_image: np_image
+                })
 
-        labels = [label.decode('utf-8') for label in predictions[self.__output_key_labels][0].tolist()]
         confidences = predictions[self.__output_key_confidences][0]
         top_prediction = predictions[self.__output_key_prediction][0].decode('utf-8')
 
-        return PredictionResult(labels=labels, confidences=confidences, prediction=top_prediction)
+        return PredictionResult(labels=self.__labels, confidences=confidences, prediction=top_prediction)