Update pipeline.py

pipeline.py

@@ -19,12 +19,12 @@ class PreTrainedPipeline():
     def __call__(self, inputs: "Image.Image")-> List[Dict[str, Any]]:
 
         # convert img to numpy array, resize and normalize to make the prediction
-        #with Image.open(inputs) as img:
-        img = np.array(inputs)
+        with Image.open(inputs) as img:
+            img = np.array(inputs)
 
         im = tf.image.resize(img, (128, 128))
         im = tf.cast(im, tf.float32) / 255.0
-        pred_mask = model.predict(im[tf.newaxis, ...])
+        pred_mask = self.model.predict(im[tf.newaxis, ...])
         
         # take the best performing class for each pixel
         # the output of argmax looks like this [[1, 2, 0], ...]