Fixed paths

pipeline.py

@@ -18,7 +18,7 @@ class PreTrainedPipeline():
         hidden_dim = 1024
         num_heads = 8
         self.nutr_names = ('energy', 'fat', 'protein', 'carbs')
-        with open(f'ingredients_metadata.json', encoding='UTF-8') as f:
+        with open(os.path.join(path, "ingredients_metadata.json"), encoding='UTF-8') as f:
             self.ingredients = json.load(f)
         self.ing_names = {ing['name']: int(ing_id) for ing_id, ing in self.ingredients.items()}
         self.vocab_size = len(self.ingredients) + 3
@@ -35,7 +35,7 @@ class PreTrainedPipeline():
         )
         self.tfing.compile()
         self.tfing((tf.zeros((1, 224, 224, 3)), tf.zeros((1, seq_length))))
-        self.tfing.load_weights(f'tfing.h5')
+        self.tfing.load_weights(os.path.join(path, 'tfing.h5'))
 
         self.tfport = TFPort(
             crop_size, 
@@ -50,7 +50,7 @@ class PreTrainedPipeline():
         )
         self.tfport.compile()
         self.tfport((tf.zeros((1, 224, 224, 3)), tf.zeros((1, seq_length)), tf.zeros((1, seq_length))))
-        self.tfport.load_weights(f'/tfport.h5')
+        self.tfport.load_weights(os.path.join(path, 'tfport.h5'))
 
     def __call__(self, inputs: "Image.Image") -> List[Dict[str, Any]]:
         image = tf.keras.preprocessing.image.img_to_array(inputs)