update readme

Author: delfosseaurelien

README.md

@@ -104,62 +104,34 @@ ScoreList = List[Score]
 
 
 class App(DeepChainApp):
-    """
-    DeepChain App template:
-        Implement score_names() and compute_score() methods.
-        Choose a a transformer available on DeepChain
-        Choose a personal keras/tensorflow model
+    """DeepChain App template:
+
+    - Implement score_names() and compute_score() methods.
+    - Choose a a transformer available on BioTranfformers
+    - Choose a personal keras/tensorflow model
     """
 
     def __init__(self, device: str = "cuda:0"):
         self._device = device
         self.transformer = BioTransformers(backend="protbert", device=device)
-        # TODO: fill _checkpoint_filename if needed
         # Make sure to put your checkpoint file in your_app/checkpoint folder
         self._checkpoint_filename: Optional[str] = "model.pt"
 
-        # TODO:  Use proper loading function
-        #        load_model for tensorflow/keras model
-        #        load for pytorch model
+        # load_model for tensorflow/keras model-load for pytorch model
         if self._checkpoint_filename is not None:
             self.model = load(self.get_checkpoint_path(__file__))
 
     @staticmethod
     def score_names() -> List[str]:
-        """
-        App Score Names. Must be specified.
+        """App Score Names. Must be specified.
 
         Example:
          return ["max_probability", "min_probability"]
         """
-        # TODO: Put your own score_names here
         return ["probability"]
 
     def compute_scores(self, sequences: List[str]) -> ScoreList:
-        """
-        Return a list of all proteins score
-        Score must be a list of dict:
-                - element of list is protein score
-                - key of dict are score_names, value is the scorexs
-
-        Example:
-            Calculate embeddings with the pre-trained Tranformer module
-            -- Use same embedding as the one for training the model!
-            -- Get available embedding with :
-                >> transformer.list_backend()
-                >> embeddings = self.transformer.compute_embedding(sequences)
-
-
-        Args:
-            sequences (List[str]): List of proteins (str)
-
-        Returns:
-            ScoreList: List of score (dictionnary with score_name as key)
-        """
-        # TODO: Fill with you own score function
-
-        if not isinstance(sequences, list):
-            sequences = [sequences]
+        """Return a list of all proteins score"""
 
         x_embedding = self.transformer.compute_embeddings(sequences)["cls"]
         probabilities = self.model(torch.tensor(x_embedding).float())