update

app.py

@@ -17,7 +17,7 @@ PH_FNS = ["charge", "charge_density", "isoelectric_point"]
 
 
 def main(property: str, seq: str, seq_file: str, amide: bool, ph: float):
-
+    print("Property", property, "Seq", seq, "seq_file", seq_file)
     prop_name = property.lower()
     algo, config = PROTEIN_PROPERTY_PREDICTOR_FACTORY[prop_name]
 
@@ -30,9 +30,9 @@ def main(property: str, seq: str, seq_file: str, amide: bool, ph: float):
     model = algo(config(**kwargs))
 
     # Read and parse data
-    if seq is not None and seq_file is not None:
+    if seq != "" and seq_file is not None:
         raise ValueError("Pass either smiles or seq_file, not both.")
-    elif seq is not None:
+    elif seq != "":
         seqs = [seq]
     elif seq_file is not None:
         seqs = pd.read_csv(seq_file.name, header=None, sep="\t")[0].tolist()
@@ -46,7 +46,6 @@ def main(property: str, seq: str, seq_file: str, amide: bool, ph: float):
 
 
 if __name__ == "__main__":
-
     # Preparation (retrieve all available algorithms)
     properties = list(PROTEIN_PROPERTY_PREDICTOR_FACTORY.keys())[::-1]
     properties = list(map(lambda x: x.capitalize(), properties))
@@ -55,7 +54,7 @@ if __name__ == "__main__":
     metadata_root = pathlib.Path(__file__).parent.joinpath("model_cards")
 
     examples = [
-        ["Aliphaticity", None, metadata_root.joinpath("examples.smi"), False, 7],
+        ["Aliphaticity", "", metadata_root.joinpath("examples.smi"), False, 7],
         ["Isoelectric_point", "KFLIYQMECSTMIFGL", None, False, 7],
         ["Charge", "KFLIYQMECSTMIFGL", None, True, 12],
     ]

utils.py

@@ -10,7 +10,11 @@ logger.addHandler(logging.NullHandler())
 
 
 def draw_grid_predict(
-    sequences: List[str], properties: np.array, property_names: List[str], domain: str
+    sequences: List[str],
+    properties: np.array,
+    property_names: List[str],
+    domain: str,
+    n_cols: int = -1,
 ) -> str:
     """
     Uses mols2grid to draw a HTML grid for the prediction
@@ -46,8 +50,9 @@ def draw_grid_predict(
         result["Seqs"] = sequences
     for i, name in enumerate(property_names):
         result[name] = properties[:, i]
-    n_cols = min(3, len(result))
-    size = (140, 200) if len(result) > 3 else (600, 700)
+    if n_cols == -1:
+        n_cols = min(3, len(result))
+    size = (250, 200) if len(result) > 3 else (600, 700)
     obj = mols2grid.display(
         result,
         tooltip=list(result.keys()),