Update app.py

app.py

@@ -358,31 +358,30 @@ def predict_inverse(antimicrobial_activity_target, substrate, microorganism, num
     domain = []
     for column in targets:
         df_columns.remove(column)
+
     constrained_columns = ['Substrate', 'Washing_cycles', 'Microorganism ']
-    not_constrained_columns = [column for column in df_columns if column not in constrained_columns]
-    ### Add input constraints
-    for c in test_data_columns:
-        if c.startswith('Substrate'):
-            if c == substrate:
-                domain.append({'name': str(c), 'type': 'categorical', 'domain': (1.0, 1.0)})
-            else:
-                domain.append({'name': str(c), 'type': 'categorical', 'domain': (0.0, 0.0)})
-        elif c == 'Microorganism ':
-            if c == microorganism:
-                domain.append({'name': str(c), 'type': 'categorical', 'domain': (1.0, 1.0)})
-            else:
-                domain.append({'name': str(c), 'type': 'categorical', 'domain': (0.0, 0.0)})
-        elif c == 'Washing_cycles':
-            domain.append({'name': str(c), 'type': 'categorical', 'domain': (int(num_washing_cycles), int(num_washing_cycles))})
-        elif c in numerical_columns:
-            domain.append({'name': str(c), 'type': 'continuous', 'domain': (0.,1.)})
+
+    ### Add input domain
+    for df_column in df_columns:
+        if df_column == "Substrate":
+            for one_hot_column in one_hot_mapping[df_column]:
+                if one_hot_column == substrate:
+                    domain.append({'name': str(one_hot_column), 'type': 'categorical', 'domain': (1.0, 1.0)})
+                else:
+                    domain.append({'name': str(one_hot_column), 'type': 'categorical', 'domain': (0.0, 0.0)})
+        elif df_column == 'Microorganism ':
+            for one_hot_column in one_hot_mapping[df_column]:
+                if one_hot_column == microorganism:
+                    domain.append({'name': str(one_hot_column), 'type': 'categorical', 'domain': (1.0, 1.0)})
+                else:
+                    domain.append({'name': str(one_hot_column), 'type': 'categorical', 'domain': (0.0, 0.0)})
+        elif df_column == 'Washing_cycles':
+            domain.append({'name': str(df_column), 'type': 'categorical', 'domain': (int(num_washing_cycles), int(num_washing_cycles))})
+        elif df_column in numerical_columns:
+            domain.append({'name': str(df_column), 'type': 'continuous', 'domain': (0.,1.)})
         else:
-            seen_not_constrained = []
-            for not_c in not_constrained_columns:
-                if c.startswith(not_c) and not_c not in seen_not_constrained:
-                    domain.append({'name': str(not_c), 'type': 'categorical', 'domain': (0,1),
-                                   'dimensionality': dimensionality_dict[not_c]})
-                    seen_not_constrained.append(str(not_c))
+            domain.append({'name': str(df_column), 'type': 'categorical', 'domain': (0,1),
+                           'dimensionality': dimensionality_dict[df_column]})
     print("Domain is ", domain)
     print(len(domain))