Update app.py

app.py

@@ -360,19 +360,18 @@ def predict_inverse(antimicrobial_activity_target, substrate, microorganism, num
         df_columns.remove(column)
     constrained_columns = ['Substrate', 'Washing_cycles', 'Microorganism ']
     for c in test_data_columns:
-        if c in constrained_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)})
-            if 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)})
-            if c == 'Washing_cycles':
-                domain.append({'name': str(c), 'type': 'categorical', 'domain': (int(num_washing_cycles), int(num_washing_cycles))})
+        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))})
         else:
             if c in numerical_columns:
                 domain.append({'name': str(c), 'type': 'continuous', 'domain': (0.,1.)})