Update app.py

app.py

@@ -316,48 +316,7 @@ test_data_columns = ['Binder_ADA',
  'Duration (h)',
  'Washing_cycles',
  'Concetration (µg/mL)']
-
     
-### Define space and constrains
-
-dimensionality_dict = {}
-one_hot_mapping = {}
-for c in categorical_columns:
-    dimensionality_dict[c] = 0
-    one_hot_mapping[c] = []
-for c in categorical_columns:
-    for t in test_data_columns:
-        if c in t:
-            dimensionality_dict[c]+=1 
-            one_hot_mapping[c].append(t)
-
-domain = []
-for column in targets:
-    df_columns.remove(column)
-constrained_columns = ['Substrate', 'Washing_cycles', 'Microorganism ']
-for c in df_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))})
-    else:
-        if c in numerical_columns:
-            domain.append({'name': str(c), 'type': 'continuous', 'domain': (0.,1.)})
-        else:
-            domain.append({'name': str(c), 'type': 'categorical', 'domain': (0,1),
-                           'dimensionality': dimensionality_dict[c]})
-
-# Constraints 
-constraints = []
 
 
 
@@ -383,6 +342,47 @@ def fit_outputs_constraints(X, antimicrobial_activity_target, request: gr.Reques
 
 def predict_inverse(antimicrobial_activity_target, request: gr.Request):
 
+    ### Define space and constrains
+
+    dimensionality_dict = {}
+    one_hot_mapping = {}
+    for c in categorical_columns:
+        dimensionality_dict[c] = 0
+        one_hot_mapping[c] = []
+    for c in categorical_columns:
+        for t in test_data_columns:
+            if c in t:
+                dimensionality_dict[c]+=1 
+                one_hot_mapping[c].append(t)
+
+    domain = []
+    for column in targets:
+        df_columns.remove(column)
+    constrained_columns = ['Substrate', 'Washing_cycles', 'Microorganism ']
+    for c in df_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))})
+        else:
+            if c in numerical_columns:
+                domain.append({'name': str(c), 'type': 'continuous', 'domain': (0.,1.)})
+            else:
+                domain.append({'name': str(c), 'type': 'categorical', 'domain': (0,1),
+                               'dimensionality': dimensionality_dict[c]})
+
+    # Constraints 
+    constraints = []
+
     def fit_outputs(x):
         return fit_outputs_constraints(x, antimicrobial_activity_target, request)
     opt = GPyOpt.methods.BayesianOptimization(f = fit_outputs,            # function to optimize