Update app.py

app.py

@@ -144,7 +144,7 @@ def fit_outputs_constraints(x, hardness_target, ys_target, request: gr.Request):
           error_hardness, error_ys)
     return error_hardness + error_ys
 
-def predict_inverse(hardness_target, ys_target, request: gr.Request):
+def predict_inverse(hardness_target, ys_target, formula, request: gr.Request):
 
     one_hot_columns = utils.return_feature_names()
 
@@ -154,16 +154,25 @@ def predict_inverse(hardness_target, ys_target, request: gr.Request):
     for c in continuous_variables:
         categorical_variables.remove(c)
 
+
+    fixed_density = utils.calculate_density(str(formula))
+    fixed_ym = utils.calculate_youngs_modulus(str(formula))
+    
     domain = []
     for c in one_hot_columns:
         if c in continuous_variables:
             if c == continuous_variables[0]:
-                domain.append({'name': str(c), 'type': 'continuous', 'domain': (0.528, 0.528)})#(0.,1.)})
+                domain_density = (fixed_density-scaling_factors[c][0])/(
+                    scaling_factors[c][1]-scaling_factors[c][0])
+                domain.append({'name': str(c), 'type': 'continuous', 'domain': (domain_density)})#(0.,1.)})
             else:
-                domain.append({'name': str(c), 'type': 'continuous', 'domain': (0.539, 0.539)})#(0.,1.)})
+                domain_ym = (fixed_ym-scaling_factors[c][0])/(
+                    scaling_factors[c][1]-scaling_factors[c][0])
+                domain.append({'name': str(c), 'type': 'continuous', 'domain': (domain_ym)})#(0.,1.)})
         else:
             domain.append({'name': str(c), 'type': 'discrete', 'domain': (0,1)})
 
+    print("Domain is ", domain)
     constraints = []
     constrained_columns = ['Single/Multiphase', 'Preprocessing method', 'BCC/FCC/other']#, 'Microstructure']