Update app.py

app.py

@@ -135,17 +135,13 @@ def predict_from_tuple(in1, in2, in3, in4, in5, request: gr.Request):
     print("One hot columns are ", one_hot.columns)
     return predict(one_hot, request)
 
-hardness_target  = 1
-ys_target = 2
-
-def fit_outputs(x):
+def fit_outputs_constraints(x, hardness_target, ys_target):
     predictions = predict(x)
     error_hardness = np.sqrt(np.square(predictions[0]-hardness_target))
     error_ys = np.sqrt(np.square(predictions[2]-ys_target))
     print(predictions, hardness_target, ys_target, error_hardness, error_ys)
     return error_hardness + error_ys
 
-
 def predict_inverse(hardness_target, ys_target, request: gr.Request):
 
     continuous_variables = ['PROPERTY: Calculated Density (g/cm$^3$)',
@@ -176,6 +172,9 @@ def predict_inverse(hardness_target, ys_target, request: gr.Request):
         constraints.append({'name': constraint + "+1", 'constraint': sum_string + '-1'})
         constraints.append({'name': constraint + "-1", 'constraint': '-1*(' + sum_string + ')+1'})
 
+    def fit_outputs(x):
+        return fit_outputs_constraints(x, harndess_target, ys_target)
+
     opt = GPyOpt.methods.BayesianOptimization(f = fit_outputs,            # function to optimize       
                                               domain = domain,        # box-constraints of the problem
                                               constraints = constraints,