Update app.py

app.py

@@ -136,8 +136,8 @@ 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)
 
-def fit_outputs_constraints(x, hardness_target, ys_target):
-    predictions = predict(x)
+def fit_outputs_constraints(x, hardness_target, ys_target, request: gr.Request):
+    predictions = predict(x, request)
     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)
@@ -176,7 +176,7 @@ def predict_inverse(hardness_target, ys_target, request: gr.Request):
         constraints.append({'name': constraint + "-1", 'constraint': '-1*(' + sum_string + ')+1'})
 
     def fit_outputs(x):
-        return fit_outputs_constraints(x, hardness_target, ys_target)
+        return fit_outputs_constraints(x, hardness_target, ys_target, request)
 
     opt = GPyOpt.methods.BayesianOptimization(f = fit_outputs,            # function to optimize       
                                               domain = domain,        # box-constraints of the problem