Update app.py

app.py

@@ -106,7 +106,9 @@ def fit_outputs_constraints(x, hardness_target, ys_target, metals_to_use, reques
 
 def predict_inverse(hardness_original_target, ys_original_target, request: gr.Request):
 
-    #one_hot_columns = utils.return_feature_names()
+    one_hot_columns = utils.return_feature_names()
+    min_df_hardness, max_df_hardness = scaling_factors["PROPERTY: HV"]
+    min_df_hardness, max_df_hardness = scaling_factors["PROPERTY: YS (MPa)"]
 
     hardness_target = (hardness_original_target-min_df_hardness)/(max_df_hardness-min_df_hardness)
     ys_target = (ys_original_target-min_df_ys)/(max_df_ys-min_df_ys)
@@ -121,7 +123,7 @@ def predict_inverse(hardness_original_target, ys_original_target, request: gr.Re
                         'PROPERTY: Metal Hf', 'PROPERTY: Metal W', 'PROPERTY: Metal Zn',
                         'PROPERTY: Metal Sn', 'PROPERTY: Metal Re', 'PROPERTY: Metal C',
                         'PROPERTY: Metal Pd', 'PROPERTY: Metal Sc', 'PROPERTY: Metal Y']
-    categorical_variables = list(one_hot.columns)
+    categorical_variables = list(one_hot_columns)
     for c in continuous_variables:
         categorical_variables.remove(c)
 
@@ -146,8 +148,8 @@ def predict_inverse(hardness_original_target, ys_original_target, request: gr.Re
     constrained_columns = ['Single/Multiphase', 'Preprocessing method', 'BCC/FCC/other'] #'PROPERTY: Metal']#, 'Microstructure']
     for constraint in constrained_columns:
         sum_string = ''
-        for i in range (len(one_hot.columns)):
-            column_one_hot = one_hot.columns[i]
+        for i in range (len(one_hot_columns)):
+            column_one_hot = one_hot_columns[i]
             if column_one_hot.startswith(constraint):
                 sum_string = sum_string+"+x[:," + str(i) + "]"
         constraints.append({'name': constraint + "+1", 'constraint': sum_string + '-1'})