Update app.py

app.py

@@ -383,7 +383,7 @@ def predict_inverse(antimicrobial_activity_target, substrate, microorganism, num
             domain.append({'name': str(df_column), 'type': 'continuous', 'domain': (washing_cycles_target_df["Washing_cycles"].iloc[0],
                                                                                     washing_cycles_target_df["Washing_cycles"].iloc[0])})
         elif df_column in numerical_columns:
-            domain.append({'name': str(df_column), 'type': 'continuous', 'domain': (0.5,1.)})
+            domain.append({'name': str(df_column), 'type': 'continuous', 'domain': (0.0,1.)})
         else:
             domain.append({'name': str(df_column), 'type': 'categorical', 'domain': (0,1),
                            'dimensionality': dimensionality_dict[df_column]})
@@ -424,7 +424,7 @@ def predict_inverse(antimicrobial_activity_target, substrate, microorganism, num
     optimal_concentration = optimized_x['Concetration (µg/mL)'].iloc[0] if optimized_x['Concetration (µg/mL)'].iloc[0] > 0 else 11.2
 
     return (optimized_x['Binder'].iloc[0], optimized_x['NMs_Type'].iloc[0], optimized_x['Primary_Size (nm)'].iloc[0],
-            optimized_x['NM-Shape'].iloc[0], optimized_x['Concetration (µg/mL)'].iloc[0],
+            optimized_x['NM-Shape'].iloc[0], optimized_x['Concetration (µg/mL)'].iloc[0] if optimized_x['Concetration (µg/mL)'].iloc[0] else 0.1,
             optimized_x['NPs_Synthesis_method'].iloc[0], optimized_x['Application method\n'].iloc[0], 
             optimized_x['Washing_Detergent'].iloc[0], optimized_x['Washing_Temp'].iloc[0])