Update app.py

app.py

@@ -376,8 +376,8 @@ def predict_inverse(antimicrobial_activity_target, substrate, microorganism, num
                 else:
                     domain.append({'name': str(one_hot_column), 'type': 'categorical', 'domain': (0.0, 0.0)})
         elif df_column == 'Washing_cycles':
-            washing_cycles_target_df = pd.DataFrame({'Washing_cycles':[reduction_target]})
-            washing_cycles_target_df = scale_numerical(reduction_target_df, ['Washing_cycles'], scaler=minmax_scaler_targets, fit=False)
+            washing_cycles_target_df = pd.DataFrame({'Washing_cycles':[num_washing_cycles]})
+            washing_cycles_target_df = scale_numerical(washing_cycles_target_df, ['Washing_cycles'], scaler=minmax_scaler_targets, fit=False)
             domain.append({'name': str(df_column), 'type': 'continuous', 'domain': (washing_cycles_target_df, washing_cycles_target_df)})
         elif df_column in numerical_columns:
             domain.append({'name': str(df_column), 'type': 'continuous', 'domain': (0.,1.)})
@@ -462,7 +462,7 @@ with gr.Blocks(css=css_styling, title=page_title, theme=osium_theme) as demo:
             antimicrobial_activity_target = gr.Text(label="Enter the minimum acceptable antimicrobial activity for your textile coating")
             gr.Markdown("### Your constraints")
             substrate = gr.Dropdown(label="Your substrate", choices=[c for c in test_data_columns if c.startswith("Substrate")])
-            num_washing_cycles = gr.Text("Your number of washing cycles")
+            num_washing_cycles = gr.Text(label="Your number of washing cycles")
             microorganism = gr.Dropdown(label="Microorganism", choices=[c for c in test_data_columns if c.startswith("Microorganism")])
 
         with gr.Column():