Update app.py

app.py

@@ -107,17 +107,18 @@ def predict(x, request: gr.Request):
     return (round(y_hardness*(maximum_hardness-minimum_hardness)+minimum_hardness, 2), 12,
             round(y_ys*(maximum_ys-minimum_ys)+minimum_ys, 2), 12)
 
-def fit_outputs(X, reduction_target):
+def fit_outputs(X, antimicrobial_activity_target):
+    reduction_target = 100 - antimicrobial_activity_target
     reduction_target_df = pd.DataFrame({'Reduction_%':[reduction_target]})
     reduction_target_df = scale_numerical(reduction_target_df, ['Reduction_%'], scaler=minmax_scaler_targets, fit=False)
     predictions = model.predict(X)[0]   
     error = np.sqrt(np.square(predictions[0]-reduction_target))
     return error
 
-def predict_inverse(reduction_target, request: gr.Request):
+def predict_inverse(antimicrobial_activity_target, request: gr.Request):
 
     def fit_outputs(x):
-        return fit_outputs_constraints(x, reduction_target, request)
+        return fit_outputs_constraints(x, antimicrobial_activity_target, request)
     opt = GPyOpt.methods.BayesianOptimization(f = fit_outputs,            # function to optimize       
                                               domain = domain,        # box-constraints of the problem
                                               constraints = constraints,
@@ -143,7 +144,7 @@ def predict_inverse(reduction_target, request: gr.Request):
     return optimized_x.transpose()
 
 
-example_inputs = [20]
+example_inputs = [80]
 
 css_styling = """#submit {background: #1eccd8} 
 #submit:hover {background: #a2f1f6} 
@@ -180,8 +181,8 @@ with gr.Blocks(css=css_styling, title=page_title, theme=osium_theme) as demo:
         prediction_button = gr.Button("Predict", elem_id="submit")
     with gr.Row():
         with gr.Column():
-            gr.Markdown("### The target performance of your alloy")
-            reduction_target = gr.Text(label="Enter your maximal acceptable antimicrobial activity reduction you would like for your coating")
+            gr.Markdown("### The target antimicrobial activity of your textile coating")
+            antimicrobial_activity_target = gr.Text(label="Enter the minimum acceptable antimicrobial activity for your textile coating")
 
         with gr.Column():
             with gr.Row():
@@ -196,7 +197,7 @@ with gr.Blocks(css=css_styling, title=page_title, theme=osium_theme) as demo:
 
     prediction_button.click(
         fn=predict_inverse,
-        inputs=[reduction_target],
+        inputs=[antimicrobial_activity_target],
         outputs=[optimal_conditions],
         show_progress=True,
     )
@@ -204,7 +205,7 @@ with gr.Blocks(css=css_styling, title=page_title, theme=osium_theme) as demo:
         lambda x: [gr.update(value=None)] * 2,
         [],
         [
-            reduction_target,
+            antimicrobial_activity_target,
             optimal_conditions,
         ],
     )