Update app.py

app.py

@@ -192,7 +192,6 @@ def predict_inverse(hardness_target, ys_target, request: gr.Request):
         [el['name'] for el in domain],
         [[x] for x in x_best]))
     optimized_x = pd.DataFrame.from_dict(best_params)
-    plot = utils.interpret(np.asarray(optimized_x))
     #for c in optimized_x.columns:
      #   if c in continuous_variables:
       #      optimized_x[c]=optimized_x[c]*(scaling_factors[c][1]-scaling_factors[c][0])+scaling_factors[c][0]
@@ -205,7 +204,7 @@ def predict_inverse(hardness_target, ys_target, request: gr.Request):
                                'Single/Multiphase ', 'Single/Multiphase M', 'Single/Multiphase S']]
     result = optimized_x
     result = result[result>0.0].dropna(axis=1)
-    return (result, plot)
+    return result
 
 
 example_inputs = [420, 10]
@@ -270,7 +269,6 @@ with gr.Blocks(css=css_styling, title=page_title) as demo:
         inputs=[input_hardness, input_yield_strength],
         outputs=[
             optimal_parameters,
-            output_interpretation,
         ],
         show_progress=True,
     )
@@ -281,7 +279,6 @@ with gr.Blocks(css=css_styling, title=page_title) as demo:
             optimal_parameters,
             input_hardness,
             input_yield_strength,
-            output_interpretation,
         ],
     )