Update app.py

app.py

@@ -204,7 +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
+    return list(result.keys())[2:]
 
 
 example_inputs = [420, 10]
@@ -250,7 +250,10 @@ with gr.Blocks(css=css_styling, title=page_title) as demo:
         with gr.Column():
             with gr.Row():
                 gr.Markdown("### Your optimal formulation and processing conditions")
-                optimal_parameters = gr.DataFrame(label="Optimal parameters", wrap=True)
+                #optimal_parameters = gr.DataFrame(label="Optimal parameters", wrap=True)
+                param1 = gr.Text(lable="Param1")
+                param2 = gr.Text(lable="Param2")
+                param3 = gr.Text(lable="Param3")
             with gr.Row():
                 with gr.Column():
                     with gr.Row():
@@ -268,7 +271,9 @@ with gr.Blocks(css=css_styling, title=page_title) as demo:
         fn=predict_inverse,
         inputs=[input_hardness, input_yield_strength],
         outputs=[
-            optimal_parameters,
+            param1,
+            param2,
+            param3,
         ],
         show_progress=True,
     )
@@ -276,7 +281,9 @@ with gr.Blocks(css=css_styling, title=page_title) as demo:
         lambda x: [gr.update(value=None)] * 4,
         [],
         [
-            optimal_parameters,
+            param1,
+            param2,
+            param3,
             input_hardness,
             input_yield_strength,
         ],