Update app.py

app.py

@@ -807,6 +807,30 @@ with demo:
 
     gr.Markdown("<hr />")
 
+    ########################## Model Prediction ##########################
+
+    gr.Markdown("## Step 3: Run the FHE evaluation.")
+    gr.Markdown("<span style='color:grey'>Server Side</span>")
+    gr.Markdown(
+        """
+        It's high time to launch our prediction, by pressing the button you will launch the 
+        fraud analysis that our fictitious bank offers you.
+        This server employs a [Random Forest (by Concrete-ML)](https://github.com/zama-ai/concrete-ml/blob/release/1.8.x/docs/references/api/concrete.ml.sklearn.rf.md#class-randomforestclassifier)
+        classifier model that has been trained on a synthetic data-set.
+        """
+    )
+
+    execute_fhe_button = gr.Button("Run the FHE evaluation.")
+    fhe_execution_time = gr.Textbox(
+        label="Total FHE execution time (in seconds):", max_lines=1, interactive=False
+    )
+
+    # Button to send the encodings to the server using post method
+    execute_fhe_button.click(predict, outputs=[fhe_execution_time, execute_fhe_button])
+
+    gr.Markdown("<hr />")
+
+
 
 
 
@@ -1247,22 +1271,6 @@ demo.launch(share=False)
 
 #     gr.Markdown("<hr />")
 
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 #     ########################## Model Prediction ##########################
 
 #     gr.Markdown("## Step 3: Run the FHE evaluation.")
@@ -1286,6 +1294,27 @@ demo.launch(share=False)
 
 #     gr.Markdown("<hr />")
 
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 #     ########################## Decrypt Prediction ##########################
 
 #     gr.Markdown("## Step 4: Receive the encrypted output from the server and decrypt.")