Update app.py

app.py

@@ -18,7 +18,7 @@ def gen_pulsive_noise(count, min_val, max_val):
         pulse = random.randint(min_val, max_val)
     
     return pulse
-def simulation(input_noise, output_noise, min, max, std, mean, min_pul, max_pul, kp, ki, kd):
+def simulation(input_noise, output_noise, min, max, std, mean, min_pul, max_pul, kp, ki, kd, show_original):
     vrs = {}
     fmu = 'Pharmacokinetics_4_comportmental_model_PI_ref_FMU_base4_OAAS_lnx.fmu'
     model_description = read_model_description(fmu)
@@ -106,13 +106,15 @@ def simulation(input_noise, output_noise, min, max, std, mean, min_pul, max_pul,
     trace1 = go.Scatter(x=df.index, y=df['BIS'], mode='lines', name='BIS')
     fig1 = go.Figure(data=trace1)
     fig1.update_layout(height=400, width=1200, title_text="BIS evolution")
-    fig1.add_trace(go.Scatter(x=df_original.index, y=df_original['BIS'], mode='lines', name='BIS original', line=dict(color="red"), opacity=0.5),)
 
     # Add a line trace for column_2 in the second subplot
     trace2 = go.Scatter(x=df.index, y=df['Infusion'], mode='lines', name='Infusion')
     fig2 = go.Figure(data=trace2)
     fig2.update_layout(height=400, width=1200, title_text="Infusion evolution")
-    fig2.add_trace(go.Scatter(x=df_original.index, y=df_original['Infusion'], mode='lines', name='Infusion original',  line=dict(color="red"), opacity=0.5))
+    
+    if show_original:
+        fig1.add_trace(go.Scatter(x=df_original.index, y=df_original['BIS'], mode='lines', name='BIS original', line=dict(color="red"), opacity=0.5))
+        fig2.add_trace(go.Scatter(x=df_original.index, y=df_original['Infusion'], mode='lines', name='Infusion original',  line=dict(color="red"), opacity=0.5))
 
     return fig1, fig2    
 import gradio as gr 
@@ -136,10 +138,11 @@ with gr.Blocks() as demo:
             with gr.Blocks():
                 kp_slider = gr.inputs.Slider(minimum=0, maximum=20, default=4, label="kp")
                 ki_slider = gr.inputs.Slider(minimum=0, maximum=1, default=0.01, label="ki")
-                kd_slider = gr.inputs.Slider(minimum=0, maximum=20, default=1, label="kd")
+                kd_slider = gr.inputs.Slider(minimum=0, maximum=200, default=1, label="kd")
+            show_original = gr.inputs.Checkbox(label="Show original")
             button = gr.Button("Simulate")
         with gr.Column(scale=5):
             plot1 = gr.Plot(label="BIS evolution")
             plot2 = gr.Plot(label="Infusion evolution")
-    button.click(simulation, inputs=[input_noise, output_noise, min_gaussian, max_gaussian, std_gaussian, mean_gaussian, min_pul, max_pul, kp_slider, ki_slider, kd_slider], outputs=[plot1, plot2])
+    button.click(simulation, inputs=[input_noise, output_noise, min_gaussian, max_gaussian, std_gaussian, mean_gaussian, min_pul, max_pul, kp_slider, ki_slider, kd_slider, show_original], outputs=[plot1, plot2])
 demo.launch()