Update app.py

app.py

@@ -50,17 +50,17 @@ st.markdown(
     'Simulate A/B optimization with policy gradient reinforcement learning')
 
 
-learning_rate = st.text_input('Learning rate', value=0.1)
+learning_rate = float(st.text_input('Learning rate', value=0.1))
 
-prob_A = st.text_input('Click probability of ad A', value=0.4)
+prob_A = float(st.text_input('Click probability of ad A', value=0.4))
 
-prob_B = st.text_input('Click probability of ad B', value=0.5)
+prob_B = float(st.text_input('Click probability of ad B', value=0.5))
 
 epochs = int(st.text_input('Number of ad impressions (epochs)', value=2000))
 
 information_for_plotting = np.zeros((epochs, 10))
 
-if st.button('Modell trainieren und Fit-Kurve darstellen'):
+if st.button('Run the ad campaign and display the results'):
 
     with st.spinner('Simulating the ad campaign may take a few seconds ...'):