Update app.py

app.py

@@ -50,13 +50,13 @@ st.markdown(
     'Simulate A/B optimization with policy gradient reinforcement learning')
 
 
-learning_rate = st.text_input('Learning rate', value=0.1, height=25)
+learning_rate = st.text_input('Learning rate', value=0.1)
 
-prob_A = st.text_input('Click probability of ad A', 0.4, height=75)
+prob_A = st.text_input('Click probability of ad A', value=0.4)
 
-prob_B = st.text_input('Click probability of ad B', 0.5, height=75)
+prob_B = st.text_input('Click probability of ad B', value=0.5)
 
-epochs = int(st.text_input('Number of ad impressions (epochs)', 2000, height=75))
+epochs = int(st.text_input('Number of ad impressions (epochs)', value=2000))
 
 information_for_plotting = np.zeros((epochs, 10))