Move demo to end

app.py

@@ -13,7 +13,7 @@ eta, a, p, D, profit, var_cost, fixed_cost = sympy.symbols("eta a p D Profit var
 np.random.seed(42)
 
 st.set_page_config(
-    page_title="💸 Dynamic Pricing 💸",
+    page_title="Dynamic Pricing",
     page_icon="💸",
     layout="centered",
     initial_sidebar_state="auto",
@@ -24,7 +24,7 @@ st.set_page_config(
     }
 )
 
-st.title("💸 Dynamic Pricing 💸")
+st.title("Dynamic Pricing")
 st.subheader("Setting optimal prices with Bayesian stats 📈")
 
 # (0) Intro
@@ -102,7 +102,7 @@ curve.  \nAnd you would be very right! But also very wrong as this leads us nice
 next issue.""")
 
 # (3) Constrained data
-st.header("Where are we getting this data anyways? 🤔")
+st.header("Where are we getting this data anyways? 🖥️")
 st.markdown("""So far, we have assumed that we get (and keep getting) data on demand levels at
 different price points.  \n
 Not only is this assumption **unrealistic**, it is also very **undesirable**""")
@@ -195,37 +195,8 @@ st.image(["assets/images/posterior_demand_sample_2.png", "assets/images/posterio
 st.markdown("""And finally we arrive at a price point of €4.04 which is eerily close to
 the actual optimum of €4.24""")
 
-# (5) Thompson sampling demo
-st.header("Demo time 🎮")
-st.markdown("Now that we have covered the theory, you can go ahead and try it our for yourself!")
-st.markdown("""You will notice a few things:  \n
-👉 As you increase price elasticity, the demand becomes more sensitive to price changes and thus the
-profit-optimizing price becomes lower (& vice versa).  \n
-👉 As you decrease price elasticity, our demand observations at €7.5, €10 and €11 become
-increasingly larger and increasingly more variable (as their variance is a constant fraction of the
-absolute value). This causes our demand posterior to become increasingly wider and thus Thompson
-sampling will lead to more exploration.
-""")
-
-thompson_sampler = ThompsonSampler()
-demo_button = st.checkbox(
-    label='Ready for the Demo? 🤯',
-    help="Starts interactive Thompson sampling demo"
-)
-elasticity = st.slider(
-    "Adjust latent elasticity",
-    key="latent_elasticity",
-    min_value=0.05,
-    max_value=0.95,
-    value=0.25,
-    step=0.05,
-)
-while demo_button:
-    thompson_sampler.run()
-    time.sleep(1)
-
-# (6) Extra topics
-st.header("Some final remarks")
+# (5) Extra topics
+st.header("Some things to think about 🤔")
 
 st.markdown("""Because we have purposefully kept the example above quite simple, you may still be
 wondering what happens when added complexities show up.  \n
@@ -309,4 +280,33 @@ get quite far with limited data, especially if you have an accurate prior belief
 likely behaves.""")
 st.markdown("""For reference, in our simple example where we showed a Thompson sampling update, we 
 were already able to gain a lot of confidence in our estimates with just 10 extra demand 
-observations.""")
+observations.""")
+
+# (6) Thompson sampling demo
+st.header("Demo time 🎮")
+st.markdown("Now that we have covered the theory, you can go ahead and try it our for yourself!")
+st.markdown("""You will notice a few things:  \n
+👉 As you increase price elasticity, the demand becomes more sensitive to price changes and thus the
+profit-optimizing price becomes lower (& vice versa).  \n
+👉 As you decrease price elasticity, our demand observations at €7.5, €10 and €11 become
+increasingly larger and increasingly more variable (as their variance is a constant fraction of the
+absolute value). This causes our demand posterior to become increasingly wider and thus Thompson
+sampling will lead to more exploration.
+""")
+
+thompson_sampler = ThompsonSampler()
+demo_button = st.checkbox(
+    label='Ready for the Demo? 🕹️',
+    help="Starts interactive Thompson sampling demo"
+)
+elasticity = st.slider(
+    "Adjust latent elasticity",
+    key="latent_elasticity",
+    min_value=0.05,
+    max_value=0.95,
+    value=0.25,
+    step=0.05,
+)
+while demo_button:
+    thompson_sampler.run()
+    time.sleep(1)