overview updates

app.py

@@ -29,7 +29,22 @@ X_names = [
 # Title
 st.title("Uplift Modeling in Retail Demo")
 
-tabs = st.sidebar.radio("Navigation", ["Data generation", "Exploratory analysis", "Model training", "Economic effects"])
+tabs = st.sidebar.radio("Navigation", [
+    "Overview"
+    "Data generation",
+    "Exploratory analysis",
+    "Model training",
+    "Economic effects"]
+)
+
+if tabs == "Overview":
+    st.header("Overview")
+    st.write("""
+    This app demonstrates the use of uplift modeling to understand the effect of different actions (like promotions) on customer behavior. We generate a simulated dataset and use it to train a model that predicts the uplift effect of different treatments on customer behavior. We then evaluate the model using the Qini curve, which measures the uplift effect of a model compared to a random model.
+    """)
+    # embed Loom video on the page
+    st.video("https://www.loom.com/share/57591e60cf7a44fdb8de5d7fb66808e8")
+    st.write("To get started, select the 'Data generation' tab from the sidebar.")
 
 if tabs == "Data generation":