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app.py

@@ -90,12 +90,24 @@ input_df = pd.DataFrame([input_data_dict])
 st.markdown("---")
 col1, col2, col3 = st.columns([2, 1, 1])
 with col1:
-    st.subheader("Customer Summary")
-    summary_df = pd.DataFrame([{
-        "Feature": ["Age", "Income", "City Tier", "Product", "Trips"],
-        "Value": [f"{age} yrs", f"₹{monthly_income:,}", f"Tier {city_tier}", product_pitched, num_trips]
-    }])
-    st.dataframe(summary_df, use_container_width=True)
+  summary_df = pd.DataFrame({
+      "Feature": ["Age", "Income", "City Tier", "Product", "Trips"],
+      "Value": [
+        f"{age} yrs",
+        f"₹{monthly_income:,}",
+        f"Tier {city_tier}",
+        product_pitched,
+        str(num_trips)
+      ]
+    })
+  st.dataframe(summary_df, use_container_width=True)
+
+    # st.subheader("Customer Summary")
+    # summary_df = pd.DataFrame([{
+    #     "Feature": ["Age", "Income", "City Tier", "Product", "Trips"],
+    #     "Value": [f"{age} yrs", f"₹{monthly_income:,}", f"Tier {city_tier}", product_pitched, num_trips]
+    # }])
+    # st.dataframe(summary_df, use_container_width=True)
 
 if st.button("Predict Package Purchase", type="primary", use_container_width=True):
     with st.spinner("Generating prediction..."):