feat: updated website

app.py

@@ -327,6 +327,32 @@ elif page == "Customer Analysis":
 
                     # Column 1: Radar chart for top manufacturers
                     with col1:
+                        # Overperforming manufacturers alert
+                        st.markdown(f"### Overperforming Manufacturers for {customer_code}")
+
+                        # Identify manufacturers that exceeded predicted sales
+                        overperforming_manufacturers = results[results['ventas_reales'] > results['ventas_predichas']].copy()
+
+                        if not overperforming_manufacturers.empty:
+                            # Calculate the extra amount (difference between actual and predicted sales)
+                            overperforming_manufacturers['extra_amount'] = overperforming_manufacturers['ventas_reales'] - overperforming_manufacturers['ventas_predichas']
+
+                            # Sort by the highest extra amount
+                            overperforming_manufacturers = overperforming_manufacturers.sort_values(by='extra_amount', ascending=False)
+
+                            # Limit to top 10 overperforming manufacturers
+                            top_overperformers = overperforming_manufacturers.head(10)
+
+                            # Display alerts for top 10 biggest overperformers
+                            st.success("Top 10 manufacturers that exceeded predicted sales:")
+                            for index, row in top_overperformers.iterrows():
+                                manufacturer_name = get_supplier_name(row['marca_id_encoded'])
+                                predicted = row['ventas_predichas']
+                                actual = row['ventas_reales']
+                                extra = row['extra_amount']
+                                st.write(f"- {manufacturer_name}: Predicted = {predicted:.2f}€, Actual = {actual:.2f}€, Exceeded by = {extra:.2f}€")
+                        else:
+                            st.info("No manufacturers exceeded predicted sales.")
                         # Radar chart logic remains the same
                         customer_df = df[df["CLIENTE"] == str(customer_code)]
                         all_manufacturers = customer_df.iloc[:, 1:].T
@@ -371,7 +397,7 @@ elif page == "Customer Analysis":
 
                     # Column 2: Alerts and additional analysis
                     with col2:
-                        st.markdown(f"### Alerts for {customer_code}")
+                        st.markdown(f"### You might need to check this!")
                         
                         # Identify manufacturers that didn't meet predicted sales
                         underperforming_manufacturers = results[results['ventas_reales'] < results['ventas_predichas']].copy()
@@ -387,7 +413,7 @@ elif page == "Customer Analysis":
                             top_misses = underperforming_manufacturers.head(10)
 
                             # Display warnings for top 10 biggest misses
-                            st.warning("Top 10 manufacturers that have not met predicted sales:")
+                            st.warning("Your customer was expected to buy more products from the following brands:")
                             for index, row in top_misses.iterrows():
                                 manufacturer_name = get_supplier_name(row['marca_id_encoded'])
                                 predicted = row['ventas_predichas']