feat: generated files

app.py

@@ -104,8 +104,20 @@ elif page == "Customer Analysis":
 
             all_manufacturers = customer_data.iloc[:, 1:].T[customer_data.iloc[:, 1:].T[customer_data.index[0]] > 0]
             
+            # Convert to numeric and handle any non-numeric values
+            all_manufacturers = all_manufacturers.apply(pd.to_numeric, errors='coerce')
+            customer_euros = customer_euros.apply(pd.to_numeric, errors='coerce')
+
             top_units = all_manufacturers.sort_values(by=customer_data.index[0], ascending=False).head(10)
-            top_sales = customer_euros.iloc[0].sort_values(ascending=False).head(10)
+            
+            # Ensure we're working with numeric data for sorting
+            numeric_euros = customer_euros.select_dtypes(include=[np.number])
+            if not numeric_euros.empty:
+                top_sales = numeric_euros.iloc[0].sort_values(ascending=False).head(10)
+            else:
+                st.warning("No numeric sales data available for this customer.")
+                top_sales = pd.Series()
+
             combined_top = pd.concat([top_units, top_sales]).index.unique()
 
             values = []
@@ -122,8 +134,11 @@ elif page == "Customer Analysis":
             for manufacturer, value, amount in zip(manufacturers, values, amounts):
                 st.write(f"{manufacturer} = {value:.4f} units, €{amount:.2f}")
 
-            fig = radar_chart(manufacturers, values, amounts, f'Radar Chart for Top {len(manufacturers)} Manufacturers of Customer {customer_code}')
-            st.pyplot(fig)
+            if manufacturers:  # Only create the chart if we have data
+                fig = radar_chart(manufacturers, values, amounts, f'Radar Chart for Top {len(manufacturers)} Manufacturers of Customer {customer_code}')
+                st.pyplot(fig)
+            else:
+                st.warning("No data available to create the radar chart.")
 
             # Customer sales 2021-2024 (if data exists)
             if 'VENTA_2021' in df.columns and 'VENTA_2022' in df.columns and 'VENTA_2023' in df.columns and 'VENTA_2024' in df.columns: