feat: generated files

app.py

@@ -26,49 +26,42 @@ def get_supplier_name(code):
     name = nombres_proveedores[nombres_proveedores['codigo'] == code]['nombre'].values
     return name[0] if len(name) > 0 else code
 
-# Function to create radar chart
+# Function to create radar chart with square root transformation
 def radar_chart(categories, values, amounts, title):
-    # Number of variables
     N = len(categories)
-    
-    # Calculate angles for each point
     angles = [n / float(N) * 2 * np.pi for n in range(N)]
     angles += angles[:1]
     
-    # Initialize the plot
     fig, ax = plt.subplots(figsize=(12, 12), subplot_kw=dict(projection='polar'))
     
-    # Normalize values and amounts
-    max_value = max(values)
-    normalized_values = [v / max_value for v in values]
-    total_amount = sum(amounts)
-    normalized_amounts = [a / total_amount for a in amounts]
+    # Apply square root transformation
+    sqrt_values = np.sqrt(values)
+    sqrt_amounts = np.sqrt(amounts)
+    
+    max_sqrt_value = max(sqrt_values)
+    normalized_values = [v / max_sqrt_value for v in sqrt_values]
+    total_sqrt_amount = sum(sqrt_amounts)
+    normalized_amounts = [a / total_sqrt_amount for a in sqrt_amounts]
     
-    # Draw polygon for units and fill it
     normalized_values += normalized_values[:1]
-    ax.plot(angles, normalized_values, 'o-', linewidth=2, color='#FF69B4', label='% Units')
+    ax.plot(angles, normalized_values, 'o-', linewidth=2, color='#FF69B4', label='% Units (sqrt)')
     ax.fill(angles, normalized_values, alpha=0.25, color='#FF69B4')
     
-    # Draw polygon for amounts and fill it
     normalized_amounts += normalized_amounts[:1]
-    ax.plot(angles, normalized_amounts, 'o-', linewidth=2, color='#4B0082', label='% Spend')
+    ax.plot(angles, normalized_amounts, 'o-', linewidth=2, color='#4B0082', label='% Spend (sqrt)')
     ax.fill(angles, normalized_amounts, alpha=0.25, color='#4B0082')
     
-    # Set axes
     ax.set_xticks(angles[:-1])
     ax.set_xticklabels(categories, size=8, wrap=True)
     ax.set_ylim(0, max(max(normalized_values), max(normalized_amounts)) * 1.1)
     
-    # Draw reference circles
     circles = np.linspace(0, 1, 5)
     for circle in circles:
         ax.plot(angles, [circle]*len(angles), '--', color='gray', alpha=0.3, linewidth=0.5)
     
-    # Remove radial labels and chart borders
     ax.set_yticklabels([])
     ax.spines['polar'].set_visible(False)
     
-    # Add title and legend
     plt.title(title, size=16, y=1.1)
     plt.legend(loc='upper right', bbox_to_anchor=(1.3, 1.1))
     
@@ -92,60 +85,43 @@ if page == "":
 # Customer Analysis Page
 elif page == "Customer Analysis":
     st.title("Customer Analysis")
-    st.markdown("""
-        Use the tools below to explore your customer data.
-    """)
+    st.markdown("Use the tools below to explore your customer data.")
 
-    # Customer filter field
     partial_code = st.text_input("Enter part of Customer Code (or leave empty to see all)")
-
-    # Filter customer options that match the partial code
     if partial_code:
         filtered_customers = df[df['CLIENTE'].str.contains(partial_code)]
     else:
         filtered_customers = df
-
-    # Create a list of filtered customers for the selectbox
     customer_list = filtered_customers['CLIENTE'].unique()
-
-    # Customer selection with filtered autocomplete
     customer_code = st.selectbox("Select Customer Code", customer_list)
 
     if customer_code:
-        # Filter data for the selected customer
         customer_data = df[df["CLIENTE"] == customer_code]
         customer_euros = euros_proveedor[euros_proveedor["CLIENTE"] == customer_code]
 
         if not customer_data.empty and not customer_euros.empty:
             st.write(f"### Analysis for Customer {customer_code}")
 
-            # Define purchase threshold
-            purchase_threshold = 0
-
-            # Get all manufacturers the customer bought from (ignore the customer column)
-            all_manufacturers = customer_data.iloc[:, 1:].T[customer_data.iloc[:, 1:].T[customer_data.index[0]] > purchase_threshold]
+            all_manufacturers = customer_data.iloc[:, 1:].T[customer_data.iloc[:, 1:].T[customer_data.index[0]] > 0]
             
-            # Sort manufacturers by value in descending order and limit to top 20
-            all_manufacturers = all_manufacturers.sort_values(by=customer_data.index[0], ascending=False).head(20)
-
-            # Prepare values and manufacturers
-            values = all_manufacturers[customer_data.index[0]].values.tolist()
-            manufacturers = [get_supplier_name(m) for m in all_manufacturers.index.tolist()]
+            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)
+            combined_top = pd.concat([top_units, top_sales]).index.unique()
 
-            # Get amounts in euros
+            values = []
+            manufacturers = []
             amounts = []
-            for m in all_manufacturers.index.tolist():
-                if m in customer_euros.columns:
-                    amounts.append(customer_euros[m].values[0])
-                else:
-                    amounts.append(0)
-
-            # Display the results for each manufacturer
-            st.write(f"### Results for top {len(manufacturers)} manufacturers (sorted):")
+
+            for m in combined_top:
+                if m in all_manufacturers.index:
+                    values.append(all_manufacturers[m])
+                    manufacturers.append(get_supplier_name(m))
+                    amounts.append(customer_euros[m].values[0] if m in customer_euros.columns else 0)
+
+            st.write(f"### Results for top {len(manufacturers)} manufacturers (balanced by units and sales):")
             for manufacturer, value, amount in zip(manufacturers, values, amounts):
                 st.write(f"{manufacturer} = {value:.4f} units, €{amount:.2f}")
 
-            # Create and display the radar chart
             fig = radar_chart(manufacturers, values, amounts, f'Radar Chart for Top {len(manufacturers)} Manufacturers of Customer {customer_code}')
             st.pyplot(fig)
 
@@ -170,32 +146,23 @@ elif page == "Customer Recommendations":
         Get tailored recommendations for your customers based on their purchasing history.
     """)
 
-    # Customer filter field
     partial_code = st.text_input("Enter part of Customer Code for Recommendations (or leave empty to see all)")
-
-    # Filter customer options that match the partial code
     if partial_code:
         filtered_customers = df[df['CLIENTE'].str.contains(partial_code)]
     else:
         filtered_customers = df
-
-    # Create a list of filtered customers for the selectbox
     customer_list = filtered_customers['CLIENTE'].unique()
-
-    # Customer selection with filtered autocomplete
     customer_code = st.selectbox("Select Customer Code for Recommendations", customer_list)
 
     if customer_code:
         customer_data = df[df["CLIENTE"] == customer_code]
 
         if not customer_data.empty:
-            # Show selected customer's purchase history
             st.write(f"### Purchase History for Customer {customer_code}")
             st.write(customer_data)
 
-            # Generate recommendations (placeholder)
             st.write(f"### Recommended Products for Customer {customer_code}")
-            # You can replace this with the logic of the recommendation model
+            # Placeholder for recommendation logic
             st.write("Product A, Product B, Product C")
         else:
             st.warning(f"No data found for customer {customer_code}. Please check the code.")