feat: generated files

app.py

@@ -4,126 +4,134 @@ import plotly.express as px
 import matplotlib.pyplot as plt
 import numpy as np
 
-# Configuración de la página principal
+# Page configuration
 st.set_page_config(page_title="Customer Insights App", page_icon=":bar_chart:")
 
-# Cargar los archivos CSV
+# Load CSV files
 df = pd.read_csv("df_clean.csv")
 nombres_proveedores = pd.read_csv("nombres_proveedores.csv", sep=';')
 
 nombres_proveedores['codigo'] = nombres_proveedores['codigo'].astype(str)
 
-# Ignorar las dos últimas columnas
+# Ignore the last two columns
 df = df.iloc[:, :-2]
 
-# Asegurarse de que el código del cliente sea una cadena (string)
+# Ensure customer code is a string
 df['CLIENTE'] = df['CLIENTE'].astype(str)
 
-# Función para obtener el nombre del proveedor
+# Function to get supplier name
 def get_supplier_name(code):
     name = nombres_proveedores[nombres_proveedores['codigo'] == code]['nombre'].values
     return name[0] if len(name) > 0 else code
 
-# Función para crear el gráfico de radar
+# Function to create radar chart
 def radar_chart(categories, values, title):
-    # Número de variables
+    # Number of variables
     N = len(categories)
     
-    # Calcular los ángulos para cada punto
+    # Calculate angles for each point
     angles = [n / float(N) * 2 * np.pi for n in range(N)]
     angles += angles[:1]
     
-    # Inicializar el gráfico
-    fig, ax = plt.subplots(figsize=(10, 10), subplot_kw=dict(projection='polar'))
+    # Initialize the plot
+    fig, ax = plt.subplots(figsize=(12, 12), subplot_kw=dict(projection='polar'))
     
-    # Dibujar el polígono y rellenarlo
+    # Draw polygon and fill it
     values += values[:1]
     ax.plot(angles, values, 'o-', linewidth=2, color='#FF69B4')
     ax.fill(angles, values, alpha=0.25, color='#FF69B4')
     
-    # Configurar los ejes
+    # Set axes
     ax.set_xticks(angles[:-1])
     ax.set_xticklabels(categories, size=8, wrap=True)
     ax.set_ylim(0, max(values) * 1.1)
     
-    # Dibujar círculos de referencia
+    # Draw reference circles
     circles = np.linspace(0, max(values), 5)
     for circle in circles:
         ax.plot(angles, [circle]*len(angles), '--', color='gray', alpha=0.3, linewidth=0.5)
     
-    # Eliminar las etiquetas radiales y los bordes del gráfico
+    # Remove radial labels and chart borders
     ax.set_yticklabels([])
     ax.spines['polar'].set_visible(False)
     
-    # Añadir el título
+    # Add title
     plt.title(title, size=16, y=1.1)
     
     return fig
 
-# Diseño de la página principal
+# Main page design
 st.title("Welcome to Customer Insights App")
 st.markdown("""
     This app helps businesses analyze customer behaviors and provide personalized recommendations based on purchase history. 
     Use the tools below to dive deeper into your customer data.
 """)
 
-# Menú de navegación
-page = st.selectbox("Selecciona la herramienta que quieres utilizar", ["", "Customer Analysis", "Customer Recommendations"])
+# Navigation menu
+page = st.selectbox("Select the tool you want to use", ["", "Customer Analysis", "Customer Recommendations"])
 
-# Página Home
+# Home Page
 if page == "":
     st.markdown("## Welcome to the Customer Insights App")
     st.write("Use the dropdown menu to navigate between the different sections.")
 
-# Página Customer Analysis
+# Customer Analysis Page
 elif page == "Customer Analysis":
     st.title("Customer Analysis")
     st.markdown("""
         Use the tools below to explore your customer data.
     """)
 
-    # Campo para filtrar clientes
+    # Customer filter field
     partial_code = st.text_input("Enter part of Customer Code (or leave empty to see all)")
 
-    # Filtrar las opciones de clientes que coincidan con el código parcial
+    # Filter customer options that match the partial code
     if partial_code:
         filtered_customers = df[df['CLIENTE'].str.contains(partial_code)]
     else:
         filtered_customers = df
 
-    # Crear una lista de clientes filtrados para el selectbox
+    # Create a list of filtered customers for the selectbox
     customer_list = filtered_customers['CLIENTE'].unique()
 
-    # Selección de cliente con autocompletar filtrado
+    # Customer selection with filtered autocomplete
     customer_code = st.selectbox("Select Customer Code", customer_list)
 
     if customer_code:
-        # Filtrar datos para el cliente seleccionado
+        # Filter data for the selected customer
         customer_data = df[df["CLIENTE"] == customer_code]
 
         if not customer_data.empty:
             st.write(f"### Analysis for Customer {customer_code}")
 
-            # Obtener las 6 columnas con los valores más altos (ignorar la columna de cliente)
-            top_6_manufacturers = customer_data.iloc[:, 1:].T.nlargest(6, customer_data.index[0])
+            # Define purchase threshold
+            purchase_threshold = 0
 
-            # Ordenar los fabricantes por valor descendente para mejor visualización
-            top_6_manufacturers = top_6_manufacturers.sort_values(by=customer_data.index[0], ascending=False)
+            # 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]
+            
+            # Sort manufacturers by value in descending order
+            all_manufacturers = all_manufacturers.sort_values(by=customer_data.index[0], ascending=False)
 
-            # Preparar los valores y fabricantes
-            values = top_6_manufacturers[customer_data.index[0]].values.tolist()
-            manufacturers = [get_supplier_name(m) for m in top_6_manufacturers.index.tolist()]
+            # 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()]
 
-            # Mostrar los resultados de cada fabricante
-            st.write("### Resultados porcentaje fabricante (ordenados):")
+            # If there are fewer than 3 manufacturers, add a third one with value 0
+            if len(manufacturers) < 3:
+                manufacturers.append("Other")
+                values.append(0)
+
+            # Display the results for each manufacturer
+            st.write(f"### Results for {len(manufacturers)} manufacturers (sorted):")
             for manufacturer, value in zip(manufacturers, values):
                 st.write(f"{manufacturer} = {value:.4f}")
 
-            # Crear y mostrar el gráfico de radar
-            fig = radar_chart(manufacturers, values, f'Radar Chart for Top 6 Manufacturers of Customer {customer_code}')
+            # Create and display the radar chart
+            fig = radar_chart(manufacturers, values, f'Radar Chart for {len(manufacturers)} Manufacturers of Customer {customer_code}')
             st.pyplot(fig)
 
-            # Ventas del cliente 2021-2024 (si los datos existen)
+            # 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:
                 years = ['2021', '2022', '2023', '2024']
                 sales_columns = ['VENTA_2021', 'VENTA_2022', 'VENTA_2023', 'VENTA_2024']
@@ -137,39 +145,39 @@ elif page == "Customer Analysis":
         else:
             st.warning(f"No data found for customer {customer_code}. Please check the code.")
 
-# Página Customer Recommendations
+# Customer Recommendations Page
 elif page == "Customer Recommendations":
     st.title("Customer Recommendations")
     st.markdown("""
         Get tailored recommendations for your customers based on their purchasing history.
     """)
 
-    # Campo para filtrar clientes
+    # Customer filter field
     partial_code = st.text_input("Enter part of Customer Code for Recommendations (or leave empty to see all)")
 
-    # Filtrar las opciones de clientes que coincidan con el código parcial
+    # Filter customer options that match the partial code
     if partial_code:
         filtered_customers = df[df['CLIENTE'].str.contains(partial_code)]
     else:
         filtered_customers = df
 
-    # Crear una lista de clientes filtrados para el selectbox
+    # Create a list of filtered customers for the selectbox
     customer_list = filtered_customers['CLIENTE'].unique()
 
-    # Selección de cliente con autocompletar filtrado
+    # 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:
-            # Mostrar historial de compras del cliente seleccionado
+            # Show selected customer's purchase history
             st.write(f"### Purchase History for Customer {customer_code}")
             st.write(customer_data)
 
-            # Generar recomendaciones (placeholder)
+            # Generate recommendations (placeholder)
             st.write(f"### Recommended Products for Customer {customer_code}")
-            # Aquí puedes reemplazar con la lógica del modelo de recomendación
+            # You can replace this with the logic of the recommendation model
             st.write("Product A, Product B, Product C")
         else:
             st.warning(f"No data found for customer {customer_code}. Please check the code.")