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| import streamlit as st | |
| import pandas as pd | |
| import plotly.express as px | |
| import plotly.graph_objects as go | |
| # Configuración de la página principal | |
| st.set_page_config(page_title="Customer Insights App", page_icon=":bar_chart:") | |
| # Cargar el archivo CSV que ya está disponible en la web | |
| # Asegúrate de que el archivo esté en el mismo directorio o en una ubicación accesible | |
| df = pd.read_csv("ruta_del_archivo.csv") # Actualiza "ruta_del_archivo.csv" con la ruta correcta | |
| # Diseño de la página principal | |
| 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"]) | |
| # Página Home | |
| 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 | |
| elif page == "Customer Analysis": | |
| st.title("Customer Analysis") | |
| st.markdown(""" | |
| Use the tools below to explore your customer data. | |
| """) | |
| # Input para código de cliente | |
| customer_code = st.text_input("Enter Customer Code") | |
| if customer_code: | |
| # Filtrar datos para el cliente seleccionado | |
| customer_data = df[df["CLIENTE"] == customer_code] | |
| if not customer_data.empty: | |
| st.write(f"### Analysis for Customer {customer_code}") | |
| # Generar un gráfico spider (radar chart) | |
| categories = ['Variable1', 'Variable2', 'Variable3', 'Variable4'] # Aquí puedes incluir tus variables específicas | |
| customer_values = [customer_data[category].values[0] for category in categories] | |
| fig_spider = go.Figure() | |
| fig_spider.add_trace(go.Scatterpolar( | |
| r=customer_values, | |
| theta=categories, | |
| fill='toself', | |
| name=f'Customer {customer_code}' | |
| )) | |
| fig_spider.update_layout( | |
| polar=dict( | |
| radialaxis=dict( | |
| visible=True, | |
| range=[0, max(customer_values) + 1] | |
| )), | |
| showlegend=False, | |
| title=f'Spider Chart for Customer {customer_code}' | |
| ) | |
| st.plotly_chart(fig_spider) | |
| # Ventas del cliente 2021-2024 | |
| years = ['2021', '2022', '2023', '2024'] | |
| sales_columns = ['VENTA_2021', 'VENTA_2022', 'VENTA_2023', 'VENTA_2024'] # Nombres de las columnas para ventas por año | |
| customer_sales = [customer_data[col].values[0] for col in sales_columns] | |
| fig_sales = px.line(x=years, y=customer_sales, markers=True, title=f'Sales Over the Years for Customer {customer_code}') | |
| fig_sales.update_layout(xaxis_title="Year", yaxis_title="Sales") | |
| st.plotly_chart(fig_sales) | |
| else: | |
| st.warning(f"No data found for customer {customer_code}. Please check the code.") | |
| # Página Customer Recommendations | |
| elif page == "Customer Recommendations": | |
| st.title("Customer Recommendations") | |
| st.markdown(""" | |
| Get tailored recommendations for your customers based on their purchasing history. | |
| """) | |
| # Input para código de cliente | |
| customer_code = st.text_input("Enter Customer Code for Recommendations") | |
| if customer_code: | |
| customer_data = df[df["CLIENTE"] == customer_code] | |
| if not customer_data.empty: | |
| # Mostrar historial de compras del cliente seleccionado | |
| st.write(f"### Purchase History for Customer {customer_code}") | |
| st.write(customer_data) | |
| # Generar recomendaciones (placeholder) | |
| st.write(f"### Recommended Products for Customer {customer_code}") | |
| # Aquí puedes reemplazar con la lógica del modelo de recomendación | |
| st.write("Product A, Product B, Product C") | |
| else: | |
| st.warning(f"No data found for customer {customer_code}. Please check the code.") | |