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| import streamlit as st | |
| import pandas as pd | |
| import plotly.express as px | |
| # Configuración de la página principal | |
| st.set_page_config(page_title="Customer Insights App", page_icon=":bar_chart:") | |
| # 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("Select a page", ["Home", "Customer Analysis", "Customer Recommendations"]) | |
| # Página Home | |
| if page == "Home": | |
| 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. | |
| """) | |
| # Cargar y visualizar datos | |
| uploaded_file = st.file_uploader("Upload your CSV file", type="csv") | |
| if uploaded_file: | |
| df = pd.read_csv(uploaded_file) | |
| st.write("## Dataset Overview", df.head()) | |
| # Mostrar un gráfico interactivo | |
| st.markdown("### Sales per Customer") | |
| customer_sales = df.groupby("CLIENTE")["VENTA_ANUAL"].sum().reset_index() | |
| fig = px.bar(customer_sales, x="CLIENTE", y="VENTA_ANUAL", title="Annual Sales per Customer") | |
| st.plotly_chart(fig) | |
| # Página Customer Recommendations | |
| elif page == "Customer Recommendations": | |
| st.title("Customer Recommendations") | |
| st.markdown(""" | |
| Get tailored recommendations for your customers based on their purchasing history. | |
| """) | |
| # Cargar los datos | |
| uploaded_file = st.file_uploader("Upload your CSV file", type="csv") | |
| if uploaded_file: | |
| df = pd.read_csv(uploaded_file) | |
| # Selección de cliente | |
| customer_id = st.selectbox("Select a Customer", df["CLIENTE"].unique()) | |
| # Mostrar historial de compras del cliente seleccionado | |
| st.write(f"### Purchase History for Customer {customer_id}") | |
| customer_data = df[df["CLIENTE"] == customer_id] | |
| st.write(customer_data) | |
| # Generar recomendaciones (placeholder) | |
| st.write(f"### Recommended Products for Customer {customer_id}") | |
| # Aquí puedes reemplazar con tu lógica de recomendación de productos | |
| st.write("Product A, Product B, Product C") | |