feat: generated files

app.py

@@ -1,6 +1,8 @@
 import streamlit as st
+import pandas as pd
+import plotly.express as px
 
-# Configuración de la página
+# Configuración de la página principal
 st.set_page_config(page_title="Customer Insights App", page_icon=":bar_chart:")
 
 # Diseño de la página principal
@@ -13,12 +15,51 @@ st.markdown("""
 # 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.write("You have selected **Customer Analysis**.")
-    # Aquí puedes incluir el contenido de la página 'customer_analysis.py'
+    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.write("You have selected **Customer Recommendations**.")
-    # Aquí puedes incluir el contenido de la página 'customer_recommendation.py'
+    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")