feat: generated files

app.py

@@ -1,4 +1,24 @@
 import streamlit as st
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+# Diseño de la página principal
+st.set_page_config(page_title="Customer Insights App", page_icon=":bar_chart:")
+
+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.
+""")
+
+# Navegación a las otras herramientas
+st.markdown("## Available Tools:")
+col1, col2 = st.columns(2)
+
+with col1:
+    st.markdown("### 🔍 Customer Analysis")
+    st.write("Analyze customer data to discover patterns and insights.")
+    st.button("Go to Customer Analysis", on_click=lambda: st.experimental_rerun())
+
+with col2:
+    st.markdown("### 📊 Customer Recommendations")
+    st.write("Generate recommendations based on customer purchase history.")
+    st.button("Go to Recommendations", on_click=lambda: st.experimental_rerun())

customer_analysis.py

@@ -0,0 +1,22 @@
+import streamlit as st
+import pandas as pd
+import plotly.express as px
+
+st.set_page_config(page_title="Customer Analysis", page_icon=":mag:")
+
+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)

customer_recommendation.py

@@ -0,0 +1,24 @@
+import streamlit as st
+import pandas as pd
+
+st.set_page_config(page_title="Customer Recommendations", page_icon=":chart_with_upwards_trend:")
+
+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)
+    customer_id = st.selectbox("Select a Customer", df["CLIENTE"].unique())
+
+    # Mostrar datos y recomendación
+    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}")
+    st.write("Product A, Product B, Product C")

requirements.txt

@@ -0,0 +1 @@
+plotly