feat: generated files

app.py

@@ -1,6 +1,7 @@
 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:")
@@ -33,11 +34,49 @@ elif page == "Customer Analysis":
         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)
+        # 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":
@@ -61,5 +100,5 @@ elif page == "Customer Recommendations":
 
         # Generar recomendaciones (placeholder)
         st.write(f"### Recommended Products for Customer {customer_id}")
-        # Aquí puedes reemplazar con tu lógica de recomendación de productos
+        # Aquí puedes reemplazar con la lógica del modelo de recomendación
         st.write("Product A, Product B, Product C")