feat: generated files

app.py

@@ -6,6 +6,10 @@ 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("""
@@ -28,55 +32,49 @@ elif page == "Customer Analysis":
         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())
-
-        # 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.")
+    # 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":
@@ -85,20 +83,20 @@ elif page == "Customer Recommendations":
         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 la lógica del modelo de recomendación
-        st.write("Product A, Product B, Product C")
+    # 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.")