feat: Translated Summary to Spanish

app.py

@@ -72,7 +72,7 @@ historical_data = pd.read_csv('historical_data.csv')
 
 with st.sidebar:
     st.sidebar.title("DeepInsightz")
-    page = st.sidebar.selectbox("Select the tool you want to use", ["Summary", "Customer Analysis", "Articles Recommendations"])
+    page = st.sidebar.selectbox("Select the tool you want to use", ["Resumen", "Análisis de Cliente", "Articles Recommendations"])
 
 # Generamos la columna total_sales
 ventas_clientes['total_sales'] = ventas_clientes[['VENTA_2021', 'VENTA_2022', 'VENTA_2023']].sum(axis=1)
@@ -205,7 +205,7 @@ def radar_chart(categories, values, amounts, title):
 
 
 
-if page == "Summary":
+if page == "Resumen":
     # st.title("Welcome to DeepInsightz")
     # st.markdown("""
     #     ### Data-driven Customer Clustering
@@ -217,17 +217,17 @@ if page == "Summary":
 
     # Left Column (Red): Metrics and Donut Charts
     with col1:
-        st.markdown('#### General Information')
-        st.metric(label="Range of Dates", value="2021-2023")
-        st.metric(label="Customers Analysed", value="3.000")
-        st.metric(label="Unique Products Sold", value="10.702")
-        st.metric(label="Total Sales Instances", value="764.396")
+        st.markdown('#### Información General')
+        st.metric(label="Rango de fechas", value="2021-2023")
+        st.metric(label="Clientes Analizados", value="3.000")
+        st.metric(label="Productos Únicos Vendidos", value="10.702")
+        st.metric(label="Líneas de Venta Totales", value="764.396")
         
 
 
     # Middle Column (White): 3D Cluster Model and Bar Chart
     with col2:
-        st.markdown('#### 3D Customer Clusters')
+        st.markdown('#### Cluster de Clientes 3D')
 
         # Create 3D PCA plot using actual data from pca_data_5
         fig_cluster = px.scatter_3d(
@@ -236,7 +236,7 @@ if page == "Summary":
             y='PC2', 
             z='PC3', 
             color='cluster_id', 
-            hover_name='CustomerID',
+            hover_name='ClienteID',
         )
         fig_cluster.update_layout(
             scene=dict(aspectratio=dict(x=1, y=1, z=0.8)),  # Adjusted aspect ratio for better balance
@@ -248,7 +248,7 @@ if page == "Summary":
     # Right Column (Blue): Key Metrics Overview and Data Preparation Summary
     with col3:
         # Mostrar la tabla con los 100 mejores clientes
-        st.markdown('#### Top 100 Clients by Total Sales')
+        st.markdown('#### Top 100 Clientes')
 
         # Configurar columnas para mostrar los clientes y las ventas totales
         st.dataframe(ventas_top_100[['codigo_cliente', 'total_sales']],
@@ -258,20 +258,19 @@ if page == "Summary":
                     height=600,  # Ajustar la altura de la tabla
                     column_config={
                         "codigo_cliente": st.column_config.TextColumn(
-                            "Client Code",
+                            "Código de Cliente",
                         ),
                         "total_sales": st.column_config.ProgressColumn(
-                            "Total Sales (€)",
+                            "Venta Total (€)",
                             format="%d",
                             min_value=0,
                             max_value=ventas_top_100['total_sales'].max()
                         )}
                     )
 # Customer Analysis Page
-# Customer Analysis Page
-elif page == "Customer Analysis":
+elif page == "Análisis de Cliente":
     st.markdown("""
-    <h2 style='text-align: center; font-size: 2.5rem;'>Customer Analysis</h2>
+    <h2 style='text-align: center; font-size: 2.5rem;'>Análisis de Cliente</h2>
     <p style='text-align: center; font-size: 1.2rem; color: gray;'> 
     Enter the customer code to explore detailed customer insights, 
     including past sales, predictions for the current year, and manufacturer-specific information.