feat: Translated Summary to Spanish

app.py

@@ -219,9 +219,9 @@ if page == "Resumen":
     with col1:
         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")
+        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")
         
 
 
@@ -272,14 +272,13 @@ elif page == "Análisis de Cliente":
     st.markdown("""
     <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.
+    Introduce el código del cliente para explorar información detallada del mismo, incluyendo ventas anteriores, predicciones para el año actual e información específica por fabricante.
     </p>
     """, unsafe_allow_html=True)
 
     # Combine text input and dropdown into a single searchable selectbox
     customer_code = st.selectbox(
-        "Search and Select Customer Code",
+        "Escribe o selecciona el código de tu cliente",
         df['CLIENTE'].unique(),  # All customer codes
 
         format_func=lambda x: str(x),  # Ensures the values are displayed as strings
@@ -288,7 +287,7 @@ elif page == "Análisis de Cliente":
 
     if st.button("Calcular"):
         if customer_code:
-            with st.spinner("We are identifying the customer's cluster..."):
+            with st.spinner("Estamos identificando el grupo del cliente..."):
                 # Find Customer's Cluster
                 customer_match = customer_clusters[customer_clusters['cliente_id'] == customer_code]
                 time.sleep(1)
@@ -296,24 +295,24 @@ elif page == "Análisis de Cliente":
                 if not customer_match.empty:
                     cluster = customer_match['cluster_id'].values[0]
                 
-            with st.spinner(f"Selecting predictive model..."):
+            with st.spinner(f"Seleccionando el modelo predictivo..."):
                 # Load the Corresponding Model
                 model_path = f'models/modelo_cluster_{cluster}.txt'
                 gbm = lgb.Booster(model_file=model_path)
 
-            with st.spinner("Getting the data ready..."):
+            with st.spinner("Preparando los datos..."):
                 # Load predict data for that cluster
                 predict_data = pd.read_csv(f'predicts/predict_cluster_{cluster}.csv')
                 
                 # Convert cliente_id to string
                 predict_data['cliente_id'] = predict_data['cliente_id'].astype(str)
 
-            with st.spinner("Filtering data..."):
+            with st.spinner("Filtrando data..."):
                 # Filter for the specific customer
                 customer_code_str = str(customer_code)
                 customer_data = predict_data[predict_data['cliente_id'] == customer_code_str]
 
-            with st.spinner("Generating sales predictions..."):
+            with st.spinner("Geneerando predicciones de venta..."):
                 if not customer_data.empty:
                     # Define features consistently with the training process
                     lag_features = [f'precio_total_lag_{lag}' for lag in range(1, 25)]
@@ -476,8 +475,8 @@ elif page == "Análisis de Cliente":
 
                     # Column 1: Radar chart for top manufacturers
                     with col1:
-                        st.subheader("This looks great!")
-                        st.info("Your customer did exceed predicted sales from the following brands:")
+                        st.subheader("¡Esto tiene buena pinta!")
+                        st.info("Su cliente ha superado las ventas predichas de las siguientes marcas:")
 
                         # Group results by manufacturer to calculate the total predicted and actual sales
                         grouped_results = results.groupby('marca_id_encoded').agg({
@@ -559,13 +558,13 @@ elif page == "Análisis de Cliente":
                             manufacturers = [get_supplier_name(m) for m in manufacturers_to_show.index]
 
                             if manufacturers:
-                                fig = radar_chart(manufacturers, values, amounts, f'Radar Chart for Top {len(manufacturers)} Manufacturers of Customer {customer_code}')
+                                fig = radar_chart(manufacturers, values, amounts, f'Gráfico de radar para los {len(manufacturers)} principales fabricantes del cliente {customer_code}')
                                 st.pyplot(fig)
 
                     # Column 2: Alerts and additional analysis
                     with col2:
-                        st.subheader("You might need to check this!")
-                        st.warning("Your customer was expected to buy more products from the following brands:")
+                        st.subheader("¡Puede que tengas que revisar esto!")
+                        st.warning("Se esperaba que tu cliente comprara más productos de las siguientes marcas:")
 
                         # Group results by manufacturer to calculate the total predicted and actual sales
                         grouped_results = results.groupby('marca_id_encoded').agg({
@@ -683,7 +682,7 @@ elif page == "Análisis de Cliente":
 
                                     # Customize layout
                                     fig_sales_bar.update_layout(
-                                        title=f"Sales Over the Years for Customer {customer_code}",
+                                        title=f"Ventas anuales de tu cliente",
                                         xaxis_title="Year",
                                         yaxis_title="Sales (€)",
                                         barmode='group',