feat: updated version app.py

app.py

@@ -9,20 +9,20 @@ from sklearn.metrics.pairwise import cosine_similarity
 # Page configuration
 st.set_page_config(page_title="Customer Insights App", page_icon=":bar_chart:")
 
-# Load CSV files
+# Load CSV files at the top, only once
 df = pd.read_csv("df_clean.csv")
 nombres_proveedores = pd.read_csv("nombres_proveedores.csv", sep=';')
 euros_proveedor = pd.read_csv("euros_proveedor.csv", sep=',')
 ventas_clientes = pd.read_csv("ventas_clientes.csv", sep=',')
+customer_clusters = pd.read_csv('predicts/customer_clusters.csv')  # Load the customer clusters here
 
 # Ensure customer codes are strings
 df['CLIENTE'] = df['CLIENTE'].astype(str)
 nombres_proveedores['codigo'] = nombres_proveedores['codigo'].astype(str)
 euros_proveedor['CLIENTE'] = euros_proveedor['CLIENTE'].astype(str)
+customer_clusters['cliente_id'] = customer_clusters['cliente_id'].astype(str)  # Ensure customer IDs are strings
 fieles_df = pd.read_csv("clientes_relevantes.csv")
-# Cargo csv del histórico de cestas
 cestas = pd.read_csv("cestas.csv")
-# Cargo csv de productos y descripcion
 productos = pd.read_csv("productos.csv")
 
 # Convert all columns except 'CLIENTE' to float in euros_proveedor
@@ -124,10 +124,15 @@ elif page == "Customer Analysis":
             if not customer_data.empty and not customer_euros.empty:
                 st.write(f"### Analysis for Customer {customer_code}")
 
-                # **Step 1: Find Customer's Cluster**
-                customer_clusters = pd.read_csv('predicts/customer_clusters.csv')
-                cluster = customer_clusters[customer_clusters['cliente_id'] == customer_code]['cluster_id'].values[0]
-                st.write(f"Customer {customer_code} belongs to cluster {cluster}")
+                # **Find Customer's Cluster**
+                customer_match = customer_clusters[customer_clusters['cliente_id'] == customer_code]
+
+                if not customer_match.empty:
+                    cluster = customer_match['cluster_id'].values[0]
+                    st.write(f"Customer {customer_code} belongs to cluster {cluster}")
+                else:
+                    st.error(f"Customer {customer_code} not found in customer_clusters.")
+                    return  # Stop further execution if no cluster is found
 
                 # **Step 2: Load the Corresponding Model**
                 model_path = f'models/modelo_cluster_{cluster}.txt'
@@ -378,4 +383,4 @@ elif page == "Articles Recommendations":
                     else:
                         st.warning("No recommendations found for the provided basket.")
                 else:
-                    st.warning("Please select at least one article and set its quantity.")
+                    st.warning("Please select at least one article and set its quantity.")