feat: updated app.py

app.py

@@ -11,84 +11,59 @@ from sklearn.metrics import mean_absolute_error, mean_squared_error
 # Page configuration
 st.set_page_config(page_title="Customer Insights App", page_icon=":bar_chart:")
 
-# Load CSV files at the top
+# Load CSV files
 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
-df_agg_2024 = pd.read_csv('predicts/df_agg_2024.csv') 
+customer_clusters = pd.read_csv('predicts/customer_clusters.csv')
+df_agg_2024 = pd.read_csv('predicts/df_agg_2024.csv')
 
 # 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
+customer_clusters['cliente_id'] = customer_clusters['cliente_id'].astype(str)
 fieles_df = pd.read_csv("clientes_relevantes.csv")
 cestas = pd.read_csv("cestas.csv")
 productos = pd.read_csv("productos.csv")
 df_agg_2024['cliente_id'] = df_agg_2024['cliente_id'].astype(str)
 
-# Convert all columns except 'CLIENTE' to float in euros_proveedor
+# Convert columns in euros_proveedor to numeric
 for col in euros_proveedor.columns:
     if col != 'CLIENTE':
         euros_proveedor[col] = pd.to_numeric(euros_proveedor[col], errors='coerce')
 
-# Check for NaN values after conversion
+# Check for NaN values in euros_proveedor
 if euros_proveedor.isna().any().any():
     st.warning("Some values in euros_proveedor couldn't be converted to numbers. Please review the input data.")
 
-# Ignore the last two columns of df
+# Ignore the last two columns in df
 df = df.iloc[:, :-2]
 
 # Function to get supplier name
 def get_supplier_name(code):
-    code = str(code)  # Ensure code is a string
+    code = str(code)
     name = nombres_proveedores[nombres_proveedores['codigo'] == code]['nombre'].values
     return name[0] if len(name) > 0 else code
 
-# Function to create radar chart with square root transformation
+# Function to create radar chart using Plotly
 def radar_chart(categories, values, amounts, title):
-    N = len(categories)
-    angles = [n / float(N) * 2 * np.pi for n in range(N)]
-    angles += angles[:1]
-    
-    fig, ax = plt.subplots(figsize=(12, 12), subplot_kw=dict(projection='polar'))
-    
-    # Apply square root transformation
-    sqrt_values = np.sqrt(values)
-    sqrt_amounts = np.sqrt(amounts)
-    
-    max_sqrt_value = max(sqrt_values)
-    normalized_values = [v / max_sqrt_value for v in sqrt_values]
-    
-    # Adjust scaling for spend values
-    max_sqrt_amount = max(sqrt_amounts)
-    scaling_factor = 0.7  # Adjust this value to control how much the spend values are scaled up
-    normalized_amounts = [min((a / max_sqrt_amount) * scaling_factor, 1.0) for a in sqrt_amounts]
-    
-    normalized_values += normalized_values[:1]
-    ax.plot(angles, normalized_values, 'o-', linewidth=2, color='#FF69B4', label='% Units (sqrt)')
-    ax.fill(angles, normalized_values, alpha=0.25, color='#FF69B4')
-    
-    normalized_amounts += normalized_amounts[:1]
-    ax.plot(angles, normalized_amounts, 'o-', linewidth=2, color='#4B0082', label='% Spend (sqrt)')
-    ax.fill(angles, normalized_amounts, alpha=0.25, color='#4B0082')
-    
-    ax.set_xticks(angles[:-1])
-    ax.set_xticklabels(categories, size=8, wrap=True)
-    ax.set_ylim(0, 1)
-    
-    circles = np.linspace(0, 1, 5)
-    for circle in circles:
-        ax.plot(angles, [circle]*len(angles), '--', color='gray', alpha=0.3, linewidth=0.5)
-    
-    ax.set_yticklabels([])
-    ax.spines['polar'].set_visible(False)
-    
-    plt.title(title, size=16, y=1.1)
-    plt.legend(loc='upper right', bbox_to_anchor=(1.3, 1.1))
-    
+    fig = px.line_polar(
+        r=values,
+        theta=categories,
+        line_close=True,
+        labels={'r': '% Units Sold', 'theta': 'Manufacturers'},
+        title=title,
+    )
+    fig.add_scatterpolar(
+        r=amounts,
+        theta=categories,
+        line_close=True,
+        name="Spend (€)",
+        mode="lines+markers"
+    )
+    fig.update_traces(fill='toself')
     return fig
 
 # Main page design
@@ -106,6 +81,7 @@ if page == "":
     st.markdown("## Welcome to the Customer Insights App")
     st.write("Use the dropdown menu to navigate between the different sections.")
 
+# Customer Analysis Page
 elif page == "Customer Analysis":
     st.title("Customer Analysis")
     st.markdown("Use the tools below to explore your customer data.")
@@ -120,368 +96,632 @@ elif page == "Customer Analysis":
 
     if st.button("Calcular"):
         if customer_code:
-            # 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}")
 
-                # Load the Corresponding Model
+                # Load the corresponding model
                 model_path = f'models/modelo_cluster_{cluster}.txt'
                 gbm = lgb.Booster(model_file=model_path)
                 st.write(f"Loaded model for cluster {cluster}")
 
-                # Inspect the model
-                st.write("### Model Information:")
-                st.write(f"Number of trees: {gbm.num_trees()}")
-                st.write(f"Number of features: {gbm.num_feature()}")
-                st.write("Feature names:")
-                st.write(gbm.feature_name())
-
                 # 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)
-                
-                st.write("### Predict Data DataFrame:")
-                st.write(predict_data.head())
-                st.write(f"Shape: {predict_data.shape}")
 
                 # Filter for the specific customer
-                customer_code_str = str(customer_code)
-                customer_data = predict_data[predict_data['cliente_id'] == customer_code_str]
-                
-                # Add debug statements
-                st.write(f"Unique customer IDs in predict data: {predict_data['cliente_id'].unique()}")
-                st.write(f"Customer code we're looking for: {customer_code_str}")
-
-                st.write("### Customer Data:")
-                st.write(customer_data.head())
-                st.write(f"Shape: {customer_data.shape}")
+                customer_data = predict_data[predict_data['cliente_id'] == customer_code]
 
                 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)]
                     features = lag_features + ['mes', 'marca_id_encoded', 'año', 'cluster_id']
-                    
-                    # Prepare data for prediction
                     X_predict = customer_data[features]
 
                     # Convert categorical features to 'category' dtype
                     categorical_features = ['mes', 'marca_id_encoded', 'cluster_id']
                     for feature in categorical_features:
                         X_predict[feature] = X_predict[feature].astype('category')
-                    
-                    st.write("### Features for Prediction:")
-                    st.write(X_predict.head())
-                    st.write(f"Shape: {X_predict.shape}")
-                    st.write("Data types:")
-                    st.write(X_predict.dtypes)
-                    
+
                     # Make Prediction for the selected customer
                     y_pred = gbm.predict(X_predict, num_iteration=gbm.best_iteration)
-                    st.write("### Prediction Results:")
-                    st.write(f"Type of y_pred: {type(y_pred)}")
-                    st.write(f"Shape of y_pred: {y_pred.shape}")
-                    st.write("First few predictions:")
-                    st.write(y_pred[:5])
-                    
-                    # Reassemble the results
+
+                    # Results DataFrame
                     results = customer_data[['cliente_id', 'marca_id_encoded', 'fecha_mes']].copy()
                     results['ventas_predichas'] = y_pred
-                    st.write("### Results DataFrame:")
-                    st.write(results.head())
-                    st.write(f"Shape: {results.shape}")
-                    
-                    st.write(f"Predicted total sales for Customer {customer_code}: {results['ventas_predichas'].sum():.2f}")
 
-                    # Load actual data
-                    actual_sales = df_agg_2024[df_agg_2024['cliente_id'] == customer_code_str]
-                    st.write("### Actual Sales DataFrame:")
-                    st.write(actual_sales.head())
-                    st.write(f"Shape: {actual_sales.shape}")
-                    
+                    # Load actual sales data
+                    actual_sales = df_agg_2024[df_agg_2024['cliente_id'] == customer_code]
                     if not actual_sales.empty:
-                        results = results.merge(actual_sales[['cliente_id', 'marca_id_encoded', 'fecha_mes', 'precio_total']], 
-                                                on=['cliente_id', 'marca_id_encoded', 'fecha_mes'], 
-                                                how='left')
+                        results = results.merge(
+                            actual_sales[['cliente_id', 'marca_id_encoded', 'fecha_mes', 'precio_total']],
+                            on=['cliente_id', 'marca_id_encoded', 'fecha_mes'],
+                            how='left'
+                        )
                         results.rename(columns={'precio_total': 'ventas_reales'}, inplace=True)
                         results['ventas_reales'].fillna(0, inplace=True)
-                        st.write("### Final Results DataFrame:")
-                        st.write(results.head())
-                        st.write(f"Shape: {results.shape}")
-                        
-                        # Calculate metrics only for non-null actual sales
+
+                        # Calculate error metrics
                         valid_results = results.dropna(subset=['ventas_reales'])
                         if not valid_results.empty:
                             mae = mean_absolute_error(valid_results['ventas_reales'], valid_results['ventas_predichas'])
                             mape = np.mean(np.abs((valid_results['ventas_reales'] - valid_results['ventas_predichas']) / valid_results['ventas_reales'])) * 100
                             rmse = np.sqrt(mean_squared_error(valid_results['ventas_reales'], valid_results['ventas_predichas']))
 
-                            st.write(f"Actual total sales for Customer {customer_code}: {valid_results['ventas_reales'].sum():.2f}")
                             st.write(f"MAE: {mae:.2f}")
                             st.write(f"MAPE: {mape:.2f}%")
                             st.write(f"RMSE: {rmse:.2f}")
 
-                        # Analysis of results
-                        threshold_good = 100  # You may want to adjust this threshold
-                        if mae < threshold_good:
-                            st.success(f"Customer {customer_code} is performing well based on the predictions.")
-                        else:
-                            st.warning(f"Customer {customer_code} is not performing well based on the predictions.")
-                    else:
-                        st.warning(f"No actual sales data found for customer {customer_code} in df_agg_2024.")
+                        # Plot radar chart
+                        top_units = df[df["CLIENTE"] == str(customer_code)].iloc[:, 1:].T
+                        top_sales = euros_proveedor[euros_proveedor["CLIENTE"] == str(customer_code)].iloc[:, 1:].T
+
+                        combined_top = pd.concat([top_units, top_sales]).index.unique()[:20]
 
-                    st.write("### Debug Information for Radar Chart:")
-                    st.write(f"Shape of customer_data: {customer_data.shape}")
-                    st.write(f"Shape of euros_proveedor: {euros_proveedor.shape}")
+                        combined_data = pd.DataFrame({
+                            'units': top_units.loc[combined_top, top_units.columns[0]],
+                            'sales': top_sales.loc[combined_top, top_sales.columns[0]]
+                        }).fillna(0)
 
-                    # Get percentage of units sold for each manufacturer
-                    customer_df = df[df["CLIENTE"] == str(customer_code)]  # Get the customer data
-                    all_manufacturers = customer_df.iloc[:, 1:].T  # Exclude CLIENTE column (manufacturers are in columns)
-                    all_manufacturers.index = all_manufacturers.index.astype(str)
+                        manufacturers = [get_supplier_name(m) for m in combined_data.index]
+                        values = combined_data['units'].tolist()
+                        amounts = combined_data['sales'].tolist()
 
-                    # Get total sales for each manufacturer from euros_proveedor
-                    customer_euros = euros_proveedor[euros_proveedor["CLIENTE"] == str(customer_code)]
-                    sales_data = customer_euros.iloc[:, 1:].T  # Exclude CLIENTE column
-                    sales_data.index = sales_data.index.astype(str)
+                        fig = radar_chart(manufacturers, values, amounts, f'Radar Chart for Customer {customer_code}')
+                        st.plotly_chart(fig)
+
+# Articles Recommendations Page
+elif page == "Articles Recommendations":
+    st.title("Articles Recommendations")
 
-                    # Remove the 'CLIENTE' row from sales_data to avoid issues with mixed types
-                    sales_data_filtered = sales_data.drop(index='CLIENTE', errors='ignore')
+    st.markdown("""
+        Get tailored recommendations for your customers based on their basket.
+    """)
+
+    partial_code = st.text_input("Enter part of Customer Code for Recommendations (or leave empty to see all)")
+    if partial_code:
+        filtered_customers = df[df['CLIENTE'].str.contains(partial_code)]
+    else:
+        filtered_customers = df
+    customer_list = filtered_customers['CLIENTE'].unique()
+    customer_code = st.selectbox("Select Customer Code for Recommendations", [""] + list(customer_list))
 
-                    # Ensure all values are numeric
-                    sales_data_filtered = sales_data_filtered.apply(pd.to_numeric, errors='coerce')
-                    all_manufacturers = all_manufacturers.apply(pd.to_numeric, errors='coerce')
+    if customer_code:
+        option = st.selectbox("Select Recommendation Type", ["Select an option", "By Purchase History", "By Current Basket"])
 
-                    # Sort manufacturers by percentage of units and get top 10
-                    top_units = all_manufacturers.sort_values(by=all_manufacturers.columns[0], ascending=False).head(10)
+        if option == "By Current Basket":
+            st.write("Select the items and assign quantities for the basket:")
 
-                    # Sort manufacturers by total sales and get top 10
-                    top_sales = sales_data_filtered.sort_values(by=sales_data_filtered.columns[0], ascending=False).head(10)
+            available_articles = productos['ARTICULO'].unique()
+            selected_articles = st.multiselect("Select Articles", available_articles)
 
-                    # Combine top manufacturers from both lists and get up to 20 unique manufacturers
-                    combined_top = pd.concat([top_units, top_sales]).index.unique()[:20]
+            quantities = {article: st.number_input(f"Quantity for {article}", min_value=0, step=1) for article in selected_articles}
 
-                    # Filter out manufacturers that are not present in both datasets
-                    combined_top = [m for m in combined_top if m in all_manufacturers.index and m in sales_data_filtered.index]
+            if st.button("Calcular"):
+                new_basket = [f"{article} x{quantities[article]}" for article in selected_articles if quantities[article] > 0]
 
-                    st.write(f"Number of combined top manufacturers: {len(combined_top)}")
+                if new_basket:
+                    def recomienda(new_basket):
+                        tfidf = TfidfVectorizer()
+                        tfidf_matrix = tfidf.fit_transform(cestas['Cestas'])
+                        new_basket_tfidf = tfidf.transform([' '.join(new_basket)])
+                        similarities = cosine_similarity(new_basket_tfidf, tfidf_matrix)
+                        similar_indices = similarities.argsort()[0][-3:]
+
+                        recommendations_count = {}
+                        total_similarity = 0
+
+                        for idx in similar_indices:
+                            sim_score = similarities[0][idx]
+                            total_similarity += sim_score
+                            products = cestas.iloc[idx]['Cestas'].split()
+
+                            for product in products:
+                                if product not in new_basket:
+                                    recommendations_count[product] = recommendations_count.get(product, 0) + sim_score
+
+                        recommendations_with_prob = [(prod, score / total_similarity) for prod, score in recommendations_count.items()]
+                        recommendations_with_prob.sort(key=lambda x: x[1], reverse=True)
+
+                        recommendations_df = pd.DataFrame({
+                            'ARTICULO': [r[0] for r in recommendations_with_prob],
+                            'PROBABILIDAD': [r[1] for r in recommendations_with_prob]
+                        })
+                        return recommendations_df
 
-                    if combined_top:
-                        # Create a DataFrame with combined data for these top manufacturers
-                        combined_data = pd.DataFrame({
-                            'units': all_manufacturers.loc[combined_top, all_manufacturers.columns[0]],
-                            'sales': sales_data_filtered.loc[combined_top, sales_data_filtered.columns[0]]
-                        }).fillna(0)
+                    recommendations_df = recomienda(new_basket)
+                    st.dataframe(recommendations_df)
+                else:
+                    st.warning("Please select at least one article and set its quantity.")
+
+
+
+# import streamlit as st
+# import pandas as pd
+# import plotly.express as px
+# import matplotlib.pyplot as plt
+# import numpy as np
+# import lightgbm as lgb
+# from sklearn.feature_extraction.text import TfidfVectorizer
+# from sklearn.metrics.pairwise import cosine_similarity
+# from sklearn.metrics import mean_absolute_error, mean_squared_error
+
+# # Page configuration
+# st.set_page_config(page_title="Customer Insights App", page_icon=":bar_chart:")
+
+# # Load CSV files at the top
+# 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
+# df_agg_2024 = pd.read_csv('predicts/df_agg_2024.csv') 
+
+# # 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")
+# cestas = pd.read_csv("cestas.csv")
+# productos = pd.read_csv("productos.csv")
+# df_agg_2024['cliente_id'] = df_agg_2024['cliente_id'].astype(str)
+
+# # Convert all columns except 'CLIENTE' to float in euros_proveedor
+# for col in euros_proveedor.columns:
+#     if col != 'CLIENTE':
+#         euros_proveedor[col] = pd.to_numeric(euros_proveedor[col], errors='coerce')
+
+# # Check for NaN values after conversion
+# if euros_proveedor.isna().any().any():
+#     st.warning("Some values in euros_proveedor couldn't be converted to numbers. Please review the input data.")
+
+# # Ignore the last two columns of df
+# df = df.iloc[:, :-2]
+
+# # Function to get supplier name
+# def get_supplier_name(code):
+#     code = str(code)  # Ensure code is a string
+#     name = nombres_proveedores[nombres_proveedores['codigo'] == code]['nombre'].values
+#     return name[0] if len(name) > 0 else code
+
+# # Function to create radar chart with square root transformation
+# def radar_chart(categories, values, amounts, title):
+#     N = len(categories)
+#     angles = [n / float(N) * 2 * np.pi for n in range(N)]
+#     angles += angles[:1]
+    
+#     fig, ax = plt.subplots(figsize=(12, 12), subplot_kw=dict(projection='polar'))
+    
+#     # Apply square root transformation
+#     sqrt_values = np.sqrt(values)
+#     sqrt_amounts = np.sqrt(amounts)
+    
+#     max_sqrt_value = max(sqrt_values)
+#     normalized_values = [v / max_sqrt_value for v in sqrt_values]
+    
+#     # Adjust scaling for spend values
+#     max_sqrt_amount = max(sqrt_amounts)
+#     scaling_factor = 0.7  # Adjust this value to control how much the spend values are scaled up
+#     normalized_amounts = [min((a / max_sqrt_amount) * scaling_factor, 1.0) for a in sqrt_amounts]
+    
+#     normalized_values += normalized_values[:1]
+#     ax.plot(angles, normalized_values, 'o-', linewidth=2, color='#FF69B4', label='% Units (sqrt)')
+#     ax.fill(angles, normalized_values, alpha=0.25, color='#FF69B4')
+    
+#     normalized_amounts += normalized_amounts[:1]
+#     ax.plot(angles, normalized_amounts, 'o-', linewidth=2, color='#4B0082', label='% Spend (sqrt)')
+#     ax.fill(angles, normalized_amounts, alpha=0.25, color='#4B0082')
+    
+#     ax.set_xticks(angles[:-1])
+#     ax.set_xticklabels(categories, size=8, wrap=True)
+#     ax.set_ylim(0, 1)
+    
+#     circles = np.linspace(0, 1, 5)
+#     for circle in circles:
+#         ax.plot(angles, [circle]*len(angles), '--', color='gray', alpha=0.3, linewidth=0.5)
+    
+#     ax.set_yticklabels([])
+#     ax.spines['polar'].set_visible(False)
+    
+#     plt.title(title, size=16, y=1.1)
+#     plt.legend(loc='upper right', bbox_to_anchor=(1.3, 1.1))
+    
+#     return fig
+
+# # Main page design
+# st.title("Welcome to Customer Insights App")
+# st.markdown("""
+#     This app helps businesses analyze customer behaviors and provide personalized recommendations based on purchase history. 
+#     Use the tools below to dive deeper into your customer data.
+# """)
+
+# # Navigation menu
+# page = st.selectbox("Select the tool you want to use", ["", "Customer Analysis", "Articles Recommendations"])
+
+# # Home Page
+# if page == "":
+#     st.markdown("## Welcome to the Customer Insights App")
+#     st.write("Use the dropdown menu to navigate between the different sections.")
+
+# # Customer Analysis Page
+# elif page == "Customer Analysis":
+#     st.title("Customer Analysis")
+#     st.markdown("Use the tools below to explore your customer data.")
+
+#     partial_code = st.text_input("Enter part of Customer Code (or leave empty to see all)")
+#     if partial_code:
+#         filtered_customers = df[df['CLIENTE'].str.contains(partial_code)]
+#     else:
+#         filtered_customers = df
+#     customer_list = filtered_customers['CLIENTE'].unique()
+#     customer_code = st.selectbox("Select Customer Code", customer_list)
+
+#     if st.button("Calcular"):
+#         if customer_code:
+#             # 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}")
+
+#                 # Load the Corresponding Model
+#                 model_path = f'models/modelo_cluster_{cluster}.txt'
+#                 gbm = lgb.Booster(model_file=model_path)
+#                 st.write(f"Loaded model for cluster {cluster}")
+
+#                 # Inspect the model
+#                 st.write("### Model Information:")
+#                 st.write(f"Number of trees: {gbm.num_trees()}")
+#                 st.write(f"Number of features: {gbm.num_feature()}")
+#                 st.write("Feature names:")
+#                 st.write(gbm.feature_name())
+
+#                 # 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)
+                
+#                 st.write("### Predict Data DataFrame:")
+#                 st.write(predict_data.head())
+#                 st.write(f"Shape: {predict_data.shape}")
+
+#                 # Filter for the specific customer
+#                 customer_code_str = str(customer_code)
+#                 customer_data = predict_data[predict_data['cliente_id'] == customer_code_str]
+                
+#                 # Add debug statements
+#                 st.write(f"Unique customer IDs in predict data: {predict_data['cliente_id'].unique()}")
+#                 st.write(f"Customer code we're looking for: {customer_code_str}")
+
+#                 st.write("### Customer Data:")
+#                 st.write(customer_data.head())
+#                 st.write(f"Shape: {customer_data.shape}")
+
+#                 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)]
+#                     features = lag_features + ['mes', 'marca_id_encoded', 'año', 'cluster_id']
+                    
+#                     # Prepare data for prediction
+#                     X_predict = customer_data[features]
+
+#                     # Convert categorical features to 'category' dtype
+#                     categorical_features = ['mes', 'marca_id_encoded', 'cluster_id']
+#                     for feature in categorical_features:
+#                         X_predict[feature] = X_predict[feature].astype('category')
+                    
+#                     st.write("### Features for Prediction:")
+#                     st.write(X_predict.head())
+#                     st.write(f"Shape: {X_predict.shape}")
+#                     st.write("Data types:")
+#                     st.write(X_predict.dtypes)
+                    
+#                     # Make Prediction for the selected customer
+#                     y_pred = gbm.predict(X_predict, num_iteration=gbm.best_iteration)
+#                     st.write("### Prediction Results:")
+#                     st.write(f"Type of y_pred: {type(y_pred)}")
+#                     st.write(f"Shape of y_pred: {y_pred.shape}")
+#                     st.write("First few predictions:")
+#                     st.write(y_pred[:5])
+                    
+#                     # Reassemble the results
+#                     results = customer_data[['cliente_id', 'marca_id_encoded', 'fecha_mes']].copy()
+#                     results['ventas_predichas'] = y_pred
+#                     st.write("### Results DataFrame:")
+#                     st.write(results.head())
+#                     st.write(f"Shape: {results.shape}")
+                    
+#                     st.write(f"Predicted total sales for Customer {customer_code}: {results['ventas_predichas'].sum():.2f}")
+
+#                     # Load actual data
+#                     actual_sales = df_agg_2024[df_agg_2024['cliente_id'] == customer_code_str]
+#                     st.write("### Actual Sales DataFrame:")
+#                     st.write(actual_sales.head())
+#                     st.write(f"Shape: {actual_sales.shape}")
+                    
+#                     if not actual_sales.empty:
+#                         results = results.merge(actual_sales[['cliente_id', 'marca_id_encoded', 'fecha_mes', 'precio_total']], 
+#                                                 on=['cliente_id', 'marca_id_encoded', 'fecha_mes'], 
+#                                                 how='left')
+#                         results.rename(columns={'precio_total': 'ventas_reales'}, inplace=True)
+#                         results['ventas_reales'].fillna(0, inplace=True)
+#                         st.write("### Final Results DataFrame:")
+#                         st.write(results.head())
+#                         st.write(f"Shape: {results.shape}")
+                        
+#                         # Calculate metrics only for non-null actual sales
+#                         valid_results = results.dropna(subset=['ventas_reales'])
+#                         if not valid_results.empty:
+#                             mae = mean_absolute_error(valid_results['ventas_reales'], valid_results['ventas_predichas'])
+#                             mape = np.mean(np.abs((valid_results['ventas_reales'] - valid_results['ventas_predichas']) / valid_results['ventas_reales'])) * 100
+#                             rmse = np.sqrt(mean_squared_error(valid_results['ventas_reales'], valid_results['ventas_predichas']))
+
+#                             st.write(f"Actual total sales for Customer {customer_code}: {valid_results['ventas_reales'].sum():.2f}")
+#                             st.write(f"MAE: {mae:.2f}")
+#                             st.write(f"MAPE: {mape:.2f}%")
+#                             st.write(f"RMSE: {rmse:.2f}")
+
+#                         # Analysis of results
+#                         threshold_good = 100  # You may want to adjust this threshold
+#                         if mae < threshold_good:
+#                             st.success(f"Customer {customer_code} is performing well based on the predictions.")
+#                         else:
+#                             st.warning(f"Customer {customer_code} is not performing well based on the predictions.")
+#                     else:
+#                         st.warning(f"No actual sales data found for customer {customer_code} in df_agg_2024.")
+
+#                     st.write("### Debug Information for Radar Chart:")
+#                     st.write(f"Shape of customer_data: {customer_data.shape}")
+#                     st.write(f"Shape of euros_proveedor: {euros_proveedor.shape}")
+
+#                     # Get percentage of units sold for each manufacturer
+#                     customer_df = df[df["CLIENTE"] == str(customer_code)]  # Get the customer data
+#                     all_manufacturers = customer_df.iloc[:, 1:].T  # Exclude CLIENTE column (manufacturers are in columns)
+#                     all_manufacturers.index = all_manufacturers.index.astype(str)
+
+#                     # Get total sales for each manufacturer from euros_proveedor
+#                     customer_euros = euros_proveedor[euros_proveedor["CLIENTE"] == str(customer_code)]
+#                     sales_data = customer_euros.iloc[:, 1:].T  # Exclude CLIENTE column
+#                     sales_data.index = sales_data.index.astype(str)
+
+#                     # Remove the 'CLIENTE' row from sales_data to avoid issues with mixed types
+#                     sales_data_filtered = sales_data.drop(index='CLIENTE', errors='ignore')
+
+#                     # Ensure all values are numeric
+#                     sales_data_filtered = sales_data_filtered.apply(pd.to_numeric, errors='coerce')
+#                     all_manufacturers = all_manufacturers.apply(pd.to_numeric, errors='coerce')
+
+#                     # Sort manufacturers by percentage of units and get top 10
+#                     top_units = all_manufacturers.sort_values(by=all_manufacturers.columns[0], ascending=False).head(10)
 
-                        # Sort by units, then by sales
-                        combined_data_sorted = combined_data.sort_values(by=['units', 'sales'], ascending=False)
+#                     # Sort manufacturers by total sales and get top 10
+#                     top_sales = sales_data_filtered.sort_values(by=sales_data_filtered.columns[0], ascending=False).head(10)
 
-                        # Filter out manufacturers with 0 units
-                        non_zero_manufacturers = combined_data_sorted[combined_data_sorted['units'] > 0]
+#                     # Combine top manufacturers from both lists and get up to 20 unique manufacturers
+#                     combined_top = pd.concat([top_units, top_sales]).index.unique()[:20]
 
-                        # If we have less than 3 non-zero manufacturers, add some zero-value ones
-                        if len(non_zero_manufacturers) < 3:
-                            zero_manufacturers = combined_data_sorted[combined_data_sorted['units'] == 0].head(3 - len(non_zero_manufacturers))
-                            manufacturers_to_show = pd.concat([non_zero_manufacturers, zero_manufacturers])
-                        else:
-                            manufacturers_to_show = non_zero_manufacturers
+#                     # Filter out manufacturers that are not present in both datasets
+#                     combined_top = [m for m in combined_top if m in all_manufacturers.index and m in sales_data_filtered.index]
 
-                        values = manufacturers_to_show['units'].tolist()
-                        amounts = manufacturers_to_show['sales'].tolist()
-                        manufacturers = [get_supplier_name(m) for m in manufacturers_to_show.index]
+#                     st.write(f"Number of combined top manufacturers: {len(combined_top)}")
 
-                        st.write(f"### Results for top {len(manufacturers)} manufacturers:")
-                        for manufacturer, value, amount in zip(manufacturers, values, amounts):
-                            st.write(f"{manufacturer} = {value:.2f}% of units, €{amount:.2f} total sales")
+#                     if combined_top:
+#                         # Create a DataFrame with combined data for these top manufacturers
+#                         combined_data = pd.DataFrame({
+#                             'units': all_manufacturers.loc[combined_top, all_manufacturers.columns[0]],
+#                             'sales': sales_data_filtered.loc[combined_top, sales_data_filtered.columns[0]]
+#                         }).fillna(0)
 
-                        if manufacturers:  # Only create the chart if we have data
-                            fig = radar_chart(manufacturers, values, amounts, f'Radar Chart for Top {len(manufacturers)} Manufacturers of Customer {customer_code}')
-                            st.pyplot(fig)
-                        else:
-                            st.warning("No data available to create the radar chart.")
+#                         # Sort by units, then by sales
+#                         combined_data_sorted = combined_data.sort_values(by=['units', 'sales'], ascending=False)
 
-                    else:
-                        st.warning("No combined top manufacturers found.")
+#                         # Filter out manufacturers with 0 units
+#                         non_zero_manufacturers = combined_data_sorted[combined_data_sorted['units'] > 0]
 
-                    # Ensure codigo_cliente in ventas_clientes is a string
-                    ventas_clientes['codigo_cliente'] = ventas_clientes['codigo_cliente'].astype(str).str.strip()
+#                         # If we have less than 3 non-zero manufacturers, add some zero-value ones
+#                         if len(non_zero_manufacturers) < 3:
+#                             zero_manufacturers = combined_data_sorted[combined_data_sorted['units'] == 0].head(3 - len(non_zero_manufacturers))
+#                             manufacturers_to_show = pd.concat([non_zero_manufacturers, zero_manufacturers])
+#                         else:
+#                             manufacturers_to_show = non_zero_manufacturers
 
-                    # Ensure customer_code is a string and strip any spaces
-                    customer_code = str(customer_code).strip()
+#                         values = manufacturers_to_show['units'].tolist()
+#                         amounts = manufacturers_to_show['sales'].tolist()
+#                         manufacturers = [get_supplier_name(m) for m in manufacturers_to_show.index]
 
-                    if customer_code in ventas_clientes['codigo_cliente'].unique():
-                        st.write(f"Customer {customer_code} found in ventas_clientes")  
-                    else:
-                        st.write(f"Customer {customer_code} not found in ventas_clientes")
+#                         st.write(f"### Results for top {len(manufacturers)} manufacturers:")
+#                         for manufacturer, value, amount in zip(manufacturers, values, amounts):
+#                             st.write(f"{manufacturer} = {value:.2f}% of units, €{amount:.2f} total sales")
 
-                    # Customer sales 2021-2024 (if data exists)
-                    sales_columns = ['VENTA_2021', 'VENTA_2022', 'VENTA_2023']
-                    if all(col in ventas_clientes.columns for col in sales_columns):
-                        customer_sales_data = ventas_clientes[ventas_clientes['codigo_cliente'] == customer_code]
+#                         if manufacturers:  # Only create the chart if we have data
+#                             fig = radar_chart(manufacturers, values, amounts, f'Radar Chart for Top {len(manufacturers)} Manufacturers of Customer {customer_code}')
+#                             st.pyplot(fig)
+#                         else:
+#                             st.warning("No data available to create the radar chart.")
+
+#                     else:
+#                         st.warning("No combined top manufacturers found.")
+
+#                     # Ensure codigo_cliente in ventas_clientes is a string
+#                     ventas_clientes['codigo_cliente'] = ventas_clientes['codigo_cliente'].astype(str).str.strip()
+
+#                     # Ensure customer_code is a string and strip any spaces
+#                     customer_code = str(customer_code).strip()
+
+#                     if customer_code in ventas_clientes['codigo_cliente'].unique():
+#                         st.write(f"Customer {customer_code} found in ventas_clientes")  
+#                     else:
+#                         st.write(f"Customer {customer_code} not found in ventas_clientes")
+
+#                     # Customer sales 2021-2024 (if data exists)
+#                     sales_columns = ['VENTA_2021', 'VENTA_2022', 'VENTA_2023']
+#                     if all(col in ventas_clientes.columns for col in sales_columns):
+#                         customer_sales_data = ventas_clientes[ventas_clientes['codigo_cliente'] == customer_code]
                         
-                        if not customer_sales_data.empty:
-                            customer_sales = customer_sales_data[sales_columns].values[0]
-                            years = ['2021', '2022', '2023']
+#                         if not customer_sales_data.empty:
+#                             customer_sales = customer_sales_data[sales_columns].values[0]
+#                             years = ['2021', '2022', '2023']
                             
-                            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 historical sales data found for customer {customer_code}")
-                    else:
-                        st.warning("Sales data for 2021-2023 not available in the dataset.")
-                else:
-                    st.warning(f"No data found for customer {customer_code}. Please check the code.")
-        else:
-            st.warning("Please select a customer.")
+#                             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 historical sales data found for customer {customer_code}")
+#                     else:
+#                         st.warning("Sales data for 2021-2023 not available in the dataset.")
+#                 else:
+#                     st.warning(f"No data found for customer {customer_code}. Please check the code.")
+#         else:
+#             st.warning("Please select a customer.")
 
 
-# Customer Recommendations Page
-elif page == "Articles Recommendations":
-    st.title("Articles Recommendations")
+# # Customer Recommendations Page
+# elif page == "Articles Recommendations":
+#     st.title("Articles Recommendations")
 
-    st.markdown("""
-        Get tailored recommendations for your customers based on their basket.
-    """)
+#     st.markdown("""
+#         Get tailored recommendations for your customers based on their basket.
+#     """)
 
-    # Campo input para cliente
-    partial_code = st.text_input("Enter part of Customer Code for Recommendations (or leave empty to see all)")
-    if partial_code:
-        filtered_customers = df[df['CLIENTE'].str.contains(partial_code)]
-    else:
-        filtered_customers = df
-    customer_list = filtered_customers['CLIENTE'].unique()
-    customer_code = st.selectbox("Select Customer Code for Recommendations", [""] + list(customer_list))
+#     # Campo input para cliente
+#     partial_code = st.text_input("Enter part of Customer Code for Recommendations (or leave empty to see all)")
+#     if partial_code:
+#         filtered_customers = df[df['CLIENTE'].str.contains(partial_code)]
+#     else:
+#         filtered_customers = df
+#     customer_list = filtered_customers['CLIENTE'].unique()
+#     customer_code = st.selectbox("Select Customer Code for Recommendations", [""] + list(customer_list))
+
+#     # Definición de la función recomienda
+#     def recomienda(new_basket):
+#         # Calcular la matriz TF-IDF
+#         tfidf = TfidfVectorizer()
+#         tfidf_matrix = tfidf.fit_transform(cestas['Cestas'])
+
+#         # Convertir la nueva cesta en formato TF-IDF
+#         new_basket_str = ' '.join(new_basket)
+#         new_basket_tfidf = tfidf.transform([new_basket_str])
+
+#         # Comparar la nueva cesta con las anteriores
+#         similarities = cosine_similarity(new_basket_tfidf, tfidf_matrix)
+
+#         # Obtener los índices de las cestas más similares
+#         similar_indices = similarities.argsort()[0][-3:]  # Las 3 más similares
+
+#         # Crear un diccionario para contar las recomendaciones
+#         recommendations_count = {}
+#         total_similarity = 0
+
+#         # Recomendar productos de cestas similares
+#         for idx in similar_indices:
+#             sim_score = similarities[0][idx]
+#             total_similarity += sim_score
+#             products = cestas.iloc[idx]['Cestas'].split()
+
+#             for product in products:
+#                 if product.strip() not in new_basket:  # Evitar recomendar lo que ya está en la cesta
+#                     if product.strip() in recommendations_count:
+#                         recommendations_count[product.strip()] += sim_score
+#                     else:
+#                         recommendations_count[product.strip()] = sim_score
 
-    # Definición de la función recomienda
-    def recomienda(new_basket):
-        # Calcular la matriz TF-IDF
-        tfidf = TfidfVectorizer()
-        tfidf_matrix = tfidf.fit_transform(cestas['Cestas'])
-
-        # Convertir la nueva cesta en formato TF-IDF
-        new_basket_str = ' '.join(new_basket)
-        new_basket_tfidf = tfidf.transform([new_basket_str])
-
-        # Comparar la nueva cesta con las anteriores
-        similarities = cosine_similarity(new_basket_tfidf, tfidf_matrix)
-
-        # Obtener los índices de las cestas más similares
-        similar_indices = similarities.argsort()[0][-3:]  # Las 3 más similares
-
-        # Crear un diccionario para contar las recomendaciones
-        recommendations_count = {}
-        total_similarity = 0
-
-        # Recomendar productos de cestas similares
-        for idx in similar_indices:
-            sim_score = similarities[0][idx]
-            total_similarity += sim_score
-            products = cestas.iloc[idx]['Cestas'].split()
-
-            for product in products:
-                if product.strip() not in new_basket:  # Evitar recomendar lo que ya está en la cesta
-                    if product.strip() in recommendations_count:
-                        recommendations_count[product.strip()] += sim_score
-                    else:
-                        recommendations_count[product.strip()] = sim_score
-
-        # Calcular la probabilidad relativa de cada producto recomendado
-        recommendations_with_prob = []
-        if total_similarity > 0:  # Verificar que total_similarity no sea cero
-            recommendations_with_prob = [(product, score / total_similarity) for product, score in recommendations_count.items()]
-        else:
-            print("No se encontraron similitudes suficientes para calcular probabilidades.")
-
-        recommendations_with_prob.sort(key=lambda x: x[1], reverse=True)  # Ordenar por puntuación
-
-        # Crear un nuevo DataFrame para almacenar las recomendaciones con descripciones y probabilidades
-        recommendations_df = pd.DataFrame(columns=['ARTICULO', 'DESCRIPCION', 'PROBABILIDAD'])
-
-        # Agregar las recomendaciones al DataFrame usando pd.concat
-        for product, prob in recommendations_with_prob:
-            # Buscar la descripción en el DataFrame de productos
-            description = productos.loc[productos['ARTICULO'] == product, 'DESCRIPCION']
-            if not description.empty:
-                # Crear un nuevo DataFrame temporal para la recomendación
-                temp_df = pd.DataFrame({
-                    'ARTICULO': [product],
-                    'DESCRIPCION': [description.values[0]],  # Obtener el primer valor encontrado
-                    'PROBABILIDAD': [prob]
-                })
-                # Concatenar el DataFrame temporal al DataFrame de recomendaciones
-                recommendations_df = pd.concat([recommendations_df, temp_df], ignore_index=True)
-
-        return recommendations_df
-
-    # Comprobar si el cliente está en el CSV de fieles
-    is_fiel = customer_code in fieles_df['Cliente'].astype(str).values
+#         # Calcular la probabilidad relativa de cada producto recomendado
+#         recommendations_with_prob = []
+#         if total_similarity > 0:  # Verificar que total_similarity no sea cero
+#             recommendations_with_prob = [(product, score / total_similarity) for product, score in recommendations_count.items()]
+#         else:
+#             print("No se encontraron similitudes suficientes para calcular probabilidades.")
+
+#         recommendations_with_prob.sort(key=lambda x: x[1], reverse=True)  # Ordenar por puntuación
+
+#         # Crear un nuevo DataFrame para almacenar las recomendaciones con descripciones y probabilidades
+#         recommendations_df = pd.DataFrame(columns=['ARTICULO', 'DESCRIPCION', 'PROBABILIDAD'])
+
+#         # Agregar las recomendaciones al DataFrame usando pd.concat
+#         for product, prob in recommendations_with_prob:
+#             # Buscar la descripción en el DataFrame de productos
+#             description = productos.loc[productos['ARTICULO'] == product, 'DESCRIPCION']
+#             if not description.empty:
+#                 # Crear un nuevo DataFrame temporal para la recomendación
+#                 temp_df = pd.DataFrame({
+#                     'ARTICULO': [product],
+#                     'DESCRIPCION': [description.values[0]],  # Obtener el primer valor encontrado
+#                     'PROBABILIDAD': [prob]
+#                 })
+#                 # Concatenar el DataFrame temporal al DataFrame de recomendaciones
+#                 recommendations_df = pd.concat([recommendations_df, temp_df], ignore_index=True)
+
+#         return recommendations_df
+
+#     # Comprobar si el cliente está en el CSV de fieles
+#     is_fiel = customer_code in fieles_df['Cliente'].astype(str).values
+
+#     if customer_code:
+#         if is_fiel:
+#             st.write(f"### Customer {customer_code} is a loyal customer.")
+#             option = st.selectbox("Select Recommendation Type", ["Select an option", "By Purchase History", "By Current Basket"])
+
+#             if option == "By Purchase History":
+#                 st.warning("Option not available... aún")
+#             elif option == "By Current Basket":
+#                 st.write("Select the items and assign quantities for the basket:")
+
+#                 # Mostrar lista de artículos disponibles
+#                 available_articles = productos['ARTICULO'].unique()
+#                 selected_articles = st.multiselect("Select Articles", available_articles)
+
+#                 # Crear inputs para ingresar las cantidades de cada artículo seleccionado
+#                 quantities = {}
+#                 for article in selected_articles:
+#                     quantities[article] = st.number_input(f"Quantity for {article}", min_value=0, step=1)
+
+#                 if st.button("Calcular"):  # Añadimos el botón "Calcular"
+#                     # Crear una lista de artículos basada en la selección
+#                     new_basket = [f"{article} x{quantities[article]}" for article in selected_articles if quantities[article] > 0]
+
+#                     if new_basket:
+#                         # Procesar la lista para recomendar
+#                         recommendations_df = recomienda(new_basket)
+
+#                         if not recommendations_df.empty:
+#                             st.write("### Recommendations based on the current basket:")
+#                             st.dataframe(recommendations_df)
+#                         else:
+#                             st.warning("No recommendations found for the provided basket.")
+#                     else:
+#                         st.warning("Please select at least one article and set its quantity.")
+#         else:
+#             st.write(f"### Customer {customer_code} is not a loyal customer.")
+#             st.write("Select items and assign quantities for the basket:")
 
-    if customer_code:
-        if is_fiel:
-            st.write(f"### Customer {customer_code} is a loyal customer.")
-            option = st.selectbox("Select Recommendation Type", ["Select an option", "By Purchase History", "By Current Basket"])
-
-            if option == "By Purchase History":
-                st.warning("Option not available... aún")
-            elif option == "By Current Basket":
-                st.write("Select the items and assign quantities for the basket:")
-
-                # Mostrar lista de artículos disponibles
-                available_articles = productos['ARTICULO'].unique()
-                selected_articles = st.multiselect("Select Articles", available_articles)
-
-                # Crear inputs para ingresar las cantidades de cada artículo seleccionado
-                quantities = {}
-                for article in selected_articles:
-                    quantities[article] = st.number_input(f"Quantity for {article}", min_value=0, step=1)
-
-                if st.button("Calcular"):  # Añadimos el botón "Calcular"
-                    # Crear una lista de artículos basada en la selección
-                    new_basket = [f"{article} x{quantities[article]}" for article in selected_articles if quantities[article] > 0]
-
-                    if new_basket:
-                        # Procesar la lista para recomendar
-                        recommendations_df = recomienda(new_basket)
-
-                        if not recommendations_df.empty:
-                            st.write("### Recommendations based on the current basket:")
-                            st.dataframe(recommendations_df)
-                        else:
-                            st.warning("No recommendations found for the provided basket.")
-                    else:
-                        st.warning("Please select at least one article and set its quantity.")
-        else:
-            st.write(f"### Customer {customer_code} is not a loyal customer.")
-            st.write("Select items and assign quantities for the basket:")
-
-            # Mostrar lista de artículos disponibles
-            available_articles = productos['ARTICULO'].unique()
-            selected_articles = st.multiselect("Select Articles", available_articles)
+#             # Mostrar lista de artículos disponibles
+#             available_articles = productos['ARTICULO'].unique()
+#             selected_articles = st.multiselect("Select Articles", available_articles)
 
-            # Crear inputs para ingresar las cantidades de cada artículo seleccionado
-            quantities = {}
-            for article in selected_articles:
-                quantities[article] = st.number_input(f"Quantity for {article}", min_value=0, step=1)
+#             # Crear inputs para ingresar las cantidades de cada artículo seleccionado
+#             quantities = {}
+#             for article in selected_articles:
+#                 quantities[article] = st.number_input(f"Quantity for {article}", min_value=0, step=1)
 
-            if st.button("Calcular"):  # Añadimos el botón "Calcular"
-                # Crear una lista de artículos basada en la selección
-                new_basket = [f"{article} x{quantities[article]}" for article in selected_articles if quantities[article] > 0]
+#             if st.button("Calcular"):  # Añadimos el botón "Calcular"
+#                 # Crear una lista de artículos basada en la selección
+#                 new_basket = [f"{article} x{quantities[article]}" for article in selected_articles if quantities[article] > 0]
 
-                if new_basket:
-                    # Procesar la lista para recomendar
-                    recommendations_df = recomienda(new_basket)
+#                 if new_basket:
+#                     # Procesar la lista para recomendar
+#                     recommendations_df = recomienda(new_basket)
 
-                    if not recommendations_df.empty:
-                        st.write("### Recommendations based on the current basket:")
-                        st.dataframe(recommendations_df)
-                    else:
-                        st.warning("No recommendations found for the provided basket.")
-                else:
-                    st.warning("Please select at least one article and set its quantity.")
+#                     if not recommendations_df.empty:
+#                         st.write("### Recommendations based on the current basket:")
+#                         st.dataframe(recommendations_df)
+#                     else:
+#                         st.warning("No recommendations found for the provided basket.")
+#                 else:
+#                     st.warning("Please select at least one article and set its quantity.")
 
 
 # Customer Analysis Page