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| 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 | |
| from joblib import dump, load | |
| # 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 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) | |
| # Combine top manufacturers from both lists and get up to 20 unique manufacturers | |
| combined_top = pd.concat([top_units, top_sales]).index.unique()[:20] | |
| # 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] | |
| st.write(f"Number of combined top manufacturers: {len(combined_top)}") | |
| 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) | |
| # Sort by units, then by sales | |
| combined_data_sorted = combined_data.sort_values(by=['units', 'sales'], ascending=False) | |
| # Filter out manufacturers with 0 units | |
| non_zero_manufacturers = combined_data_sorted[combined_data_sorted['units'] > 0] | |
| # 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 | |
| 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"### 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 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'] | |
| 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.") | |
| def recomienda_tfid(new_basket): | |
| cestas = pd.read_csv('../data/processed/cestas.csv') | |
| productos = pd.read_csv('../data/processed/productos.csv') | |
| # Cargar la matriz TF-IDF y el modelo | |
| tfidf_matrix = load('../models/tfidf_matrix.joblib') | |
| # MAtriz que tienen cada columna los diferentes artículos y las diferentes cestas en las filas | |
| # Los valores son la importancia de cada artículo en la cesta según las veces que aparece en la misma y el total de artículos | |
| tfidf = load('../models/tfidf_model.joblib') | |
| # 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 | |
| # Calculando la distancia coseoidal, distancia entre rectas | |
| similarities = cosine_similarity(new_basket_tfidf, tfidf_matrix) | |
| # La similitud coseno devuelve un valor entre 0 y 1, donde 1 significa | |
| # que las cestas son idénticas en términos de productos y 0 que no comparten ningún producto. | |
| # Obtener los índices de las cestas más similares | |
| # Muestra los índices de Las 3 cestas más parecidas atendiendo a la distancia calculada anteriormente | |
| similar_indices = similarities.argsort()[0][-4:] # 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] | |
| # sim_score es el valor de similitud de la cesta actual con la cesta similar. | |
| total_similarity += sim_score # Suma de las similitudes entre 0 y el nº de cestas similares | |
| 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 | |
| recommendations_count[product.strip()] = recommendations_count.get(product.strip(), 0) + sim_score | |
| # se utiliza para incrementar el conteo del producto en recommendations_count. | |
| # almacena el conteo de la relevancia de cada producto basado en cuántas veces aparece en las cestas similares, ponderado por la similitud de cada cesta. | |
| # sumandole sim_score se incrementa el score cuando la cesta es mas similar | |
| # 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()] | |
| # Se guarda cada producto junto su score calculada | |
| 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 | |
| recommendations_data = [] | |
| for product, score in recommendations_with_prob: | |
| # Buscar la descripción en el DataFrame de productos | |
| description = productos.loc[productos['ARTICULO'] == product, 'DESCRIPCION'] | |
| if not description.empty: | |
| recommendations_data.append({ | |
| 'ARTICULO': product, | |
| 'DESCRIPCION': description.values[0], # Obtener el primer valor encontrado | |
| 'RELEVANCIA': score | |
| }) | |
| recommendations_df = pd.DataFrame(recommendations_data) | |
| return recommendations_df | |
| # # Customer Recommendations Page | |
| # elif page == "Articles Recommendations": | |
| # st.title("Articles Recommendations") | |
| # 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)) | |
| # # 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 | |
| # 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) | |
| # # 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.") | |
| # 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}") | |
| # # Load X_predict for that cluster | |
| # X_predict_cluster = pd.read_csv(f'predicts/X_predict_cluster_{cluster}.csv') | |
| # # Filter for the specific customer | |
| # X_cliente = X_predict_cluster[X_predict_cluster['cliente_id'] == customer_code] | |
| # if not X_cliente.empty: | |
| # # Prepare data for prediction | |
| # features_for_prediction = X_cliente.drop(columns=['cliente_id', 'fecha_mes']) | |
| # # Make Prediction for the selected customer | |
| # y_pred = gbm.predict(features_for_prediction, num_iteration=gbm.best_iteration) | |
| # # Reassemble the results | |
| # results = X_cliente[['cliente_id', 'marca_id_encoded', 'fecha_mes']].copy() | |
| # results['ventas_predichas'] = y_pred | |
| # st.write(f"Predicted total sales for Customer {customer_code}: {results['ventas_predichas'].sum():.2f}") | |
| # # Load actual data | |
| # df_agg_2024 = pd.read_csv('predicts/df_agg_2024.csv') | |
| # 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.rename(columns={'precio_total': 'ventas_reales'}, inplace=True) | |
| # # 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.") | |
| # # Show the radar chart | |
| # all_manufacturers = customer_data.iloc[:, 1:].T # Exclude CLIENTE column | |
| # all_manufacturers.index = all_manufacturers.index.astype(str) | |
| # sales_data = customer_euros.iloc[:, 1:].T # Exclude CLIENTE column | |
| # sales_data.index = sales_data.index.astype(str) | |
| # sales_data_filtered = sales_data.drop(index='CLIENTE', errors='ignore') | |
| # sales_data_filtered = sales_data_filtered.apply(pd.to_numeric, errors='coerce') | |
| # top_units = all_manufacturers.sort_values(by=all_manufacturers.columns[0], ascending=False).head(10) | |
| # top_sales = sales_data_filtered.sort_values(by=sales_data_filtered.columns[0], ascending=False).head(10) | |
| # combined_top = pd.concat([top_units, top_sales]).index.unique()[:20] | |
| # combined_top = [m for m in combined_top if m in all_manufacturers.index and m in sales_data_filtered.index] | |
| # 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) | |
| # combined_data_sorted = combined_data.sort_values(by=['units', 'sales'], ascending=False) | |
| # non_zero_manufacturers = combined_data_sorted[combined_data_sorted['units'] > 0] | |
| # 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 | |
| # 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"### 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 manufacturers: | |
| # 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.") | |
| # # Show sales over the years graph | |
| # sales_columns = ['VENTA_2021', 'VENTA_2022', 'VENTA_2023'] | |
| # if all(col in ventas_clientes.columns for col in sales_columns): | |
| # years = ['2021', '2022', '2023'] | |
| # customer_sales = ventas_clientes[ventas_clientes['codigo_cliente'] == customer_code][sales_columns].values[0] | |
| # 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("Sales data for 2021-2023 not available.") | |
| # else: | |
| # st.warning(f"No prediction data found for customer {customer_code}.") | |
| # else: | |
| # st.warning(f"No data found for customer {customer_code}. Please check the code.") | |
| # else: | |
| # st.warning("Please select a customer.") | |