app.py

import streamlit as st
import pandas as pd
import plotly.express as px
import matplotlib.pyplot as plt
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
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
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=',')

# 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)
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
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 customer_code:
        customer_data = df[df["CLIENTE"] == str(customer_code)]
        customer_euros = euros_proveedor[euros_proveedor["CLIENTE"] == str(customer_code)]

        if not customer_data.empty and not customer_euros.empty:
            st.write(f"### Analysis for Customer {customer_code}")

            # Get percentage of units sold for each manufacturer
            all_manufacturers = customer_data.iloc[:, 1:].T  # Exclude CLIENTE column
            all_manufacturers.index = all_manufacturers.index.astype(str)

            # Get total sales for each manufacturer
            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')

            # 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]

            # 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.")

            # Customer sales 2021-2024 (if data exists)
            sales_columns = ['VENTA_2021', 'VENTA_2022', 'VENTA_2023', 'VENTA_2024']
            if all(col in df.columns for col in sales_columns):
                years = ['2021', '2022', '2023', '2024']
                customer_sales = customer_data[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 2021-2024 not available.")
        else:
            st.warning(f"No data found for customer {customer_code}. Please check the code.")

# 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", customer_list)

    # DEfinicion de la funcion 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("Enter the items in the basket:")
                
                # Input para los artículos y unidades
                items = st.text_input("Enter items (comma-separated):").split(',')
                quantities = st.text_input("Enter quantities (comma-separated):").split(',')

                # Crear una lista de artículos basada en la entrada
                new_basket = [item.strip() for item in items]

                # Asegurarse de que las longitudes de artículos y cantidades coincidan
                if len(new_basket) == len(quantities):
                    # 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("The number of items must match the number of quantities.")
        else:
            st.write(f"### Customer {customer_code} is not a loyal customer.")
            st.write("Recommendation based on the basket. Please enter the items:")
            
            # Input para los artículos y unidades
            items = st.text_input("Enter items (comma-separated):").split(',')
            quantities = st.text_input("Enter quantities (comma-separated):").split(',')

            # Crear una lista de artículos basada en la entrada
            new_basket = [item.strip() for item in items]

            # Asegurarse de que las longitudes de artículos y cantidades coincidan
            if len(new_basket) == len(quantities):
                # 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("The number of items must match the number of quantities.")