app.py

import streamlit as st
import pandas as pd
import plotly.express as px
import matplotlib.pyplot as plt
import numpy as np

# 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=',')

nombres_proveedores['codigo'] = nombres_proveedores['codigo'].astype(str)
euros_proveedor['CLIENTE'] = euros_proveedor['CLIENTE'].astype(str)

# Ignore the last two columns
df = df.iloc[:, :-2]

# Ensure customer code is a string
df['CLIENTE'] = df['CLIENTE'].astype(str)

# Function to get supplier name
def get_supplier_name(code):
    name = nombres_proveedores[nombres_proveedores['codigo'] == code]['nombre'].values
    return name[0] if len(name) > 0 else code

# Function to create radar chart
def radar_chart(categories, values, amounts, title):
    # Number of variables
    N = len(categories)
    
    # Calculate angles for each point
    angles = [n / float(N) * 2 * np.pi for n in range(N)]
    angles += angles[:1]
    
    # Initialize the plot
    fig, ax = plt.subplots(figsize=(12, 12), subplot_kw=dict(projection='polar'))
    
    # Normalize values and amounts
    max_value = max(values)
    normalized_values = [v / max_value for v in values]
    total_amount = sum(amounts)
    normalized_amounts = [a / total_amount for a in amounts]
    
    # Draw polygon for units and fill it
    normalized_values += normalized_values[:1]
    ax.plot(angles, normalized_values, 'o-', linewidth=2, color='#FF69B4', label='% Units')
    ax.fill(angles, normalized_values, alpha=0.25, color='#FF69B4')
    
    # Draw polygon for amounts and fill it
    normalized_amounts += normalized_amounts[:1]
    ax.plot(angles, normalized_amounts, 'o-', linewidth=2, color='#4B0082', label='% Spend')
    ax.fill(angles, normalized_amounts, alpha=0.25, color='#4B0082')
    
    # Set axes
    ax.set_xticks(angles[:-1])
    ax.set_xticklabels(categories, size=8, wrap=True)
    ax.set_ylim(0, max(max(normalized_values), max(normalized_amounts)) * 1.1)
    
    # Draw reference circles
    circles = np.linspace(0, 1, 5)
    for circle in circles:
        ax.plot(angles, [circle]*len(angles), '--', color='gray', alpha=0.3, linewidth=0.5)
    
    # Remove radial labels and chart borders
    ax.set_yticklabels([])
    ax.spines['polar'].set_visible(False)
    
    # Add title and legend
    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", "Customer 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.
    """)

    # Customer filter field
    partial_code = st.text_input("Enter part of Customer Code (or leave empty to see all)")

    # Filter customer options that match the partial code
    if partial_code:
        filtered_customers = df[df['CLIENTE'].str.contains(partial_code)]
    else:
        filtered_customers = df

    # Create a list of filtered customers for the selectbox
    customer_list = filtered_customers['CLIENTE'].unique()

    # Customer selection with filtered autocomplete
    customer_code = st.selectbox("Select Customer Code", customer_list)

    if customer_code:
        # Filter data for the selected customer
        customer_data = df[df["CLIENTE"] == customer_code]
        customer_euros = euros_proveedor[euros_proveedor["CLIENTE"] == customer_code]

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

            # Define purchase threshold
            purchase_threshold = 0

            # Get all manufacturers the customer bought from (ignore the customer column)
            all_manufacturers = customer_data.iloc[:, 1:].T[customer_data.iloc[:, 1:].T[customer_data.index[0]] > purchase_threshold]
            
            # Sort manufacturers by value in descending order
            all_manufacturers = all_manufacturers.sort_values(by=customer_data.index[0], ascending=False)

            # Prepare values and manufacturers
            values = all_manufacturers[customer_data.index[0]].values.tolist()
            manufacturers = [get_supplier_name(m) for m in all_manufacturers.index.tolist()]

            # Get amounts in euros
            amounts = []
            for m in all_manufacturers.index.tolist():
                if m in customer_euros.columns:
                    amounts.append(customer_euros[m].values[0])
                else:
                    amounts.append(0)

            # If there are fewer than 3 manufacturers, add a third one with value 0
            if len(manufacturers) < 3:
                manufacturers.append("Other")
                values.append(0)
                amounts.append(0)

            # Display the results for each manufacturer
            st.write(f"### Results for {len(manufacturers)} manufacturers (sorted):")
            for manufacturer, value, amount in zip(manufacturers, values, amounts):
                st.write(f"{manufacturer} = {value:.4f} units, €{amount:.2f}")

            # Create and display the radar chart
            fig = radar_chart(manufacturers, values, amounts, f'Radar Chart for {len(manufacturers)} Manufacturers of Customer {customer_code}')
            st.pyplot(fig)

            # Customer sales 2021-2024 (if data exists)
            if 'VENTA_2021' in df.columns and 'VENTA_2022' in df.columns and 'VENTA_2023' in df.columns and 'VENTA_2024' in df.columns:
                years = ['2021', '2022', '2023', '2024']
                sales_columns = ['VENTA_2021', 'VENTA_2022', 'VENTA_2023', 'VENTA_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 == "Customer Recommendations":
    st.title("Customer Recommendations")
    st.markdown("""
        Get tailored recommendations for your customers based on their purchasing history.
    """)

    # Customer filter field
    partial_code = st.text_input("Enter part of Customer Code for Recommendations (or leave empty to see all)")

    # Filter customer options that match the partial code
    if partial_code:
        filtered_customers = df[df['CLIENTE'].str.contains(partial_code)]
    else:
        filtered_customers = df

    # Create a list of filtered customers for the selectbox
    customer_list = filtered_customers['CLIENTE'].unique()

    # Customer selection with filtered autocomplete
    customer_code = st.selectbox("Select Customer Code for Recommendations", customer_list)

    if customer_code:
        customer_data = df[df["CLIENTE"] == customer_code]

        if not customer_data.empty:
            # Show selected customer's purchase history
            st.write(f"### Purchase History for Customer {customer_code}")
            st.write(customer_data)

            # Generate recommendations (placeholder)
            st.write(f"### Recommended Products for Customer {customer_code}")
            # You can replace this with the logic of the recommendation model
            st.write("Product A, Product B, Product C")
        else:
            st.warning(f"No data found for customer {customer_code}. Please check the code.")