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 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]
    total_sqrt_amount = sum(sqrt_amounts)
    normalized_amounts = [a / total_sqrt_amount 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, max(max(normalized_values), max(normalized_amounts)) * 1.1)
    
    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", "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.")

    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"] == 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}")

            all_manufacturers = customer_data.iloc[:, 1:].T[customer_data.iloc[:, 1:].T[customer_data.index[0]] > 0]
            
            # Convert to numeric and handle any non-numeric values
            all_manufacturers = all_manufacturers.apply(pd.to_numeric, errors='coerce')
            customer_euros = customer_euros.apply(pd.to_numeric, errors='coerce')

            top_units = all_manufacturers.sort_values(by=customer_data.index[0], ascending=False).head(10)
            
            # Ensure we're working with numeric data for sorting
            numeric_euros = customer_euros.select_dtypes(include=[np.number])
            if not numeric_euros.empty:
                top_sales = numeric_euros.iloc[0].sort_values(ascending=False).head(10)
            else:
                st.warning("No numeric sales data available for this customer.")
                top_sales = pd.Series()

            combined_top = pd.concat([top_units, top_sales]).index.unique()

            values = []
            manufacturers = []
            amounts = []

            for m in combined_top:
                if m in all_manufacturers.index:
                    values.append(all_manufacturers[m])
                    manufacturers.append(get_supplier_name(m))
                    amounts.append(customer_euros[m].values[0] if m in customer_euros.columns else 0)

            st.write(f"### Results for top {len(manufacturers)} manufacturers (balanced by units and sales):")
            for manufacturer, value, amount in zip(manufacturers, values, amounts):
                st.write(f"{manufacturer} = {value:.4f} units, €{amount:.2f}")

            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)
            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.
    """)

    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)

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

        if not customer_data.empty:
            st.write(f"### Purchase History for Customer {customer_code}")
            st.write(customer_data)

            st.write(f"### Recommended Products for Customer {customer_code}")
            # Placeholder for recommendation logic
            st.write("Product A, Product B, Product C")
        else:
            st.warning(f"No data found for customer {customer_code}. Please check the code.")