import streamlit as st
import pandas as pd
import joblib

# --- Configuration ---
MODEL_PATH = 'src/customer_model.joblib'
SCALER_PATH = 'src/scaler.joblib' 

FEATURES = ['Income', 'Seniority', 'Spending'] 

@st.cache_resource
def load_assets():
    try:
        model = joblib.load(MODEL_PATH)
        scaler = joblib.load(SCALER_PATH)
        return model, scaler
    except FileNotFoundError:
        st.error(f"Error: Model or Scaler file not found. Ensure both '{MODEL_PATH}' and '{SCALER_PATH}' are uploaded to the Space.")
        return None, None
    except Exception as e:
        st.error(f"Error loading assets: {e}")
        return None, None

def predict_cluster(model, scaler, input_data):
    input_df = pd.DataFrame([input_data])
    
    scaled_data = scaler.transform(input_df[FEATURES])
    
    prediction = model.predict(scaled_data) 
    
    return prediction[0]

# --- Streamlit Interface ---
st.set_page_config(page_title="Customer Clustering App", layout="wide")
st.title("🥇 Customer Personality Cluster Prediction")
st.markdown("Use the sidebar to input customer features and predict their cluster.")

model, scaler = load_assets()

if model is not None and scaler is not None:
    st.sidebar.header("Input Customer Features")

    income = st.sidebar.slider("Income ($):", min_value=1000, max_value=200000, value=50000)
    seniority = st.sidebar.slider("Seniority (Years as customer):", min_value=0, max_value=50, value=10)
    spending = st.sidebar.slider("Total Spending ($):", min_value=0, max_value=3000, value=500)
    
    input_data = {
        'Income': income,
        'Seniority': seniority,
        'Spending': spending
    }

    st.subheader("Current Input Data:")
    st.write(pd.DataFrame([input_data]))

    if st.button("Predict Cluster"):
        with st.spinner('Predicting...'):
            cluster_id = predict_cluster(model, scaler, input_data)
            
            cluster_descriptions = {
                0: "👑 **Cluster 0: High Potential**",
                1: "🚨 **Cluster 1: Need Attention**",
                2: "⏳ **Cluster 2: Leaky Bucket**",
                3: "⭐ **Cluster 3: Stars**",
            }
            
            description = cluster_descriptions.get(cluster_id, f"🔍 Cluster ID **{cluster_id}** (Undefined)")
            
            st.success(f"Prediction Complete! The customer belongs to:")
            st.markdown(f"## {description}")