import streamlit as st
import pandas as pd
import numpy as np
import joblib

# Load the trained model
def load_model():
  return joblib.load("churn_prediction_model_v1_0.joblib")

model = load_model()


# Streamlit UI for Customer Churn Prediction
st.title("Customer Churn Prediction App")
st.write("App to predict churn")
st.write("Enter Customer details to predict churn")

# Collect user input
CreditScore = st.number_input("Credit Score", min_value=0, max_value=1000, value=500)
Geography = st.selectbox("Geography (country where the customer resides)", ["France", "Germany", "Spain"])
Age = st.number_input("Age (customer's age in years)", min_value=18, max_value=100, value=30)
Tenure = st.number_input("Tenure (number of years the customer has been with the bank)", value=12)
Balance = st.number_input("Account Balance (customer’s account balance)", min_value=0.0, value=10000.0)
NumOfProducts = st.number_input("Number of Products (number of products the customer has with the bank)", min_value=1, value=1)
HasCrCard = st.selectbox("Has Credit Card?", ["Yes", "No"])
IsActiveMember = st.selectbox("Is Active Member?", ["Yes", "No"])
EstimatedSalary = st.number_input("Estimated Salary (customer’s estimated salary)", min_value=0.0, value=50000.0)

# Convert categorical inputs to match model training
input_data = pd.DataFrame([{
    'CreditScore': CreditScore,
    'Geography': Geography,
    'Age': Age,
    'Tenure': Tenure,
    'Balance': Balance,
    'NumOfProducts': NumOfProducts,
    'HasCrCard': 1 if HasCrCard == "Yes" else 0,
    'IsActiveMember': 1 if IsActiveMember == "Yes" else 0,
    'EstimatedSalary': EstimatedSalary
}])

# Set the classification threshold
classification_threshold = 0.45

# Predict button
if st.button("Predict"):
    prediction_proba = model.predict_proba(input_data)[0, 1]
    prediction = (prediction_proba >= classification_threshold).astype(int)
    result = "churn" if prediction == 1 else "not churn"
    st.write(f"Based on the information provided, the customer is likely to {result}.")