import streamlit as st
import pandas as pd
import joblib
from custom_transformers import ColumnSelectorTransformer, CastCategoricalTransformer

# Load the model
model = joblib.load('model.pkl')

# Title and Description
st.title('Tourism Package Purchase Predictor')
st.write('Enter customer information to predict likelihood of package purchase')

# Personal Information
st.header('Personal Details')
col1, col2 = st.columns(2)
with col1:
    age = st.number_input('Age', min_value=18, max_value=100)
    gender = st.selectbox('Gender', ['Male', 'Female'])
    marital_status = st.selectbox('Marital Status', ['Single', 'Married', 'Divorced'])
with col2:
    occupation = st.selectbox('Occupation', ['Salaried', 'Free Lancer', 'Business'])
    monthly_income = st.number_input('Monthly Income', min_value=0)
    own_car = st.selectbox('Owns Car?', ['Yes', 'No'])
    designation = st.selectbox('Designation', ['Executive', 'Manager', 'Senior Manager', 'AVP', 'VP'])

# Travel Preferences
st.header('Travel Information')
col1, col2 = st.columns(2)
with col1:
    num_trips = st.number_input('Number of Past Trips', min_value=0)
    preferred_star = st.selectbox('Preferred Property Star Rating', [3, 4, 5])
    num_persons = st.number_input('Number of Persons Visiting', min_value=1)
with col2:
    num_children = st.number_input('Number of Children Visiting', min_value=0)
    passport = st.selectbox('Has Passport?', ['Yes', 'No'])
    city_tier = st.selectbox('City Tier', [1, 2, 3])

# Sales Information
st.header('Sales Interaction')
col1, col2 = st.columns(2)
with col1:
    type_contact = st.selectbox('Type of Contact', ['Self Enquiry', 'Company Invited'])
    pitch_duration = st.number_input('Duration of Pitch (minutes)', min_value=1)
    product_pitched = st.selectbox('Product Pitched', ['Basic', 'Standard', 'Deluxe', 'Super Deluxe', 'King'])
with col2:
    num_followups = st.number_input('Number of Followups', min_value=0)
    pitch_satisfaction = st.slider('Pitch Satisfaction Score', 1, 5)

# Predict button
if st.button('Predict Purchase Likelihood'):
    input_data = pd.DataFrame({
        'Age': [age],
        'Gender': [gender],
        'MaritalStatus': [marital_status],
        'Occupation': [occupation],
        'MonthlyIncome': [monthly_income],
        'Designation': [designation],
        'NumberOfTrips': [num_trips],
        'PreferredPropertyStar': [preferred_star],
        'NumberOfPersonVisiting': [num_persons],
        'NumberOfChildrenVisiting': [num_children],
        'Passport': [1 if passport == 'Yes' else 0],
        'CityTier': [city_tier],
        'TypeofContact': [type_contact],
        'DurationOfPitch': [pitch_duration],
        'NumberOfFollowups': [num_followups],
        'PitchSatisfactionScore': [pitch_satisfaction],
        'OwnCar': [1 if own_car == 'Yes' else 0],
        'ProductPitched': [product_pitched]
    })

    # Make prediction
    prediction = model.predict_proba(input_data)[0]

    # Show results
    st.subheader('Prediction Results')
    st.write(f'Likelihood of purchasing package: {prediction[1]:.2%}')