import streamlit as st
import pandas as pd
from huggingface_hub import hf_hub_download
import joblib

# Download the model from the Model Hub
model_path = hf_hub_download(repo_id="hkbindhu/Tourism-Package-Model", filename="best_tourism_prediction_model_v1.joblib")

# Load the model
model = joblib.load(model_path)

# Streamlit UI for Customer Churn Prediction
# Streamlit UI for Customer Churn Prediction
st.title("Tourism Package Prediction")
st.write("Fill the customer details below to predict if they'll purchase a travel package")

# Collect user input
Age = st.number_input("Age (customer's age in years)", min_value=18.0, max_value=110.0, value=18.0,step=1.0)
CityTier = st.selectbox("The city category based on development, population, and living standards (Tier 1 > Tier 2 > Tier 3)",
                        ["Tier 1", "Tier 2", "Tier 3"])
NumberOfPersonVisiting = st.number_input("Total number of people accompanying the customer on the trip", min_value=0, max_value=30, value=0,step=1)
PreferredPropertyStar = st.number_input("Preferred hotel rating by the customer",min_value=1.0, max_value=7.0, value=3.0,step=1.0)
NumberOfTrips = st.number_input("Average number of trips the customer takes annually",min_value=0.0, value=1.0,step=1.0)
Passport = st.selectbox("Whether the customer holds a valid passport ?",["Yes", "No"])
OwnCar = st.selectbox("Whether the customer owns a car ?",["Yes", "No"])
NumberOfChildrenVisiting = st.number_input("Number of children below age 5 accompanying the customer",min_value=0.0, value=0.0,step=1.0)
MonthlyIncome = st.number_input("Gross monthly income of the customer", min_value=0.0, value=5000.0)
PitchSatisfactionScore = st.number_input("Score indicating the customer's satisfaction with the sales pitch", min_value=1, value=1,max_value=5,step=1)
NumberOfFollowups = st.number_input("Total number of follow-ups by the salesperson after the sales pitch.",min_value=0.0, value=1.0,step=1.0)
DurationOfPitch = st.number_input("Duration of the sales pitch delivered to the customer.",min_value=1.0, value=1.0,step=1.0)

TypeofContact = st.selectbox("The method by which the customer was contacted",["Self Enquiry", "Company Invited"])
Occupation = st.selectbox("Customer's occupation",["Salaried", "Small Business","Large Business","Free Lancer"])
Gender = st.selectbox("Gender of the customer",["Male", "Female"])
MaritalStatus = st.selectbox("Marital status of the customer",["Married", "Divorced","Unmarried","Single"])
Designation = st.selectbox("Customer's designation in their current organization",["Executive", "Manager","Senior Manager", "AVP","VP"])
ProductPitched = st.selectbox("The type of product pitched to the customer",["Basic", "Deluxe","Standard","Super Deluxe","King"])

citytier_mapping = {'Tier 1':1,'Tier 2':2,'Tier 3':3}
                                                                             

                                                                             
# Convert categorical inputs to match model training
input_data = pd.DataFrame([{
    'Age': Age,
    'CityTier': citytier_mapping[CityTier],
    'NumberOfPersonVisiting': NumberOfPersonVisiting,
    'PreferredPropertyStar': PreferredPropertyStar,
    'NumberOfTrips': NumberOfTrips,
    'Passport': 1 if Passport == "Yes" else 0,
    'OwnCar': 1 if OwnCar == "Yes" else 0,
    'NumberOfChildrenVisiting': NumberOfChildrenVisiting,
    'MonthlyIncome': MonthlyIncome,
    'PitchSatisfactionScore': PitchSatisfactionScore,
    'NumberOfFollowups': NumberOfFollowups,
    'DurationOfPitch': DurationOfPitch,
    'TypeofContact': TypeofContact,
    'Occupation': Occupation,
    'Gender': Gender,
    'MaritalStatus': MaritalStatus,
    'Designation': Designation,
    'ProductPitched': ProductPitched
}])

# 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 = "Opted For Tourism Package" if prediction == 1 else "Not Opted For Tourism Package"
    st.write(f"Prediction: Customer {result}")