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

# ================= Load Model =================
model_path = hf_hub_download(
    repo_id="Disha252001/tourism-prediction-model",
    filename="best_model.joblib"
)
model = joblib.load(model_path)

st.title("Wellness Tourism Package Prediction")

# ================= Encoding Maps =================
gender_map = {"Female": 0, "Male": 1}
occupation_map = {
    "Free Lancer": 0,
    "Salaried": 1,
    "Small Business": 2
}
marital_map = {
    "Single": 0,
    "Married": 1,
    "Divorced": 2
}

# ================= User Inputs =================
Age = st.number_input("Age", 18, 80, 30)
CityTier = st.selectbox("City Tier", [1, 2, 3])
Gender = st.selectbox("Gender", list(gender_map.keys()))
Occupation = st.selectbox("Occupation", list(occupation_map.keys()))
MaritalStatus = st.selectbox("Marital Status", list(marital_map.keys()))
MonthlyIncome = st.number_input("Monthly Income", 10000, 500000, 50000)
NumberOfTrips = st.number_input("Number of Trips per Year", 0, 20, 2)
PitchSatisfactionScore = st.slider("Pitch Satisfaction Score", 1, 5, 3)

# ================= Build Input Row =================
input_data = {
    "Age": Age,
    "TypeofContact": 0,
    "CityTier": CityTier,
    "DurationOfPitch": 15,
    "Occupation": occupation_map[Occupation],
    "Gender": gender_map[Gender],
    "NumberOfPersonVisiting": 2,
    "NumberOfFollowups": 2,
    "ProductPitched": 0,
    "PreferredPropertyStar": 3,
    "MaritalStatus": marital_map[MaritalStatus],
    "NumberOfTrips": NumberOfTrips,
    "Passport": 1,
    "PitchSatisfactionScore": PitchSatisfactionScore,
    "OwnCar": 1,
    "NumberOfChildrenVisiting": 0,
    "Designation": 0,
    "MonthlyIncome": MonthlyIncome
}

input_df = pd.DataFrame([input_data])

# Handle accidental index column
if "Unnamed: 0" in model.feature_names_in_:
    input_df["Unnamed: 0"] = 0

# Ensure correct order
input_df = input_df[model.feature_names_in_]

# ================= Predict =================
if st.button("Predict"):
    prediction = model.predict(input_df)[0]
    result = "Purchased Package" if prediction == 1 else "Did Not Purchase"
    st.success(f"Prediction Result: **{result}**")