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import streamlit as st
import pandas as pd
import joblib
from huggingface_hub import hf_hub_download
# Download and load the model
model_path = hf_hub_download(
repo_id="Pushpak21/tourism-package-model",
filename="best_tourism_package_model.joblib"
)
model = joblib.load(model_path)
# Feature descriptions for UI
feature_info = {
"Age": "Age of the customer (years).",
"TypeofContact": "How the customer was contacted (Company Invited / Self Inquiry).",
"CityTier": "City category (1=Tier1, 2=Tier2, 3=Tier3).",
"Occupation": "Customer occupation (Salaried, Freelancer, etc.).",
"Gender": "Male or Female.",
"NumberOfPersonVisiting": "Total number of people visiting together.",
"PreferredPropertyStar": "Preferred hotel star rating (3,4,5).",
"MaritalStatus": "Single / Married / Divorced.",
"NumberOfTrips": "Average trips per year.",
"Passport": "Has passport? (0 = No, 1 = Yes).",
"OwnCar": "Owns car? (0 = No, 1 = Yes).",
"NumberOfChildrenVisiting": "Children under 5 accompanying.",
"Designation": "Job designation/title.",
"MonthlyIncome": "Gross monthly income.",
"PitchSatisfactionScore": "Satisfaction score for the sales pitch (1-5).",
"ProductPitched": "Product variant pitched to the customer.",
"NumberOfFollowups": "Number of follow-ups by salesperson.",
"DurationOfPitch": "Duration of pitch in minutes."
}
st.sidebar.title("Feature descriptions")
for k, v in feature_info.items():
st.sidebar.write(f"**{k}** — {v}")
st.title("🧳 Tourism Package Purchase Prediction")
# Form with two-column layout
with st.form("input_form"):
col1, col2 = st.columns(2)
with col1:
age = st.number_input("Age", min_value=18, max_value=100, value=30, help=feature_info["Age"])
typeof_contact = st.selectbox("Type of Contact", ["Self Enquiry", "Company Invited"], help=feature_info["TypeofContact"])
city_tier = st.selectbox("City Tier", [1,2,3], help=feature_info["CityTier"])
occupation = st.selectbox("Occupation", ["Salaried", "Free Lancer", "Small Business", "Large Business"], help=feature_info["Occupation"])
gender = st.selectbox("Gender", ["Male", "Female"], help=feature_info["Gender"])
persons = st.number_input("Number Of Person Visiting", min_value=1, max_value=5, value=2, help=feature_info["NumberOfPersonVisiting"])
star = st.selectbox("Preferred Property Star", [3,4,5], help=feature_info["PreferredPropertyStar"])
marital = st.selectbox("Marital Status", ["Single", "Married", "Divorced","Unmarried"], help=feature_info["MaritalStatus"])
with col2:
trips = st.number_input("Number Of Trips", min_value=1, max_value=25, value=2, help=feature_info["NumberOfTrips"])
passport = st.radio("Passport", [0,1], help=feature_info["Passport"])
owncar = st.radio("Own Car", [0,1], help=feature_info["OwnCar"])
children = st.number_input("Number Of Children Visiting", min_value=0, max_value=3, value=0, help=feature_info["NumberOfChildrenVisiting"])
designation = st.selectbox("Designation", ["Executive", "Manager", "Senior Manager", "AVP","VP"], help=feature_info["Designation"])
income = st.number_input("Monthly Income", min_value=1000, max_value=100000, value=30000, help=feature_info["MonthlyIncome"])
satisfaction = st.slider("Pitch Satisfaction Score", min_value=1, max_value=5, value=3, help=feature_info["PitchSatisfactionScore"])
product = st.selectbox("Product Pitched", ["Basic", "Standard","King", "Deluxe", "Super Deluxe"], help=feature_info["ProductPitched"])
followups = st.number_input("Number Of Followups", min_value=1, max_value=6, value=2, help=feature_info["NumberOfFollowups"])
duration = st.number_input("Duration Of Pitch (minutes)", min_value=0, max_value=300, value=10, help=feature_info["DurationOfPitch"])
submitted = st.form_submit_button("Predict")
if submitted:
input_df = pd.DataFrame([{
"Age": age,
"TypeofContact": typeof_contact,
"CityTier": city_tier,
"Occupation": occupation,
"Gender": gender,
"NumberOfPersonVisiting": persons,
"PreferredPropertyStar": star,
"MaritalStatus": marital,
"NumberOfTrips": trips,
"Passport": passport,
"OwnCar": owncar,
"NumberOfChildrenVisiting": children,
"Designation": designation,
"MonthlyIncome": income,
"PitchSatisfactionScore": satisfaction,
"ProductPitched": product,
"NumberOfFollowups": followups,
"DurationOfPitch": duration
}])
proba = model.predict_proba(input_df)[0,1]
pred = model.predict(input_df)[0]
st.write("Probability:", round(proba,3))
st.write("Prediction:", "✅ Will buy (1)" if pred==1 else "❌ Will not buy (0)")
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