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import streamlit as st
import pandas as pd
from huggingface_hub import hf_hub_download
import joblib
# -------------------------------
# LOAD MODEL FROM HUGGING FACE HUB
# -------------------------------
model_path = hf_hub_download(
repo_id="vsardey/tourism-package-prediction-model",
filename="tourism-package-prediction_model.joblib"
)
model = joblib.load(model_path)
# -------------------------------
# STREAMLIT APP
# -------------------------------
st.title("Tourism Package Purchase Prediction App")
st.write("""
This application predicts whether a customer is likely to purchase the **Tourism Package**
offered by *Visit with Us*.
Please enter the customer details below to get the prediction.
""")
# -------------------------------
# USER INPUT FIELDS
# -------------------------------
Age = st.number_input("Customer Age", min_value=0, max_value=100, value=30)
Gender = st.selectbox("Gender", ["Male", "Female"])
TypeofContact = st.selectbox("Type of Contact", ["Company Invited", "Self Inquiry"])
CityTier = st.selectbox("City Tier", [1, 2, 3])
Occupation = st.selectbox(
"Occupation",
["Salaried", "Self Employed", "Freelancer", "Company Owner", "Other"]
)
MaritalStatus = st.selectbox(
"Marital Status",
["Single", "Married", "Divorced"]
)
ProductPitched = st.selectbox(
"Product Pitched",
["Basic", "Deluxe", "Standard", "King", "Super Deluxe"]
)
Designation = st.selectbox(
"Designation",
["Manager", "Executive", "Senior Manager", "AVP", "VP"]
)
MonthlyIncome = st.number_input("Monthly Income", min_value=0, value=50000)
NumberOfTrips = st.number_input("Average Trips per Year", min_value=0, value=1)
NumberOfPersonVisiting = st.number_input("Number of Persons Visiting", min_value=1, value=2)
PreferredPropertyStar = st.selectbox("Preferred Hotel Star Rating", [1, 2, 3, 4, 5])
NumberOfChildrenVisiting = st.number_input("Number of Children Visiting", min_value=0, value=0)
Passport = st.selectbox("Passport Available?", [0, 1])
OwnCar = st.selectbox("Owns a Car?", [0, 1])
PitchSatisfactionScore = st.slider("Pitch Satisfaction Score", 1, 5, 3)
NumberOfFollowups = st.number_input("Number of Follow-ups", min_value=0, value=2)
DurationOfPitch = st.number_input("Duration of Pitch (minutes)", min_value=0, value=15)
# -------------------------------
# CREATE INPUT DATAFRAME
# -------------------------------
input_data = pd.DataFrame([{
"Age": Age,
"Gender": Gender,
"TypeofContact": TypeofContact,
"CityTier": CityTier,
"Occupation": Occupation,
"MaritalStatus": MaritalStatus,
"NumberOfPersonVisiting": NumberOfPersonVisiting,
"PreferredPropertyStar": PreferredPropertyStar,
"NumberOfTrips": NumberOfTrips,
"Passport": Passport,
"OwnCar": OwnCar,
"NumberOfChildrenVisiting": NumberOfChildrenVisiting,
"Designation": Designation,
"MonthlyIncome": MonthlyIncome,
"PitchSatisfactionScore": PitchSatisfactionScore,
"ProductPitched": ProductPitched,
"NumberOfFollowups": NumberOfFollowups,
"DurationOfPitch": DurationOfPitch
}])
# -------------------------------
# PREDICTION
# -------------------------------
if st.button("Predict Purchase Likelihood"):
prediction = model.predict(input_data)[0]
result = "Will Purchase Package" if prediction == 1 else "Will Not Purchase Package"
st.subheader("Prediction Result:")
st.success(f"The model predicts: **{result}**")
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