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import streamlit as st
import pandas as pd
from huggingface_hub import hf_hub_download
import joblib
# Download and load the trained model
model_path = hf_hub_download(repo_id="lcsekar/tourism-project-model", filename="best_model_v1.joblib")
model = joblib.load(model_path)
# Streamlit UI
st.title("Visit with Us - Purchase Prediction App")
st.write("""
This application predicts whether the user will buy a tourism package from Visit with Us
based on user details such as age, occupation, gender, marital status and income.
Please enter the app details below to get the prediction.
""")
# User input
# create two columns for better layout
col1, col2 = st.columns(2)
with col1:
customer_id = st.number_input("Customer ID", min_value=1)
age = st.number_input("Age", min_value=18, max_value=100, value=30)
type_of_contact = st.selectbox("Type of Contact", ["Self Enquiry", "Company Invited"])
city_tier = st.selectbox("City Tier", [1, 2, 3])
occupation = st.selectbox("Occupation", ["Salaried", "Free Lancer", "Small Business", "Large Business"])
gender = st.selectbox("Gender", ["Male", "Female"])
num_person_visiting = st.number_input("Number of Persons Visiting", min_value=1, max_value=20, value=2)
preferred_property_star = st.selectbox("Preferred Property Star", [3, 4, 5])
marital_status = st.selectbox("Marital Status", ["Single", "Divorced", "Married", "Unmarried"])
num_trips = st.number_input("Number of Trips", min_value=0, max_value=50, value=5)
with col2:
passport = st.selectbox("Passport", ["Yes", "No"])
own_car = st.selectbox("Own Car", ["Yes", "No"])
num_children_visiting = st.number_input("Number of Children Visiting", min_value=0, max_value=10, value=0)
designation = st.selectbox("Designation", ["Executive", "Manager", "Senior Manager", "AVP", "VP"])
monthly_income = st.number_input("Monthly Income (USD)", min_value=1000, max_value=100000, value=5000)
pitch_satisfaction_score = st.selectbox("Pitch Satisfaction Score", [1, 2, 3, 4, 5])
product_pitched = st.selectbox("Product Pitched", ["Super Deluxe", "Deluxe", "Standard", "Basic", "King"])
num_followups = st.number_input("Number of Followups", min_value=0, max_value=20, value=2)
duration_of_pitch = st.number_input("Duration of Pitch (minutes)", min_value=1, max_value=120, value=30)
# Assemble input into DataFrame
input_data = pd.DataFrame([{
'Age': age,
'TypeofContact': type_of_contact,
'CityTier': city_tier,
'Occupation': occupation,
'Gender': gender,
'NumberOfPersonVisiting': num_person_visiting,
'PreferredPropertyStar': preferred_property_star,
'MaritalStatus': marital_status,
'NumberOfTrips': num_trips,
'Passport': 1 if passport == "Yes" else 0,
'OwnCar': 1 if own_car == "Yes" else 0,
'NumberOfChildrenVisiting': num_children_visiting,
'Designation': designation,
'MonthlyIncome': monthly_income,
'PitchSatisfactionScore': pitch_satisfaction_score,
'ProductPitched': product_pitched,
'NumberOfFollowups': num_followups,
'DurationOfPitch': duration_of_pitch
}])
print(input_data)
# Predict button
if st.button("Predict"):
prediction = model.predict(input_data)[0]
print(f"Prediction: {prediction}")
st.subheader("Prediction Result:")
st.success(f"The customer is {'likely' if prediction == 1 else 'not likely'} to purchase the tourism package.")
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