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import streamlit as st
import pandas as pd
from huggingface_hub import hf_hub_download
import joblib
import numpy as np
# Page config
st.set_page_config(page_title="Tourism Package Prediction", layout="wide")
# Download and load the model
model_path = hf_hub_download(repo_id="pragmat/Tourism", filename="best_prediction_model_v1.joblib")
model = joblib.load(model_path)
# Main title and description
st.title("Tourism Package Purchase Prediction")
st.markdown("""
**Predict whether a customer will purchase a tourism package** based on their demographic and travel preferences.
Enter customer details below to get a prediction!
""")
# Sidebar for inputs with proper feature names from your dataset
st.sidebar.header("Customer Profile")
st.sidebar.markdown("---")
# Numeric features (matching your dataset)
col1, col2 = st.columns(2)
with col1:
age = st.number_input("Age", min_value=18, max_value=80, value=35)
num_person = st.number_input("Number of Persons Visiting", min_value=1, max_value=6, value=2)
num_trips = st.number_input("Number of Trips", min_value=0, max_value=10, value=1)
with col2:
monthly_income = st.number_input("Monthly Income", min_value=10000, max_value=100000, value=50000)
num_children = st.number_input("Number of Children Visiting", min_value=0, max_value=5, value=0)
passport = st.selectbox("Has Passport?", [0, 1], format_func=lambda x: "Yes" if x else "No")
col3, col4 = st.columns(2)
with col3:
own_car = st.selectbox("Owns Car?", [0, 1], format_func=lambda x: "Yes" if x else "No")
pitch_satisfaction = st.slider("Pitch Satisfaction Score", 1, 5, 3)
num_followups = st.number_input("Number of Follow-ups", min_value=0, max_value=10, value=2)
with col4:
duration_pitch = st.slider("Duration of Pitch (days)", 1, 30, 7)
preferred_star = st.slider("Preferred Property Star Rating", 1, 5, 3)
# Categorical features
st.sidebar.markdown("---")
st.sidebar.subheader("Demographics & Preferences")
city_tier = st.sidebar.selectbox("City Tier", [1, 2, 3], format_func=lambda x: f"Tier {x}")
occupation = st.sidebar.selectbox("Occupation", [
"Employee", "Self Employed", "Housewife", "Student", "Business"
])
gender = st.sidebar.selectbox("Gender", ["Male", "Female"])
marital_status = st.sidebar.selectbox("Marital Status", [
"Single", "Married", "Divorced"
])
designation = st.sidebar.selectbox("Designation", [
"Executive", "Manager", "Senior Manager", "Director"
])
product_pitched = st.sidebar.selectbox("Product Pitched", [
"Basic", "Standard", "Premium", "Deluxe"
])
type_of_contact = st.sidebar.selectbox("Type of Contact", ["Email", "Self Enquiry"])
# Prepare input data with correct column names and proper encoding
input_data_dict = {
'Age': age,
'NumberOfPersonVisiting': num_person,
'PreferredPropertyStar': preferred_star,
'NumberOfTrips': num_trips,
'Passport': passport,
'OwnCar': own_car,
'NumberOfChildrenVisiting': num_children,
'MonthlyIncome': monthly_income,
'PitchSatisfactionScore': pitch_satisfaction,
'NumberOfFollowups': num_followups,
'DurationOfPitch': duration_pitch,
'TypeofContact': type_of_contact,
'CityTier': city_tier,
'Occupation': occupation,
'Gender': gender,
'MaritalStatus': marital_status,
'Designation': designation,
'ProductPitched': product_pitched
}
input_df = pd.DataFrame([input_data_dict])
# Main prediction section
st.markdown("---")
col1, col2, col3 = st.columns([2, 1, 1])
with col1:
summary_df = pd.DataFrame({
"Feature": ["Age", "Income", "City Tier", "Product", "Trips"],
"Value": [
f"{age} yrs",
f"₹{monthly_income:,}",
f"Tier {city_tier}",
product_pitched,
str(num_trips)
]
})
st.dataframe(summary_df, use_container_width=True)
# st.subheader("Customer Summary")
# summary_df = pd.DataFrame([{
# "Feature": ["Age", "Income", "City Tier", "Product", "Trips"],
# "Value": [f"{age} yrs", f"₹{monthly_income:,}", f"Tier {city_tier}", product_pitched, num_trips]
# }])
# st.dataframe(summary_df, use_container_width=True)
if st.button("Predict Package Purchase", type="primary", use_container_width=True):
with st.spinner("Generating prediction..."):
try:
# Get prediction probabilities
prediction_proba = model.predict_proba(input_df)[:, 1][0]
prediction = model.predict(input_df)[0]
# Results
st.subheader("Prediction Results")
col_a, col_b = st.columns(2)
with col_a:
probability = prediction_proba * 100
st.metric(
label="Purchase Probability",
value=f"{probability:.1f}%",
delta=f"{probability:.1f}% chance"
)
with col_b:
result = "**Will Purchase**" if prediction == 1 else "**Won't Purchase**"
st.markdown(result)
# Confidence bar
st.progress(prediction_proba)
# Recommendation
if prediction == 1:
st.success("**High conversion potential!** Prioritize follow-up calls and personalized offers.")
else:
st.warning("**Low conversion likelihood.** Consider alternative products or nurturing strategy.")
except Exception as e:
st.error(f"Prediction failed: {str(e)}")
st.info("Ensure all input features match your training data exactly.")
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