app.py

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

# Download and load the model

# replace with your repoid
model_path = hf_hub_download(repo_id="varun109/Tourism-Package-Prediction", filename="best_tourism_package_prediction_model_v1.joblib")

model = joblib.load(model_path)

# Streamlit UI for Machine Failure Prediction
st.title("Tourism Package Prediction App")
st.write("""
This application predicts whether a customer will purchase the newly introduced Wellness Tourism Package before contacting them.
Please enter the Customer data below to get a prediction.
""")

# User input
# Type = st.selectbox("Machine Type", ["H", "L", "M"])
# air_temp = st.number_input("Air Temperature (K)", min_value=250.0, max_value=400.0, value=298.0, step=0.1)
# process_temp = st.number_input("Process Temperature (K)", min_value=250.0, max_value=500.0, value=324.0, step=0.1)
# rot_speed = st.number_input("Rotational Speed (RPM)", min_value=0, max_value=3000, value=1400)
# torque = st.number_input("Torque (Nm)", min_value=0.0, max_value=100.0, value=40.0, step=0.1)
# tool_wear = st.number_input("Tool Wear (min)", min_value=0, max_value=300, value=10)

age = st.slider("Age", 18, 90, 30)
num_persons_visiting = st.slider("Number of People Visiting", 1, 10, 1)
num_trips = st.slider("Number of Trips Annually", 0, 20, 2)

num_children_visiting = st.slider("Number of Children Visiting (under 5)", 0, 5, 0)
monthly_income = st.slider("Monthly Income", 10000, 200000, 50000, step=1000)




# Assemble input into DataFrame
input_data = pd.DataFrame([{

    # 'Air_temperature': air_temp,
    # 'Process_temperature': process_temp,
    # 'Rotational_speed': rot_speed,
    # 'Torque': torque,
    # 'Tool_wear': tool_wear,
    # 'Type': Type

    'Age': age,
    'NumberOfPersonVisiting' : num_persons_visiting,
    'NumberOfTrips' : num_trips ,
    'NumberOfChildrenVisiting' : num_children_visiting,
    'MonthlyIncome' : monthly_income

}])


if st.button("Tourism Package Prediction"):
    prediction = model.predict(input_data)[0]
    result = "Tourism Package Prediction" if prediction == 1 else "No Prediction"
    st.subheader("Prediction Result:")
    st.success(f"The model predicts: **{result}**")