app.py

import streamlit as st
import pandas as pd
import joblib

with open('model_iterative.pkl', 'rb') as model_file:
    model = joblib.load(model_file)

def predict_total_order(week_of_month, day_of_week, order_type_a, order_type_b, order_type_c):

    input_data = {
        'Week of the month': week_of_month,
        'Day of the week': day_of_week,
        'Order type A': order_type_a,
        'Order type B': order_type_b,
        'Order type C': order_type_c,
    }

    input_df = pd.DataFrame([input_data])
    total_order_prediction = model.predict(input_df)
    return total_order_prediction

def run():
    st.title('Total Order Prediction')
    st.subheader('Input Features')

    week_of_month = st.number_input('Week of the month', min_value=0, max_value=4, step=1)
    day_of_week = st.number_input('Day of the week', min_value=0, max_value=7, step=1)
    order_type_a = st.number_input('Order type A', value=0.0)
    order_type_b = st.number_input('Order type B', value=0.0)
    order_type_c = st.number_input('Order type C', value=0.0)

    if st.button('Predict Total Order'):
        total_order_prediction = predict_total_order(week_of_month, day_of_week, order_type_a, order_type_b, order_type_c)
        st.success(f'Predicted Total Order: {total_order_prediction[0]:.2f}')

if __name__ == '__main__':
    run()