prediction.py

import streamlit as st
from PIL import Image
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import joblib

# Load All Files
with open('model.pkl', 'rb') as file_1:
  model = joblib.load(file_1)

with open('pipeline.pkl', 'rb') as file_2:
  preprocessor = joblib.load(file_2)

def run():
    # Membuat Form
    with st.form(key='form_parameters'):
        income = st.number_input('Average Income', min_value=0, max_value=150000, value=50000, step=1000)
        age = st.number_input('House Age', min_value=0, max_value=50, value=5, step=1)
        rooms = st.number_input('Number of Rooms', min_value=0, max_value=25, value=5, step=1)
        bedrooms = st.number_input('Number of Bedrooms', min_value=0, max_value=10, value=3, step=1)
        population = st.number_input('Area Population', min_value=0, max_value=100000, value=50000, step=1000)
        st.markdown('---')

        submitted = st.form_submit_button('Predict')

    data_inf = {
        'Income': income, 
        'Age': age,
        'Rooms': rooms,
        'Bedrooms': bedrooms,
        'Population': population
    }

    data_inf = pd.DataFrame([data_inf])
    st.dataframe(data_inf)

    if submitted:
        # Feature Preprocessing
        X_inf = preprocessor.transform(data_inf)

        # Predict using Linear regression
        y_pred_inf = model.predict(X_inf)

        st.write('# House Price : ', str(int(y_pred_inf)))

if __name__ == '__main__':
    run()