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| import streamlit as st | |
| import pandas as pd | |
| import seaborn as sns | |
| import matplotlib.pyplot as plt | |
| import plotly.express as px | |
| from PIL import Image | |
| import numpy as np | |
| import joblib | |
| import json | |
| # 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('---') | |
| #attacking_work_rate = st.selectbox('AttackingWorkrate', ('Low', 'Medium', 'High'), index=1) | |
| #defensive_work_rate = st.selectbox('DefensiveWorkRate', ('Low', 'Medium', 'High'), index=1) | |
| st.markdown('---') | |
| #pace = st.number_input('Pace', min_value=0, max_value=100, value=50) | |
| #shooting = st.number_input('Shooting', min_value=0, max_value=100, value=50) | |
| #passing = st.number_input('Passing', min_value=0, max_value=100, value=50) | |
| #dribbling = st.number_input('Dribbling', min_value=0, max_value=100, value=50) | |
| #defending = st.number_input('Defending', min_value=0, max_value=100, value=50) | |
| #physicality = st.number_input('Physicality', min_value=0, max_value=100, value=50) | |
| 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() |