prediction.py

import streamlit as st
import pandas as pd
import numpy as np
import pickle
import json

#Load all files

with open('list_cat_cols.txt', 'r') as file_1:
  list_cat_cols = json.load(file_1)

with open('list_num_cols.txt', 'r') as file_2:
  list_num_cols = json.load(file_2)

with open('model_scaler.pkl', 'rb') as file_3:
  scaler = pickle.load(file_3)

with open('model_encoder.pkl', 'rb') as file_4:
  encoder = pickle.load(file_4)

with open('model_lin_reg.pkl', 'rb') as file_5:
  model_lin_reg = pickle.load(file_5)
  
def run():  
    # Membuat Form
    with st.form(key='Form FIFA 2022'):
        name = st.text_input('Name', value='')
        age  = st.number_input('Age', min_value=16, max_value=60, value=25, step=1, help='Usia Pemain')
        weight = st.number_input('Weight', min_value=50, max_value=150, value=70)
        height = st.slider('Height', 50, 250, 180)
        price = st.number_input('Price', min_value=0, max_value=10000000, value=0)
        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)
        dribling = st.number_input('Dribling', 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)
        
        submited = st.form_submit_button('Predict')
        
    data_inf = {
        'Name'  : name,
        'Age'   : age,
        'Height': height,
        'Weight': weight,
        'Price' : price,
        'AttackingWorkRate' : attacking_work_rate,
        'DefensiveWorkRate' : defensive_work_rate,
        'PaceTotal' : pace,
        'ShootingTotal' : shooting,
        'PassingTotal'  : passing,
        'DribblingTotal': dribling,
        'DefendingTotal': defending,
        'PhysicalityTotal' : physicality
    }

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

    if submited:  
        #Split between numerical columns and categorical columns
        data_inf_num = data_inf[list_num_cols]
        data_inf_cat = data_inf[list_cat_cols]
        #Feature scaling and feature encoding
        data_inf_num_scaled = scaler.transform(data_inf_num)
        data_inf_cat_encoded = encoder.transform(data_inf_cat)
        data_inf_final = np.concatenate([data_inf_num_scaled, data_inf_cat_encoded], axis = 1)
        #Predict usiing linear regression
        y_pred_inf = model_lin_reg.predict(data_inf_final)
        st.write('# Rating : ', str(int(y_pred_inf)))

if __name__ == '__main__':
    run()