prediction.py

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

# Load All Files

with open('model_lin_reg.pkl', 'rb') as file_1:
  model_lin_reg = pickle.load(file_1)

with open('model_scaler.pkl', 'rb') as file_2:
  model_scaler = pickle.load(file_2)

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

with open('list_num_cols.txt', 'r') as file_4:
  list_num_cols = json.load(file_4)

with open('list_cat_cols.txt', 'r') as file_5:
  list_cat_cols = json.load(file_5)

def run():
  with st.form(key='from_fifa_2022'):
      name = st.text_input('Full Name', value='')
      age = st.number_input('Age', min_value=16, max_value=50, 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, 170)
      price = st.number_input('Price', min_value=0, max_value=100000000, value=0)
      st.markdown('---')

      attacking_work_rate = st.selectbox('Attacking Work Rate', ('Low', 'Medium', 'High'), index=1)
      defensive_work_rate = st.selectbox('Defensive Work Rate', ('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)

      submitted = 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': dribbling,
      'DefendingTotal': defending,
      'PhysicalityTotal': physicality
  }

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

  if submitted:
    # 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 = model_scaler.transform(data_inf_num)
    data_inf_cat_encoded = model_encoder.transform(data_inf_cat)
    data_inf_final = np.concatenate([data_inf_num_scaled, data_inf_cat_encoded], axis=1)

    # Predict using Linear Regression
    y_pred_inf = model_lin_reg.predict(data_inf_final)

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

if __name__=='__main__':
    run()