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=form_fifa_2022') :
      name = st.text_input('Full 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', 150,250,170)
      price = st.number_input ('price',min_value=0, max_value=10000000000, value= 0)
      st.markdown('---')

      attacking_work_rate = st.radio('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()