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_col = json.load(file_1)

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

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

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

with open('model_lin_reg.pkl', 'rb') as file_5:
  model_lin_reg = pickle.load(file_5)

def run():
  
  # Make form to fill data
    with st.form('form_fifa_2022'):
        # Use text_input
        name = st.text_input('Name: ', value = '')
        # Use number_input
        age = st.number_input('Age: ', value = 25, min_value = 15, max_value = 60, help = 'Fill with player age')
        height = st.number_input('Height', value = 170, min_value = 150, max_value = 250)
        # Use a slider
        weight = st.slider('Weight: ', min_value = 50, max_value = 100, value = 70)
        # Price
        price  = st.number_input('Price: ', value = 0, min_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)

        pace = st.number_input('Pace: ', min_value =0, max_value = 100, value = 10)
        shooting = st.number_input('Shooting: ', min_value =0, max_value = 100, value = 10)
        passing = st.number_input('Passing: ', min_value =0, max_value = 100, value = 10)
        dribbling = st.number_input('Dribbling: ', min_value =0, max_value = 100, value = 10)
        defending = st.number_input('Defending: ', min_value =0, max_value = 100, value = 10)
        physicality = st.number_input('Physicality: ', min_value =0, max_value = 100, value = 10)

        # Define submit button form
        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
        data_inf_num = data_inf[list_num_col]
        data_inf_cat = data_inf[list_cat_col]

        # Scaling, Encoding, Concatenate
        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
        y_pred_inf = model_lin_reg.predict(data_inf_final)

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

if __name__ == '__main__':
    run()