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

st.set_page_config(
   page_title='FIFA 2022',
   layout='wide',
   initial_sidebar_state='expanded'
)

#Load 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('encoder.pkl', 'rb') as file_3:
  encoder = pickle.load(file_3)

with open('scaler.pkl', 'rb') as file_4:
  scaler = 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, 170)
        price = st.number_input('Price', min_value=0, max_value=1000000000, value=0)
        st.markdown('---')

        attacking_work_rate = st.selectbox('AttackingWorkRate', ('Low', 'Medium', 'High'), index=0)
        defensive_work_rate = st.radio('DefensiveWorkRate', ('Low', 'Medium', 'High'), index=1)
        st.markdown('---')

        pace = st.number_input('Kecepatan Lari', 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 categorical and numerical
        data_inf_num = data_inf[list_num_cols]
        data_inf_cat = data_inf[list_cat_cols]
        
        #Scaling and Encoding
        data_inf_scaled = scaler.transform(data_inf_num)
        data_inf_encoded = encoder.transform(data_inf_cat)
        data_inf_final = np.concatenate([data_inf_scaled, data_inf_encoded], axis = 1)

        #predict using linreg
        y_pred_inf = model_lin_reg.predict(data_inf_final)

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





if __name__ == '__main__':
  run()