import pickle
import streamlit as st
import json
import pandas as pd
import numpy as np
import sklearn
# Load All Files
def run():
  with st.form(key='formfifa2022'):
      name =st.text_input('Name', value='')
      age =st.number_input('Age', min_value=16, max_value=60,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=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)
      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)
      Passing =st.number_input('Passing', min_value=0, max_value=100, value=50)
      Shooting =st.number_input(' Shooting ', min_value=0, max_value=100, value=50)
      Dribbling =st.number_input('Dribbling', min_value=0, max_value=100, value=50)
      submitted =st.form_submit_button('Predict')

  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)

  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])
  data_inf

  data_inf_num = data_inf[list_num_cols]
  data_inf_cat = data_inf[list_cat_cols]
  data_inf_num

  # 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)

  y_pred_inf = model_lin_reg.predict(data_inf_final)
  st.write('# Rating: ', str(int(y_pred_inf)))

if __name__== '__main__':
    run()