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('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=1)
      defensive_work_rate = st.radio('DefensiveWorkRate',('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()