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

def run():

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

    with open('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)




    with st.form('form_input'):
        st.subheader('Personal Info', divider='orange')
        name = st.text_input('Name', value='')
        age = st.number_input('Age', min_value=15, max_value=50, value=20, step=1, help='Usia Pemain')
        weight = st.number_input('Weight', min_value=50, max_value=150, step=1, help='Berat Pemain')
        height = st.slider('Height', 150, 250, 180)
        price = st.number_input('Harga', min_value=0, max_value=1000000000, value=0)
        st.markdown('---')

        st.subheader('Work Rate', divider='orange')
        attacking = st.selectbox('Attacking Work Rate', ('Low', 'Medium', 'High'), index=0)
        defensive = st.selectbox('Defensive Work Rate', ('Low', 'Medium', 'High'), index=1)
        st.markdown('---')

        st.subheader('Skills', divider='orange')
        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,
        'DefensiveWorkRate': defensive,
        'PaceTotal': pace,
        'ShootingTotal': shooting,
        'PassingTotal': passing,
        'DribblingTotal': dribbling,
        'DefendingTotal': defending,
        'PhysicalityTotal': physicality
    }
    data_inf = pd.DataFrame([data_inf])
    st.dataframe(data_inf)

    if submitted:
        # Menghitung nilai rating

        #split between numerical and categorical columns
        data_inf_num = data_inf[list_num_col]
        data_inf_cat = data_inf[list_cat_col]

        #feature scaling and 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 reg model
        y_pred_inf = model_lin_reg.predict(data_inf_final)
        
        st.write('# Rating : ', str(int(y_pred_inf)))