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

# Load model
with open('list_num_columns.txt', 'r') as file_1:
    list_num_cols = json.load(file_1)

with open('list_cat_columns.txt', 'r') as file_2:
    list_cat_cols = json.load(file_2)

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

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

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

def run():
    with st.form('form_fifa_2022'):
        name = st.text_input('Name', value='', help='Player name')
        age = st.number_input('Age', 
                            min_value=12, 
                            max_value=48, 
                            value=28, 
                            step=1,
                            help='Player age')
        weight = st.number_input('Weight',
                                min_value=30,
                                max_value=100,
                                value=80,
                                help='Player weight')
        height = st.number_input('Height',
                                min_value=140,
                                max_value=210,
                                value=180,
                                help='Player height')
        price = st.slider('Price', 0, 200000000, 0)
        st.markdown('---')
        defense = st.radio('Defending Work Rate',
                        ('Low','Medium','High'),
                        index=1)
        attack = st.radio('Attacking Work Rate',
                        ('Low','Medium','High'),
                        index=1)
        st.markdown('---')
        pace = st.number_input('Pace', 0, 100, 50)
        shoot = st.number_input('Shooting', 0, 100, 50)
        passing = st.number_input('Passing', 0, 100, 50)
        dribble = st.number_input('Dribbling', 0, 100, 50)
        defend = st.number_input('Defending', 0, 100, 50)
        physical = st.number_input('Phisicality', 0, 100, 50)
        st.markdown('---')
        submitted = st.form_submit_button('PREDICT')

    data_inf = {
        'Name': name,
        'Age': age,
        'Weight': weight,
        'Height': height,
        'Price': price,
        'AttackRate': attack,
        'DefenseRate': defense,
        'PaceTotal': pace,
        'ShootingTotal': shoot,
        'PassingTotal': passing,
        'DribblingTotal': dribble,
        'DefendingTotal': defend,
        'PhysicalityTotal': physical
    }
    data_inf = pd.DataFrame([data_inf])
    st.dataframe(data_inf)

    if submitted:
        # Numeric-Categoric split
        data_inf_num = data_inf[list_num_cols]
        data_inf_cat = data_inf[list_cat_cols]
        # Numeric
        data_inf_num_scaled = model_scaler.transform(data_inf_num)
        # Categoric
        data_inf_cat_encoded = model_encoder.transform(data_inf_cat)
        # Concatenate
        data_inf_final = np.concatenate([data_inf_num_scaled,data_inf_cat_encoded], axis=1)
        # Predict
        y_inf_pred = model_lin_reg.predict(data_inf_final)
        # Show prediction
        st.write('# Rating: ', str(int(y_inf_pred)))

if __name__ == '__main__':
    run()