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import streamlit as st
import pandas as pd
import numpy as np
import pickle
import json
import sklearn
def run():
with st.form('form-fifa_2022'):
#field nama
name = st.text_input('Name', value='')
#field umur
age = st.number_input('Age', min_value=16, max_value=60, value = 25, step=1, help='Usia pemain')
#field tinggi badan
height = st.slider('Height', 100, 250, 170)
#field weight
weight = st.number_input('Weight', 50, 150, 70)
#field price
price = st.number_input('Price', value=0)
st.markdown('-----')
#field attacking work rate
attacking_work_rate = st.selectbox('Attacking Work Rate', ('Low', 'Medium', 'High'), index=1)
#field defensive work rate
defensive_work_rate = st.selectbox('Defensive Work Rate', ('Low', 'Medium', 'High'), index=1)
#field pace total
pace_total = st.number_input('Pace', min_value=0, max_value=100, value=50)
#field shooting total
shooting_total = st.number_input('Shooting', min_value=0, max_value=100, value=50)
#filed passing total
passing_total = st.number_input('Passing', min_value=0, max_value=100, value=50)
#field dribbling total
dribbling_total = st.number_input('Dribbling', min_value=0, max_value=100, value=50)
#filed defending total
defending_total = st.number_input('Defending', min_value=0, max_value=100, value=50)
#field physicality
physicality = st.number_input('Physicality', min_value=0, max_value=100, value=50)
#bikin submit button
submitted = st.form_submit_button('Predict')
#inference
#load all 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('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)
data_inf = {
'Name' : name,
'Age' : age,
'Height' : height,
'Weight' : weight,
'Price' : price,
'AttackingWorkRate' : attacking_work_rate,
'DefensiveWorkRate' : defensive_work_rate,
'PaceTotal' :pace_total,
'ShootingTotal': shooting_total,
'PassingTotal' : passing_total,
'DribblingTotal': dribbling_total,
'DefendingTotal' :defending_total,
'PhysicalityTotal':physicality,
}
data_inf = pd.DataFrame([data_inf])
st.dataframe(data_inf)
#logic ketika predic button ditekan
if submitted:
#split between numerical and categorical collumn
data_inf_num = data_inf[list_num_cols]
data_inf_cat = data_inf[list_cat_cols]
#scalling dan 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)
#preedict using linear reg model
y_pred_inf = model_lin_reg.predict(data_inf_final)
st.write('## Rating :', str(int(y_pred_inf)))
if __name__ == '__main__':
run() |