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import streamlit as st
import pandas as pd
import numpy as np
import pickle
import json
#Load All files
#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)
def run():
with st.form('form_fifa_2022'):
#nama, value untuk default value
name = st.text_input('Name', value = ' ')
#age, min_value untuk minimum nilai yang bisa diisi, max_value maksimum nilai yang bisa diisi
age = st.number_input('Age', value = 25, min_value = 15, max_value = 60, help = 'isi dengan usia pemain')
#height
height = st.number_input('Height', value = 170, min_value = 100, help = 'in cm')
weight = st.slider('Weight', value = 70, min_value = 50, max_value = 150)
price = st.number_input('Price', value = 0)
st.markdown('---')
#index untuk default value di selctbox/radio button
attacking_work_rate = st.selectbox('Attacking Work Rate', ('Low', 'Medium', 'High'), index = 1)
defensive_work_rate = st.radio('Defensive Work Rate', ('Low', 'Medium', 'High'), index = 1)
pace = st.number_input('Pace', min_value = 0, max_value = 100, value = 50)
shooting = st.number_input('Shooting Score', min_value = 0, max_value = 100, value = 50)
passing = st.number_input('Passing Score', min_value = 0, max_value = 100, value = 50)
dribbling = st.number_input('Dribbling Score', min_value = 0, max_value = 100, value = 50)
defending = st.number_input('Defending Score', min_value = 0, max_value = 100, value = 50)
physicality = st.number_input('Pysicality Score', min_value = 0, max_value = 100, value = 50)
#bikin submit button form
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 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)))
if __name__ == '__main__':
run()
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