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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='from_fifa_2022'):
name = st.text_input('Full Name', value='')
age = st.number_input('Age', min_value=16, max_value=50, 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=100000000, value=0)
st.markdown('---')
attacking_work_rate = st.selectbox('Attacking Work Rate', ('Low', 'Medium', 'High'), index=1)
defensive_work_rate = st.selectbox('Defensive Work Rate', ('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() |