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import streamlit as st
import pandas as pd
import numpy as np
import pickle
import json
# Load datanya
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(key='Form Parameter'):
name = st.text_input('Name', value='')
age = st.number_input('Age', min_value=0, max_value=70, step=1)
weight = st.number_input('Weight', min_value=0, max_value=150, step=1)
height = st.slider('Height', 150, 225, 165)
price = st.number_input('Price', min_value=0, max_value=1000000000, step=1000)
st.markdown('---')
AttackingWorkRate = st.selectbox('Attacking Work Rate', ('Low','Medium','High'),index=0)
DefensiveWorkRate = st.selectbox('Defensive Work Rate', ('Low','Medium','High'),index=1)
st.markdown('---')
pace = st.number_input('Pace', min_value=0, max_value=100, step=1)
shooting = st.number_input('Shooting Total', min_value=0, max_value=100, step=1)
passing = st.number_input('Passing', min_value=0, max_value=100, step=1)
dribbling = st.number_input('Dribbling', min_value=0, max_value=100, step=1)
defending = st.number_input('Defending', min_value=0, max_value=100, step=1)
physicality = st.number_input('Physicality', min_value=0, max_value=100, step=1)
submitted = st.form_submit_button('Predict')
data_inf = {'Name': name,
'Age': age,
'Height': height,
'Weight': weight,
'Price': price,
'AttackRate':AttackingWorkRate,
'DefenseRate': DefensiveWorkRate,
'PaceTotal': pace,
'ShootingTotal':shooting,
'PassingTotal':passing ,
'DribblingTotal':dribbling,
'DefendingTotal': defending,
'PhysicalityTotal': physicality}
df = pd.DataFrame([data_inf])
st.dataframe(df)
if submitted:
# split antara num and cat column
data_inf_num = df[list_num_cols]
data_inf_cat = df[list_cat_cols]
# scaling and encoding
data_inf_num_scaled = model_scaler.transform(data_inf_num)
data_inf_cat_encoded = model_encoder.transform(data_inf_cat)
# merge
data_inf_final = np.concatenate([data_inf_num_scaled, data_inf_cat_encoded], axis=1)
# predict
y_pred_inf = model_lin_reg.predict(data_inf_final)
st.write('# Rating: ',str(np.round(y_pred_inf))[1:3])
if __name__ == '__main__':
run() |