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import streamlit as st
import pandas as pd
import numpy as np
import pickle
import json
# Load all file
# Load the model
with open('model_lin_reg.pkl', 'rb') as file_1:
loaded_model = pickle.load(file_1)
# Load the scaler model
with open('model_scaler.pkl', 'rb') as file_2:
model_scaler = pickle.load(file_2)
# Load the list of numerical columns
with open('list_num_cols.txt', 'r') as file_3:
list_num_cols = json.load(file_3)
# Load the list of categorical columns
with open('list_cat_cols.txt', 'r') as file_4:
list_cat_cols = json.load(file_4)
def run():
with st.form(key='from_harga_saham_BBCA'):
open = st.number_input('open', min_value=0, max_value=100000, value=7775)
low = st.number_input('low', min_value=0, max_value=100000, value=6060)
high = st.number_input('high', min_value=0, max_value=100000, value=8050)
volume = st.number_input('volume', min_value=0, max_value=200000000, value=135519500)
st.markdown('---')
Tahun = st.number_input('Tahun', min_value=2020, max_value=2023, value=2020)
Bulan = st.number_input('Bulan', min_value=1, max_value=12, value=7)
Hari = st.number_input('Hari', min_value=1, max_value=31, value=28)
st.markdown('---')
Quarter_End = st.selectbox('quarter_end', ['0','1'], help='Akhir Quartal Atau Bukan')
Open_close = st.number_input('open-close', min_value=0, max_value=10000, value=50, help='selisih harga pembukaan dengan harga penutupan sebelumnya')
High_low = st.number_input('low-high', min_value=0, max_value=10000, value=-1990, help='selisih harga pembukaan dengan harga penutupan sebelumnya')
G_L = st.selectbox('G/L', ['0','1'], help='0 untuk harga pembukaan lebih tinggi dari harga sebelumnya dan 1 kebalikannya')
submitted = st.form_submit_button('Prediksi Harga Penutupan')
data_inf = {
'open': 7780,
'low': 7760,
'high': 8050,
'volume': 155519500,
'Tahun': 2020,
'Bulan': 7,
'Hari': 28,
'quarter_end': 0,
'open-close': 0,
'low-high': -1990,
'G/L': 0
}
data_inf = pd.DataFrame([data_inf])
st.dataframe(data_inf)
if submitted:
# Split antara kolom numerik dan kategorik
data_inf_num = data_inf[list_num_cols]
data_inf_cat = data_inf[list_cat_cols]
# Feature Scalling
data_inf_num_scaled = model_scaler.transform(data_inf_num)
data_inf_final = np.concatenate([data_inf_num_scaled], axis=1)
# Prediksi menggunakan Linear regression
y_pred_inf = loaded_model.predict(data_inf_final)
harga_saham = y_pred_inf[0]
st.write('# Harga Penutupan : Rp', str(int(harga_saham )))
if __name__== '__main__':
run() |