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()