import yfinance as yf
import streamlit as st
import pandas as pd
import datetime

import numpy as np
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import LSTM
from keras.layers import Dense
from keras.layers import Bidirectional


st.write("""
# Indonesian LQ45 Stock Price Prediction

Shown are the stock **closing price** and **volume**.
""")

def user_input_features() :
    stock_symbol = st.sidebar.selectbox('Symbol',('ADHI', 'ADRO', 'AKRO', 'ANTM', 'ASII', 'BBCA', 'BBNI', 
    'BBRI', 'BBTN', 'BKSL', 'BMRI', 'BSDE', 'CPIN', 'ELSA', 'EXCL', 'GGRM', 'HMSP', 'ICBP', 'INCO', 'INDF',
    'INDY', 'INKP', 'INTP', 'ITMG', 'JSMR', 'KLBF', 'LPKR', 'LPPF', 'MEDC', 'MNCN', 'PGAS', 'PTBA', 'PTPP',
    'SCMA', 'SMGR', 'TLKM', 'TPIA', 'UNTR', 'UNVR', 'WIKA', 'WSKT'))
    date_start = st.sidebar.date_input("Start Date", datetime.date(2015, 5, 31))
    date_end = st.sidebar.date_input("End Date", datetime.date.today())

    tickerData = yf.Ticker(stock_symbol+'.JK')
    tickerDf = tickerData.history(period='1d', start=date_start, end=date_end)
    return tickerDf, stock_symbol

input_df, stock_symbol = user_input_features()

st.line_chart(input_df.Close)
st.line_chart(input_df.Volume)

st.write("""
# Stock Price Prediction

Shown are the stock prediction for next 20 days.
""")

n_steps = 100
n_features = 1

model = Sequential()
model.add(Bidirectional(LSTM(300, activation='relu'), input_shape=(n_steps, n_features)))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')

model.load_weights(stock_symbol + ".h5")
df = input_df.dropna(axis=0, how='any', thresh=None, subset=None, inplace=False)
df = df[df.Volume > 0]

close = df['Close'][-n_steps:].to_list()
min_in = min(close)
max_in = max(close)
in_seq = []
for i in close :
  in_seq.append((i - min_in) / (max_in - min_in))

for i in range(20) :
  x_input = np.array(in_seq[-100:])
  x_input = x_input.reshape((1, n_steps, n_features))
  yhat = model.predict(x_input, verbose=0)
  in_seq.append(yhat[0][0])

norm_res = in_seq[-20:]
res = []
for i in norm_res :
  res.append(i * (max_in - min_in) + min_in)

closepred = close[-80:]
for x in res :
  closepred.append(x)

plt.figure(figsize = (20,10))
plt.plot(closepred, label="Prediction")
plt.plot(close[-80:], label="Previous")
plt.ylabel('Price (Rp)', fontsize = 15 )
plt.xlabel('Days', fontsize = 15 )
plt.title(stock_symbol + " Stock Prediction", fontsize = 20)
plt.legend()
plt.grid()

st.pyplot(plt)