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BBRI.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e0361196e71c11258c8c5e8514369ad99d779c7a5319945650bd52225aef7c76
+size 2919964

HMSP.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b8824250763e540e72f8510fefe2e408d5f48d64a8344b7fe2788bc6af455f1e
+size 2919964

JSMR.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:772c7e0bcea6c29e9b92e8f4dcd0bc38084c6547f08636fc16149c188555086a
+size 2919964

PGAS.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:03b4348a6864f7bc763220084e7c644738e666d8e374c38ab6f75285c862ced6
+size 2919964

Procfile.txt

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+web: sh setup.sh && streamlit run app.py

WSKT.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2d94d6e52735a9371c291653c5ef9f6251480121804e5b666443e5d646b299ea
+size 2919964

app.py

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+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("""
+# Simple Stock Price App
+
+Shown are the stock **closing price** and **volume**.
+""")
+
+def user_input_features() :
+    stock_symbol = st.sidebar.selectbox('Symbol',('BBRI', 'HMSP', 'WSKT', 'JSMR', 'PGAS'))
+    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)

requirements.txt

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+keras==2.9.0
+matplotlib==3.5.2
+numpy==1.23.1
+pandas==1.4.3
+streamlit==1.12.0
+yfinance==0.1.74
+tensorflow

setup.sh

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+mkdir -p ~/.streamlit/
+
+echo "\
+[general]\n\
+email = \"[email protected]\"\n\
+" > ~/.streamlit/credentials.toml
+
+echo "\
+[server]\n\
+headless = true\n\
+enableCORS=false\n\
+port = $PORT\n\
+" > ~/.streamlit/config.toml