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import streamlit as st
import pandas as pd
import plotly.graph_objects as go
import yfinance as yf
import matplotlib.pyplot as plt
# import numpy as np
import plotly.express as px
from st_aggrid import GridOptionsBuilder, AgGrid
from style import add_logo
hide_menu_style = """
<style>
#MainMenu{visibility: hidden;}
footer{visibility:hidden;}
</style>
"""
# page expands to full width
st.set_page_config(page_title="Predicta.oil | Explore",
layout='wide', page_icon="⛽")
st.markdown(hide_menu_style, unsafe_allow_html=True)
st.title("Explore Models")
add_logo()
# ARIMA
# slider interval
interv = st.select_slider('Select Time Series Data Interval for Prediction', options=[
'Daily', 'Weekly', 'Monthly', 'Quarterly'], value='Weekly')
# dropdown 50 60 80
st.write("Select Split")
intervals = st.selectbox(
"Select Interval:", ('80', '60', '50'))
# read file from select interval and dropdown split
def get_location(interv, intervals):
# location = 'Explore/ARIMA/' + interv + '/' + intervals + '.csv'
location = 'Explore/PREDICTIONS/' + interv + '/' + intervals + '.csv'
return location
location = get_location(interv, intervals)
# pagination function for aggrid
def pagination(df):
gb = GridOptionsBuilder.from_dataframe(df)
gb.configure_pagination(paginationAutoPageSize=True)
return gb.build()
# read file
file = pd.read_csv(get_location(interv, intervals))
page = pagination(file)
file.drop("Unnamed: 0", axis=1, inplace=True)
# select columns
columns = file.columns.to_list()
# st.write(columns)
selectedCols = st.multiselect("Select models", columns, default=[
"Date", "Close Prices"])
df = file[selectedCols]
st.dataframe(df)
# print(df.columns.values)
fig = go.Figure()
for idx, col in enumerate(df.columns, 0):
# print(df.iloc[1:,idx])
if col != 'Date':
fig.add_trace(go.Scatter(
x=file['Date'], y=df.iloc[1:, idx], mode='lines', name=col))
st.plotly_chart(fig, use_container_width=True)
# LSTM
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