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import streamlit as st
import pandas as pd
import os
from PIL import Image
st.title('Price Forecasting - Crude Oil Futures')
st.subheader('This page is not interactive - only for prototype purposes*')
st.text('*Due to not having access to GPU for cloud computation yet.')
st.header('Univariate Forecasting with Exogenous Predictors')
col1, col2, col3 = st.columns(3)
uni_df = pd.read_csv(os.path.join('price_forecasting_ml',
'artifacts',
'crude_oil_8998a364-2ecc-483d-8079-f04d455b4522',
'train_data.csv')).drop(columns=['Unnamed: 0'])
with col1:
horizon_uni = st.text_input('Univariate Forecasting Horizon')
with col2:
target_uni = st.multiselect('Univariate Target Variable', uni_df.columns
,default='y')
with col3:
agg_uni = st.selectbox('Univariate Data Aggregation',
['Daily', 'Weekly', 'Monthly', 'Yearly'])
st.dataframe(uni_df)
img1 = Image.open(os.path.join('price_forecasting_ml',
'artifacts',
'crude_oil_8998a364-2ecc-483d-8079-f04d455b4522',
'forecast_plot.jpg'))
st.image(img1, caption="Crude Oil Futures Price Forecasting - Univariate with Exogenous Features (Horizon = 5)")
st.markdown("---")
st.header('Multivariate Forecasting')
col4, col5, col6 = st.columns(3)
multi_df = pd.read_csv(os.path.join('price_forecasting_ml',
'artifacts',
'crude_oil_df1ce299-117d-43c7-bcd5-7ecaeac0bc89',
'train_data.csv')).drop(columns=['Unnamed: 0'])
with col4:
horizon_multi = st.text_input('Multivariate Forecasting Horizon')
with col5:
target_multi = st.multiselect('Multivariate Target Variable', multi_df.columns
,default='y')
with col6:
agg_multi = st.selectbox('Multivariate Data Aggregation',
['Daily', 'Weekly', 'Monthly', 'Yearly'])
st.dataframe(multi_df)
img2 = Image.open(os.path.join('price_forecasting_ml',
'artifacts',
'crude_oil_df1ce299-117d-43c7-bcd5-7ecaeac0bc89',
'forecast_plot.jpg'))
st.image(img2, caption="Crude Oil Futures Price Forecasting - Multivariate (Horizon = 5)")
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