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from data_loader_functions import *
from sentiment_analysis import *
from stock_prediction import *
from datetime import datetime
import pandas as pd
import streamlit as st
from bs4 import BeautifulSoup
import requests
st.set_page_config(layout="wide")
st.title("Stock Prediction via News Sentiments")
left_column, right_column = st.columns(2)
with left_column:
all_tickers = {
"Apple":"AAPL",
"Amazon":"AMZN",
"Meta":"META",
}
st.subheader("Select Stock to Analyze")
option_name = st.selectbox('Choose a stock:', all_tickers.keys())
option_ticker = all_tickers[option_name]
'Your selection: ', option_name, "(",option_ticker,")"
st.subheader("Vader-based Sentiment Analysis")
with st.spinner("Connecting with Hopsworks..."):
df = sentiment_analysis(option_name, datetime(2023, 1, 5))
df_copy = df.copy()
df_copy = df_copy.set_index('publish_date')
st.table(df_copy.drop(['body_text', 'text_w_puncts', 'text_tokenized', 'text_w_stopwords', 'text_lemmatized', 'text_stemmed', 'text_processed', 'predicted_class'], axis=1))
daily_df = aggregate_by_date(df)
"Current sentiment:", daily_df.iloc[0]['compound']
with right_column:
st.subheader("Latest Stock Price")
with st.spinner('Loading stock data from Hopsworks...'):
stock_df = get_stock_price_from_hopsworks(option_name)
st.table(stock_df)
st.subheader("LSTM-based stock price prediction model")
get_history_plot_from_hopsworks(option_ticker)
st.image(option_name.lower() + "_stock_prediction.png", caption="Latest Model Performance")
with st.spinner("Loading LSTM model from Hopsworks.."):
date, value = model(option_ticker)
"The predicted stock value on ", date, "is", value |