| import pandas as pd | |
| import streamlit as st | |
| from model import Model | |
| from plots import Plots | |
| from stock_data_loader import StockDataLoader | |
| class StockModelPage: | |
| def __init__(self): | |
| self.tickers = ['NVDA', 'AAPL', 'GOOGL', 'MSFT', 'AMZN'] | |
| self.setup_sidebar() | |
| def setup_sidebar(self): | |
| self.ticker = st.sidebar.selectbox('Choose Stock Ticker', self.tickers) | |
| self.start_date = st.sidebar.date_input('Start Date', value=pd.to_datetime('2010-01-01')) | |
| self.end_date = st.sidebar.date_input('End Date', value=pd.to_datetime('today')) | |
| self.load_button_clicked = st.sidebar.button('Load Data') | |
| def load_data(self): | |
| if self.load_button_clicked: | |
| loader = StockDataLoader(self.ticker, self.start_date, self.end_date) | |
| st.session_state['stock_data'] = loader.get_stock_data() | |
| st.write("--------------------------------------------") | |
| st.write(f"Data for {self.ticker} from {self.start_date} to {self.end_date} loaded successfully!") | |
| def handle_model_training(self): | |
| if 'stock_data' in st.session_state: | |
| stock_data = st.session_state['stock_data'] | |
| if st.button('Train Model'): | |
| st.write("Training Model...") | |
| model = Model(stock_data) | |
| model.train_lstm() | |
| predictions = model.make_predictions() | |
| future_predictions = model.forecast_future(days=5) | |
| self.plot_predictions(stock_data, predictions, future_predictions) | |
| else: | |
| st.write("Click the button above to train the model.") | |
| else: | |
| st.write("--------------------------------------------") | |
| st.write("Please load data before training the model.") | |
| def plot_predictions(self, stock_data, predictions, future_predictions): | |
| plot_instance = Plots(stock_data) | |
| plot_instance.plot_predictions(predictions, future_predictions) | |
| def run(self): | |
| st.write("--------------------------------------------") | |
| st.write(f'<div style="font-size:50px">🤖 Real-Time Stock Prediction', unsafe_allow_html=True) | |
| self.load_data() | |
| self.handle_model_training() |