app.py

import os

os.system('pip install streamlit_analytics')

import streamlit as st
import streamlit_analytics 

try:
    streamlit_analytics.track(load_from_json="demand.json")
except:
    pass

# Tickers to choose from
tickers = ['AAPL', 'AMZN', 'NIO', 'AMD', 'NVDA', 'META', 'PLUG', 'INTC', 'FORD', 'TSLA', 'GOOGL']

# Image options for each ticker
image_options = {
    'AAPL': 'AAPL.jpg',
    'AMZN': 'AMZN.jpg',
    'NIO': 'NIO.jpg',
    'AMD': 'AMD.jpg',
    'NVDA': 'NVDA.jpg',
    'META': 'META.jpg',
    'PLUG': 'PLUG.jpg',
    'INTC': 'INTC.jpg',
    'FORD': 'FORD.jpg',
    'TSLA': 'TSLA.jpg',
    'GOOGL': 'GOOGL.jpg',
}

# Stock names for each ticker
stock_names = {
    'AAPL': 'Apple Inc.',
    'AMZN': 'Amazon.com Inc.',
    'NIO': 'NIO Inc.',
    'AMD': 'Advanced Micro Devices Inc.',
    'NVDA': 'NVIDIA Corporation',
    'META': 'Meta Platforms Inc.',
    'PLUG': 'Plug Power Inc.',
    'INTC': 'Intel Corporation',
    'FORD': 'Ford Motor Company',
    'TSLA': 'Tesla Inc.',
    'GOOGL': 'Alphabet Inc. (Google)',
}


st.title("Stock Forecaster")

# Create a dropdown to select a ticker
with streamlit_analytics.track(save_to_json="demand.json"):
    selected_ticker = st.selectbox("Select a ticker:", tickers)

# Display the image for the selected ticker
if selected_ticker:
    image_path = image_options[selected_ticker]
    image = st.image(image_path)

    # Display the stock name for the selected ticker
    stock_name = stock_names[selected_ticker]
    st.write(f"Stock name: {stock_name}")

st.markdown(":warning: The content of this website is for educational purposes and is not a financial advice")

st.markdown(":information_source: This model has been trained on the past 6 years of data until November 22nd, 2023 for each of the selected stocks. For a more comprehensive analysis with a different date range, access to thousands of stocks, hundreds of cryptocurrencies, and more up-to-date predictions, please visit our website: https://stock.quu.fr")