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
# Updated crypto tickers
tickers = ['BTC-USD', 'ETH-USD', 'BNB-USD', 'XRP-USD', 'ADA-USD', 'DOT-USD', 'DOGE-USD', 'SOL-USD']

# Image options for each crypto ticker
image_options = {
  'BTC-USD': 'BTC-USD.jpg',
  'ETH-USD': 'ETH-USD.jpg',
  'BNB-USD': 'BNB-USD.jpg',
  'XRP-USD': 'XRP-USD.jpg',
  'ADA-USD': 'ADA-USD.jpg',
  'DOT-USD': 'DOT-USD.jpg',
  'DOGE-USD': 'DOGE-USD.jpg',
  'SOL-USD': 'SOL-USD.jpg',
  'DOT-USD': 'DOT-USD.jpg',
}

# Crypto names for each ticker
stock_names = {
  'BTC-USD': 'Bitcoin',
  'ETH-USD': 'Ethereum',
  'BNB-USD': 'Binance Coin',
  'XRP-USD': 'XRP',
  'ADA-USD': 'Cardano',
  'DOT-USD': 'Polkadot',
  'DOGE-USD': 'Dogecoin',
  'SOL-USD': 'Solana',
  'DOT-USD': 'Polkadot',
}


st.title("Crypto 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"Crypto 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://crypto.quu.fr")