import yfinance as yf
import datetime as datetime
from datetime import date, timedelta
import streamlit as st
import plotly.graph_objects as go
import joblib
# from autots import AutoTS


st.title('BTC AutoTS Price Prediction 📢')
st.write('This is a bitcoin price prediction time series analysis using pretrained AutoTS model that took 10 minutes.')
st.image('https://theounce.ca/wp-content/uploads/2021/02/Corinne-Doan-wallstreetbets-WSB-cannabis-2.jpg')


# DATA IMPORT
today = date.today()
d1 = today.strftime("%Y-%m-%d")
end_date = d1
d2 = date.today() - timedelta(days=1460)
d2 = d2.strftime("%Y-%m-%d")
start_date = d2

data = yf.download('BTC-USD', 
                      start=start_date, 
                      end=end_date, 
                      progress=False)
data["Date"] = data.index
data = data[["Date", "Open", "High", "Low", "Close", "Adj Close", "Volume"]]
data.reset_index(drop=True, inplace=True)

st.write("### Data of THE Last 4 Years")
st.write(data)


st.write("### BTC Price Chart")
figure = go.Figure(data=[go.Candlestick(x=data["Date"],
                                        open=data["Open"], 
                                        high=data["High"],
                                        low=data["Low"], 
                                        close=data["Close"])])
figure.update_layout(xaxis_rangeslider_visible=False)
figure.update_layout(xaxis_title='Date', yaxis_title='Close Price')
st.plotly_chart(figure)


st.write("### AutoTS Prediction")
#model = AutoTS(forecast_length=30, frequency='infer', ensemble='simple').fit(data, date_col='Date', value_col='Close', id_col=None)
#joblib.dump(model, 'autots_model.pkl')
model = joblib.load('autots_model.pkl')
prediction = model.predict()
forecast = prediction.forecast

fig = go.Figure()
fig.add_trace(go.Scatter(x=data['Date'], y=data['Close'], mode='lines', name='Original'))
fig.add_trace(go.Scatter(x=forecast.index, y=forecast['Close'], mode='lines', name='Forecast',line=dict(color='yellow')))
fig.update_layout(xaxis_title='Date', yaxis_title='Close Price')

# Display the plot in Streamlit
st.plotly_chart(fig)

st.write("### Predicted Prices by Day")
st.write(forecast)
st.write('#### Looks like a fall is ahead of us! 😱')