import streamlit as st
from datetime import date
import yfinance as yf
from prophet import Prophet
from prophet.plot import plot_plotly
from plotly import graph_objs as go

START = "2015-01-01"
TODAY = date.today().strftime("%Y-%m-%d")
st.title("Stock Price Prediction App")

stocks = ("AAPL", "GOOG", "MSFT", "AMZN")
selected_stock = st.selectbox("Select stock for prediction", stocks)
n_years = st.slider("Years of prediction", 1, 4)
period = n_years * 365

@st.cache_data
def load_data(ticker):
    data = yf.download(ticker, START, TODAY)
    data.reset_index(inplace=True)
    return data

data_load_state = st.text("Loading data...")
data = load_data(selected_stock)
data_load_state.text("Loading data...done!")
st.subheader("Raw data")
data.columns = data.columns.droplevel(1)
st.write(data.tail())

#PLOT Raw Data
def plot_raw_data():
    fig = go.Figure()
    fig.add_trace(go.Scatter(x=data["Date"], y=data["Open"], 
        name="Stock Open"))
    fig.add_trace(go.Scatter(x=data["Date"], y=data["Close"], 
        name="Stock Close"))
    fig.layout.update(title_text="Time Series Data", 
        xaxis_rangeslider_visible=True)
    st.plotly_chart(fig)
plot_raw_data()

#PREDICTION AVEC PROPHET
df_train = data[["Date", "Close"]]
df_train = df_train.rename(columns={"Date": "ds", "Close": "y"})
m = Prophet()
m.fit(df_train)
future = m.make_future_dataframe(periods=period)
forecast = m.predict(future)
#Show and plot forecast
st.subheader("Forecast data")
st.write(forecast.tail())
st.write(f"Forecast plot for {n_years} years")
fig1 = plot_plotly(m, forecast)
st.plotly_chart(fig1)

st.write("Forecast components")
fig2 = m.plot_components(forecast)
st.write(fig2)