import datetime
import gradio as gr
import pandas as pd
import yfinance as yf
import seaborn as sns
sns.set()
import matplotlib.pyplot as plt
import plotly.graph_objects as go

from datetime import date, timedelta
from matplotlib import pyplot as plt
from plotly.subplots import make_subplots
from pytickersymbols import PyTickerSymbols
from statsmodels.tsa.arima.model import ARIMA
from pandas.plotting import autocorrelation_plot
from dateutil.relativedelta import relativedelta

index_options = ['FTSE 100(UK)', 'NASDAQ(USA)', 'CAC 40(FRANCE)']
ticker_dict = {'FTSE 100(UK)': 'FTSE 100', 'NASDAQ(USA)': 'NASDAQ 100', 'CAC 40(FRANCE)': 'CAC 40'}
time_intervals = ['1d', '1m', '5m', '15m', '60m']

global START_DATE, END_DATE

END_DATE = date.today()
START_DATE = END_DATE - relativedelta(years=1)
FORECAST_PERIOD = 7
demo = gr.Blocks()
stock_names = []

with demo:
    d1 = gr.Dropdown(index_options, label='Please select Index...', info='Will be adding more indices later on', interactive=True)
    d2 = gr.Dropdown([], label='Please Select Stock from your selected index', interactive=True)
    d3 = gr.Dropdown(time_intervals, label='Select Time Interval', value='1d', interactive=True)
    d4 = gr.Radio(['Line Graph', 'Candlestick Graph'], label='Select Graph Type', value='Line Graph', interactive=True)
    d5 = gr.Dropdown(['ARIMA', 'Prophet', 'LSTM'], label='Select Forecasting Method', value='ARIMA', interactive=True)

    def forecast_series(series, model="ARIMA", forecast_period=7):
        predictions = list()
        if series.shape[1] > 1:
            series = series['Close'].values.tolist()
        
        if model == "ARIMA":
            for i in range(forecast_period):
                model = ARIMA(series, order=(5, 1, 0))
                model_fit = model.fit()
                output = model_fit.forecast()
                yhat = output[0]
                predictions.append(yhat)
                series.append(yhat)
        elif model == "Prophet":
            # Implement Prophet forecasting method
            pass
        elif model == "LSTM":
            # Implement LSTM forecasting method
            pass

        return predictions

    def is_business_day(a_date):
        return a_date.weekday() < 5

    def get_stocks_from_index(idx):
        stock_data = PyTickerSymbols()
        index = ticker_dict[idx]
        stocks = list(stock_data.get_stocks_by_index(index))
        stock_names = [f"{stock['name']}:{stock['symbol']}" for stock in stocks]
        return gr.Dropdown(choices=stock_names, label='Please Select Stock from your selected index', interactive=True)

    d1.input(get_stocks_from_index, d1, d2)

    def get_stock_graph(idx, stock, interval, graph_type, forecast_method):
        stock_name, ticker_name = stock.split(":")
        
        if ticker_dict[idx] == 'FTSE 100':
            ticker_name += '.L' if ticker_name[-1] != '.' else 'L'
        elif ticker_dict[idx] == 'CAC 40':
            ticker_name += '.PA'

        series = yf.download(tickers=ticker_name, start=START_DATE, end=END_DATE, interval=interval)
        series = series.reset_index()

        predictions = forecast_series(series, model=forecast_method)

        last_date = pd.to_datetime(series['Date'].values[-1])
        forecast_week = [last_date + timedelta(days=i) for i in range(1, FORECAST_PERIOD + 1) if is_business_day(last_date + timedelta(days=i))]

        forecast = pd.DataFrame({"Date": forecast_week, "Forecast": predictions})

        if graph_type == 'Line Graph':
            fig = go.Figure()
            fig.add_trace(go.Scatter(x=series['Date'], y=series['Close'], mode='lines', name='Historical'))
            fig.add_trace(go.Scatter(x=forecast['Date'], y=forecast['Forecast'], mode='lines', name='Forecast'))
        else:  # Candlestick Graph
            fig = go.Figure(data=[go.Candlestick(x=series['Date'],
                                                 open=series['Open'],
                                                 high=series['High'],
                                                 low=series['Low'],
                                                 close=series['Close'],
                                                 name='Historical')])
            fig.add_trace(go.Scatter(x=forecast['Date'], y=forecast['Forecast'], mode='lines', name='Forecast'))

        fig.update_layout(title=f"Stock Price of {stock_name}",
                          xaxis_title="Date",
                          yaxis_title="Price")

        return fig

    out = gr.Plot()
    inputs = [d1, d2, d3, d4, d5]
    d2.input(get_stock_graph, inputs, out)
    d3.input(get_stock_graph, inputs, out)
    d4.input(get_stock_graph, inputs, out)
    d5.input(get_stock_graph, inputs, out)

demo.launch()