import gradio as gr
import yfinance as yf
import plotly.graph_objects as go
from statsmodels.tsa.arima.model import ARIMA
import pandas as pd
import logging

logging.basicConfig(level=logging.INFO)

def fetch_eth_price(period):
    eth = yf.Ticker("ETH-USD")
    if period == '1d':
        data = eth.history(period="1d", interval="1m")
        predict_steps = 60  # Next 60 minutes
        freq = 'min'  # Minute frequency
    elif period == '5d':
        data = eth.history(period="5d", interval="15m")
        predict_steps = 96  # Next 24 hours
        freq = '15min'  # 15 minutes frequency
    elif period == '1wk':
        data = eth.history(period="1wk", interval="30m")
        predict_steps = 336  # Next 7 days
        freq = '30min'  # 30 minutes frequency
    elif period == '1mo':
        data = eth.history(period="1mo", interval="1h")
        predict_steps = 720  # Next 30 days
        freq = 'H'  # Hourly frequency
    else:
        return None, None, None
    
    data.index = pd.DatetimeIndex(data.index)
    data = data.asfreq(freq)  # Ensure the data has a consistent frequency
    
    # Limit the data to the last 200 points to reduce prediction time
    data = data[-200:]
    
    return data, predict_steps, freq

def make_predictions(data, predict_steps, freq):
    if data is None or data.empty:
        logging.error("No data available for prediction.")
        return pd.DataFrame(index=pd.date_range(start=pd.Timestamp.now(), periods=predict_steps+1, freq=freq)[1:])

    logging.info(f"Starting model training with {len(data)} data points...")
    model = ARIMA(data['Close'], order=(5, 1, 0))
    model_fit = model.fit()
    logging.info("Model training completed.")
    
    forecast = model_fit.forecast(steps=predict_steps)
    future_dates = pd.date_range(start=data.index[-1], periods=predict_steps+1, freq=freq, inclusive='right')
    forecast_df = pd.DataFrame(forecast, index=future_dates[1:], columns=['Prediction'])
    
    logging.info("Predictions generated successfully.")
    return forecast_df

def plot_eth(period):
    data, predict_steps, freq = fetch_eth_price(period)
    forecast_df = make_predictions(data, predict_steps, freq)
    
    fig = go.Figure()
    fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='ETH Price'))
    fig.add_trace(go.Scatter(x=forecast_df.index, y=forecast_df['Prediction'], mode='lines', name='Prediction', line=dict(dash='dash', color='orange')))
    fig.update_layout(title=f"ETH Price and Predictions ({period})", xaxis_title="Date", yaxis_title="Price (USD)")
    
    logging.info("Plotting completed.")
    return fig

def refresh_predictions(period):
    return plot_eth(period)

with gr.Blocks() as iface:
    period = gr.Radio(["1d", "5d", "1wk", "1mo"], label="Select Period")
    plot = gr.Plot()
    refresh_button = gr.Button("Refresh Predictions and Prices")
    
    period.change(fn=plot_eth, inputs=period, outputs=plot)
    refresh_button.click(fn=refresh_predictions, inputs=period, outputs=plot)
    
iface.launch()