import pandas as pd
import mplfinance as mpf
from io import BytesIO
import base64
import matplotlib.pyplot as plt

def create_mplfinance_chart(df, ticker, predictions=None):
    """
    Creates a custom mplfinance candlestick chart and returns it as a base64 encoded image.
    Implements the exact layout: Candlestick + Volume + MACD + Stochastic.
    """
    if df.empty:
        return None

    # Define style - Yahoo style as requested
    mc = mpf.make_marketcolors(
        up='#00ff00', down='#ff0000',  # Green/Red candles
        wick='black',
        edge='black',
        volume='#00bfff',
        inherit=True
    )

    s = mpf.make_mpf_style(
        base_mpf_style='yahoo',
        marketcolors=mc,
        facecolor='white',
        edgecolor='black',
        gridcolor='lightgray',
        gridstyle='-',
        figcolor='white',
        rc={'axes.labelcolor': 'black',
            'xtick.color': 'black',
            'ytick.color': 'black',
            'figure.titlesize': 16,
            'axes.titlesize': 14,
            'axes.titleweight': 'bold'}
    )

    # Define panels: [Candlestick, Volume, MACD, Stochastic]
    apds = []
    
    # MACD Panel (Panel 2 - index 1 for addplot)
    # MACD Line
    apds.append(
        mpf.make_addplot(df['MACD'], color='#606060', panel=2, ylabel='MACD', secondary_y=False)
    )
    # Signal Line
    apds.append(
        mpf.make_addplot(df['MACD_signal'], color='#1f77b4', panel=2, secondary_y=False)
    )
    # Positive Histogram Bars
    apds.append(
        mpf.make_addplot(df['MACD_bar_positive'], type='bar', color='#4dc790', panel=2, width=0.8)
    )
    # Negative Histogram Bars
    apds.append(
        mpf.make_addplot(df['MACD_bar_negative'], type='bar', color='#fd6b6c', panel=2, width=0.8)
    )
    
    # Stochastic Panel (Panel 3 - index 2 for addplot)
    apds.append(
        mpf.make_addplot(df[['%D', '%SD', 'UL', 'DL']], panel=3, ylabel='Stoch (14,3)', ylim=[0, 100])
    )

    # Prediction overlay on main chart
    if predictions is not None and predictions.any():
        last_date = df.index[-1]
        future_index = pd.date_range(start=last_date, periods=len(predictions) + 1, freq=df.index.freq or 'D')[1:]
        future_series = pd.Series(predictions, index=future_index)
        
        apds.append(
            mpf.make_addplot(future_series, color='blue', linestyle='-.', width=2, marker='o', markersize=4)
        )

    # Plotting
    fig, axes = mpf.plot(
        df,
        type='candle',
        style=s,
        title=f'{ticker} Price Chart and Analysis',
        ylabel='Price (USD)',
        volume=True,
        addplot=apds,
        mav=(5, 20),  # Moving averages as requested
        figratio=(16, 9),
        figscale=1.5,
        panel_ratios=(3, 1, 3, 3),  # Ratio as requested
        returnfig=True
    )
    
    # Convert to Base64
    buf = BytesIO()
    fig.savefig(buf, format='png', bbox_inches='tight', dpi=100)
    plt.close(fig)
    image_base64 = base64.b64encode(buf.getvalue()).decode('utf-8')
    
    return f'<img src="data:image/png;base64,{image_base64}" style="width: 100%; height: auto;">'