import gradio as gr
import pandas as pd
import plotly.graph_objects as go
from data_processor import DataProcessor
from sentiment_analyzer import SentimentAnalyzer
from model_handler import ModelHandler
from trading_logic import TradingLogic
import numpy as np

# Global instances
data_processor = DataProcessor()
sentiment_analyzer = SentimentAnalyzer()
model_handler = ModelHandler()
trading_logic = TradingLogic()

def create_chart_analysis(interval):
    """Create chart with technical indicators"""
    try:
        df = data_processor.get_gold_data(interval)
        if df.empty:
            return "No data available", None, None
        
        # Calculate indicators
        df = data_processor.calculate_indicators(df)
        
        # Create candlestick chart
        fig = go.Figure(data=[
            go.Candlestick(
                x=df.index,
                open=df['Open'],
                high=df['High'],
                low=df['Low'],
                close=df['Close'],
                name='Gold Price'
            )
        ])
        
        # Add Bollinger Bands
        fig.add_trace(go.Scatter(
            x=df.index, y=df['BB_upper'],
            line=dict(color='rgba(255,255,255,0.3)', width=1),
            name='BB Upper', showlegend=False
        ))
        fig.add_trace(go.Scatter(
            x=df.index, y=df['BB_lower'],
            line=dict(color='rgba(255,255,255,0.3)', width=1),
            fill='tonexty', fillcolor='rgba(255,255,255,0.1)',
            name='BB Lower', showlegend=False
        ))
        
        # Add moving averages
        fig.add_trace(go.Scatter(
            x=df.index, y=df['SMA_20'],
            line=dict(color='#FFD700', width=2),
            name='SMA 20'
        ))
        fig.add_trace(go.Scatter(
            x=df.index, y=df['SMA_50'],
            line=dict(color='#FFA500', width=2),
            name='SMA 50'
        ))
        
        fig.update_layout(
            title=f'Gold Futures (GC=F) - {interval}',
            yaxis_title='Price (USD)',
            xaxis_title='Date',
            template='plotly_dark',
            height=500,
            margin=dict(l=50, r=50, t=50, b=50),
            xaxis_rangeslider_visible=False,
            paper_bgcolor='rgba(0,0,0,0)',
            plot_bgcolor='rgba(0,0,0,0)',
            font=dict(color='white')
        )
        
        # KOREKSI: Panggil prepare_for_chronos sebelum prediksi
        prepared_data = data_processor.prepare_for_chronos(df)

        # Generate predictions
        predictions = model_handler.predict(prepared_data, horizon=10)

        current_price = df['Close'].iloc[-1]
        
        # Get signal
        signal, confidence = trading_logic.generate_signal(
            predictions, current_price, df
        )
        
        # Calculate TP/SL
        tp, sl = trading_logic.calculate_tp_sl(
            current_price, df['ATR'].iloc[-1], signal
        )
        
        # Create metrics display
        metrics = {
            "Current Price": f"${current_price:.2f}",
            "Signal": signal.upper(),
            "Confidence": f"{confidence:.1%}",
            "Take Profit": f"${tp:.2f}" if tp else "N/A",
            "Stop Loss": f"${sl:.2f}" if sl else "N/A",
            "RSI": f"{df['RSI'].iloc[-1]:.1f}",
            "MACD": f"{df['MACD'].iloc[-1]:.4f}",
            "Volume": f"{df['Volume'].iloc[-1]:,.0f}"
        }
        
        # Create prediction chart
        pred_fig = go.Figure()
        
        # Check if predictions are valid before plotting
        if predictions.any():
            future_dates = pd.date_range(
                start=df.index[-1], periods=len(predictions), freq='D'
            )
            
            pred_fig.add_trace(go.Scatter(
                x=future_dates, y=predictions,
                mode='lines+markers',
                line=dict(color='#FFD700', width=3),
                marker=dict(size=6),
                name='Predictions'
            ))
            
            pred_fig.add_trace(go.Scatter(
                x=[df.index[-1], future_dates[0]],
                y=[current_price, predictions[0]],
                mode='lines',
                line=dict(color='rgba(255,215,0,0.5)', width=2, dash='dash'),
                showlegend=False
            ))
        
        pred_fig.update_layout(
            title='Price Prediction (Next 10 Periods)',
            yaxis_title='Price (USD)',
            xaxis_title='Date',
            template='plotly_dark',
            height=300,
            paper_bgcolor='rgba(0,0,0,0)',
            plot_bgcolor='rgba(0,0,0,0)',
            font=dict(color='white')
        )
        
        return fig, metrics, pred_fig
        
    except Exception as e:
        return str(e), None, None

def analyze_sentiment():
    """Analyze gold market sentiment"""
    try:
        sentiment_score, news_summary = sentiment_analyzer.analyze_gold_sentiment()
        
        # Create sentiment gauge
        fig = go.Figure(go.Indicator(
            mode="gauge+number+delta",
            value=sentiment_score,
            domain={'x': [0, 1], 'y': [0, 1]},
            title={'text': "Gold Market Sentiment"},
            delta={'reference': 0},
            gauge={
                'axis': {'range': [-1, 1]},
                'bar': {'color': "#FFD700"},
                'steps': [
                    {'range': [-1, -0.5], 'color': "rgba(255,0,0,0.5)"},
                    {'range': [-0.5, 0.5], 'color': "rgba(255,255,255,0.3)"},
                    {'range': [0.5, 1], 'color': "rgba(0,255,0,0.5)"}
                ],
                'threshold': {
                    'line': {'color': "white", 'width': 4},
                    'thickness': 0.75,
                    'value': 0
                }
            }
        ))
        
        fig.update_layout(
            template='plotly_dark',
            height=300,
            paper_bgcolor='rgba(0,0,0,0)',
            plot_bgcolor='rgba(0,0,0,0)',
            font=dict(color='white')
        )
        
        return fig, news_summary
        
    except Exception as e:
        return str(e), None

def get_fundamentals():
    """Get fundamental analysis data"""
    try:
        fundamentals = data_processor.get_fundamental_data()
        
        # Create fundamentals table
        table_data = []
        for key, value in fundamentals.items():
            table_data.append([key, value])
        
        df = pd.DataFrame(table_data, columns=['Metric', 'Value'])
        
        # Create fundamentals gauge chart
        fig = go.Figure(go.Indicator(
            mode="gauge+number",
            value=fundamentals.get('Gold Strength Index', 50),
            title={'text': "Gold Strength Index"},
            gauge={
                'axis': {'range': [0, 100]},
                'bar': {'color': "#FFD700"},
                'steps': [
                    {'range': [0, 30], 'color': "rgba(255,0,0,0.5)"},
                    {'range': [30, 70], 'color': "rgba(255,255,255,0.3)"},
                    {'range': [70, 100], 'color': "rgba(0,255,0,0.5)"}
                ]
            }
        ))
        
        fig.update_layout(
            template='plotly_dark',
            height=300,
            paper_bgcolor='rgba(0,0,0,0)',
            plot_bgcolor='rgba(0,0,0,0)',
            font=dict(color='white')
        )
        
        return fig, df
        
    except Exception as e:
        return str(e), None

# Create Gradio interface
with gr.Blocks(
    theme=gr.themes.Default(primary_hue="yellow", secondary_hue="yellow"),
    title="Gold Trading Analysis & Prediction",
    css="""
        .gradio-container {background-color: #000000; color: #FFFFFF}
        .gr-button-primary {background-color: #FFD700 !important; color: #000000 !important}
        .gr-button-secondary {border-color: #FFD700 !important; color: #FFD700 !important}
        .gr-tab button {color: #FFFFFF !important}
        .gr-tab button.selected {background-color: #FFD700 !important; color: #000000 !important}
        .gr-highlighted {background-color: #1a1a1a !important}
        .anycoder-link {color: #FFD700 !important; text-decoration: none; font-weight: bold}
    """
) as demo:
    
    # Header with anycoder link
    gr.HTML("""
        <div style="text-align: center; padding: 20px;">
            <h1 style="color: #FFD700;">Gold Trading Analysis & Prediction</h1>
            <p>Advanced AI-powered analysis for Gold Futures (GC=F)</p>
            <a href="https://huggingface.co/spaces/akhaliq/anycoder" target="_blank" class="anycoder-link">Built with anycoder</a>
        </div>
    """)
    
    with gr.Row():
        interval_dropdown = gr.Dropdown(
            choices=[
                "5m", "15m", "30m", "1h", "4h", "1d", "1wk", "1mo", "3mo"
            ],
            value="1d",
            label="Time Interval",
            info="Select analysis timeframe"
        )
        refresh_btn = gr.Button("売 Refresh Data", variant="primary")
    
    with gr.Tabs():
        with gr.TabItem("投 Chart Analysis"):
            with gr.Row():
                chart_plot = gr.Plot(label="Price Chart")
                pred_plot = gr.Plot(label="Predictions")
            
            with gr.Row():
                metrics_output = gr.JSON(label="Trading Metrics")
        
        with gr.TabItem("堂 Sentiment Analysis"):
            with gr.Row():
                sentiment_gauge = gr.Plot(label="Sentiment Score")
                news_display = gr.HTML(label="Market News")
        
        with gr.TabItem("嶋 Fundamentals"):
            with gr.Row():
                fundamentals_gauge = gr.Plot(label="Strength Index")
                fundamentals_table = gr.Dataframe(
                    headers=["Metric", "Value"],
                    label="Key Fundamentals",
                    interactive=False
                )
    
    # Event handlers
    def update_all(interval):
        chart, metrics, pred = create_chart_analysis(interval)
        sentiment, news = analyze_sentiment()
        fund_gauge, fund_table = get_fundamentals()
        
        return chart, metrics, pred, sentiment, news, fund_gauge, fund_table
    
    refresh_btn.click(
        fn=update_all,
        inputs=interval_dropdown,
        outputs=[
            chart_plot, metrics_output, pred_plot,
            sentiment_gauge, news_display,
            fundamentals_gauge, fundamentals_table
        ]
    )
    
    demo.load(
        fn=update_all,
        inputs=interval_dropdown,
        outputs=[
            chart_plot, metrics_output, pred_plot,
            sentiment_gauge, news_display,
            fundamentals_gauge, fundamentals_table
        ]
    )

if __name__ == "__main__":
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False,
        show_api=True
    )