app.py

# app.py

import gradio as gr
import pandas as pd
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from datetime import datetime
from geo_macro import UnifiedMarketDataDownloader, FRED_API_KEY
from feature_engineering import MarketRegimeDetector


# ==================== PROFESSIONAL COLOR SCHEME ====================
COLORS = {
    'crisis': '#DC2626',        # Red
    'recession': '#F59E0B',     # Amber
    'stagflation': '#8B5CF6',   # Purple
    'expansion': '#10B981',     # Green
    'transition': '#6B7280',    # Gray
    'primary': '#2563EB',       # Blue
    'secondary': '#64748B',     # Slate
}

REGIME_CONFIG = {
    'FINANCIAL_CRISIS': {'color': COLORS['crisis'], 'icon': '🚨'},
    'RECESSION_WARNING': {'color': COLORS['recession'], 'icon': '⚠️'},
    'STAGFLATION': {'color': COLORS['stagflation'], 'icon': '📉'},
    'EXPANSION': {'color': COLORS['expansion'], 'icon': '📈'},
    'TRANSITION': {'color': COLORS['transition'], 'icon': '🔄'},
}


# ==================== DATA CACHING ====================
_cached_df = None
_cached_dates = (None, None)


def get_data(start_date: str, end_date: str):
    """Fetch market data with caching"""
    global _cached_df, _cached_dates
    if _cached_df is not None and _cached_dates == (start_date, end_date):
        return _cached_df.copy()
    
    print(f"📥 Downloading data from {start_date} to {end_date}...")
    downloader = UnifiedMarketDataDownloader(fred_api_key=FRED_API_KEY)
    df = downloader.download_all_data(start_date=start_date, end_date=end_date)
    
    _cached_df = df.copy()
    _cached_dates = (start_date, end_date)
    return df


# ==================== VISUALIZATION FUNCTIONS ====================

def create_summary_card(latest):
    """Professional HTML summary card with key metrics"""
    regime = str(latest['regime'])
    config = REGIME_CONFIG.get(regime, REGIME_CONFIG['TRANSITION'])
    confidence = latest.get('regime_confidence', 0)
    
    html = f"""
    <div style="
        background: linear-gradient(135deg, {config['color']}15 0%, {config['color']}05 100%);
        border-left: 5px solid {config['color']};
        padding: 30px;
        border-radius: 12px;
        box-shadow: 0 4px 12px rgba(0,0,0,0.1);
        font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif;
    ">
        <div style="display: flex; align-items: center; justify-content: space-between; margin-bottom: 25px;">
            <h2 style="margin: 0; color: #1F2937; font-size: 26px; font-weight: 700;">
                {config['icon']} Market Regime Analysis
            </h2>
            <div style="
                background: white;
                padding: 8px 16px;
                border-radius: 20px;
                font-size: 12px;
                color: #6B7280;
                font-weight: 600;
            ">
                {latest.name.strftime('%b %d, %Y') if hasattr(latest.name, 'strftime') else 'Latest'}
            </div>
        </div>
        
        <div style="
            background: white;
            padding: 25px;
            border-radius: 10px;
            margin-bottom: 20px;
            text-align: center;
            box-shadow: 0 2px 8px rgba(0,0,0,0.05);
        ">
            <div style="font-size: 13px; color: #6B7280; margin-bottom: 8px; text-transform: uppercase; letter-spacing: 1px; font-weight: 600;">
                Current Regime
            </div>
            <div style="
                font-size: 32px;
                font-weight: 800;
                color: {config['color']};
                margin-bottom: 10px;
                letter-spacing: -0.5px;
            ">{regime.replace('_', ' ')}</div>
            <div style="
                display: inline-block;
                background: {config['color']}15;
                color: {config['color']};
                padding: 6px 14px;
                border-radius: 20px;
                font-size: 13px;
                font-weight: 600;
            ">
                Confidence: {confidence:.0%}
            </div>
        </div>
        
        <div style="display: grid; grid-template-columns: repeat(2, 1fr); gap: 15px; margin-bottom: 20px;">
            <div style="background: white; padding: 18px; border-radius: 10px; box-shadow: 0 2px 8px rgba(0,0,0,0.05);">
                <div style="font-size: 11px; color: #6B7280; margin-bottom: 6px; text-transform: uppercase; letter-spacing: 0.5px; font-weight: 600;">
                    Recession Risk
                </div>
                <div style="font-size: 24px; font-weight: 700; color: {COLORS['recession']};">
                    {latest.get('recession_probability', 0):.0%}
                </div>
            </div>
            <div style="background: white; padding: 18px; border-radius: 10px; box-shadow: 0 2px 8px rgba(0,0,0,0.05);">
                <div style="font-size: 11px; color: #6B7280; margin-bottom: 6px; text-transform: uppercase; letter-spacing: 0.5px; font-weight: 600;">
                    Crisis Risk
                </div>
                <div style="font-size: 24px; font-weight: 700; color: {COLORS['crisis']};">
                    {latest.get('financial_crisis_risk', 0):.0%}
                </div>
            </div>
            <div style="background: white; padding: 18px; border-radius: 10px; box-shadow: 0 2px 8px rgba(0,0,0,0.05);">
                <div style="font-size: 11px; color: #6B7280; margin-bottom: 6px; text-transform: uppercase; letter-spacing: 0.5px; font-weight: 600;">
                    Stagflation Risk
                </div>
                <div style="font-size: 24px; font-weight: 700; color: {COLORS['stagflation']};">
                    {latest.get('stagflation_risk', 0):.0%}
                </div>
            </div>
            <div style="background: white; padding: 18px; border-radius: 10px; box-shadow: 0 2px 8px rgba(0,0,0,0.05);">
                <div style="font-size: 11px; color: #6B7280; margin-bottom: 6px; text-transform: uppercase; letter-spacing: 0.5px; font-weight: 600;">
                    Expansion Probability
                </div>
                <div style="font-size: 24px; font-weight: 700; color: {COLORS['expansion']};">
                    {latest.get('expansion_probability', 0):.0%}
                </div>
            </div>
        </div>
        
        <div style="
            background: white;
            padding: 15px;
            border-radius: 10px;
            display: flex;
            align-items: center;
            gap: 10px;
            box-shadow: 0 2px 8px rgba(0,0,0,0.05);
        ">
            <div style="color: {COLORS['primary']}; font-size: 20px;">ℹ️</div>
            <div style="font-size: 12px; color: #4B5563; line-height: 1.5;">
                <strong>Methodology:</strong> Empirically validated indicators from 50+ years of market history. 
                Leading indicators provide 6-18 month predictive signals.
            </div>
        </div>
    </div>
    """
    return html


def create_regime_probabilities_chart(latest):
    """Horizontal bar chart for regime probabilities"""
    probs = {
        'Expansion': latest.get('expansion_probability', 0),
        'Stagflation': latest.get('stagflation_risk', 0),
        'Recession': latest.get('recession_probability', 0),
        'Crisis': latest.get('financial_crisis_risk', 0),
    }
    
    colors = [COLORS['expansion'], COLORS['stagflation'], COLORS['recession'], COLORS['crisis']]
    
    fig = go.Figure(go.Bar(
        y=list(probs.keys()),
        x=list(probs.values()),
        orientation='h',
        marker=dict(
            color=colors,
            line=dict(color='white', width=2)
        ),
        text=[f"{v:.0%}" for v in probs.values()],
        textposition='outside',
        textfont=dict(size=14, color='#1F2937', weight=600),
        hovertemplate='<b>%{y}</b><br>Probability: %{x:.1%}<extra></extra>'
    ))
    
    fig.update_layout(
        title=dict(
            text="<b>Regime Probability Analysis</b>",
            font=dict(size=18, color='#1F2937'),
            x=0.5,
            xanchor='center'
        ),
        xaxis=dict(
            title="Probability",
            tickformat='.0%',
            range=[0, 1],
            gridcolor='#E5E7EB',
            showgrid=True
        ),
        yaxis=dict(
            title="",
            tickfont=dict(size=13, color='#1F2937')
        ),
        height=350,
        plot_bgcolor='white',
        paper_bgcolor='white',
        margin=dict(t=60, b=50, l=120, r=100),
        font=dict(family="Inter, Arial, sans-serif")
    )
    
    return fig


def create_leading_indicators_dashboard(latest):
    """Multi-panel dashboard for key leading indicators"""
    
    fig = make_subplots(
        rows=2, cols=2,
        subplot_titles=(
            'Yield Curve Spread',
            'Credit Stress Index',
            'Copper/Gold Ratio',
            'Consumer Rotation'
        ),
        specs=[[{'type': 'indicator'}, {'type': 'indicator'}],
               [{'type': 'indicator'}, {'type': 'indicator'}]],
        vertical_spacing=0.25,
        horizontal_spacing=0.15
    )
    
    # Yield Curve
    spread = latest.get('yield_curve_spread', 0)
    spread_color = COLORS['crisis'] if spread < -0.15 else COLORS['expansion']
    fig.add_trace(go.Indicator(
        mode="number+delta+gauge",
        value=spread,
        delta={'reference': 0, 'valueformat': '.2f'},
        gauge={
            'axis': {'range': [-1.5, 1.5]},
            'bar': {'color': spread_color},
            'threshold': {
                'line': {'color': COLORS['crisis'], 'width': 3},
                'thickness': 0.75,
                'value': -0.15
            },
            'steps': [
                {'range': [-1.5, -0.15], 'color': '#FEE2E2'},
                {'range': [-0.15, 0], 'color': '#FEF3C7'},
                {'range': [0, 1.5], 'color': '#D1FAE5'}
            ]
        },
        number={'suffix': '%', 'font': {'size': 28}},
        domain={'row': 0, 'column': 0}
    ), row=1, col=1)
    
    # Credit Stress
    credit_stress = latest.get('credit_spread_proxy', 0)
    fig.add_trace(go.Indicator(
        mode="number+gauge",
        value=credit_stress * 100,
        gauge={
            'axis': {'range': [0, 10]},
            'bar': {'color': COLORS['recession']},
            'threshold': {
                'line': {'color': COLORS['crisis'], 'width': 3},
                'thickness': 0.75,
                'value': 5
            },
            'steps': [
                {'range': [0, 3], 'color': '#D1FAE5'},
                {'range': [3, 5], 'color': '#FEF3C7'},
                {'range': [5, 10], 'color': '#FEE2E2'}
            ]
        },
        number={'suffix': '', 'font': {'size': 28}},
        domain={'row': 0, 'column': 1}
    ), row=1, col=2)
    
    # Copper/Gold
    cu_au = latest.get('copper_gold_ratio', 0)
    cu_au_color = COLORS['crisis'] if cu_au < 0.002 else COLORS['expansion']
    fig.add_trace(go.Indicator(
        mode="number+gauge",
        value=cu_au * 1000,
        gauge={
            'axis': {'range': [0, 5]},
            'bar': {'color': cu_au_color},
            'threshold': {
                'line': {'color': COLORS['crisis'], 'width': 3},
                'thickness': 0.75,
                'value': 2
            },
            'steps': [
                {'range': [0, 2], 'color': '#FEE2E2'},
                {'range': [2, 3], 'color': '#FEF3C7'},
                {'range': [3, 5], 'color': '#D1FAE5'}
            ]
        },
        number={'suffix': ' ×10⁻³', 'font': {'size': 24}},
        domain={'row': 1, 'column': 0}
    ), row=2, col=1)
    
    # Consumer Rotation
    rotation = latest.get('consumer_rotation_ratio', 0)
    rotation_color = COLORS['recession'] if rotation < 1.5 else COLORS['expansion']
    fig.add_trace(go.Indicator(
        mode="number+gauge",
        value=rotation,
        gauge={
            'axis': {'range': [1, 3]},
            'bar': {'color': rotation_color},
            'threshold': {
                'line': {'color': COLORS['recession'], 'width': 3},
                'thickness': 0.75,
                'value': 1.5
            },
            'steps': [
                {'range': [1, 1.5], 'color': '#FEE2E2'},
                {'range': [1.5, 2], 'color': '#FEF3C7'},
                {'range': [2, 3], 'color': '#D1FAE5'}
            ]
        },
        number={'font': {'size': 28}},
        domain={'row': 1, 'column': 1}
    ), row=2, col=2)
    
    fig.update_layout(
        height=600,
        showlegend=False,
        paper_bgcolor='white',
        font=dict(family="Inter, Arial, sans-serif", color='#1F2937'),
        margin=dict(t=80, b=40, l=40, r=40)
    )
    
    return fig


def create_regime_timeline(features):
    """Enhanced timeline showing regime history"""
    tail = features[['regime', 'regime_confidence']].tail(252).copy()
    
    if tail.empty:
        return go.Figure()
    
    tail['date'] = tail.index
    tail['color'] = tail['regime'].map(lambda x: REGIME_CONFIG.get(x, REGIME_CONFIG['TRANSITION'])['color'])
    
    fig = go.Figure()
    
    # Add scatter with color coding
    for regime, config in REGIME_CONFIG.items():
        mask = tail['regime'] == regime
        if mask.any():
            fig.add_trace(go.Scatter(
                x=tail[mask]['date'],
                y=tail[mask]['regime_confidence'],
                mode='markers',
                name=regime.replace('_', ' ').title(),
                marker=dict(
                    color=config['color'],
                    size=10,
                    line=dict(color='white', width=1.5),
                    symbol='circle'
                ),
                hovertemplate=(
                    f'<b>{regime.replace("_", " ")}</b><br>' +
                    'Date: %{x|%Y-%m-%d}<br>' +
                    'Confidence: %{y:.0%}<extra></extra>'
                )
            ))
    
    fig.update_layout(
        title=dict(
            text="<b>12-Month Regime History</b>",
            font=dict(size=18, color='#1F2937'),
            x=0.5,
            xanchor='center'
        ),
        xaxis=dict(
            gridcolor='#E5E7EB',
            showgrid=True
        ),
        yaxis=dict(
            title="Regime Confidence",
            tickformat='.0%',
            gridcolor='#E5E7EB',
            showgrid=True,
            range=[0, 1]
        ),
        height=400,
        plot_bgcolor='white',
        paper_bgcolor='white',
        margin=dict(t=60, b=50, l=70, r=40),
        legend=dict(
            orientation="h",
            yanchor="bottom",
            y=-0.35,
            xanchor="center",
            x=0.5,
            font=dict(size=11)
        ),
        font=dict(family="Inter, Arial, sans-serif"),
        hovermode='closest'
    )
    
    return fig


def create_cross_asset_signals(features):
    """Multi-line chart showing key cross-asset signals"""
    tail = features.tail(252)
    
    fig = go.Figure()
    
    signals = [
        ('yield_curve_spread', 'Yield Curve', COLORS['primary']),
        ('copper_gold_zscore', 'Copper/Gold Z-Score', COLORS['expansion']),
        ('credit_spread_proxy', 'Credit Spread', COLORS['recession']),
        ('consumer_confidence_zscore', 'Consumer Confidence', COLORS['stagflation']),
    ]
    
    for col, name, color in signals:
        if col in tail.columns:
            fig.add_trace(go.Scatter(
                x=tail.index,
                y=tail[col],
                mode='lines',
                name=name,
                line=dict(color=color, width=2),
                hovertemplate=f'<b>{name}</b><br>Date: %{{x|%Y-%m-%d}}<br>Value: %{{y:.2f}}<extra></extra>'
            ))
    
    fig.update_layout(
        title=dict(
            text="<b>Cross-Asset Leading Indicators</b>",
            font=dict(size=18, color='#1F2937'),
            x=0.5,
            xanchor='center'
        ),
        xaxis=dict(
            gridcolor='#E5E7EB',
            showgrid=True
        ),
        yaxis=dict(
            title="Normalized Value",
            gridcolor='#E5E7EB',
            showgrid=True,
            zeroline=True,
            zerolinecolor='#9CA3AF',
            zerolinewidth=2
        ),
        height=400,
        plot_bgcolor='white',
        paper_bgcolor='white',
        margin=dict(t=60, b=50, l=70, r=40),
        legend=dict(
            orientation="h",
            yanchor="bottom",
            y=-0.3,
            xanchor="center",
            x=0.5
        ),
        font=dict(family="Inter, Arial, sans-serif"),
        hovermode='x unified'
    )
    
    return fig


# ==================== MAIN PIPELINE ====================

def run_pipeline(days_back: int = 1825):
    """Execute the full analysis pipeline"""
    try:
        today = pd.Timestamp.today()
        start_date = (today - pd.Timedelta(days=days_back)).strftime('%Y-%m-%d')
        end_date = today.strftime('%Y-%m-%d')
        
        # Fetch data
        df = get_data(start_date, end_date)
        if len(df) < 300:
            error_html = """
            <div style="padding: 30px; background: #FEE2E2; border-radius: 12px; border-left: 5px solid #DC2626;">
                <h3 style="color: #DC2626; margin: 0 0 12px 0;">⚠️ Insufficient Data</h3>
                <p style="margin: 0; color: #1F2937; line-height: 1.6;">
                    Not enough data points for reliable analysis. Please increase the lookback window to at least 1000 days.
                </p>
            </div>
            """
            return error_html, None, None, None, None, None
        
        # Build features
        print("Building regime features...")
        detector = MarketRegimeDetector(df)
        features = detector.build_all_features()
        
        # Get latest data point with valid regime
        latest = features.dropna(subset=['regime']).iloc[-1]
        
        # Create visualizations
        summary_html = create_summary_card(latest)
        prob_chart = create_regime_probabilities_chart(latest)
        indicators_dash = create_leading_indicators_dashboard(latest)
        timeline = create_regime_timeline(features)
        cross_asset = create_cross_asset_signals(features)
        
        # Detailed JSON output
        json_output = {
            "📊 Current Status": {
                "Regime": str(latest['regime']),
                "Confidence": f"{latest.get('regime_confidence', 0):.1%}",
                "Date": latest.name.strftime('%Y-%m-%d') if hasattr(latest.name, 'strftime') else 'N/A'
            },
            "🎯 Regime Probabilities": {
                "Recession": f"{latest.get('recession_probability', 0):.1%}",
                "Financial Crisis": f"{latest.get('financial_crisis_risk', 0):.1%}",
                "Stagflation": f"{latest.get('stagflation_risk', 0):.1%}",
                "Expansion": f"{latest.get('expansion_probability', 0):.1%}"
            },
            "📈 Leading Indicators": {
                "Yield Curve Spread": f"{latest.get('yield_curve_spread', 0):.2f}%",
                "Yield Curve Inverted": bool(latest.get('yield_curve_inverted', 0)),
                "Copper/Gold Ratio": f"{latest.get('copper_gold_ratio', 0):.4f}",
                "Consumer Rotation": f"{latest.get('consumer_rotation_ratio', 0):.2f}",
                "Credit Stress": bool(latest.get('credit_stress', 0))
            },
            "🌡️ Market Health": {
                "VIX Level": f"{latest.get('vix_level', 0):.1f}",
                "S&P 500 3M Return": f"{latest.get('sp500_return_3m', 0):.1%}",
                "Dollar Strength": f"{latest.get('dollar_strength', 0):.1f}",
                "Inflation YoY": f"{latest.get('inflation_yoy', 0):.1f}%",
                "Unemployment Rate": f"{latest.get('unemployment_rate', 0):.1f}%"
            }
        }
        
        return summary_html, json_output, prob_chart, indicators_dash, timeline, cross_asset

    except Exception as e:
        import traceback
        error_detail = traceback.format_exc()
        error_html = f"""
        <div style="padding: 30px; background: #FEE2E2; border-radius: 12px; border-left: 5px solid #DC2626;">
            <h3 style="color: #DC2626; margin: 0 0 12px 0;">❌ Error</h3>
            <p style="margin: 0 0 10px 0; color: #1F2937; font-weight: 600;">
                {str(e)}
            </p>
            <details style="margin-top: 15px;">
                <summary style="cursor: pointer; color: #6B7280; font-size: 13px;">
                    Show technical details
                </summary>
                <pre style="
                    margin-top: 10px;
                    padding: 15px;
                    background: #F9FAFB;
                    border-radius: 6px;
                    font-size: 11px;
                    color: #374151;
                    overflow-x: auto;
                ">{error_detail}</pre>
            </details>
        </div>
        """
        return error_html, {"Error": str(e)}, None, None, None, None


# ==================== GRADIO UI ====================

custom_css = """
@import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;600;700;800&display=swap');

.gradio-container {
    font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif !important;
    max-width: 1600px !important;
    margin: auto !important;
}

.header-banner {
    background: linear-gradient(135deg, #2563EB 0%, #1E40AF 100%);
    color: white;
    padding: 40px 30px;
    border-radius: 12px;
    margin-bottom: 30px;
    box-shadow: 0 10px 25px rgba(37, 99, 235, 0.2);
}

.header-banner h1 {
    margin: 0;
    color: white;
    font-size: 36px;
    font-weight: 800;
    letter-spacing: -0.5px;
}

.header-banner p {
    margin: 12px 0 0 0;
    color: white;
    font-size: 16px;
    opacity: 0.95;
    font-weight: 500;
}

.btn-primary {
    background: linear-gradient(135deg, #2563EB 0%, #1E40AF 100%) !important;
    border: none !important;
    font-weight: 700 !important;
    font-size: 15px !important;
    padding: 12px 24px !important;
    border-radius: 8px !important;
    box-shadow: 0 4px 12px rgba(37, 99, 235, 0.3) !important;
    transition: all 0.2s !important;
}

.btn-primary:hover {
    transform: translateY(-2px) !important;
    box-shadow: 0 6px 16px rgba(37, 99, 235, 0.4) !important;
}
"""

with gr.Blocks(css=custom_css, title="Geopolitics Risk Analysis", theme=gr.themes.Soft()) as demo:
    
    gr.HTML("""
        <div class="header-banner">
            <h1>Geopolitics Risk Analysis</h1>
            <p>Market Regime Detector</p>
        </div>
    """)
    
    with gr.Row():
        with gr.Column(scale=3):
            days = gr.Slider(
                365, 3000, 
                value=1825, 
                step=90, 
                label="📅 Lookback Window (days)",
                info="Minimum 1000 days recommended for stable regime detection"
            )
        with gr.Column(scale=1):
            run_btn = gr.Button(
                "🔄 Update Analysis", 
                variant="primary",
                size="lg"
            )
    
    gr.Markdown("---")
    
    with gr.Row():
        with gr.Column(scale=2):
            summary_html = gr.HTML(label="Executive Summary")
        with gr.Column(scale=1):
            json_output = gr.JSON(label="📋 Detailed Metrics", show_label=True)
    
    gr.Markdown("---")
    gr.Markdown("## 📊 Regime Probability Analysis")
    
    with gr.Row():
        prob_chart = gr.Plot(label="Regime Probabilities")
        indicators_dash = gr.Plot(label="Leading Indicators Dashboard")
    
    gr.Markdown("---")
    gr.Markdown("## 📈 Historical Analysis & Cross-Asset Signals")
    
    with gr.Row():
        timeline_plot = gr.Plot(label="12-Month Regime Timeline")
        cross_asset_plot = gr.Plot(label="Cross-Asset Leading Indicators")
    
    gr.Markdown("---")
    gr.HTML("""
    <div style="
        background: #F9FAFB;
        padding: 25px;
        border-radius: 12px;
        border: 1px solid #E5E7EB;
        margin-top: 20px;
    ">
        <h3 style="margin: 0 0 15px 0; color: #1F2937; font-size: 18px; font-weight: 700;">
            📚 Methodology & Data Sources
        </h3>
        <div style="color: #4B5563; line-height: 1.8; font-size: 14px;">
            <p style="margin: 0 0 12px 0;">
                <strong>Leading Indicators (6-18 month predictive):</strong> Yield curve inversion, credit spreads (HYG/TLT), 
                copper/gold ratio, consumer rotation (XLY/XLP). These signals have preceded major recessions since 1970s.
            </p>
            <p style="margin: 0 0 12px 0;">
                <strong>Historical Validation:</strong> All thresholds derived from documented episodes including 
                2000 dot-com crash, 2008 GFC, 2020 COVID recession, and 2022 inflation surge.
            </p>
            <p style="margin: 0;">
                <strong>Data Sources:</strong> Yahoo Finance (equity/commodity prices), FRED Economic Data (macro indicators), 
                updated daily. Framework based on peer-reviewed research and central bank methodologies.
            </p>
        </div>
    </div>
    """)
    
    # Event handlers
    run_btn.click(
        run_pipeline,
        inputs=[days],
        outputs=[summary_html, json_output, prob_chart, indicators_dash, timeline_plot, cross_asset_plot]
    )
    
    # Auto-run on load
    demo.load(
        run_pipeline,
        inputs=[days],
        outputs=[summary_html, json_output, prob_chart, indicators_dash, timeline_plot, cross_asset_plot]
    )


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