Update app.py

app.py

@@ -1,413 +1,347 @@
 import gradio as gr
+import matplotlib.pyplot as plt
+import seaborn as sns
 import pandas as pd
 import numpy as np
-import plotly.graph_objects as go
-from plotly.subplots import make_subplots
-from datetime import datetime
+from datetime import datetime, timedelta
 import warnings
-import os
-
 warnings.filterwarnings('ignore')
 
-from geo_macro import UnifiedMarketDataDownloader
+# Import from your geo_macro.py
+from geo_macro import UnifiedMarketDataDownloader, FRED_API_KEY
 
-# Configuration
-FRED_API_KEY = os.getenv("FRED_API_KEY", "23f3511b0ca43918ccd503ef64cb844e")
+# ======================
+# CUSTOM STYLING & COLORS
+# ======================
 
-# Professional color palette
-COLORS = {
-    'primary': '#1a1a1a',
-    'secondary': '#4a4a4a',
-    'accent': '#0066cc',
-    'grid': '#e5e5e5',
-    'bg': '#ffffff',
-    'green': '#00a86b',
-    'red': '#d32f2f',
+# Custom color palette matching your theme
+CUSTOM_COLORS = {
+    'primary': '#00ff9b',
+    'secondary': '#050505', 
+    'accent': '#0066ff',
+    'background': '#0a0a0a',
+    'text': '#ffffff',
+    'grid': '#1a1a1a'
 }
 
-# Cache data globally
-_cached_data = None
-
-def load_data():
-    """Load and cache market data"""
-    global _cached_data
-    if _cached_data is None:
-        downloader = UnifiedMarketDataDownloader(fred_api_key=FRED_API_KEY)
-        _cached_data = downloader.download_all_data(start_date='2020-01-01')
-    return _cached_data
-
-def calculate_momentum_zscore(series, window=60):
-    """Calculate momentum z-score"""
-    returns = series.pct_change(window)
-    mean = returns.rolling(252).mean()
-    std = returns.rolling(252).std()
-    return (returns - mean) / std
-
-def create_layout(title, height=450):
-    """Standard plot layout"""
-    return go.Layout(
-        title=dict(text=title, font=dict(size=16, color=COLORS['primary']), x=0.5, xanchor='center'),
-        plot_bgcolor=COLORS['bg'],
-        paper_bgcolor=COLORS['bg'],
-        font=dict(color=COLORS['secondary'], size=11),
-        xaxis=dict(gridcolor=COLORS['grid'], showline=True, linecolor=COLORS['grid']),
-        yaxis=dict(gridcolor=COLORS['grid'], showline=True, linecolor=COLORS['grid']),
-        hovermode='x unified',
-        height=height,
-        margin=dict(l=60, r=40, t=60, b=40),
-    )
+# Apply custom styling
+plt.style.use('dark_background')
+sns.set_palette([CUSTOM_COLORS['primary'], CUSTOM_COLORS['accent'], '#ff6b6b', '#ffd93d'])
 
-# === PLOT FUNCTIONS ===
+# ======================
+# ANALYSIS FUNCTIONS
+# ======================
 
-def plot_geopolitical_risk(start_date, end_date):
-    """Geopolitical risk composite index"""
-    df = load_data().loc[start_date:end_date]
-    
-    # Composite: Defense, Oil, Gold, VIX
-    components = ['Defense', 'Oil', 'Gold', 'VIX']
-    available = [c for c in components if c in df.columns]
-    
-    if len(available) < 2:
-        return go.Figure().update_layout(create_layout("Insufficient Data"))
-    
-    # Normalize each component
-    normalized = df[available].apply(lambda x: (x / x.iloc[0]) - 1)
-    risk_index = normalized.mean(axis=1)
-    
-    # Calculate z-score
-    zscore = (risk_index - risk_index.rolling(252).mean()) / risk_index.rolling(252).std()
-    
-    fig = go.Figure()
-    
-    # Add risk index
-    fig.add_trace(go.Scatter(
-        x=zscore.index, 
-        y=zscore,
-        fill='tozeroy',
-        line=dict(color=COLORS['accent'], width=2),
-        name='Risk Index'
-    ))
+class GradioMarketAnalyzer:
+    def __init__(self):
+        self.data = None
+        self.downloader = UnifiedMarketDataDownloader(fred_api_key=FRED_API_KEY)
+    
+    def download_data(self, start_date, end_date):
+        """Download market data with progress tracking"""
+        try:
+            self.data = self.downloader.download_all_data(start_date=start_date, end_date=end_date)
+            return f"✅ Data downloaded successfully! Shape: {self.data.shape}"
+        except Exception as e:
+            return f"❌ Error downloading data: {str(e)}"
+    
+    def plot_market_overview(self):
+        """Create market overview plot with custom styling"""
+        if self.data is None:
+            return None
+        
+        fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(16, 12))
+        fig.patch.set_facecolor(CUSTOM_COLORS['background'])
+        
+        # Plot 1: Major US Indices
+        equity_cols = [col for col in ['SP500', 'NASDAQ', 'DJI'] if col in self.data.columns]
+        if equity_cols:
+            for col in equity_cols:
+                ax1.plot(self.data.index, self.data[col] / self.data[col].iloc[0], 
+                        label=col, linewidth=2)
+            ax1.set_title('US Equity Indices (Normalized)', color=CUSTOM_COLORS['text'], fontsize=14, fontweight='bold')
+            ax1.legend()
+            ax1.grid(True, alpha=0.3, color=CUSTOM_COLORS['grid'])
+        
+        # Plot 2: Key Rates
+        rate_cols = [col for col in ['DGS10', 'DGS2', 'DGS3MO'] if col in self.data.columns]
+        if rate_cols:
+            for col in rate_cols:
+                ax2.plot(self.data.index, self.data[col], label=col, linewidth=2)
+            ax2.set_title('Treasury Yields', color=CUSTOM_COLORS['text'], fontsize=14, fontweight='bold')
+            ax2.legend()
+            ax2.grid(True, alpha=0.3, color=CUSTOM_COLORS['grid'])
+        
+        # Plot 3: Commodities
+        commodity_cols = [col for col in ['Gold', 'Oil', 'Copper'] if col in self.data.columns]
+        if commodity_cols:
+            for col in commodity_cols:
+                ax3.plot(self.data.index, self.data[col] / self.data[col].iloc[0], 
+                        label=col, linewidth=2)
+            ax3.set_title('Commodities (Normalized)', color=CUSTOM_COLORS['text'], fontsize=14, fontweight='bold')
+            ax3.legend()
+            ax3.grid(True, alpha=0.3, color=CUSTOM_COLORS['grid'])
+        
+        # Plot 4: Risk Indicators
+        risk_cols = [col for col in ['VIX', 'HYG'] if col in self.data.columns]
+        if risk_cols:
+            for col in risk_cols:
+                ax4.plot(self.data.index, self.data[col], label=col, linewidth=2)
+            ax4.set_title('Risk Indicators', color=CUSTOM_COLORS['text'], fontsize=14, fontweight='bold')
+            ax4.legend()
+            ax4.grid(True, alpha=0.3, color=CUSTOM_COLORS['grid'])
+        
+        plt.tight_layout()
+        return fig
     
-    # Add threshold lines
-    fig.add_hline(y=0, line_dash="dash", line_color=COLORS['secondary'], line_width=1)
-    fig.add_hline(y=2, line_dash="dot", line_color=COLORS['red'], line_width=1)
-    fig.add_hline(y=-2, line_dash="dot", line_color=COLORS['green'], line_width=1)
+    def plot_economic_indicators(self):
+        """Plot key economic indicators"""
+        if self.data is None:
+            return None
+        
+        fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(16, 12))
+        fig.patch.set_facecolor(CUSTOM_COLORS['background'])
+        
+        # Plot 1: Inflation
+        inflation_cols = [col for col in ['CPIAUCSL', 'CPILFESL'] if col in self.data.columns]
+        if inflation_cols:
+            for col in inflation_cols:
+                ax1.plot(self.data.index, self.data[col], label=col, linewidth=2)
+            ax1.set_title('Inflation Indicators', color=CUSTOM_COLORS['text'], fontsize=14, fontweight='bold')
+            ax1.legend()
+            ax1.grid(True, alpha=0.3, color=CUSTOM_COLORS['grid'])
+        
+        # Plot 2: Labor Market
+        labor_cols = [col for col in ['UNRATE', 'PAYEMS'] if col in self.data.columns]
+        if labor_cols:
+            for col in labor_cols:
+                if col == 'PAYEMS':  # Scale payrolls for better visualization
+                    scaled_data = self.data[col] / 1000
+                    ax2.plot(self.data.index, scaled_data, label=f"{col} (thousands)", linewidth=2)
+                else:
+                    ax2.plot(self.data.index, self.data[col], label=col, linewidth=2)
+            ax2.set_title('Labor Market', color=CUSTOM_COLORS['text'], fontsize=14, fontweight='bold')
+            ax2.legend()
+            ax2.grid(True, alpha=0.3, color=CUSTOM_COLORS['grid'])
+        
+        # Plot 3: Yield Curve Spread
+        if 'T10Y2Y' in self.data.columns:
+            ax3.plot(self.data.index, self.data['T10Y2Y'], 
+                    color=CUSTOM_COLORS['primary'], linewidth=2)
+            ax3.axhline(y=0, color='white', linestyle='--', alpha=0.5)
+            ax3.set_title('10Y-2Y Yield Spread (Recession Indicator)', 
+                         color=CUSTOM_COLORS['text'], fontsize=14, fontweight='bold')
+            ax3.grid(True, alpha=0.3, color=CUSTOM_COLORS['grid'])
+        
+        # Plot 4: Consumer Sentiment
+        sentiment_cols = [col for col in ['UMCSENT'] if col in self.data.columns]
+        if sentiment_cols:
+            for col in sentiment_cols:
+                ax4.plot(self.data.index, self.data[col], 
+                        color=CUSTOM_COLORS['accent'], linewidth=2)
+            ax4.set_title('Consumer Sentiment', color=CUSTOM_COLORS['text'], fontsize=14, fontweight='bold')
+            ax4.grid(True, alpha=0.3, color=CUSTOM_COLORS['grid'])
+        
+        plt.tight_layout()
+        return fig
     
-    fig.update_layout(create_layout("Geopolitical Risk Index (Z-Score)"))
-    fig.update_yaxes(title="Standard Deviations from Mean")
+    def plot_geopolitical_indicators(self):
+        """Plot geopolitical risk indicators"""
+        if self.data is None:
+            return None
+        
+        fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(16, 12))
+        fig.patch.set_facecolor(CUSTOM_COLORS['background'])
+        
+        # Plot 1: Defense vs Market
+        if all(col in self.data.columns for col in ['Defense_Stocks', 'SP500']):
+            defense_relative = self.data['Defense_Stocks'] / self.data['SP500']
+            ax1.plot(self.data.index, defense_relative, 
+                    color=CUSTOM_COLORS['primary'], linewidth=2)
+            ax1.set_title('Defense Stocks Relative to S&P 500', 
+                         color=CUSTOM_COLORS['text'], fontsize=14, fontweight='bold')
+            ax1.grid(True, alpha=0.3, color=CUSTOM_COLORS['grid'])
+        
+        # Plot 2: Safe Havens
+        safe_haven_cols = [col for col in ['Gold', 'TLT', 'Bitcoin'] if col in self.data.columns]
+        if safe_haven_cols:
+            for col in safe_haven_cols:
+                ax2.plot(self.data.index, self.data[col] / self.data[col].iloc[0], 
+                        label=col, linewidth=2)
+            ax2.set_title('Safe Haven Assets (Normalized)', 
+                         color=CUSTOM_COLORS['text'], fontsize=14, fontweight='bold')
+            ax2.legend()
+            ax2.grid(True, alpha=0.3, color=CUSTOM_COLORS['grid'])
+        
+        # Plot 3: Global Markets
+        global_cols = [col for col in ['China', 'Europe', 'Japan', 'Emerging_Markets'] if col in self.data.columns]
+        if global_cols:
+            for col in global_cols:
+                ax3.plot(self.data.index, self.data[col] / self.data[col].iloc[0], 
+                        label=col, linewidth=2)
+            ax3.set_title('Global Markets (Normalized)', 
+                         color=CUSTOM_COLORS['text'], fontsize=14, fontweight='bold')
+            ax3.legend()
+            ax3.grid(True, alpha=0.3, color=CUSTOM_COLORS['grid'])
+        
+        # Plot 4: VIX - Fear Index
+        if 'VIX' in self.data.columns:
+            ax4.plot(self.data.index, self.data['VIX'], 
+                    color='#ff6b6b', linewidth=2)
+            ax4.axhline(y=20, color='white', linestyle='--', alpha=0.5, label='High Volatility')
+            ax4.set_title('VIX - Volatility Index', 
+                         color=CUSTOM_COLORS['text'], fontsize=14, fontweight='bold')
+            ax4.legend()
+            ax4.grid(True, alpha=0.3, color=CUSTOM_COLORS['grid'])
+        
+        plt.tight_layout()
+        return fig
     
-    return fig
+    def get_market_summary(self):
+        """Generate market summary statistics"""
+        if self.data is None:
+            return "No data available. Please download data first."
+        
+        summary_text = "## 📊 Market Summary\n\n"
+        
+        # Equity Performance
+        if 'SP500' in self.data.columns:
+            spx_current = self.data['SP500'].iloc[-1]
+            spx_1m = (self.data['SP500'].iloc[-1] / self.data['SP500'].iloc[-22] - 1) * 100
+            summary_text += f"**S&P 500**: {spx_current:,.0f} ({spx_1m:+.2f}% 1M)\n"
+        
+        # Key Rates
+        if 'DGS10' in self.data.columns:
+            ten_yr = self.data['DGS10'].iloc[-1]
+            summary_text += f"**10Y Yield**: {ten_yr:.2f}%\n"
+        
+        # VIX
+        if 'VIX' in self.data.columns:
+            vix = self.data['VIX'].iloc[-1]
+            summary_text += f"**VIX**: {vix:.1f}\n"
+        
+        # Gold
+        if 'Gold' in self.data.columns:
+            gold = self.data['Gold'].iloc[-1]
+            summary_text += f"**Gold**: ${gold:,.0f}\n"
+        
+        # Yield Curve
+        if 'T10Y2Y' in self.data.columns:
+            spread = self.data['T10Y2Y'].iloc[-1]
+            curve_status = "INVERTED 🔴" if spread < 0 else "NORMAL 🟢"
+            summary_text += f"**Yield Curve**: {spread:.2f}% ({curve_status})\n"
+        
+        # Unemployment
+        if 'UNRATE' in self.data.columns:
+            unemployment = self.data['UNRATE'].iloc[-1]
+            summary_text += f"**Unemployment**: {unemployment:.1f}%\n"
+        
+        return summary_text
 
-def plot_macro_indicators(start_date, end_date):
-    """Key macro indicators dashboard"""
-    df = load_data().loc[start_date:end_date]
-    
-    indicators = {
-        'CPI': ('Inflation (YoY %)', lambda x: x.pct_change(252) * 100),
-        'Yield_Curve': ('Yield Curve (10Y-2Y)', lambda x: x),
-        'VIX': ('Market Volatility', lambda x: x),
-        'USD_Index': ('Dollar Strength', lambda x: x),
-    }
-    
-    available = {k: v for k, v in indicators.items() if k in df.columns}
-    
-    if not available:
-        return go.Figure().update_layout(create_layout("No Data Available"))
-    
-    n_plots = len(available)
-    fig = make_subplots(
-        rows=n_plots, cols=1,
-        shared_xaxes=True,
-        vertical_spacing=0.05,
-        subplot_titles=[v[0] for v in available.values()]
-    )
-    
-    for i, (col, (title, transform)) in enumerate(available.items(), 1):
-        series = transform(df[col]).dropna()
-        
-        # Color based on last value
-        color = COLORS['red'] if series.iloc[-1] > series.mean() else COLORS['green']
-        if col == 'Yield_Curve':
-            color = COLORS['red'] if series.iloc[-1] < 0 else COLORS['green']
-        
-        fig.add_trace(
-            go.Scatter(
-                x=series.index,
-                y=series,
-                line=dict(color=color, width=2),
-                showlegend=False
-            ),
-            row=i, col=1
+# ======================
+# GRADIO INTERFACE
+# ======================
+
+def create_interface():
+    analyzer = GradioMarketAnalyzer()
+    
+    with gr.Blocks(theme='JohnSmith9982/small_and_pretty', css=".gradio-container {background: linear-gradient(135deg, #050505, #00ff9b);}") as demo:
+        gr.HTML("""
+        <div style='text-align: center; padding: 20px; background: linear-gradient(135deg, #050505, #00ff9b); 
+                    border-radius: 15px; margin: 20px 0;'>
+            <h1 style='color: white; margin: 0;'>🌍 Geo-Macro Analysis Dashboard</h1>
+            <p style='color: white; margin: 10px 0 0 0;'>Comprehensive Geopolitical & Macroeconomic Analysis</p>
+        </div>
+        """)
+        
+        with gr.Row():
+            with gr.Column(scale=1):
+                start_date = gr.Textbox(value="2020-01-01", label="Start Date")
+                end_date = gr.Textbox(value=datetime.now().strftime('%Y-%m-%d'), label="End Date")
+                download_btn = gr.Button("📥 Download Market Data", variant="primary")
+                download_status = gr.Textbox(label="Download Status", interactive=False)
+                
+            with gr.Column(scale=2):
+                summary = gr.Markdown(label="Market Summary")
+        
+        with gr.Tab("Market Overview"):
+            market_plot = gr.Plot(label="Market Overview")
+        
+        with gr.Tab("Economic Indicators"):
+            economic_plot = gr.Plot(label="Economic Indicators")
+        
+        with gr.Tab("Geopolitical Analysis"):
+            geo_plot = gr.Plot(label="Geopolitical Indicators")
+        
+        # Event handlers
+        download_btn.click(
+            fn=analyzer.download_data,
+            inputs=[start_date, end_date],
+            outputs=[download_status]
         )
         
-        # Add mean line
-        mean_val = series.mean()
-        fig.add_hline(
-            y=mean_val,
-            line_dash="dash",
-            line_color=COLORS['secondary'],
-            line_width=1,
-            row=i, col=1
+        download_btn.click(
+            fn=analyzer.get_market_summary,
+            inputs=[],
+            outputs=[summary]
+        )
+        
+        download_btn.click(
+            fn=analyzer.plot_market_overview,
+            inputs=[],
+            outputs=[market_plot]
+        )
+        
+        download_btn.click(
+            fn=analyzer.plot_economic_indicators,
+            inputs=[],
+            outputs=[economic_plot]
+        )
+        
+        download_btn.click(
+            fn=analyzer.plot_geopolitical_indicators,
+            inputs=[],
+            outputs=[geo_plot]
+        )
+        
+        # Initial load
+        demo.load(
+            fn=analyzer.download_data,
+            inputs=[start_date, end_date],
+            outputs=[download_status]
+        )
+        
+        demo.load(
+            fn=analyzer.get_market_summary,
+            inputs=[],
+            outputs=[summary]
+        )
+        
+        demo.load(
+            fn=analyzer.plot_market_overview,
+            inputs=[],
+            outputs=[market_plot]
+        )
+        
+        demo.load(
+            fn=analyzer.plot_economic_indicators,
+            inputs=[],
+            outputs=[economic_plot]
+        )
+        
+        demo.load(
+            fn=analyzer.plot_geopolitical_indicators,
+            inputs=[],
+            outputs=[geo_plot]
         )
     
-    fig.update_layout(
-        create_layout("Macroeconomic Indicators", height=150 * n_plots),
-        showlegend=False
-    )
-    
-    return fig
-
-def plot_safe_haven_flows(start_date, end_date):
-    """Safe haven vs risk asset flows"""
-    df = load_data().loc[start_date:end_date]
-    
-    safe_havens = ['Gold', 'US_10Y', 'CHF']
-    risk_assets = ['US_Equity', 'Emerging_Markets', 'High_Yield']
-    
-    safe_available = [c for c in safe_havens if c in df.columns]
-    risk_available = [c for c in risk_assets if c in df.columns]
-    
-    if not safe_available or not risk_available:
-        return go.Figure().update_layout(create_layout("Insufficient Data"))
-    
-    # Normalize to 100
-    safe_norm = df[safe_available].apply(lambda x: x / x.iloc[0] * 100).mean(axis=1)
-    risk_norm = df[risk_available].apply(lambda x: x / x.iloc[0] * 100).mean(axis=1)
-    
-    fig = go.Figure()
-    
-    fig.add_trace(go.Scatter(
-        x=safe_norm.index,
-        y=safe_norm,
-        name='Safe Havens',
-        line=dict(color=COLORS['green'], width=2)
-    ))
-    
-    fig.add_trace(go.Scatter(
-        x=risk_norm.index,
-        y=risk_norm,
-        name='Risk Assets',
-        line=dict(color=COLORS['red'], width=2)
-    ))
-    
-    fig.update_layout(create_layout("Safe Haven vs Risk Asset Performance"))
-    fig.update_yaxes(title="Indexed to 100")
-    
-    return fig
-
-def plot_relative_strength(start_date, end_date):
-    """Regional equity performance"""
-    df = load_data().loc[start_date:end_date]
-    
-    regions = ['US_Equity', 'Europe', 'China', 'Emerging_Markets']
-    available = [r for r in regions if r in df.columns]
-    
-    if len(available) < 2:
-        return go.Figure().update_layout(create_layout("Insufficient Data"))
-    
-    # Calculate 3-month momentum
-    momentum = df[available].pct_change(60).iloc[-1] * 100
-    momentum = momentum.sort_values(ascending=False)
-    
-    colors = [COLORS['green'] if x > 0 else COLORS['red'] for x in momentum.values]
-    
-    fig = go.Figure(go.Bar(
-        x=momentum.values,
-        y=[name.replace('_', ' ') for name in momentum.index],
-        orientation='h',
-        marker_color=colors,
-        text=[f"{x:.1f}%" for x in momentum.values],
-        textposition='auto',
-    ))
-    
-    fig.add_vline(x=0, line_color=COLORS['secondary'], line_width=1)
-    
-    fig.update_layout(create_layout("Regional Equity Strength (3M Return)"))
-    fig.update_xaxes(title="Return (%)")
-    
-    return fig
-
-def plot_correlation_matrix(start_date, end_date, assets):
-    """Correlation heatmap for selected assets"""
-    df = load_data().loc[start_date:end_date]
-    
-    available = [a for a in assets if a in df.columns]
-    if len(available) < 2:
-        return go.Figure().update_layout(create_layout("Select at least 2 assets"))
-    
-    corr = df[available].pct_change().corr()
-    
-    fig = go.Figure(go.Heatmap(
-        z=corr.values,
-        x=[c.replace('_', ' ') for c in corr.columns],
-        y=[c.replace('_', ' ') for c in corr.columns],
-        colorscale='RdBu_r',
-        zmid=0,
-        zmin=-1,
-        zmax=1,
-        text=np.round(corr.values, 2),
-        texttemplate='%{text}',
-        textfont=dict(size=10),
-        colorbar=dict(title="Correlation")
-    ))
-    
-    fig.update_layout(create_layout("Asset Correlation Matrix", height=500))
-    
-    return fig
-
-def plot_drawdown(start_date, end_date, assets):
-    """Drawdown analysis"""
-    df = load_data().loc[start_date:end_date]
-    
-    available = [a for a in assets if a in df.columns]
-    if not available:
-        return go.Figure().update_layout(create_layout("Select assets to analyze"))
-    
-    fig = go.Figure()
-    
-    for asset in available:
-        prices = df[asset].dropna()
-        if len(prices) > 0:
-            rolling_max = prices.expanding().max()
-            drawdown = ((prices - rolling_max) / rolling_max) * 100
-            
-            fig.add_trace(go.Scatter(
-                x=drawdown.index,
-                y=drawdown,
-                name=asset.replace('_', ' '),
-                line=dict(width=2)
-            ))
-    
-    fig.update_layout(create_layout("Drawdown Analysis"))
-    fig.update_yaxes(title="Drawdown (%)")
-    
-    return fig
-
-# === GRADIO APP ===
+    return demo
 
-custom_css = """
-.gradio-container { 
-    font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
-    max-width: 1400px;
-    margin: auto;
-}
-h1 { 
-    font-size: 2.2em !important;
-    font-weight: 600 !important;
-    color: #1a1a1a !important;
-    text-align: center;
-    margin-bottom: 0.3em !important;
-}
-h3 { 
-    font-size: 1.1em !important;
-    font-weight: 400 !important;
-    color: #4a4a4a !important;
-    text-align: center;
-    margin-top: 0 !important;
-}
-.tabs button {
-    font-size: 0.95em !important;
-    font-weight: 500 !important;
-}
-.tabs button.selected {
-    border-bottom: 2px solid #0066cc !important;
-}
-button.primary {
-    background: #0066cc !important;
-    font-weight: 500 !important;
-}
-"""
-
-AVAILABLE_ASSETS = [
-    'US_Equity', 'Europe', 'China', 'Emerging_Markets',
-    'Gold', 'Oil', 'Copper', 'US_10Y', 'High_Yield',
-    'VIX', 'USD_Index', 'Defense', 'Technology'
-]
-
-with gr.Blocks(title="Geopolitical & Macro Intelligence", css=custom_css, theme=gr.themes.Soft()) as demo:
-    
-    gr.Markdown("# Geopolitical & Macro Intelligence")
-    gr.Markdown("### Professional analysis of global markets and economic indicators")
-    
-    with gr.Tabs():
-        
-        # TAB 1: Geopolitical Overview
-        with gr.Tab("🌍 Geopolitical Risk"):
-            with gr.Row():
-                start_1 = gr.Textbox("2023-01-01", label="Start Date", scale=1)
-                end_1 = gr.Textbox(datetime.today().strftime('%Y-%m-%d'), label="End Date", scale=1)
-                btn_1 = gr.Button("Update", variant="primary", scale=1)
-            
-            with gr.Row():
-                plot_geo_risk = gr.Plot()
-                plot_safe_haven = gr.Plot()
-            
-            btn_1.click(
-                fn=plot_geopolitical_risk,
-                inputs=[start_1, end_1],
-                outputs=plot_geo_risk
-            ).then(
-                fn=plot_safe_haven_flows,
-                inputs=[start_1, end_1],
-                outputs=plot_safe_haven
-            )
-        
-        # TAB 2: Macro Dashboard
-        with gr.Tab("📊 Macro Indicators"):
-            with gr.Row():
-                start_2 = gr.Textbox("2023-01-01", label="Start Date", scale=1)
-                end_2 = gr.Textbox(datetime.today().strftime('%Y-%m-%d'), label="End Date", scale=1)
-                btn_2 = gr.Button("Update", variant="primary", scale=1)
-            
-            with gr.Row():
-                plot_macro = gr.Plot(scale=2)
-                plot_regional = gr.Plot(scale=1)
-            
-            btn_2.click(
-                fn=plot_macro_indicators,
-                inputs=[start_2, end_2],
-                outputs=plot_macro
-            ).then(
-                fn=plot_relative_strength,
-                inputs=[start_2, end_2],
-                outputs=plot_regional
-            )
-        
-        # TAB 3: Custom Analysis
-        with gr.Tab("🔍 Custom Analysis"):
-            with gr.Row():
-                start_3 = gr.Textbox("2023-01-01", label="Start Date", scale=1)
-                end_3 = gr.Textbox(datetime.today().strftime('%Y-%m-%d'), label="End Date", scale=1)
-            
-            assets_select = gr.Dropdown(
-                AVAILABLE_ASSETS,
-                value=['US_Equity', 'Gold', 'Oil', 'VIX'],
-                multiselect=True,
-                label="Select Assets"
-            )
-            
-            btn_3 = gr.Button("Analyze", variant="primary")
-            
-            with gr.Row():
-                plot_corr = gr.Plot()
-                plot_dd = gr.Plot()
-            
-            btn_3.click(
-                fn=plot_correlation_matrix,
-                inputs=[start_3, end_3, assets_select],
-                outputs=plot_corr
-            ).then(
-                fn=plot_drawdown,
-                inputs=[start_3, end_3, assets_select],
-                outputs=plot_dd
-            )
-    
-    # Load initial data
-    demo.load(
-        fn=plot_geopolitical_risk,
-        inputs=[start_1, end_1],
-        outputs=plot_geo_risk
-    )
+# ======================
+# MAIN EXECUTION
+# ======================
 
 if __name__ == "__main__":
+    # Create and launch the interface
+    demo = create_interface()
     demo.launch()