#!/usr/bin/env python3
"""
FRED ML Simple Demo
Shows system capabilities without requiring real credentials
"""

import os
import sys
import json
import time
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import seaborn as sns
from datetime import datetime, timedelta

def demo_data_processing():
    """Demo data processing capabilities"""
    print("📊 Data Processing Demo")
    print("=" * 40)
    
    # Create sample economic data
    np.random.seed(42)
    dates = pd.date_range('2020-01-01', '2024-01-01', freq='M')
    
    # Simulate economic indicators
    data = {
        'GDP': np.random.normal(100, 5, len(dates)) + np.cumsum(np.random.normal(0, 0.5, len(dates))),
        'UNRATE': np.random.normal(5, 1, len(dates)),
        'CPIAUCSL': np.random.normal(200, 10, len(dates)) + np.cumsum(np.random.normal(0, 1, len(dates))),
        'FEDFUNDS': np.random.normal(2, 0.5, len(dates)),
        'DGS10': np.random.normal(3, 0.3, len(dates))
    }
    
    df = pd.DataFrame(data, index=dates)
    
    print(f"✅ Generated {len(df)} data points for {len(df.columns)} indicators")
    print(f"📈 Date range: {df.index.min()} to {df.index.max()}")
    
    # Basic statistics
    print("\n📊 Summary Statistics:")
    print(df.describe().round(2))
    
    # Correlation analysis
    print("\n🔗 Correlation Matrix:")
    correlation = df.corr()
    print(correlation.round(3))
    
    return df

def demo_visualization(df):
    """Demo visualization capabilities"""
    print("\n🎨 Visualization Demo")
    print("=" * 40)
    
    # 1. Time series plot
    print("📈 Creating time series visualization...")
    fig1 = go.Figure()
    
    for col in df.columns:
        fig1.add_trace(go.Scatter(
            x=df.index,
            y=df[col],
            name=col,
            mode='lines'
        ))
    
    fig1.update_layout(
        title="Economic Indicators Over Time",
        xaxis_title="Date",
        yaxis_title="Value",
        height=500
    )
    
    # Save the plot
    fig1.write_html("demo_time_series.html")
    print("✅ Time series plot saved as demo_time_series.html")
    
    # 2. Correlation heatmap
    print("🔥 Creating correlation heatmap...")
    correlation = df.corr()
    
    fig2 = px.imshow(
        correlation,
        text_auto=True,
        aspect="auto",
        color_continuous_scale="RdBu",
        title="Correlation Matrix Heatmap"
    )
    
    fig2.write_html("demo_correlation.html")
    print("✅ Correlation heatmap saved as demo_correlation.html")
    
    # 3. Distribution plots
    print("📊 Creating distribution plots...")
    fig3 = make_subplots(
        rows=2, cols=3,
        subplot_titles=df.columns,
        specs=[[{"secondary_y": False}, {"secondary_y": False}, {"secondary_y": False}],
               [{"secondary_y": False}, {"secondary_y": False}, {"secondary_y": False}]]
    )
    
    for i, col in enumerate(df.columns):
        row = (i // 3) + 1
        col_num = (i % 3) + 1
        fig3.add_trace(
            go.Histogram(x=df[col], name=col),
            row=row, col=col_num
        )
    
    fig3.update_layout(height=600, title_text="Distribution of Economic Indicators")
    fig3.write_html("demo_distributions.html")
    print("✅ Distribution plots saved as demo_distributions.html")
    
    return True

def demo_analysis(df):
    """Demo analysis capabilities"""
    print("\n🔍 Analysis Demo")
    print("=" * 40)
    
    # Trend analysis
    print("📈 Trend Analysis:")
    trends = {}
    for col in df.columns:
        # Simple linear trend
        x = np.arange(len(df))
        y = df[col].values
        slope, intercept = np.polyfit(x, y, 1)
        trends[col] = {
            'slope': slope,
            'trend_direction': 'Increasing' if slope > 0 else 'Decreasing',
            'trend_strength': abs(slope)
        }
    
    for indicator, trend in trends.items():
        print(f"   {indicator}: {trend['trend_direction']} (slope: {trend['slope']:.4f})")
    
    # Volatility analysis
    print("\n📊 Volatility Analysis:")
    volatility = df.pct_change().std() * np.sqrt(252)  # Annualized
    for indicator, vol in volatility.items():
        print(f"   {indicator}: {vol:.2%} annualized volatility")
    
    # Correlation analysis
    print("\n🔗 Correlation Analysis:")
    correlation = df.corr()
    for i, col1 in enumerate(df.columns):
        for j, col2 in enumerate(df.columns):
            if i < j:  # Avoid duplicates
                corr = correlation.loc[col1, col2]
                strength = 'Strong' if abs(corr) > 0.7 else 'Moderate' if abs(corr) > 0.3 else 'Weak'
                print(f"   {col1} vs {col2}: {corr:.3f} ({strength})")
    
    return trends, volatility

def demo_system_architecture():
    """Demo system architecture"""
    print("\n🏗️ System Architecture Demo")
    print("=" * 40)
    
    architecture = {
        "Frontend": {
            "Technology": "Streamlit",
            "Features": ["Interactive dashboard", "Real-time visualization", "User-friendly interface"],
            "Status": "✅ Ready"
        },
        "Backend": {
            "Technology": "AWS Lambda",
            "Features": ["Serverless processing", "Event-driven", "Auto-scaling"],
            "Status": "✅ Ready"
        },
        "Storage": {
            "Technology": "AWS S3",
            "Features": ["Scalable storage", "Lifecycle policies", "Versioning"],
            "Status": "✅ Ready"
        },
        "Scheduling": {
            "Technology": "EventBridge",
            "Features": ["Automated triggers", "Quarterly analysis", "CloudWatch monitoring"],
            "Status": "✅ Ready"
        },
        "Data Source": {
            "Technology": "FRED API",
            "Features": ["Economic indicators", "Real-time data", "Historical analysis"],
            "Status": "✅ Ready"
        }
    }
    
    for component, details in architecture.items():
        print(f"\n{component}:")
        print(f"   Technology: {details['Technology']}")
        print(f"   Features: {', '.join(details['Features'])}")
        print(f"   Status: {details['Status']}")

def demo_workflow():
    """Demo complete workflow"""
    print("\n🔄 Complete Workflow Demo")
    print("=" * 40)
    
    steps = [
        ("Data Retrieval", "Fetching economic data from FRED API"),
        ("Data Processing", "Cleaning and preparing data for analysis"),
        ("Statistical Analysis", "Calculating correlations and trends"),
        ("Visualization", "Creating charts and graphs"),
        ("Report Generation", "Compiling results into reports"),
        ("Cloud Storage", "Uploading results to S3"),
        ("Scheduling", "Setting up automated quarterly analysis")
    ]
    
    for i, (step, description) in enumerate(steps, 1):
        print(f"{i}. {step}: {description}")
        time.sleep(0.5)  # Simulate processing time
    
    print("\n✅ Complete workflow demonstrated!")

def main():
    """Main demo function"""
    print("🚀 FRED ML System Demo")
    print("=" * 50)
    print("This demo shows the capabilities of the FRED ML system")
    print("without requiring real AWS credentials or FRED API key.")
    print()
    
    # Demo system architecture
    demo_system_architecture()
    
    # Demo data processing
    df = demo_data_processing()
    
    # Demo analysis
    trends, volatility = demo_analysis(df)
    
    # Demo visualization
    demo_visualization(df)
    
    # Demo complete workflow
    demo_workflow()
    
    print("\n" + "=" * 50)
    print("🎉 Demo completed successfully!")
    print("\n📁 Generated files:")
    print("   - demo_time_series.html")
    print("   - demo_correlation.html") 
    print("   - demo_distributions.html")
    print("\n🎯 Next steps:")
    print("1. Set up real AWS credentials and FRED API key")
    print("2. Run: python scripts/test_dev.py")
    print("3. Launch: streamlit run frontend/app.py")
    print("4. Deploy to production using CI/CD pipeline")

if __name__ == '__main__':
    main()