┌─────────────────────────────────────────────────────────────────────────────┐
│                    FUTURE TECHNOLOGY CONVERGENCE                            │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│                          ┌─────────────────┐                               │
│                          │    BLOCKCHAIN    │                               │
│                          │     (Trust)      │                               │
│                          └────────┬────────┘                               │
│                                   │                                         │
│              ┌────────────────────┼────────────────────┐                   │
│              │                    │                    │                   │
│              v                    v                    v                   │
│   ┌──────────────────┐  ┌──────────────────┐  ┌──────────────────┐        │
│   │       AI/ML      │  │  Quantum Computing│  │     IoT/5G      │        │
│   │  (Intelligence)  │  │ (Computing Power) │  │ (Connectivity)   │        │
│   └────────┬─────────┘  └────────┬─────────┘  └────────┬─────────┘        │
│            │                     │                     │                   │
│            └─────────────────────┼─────────────────────┘                   │
│                                  v                                         │
│                          ┌─────────────────┐                               │
│                          │     Web3/Metaverse                              │
│                          │  (Decentralised World)                         │
│                          └─────────────────┘                               │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

# Simplified AI oracle simulation
class AIOracle:
    def __init__(self):
        self.predictions = []
    
    def predict_price(self, asset: str, days_ahead: int) -> float:
        # Simulate AI prediction
        base_price = {"BTC": 60000, "ETH": 3000, "SOL": 100}.get(asset, 100)
        volatility = random.uniform(-0.05, 0.05) * days_ahead
        predicted = base_price * (1 + volatility)
        return predicted

2025-2030: Quantum computers reach 50-100 qubits (Noisy Intermediate-Scale)
2030-2035: Quantum advantage for specific problems
2035-2040: Sufficient qubits to break RSA/ECC (≈4000+ qubits)
2040+: Widespread quantum computing, need for post-quantum crypto

┌─────────────────────────────────────────────────────────────────────────────┐
│                    BLOCKCHAIN ENERGY EFFICIENCY                            │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  Consensus Mechanism    │  Energy per Transaction   │  Scalability        │
│  ──────────────────────┼──────────────────────────┼─────────────────────│
│  PoW (Bitcoin)          │  Very High (≈800 kWh)    │  Low                │
│  PoS (Ethereum)         │  Low (≈0.01 kWh)         │  Medium             │
│  PoA                     │  Very Low                │  High               │
│  DPoS                    │  Very Low                │  High               │
│  PBFT                    │  Very Low                │  Medium             │
│  Avalanche               │  Low                     │  High               │
│  DAG (IOTA)             │  Very Low                │  Very High          │
│                                                                             │
│  Note: Ethereum PoS reduced energy consumption by 99.95%                   │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

# ===================================================================
# MODULE 3, LESSON 8: FUTURE TRENDS AND EMERGING TECHNOLOGIES
# ===================================================================

import hashlib
import time
import random
import json
from typing import Dict, List, Optional, Tuple
from datetime import datetime, timedelta
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')

print("="*70)
print("FUTURE TRENDS AND EMERGING TECHNOLOGIES")
print("="*70)

# ----------------------------------------------------------------
# PART A: AI + BLOCKCHAIN SIMULATION (AI Oracle)
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART A: AI + Blockchain – AI Oracle Simulation")
print("-"*60)

class AIOracle:
    """
    Simulated AI oracle providing predictions to smart contracts.
    """
    def __init__(self, name: str, accuracy: float = 0.85):
        self.name = name
        self.accuracy = accuracy
        self.predictions = []
        self.performance = []
    
    def train(self, historical_data: List[float]) -> Dict:
        """Simulate training on historical data."""
        # Calculate some statistics
        if historical_data:
            mean_val = np.mean(historical_data)
            std_val = np.std(historical_data)
            trend = (historical_data[-1] - historical_data[0]) / len(historical_data)
        else:
            mean_val, std_val, trend = 0, 0, 0
        
        return {
            'mean': mean_val,
            'std': std_val,
            'trend': trend,
            'trained_at': datetime.now()
        }
    
    def predict(self, asset: str, days_ahead: int, current_price: float) -> Dict:
        """Make a prediction for asset price."""
        # Simulate prediction with some noise
        daily_volatility = random.uniform(0.01, 0.03)
        expected_move = random.uniform(-0.02, 0.02)
        
        # Add some accuracy factor
        noise = (1 - self.accuracy) * random.uniform(-0.1, 0.1)
        
        predicted_price = current_price * (1 + expected_move * days_ahead + noise)
        confidence = self.accuracy * random.uniform(0.8, 1.0)
        
        prediction = {
            'asset': asset,
            'current_price': current_price,
            'predicted_price': predicted_price,
            'days_ahead': days_ahead,
            'confidence': confidence,
            'timestamp': datetime.now(),
            'oracle': self.name
        }
        self.predictions.append(prediction)
        return prediction
    
    def verify_prediction(self, actual_price: float) -> bool:
        """Verify if the latest prediction was accurate."""
        if not self.predictions:
            return False
        latest = self.predictions[-1]
        error_pct = abs(actual_price - latest['predicted_price']) / latest['predicted_price']
        is_accurate = error_pct < 0.05  # Within 5%
        self.performance.append({
            'predicted': latest['predicted_price'],
            'actual': actual_price,
            'error_pct': error_pct,
            'accurate': is_accurate,
            'timestamp': datetime.now()
        })
        return is_accurate
    
    def get_performance_metrics(self) -> Dict:
        if not self.performance:
            return {'accuracy': 0, 'avg_error': 0, 'predictions': 0}
        
        accurate_count = sum(1 for p in self.performance if p['accurate'])
        avg_error = np.mean([p['error_pct'] for p in self.performance])
        
        return {
            'accuracy': accurate_count / len(self.performance),
            'avg_error': avg_error,
            'predictions': len(self.performance)
        }

# Create AI oracle
oracle = AIOracle("AlphaPredict", accuracy=0.82)

print("AI Oracle Created:")
print(f"  Name: {oracle.name}")
print(f"  Accuracy: {oracle.accuracy:.1%}")

# Simulate predictions
print("\nSimulating AI predictions...")
btc_prices = [60000 + i * random.uniform(-500, 500) for i in range(30)]
current_price = btc_prices[-1]

for i in range(5):
    days = random.randint(1, 10)
    prediction = oracle.predict("BTC", days, current_price)
    print(f"  Day {days}: ${prediction['predicted_price']:,.2f} (Confidence: {prediction['confidence']:.1%})")

# Verify some predictions
print("\nVerifying predictions...")
for i in range(3):
    actual = current_price * (1 + random.uniform(-0.03, 0.03))
    is_accurate = oracle.verify_prediction(actual)
    print(f"  Prediction {i+1}: {'✅ Accurate' if is_accurate else '❌ Inaccurate'}")

# Performance metrics
metrics = oracle.get_performance_metrics()
print(f"\nOracle Performance:")
print(f"  Accuracy: {metrics['accuracy']:.1%}")
print(f"  Avg Error: {metrics['avg_error']:.2%}")

# ----------------------------------------------------------------
# PART B: QUANTUM COMPUTING IMPACT SIMULATION
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART B: Quantum Computing Impact Simulation")
print("-"*60)

class QuantumSimulator:
    """
    Simulate quantum computing impact on blockchain security.
    """
    def __init__(self):
        self.quantum_qubits = 0
        self.quantum_power = 0
        self.year = 2024
        self.threat_level = 'Low'
        self.milestones = []
    
    def advance_year(self):
        """Advance quantum computing capability by one year."""
        self.year += 1
        # Simulate qubit growth (doubling every 2 years)
        self.quantum_qubits = max(50, int(50 * (1.3) ** (self.year - 2024)))
        self.quantum_power = min(100, self.quantum_qubits / 4000 * 100)
        
        # Determine threat level
        if self.quantum_power < 25:
            self.threat_level = 'Low'
        elif self.quantum_power < 50:
            self.threat_level = 'Medium'
        elif self.quantum_power < 75:
            self.threat_level = 'High'
        else:
            self.threat_level = 'Critical'
        
        # Record milestones
        if self.quantum_qubits >= 100 and len(self.milestones) == 0:
            self.milestones.append({'year': self.year, 'event': '100-qubit milestone'})
        if self.quantum_qubits >= 1000 and len(self.milestones) == 1:
            self.milestones.append({'year': self.year, 'event': '1000-qubit milestone'})
        if self.quantum_qubits >= 4000 and len(self.milestones) == 2:
            self.milestones.append({'year': self.year, 'event': 'RSA-2048 break threshold'})
    
    def get_timeline(self, years: int = 20) -> pd.DataFrame:
        """Generate a timeline of quantum computing evolution."""
        data = []
        for _ in range(years):
            self.advance_year()
            data.append({
                'year': self.year,
                'qubits': self.quantum_qubits,
                'power': self.quantum_power,
                'threat_level': self.threat_level
            })
        return pd.DataFrame(data)

# Simulate quantum timeline
quantum = QuantumSimulator()
timeline = quantum.get_timeline(20)

print("Quantum Computing Timeline (2024-2044):")
print(timeline.head(10).to_string(index=False))
print("\n...")

print("\nKey Milestones:")
for milestone in quantum.milestones:
    print(f"  {milestone['year']}: {milestone['event']}")

# Visualise quantum threat
fig, ax = plt.subplots(figsize=(12, 5))
ax.plot(timeline['year'], timeline['power'], color='red', linewidth=2, label='Quantum Power')
ax.axhline(y=25, color='orange', linestyle='--', alpha=0.5, label='Medium Threat')
ax.axhline(y=50, color='orange', linestyle='--', alpha=0.5)
ax.axhline(y=75, color='red', linestyle='--', alpha=0.5, label='Critical Threat')
ax.fill_between(timeline['year'], 0, timeline['power'], alpha=0.3, color='red')
ax.set_xlabel('Year')
ax.set_ylabel('Quantum Power (%)')
ax.set_title('Quantum Computing Threat Evolution')
ax.legend()
ax.grid(True, alpha=0.3)

plt.tight_layout()
plt.savefig('quantum_threat.png', dpi=300, bbox_inches='tight')
plt.show()
print("Quantum threat chart saved as 'quantum_threat.png'")

# ----------------------------------------------------------------
# PART C: ENERGY EFFICIENCY COMPARISON
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART C: Blockchain Energy Efficiency Comparison")
print("-"*60)

energy_data = {
    'Consensus': ['PoW (Bitcoin)', 'PoS (Ethereum)', 'DPoS', 'PBFT', 'Avalanche', 'DAG'],
    'Energy per Tx (kWh)': [800, 0.01, 0.005, 0.001, 0.05, 0.001],
    'TPS': [7, 30, 1000, 1000, 4500, 1000],
    'Decentralisation Score': [10, 7, 4, 3, 8, 6]
}

energy_df = pd.DataFrame(energy_data)
print(energy_df.to_string(index=False))

# Visualise
fig, axes = plt.subplots(1, 2, figsize=(14, 5))

# 1. Energy comparison
ax1 = axes[0]
# Use log scale for energy
energy_values = energy_df['Energy per Tx (kWh)']
consensus_names = energy_df['Consensus']
colors = ['red' if e > 100 else 'orange' if e > 1 else 'green' for e in energy_values]

ax1.barh(consensus_names, energy_values, color=colors, alpha=0.7)
ax1.set_xlabel('Energy per Transaction (kWh)')
ax1.set_title('Energy Consumption by Consensus')
ax1.set_xscale('log')
ax1.grid(True, alpha=0.3)

# 2. Efficiency quadrant
ax2 = axes[1]
scatter = ax2.scatter(energy_df['TPS'], energy_df['Energy per Tx (kWh)'], 
                      s=energy_df['Decentralisation Score'] * 100,
                      c=range(len(energy_df)), cmap='viridis', alpha=0.7)
for i, row in energy_df.iterrows():
    ax2.annotate(row['Consensus'][:6], (row['TPS'], row['Energy per Tx (kWh)']),
                 xytext=(5, 5), textcoords='offset points', fontsize=8)
ax2.set_xlabel('TPS')
ax2.set_ylabel('Energy per Tx (kWh)')
ax2.set_title('Energy vs Throughput')
ax2.set_yscale('log')
ax2.grid(True, alpha=0.3)

plt.tight_layout()
plt.savefig('energy_efficiency.png', dpi=300, bbox_inches='tight')
plt.show()
print("Energy efficiency chart saved as 'energy_efficiency.png'")

# ----------------------------------------------------------------
# PART D: EMERGING TRENDS DASHBOARD
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART D: Emerging Trends Dashboard")
print("-"*60)

trends_data = {
    'Trend': [
        'AI + Blockchain',
        'Zero-Knowledge Proofs',
        'Quantum-Resistant Crypto',
        'Web3 / DePIN',
        'Green Blockchain',
        'Decentralised Identity',
        'RWA Tokenisation',
        'Cross-Chain Interoperability',
        'Regulatory Clarity',
        'Metaverse Integration'
    ],
    'Maturity': [
        'Growth',
        'Growth',
        'Emerging',
        'Growth',
        'Growth',
        'Growth',
        'Growth',
        'Growth',
        'Emerging',
        'Pilot'
    ],
    'Impact (1-10)': [9, 8, 9, 8, 8, 9, 9, 8, 8, 7],
    'Adoption (1-10)': [6, 5, 2, 5, 6, 5, 5, 5, 4, 3],
    'Investment Trend': [
        '↑↑↑',
        '↑↑',
        '↑↑',
        '↑↑',
        '↑↑↑',
        '↑↑',
        '↑↑↑',
        '↑↑',
        '↑',
        '↑↑'
    ]
}

trends_df = pd.DataFrame(trends_data)
print(trends_df.to_string(index=False))

# ----------------------------------------------------------------
# PART E: FUTURE FORECAST
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART E: Future Forecast (2025-2035)")
print("-"*60)

forecast = {
    "2025-2026: Foundation": {
        "Events": [
            "MiCA fully implemented in EU",
            "Major institutional adoption of crypto",
            "First quantum-resistant blockchains emerge",
            "AI agents integrated with smart contracts"
        ],
        "Impact": "Regulatory clarity drives institutional investment."
    },
    "2027-2028: Integration": {
        "Events": [
            "Widespread CBDC adoption (10+ countries)",
            "RWA tokenisation reaches $500B+",
            "Cross-chain interoperability matures",
            "ZK-rollups become standard"
        ],
        "Impact": "Blockchain becomes mainstream financial infrastructure."
    },
    "2029-2030: Maturity": {
        "Events": [
            "Post-quantum cryptography standardised",
            "DeFi integrated with traditional finance",
            "Self-sovereign identity widely adopted",
            "Green blockchain regulatory requirements"
        ],
        "Impact": "Full convergence of CeFi and DeFi."
    },
    "2031-2035: Transformation": {
        "Events": [
            "Autonomous AI DAOs operate",
            "Metaverse with full asset ownership",
            "Decentralised governance at scale",
            "Quantum-safe blockchain networks"
        ],
        "Impact": "Decentralised systems become primary infrastructure."
    }
}

for period, details in forecast.items():
    print(f"\n{period.upper()}:")
    print(f"  Impact: {details['Impact']}")
    print("  Key Events:")
    for event in details['Events']:
        print(f"    • {event}")

# ----------------------------------------------------------------
# PART F: RECOMMENDATIONS FOR THE FUTURE
# ----------------------------------------------------------------

print("\n" + "="*70)
print("PART F: Recommendations for the Future")
print("="*70)

print("""
Future Trends and Emerging Technologies – Key Takeaways:

1. AI + Blockchain: AI agents, predictive analytics, autonomous organisations.
2. Quantum Computing: Threat to cryptography; post-quantum solutions needed.
3. Web3 / Metaverse: Decentralised internet and immersive digital worlds.
4. Green Blockchain: Energy-efficient consensus, carbon offsetting.
5. Zero-Knowledge Proofs: Privacy-preserving verification.
6. RWA Tokenisation: Real-world assets on-chain.
7. Regulatory Clarity: Frameworks enabling institutional adoption.

Strategic Recommendations:
  - Invest in quantum-resistant cryptography research.
  - Explore AI-blockchain integration for automation.
  - Build for interoperability across chains.
  - Prioritise sustainability and energy efficiency.
  - Engage with regulatory developments early.
  - Develop talent in emerging technologies.
  - Focus on user-centric design and accessibility.
  - Prepare for convergence of traditional and decentralised finance.
""")