#!/usr/bin/env python3
"""
Client-to-Miner Workflow Demo with Enhanced Services
Demonstrates complete workflow from client request to miner processing
"""

import requests
import json
import time
from datetime import datetime

# Enhanced service endpoint
BASE_URL = "http://127.0.0.1:8002"

def simulate_client_request():
    """Simulate a client requesting AI agent services"""
    print("👤 CLIENT: Requesting AI Agent Services")
    print("=" * 50)
    
    # Client request data
    client_request = {
        "client_id": "client_demo_001",
        "request_type": "multimodal_inference",
        "data": {
            "text": "Analyze this sentiment: 'I love the new AITBC enhanced services!'",
            "image_url": "https://example.com/test_image.jpg",
            "audio_url": "https://example.com/test_audio.wav",
            "requirements": {
                "gpu_acceleration": True,
                "performance_target": 0.95,
                "cost_optimization": True
            }
        },
        "timestamp": datetime.now().isoformat()
    }
    
    print(f"📋 Client Request:")
    print(f"   Client ID: {client_request['client_id']}")
    print(f"   Request Type: {client_request['request_type']}")
    print(f"   Data Types: text, image, audio")
    print(f"   Requirements: {client_request['data']['requirements']}")
    
    return client_request

def process_multimodal_data(request_data):
    """Process multi-modal data through enhanced services"""
    print("\n🧠 MULTI-MODAL PROCESSING")
    print("=" * 50)
    
    # Test multi-modal processing
    try:
        response = requests.post(f"{BASE_URL}/test-multimodal", 
                                json=request_data,
                                timeout=10)
        
        if response.status_code == 200:
            result = response.json()
            print(f"✅ Multi-Modal Processing: SUCCESS")
            print(f"   Service: {result['service']}")
            print(f"   Status: {result['status']}")
            print(f"   Features Available:")
            for feature in result['features']:
                print(f"     - {feature}")
            
            # Simulate processing results
            processing_results = {
                "text_analysis": {
                    "sentiment": "positive",
                    "confidence": 0.92,
                    "entities": ["AITBC", "enhanced services"]
                },
                "image_analysis": {
                    "objects_detected": ["logo", "text"],
                    "confidence": 0.87,
                    "processing_time": "0.15s"
                },
                "audio_analysis": {
                    "speech_detected": True,
                    "language": "en",
                    "confidence": 0.89,
                    "processing_time": "0.22s"
                }
            }
            
            print(f"\n📊 Processing Results:")
            for modality, results in processing_results.items():
                print(f"   {modality}:")
                for key, value in results.items():
                    print(f"     {key}: {value}")
            
            return processing_results
        else:
            print(f"❌ Multi-Modal Processing: FAILED")
            return None
            
    except Exception as e:
        print(f"❌ Multi-Modal Processing: ERROR - {e}")
        return None

def route_to_openclaw_agents(processing_results):
    """Route processing to OpenClaw agents for optimization"""
    print("\n🤖 OPENCLAW AGENT ROUTING")
    print("=" * 50)
    
    # Test OpenClaw integration
    try:
        response = requests.post(f"{BASE_URL}/test-openclaw",
                                json=processing_results,
                                timeout=10)
        
        if response.status_code == 200:
            result = response.json()
            print(f"✅ OpenClaw Integration: SUCCESS")
            print(f"   Service: {result['service']}")
            print(f"   Status: {result['status']}")
            print(f"   Agent Capabilities:")
            for capability in result['features']:
                print(f"     - {capability}")
            
            # Simulate agent routing
            agent_routing = {
                "selected_agent": "agent_inference_001",
                "routing_strategy": "performance_optimized",
                "expected_performance": 0.94,
                "estimated_cost": 0.15,
                "gpu_required": True,
                "processing_time": "0.08s"
            }
            
            print(f"\n🎯 Agent Routing:")
            for key, value in agent_routing.items():
                print(f"   {key}: {value}")
            
            return agent_routing
        else:
            print(f"❌ OpenClaw Integration: FAILED")
            return None
            
    except Exception as e:
        print(f"❌ OpenClaw Integration: ERROR - {e}")
        return None

def process_marketplace_transaction(agent_routing):
    """Process marketplace transaction for agent services"""
    print("\n💰 MARKETPLACE TRANSACTION")
    print("=" * 50)
    
    # Test marketplace enhancement
    try:
        response = requests.post(f"{BASE_URL}/test-marketplace",
                                json=agent_routing,
                                timeout=10)
        
        if response.status_code == 200:
            result = response.json()
            print(f"✅ Marketplace Enhancement: SUCCESS")
            print(f"   Service: {result['service']}")
            print(f"   Status: {result['status']}")
            print(f"   Marketplace Features:")
            for feature in result['features']:
                print(f"     - {feature}")
            
            # Simulate marketplace transaction
            transaction = {
                "transaction_id": "txn_demo_001",
                "agent_id": agent_routing['selected_agent'],
                "client_payment": agent_routing['estimated_cost'],
                "royalty_distribution": {
                    "primary": 0.70,
                    "secondary": 0.20,
                    "tertiary": 0.10
                },
                "license_type": "commercial",
                "verification_status": "verified",
                "timestamp": datetime.now().isoformat()
            }
            
            print(f"\n💸 Transaction Details:")
            for key, value in transaction.items():
                if key != "royalty_distribution":
                    print(f"   {key}: {value}")
            
            print(f"   Royalty Distribution:")
            for tier, percentage in transaction['royalty_distribution'].items():
                print(f"     {tier}: {percentage * 100}%")
            
            return transaction
        else:
            print(f"❌ Marketplace Enhancement: FAILED")
            return None
            
    except Exception as e:
        print(f"❌ Marketplace Enhancement: ERROR - {e}")
        return None

def simulate_miner_processing(transaction):
    """Simulate miner processing the job"""
    print("\n⛏️  MINER PROCESSING")
    print("=" * 50)
    
    # Simulate miner job processing
    miner_processing = {
        "miner_id": "miner_demo_001",
        "job_id": f"job_{transaction['transaction_id']}",
        "agent_id": transaction['agent_id'],
        "processing_status": "completed",
        "start_time": datetime.now().isoformat(),
        "end_time": (datetime.now().timestamp() + 0.08).__str__(),
        "gpu_utilization": 0.85,
        "memory_usage": "2.1GB",
        "output": {
            "final_result": "positive_sentiment_high_confidence",
            "confidence_score": 0.94,
            "processing_summary": "Multi-modal analysis completed successfully with GPU acceleration"
        }
    }
    
    print(f"🔧 Miner Processing:")
    for key, value in miner_processing.items():
        if key != "output":
            print(f"   {key}: {value}")
    
    print(f"   Output:")
    for key, value in miner_processing['output'].items():
        print(f"     {key}: {value}")
    
    return miner_processing

def return_result_to_client(miner_processing, original_request):
    """Return final result to client"""
    print("\n📤 CLIENT RESPONSE")
    print("=" * 50)
    
    client_response = {
        "request_id": original_request['client_id'],
        "status": "completed",
        "processing_time": "0.08s",
        "miner_result": miner_processing['output'],
        "transaction_id": miner_processing['job_id'],
        "cost": 0.15,
        "performance_metrics": {
            "gpu_utilization": miner_processing['gpu_utilization'],
            "accuracy": miner_processing['output']['confidence_score'],
            "throughput": "12.5 requests/second"
        },
        "timestamp": datetime.now().isoformat()
    }
    
    print(f"🎉 Final Response to Client:")
    for key, value in client_response.items():
        if key not in ["miner_result", "performance_metrics"]:
            print(f"   {key}: {value}")
    
    print(f"   Miner Result:")
    for key, value in client_response['miner_result'].items():
        print(f"     {key}: {value}")
    
    print(f"   Performance Metrics:")
    for key, value in client_response['performance_metrics'].items():
        print(f"     {key}: {value}")
    
    return client_response

def run_complete_workflow():
    """Run complete client-to-miner workflow"""
    print("🚀 AITBC Enhanced Services - Client-to-Miner Workflow Demo")
    print("=" * 60)
    print("Demonstrating complete AI agent processing pipeline")
    print("with multi-modal processing, OpenClaw integration, and marketplace")
    print("=" * 60)
    
    # Step 1: Client Request
    client_request = simulate_client_request()
    
    # Step 2: Multi-Modal Processing
    processing_results = process_multimodal_data(client_request)
    if not processing_results:
        print("\n❌ Workflow failed at multi-modal processing")
        return False
    
    # Step 3: OpenClaw Agent Routing
    agent_routing = route_to_openclaw_agents(processing_results)
    if not agent_routing:
        print("\n❌ Workflow failed at agent routing")
        return False
    
    # Step 4: Marketplace Transaction
    transaction = process_marketplace_transaction(agent_routing)
    if not transaction:
        print("\n❌ Workflow failed at marketplace transaction")
        return False
    
    # Step 5: Miner Processing
    miner_processing = simulate_miner_processing(transaction)
    
    # Step 6: Return Result to Client
    client_response = return_result_to_client(miner_processing, client_request)
    
    # Summary
    print("\n✅ WORKFLOW COMPLETED SUCCESSFULLY!")
    print("=" * 60)
    
    print("🎯 Workflow Summary:")
    print("   1. ✅ Client Request Received")
    print("   2. ✅ Multi-Modal Data Processed (Text, Image, Audio)")
    print("   3. ✅ OpenClaw Agent Routing Applied")
    print("   4. ✅ Marketplace Transaction Processed")
    print("   5. ✅ Miner Job Completed")
    print("   6. ✅ Result Returned to Client")
    
    print(f"\n📊 Performance Metrics:")
    print(f"   Total Processing Time: 0.08s")
    print(f"   GPU Utilization: 85%")
    print(f"   Accuracy Score: 94%")
    print(f"   Cost: $0.15")
    print(f"   Throughput: 12.5 requests/second")
    
    print(f"\n🔗 Enhanced Services Demonstrated:")
    print(f"   ✅ Multi-Modal Processing: Text, Image, Audio analysis")
    print(f"   ✅ OpenClaw Integration: Agent routing and optimization")
    print(f"   ✅ Marketplace Enhancement: Royalties, licensing, verification")
    print(f"   ✅ GPU Acceleration: High-performance processing")
    print(f"   ✅ Client-to-Miner: Complete workflow pipeline")
    
    print(f"\n🚀 Next Steps:")
    print(f"   1. Deploy additional enhanced services to other ports")
    print(f"   2. Integrate with production AITBC infrastructure")
    print(f"   3. Scale to handle multiple concurrent requests")
    print(f"   4. Add monitoring and analytics")
    
    return True

if __name__ == "__main__":
    run_complete_workflow()
