#!/usr/bin/env python3
"""
Advanced Features Demo for Cost Katana Python SDK

This script demonstrates enterprise-level features:
- Batch processing and parallel execution
- Streaming responses
- Custom provider integration
- Advanced analytics and monitoring
- Cost optimization strategies
- Multi-agent orchestration
- Custom model fine-tuning
"""

from pathlib import Path
from dataclasses import dataclass

# Add the parent directory to the path so we can import cost_katana
import sys

sys.path.insert(0, str(Path(__file__).parent.parent))


@dataclass
class BenchmarkResult:
    """Result from model benchmarking"""

    model: str
    latency: float
    cost: float
    tokens: int
    quality_score: float


def demo_batch_processing():
    """Demonstrate batch processing capabilities"""
    print("🔄 Batch Processing")
    print("=" * 40)

    try:
        print("Cost Katana supports efficient batch processing:")
        print()
        print("```python")
        print("import asyncio")
        print("import cost_katana as ck")
        print("")
        print(
            "async def process_batch(prompts: List[str], "
            "model_name: str = 'nova-lite'):"
        )
        print("    ck.configure(api_key='dak_your_key_here')")
        print("    model = ck.create_generative_model(model_name)")
        print("    ")
        print("    # Create tasks for parallel processing")
        print(
            "    tasks = [model.generate_content_async(prompt) "
            "for prompt in prompts]"
        )
        print("    ")
        print("    # Execute all tasks concurrently")
        print("    responses = await asyncio.gather(*tasks)")
        print("    ")
        print("    # Process results")
        print("    total_cost = sum(r.usage_metadata.cost for r in responses)")
        print(
            "    total_tokens = sum(r.usage_metadata.total_tokens "
            "for r in responses)"
        )
        print("    ")
        print("    print(f'Processed {len(prompts)} prompts')")
        print("    print(f'Total cost: ${total_cost:.4f}')")
        print("    print(f'Total tokens: {total_tokens}')")
        print("    ")
        print("    return responses")
        print("")
        print("# Example usage")
        print("prompts = [")
        print("    'Explain quantum computing',")
        print("    'What is machine learning?',")
        print("    'Describe blockchain technology'")
        print("]")
        print("")
        print("responses = await process_batch(prompts)")
        print("```")

        return True
    except Exception as e:
        print(f"✗ Batch processing demo failed: {e}")
        return False


def demo_streaming_responses():
    """Demonstrate streaming response capabilities"""
    print("\n🌊 Streaming Responses")
    print("=" * 40)

    try:
        print("Cost Katana supports real-time streaming responses:")
        print()
        print("```python")
        print("import cost_katana as ck")
        print("")
        print("async def stream_response(prompt: str):")
        print("    ck.configure(api_key='dak_your_key_here')")
        print("    model = ck.create_generative_model('nova-lite')")
        print("    ")
        print("    # Start streaming response")
        print("    async for chunk in model.generate_content_stream(prompt):")
        print("        if chunk.text:")
        print("            print(chunk.text, end='', flush=True)")
        print("    print()  # New line at end")
        print("")
        print("# Example usage")
        print("await stream_response('Write a short story about AI')")
        print("```")
        print()
        print("Streaming Benefits:")
        print("✓ Real-time response display")
        print("✓ Reduced perceived latency")
        print("✓ Better user experience")
        print("✓ Progressive content generation")
        print("✓ Token-by-token cost tracking")

        return True
    except Exception as e:
        print(f"✗ Streaming demo failed: {e}")
        return False


def demo_cost_optimization():
    """Demonstrate cost optimization strategies"""
    print("\n💰 Cost Optimization")
    print("=" * 40)

    try:
        print("Cost Katana provides intelligent cost optimization:")
        print()
        print("```python")
        print("import cost_katana as ck")
        print("from cost_katana.models import GenerationConfig")
        print("")
        print("def optimize_for_cost(prompt: str, budget: float = 0.01):")
        print("    ck.configure(api_key='dak_your_key_here')")
        print("    ")
        print("    # Try different models based on cost")
        print("    models = ['nova-lite', 'nova-pro', 'claude-3-haiku']")
        print("    ")
        print("    for model_name in models:")
        print("        model = ck.create_generative_model(model_name)")
        print("        ")
        print("        # Optimize generation config")
        print("        config = GenerationConfig(")
        print("            max_output_tokens=500,  # Limit output")
        print("            temperature=0.7,        # Balanced creativity")
        print("            top_p=0.9               # Focus on likely tokens")
        print("        )")
        print("        ")
        print("        try:")
        print("            response = model.generate_content(prompt, " "config=config)")
        print("            cost = response.usage_metadata.cost")
        print("            ")
        print("            if cost <= budget:")
        print("                print(f'✓ Generated with {model_name}')")
        print("                print(f'Cost: ${cost:.4f}')")
        print("                return response")
        print("            else:")
        print("                print(f'✗ {model_name} too expensive: " "${cost:.4f}')")
        print("        except Exception as e:")
        print("            print(f'✗ {model_name} failed: {e}')")
        print("    ")
        print("    raise ValueError('No model fits within budget')")
        print("```")

        print("\nOptimization Strategies:")
        print("✓ Model selection based on cost")
        print("✓ Token limit optimization")
        print("✓ Parameter tuning")
        print("✓ Budget enforcement")
        print("✓ Quality-cost trade-offs")

        return True
    except Exception as e:
        print(f"✗ Cost optimization demo failed: {e}")
        return False


def demo_advanced_analytics():
    """Demonstrate advanced analytics capabilities"""
    print("\n📊 Advanced Analytics")
    print("=" * 40)

    try:
        print("Cost Katana provides comprehensive analytics:")
        print()
        print("```python")
        print("import cost_katana as ck")
        print("from datetime import datetime, timedelta")
        print("")
        print("def analyze_usage_patterns(days: int = 7):")
        print("    ck.configure(api_key='dak_your_key_here')")
        print("    ")
        print("    # Get usage analytics")
        print("    analytics = ck.get_usage_analytics()")
        print("    ")
        print("    print('📈 Usage Analytics')")
        print("    print('=' * 30)")
        print("    ")
        print("    # Cost breakdown")
        print("    print(f'Total cost: ${analytics.total_cost:.4f}')")
        print(
            "    print(f'Average cost per request: "
            "${analytics.avg_cost_per_request:.4f}')"
        )
        print("    print(f'Total requests: {analytics.total_requests}')")
        print("    ")
        print("    # Model performance")
        print("    print('\\nModel Performance:')")
        print("    for model_stats in analytics.model_stats:")
        print("        print(f'  {model_stats.model}:')")
        print("        print(f'    Requests: {model_stats.requests}')")
        print("        print(f'    Avg latency: " "{model_stats.avg_latency:.2f}s')")
        print("        print(f'    Success rate: " "{model_stats.success_rate:.1%}')")
        print("    ")
        print("    # Cost trends")
        print("    print('\\nCost Trends:')")
        print("    for trend in analytics.cost_trends:")
        print("        print(f'  {trend.date}: ${trend.cost:.4f}')")
        print("    ")
        print("    # Recommendations")
        print("    print('\\nOptimization Recommendations:')")
        print("    for rec in analytics.recommendations:")
        print("        print(f'  • {rec.description}')")
        print(
            "        print(f'    Potential savings: " "${rec.potential_savings:.4f}')"
        )
        print("```")

        print("\nAnalytics Features:")
        print("✓ Real-time usage monitoring")
        print("✓ Cost trend analysis")
        print("✓ Model performance comparison")
        print("✓ Optimization recommendations")
        print("✓ Budget tracking")
        print("✓ Usage forecasting")

        return True
    except Exception as e:
        print(f"✗ Advanced analytics demo failed: {e}")
        return False


def demo_multi_agent_orchestration():
    """Demonstrate multi-agent orchestration"""
    print("\n🤖 Multi-Agent Orchestration")
    print("=" * 40)

    try:
        print("Cost Katana supports complex multi-agent workflows:")
        print()
        print("```python")
        print("import asyncio")
        print("import cost_katana as ck")
        print("from typing import List, Dict")
        print("")
        print("class AgentOrchestrator:")
        print("    def __init__(self):")
        print("        ck.configure(api_key='dak_your_key_here')")
        print("        self.models = {")
        print("            'researcher': ck.create_generative_model('nova-pro'),")
        print("            'writer': ck.create_generative_model('nova-lite'),")
        print("            'reviewer': ck.create_generative_model(" "'claude-3-haiku')")
        print("        }")
        print("    ")
        print("    async def research_topic(self, topic: str) -> str:")
        print("        model = self.models['researcher']")
        print("        prompt = f'Research and provide key facts about: " "{topic}'")
        print("        response = await model.generate_content_async(prompt)")
        print("        return response.text")
        print("    ")
        print("    async def write_content(self, research: str, " "style: str) -> str:")
        print("        model = self.models['writer']")
        print(
            "        prompt = f'Write content in {style} style based on: " "{research}'"
        )
        print("        response = await model.generate_content_async(prompt)")
        print("        return response.text")
        print("    ")
        print("    async def review_content(self, content: str) -> " "Dict[str, Any]:")
        print("        model = self.models['reviewer']")
        print(
            "        prompt = f'Review this content and provide feedback: " "{content}'"
        )
        print("        response = await model.generate_content_async(prompt)")
        print("        return {'feedback': response.text, " "'quality_score': 0.85}")
        print("    ")
        print(
            "    async def create_content(self, topic: str, "
            "style: str = 'professional'):"
        )
        print("        # Step 1: Research")
        print("        research = await self.research_topic(topic)")
        print("        ")
        print("        # Step 2: Write")
        print("        content = await self.write_content(research, style)")
        print("        ")
        print("        # Step 3: Review")
        print("        review = await self.review_content(content)")
        print("        ")
        print("        return {")
        print("            'content': content,")
        print("            'research': research,")
        print("            'review': review")
        print("        }")
        print("")
        print("# Usage example")
        print("orchestrator = AgentOrchestrator()")
        print(
            "result = await orchestrator.create_content(" "'artificial intelligence')"
        )
        print("print(result['content'])")
        print("```")

        print("\nOrchestration Benefits:")
        print("✓ Parallel agent execution")
        print("✓ Specialized model selection")
        print("✓ Workflow automation")
        print("✓ Quality assurance")
        print("✓ Cost optimization")

        return True
    except Exception as e:
        print(f"✗ Multi-agent orchestration demo failed: {e}")
        return False


def demo_custom_providers():
    """Demonstrate custom provider integration"""
    print("\n🔧 Custom Provider Integration")
    print("=" * 40)

    try:
        print("Cost Katana supports custom provider integration:")
        print()
        print("```python")
        print("import cost_katana as ck")
        print("from cost_katana.providers.base import BaseProvider")
        print("from cost_katana.models import GenerationResponse, UsageMetadata")
        print("")
        print("class CustomProvider(BaseProvider):")
        print("    def __init__(self, api_key: str, base_url: str = None):")
        print("        super().__init__('custom-provider')")
        print("        self.api_key = api_key")
        print(
            "        self.base_url = base_url or " "'https://api.custom-provider.com'"
        )
        print("    ")
        print(
            "    async def generate_content(self, prompt: str, "
            "config: dict) -> GenerationResponse:"
        )
        print("        # Custom implementation")
        print("        # This would make actual API calls to your custom provider")
        print("        ")
        print("        # Simulate response")
        print("        response_text = f'Custom response to: {prompt}'")
        print("        ")
        print("        return GenerationResponse(")
        print("            text=response_text,")
        print("            usage_metadata=UsageMetadata(")
        print("                cost=0.001,  # Custom pricing")
        print(
            "                total_tokens=len(prompt.split()) + len(response_text.split()),"
        )
        print("                model='custom-model',")
        print("                provider='custom-provider'")
        print("            )")
        print("        )")
        print("")
        print("# Register custom provider")
        print("ck.register_provider('custom-provider', CustomProvider)")
        print("")
        print("# Use custom provider")
        print(
            "model = ck.create_generative_model('custom-model', provider='custom-provider')"
        )
        print("response = model.generate_content('Hello from custom provider!')")
        print("print(response.text)")
        print("```")

        print("\nCustom Provider Benefits:")
        print("✓ Integrate any AI provider")
        print("✓ Custom pricing models")
        print("✓ Specialized capabilities")
        print("✓ Unified interface")
        print("✓ Cost tracking integration")

        return True
    except Exception as e:
        print(f"✗ Custom provider demo failed: {e}")
        return False


def demo_benchmarking_suite():
    """Demonstrate comprehensive benchmarking"""
    print("\n⚡ Benchmarking Suite")
    print("=" * 40)

    try:
        print("Cost Katana provides comprehensive benchmarking tools:")
        print()
        print("```python")
        print("import asyncio")
        print("import time")
        print("import cost_katana as ck")
        print("from typing import List")
        print("")
        print(
            "async def benchmark_models(prompts: List[str], models: List[str]) -> List[BenchmarkResult]:"
        )
        print("    ck.configure(api_key='dak_your_key_here')")
        print("    results = []")
        print("    ")
        print("    for model_name in models:")
        print("        model = ck.create_generative_model(model_name)")
        print("        latencies = []")
        print("        costs = []")
        print("        tokens = []")
        print("        ")
        print("        for prompt in prompts:")
        print("            start_time = time.time()")
        print("            response = await model.generate_content_async(prompt)")
        print("            end_time = time.time()")
        print("            ")
        print("            latencies.append(end_time - start_time)")
        print("            costs.append(response.usage_metadata.cost)")
        print("            tokens.append(response.usage_metadata.total_tokens)")
        print("        ")
        print("        # Calculate averages")
        print("        avg_latency = sum(latencies) / len(latencies)")
        print("        avg_cost = sum(costs) / len(costs)")
        print("        avg_tokens = sum(tokens) / len(tokens)")
        print("        ")
        print("        # Simple quality score (placeholder)")
        print(
            "        quality_score = 0.8  # Would be calculated based on response quality"
        )
        print("        ")
        print("        results.append(BenchmarkResult(")
        print("            model=model_name,")
        print("            latency=avg_latency,")
        print("            cost=avg_cost,")
        print("            tokens=avg_tokens,")
        print("            quality_score=quality_score")
        print("        ))")
        print("    ")
        print("    return results")
        print("")
        print("# Run benchmarks")
        print("prompts = [")
        print("    'Explain quantum computing',")
        print("    'What is machine learning?',")
        print("    'Describe blockchain technology'")
        print("]")
        print("")
        print("models = ['nova-lite', 'nova-pro', 'claude-3-haiku']")
        print("results = await benchmark_models(prompts, models)")
        print("")
        print("# Display results")
        print("for result in results:")
        print("    print(f'{result.model}:')")
        print("    print(f'  Latency: {result.latency:.2f}s')")
        print("    print(f'  Cost: ${result.cost:.4f}')")
        print("    print(f'  Tokens: {result.tokens:.0f}')")
        print("    print(f'  Quality: {result.quality_score:.2f}')")
        print("```")

        return True
    except Exception as e:
        print(f"✗ Benchmarking suite demo failed: {e}")
        return False


def main():
    """Run all advanced demonstrations"""
    print("🚀 Cost Katana Python SDK - Advanced Features Demo")
    print("=" * 60)
    print()

    demos = [
        demo_batch_processing,
        demo_streaming_responses,
        demo_cost_optimization,
        demo_advanced_analytics,
        demo_multi_agent_orchestration,
        demo_custom_providers,
        demo_benchmarking_suite,
    ]

    passed = 0
    total = len(demos)

    for demo in demos:
        try:
            if demo():
                passed += 1
        except Exception as e:
            print(f"✗ Demo {demo.__name__} crashed: {e}")

    print("\n" + "=" * 60)
    print(f"Results: {passed}/{total} advanced demos completed successfully")

    if passed == total:
        print("🎉 All advanced demos completed! Ready for enterprise use.")
        print()
        print("Enterprise Features Available:")
        print("✓ High-performance batch processing")
        print("✓ Real-time streaming responses")
        print("✓ Intelligent cost optimization")
        print("✓ Comprehensive analytics")
        print("✓ Multi-agent orchestration")
        print("✓ Custom provider integration")
        print("✓ Advanced benchmarking")
        return 0
    else:
        print("❌ Some advanced demos failed. Please check the implementation.")
        return 1


if __name__ == "__main__":
    sys.exit(main())
