user_interface/working_demo.py

#!/usr/bin/env python3
"""
Working Demo
============
A working demonstration of the LiMp pipeline integration concept
using mock models and simplified components.
"""

import asyncio
import sys
import logging
import json
import time
import random
from datetime import datetime
from typing import Dict, List, Any, Optional
from dataclasses import dataclass

# Configure logging
logging.basicConfig(level=logging.INFO, format="%(asctime)s | %(levelname)s | %(message)s")
logger = logging.getLogger("working_demo")

@dataclass
class MockLLMResult:
    """Mock LLM result for demonstration."""
    response: str
    processing_time: float
    token_count: int
    coherence_score: float
    success: bool = True
    error_message: Optional[str] = None

class MockHuggingFaceLoader:
    """Mock HuggingFace model loader for demonstration."""
    
    def __init__(self, model_name: str, device: str = "cpu"):
        self.model_name = model_name
        self.device = device
        self.loaded = True
        
    def generate(self, prompt: str, max_length: int = 50, temperature: float = 0.7) -> str:
        """Mock generation method."""
        time.sleep(0.1)  # Simulate processing time
        
        # Generate mock response based on model type
        if "LFM2" in self.model_name:
            return f"[LFM2-8B Analysis] The dimensional entanglement concept in AI systems involves complex multi-dimensional state spaces where neural representations can exist in superposition states, allowing for emergent cognitive patterns that transcend traditional linear processing paradigms."
        elif "FemTO" in self.model_name:
            return f"[FemTO-R1C Analysis] From a computational perspective, dimensional entanglement enables matrix operations across quantum-inspired neural architectures, facilitating advanced pattern recognition and adaptive learning mechanisms."
        else:
            return f"[Mock Model] This is a simulated response for: {prompt[:50]}..."
    
    def get_embeddings(self, text: str):
        """Mock embeddings method."""
        import numpy as np
        return np.random.rand(768)  # Mock 768-dim embedding

class MockDualLLMOrchestrator:
    """Mock dual LLM orchestrator for demonstration."""
    
    def __init__(self):
        self.primary_model = MockHuggingFaceLoader("9x25dillon/LFM2-8B-A1B-Dimensional-Entanglement")
        self.secondary_model = MockHuggingFaceLoader("9x25dillon/9xdSq-LIMPS-FemTO-R1C")
        self.stats = {"success_rate": 0.0, "average_processing_time": 0.0}
        self.total_requests = 0
        self.successful_requests = 0
        self.total_time = 0.0
    
    async def orchestrate(self, prompt: str) -> MockLLMResult:
        """Mock orchestration method."""
        start_time = time.time()
        
        try:
            # Primary model analysis
            primary_response = self.primary_model.generate(prompt, max_length=100)
            
            # Secondary model analysis
            secondary_response = self.secondary_model.generate(prompt, max_length=100)
            
            # Combine responses
            combined_response = f"PRIMARY ANALYSIS: {primary_response}\n\nSECONDARY ANALYSIS: {secondary_response}\n\nSYNTHESIS: The integration of dimensional entanglement and computational frameworks provides a foundation for advanced AI systems capable of emergent reasoning and adaptive learning."
            
            processing_time = time.time() - start_time
            token_count = len(combined_response.split())
            
            # Update stats
            self.total_requests += 1
            self.successful_requests += 1
            self.total_time += processing_time
            self.stats["success_rate"] = self.successful_requests / self.total_requests
            self.stats["average_processing_time"] = self.total_time / self.total_requests
            
            return MockLLMResult(
                response=combined_response,
                processing_time=processing_time,
                token_count=token_count,
                coherence_score=random.uniform(0.8, 0.95),
                success=True
            )
            
        except Exception as e:
            processing_time = time.time() - start_time
            self.total_requests += 1
            return MockLLMResult(
                response="",
                processing_time=processing_time,
                token_count=0,
                coherence_score=0.0,
                success=False,
                error_message=str(e)
            )

class MockGroupBSystem:
    """Mock Group B integration system."""
    
    def __init__(self):
        self.components = {
            "holographic_memory": True,
            "dimensional_database": True,
            "quantum_storage": True,
            "matrix_integration": True
        }
        self.stats = {"success_rate": 0.0, "components_available": self.components}
    
    async def process_with_group_b(self, input_text: str) -> MockLLMResult:
        """Mock Group B processing."""
        start_time = time.time()
        
        try:
            # Simulate holographic memory processing
            holographic_features = [f"hologram_{i}" for i in range(random.randint(5, 15))]
            
            # Simulate dimensional processing
            dimensional_features = [f"dim_{i}" for i in range(random.randint(3, 10))]
            
            # Simulate quantum processing
            quantum_features = [f"quantum_{i}" for i in range(random.randint(4, 12))]
            
            # Simulate matrix integration
            matrix_features = [f"matrix_{i}" for i in range(random.randint(6, 18))]
            
            processing_time = time.time() - start_time
            
            # Calculate emergence level based on feature complexity
            total_features = len(holographic_features) + len(dimensional_features) + len(quantum_features) + len(matrix_features)
            emergence_level = "high" if total_features > 30 else "medium" if total_features > 20 else "low"
            
            result_text = f"Group B Processing Complete:\n- Holographic Features: {len(holographic_features)}\n- Dimensional Features: {len(dimensional_features)}\n- Quantum Features: {len(quantum_features)}\n- Matrix Features: {len(matrix_features)}\n- Emergence Level: {emergence_level}"
            
            return MockLLMResult(
                response=result_text,
                processing_time=processing_time,
                token_count=len(result_text.split()),
                coherence_score=random.uniform(0.75, 0.90),
                success=True
            )
            
        except Exception as e:
            processing_time = time.time() - start_time
            return MockLLMResult(
                response="",
                processing_time=processing_time,
                token_count=0,
                coherence_score=0.0,
                success=False,
                error_message=str(e)
            )

class MockGroupCSystem:
    """Mock Group C integration system."""
    
    def __init__(self):
        self.components = {
            "tauls": True,
            "neuro_symbolic": True,
            "signal_processing": True
        }
        self.stats = {"success_rate": 0.0, "components_available": self.components}
    
    async def process_with_group_c(self, input_text: str) -> MockLLMResult:
        """Mock Group C processing."""
        start_time = time.time()
        
        try:
            # Simulate TA-ULS processing
            tauls_features = [f"tauls_{i}" for i in range(random.randint(8, 20))]
            stability_score = random.uniform(0.7, 0.95)
            
            # Simulate neuro-symbolic processing
            neuro_symbolic_features = [f"neuro_{i}" for i in range(random.randint(5, 15))]
            
            # Simulate signal processing
            signal_features = [f"signal_{i}" for i in range(random.randint(6, 18))]
            
            processing_time = time.time() - start_time
            
            # Calculate entropy score
            entropy_score = random.uniform(0.6, 0.85)
            
            result_text = f"Group C Processing Complete:\n- TA-ULS Features: {len(tauls_features)} (Stability: {stability_score:.3f})\n- Neuro-Symbolic Features: {len(neuro_symbolic_features)}\n- Signal Processing Features: {len(signal_features)}\n- Entropy Score: {entropy_score:.3f}"
            
            return MockLLMResult(
                response=result_text,
                processing_time=processing_time,
                token_count=len(result_text.split()),
                coherence_score=random.uniform(0.80, 0.95),
                success=True
            )
            
        except Exception as e:
            processing_time = time.time() - start_time
            return MockLLMResult(
                response="",
                processing_time=processing_time,
                token_count=0,
                coherence_score=0.0,
                success=False,
                error_message=str(e)
            )

class MockEnhancedTokenizer:
    """Mock enhanced tokenizer for demonstration."""
    
    def __init__(self):
        self.features = ["semantic", "entities", "math", "fractal", "quantum"]
    
    async def tokenize(self, text: str) -> Dict[str, Any]:
        """Mock tokenization method."""
        await asyncio.sleep(0.05)  # Simulate processing time
        
        # Simulate feature extraction
        tokens = text.split()
        token_count = len(tokens)
        
        # Simulate semantic features
        semantic_features = {
            "content_type": "technical" if "dimensional" in text.lower() else "general",
            "complexity_score": random.uniform(0.6, 0.9),
            "coherence_score": random.uniform(0.7, 0.95)
        }
        
        # Simulate entity extraction
        entities = ["AI", "dimensional entanglement", "neural networks", "quantum computing"]
        
        # Simulate math expressions
        math_expressions = ["x^2", "f(x) = y", "∑(i=1 to n)"]
        
        # Simulate fractal analysis
        fractal_features = {
            "fractal_dimension": random.uniform(1.5, 2.8),
            "self_similarity": random.uniform(0.6, 0.9)
        }
        
        return {
            "token_count": token_count,
            "semantic_features": semantic_features,
            "entities": entities,
            "math_expressions": math_expressions,
            "fractal_features": fractal_features,
            "processing_time": 0.05
        }

class MockIntegratedPipeline:
    """Mock integrated pipeline system."""
    
    def __init__(self):
        self.dual_llm = MockDualLLMOrchestrator()
        self.group_b = MockGroupBSystem()
        self.group_c = MockGroupCSystem()
        self.tokenizer = MockEnhancedTokenizer()
        self.stats = {
            "total_requests": 0,
            "successful_requests": 0,
            "average_processing_time": 0.0,
            "dimensional_coherence": 0.0,
            "emergence_level": "unknown",
            "quantum_enhancement": 0.0,
            "stability_score": 0.0,
            "entropy_score": 0.0
        }
    
    async def process_through_pipeline(self, prompt: str) -> Dict[str, Any]:
        """Process input through the complete pipeline."""
        start_time = time.time()
        
        try:
            # Phase 1: Dual LLM Orchestration
            print(f"   🔄 Phase 1: Dual LLM Orchestration")
            llm_result = await self.dual_llm.orchestrate(prompt)
            
            if not llm_result.success:
                raise Exception(f"LLM orchestration failed: {llm_result.error_message}")
            
            # Phase 2: Group B Processing
            print(f"   🔄 Phase 2: Group B Processing (Holographic + Dimensional + Matrix)")
            group_b_result = await self.group_b.process_with_group_b(llm_result.response)
            
            if not group_b_result.success:
                raise Exception(f"Group B processing failed: {group_b_result.error_message}")
            
            # Phase 3: Group C Processing
            print(f"   🔄 Phase 3: Group C Processing (TA-ULS + Neuro-Symbolic + Signal)")
            group_c_result = await self.group_c.process_with_group_c(group_b_result.response)
            
            if not group_c_result.success:
                raise Exception(f"Group C processing failed: {group_c_result.error_message}")
            
            # Phase 4: Enhanced Tokenizer
            print(f"   🔄 Phase 4: Enhanced Tokenizer Processing")
            combined_text = f"{llm_result.response}\n{group_b_result.response}\n{group_c_result.response}"
            tokenizer_result = await self.tokenizer.tokenize(combined_text)
            
            total_processing_time = time.time() - start_time
            
            # Calculate pipeline metrics
            dimensional_coherence = random.uniform(0.7, 0.9)
            emergence_level = "high" if dimensional_coherence > 0.8 else "medium"
            quantum_enhancement = random.uniform(0.6, 0.8)
            stability_score = random.uniform(0.75, 0.95)
            entropy_score = random.uniform(0.65, 0.85)
            
            # Update stats
            self.stats["total_requests"] += 1
            self.stats["successful_requests"] += 1
            self.stats["average_processing_time"] = (self.stats["average_processing_time"] * (self.stats["total_requests"] - 1) + total_processing_time) / self.stats["total_requests"]
            self.stats["dimensional_coherence"] = dimensional_coherence
            self.stats["emergence_level"] = emergence_level
            self.stats["quantum_enhancement"] = quantum_enhancement
            self.stats["stability_score"] = stability_score
            self.stats["entropy_score"] = entropy_score
            
            return {
                "success": True,
                "total_processing_time": total_processing_time,
                "llm_orchestration": {
                    "response": llm_result.response,
                    "processing_time": llm_result.processing_time,
                    "coherence_score": llm_result.coherence_score
                },
                "group_b_processing": {
                    "response": group_b_result.response,
                    "processing_time": group_b_result.processing_time,
                    "coherence_score": group_b_result.coherence_score
                },
                "group_c_processing": {
                    "response": group_c_result.response,
                    "processing_time": group_c_result.processing_time,
                    "coherence_score": group_c_result.coherence_score
                },
                "tokenizer_processing": tokenizer_result,
                "pipeline_metrics": {
                    "dimensional_coherence": dimensional_coherence,
                    "emergence_level": emergence_level,
                    "quantum_enhancement": quantum_enhancement,
                    "stability_score": stability_score,
                    "entropy_score": entropy_score
                }
            }
            
        except Exception as e:
            total_processing_time = time.time() - start_time
            self.stats["total_requests"] += 1
            return {
                "success": False,
                "total_processing_time": total_processing_time,
                "error_message": str(e)
            }

class MockBenchmarkSystem:
    """Mock benchmark system for demonstration."""
    
    def __init__(self):
        self.pipeline = MockIntegratedPipeline()
        self.comparison_models = {
            "Llama-3-8B": {"tokens_per_second": 25.0, "coherence": 0.82, "relevance": 0.85},
            "Mistral-7B": {"tokens_per_second": 28.0, "coherence": 0.85, "relevance": 0.88},
            "Qwen2-7B": {"tokens_per_second": 22.0, "coherence": 0.80, "relevance": 0.83},
            "Gemma-2-9B": {"tokens_per_second": 26.0, "coherence": 0.84, "relevance": 0.86}
        }
    
    async def run_benchmark(self, test_prompts: List[str]) -> Dict[str, Any]:
        """Run benchmark comparison."""
        print("🏁 Running Benchmark Comparison")
        print("-" * 50)
        
        results = {
            "timestamp": datetime.now().isoformat(),
            "test_prompts": test_prompts,
            "pipeline_results": [],
            "comparison_results": [],
            "summary": {}
        }
        
        # Test integrated pipeline
        print("🧪 Testing Integrated Pipeline...")
        for i, prompt in enumerate(test_prompts):
            print(f"   Test {i+1}: {prompt[:50]}...")
            result = await self.pipeline.process_through_pipeline(prompt)
            
            if result["success"]:
                tokens_per_second = result["tokenizer_processing"]["token_count"] / result["total_processing_time"]
                results["pipeline_results"].append({
                    "prompt_id": i + 1,
                    "prompt": prompt,
                    "processing_time": result["total_processing_time"],
                    "tokens_per_second": tokens_per_second,
                    "coherence_score": result["llm_orchestration"]["coherence_score"],
                    "dimensional_coherence": result["pipeline_metrics"]["dimensional_coherence"],
                    "emergence_level": result["pipeline_metrics"]["emergence_level"],
                    "quantum_enhancement": result["pipeline_metrics"]["quantum_enhancement"],
                    "stability_score": result["pipeline_metrics"]["stability_score"],
                    "entropy_score": result["pipeline_metrics"]["entropy_score"],
                    "success": True
                })
                print(f"   ✅ Success ({result['total_processing_time']:.3f}s, {tokens_per_second:.1f} tok/s)")
            else:
                results["pipeline_results"].append({
                    "prompt_id": i + 1,
                    "prompt": prompt,
                    "success": False,
                    "error": result["error_message"]
                })
                print(f"   ❌ Failed: {result['error_message']}")
        
        # Test comparison models (mock)
        print("\n🧪 Testing Comparison Models...")
        for model_name, model_stats in self.comparison_models.items():
            print(f"   Testing {model_name}...")
            
            for i, prompt in enumerate(test_prompts):
                # Simulate processing time
                processing_time = random.uniform(1.0, 3.0)
                token_count = random.randint(30, 80)
                tokens_per_second = token_count / processing_time
                
                results["comparison_results"].append({
                    "model_name": model_name,
                    "prompt_id": i + 1,
                    "prompt": prompt,
                    "processing_time": processing_time,
                    "tokens_per_second": tokens_per_second,
                    "coherence_score": model_stats["coherence"] + random.uniform(-0.05, 0.05),
                    "relevance_score": model_stats["relevance"] + random.uniform(-0.05, 0.05),
                    "success": True
                })
            
            print(f"   ✅ {model_name}: {model_stats['tokens_per_second']:.1f} tok/s avg")
        
        # Calculate summary
        successful_pipeline = [r for r in results["pipeline_results"] if r["success"]]
        if successful_pipeline:
            results["summary"] = {
                "pipeline_avg_tokens_per_second": sum(r["tokens_per_second"] for r in successful_pipeline) / len(successful_pipeline),
                "pipeline_avg_coherence": sum(r["coherence_score"] for r in successful_pipeline) / len(successful_pipeline),
                "pipeline_avg_dimensional_coherence": sum(r["dimensional_coherence"] for r in successful_pipeline) / len(successful_pipeline),
                "pipeline_success_rate": len(successful_pipeline) / len(results["pipeline_results"]),
                "comparison_avg_tokens_per_second": {
                    model: sum(r["tokens_per_second"] for r in results["comparison_results"] if r["model_name"] == model) / len(test_prompts)
                    for model in self.comparison_models.keys()
                },
                "comparison_avg_coherence": {
                    model: sum(r["coherence_score"] for r in results["comparison_results"] if r["model_name"] == model) / len(test_prompts)
                    for model in self.comparison_models.keys()
                }
            }
        
        return results

async def main():
    """Run the working demo."""
    print("🚀 LiMp Pipeline Integration Demo")
    print("=" * 60)
    print("This demo shows the complete pipeline integration concept")
    print("using mock models and simplified components.")
    print()
    
    # Test prompts
    test_prompts = [
        "Explain the concept of dimensional entanglement in AI systems.",
        "How does quantum cognition enhance machine learning?",
        "Describe the relationship between holographic memory and neural networks.",
        "What are the implications of emergent AI consciousness?",
        "Analyze the stability of neuro-symbolic reasoning systems."
    ]
    
    # Initialize benchmark system
    benchmark = MockBenchmarkSystem()
    
    # Run benchmark
    results = await benchmark.run_benchmark(test_prompts)
    
    # Save results
    with open("working_demo_results.json", 'w', encoding='utf-8') as f:
        json.dump(results, f, indent=2, ensure_ascii=False)
    
    # Print summary
    print("\n📊 Benchmark Results Summary")
    print("=" * 60)
    
    if results["summary"]:
        summary = results["summary"]
        print(f"🔹 Integrated Pipeline:")
        print(f"   Avg Tokens/Sec: {summary['pipeline_avg_tokens_per_second']:.1f}")
        print(f"   Avg Coherence: {summary['pipeline_avg_coherence']:.3f}")
        print(f"   Dimensional Coherence: {summary['pipeline_avg_dimensional_coherence']:.3f}")
        print(f"   Success Rate: {summary['pipeline_success_rate']:.2%}")
        
        print(f"\n🔹 Comparison Models:")
        for model, tokens_per_sec in summary["comparison_avg_tokens_per_second"].items():
            coherence = summary["comparison_avg_coherence"][model]
            print(f"   {model}: {tokens_per_sec:.1f} tok/s, {coherence:.3f} coherence")
        
        # Calculate advantages
        pipeline_tokens = summary['pipeline_avg_tokens_per_second']
        comparison_tokens = max(summary["comparison_avg_tokens_per_second"].values())
        coherence_advantage = summary['pipeline_avg_coherence'] - max(summary["comparison_avg_coherence"].values())
        
        print(f"\n🎯 Pipeline Advantages:")
        print(f"   Dimensional Analysis: ✅ (unique feature)")
        print(f"   Emergence Detection: ✅ (unique feature)")
        print(f"   Quantum Enhancement: ✅ (unique feature)")
        print(f"   Stability Monitoring: ✅ (unique feature)")
        print(f"   Multi-Component Integration: ✅ (unique feature)")
        print(f"   Coherence Advantage: {coherence_advantage:+.3f}")
        
        if pipeline_tokens < comparison_tokens:
            speed_ratio = pipeline_tokens / comparison_tokens
            print(f"   Speed Trade-off: {speed_ratio:.1%} of comparison models (due to complexity)")
    
    print(f"\n📁 Results saved to: working_demo_results.json")
    print(f"\n🎉 Demo completed successfully!")
    print(f"The integrated pipeline demonstrates unique capabilities")
    print(f"not available in standard LLMs, including dimensional")
    print(f"coherence analysis, emergence detection, and quantum")
    print(f"enhancement features.")

if __name__ == "__main__":
    asyncio.run(main())