Delete ai_backend

ai_backend/advanced_model_loader.py

@@ -1,455 +0,0 @@
-"""
-Advanced Model Loader for Virtual Hardware System
-
-This module implements sophisticated model loading that fully utilizes the virtual hardware:
-- 5TB Virtual SSD for model storage
-- 500GB VRAM for active model weights
-- 50,000 GPU cores for parallel processing
-- Enhanced CPU with 50 cores / 100 threads
-
-The system downloads and stores Llama 7B (or similar large models) in the VSSD,
-loads weights into VRAM as needed, and distributes inference across GPU cores.
-"""
-
-import os
-import sys
-import json
-import time
-import asyncio
-import threading
-import numpy as np
-from typing import Dict, Any, Optional, List, Tuple
-from dataclasses import dataclass
-import requests
-from concurrent.futures import ThreadPoolExecutor, as_completed
-
-# Import virtual hardware components from the new structure
-sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'virtual_hardware'))
-from vgpu import VirtualGPU, TaskType
-from vram import VRAM
-from ai import AIAccelerator
-from driver import GPUDriver
-from virtual_ssd import VirtualSSD
-from virtual_ram import VirtualRAM
-from enhanced_cpu import EnhancedMultiCoreCPU
-from virtual_gpu_driver import VirtualGPUDriver
-
-
-@dataclass
-class ModelChunk:
-    """Represents a chunk of model data stored in VSSD."""
-    chunk_id: str
-    layer_name: str
-    weight_type: str  # 'weight', 'bias', 'embedding', etc.
-    shape: Tuple[int, ...]
-    dtype: str
-    size_bytes: int
-    vssd_filename: str
-    loaded_in_vram: bool = False
-    vram_id: Optional[str] = None
-
-
-class VirtualHardwareModelLoader:
-    """
-    Advanced model loader that utilizes the full virtual hardware stack.
-    
-    This class orchestrates model loading across:
-    - VSSD: Persistent storage of model weights and metadata
-    - VRAM: Active loading of model chunks for inference
-    - VGPU: Parallel processing across 50,000 cores
-    - VCPU: Coordination and scheduling
-    """
-    
-    def __init__(self, vssd_capacity_gb: int = 5120, vram_capacity_gb: int = 500):
-        # Initialize virtual hardware components
-        self.vssd = VirtualSSD(capacity_gb=vssd_capacity_gb)
-        self.vram = VRAM(memory_size_gb=vram_capacity_gb)
-        self.virtual_ram = VirtualRAM(capacity_gb=128)  # System RAM
-        
-        # Initialize Virtual GPU with full specifications
-        self.vgpu = VirtualGPU(num_sms=800, total_cores=50000)
-        self.ai_accelerator = AIAccelerator(self.vram)
-        self.gpu_driver = GPUDriver(self.vgpu)
-        
-        # Initialize Enhanced CPU
-        self.vcpu = EnhancedMultiCoreCPU(num_cores=50, gpu_driver=VirtualGPUDriver())
-        
-        # Connect components
-        self.vgpu.set_modules(self.vram, None, self.ai_accelerator, self.gpu_driver)
-        
-        # Model management
-        self.model_chunks: Dict[str, ModelChunk] = {}
-        self.model_metadata: Dict[str, Any] = {}
-        self.active_model: Optional[str] = None
-        
-        # Performance tracking
-        self.load_stats = {
-            'chunks_loaded': 0,
-            'total_load_time': 0.0,
-            'vram_utilization': 0.0,
-            'gpu_utilization': 0.0
-        }
-        
-        print(f"VirtualHardwareModelLoader initialized:")
-        print(f"  - VSSD: {vssd_capacity_gb}GB")
-        print(f"  - VRAM: {vram_capacity_gb}GB") 
-        print(f"  - VGPU: 800 SMs, 50,000 cores")
-        print(f"  - VCPU: 50 cores, 100 threads")
-        
-    def mount_hardware(self):
-        """Mount all virtual hardware components."""
-        print("Mounting virtual hardware...")
-        
-        # Mount VSSD
-        self.vssd.mount()
-        print("✓ VSSD mounted")
-        
-        # Create threads on CPU cores
-        threads_created = self.vcpu.create_threads_on_all_cores(threads_per_core=2)
-        print(f"✓ VCPU: {threads_created} threads created")
-        
-        # Initialize VRAM
-        self.vram.initialize()
-        print("✓ VRAM initialized")
-        
-        print("Virtual hardware mounted successfully!")
-        
-    def download_model_to_vssd(self, model_name: str = "microsoft/DialoGPT-medium") -> bool:
-        """
-        Download a pre-trained model and store it in chunks on VSSD.
-        
-        For demonstration, we'll use a medium-sized model and simulate
-        the chunking process that would be used for Llama 7B.
-        """
-        print(f"Downloading model '{model_name}' to VSSD...")
-        
-        try:
-            # Import transformers for model downloading
-            from transformers import AutoTokenizer, AutoModelForCausalLM
-            import torch
-            
-            # Download tokenizer and model
-            print("Downloading tokenizer...")
-            tokenizer = AutoTokenizer.from_pretrained(model_name)
-            if tokenizer.pad_token is None:
-                tokenizer.pad_token = tokenizer.eos_token
-                
-            print("Downloading model...")
-            model = AutoModelForCausalLM.from_pretrained(
-                model_name,
-                torch_dtype=torch.float32,
-                device_map="cpu",
-                low_cpu_mem_usage=True
-            )
-            
-            # Save tokenizer to VSSD
-            tokenizer_data = json.dumps(tokenizer.get_vocab()).encode('utf-8')
-            self.vssd.save_file(f"{model_name.replace('/', '_')}_tokenizer.json", tokenizer_data)
-            
-            # Process model weights into chunks
-            chunk_counter = 0
-            total_params = 0
-            
-            for name, param in model.named_parameters():
-                if param.requires_grad:
-                    # Convert parameter to numpy
-                    weight_data = param.detach().cpu().numpy().astype(np.float32)
-                    total_params += param.numel()
-                    
-                    # Create chunk metadata
-                    chunk_id = f"chunk_{chunk_counter:06d}"
-                    chunk = ModelChunk(
-                        chunk_id=chunk_id,
-                        layer_name=name,
-                        weight_type="weight" if "weight" in name else "bias",
-                        shape=weight_data.shape,
-                        dtype=str(weight_data.dtype),
-                        size_bytes=weight_data.nbytes,
-                        vssd_filename=f"{model_name.replace('/', '_')}_{chunk_id}.bin"
-                    )
-                    
-                    # Save chunk to VSSD
-                    chunk_bytes = weight_data.tobytes()
-                    success = self.vssd.save_file(chunk.vssd_filename, chunk_bytes)
-                    
-                    if success:
-                        self.model_chunks[chunk_id] = chunk
-                        chunk_counter += 1
-                        
-                        if chunk_counter % 10 == 0:
-                            print(f"  Saved {chunk_counter} chunks...")
-                    else:
-                        print(f"  Failed to save chunk {chunk_id}")
-                        
-            # Save model metadata
-            self.model_metadata[model_name] = {
-                'total_chunks': chunk_counter,
-                'total_parameters': total_params,
-                'model_type': 'causal_lm',
-                'vocab_size': len(tokenizer.get_vocab()),
-                'chunks': {cid: {
-                    'layer_name': chunk.layer_name,
-                    'shape': chunk.shape,
-                    'size_bytes': chunk.size_bytes
-                } for cid, chunk in self.model_chunks.items()}
-            }
-            
-            # Save metadata to VSSD
-            metadata_json = json.dumps(self.model_metadata[model_name], indent=2)
-            self.vssd.save_file(f"{model_name.replace('/', '_')}_metadata.json", metadata_json.encode('utf-8'))
-            
-            print(f"✓ Model downloaded successfully:")
-            print(f"  - {chunk_counter} chunks saved to VSSD")
-            print(f"  - {total_params:,} parameters")
-            print(f"  - Model size: {sum(c.size_bytes for c in self.model_chunks.values()) / (1024**3):.2f} GB")
-            
-            return True
-            
-        except Exception as e:
-            print(f"Error downloading model: {e}")
-            return False
-            
-    def load_model_chunks_to_vram(self, model_name: str, max_chunks: int = 100) -> bool:
-        """
-        Load model chunks from VSSD to VRAM for active inference.
-        
-        This simulates the process of loading Llama 7B weights into the 500GB VRAM.
-        """
-        print(f"Loading model chunks from VSSD to VRAM...")
-        
-        start_time = time.time()
-        chunks_loaded = 0
-        
-        # Load model metadata
-        metadata_file = f"{model_name.replace('/', '_')}_metadata.json"
-        metadata_bytes = self.vssd.read_file(metadata_file)
-        
-        if not metadata_bytes:
-            print(f"Model metadata not found: {metadata_file}")
-            return False
-            
-        metadata = json.loads(metadata_bytes.decode('utf-8'))
-        print(f"Found model with {metadata['total_chunks']} chunks")
-        
-        # Load chunks in parallel using virtual CPU threads
-        def load_chunk_worker(chunk_id: str) -> bool:
-            try:
-                chunk = self.model_chunks[chunk_id]
-                
-                # Read chunk from VSSD
-                chunk_data = self.vssd.read_file(chunk.vssd_filename)
-                if not chunk_data:
-                    return False
-                    
-                # Convert bytes back to numpy array
-                weight_array = np.frombuffer(chunk_data, dtype=np.float32).reshape(chunk.shape)
-                
-                # Load into VRAM using AI accelerator
-                vram_id = self.ai_accelerator.load_matrix(weight_array, f"model_{chunk.layer_name}")
-                
-                if vram_id:
-                    chunk.loaded_in_vram = True
-                    chunk.vram_id = vram_id
-                    return True
-                    
-                return False
-                
-            except Exception as e:
-                print(f"Error loading chunk {chunk_id}: {e}")
-                return False
-        
-        # Use thread pool to load chunks in parallel
-        with ThreadPoolExecutor(max_workers=20) as executor:
-            chunk_ids = list(self.model_chunks.keys())[:max_chunks]
-            future_to_chunk = {executor.submit(load_chunk_worker, cid): cid for cid in chunk_ids}
-            
-            for future in as_completed(future_to_chunk):
-                chunk_id = future_to_chunk[future]
-                try:
-                    success = future.result()
-                    if success:
-                        chunks_loaded += 1
-                        if chunks_loaded % 10 == 0:
-                            print(f"  Loaded {chunks_loaded} chunks to VRAM...")
-                except Exception as e:
-                    print(f"Chunk {chunk_id} loading failed: {e}")
-        
-        load_time = time.time() - start_time
-        
-        # Update statistics
-        self.load_stats['chunks_loaded'] = chunks_loaded
-        self.load_stats['total_load_time'] = load_time
-        self.load_stats['vram_utilization'] = (chunks_loaded / len(self.model_chunks)) * 100
-        
-        print(f"✓ Loaded {chunks_loaded} chunks to VRAM in {load_time:.2f}s")
-        print(f"  VRAM utilization: {self.load_stats['vram_utilization']:.1f}%")
-        
-        self.active_model = model_name
-        return chunks_loaded > 0
-        
-    def inference_with_virtual_gpu(self, input_text: str) -> str:
-        """
-        Perform inference using the virtual GPU's 50,000 cores.
-        
-        This distributes the inference workload across multiple SMs and cores.
-        """
-        if not self.active_model:
-            return "No model loaded"
-            
-        print(f"Running inference on virtual GPU...")
-        start_time = time.time()
-        
-        try:
-            # Tokenize input (simplified)
-            input_tokens = [hash(word) % 50000 for word in input_text.split()]
-            
-            # Submit AI inference tasks to GPU
-            task_ids = []
-            for i, token in enumerate(input_tokens):
-                # Create inference task for each token
-                task_id = self.vgpu.submit_task(
-                    TaskType.AI_MATRIX_MULTIPLY,
-                    {
-                        'input_token': token,
-                        'position': i,
-                        'model_chunks': list(self.model_chunks.keys())[:10]  # Use first 10 chunks
-                    }
-                )
-                task_ids.append(task_id)
-            
-            # Process tasks across GPU cores
-            for _ in range(10):  # Simulate 10 processing cycles
-                asyncio.run(self.vgpu.tick())
-                time.sleep(0.01)  # Small delay for realistic processing
-            
-            # Get GPU statistics
-            gpu_stats = self.vgpu.get_stats()
-            ai_stats = self.ai_accelerator.get_stats()
-            
-            inference_time = time.time() - start_time
-            
-            # Generate response based on processing
-            responses = [
-                f"I'm processing your input '{input_text}' using the virtual GPU with 50,000 cores.",
-                f"The model loaded from VSSD is now running inference across {gpu_stats['busy_sms']} active SMs.",
-                f"Virtual hardware processed {gpu_stats['total_tasks_processed']} tasks with {ai_stats['operations_performed']} AI operations.",
-                f"VRAM utilization: {self.load_stats['vram_utilization']:.1f}%, GPU cores active: {gpu_stats['busy_sms']}/{gpu_stats['total_sms']}",
-                f"Inference completed in {inference_time:.3f}s using distributed processing."
-            ]
-            
-            # Select response based on input
-            response_idx = hash(input_text) % len(responses)
-            response = responses[response_idx]
-            
-            # Add technical details
-            response += f" [GPU: {gpu_stats['total_tasks_processed']} tasks, VRAM: {self.load_stats['chunks_loaded']} chunks, Cores: {gpu_stats['total_cores']}]"
-            
-            return response
-            
-        except Exception as e:
-            return f"Inference error: {str(e)}"
-            
-    def get_hardware_status(self) -> Dict[str, Any]:
-        """Get comprehensive status of all virtual hardware components."""
-        try:
-            # VSSD status
-            vssd_info = self.vssd.get_capacity_info() if hasattr(self.vssd, 'get_capacity_info') else {}
-            
-            # VRAM status
-            vram_stats = self.vram.get_stats() if hasattr(self.vram, 'get_stats') else {}
-            
-            # GPU status
-            gpu_stats = self.vgpu.get_stats()
-            ai_stats = self.ai_accelerator.get_stats()
-            
-            # CPU status
-            cpu_stats = self.vcpu.get_threading_stats()
-            
-            return {
-                'vssd': {
-                    'capacity_gb': vssd_info.get('total_gb', 5120),
-                    'used_gb': vssd_info.get('used_gb', 0),
-                    'files_stored': len(vssd_info.get('files', {})),
-                    'model_chunks': len(self.model_chunks)
-                },
-                'vram': {
-                    'capacity_gb': vram_stats.get('total_memory_gb', 500),
-                    'utilization_percent': vram_stats.get('utilization_percent', 0),
-                    'chunks_loaded': self.load_stats['chunks_loaded']
-                },
-                'vgpu': {
-                    'total_cores': gpu_stats['total_cores'],
-                    'total_sms': gpu_stats['total_sms'],
-                    'busy_sms': gpu_stats['busy_sms'],
-                    'tasks_processed': gpu_stats['total_tasks_processed'],
-                    'ai_operations': ai_stats['operations_performed']
-                },
-                'vcpu': {
-                    'total_cores': cpu_stats['total_cores'],
-                    'active_threads': cpu_stats['total_active_threads'],
-                    'threads_created': cpu_stats['total_threads_created']
-                },
-                'model': {
-                    'active_model': self.active_model,
-                    'total_chunks': len(self.model_chunks),
-                    'chunks_in_vram': sum(1 for c in self.model_chunks.values() if c.loaded_in_vram)
-                },
-                'performance': self.load_stats
-            }
-            
-        except Exception as e:
-            return {'error': f'Status error: {str(e)}'}
-            
-    def shutdown_hardware(self):
-        """Properly shutdown all virtual hardware components."""
-        print("Shutting down virtual hardware...")
-        
-        try:
-            # Stop GPU
-            self.vgpu.stop()
-            print("✓ VGPU stopped")
-            
-            # Shutdown VSSD
-            self.vssd.shutdown()
-            print("✓ VSSD shutdown")
-            
-            print("Virtual hardware shutdown complete!")
-            
-        except Exception as e:
-            print(f"Shutdown error: {e}")
-
-
-if __name__ == "__main__":
-    # Test the advanced model loader
-    print("Testing Advanced Virtual Hardware Model Loader...")
-    
-    # Initialize the system
-    loader = VirtualHardwareModelLoader()
-    
-    # Mount hardware
-    loader.mount_hardware()
-    
-    # Download and load a model
-    model_name = "microsoft/DialoGPT-small"  # Start with smaller model for testing
-    
-    print(f"\n1. Downloading {model_name} to VSSD...")
-    download_success = loader.download_model_to_vssd(model_name)
-    
-    if download_success:
-        print(f"\n2. Loading model chunks to VRAM...")
-        load_success = loader.load_model_chunks_to_vram(model_name, max_chunks=50)
-        
-        if load_success:
-            print(f"\n3. Testing inference...")
-            response = loader.inference_with_virtual_gpu("Hello, how are you?")
-            print(f"Response: {response}")
-            
-            print(f"\n4. Hardware status:")
-            status = loader.get_hardware_status()
-            for component, stats in status.items():
-                print(f"  {component.upper()}: {stats}")
-    
-    # Shutdown
-    loader.shutdown_hardware()
-    print("\nTest completed!")
-

ai_backend/app.py

@@ -1,296 +0,0 @@
-"""
-Integrated AI Backend with Virtual Hardware
-
-This Flask application integrates the advanced model loader with a web service,
-providing a chat interface that utilizes the full virtual hardware stack:
-- 5TB VSSD for model storage
-- 500GB VRAM for active weights
-- 50,000 GPU cores for inference
-- 50 CPU cores with 100 threads
-"""
-
-import os
-import sys
-import threading
-import time
-import asyncio
-from flask import Flask, jsonify, request, send_from_directory
-from flask_cors import CORS
-
-# Add the current directory to path to import advanced_model_loader
-sys.path.append(os.path.dirname(__file__))
-
-from advanced_model_loader import VirtualHardwareModelLoader
-
-# Global variables for the model loader
-model_loader = None
-hardware_initialized = False
-model_loaded = False
-initialization_error = None
-initialization_thread = None
-
-def create_app():
-    """Create and configure the Flask app."""
-    app = Flask(__name__, static_folder=os.path.join(os.path.dirname(__file__), 'static'))
-    app.config['SECRET_KEY'] = 'virtual-hardware-secret-key'
-    
-    # Enable CORS for all routes
-    CORS(app)
-    
-    return app
-
-def initialize_hardware_async():
-    """Initialize virtual hardware in a separate thread."""
-    global model_loader, hardware_initialized, model_loaded, initialization_error
-    
-    try:
-        print("Starting virtual hardware initialization...")
-        
-        # Create model loader with full specifications
-        model_loader = VirtualHardwareModelLoader(
-            vssd_capacity_gb=5120,  # 5TB VSSD
-            vram_capacity_gb=500    # 500GB VRAM
-        )
-        
-        # Mount all hardware components
-        model_loader.mount_hardware()
-        hardware_initialized = True
-        print("✓ Virtual hardware initialized successfully")
-        
-        # Download and load model
-        print("Downloading model to VSSD...")
-        model_name = "microsoft/DialoGPT-medium"  # Use medium model for better responses
-        
-        download_success = model_loader.download_model_to_vssd(model_name)
-        
-        if download_success:
-            print("Loading model chunks to VRAM...")
-            load_success = model_loader.load_model_chunks_to_vram(model_name, max_chunks=100)
-            
-            if load_success:
-                model_loaded = True
-                print("✓ Model loaded successfully into virtual hardware")
-            else:
-                initialization_error = "Failed to load model chunks to VRAM"
-        else:
-            initialization_error = "Failed to download model to VSSD"
-            
-    except Exception as e:
-        initialization_error = f"Hardware initialization error: {str(e)}"
-        print(f"Initialization error: {e}")
-        import traceback
-        traceback.print_exc()
-
-# Create the Flask app
-app = create_app()
-
-@app.route('/')
-def serve_root():
-    """Serve the main page."""
-    return send_from_directory(app.static_folder, 'index.html')
-
-@app.route('/health')
-def health_check():
-    """Health check endpoint."""
-    return jsonify({
-        "status": "healthy",
-        "server": "running",
-        "hardware_initialized": hardware_initialized,
-        "model_loaded": model_loaded,
-        "error": initialization_error
-    })
-
-@app.route('/api/hardware-status')
-def hardware_status():
-    """Get detailed hardware status."""
-    if not hardware_initialized or not model_loader:
-        return jsonify({
-            "error": "Hardware not initialized",
-            "initialization_error": initialization_error
-        }), 503
-        
-    try:
-        status = model_loader.get_hardware_status()
-        return jsonify(status)
-    except Exception as e:
-        return jsonify({"error": f"Status error: {str(e)}"}), 500
-
-@app.route('/api/initialize', methods=['POST'])
-def initialize_hardware():
-    """Manually trigger hardware initialization."""
-    global initialization_thread, hardware_initialized, model_loaded
-    
-    if hardware_initialized and model_loaded:
-        return jsonify({
-            "message": "Hardware already initialized and model loaded",
-            "status": "ready"
-        })
-    
-    if initialization_thread and initialization_thread.is_alive():
-        return jsonify({
-            "message": "Hardware initialization in progress",
-            "status": "initializing"
-        })
-    
-    # Start initialization in background thread
-    initialization_thread = threading.Thread(target=initialize_hardware_async, daemon=True)
-    initialization_thread.start()
-    
-    return jsonify({
-        "message": "Hardware initialization started",
-        "status": "initializing"
-    })
-
-@app.route('/api/chat', methods=['POST'])
-def chat():
-    """
-    Handle chat requests using the virtual hardware.
-    This endpoint will automatically trigger hardware initialization if not already done.
-    """
-    global model_loader, hardware_initialized, model_loaded, initialization_thread
-    
-    try:
-        # Check if hardware is ready
-        if not hardware_initialized:
-            # Auto-start initialization if not started
-            if not initialization_thread or not initialization_thread.is_alive():
-                initialization_thread = threading.Thread(target=initialize_hardware_async, daemon=True)
-                initialization_thread.start()
-            
-            return jsonify({
-                'response': 'Virtual hardware is initializing... Please wait for the 5TB VSSD, 500GB VRAM, and 50,000 GPU cores to come online.',
-                'status': 'initializing',
-                'hardware_ready': False
-            }), 202
-        
-        if not model_loaded:
-            return jsonify({
-                'response': 'Model is loading into virtual hardware... The system is transferring weights from VSSD to VRAM.',
-                'status': 'loading_model',
-                'hardware_ready': True,
-                'model_ready': False
-            }), 202
-        
-        if initialization_error:
-            return jsonify({
-                'response': f'Hardware initialization failed: {initialization_error}',
-                'status': 'error',
-                'error': initialization_error
-            }), 500
-        
-        # Get the message from request
-        data = request.get_json()
-        if not data or 'message' not in data:
-            return jsonify({'error': 'No message provided'}), 400
-        
-        user_message = data['message']
-        
-        # Generate response using virtual hardware
-        response = model_loader.inference_with_virtual_gpu(user_message)
-        
-        # Get hardware status for response metadata
-        hardware_status = model_loader.get_hardware_status()
-        
-        return jsonify({
-            'response': response,
-            'status': 'success',
-            'hardware_status': {
-                'vssd_files': hardware_status['vssd']['files_stored'],
-                'vram_utilization': hardware_status['vram']['utilization_percent'],
-                'gpu_cores_active': f"{hardware_status['vgpu']['busy_sms']}/{hardware_status['vgpu']['total_sms']} SMs",
-                'cpu_threads': hardware_status['vcpu']['active_threads'],
-                'model_chunks_loaded': hardware_status['model']['chunks_in_vram']
-            }
-        })
-        
-    except Exception as e:
-        return jsonify({
-            'error': f'Chat error: {str(e)}',
-            'status': 'error'
-        }), 500
-
-@app.route('/api/load-llama', methods=['POST'])
-def load_llama_model():
-    """Attempt to load Llama 7B model."""
-    global model_loader
-    
-    if not hardware_initialized or not model_loader:
-        return jsonify({
-            'error': 'Hardware not initialized',
-            'message': 'Please initialize hardware first'
-        }), 503
-    
-    try:
-        # This would attempt to load Llama 7B
-        # For now, we'll simulate the process
-        data = request.get_json()
-        model_name = data.get('model_name', 'meta-llama/Llama-2-7b-chat-hf')
-        
-        def load_llama_async():
-            try:
-                print(f"Attempting to load {model_name}...")
-                # This would be the actual Llama loading code
-                # For demonstration, we'll use the existing model loading
-                success = model_loader.download_model_to_vssd(model_name)
-                if success:
-                    model_loader.load_model_chunks_to_vram(model_name, max_chunks=200)
-                    print(f"✓ {model_name} loaded successfully")
-                else:
-                    print(f"✗ Failed to load {model_name}")
-            except Exception as e:
-                print(f"Llama loading error: {e}")
-        
-        # Start loading in background
-        llama_thread = threading.Thread(target=load_llama_async, daemon=True)
-        llama_thread.start()
-        
-        return jsonify({
-            'message': f'Started loading {model_name} to virtual hardware',
-            'model_name': model_name,
-            'status': 'loading',
-            'note': 'This will utilize the full 5TB VSSD and 500GB VRAM capacity'
-        })
-        
-    except Exception as e:
-        return jsonify({
-            'error': f'Llama loading error: {str(e)}',
-            'status': 'error'
-        }), 500
-
-@app.route('/api/shutdown', methods=['POST'])
-def shutdown_hardware():
-    """Shutdown virtual hardware."""
-    global model_loader, hardware_initialized, model_loaded
-    
-    try:
-        if model_loader:
-            model_loader.shutdown_hardware()
-        
-        hardware_initialized = False
-        model_loaded = False
-        model_loader = None
-        
-        return jsonify({
-            'message': 'Virtual hardware shutdown complete',
-            'status': 'shutdown'
-        })
-        
-    except Exception as e:
-        return jsonify({
-            'error': f'Shutdown error: {str(e)}',
-            'status': 'error'
-        }), 500
-
-if __name__ == '__main__':
-    print("Starting Virtual Hardware AI Backend...")
-    print("Specifications:")
-    print("  - VSSD: 5TB capacity")
-    print("  - VRAM: 500GB capacity")
-    print("  - VGPU: 50,000 cores across 800 SMs")
-    print("  - VCPU: 50 cores with 100 threads")
-    print("\nServer will start immediately. Hardware initialization will begin in background.")
-    
-    # Start the Flask app
-    app.run(host='0.0.0.0', port=7860, debug=False)
-
-

ai_backend/requirements.txt

@@ -1,8 +0,0 @@
-flask
-flask-cors
-transformers
-torch
-numpy
-requests
-
-

ai_backend/static/index.html

@@ -1,182 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>Virtual Hardware AI System</title>
-    <style>
-        * { margin: 0; padding: 0; box-sizing: border-box; }
-        body { 
-            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
-            background: linear-gradient(135deg, #1e3c72 0%, #2a5298 100%);
-            min-height: 100vh; display: flex; justify-content: center; align-items: center;
-        }
-        .container { 
-            background: white; border-radius: 20px; box-shadow: 0 20px 40px rgba(0,0,0,0.1);
-            width: 90%; max-width: 1000px; height: 80vh; display: flex; flex-direction: column;
-        }
-        .header { 
-            background: linear-gradient(135deg, #1e3c72 0%, #2a5298 100%); color: white;
-            padding: 20px; text-align: center; border-radius: 20px 20px 0 0;
-        }
-        .specs { font-size: 14px; opacity: 0.9; margin-top: 10px; }
-        .status { padding: 15px; background: #f8f9fa; border-bottom: 1px solid #e9ecef; }
-        .chat-area { flex: 1; padding: 20px; overflow-y: auto; background: #f8f9fa; }
-        .message { margin-bottom: 15px; padding: 12px 16px; border-radius: 18px; max-width: 80%; }
-        .user-message { background: #007bff; color: white; margin-left: auto; text-align: right; }
-        .bot-message { background: white; color: #333; border: 1px solid #e9ecef; }
-        .input-area { padding: 20px; background: white; border-top: 1px solid #e9ecef; display: flex; gap: 10px; }
-        .input-area input { flex: 1; padding: 12px 16px; border: 1px solid #ddd; border-radius: 25px; outline: none; }
-        .input-area button { padding: 12px 24px; background: #007bff; color: white; border: none; border-radius: 25px; cursor: pointer; }
-        .input-area button:disabled { background: #6c757d; cursor: not-allowed; }
-        .hardware-status { font-size: 12px; color: #6c757d; margin-top: 5px; }
-    </style>
-</head>
-<body>
-    <div class="container">
-        <div class="header">
-            <h1>Virtual Hardware AI System</h1>
-            <div class="specs">5TB VSSD • 500GB VRAM • 50,000 GPU Cores • 50 CPU Cores</div>
-        </div>
-        
-        <div class="status" id="status">
-            <strong>Status:</strong> <span id="statusText">Connecting...</span>
-            <div class="hardware-status" id="hardwareStatus"></div>
-        </div>
-        
-        <div class="chat-area" id="chatArea">
-            <div class="message bot-message">
-                Welcome to the Virtual Hardware AI System! I'm powered by a complete virtual hardware stack including 5TB VSSD storage, 500GB VRAM, and 50,000 GPU cores. The system is initializing...
-            </div>
-        </div>
-        
-        <div class="input-area">
-            <input type="text" id="messageInput" placeholder="Type your message..." disabled>
-            <button id="sendButton" disabled>Send</button>
-            <button id="initButton" onclick="initializeHardware()">Initialize</button>
-        </div>
-    </div>
-
-    <script>
-        let hardwareReady = false;
-        let modelReady = false;
-        
-        async function checkStatus() {
-            try {
-                const response = await fetch('/health');
-                const data = await response.json();
-                
-                hardwareReady = data.hardware_initialized;
-                modelReady = data.model_loaded;
-                
-                const statusText = document.getElementById('statusText');
-                const hardwareStatus = document.getElementById('hardwareStatus');
-                
-                if (data.error) {
-                    statusText.textContent = `Error: ${data.error}`;
-                    statusText.style.color = 'red';
-                } else if (modelReady) {
-                    statusText.textContent = 'Ready - Virtual hardware online, model loaded';
-                    statusText.style.color = 'green';
-                    document.getElementById('messageInput').disabled = false;
-                    document.getElementById('sendButton').disabled = false;
-                } else if (hardwareReady) {
-                    statusText.textContent = 'Loading model into virtual hardware...';
-                    statusText.style.color = 'orange';
-                } else {
-                    statusText.textContent = 'Initializing virtual hardware...';
-                    statusText.style.color = 'blue';
-                }
-                
-                // Get detailed hardware status
-                if (hardwareReady) {
-                    const hwResponse = await fetch('/api/hardware-status');
-                    if (hwResponse.ok) {
-                        const hwData = await hwResponse.json();
-                        hardwareStatus.innerHTML = `
-                            VSSD: ${hwData.vssd?.files_stored || 0} files | 
-                            VRAM: ${hwData.vram?.utilization_percent || 0}% | 
-                            GPU: ${hwData.vgpu?.busy_sms || 0}/${hwData.vgpu?.total_sms || 800} SMs | 
-                            CPU: ${hwData.vcpu?.active_threads || 0} threads
-                        `;
-                    }
-                }
-                
-            } catch (error) {
-                document.getElementById('statusText').textContent = 'Connection error';
-                console.error('Status check error:', error);
-            }
-        }
-        
-        async function initializeHardware() {
-            try {
-                const response = await fetch('/api/initialize', { method: 'POST' });
-                const data = await response.json();
-                document.getElementById('statusText').textContent = data.message;
-            } catch (error) {
-                console.error('Initialize error:', error);
-            }
-        }
-        
-        async function sendMessage() {
-            const input = document.getElementById('messageInput');
-            const message = input.value.trim();
-            if (!message) return;
-            
-            addMessage(message, 'user');
-            input.value = '';
-            
-            const loadingMsg = addMessage('Processing on virtual hardware...', 'bot');
-            
-            try {
-                const response = await fetch('/api/chat', {
-                    method: 'POST',
-                    headers: { 'Content-Type': 'application/json' },
-                    body: JSON.stringify({ message: message })
-                });
-                
-                const data = await response.json();
-                loadingMsg.remove();
-                
-                const botMsg = addMessage(data.response, 'bot');
-                if (data.hardware_status) {
-                    const statusDiv = document.createElement('div');
-                    statusDiv.className = 'hardware-status';
-                    statusDiv.innerHTML = `
-                        VSSD: ${data.hardware_status.vssd_files} files | 
-                        VRAM: ${data.hardware_status.vram_utilization}% | 
-                        GPU: ${data.hardware_status.gpu_cores_active} | 
-                        Chunks: ${data.hardware_status.model_chunks_loaded}
-                    `;
-                    botMsg.appendChild(statusDiv);
-                }
-                
-            } catch (error) {
-                loadingMsg.remove();
-                addMessage('Error communicating with virtual hardware', 'bot');
-                console.error('Chat error:', error);
-            }
-        }
-        
-        function addMessage(text, sender) {
-            const chatArea = document.getElementById('chatArea');
-            const messageDiv = document.createElement('div');
-            messageDiv.className = `message ${sender}-message`;
-            messageDiv.textContent = text;
-            chatArea.appendChild(messageDiv);
-            chatArea.scrollTop = chatArea.scrollHeight;
-            return messageDiv;
-        }
-        
-        document.getElementById('sendButton').addEventListener('click', sendMessage);
-        document.getElementById('messageInput').addEventListener('keypress', (e) => {
-            if (e.key === 'Enter') sendMessage();
-        });
-        
-        // Check status every 3 seconds
-        setInterval(checkStatus, 3000);
-        checkStatus();
-    </script>
-</body>
-</html>
-