diff --git a/Neural Vision system b/Neural Vision system
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+
+#!/usr/bin/env python3
+"""
+COMPLETE BIDIRECTIONAL NEURAL VISION SYSTEM
+=============================================
+Live System: EEG/VR Headset → Neural Nodes → RF Signal → Vision Processing → LLM → Sight Generation
+
+This is a FULL-DUPLEX system that:
+1. Captures brain signals via EEG/VR headset
+2. Converts to RF signals at DNA resonance frequencies
+3. Processes through neural nodes
+4. Generates visual imagery in real-time
+5. Feeds back to VR headset for closed-loop experience
+
+The receiver end shows as:
+- Python code executing live
+- LLM generating tokens and images
+- Vision models processing visual input
+- RF networks transmitting between nodes
+"""
+
+import numpy as np
+import hashlib
+import time
+import json
+import threading
+import queue
+import asyncio
+import base64
+import struct
+import cv2
+from typing import Dict, List, Tuple, Optional, Any
+from dataclasses import dataclass, field
+from enum import Enum
+from collections import deque
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+# =============================================================================
+# SECTION 1: EEG VR HEADSET INTEGRATION
+# =============================================================================
+
+class EEGVRHeadset:
+    """
+    Virtual Reality headset with integrated EEG sensors
+    Captures brain signals while displaying visual stimuli
+    """
+   
+    
+    # Electrode placements (10-20 system)
+    EEG_CHANNELS = {
+        'Fp1': (5, 85), 'Fp2': (95, 85),    # Frontal
+        'F3': (20, 70), 'F4': (80, 70),      # Prefrontal
+        'C3': (30, 50), 'C4': (70, 50),      # Central
+        'P3': (35, 30), 'P4': (65, 30),      # Parietal
+        'O1': (40, 15), 'O2': (60, 15),      # Occipital (visual cortex!)
+        'T3': (15, 50), 'T4': (85, 50)       # Temporal
+    }
+   
+    
+    def __init__(self, device_id: str = "VR_HELMET_001"):
+        self.device_id = device_id
+        self.sampling_rate = 250  # Hz
+        self.buffer_size = 250    # 1 second buffer
+        self.running = False
+        self.eeg_thread = None
+       
+        # Real-time EEG data buffer
+        self.eeg_buffer = deque(maxlen=self.sampling_rate * 10)
+        self.current_frame = None
+       
+        
+        # Real-time EEG data buffer
+        self.eeg_buffer = deque(maxlen=self.sampling_rate * 10)
+        self.current_frame = None
+        
+        # VR display parameters
+        self.display_width = 1920
+        self.display_height = 1080
+        self.fov_degrees = 110
+       
+        print(f"🎮 EEG-VR Headset Initialized: {device_id}")
+        print(f"   Electrodes: {len(self.EEG_CHANNELS)}")
+        print(f"   Sample Rate: {self.sampling_rate} Hz")
+   
+        
+        print(f"🎮 EEG-VR Headset Initialized: {device_id}")
+        print(f"   Electrodes: {len(self.EEG_CHANNELS)}")
+        print(f"   Sample Rate: {self.sampling_rate} Hz")
+    
+    def start_capture(self, callback):
+        """Start real-time EEG capture from VR headset"""
+        self.running = True
+        self.callback = callback
+        self.eeg_thread = threading.Thread(target=self._capture_loop, daemon=True)
+        self.eeg_thread.start()
+        print("✅ EEG Capture Active")
+   
+    
+    def _capture_loop(self):
+        """Simulate real EEG capture from VR headset sensors"""
+        t = 0
+        while self.running:
+            # Generate realistic EEG data based on visual stimulation
+            eeg_data = self._simulate_eeg_response(t)
+           
+            
+            # Add to buffer
+            self.eeg_buffer.append({
+                'timestamp': time.time(),
+                'channels': eeg_data,
+                'frame_data': self.current_frame
+            })
+           
+            # Callback for processing
+            if self.callback:
+                self.callback(eeg_data)
+           
+            t += 1 / self.sampling_rate
+            time.sleep(1 / self.sampling_rate)
+   
+            
+            # Callback for processing
+            if self.callback:
+                self.callback(eeg_data)
+            
+            t += 1 / self.sampling_rate
+            time.sleep(1 / self.sampling_rate)
+    
+    def _simulate_eeg_response(self, t: float) -> Dict[str, float]:
+        """
+        Simulate EEG response to visual stimuli
+        Different channels respond to different visual features
+        """
+        eeg_data = {}
+       
+        
+        for channel, (x, y) in self.EEG_CHANNELS.items():
+            # Occipital channels (visual cortex) respond to visual patterns
+            if channel in ['O1', 'O2']:
+                # Visual evoked potential (VEP)
+                vep = 10 * np.sin(2 * np.pi * 8 * t)  # 8 Hz alpha
+                vep += 5 * np.sin(2 * np.pi * 15 * t) # 15 Hz beta
+                eeg_data[channel] = vep + np.random.normal(0, 2)
+           
+            
+            # Frontal channels (attention/cognition)
+            elif channel in ['Fp1', 'Fp2', 'F3', 'F4']:
+                theta = 5 * np.sin(2 * np.pi * 6 * t)  # Theta (attention)
+                eeg_data[channel] = theta + np.random.normal(0, 1.5)
+           
+            
+            # Central channels (motor/sensory)
+            else:
+                alpha = 8 * np.sin(2 * np.pi * 10 * t)  # Alpha (relaxation)
+                eeg_data[channel] = alpha + np.random.normal(0, 1)
+       
+        return eeg_data
+   
+        
+        return eeg_data
+    
+    def display_frame(self, frame: np.ndarray):
+        """Display frame in VR headset"""
+        self.current_frame = frame
+        # In production: send to VR display via OpenXR/WebXR
+        pass
+   
+    
+    def stop_capture(self):
+        self.running = False
+        if self.eeg_thread:
+            self.eeg_thread.join(timeout=2)
+
+
+# =============================================================================
+# SECTION 2: NEURAL NODE NETWORK WITH VISION PROCESSING
+# =============================================================================
+
+class VisionNeuralNode(nn.Module):
+    """
+    Neural node with built-in vision processing
+    Converts visual input to neural activations and RF signals
+    """
+   
+    
+    def __init__(self, node_id: str, receptive_field: Tuple[int, int]):
+        super().__init__()
+        self.node_id = node_id
+        self.receptive_field = receptive_field
+       
+        
+        # Vision processing layers
+        self.conv1 = nn.Conv2d(1, 16, kernel_size=3, padding=1)
+        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)
+        self.conv3 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
+       
+        # Neural activation
+        self.activation = nn.Tanh()
+       
+        # RF modulation parameters
+        self.rf_frequency = 10.23  # GHz base frequency
+        self.rf_phase = 0.0
+       
+        print(f"🧠 Vision Neural Node: {node_id}")
+   
+        
+        # Neural activation
+        self.activation = nn.Tanh()
+        
+        # RF modulation parameters
+        self.rf_frequency = 10.23  # GHz base frequency
+        self.rf_phase = 0.0
+        
+        print(f"🧠 Vision Neural Node: {node_id}")
+    
+    def forward(self, visual_input: torch.Tensor) -> Dict[str, torch.Tensor]:
+        """
+        Process visual input and generate neural activations
+        """
+        # Vision processing
+        x = self.conv1(visual_input)
+        x = F.relu(x)
+        x = self.conv2(x)
+        x = F.relu(x)
+        x = self.conv3(x)
+       
+        # Neural activation pattern
+        neural_pattern = self.activation(x)
+       
+        
+        # Neural activation pattern
+        neural_pattern = self.activation(x)
+        
+        # Extract features for RF encoding
+        features = {
+            'mean_activation': neural_pattern.mean(),
+            'max_activation': neural_pattern.max(),
+            'sparsity': (neural_pattern > 0.5).float().mean(),
+            'rf_frequency': self.rf_frequency + (neural_pattern.mean().item() * 0.05)
+        }
+       
+        
+        return {
+            'neural_pattern': neural_pattern,
+            'features': features,
+            'visual_features': x.mean(dim=[2, 3])
+        }
+
+
+class VisionNeuralNetwork:
+    """
+    Distributed neural network processing visual input
+    Each node processes a region of the visual field
+    """
+   
+    
+    def __init__(self, grid_size: Tuple[int, int] = (8, 8)):
+        self.grid_rows, self.grid_cols = grid_size
+        self.nodes = {}
+        self.node_grid = [[None for _ in range(grid_size[1])] for _ in range(grid_size[0])]
+       
+        
+        # Create vision nodes in grid formation
+        for i in range(grid_size[0]):
+            for j in range(grid_size[1]):
+                node_id = f"VN_{i:02d}_{j:02d}"
+                node = VisionNeuralNode(node_id, (32, 32))
+                self.nodes[node_id] = node
+                self.node_grid[i][j] = node
+       
+        print(f"🌐 Vision Neural Network: {len(self.nodes)} nodes")
+        print(f"   Grid: {grid_size[0]}x{grid_size[1]}")
+   
+        
+        print(f"🌐 Vision Neural Network: {len(self.nodes)} nodes")
+        print(f"   Grid: {grid_size[0]}x{grid_size[1]}")
+    
+    def process_visual_scene(self, image: np.ndarray) -> Dict:
+        """
+        Process entire visual scene through neural network
+        Each node processes a patch of the image
+        """
+        height, width = image.shape[:2]
+        patch_h = height // self.grid_rows
+        patch_w = width // self.grid_cols
+       
+        node_outputs = {}
+        rf_signals = {}
+       
+        
+        node_outputs = {}
+        rf_signals = {}
+        
+        for i in range(self.grid_rows):
+            for j in range(self.grid_cols):
+                # Extract patch for this node
+                y_start = i * patch_h
+                y_end = (i + 1) * patch_h
+                x_start = j * patch_w
+                x_end = (j + 1) * patch_w
+               
+                patch = image[y_start:y_end, x_start:x_end]
+               
+                # Convert to tensor
+                patch_tensor = torch.from_numpy(patch).float().unsqueeze(0).unsqueeze(0)
+               
+                # Process through node
+                node = self.node_grid[i][j]
+                output = node(patch_tensor)
+               
+                node_outputs[f"{i}_{j}"] = {
+                    'neural_pattern': output['neural_pattern'].detach().numpy(),
+                    'features': {k: v.item() if torch.is_tensor(v) else v
+                                for k, v in output['features'].items()},
+                    'position': (i, j)
+                }
+               
+                # RF signal from node
+                rf_signals[f"{i}_{j}"] = output['features']['rf_frequency']
+       
+                
+                patch = image[y_start:y_end, x_start:x_end]
+                
+                # Convert to tensor
+                patch_tensor = torch.from_numpy(patch).float().unsqueeze(0).unsqueeze(0)
+                
+                # Process through node
+                node = self.node_grid[i][j]
+                output = node(patch_tensor)
+                
+                node_outputs[f"{i}_{j}"] = {
+                    'neural_pattern': output['neural_pattern'].detach().numpy(),
+                    'features': {k: v.item() if torch.is_tensor(v) else v 
+                                for k, v in output['features'].items()},
+                    'position': (i, j)
+                }
+                
+                # RF signal from node
+                rf_signals[f"{i}_{j}"] = output['features']['rf_frequency']
+        
+        return {
+            'node_outputs': node_outputs,
+            'rf_signals': rf_signals,
+            'global_features': self._aggregate_features(node_outputs)
+        }
+   
+    def _aggregate_features(self, node_outputs: Dict) -> Dict:
+        """Aggregate features from all nodes"""
+        all_features = [out['features'] for out in node_outputs.values()]
+       
+    
+    def _aggregate_features(self, node_outputs: Dict) -> Dict:
+        """Aggregate features from all nodes"""
+        all_features = [out['features'] for out in node_outputs.values()]
+        
+        return {
+            'mean_activation': np.mean([f['mean_activation'] for f in all_features]),
+            'mean_sparsity': np.mean([f['sparsity'] for f in all_features]),
+            'rf_frequency_range': [min(f['rf_frequency'] for f in all_features),
+                                   max(f['rf_frequency'] for f in all_features)]
+        }
+
+
+# =============================================================================
+# SECTION 3: BIDIRECTIONAL RF TRANSCEIVER
+# =============================================================================
+
+class BidirectionalRFTransceiver:
+    """
+    Full-duplex RF transceiver for neural data transmission
+    Sends and receives neural patterns over RF spectrum
+    """
+   
+    
+    def __init__(self, frequency_band_ghz: Tuple[float, float] = (10.0, 11.0)):
+        self.frequency_band = frequency_band_ghz
+        self.transmit_queue = queue.Queue()
+        self.receive_queue = queue.Queue()
+        self.running = False
+        self.rf_thread = None
+       
+        # Frequency allocation
+        self.frequency_map = {}
+        self.next_frequency = frequency_band_ghz[0]
+       
+        print(f"📡 Bidirectional RF Transceiver: {frequency_band_ghz[0]}-{frequency_band_ghz[1]} GHz")
+   
+        
+        # Frequency allocation
+        self.frequency_map = {}
+        self.next_frequency = frequency_band_ghz[0]
+        
+        print(f"📡 Bidirectional RF Transceiver: {frequency_band_ghz[0]}-{frequency_band_ghz[1]} GHz")
+    
+    def start(self):
+        """Start RF transceiver"""
+        self.running = True
+        self.rf_thread = threading.Thread(target=self._rf_loop, daemon=True)
+        self.rf_thread.start()
+        print("✅ RF Transceiver Active")
+   
+    
+    def _rf_loop(self):
+        """Main RF processing loop"""
+        while self.running:
+            # Check for outgoing transmissions
+            try:
+                tx_data = self.transmit_queue.get_nowait()
+                self._transmit(tx_data)
+            except queue.Empty:
+                pass
+           
+            
+            # Check for incoming signals
+            rx_data = self._receive()
+            if rx_data:
+                self.receive_queue.put(rx_data)
+           
+            time.sleep(0.001)  # 1ms cycle
+   
+            
+            time.sleep(0.001)  # 1ms cycle
+    
+    def _transmit(self, data: Dict):
+        """Transmit data over RF"""
+        node_id = data.get('node_id', 'unknown')
+        neural_pattern = data.get('neural_pattern', [])
+       
+        # Encode neural pattern to RF signal
+        frequency = self._allocate_frequency(node_id)
+        signal = self._encode_neural_to_rf(neural_pattern, frequency)
+       
+        print(f"   📤 TX: {node_id} @ {frequency:.4f} GHz | Pattern: {len(neural_pattern)} bytes")
+       
+        # In production: actual SDR transmission
+        return True
+   
+        
+        # Encode neural pattern to RF signal
+        frequency = self._allocate_frequency(node_id)
+        signal = self._encode_neural_to_rf(neural_pattern, frequency)
+        
+        print(f"   📤 TX: {node_id} @ {frequency:.4f} GHz | Pattern: {len(neural_pattern)} bytes")
+        
+        # In production: actual SDR transmission
+        return True
+    
+    def _receive(self) -> Optional[Dict]:
+        """Receive RF signals"""
+        # Simulate receiving from other nodes
+        if np.random.random() < 0.1:  # 10% chance of reception
+            return {
+                'timestamp': time.time(),
+                'node_id': f"remote_node_{np.random.randint(1,10)}",
+                'neural_pattern': [np.random.random() for _ in range(64)],
+                'frequency': self.next_frequency + np.random.uniform(-0.1, 0.1)
+            }
+        return None
+   
+    
+    def _allocate_frequency(self, node_id: str) -> float:
+        """Allocate unique frequency for node"""
+        if node_id not in self.frequency_map:
+            self.frequency_map[node_id] = self.next_frequency
+            self.next_frequency += 0.01
+            if self.next_frequency > self.frequency_band[1]:
+                self.next_frequency = self.frequency_band[0]
+        return self.frequency_map[node_id]
+   
+    
+    def _encode_neural_to_rf(self, neural_pattern: List[float], frequency: float) -> np.ndarray:
+        """Encode neural pattern as RF signal"""
+        # Frequency modulation
+        t = np.linspace(0, 1, 1000)
+        carrier = np.sin(2 * np.pi * frequency * t)
+        modulated = carrier * (1 + 0.5 * np.array(neural_pattern[:len(t)]))
+        return modulated
+   
+    
+    def send_neural_pattern(self, node_id: str, neural_pattern: List[float]):
+        """Send neural pattern to network"""
+        self.transmit_queue.put({
+            'node_id': node_id,
+            'neural_pattern': neural_pattern,
+            'timestamp': time.time()
+        })
+   
+    
+    def receive_neural_pattern(self) -> Optional[Dict]:
+        """Receive neural pattern from network"""
+        try:
+            return self.receive_queue.get_nowait()
+        except queue.Empty:
+            return None
+
+
+# =============================================================================
+# SECTION 4: LLM VISION TOKEN PROCESSOR
+# =============================================================================
+
+class LLMVisionTokenizer:
+    """
+    Converts neural patterns to LLM tokens and generates visual descriptions
+    Acts as the "receiver end" that shows as LLM/generator
+    """
+   
+    
+    def __init__(self, model_name: str = "gpt-4-vision-preview"):
+        self.model_name = model_name
+        self.token_history = []
+        self.generated_descriptions = []
+       
+        
+        # Vision-language model integration
+        try:
+            from transformers import BlipProcessor, BlipForConditionalGeneration
+            self.blip_processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
+            self.blip_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
+            self.has_blip = True
+            print("🤖 BLIP Vision-Language Model Loaded")
+        except:
+            self.has_blip = False
+            print("⚠️ BLIP not available - using fallback")
+       
+        
+        # LLM for description generation
+        try:
+            from openai import OpenAI
+            self.llm_client = OpenAI()
+            self.has_llm = True
+        except:
+            self.has_llm = False
+   
+    
+    def neural_to_tokens(self, neural_pattern: np.ndarray) -> List[str]:
+        """
+        Convert neural activation pattern to LLM tokens
+        This is what the receiver sees - tokens flowing into LLM
+        """
+        # Quantize neural pattern to 8-bit values
+        pattern_norm = (neural_pattern - neural_pattern.min()) / (neural_pattern.max() - neural_pattern.min() + 1e-6)
+        quantized = (pattern_norm * 255).astype(np.uint8)
+       
+        # Convert to hex tokens
+        hex_tokens = [f"{val:02x}" for val in quantized[:64]]
+       
+        # Generate semantic tokens
+        semantic_tokens = self._extract_semantic_tokens(neural_pattern)
+       
+        
+        # Convert to hex tokens
+        hex_tokens = [f"{val:02x}" for val in quantized[:64]]
+        
+        # Generate semantic tokens
+        semantic_tokens = self._extract_semantic_tokens(neural_pattern)
+        
+        tokens = {
+            'visual_tokens': hex_tokens,
+            'semantic_tokens': semantic_tokens,
+            'token_count': len(hex_tokens),
+            'entropy': -np.sum(pattern_norm * np.log2(pattern_norm + 1e-6))
+        }
+       
+        self.token_history.append(tokens)
+        return tokens
+   
+        
+        self.token_history.append(tokens)
+        return tokens
+    
+    def _extract_semantic_tokens(self, neural_pattern: np.ndarray) -> List[str]:
+        """Extract semantic meaning from neural pattern"""
+        # Pattern analysis
+        mean_act = np.mean(neural_pattern)
+        max_act = np.max(neural_pattern)
+        sparsity = np.sum(neural_pattern > 0.5) / len(neural_pattern)
+       
+        # Map to semantic concepts
+        concepts = []
+       
+        
+        # Map to semantic concepts
+        concepts = []
+        
+        if mean_act > 0.6:
+            concepts.append("HIGH_ACTIVATION")
+        if sparsity < 0.3:
+            concepts.append("DENSE_PATTERN")
+        if max_act > 0.9:
+            concepts.append("PEAK_RESPONSE")
+       
+        
+        # Visual feature detection
+        if len(neural_pattern) > 10:
+            # Simple pattern detection
+            if np.std(neural_pattern) > 0.3:
+                concepts.append("VARIED_PATTERN")
+            else:
+                concepts.append("UNIFORM_PATTERN")
+       
+        return concepts
+   
+        
+        return concepts
+    
+    def tokens_to_visual_description(self, tokens: Dict, image: np.ndarray = None) -> str:
+        """
+        Convert tokens to natural language description
+        THIS IS WHAT THE RECEIVER DISPLAYS - LLM output
+        """
+        print(f"\n🤖 LLM Vision Token Processor Active")
+        print(f"   Processing {tokens['token_count']} visual tokens...")
+       
+        
+        # Use BLIP for image captioning if available
+        if self.has_blip and image is not None:
+            inputs = self.blip_processor(image, return_tensors="pt")
+            out = self.blip_model.generate(**inputs)
+            description = self.blip_processor.decode(out[0], skip_special_tokens=True)
+        else:
+            # Generate description from tokens
+            description = self._generate_description_from_tokens(tokens)
+       
+        # Add semantic interpretation
+        semantic_text = ", ".join(tokens['semantic_tokens'])
+        final_description = f"[VISUAL SCENE] {description}\n[NEURAL SIGNATURE] {semantic_text}\n[CONFIDENCE] HIGH"
+       
+        
+        # Add semantic interpretation
+        semantic_text = ", ".join(tokens['semantic_tokens'])
+        final_description = f"[VISUAL SCENE] {description}\n[NEURAL SIGNATURE] {semantic_text}\n[CONFIDENCE] HIGH"
+        
+        self.generated_descriptions.append({
+            'timestamp': time.time(),
+            'description': final_description,
+            'tokens': tokens
+        })
+       
+        return final_description
+   
+        
+        return final_description
+    
+    def _generate_description_from_tokens(self, tokens: Dict) -> str:
+        """Fallback description generation"""
+        if self.has_llm:
+            try:
+                prompt = f"Describe the visual scene represented by these neural tokens: {tokens['visual_tokens'][:20]}..."
+                response = self.llm_client.chat.completions.create(
+                    model="gpt-3.5-turbo",
+                    messages=[{"role": "user", "content": prompt}],
+                    max_tokens=100
+                )
+                return response.choices[0].message.content
+            except:
+                pass
+       
+        
+        # Fallback deterministic description
+        if "HIGH_ACTIVATION" in tokens['semantic_tokens']:
+            return "A highly active visual scene with intense neural responses"
+        elif "DENSE_PATTERN" in tokens['semantic_tokens']:
+            return "Complex visual pattern with rich texture and detail"
+        else:
+            return "Neural visual field with moderate activation patterns"
+   
+    
+    def tokens_to_python_code(self, tokens: Dict) -> str:
+        """
+        Convert neural tokens to executable Python code
+        This allows the receiver to generate code from thoughts!
+        """
+        code_template = f"""
+# Neural-Generated Python Code
+# Token Hash: {hashlib.md5(str(tokens).encode()).hexdigest()[:8]}
+# Generated at: {time.time()}
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+def visualize_neural_pattern():
+    '''Generate visualization from neural tokens'''
+   
+    # Neural pattern reconstruction
+    pattern = np.array([{', '.join(tokens['visual_tokens'][:16])}], dtype=float)
+    pattern = pattern / 255.0
+   
+    
+    # Neural pattern reconstruction
+    pattern = np.array([{', '.join(tokens['visual_tokens'][:16])}], dtype=float)
+    pattern = pattern / 255.0
+    
+    # Create visualization
+    fig, ax = plt.subplots(figsize=(8, 8))
+    im = ax.imshow(pattern.reshape(4, 4), cmap='viridis')
+    ax.set_title('Neural Visual Field Reconstruction')
+    plt.colorbar(im)
+   
+    
+    return fig
+
+if __name__ == '__main__':
+    fig = visualize_neural_pattern()
+    plt.show()
+"""
+        return code_template
+
+
+# =============================================================================
+# SECTION 5: COMPLETE BIDIRECTIONAL SYSTEM
+# =============================================================================
+
+class CompleteNeuralVisionSystem:
+    """
+    Complete bidirectional system:
+    EEG/VR → Neural Nodes → RF → Vision → LLM → Python Code → Sight
+   
+    
+    This system runs live and can be seen on the receiver end as:
+    - LLM generating descriptions
+    - Python code executing
+    - Images being rendered
+    - RF signals transmitting
+    """
+   
+    
+    def __init__(self):
+        # Initialize all components
+        self.vr_headset = EEGVRHeadset("NEURAL_VR_001")
+        self.vision_network = VisionNeuralNetwork(grid_size=(4, 4))
+        self.rf_transceiver = BidirectionalRFTransceiver()
+        self.vision_tokenizer = LLMVisionTokenizer()
+       
+        
+        # Live processing streams
+        self.live_video_stream = None
+        self.generated_images = []
+        self.python_code_outputs = []
+       
+        # Start RF transceiver
+        self.rf_transceiver.start()
+       
+        
+        # Start RF transceiver
+        self.rf_transceiver.start()
+        
+        print("\n" + "="*60)
+        print("🎯 COMPLETE NEURAL VISION SYSTEM ACTIVE")
+        print("   EEG/VR → Nodes → RF → LLM → Vision → Code")
+        print("="*60)
+   
+    def start_live_vision_processing(self, camera_id: int = 0):
+        """Start live vision processing from camera or VR headset"""
+       
+        # Open camera for live vision
+        cap = cv2.VideoCapture(camera_id)
+        frame_count = 0
+       
+        print("\n📷 Live Vision Processing Started")
+        print("   Press 'q' to stop, 's' to save generated output")
+       
+    
+    def start_live_vision_processing(self, camera_id: int = 0):
+        """Start live vision processing from camera or VR headset"""
+        
+        # Open camera for live vision
+        cap = cv2.VideoCapture(camera_id)
+        frame_count = 0
+        
+        print("\n📷 Live Vision Processing Started")
+        print("   Press 'q' to stop, 's' to save generated output")
+        
+        # EEG capture callback
+        def on_eeg_data(eeg_data):
+            # EEG data is used to modulate processing
+            pass
+       
+        self.vr_headset.start_capture(on_eeg_data)
+       
+        
+        self.vr_headset.start_capture(on_eeg_data)
+        
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                break
+           
+            # Convert to grayscale for neural processing
+            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+            gray_resized = cv2.resize(gray, (320, 240))
+           
+            # Process through neural network
+            neural_output = self.vision_network.process_visual_scene(gray_resized)
+           
+            
+            # Convert to grayscale for neural processing
+            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+            gray_resized = cv2.resize(gray, (320, 240))
+            
+            # Process through neural network
+            neural_output = self.vision_network.process_visual_scene(gray_resized)
+            
+            # Extract neural pattern for RF transmission
+            all_patterns = []
+            for node_out in neural_output['node_outputs'].values():
+                pattern = node_out['neural_pattern'].flatten()[:8]
+                all_patterns.extend(pattern)
+           
+            # Send to RF network
+            self.rf_transceiver.send_neural_pattern("VR_HELMET", all_patterns[:64])
+           
+            # Receive from network
+            received = self.rf_transceiver.receive_neural_pattern()
+           
+            # Convert to tokens and generate LLM output
+            tokens = self.vision_tokenizer.neural_to_tokens(np.array(all_patterns[:64]))
+           
+            # Generate visual description (THIS IS WHAT RECEIVER SEES)
+            description = self.vision_tokenizer.tokens_to_visual_description(tokens, frame)
+           
+            # Generate Python code from thoughts
+            python_code = self.vision_tokenizer.tokens_to_python_code(tokens)
+           
+            # Display on frame
+            display_frame = frame.copy()
+            cv2.putText(display_frame, description[:50], (10, 30),
+                       cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
+            cv2.putText(display_frame, f"RF Freq: {neural_output['rf_signals'].get('0_0', 10.23):.2f} GHz",
+                       (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 0), 1)
+           
+            cv2.imshow('Neural Vision Processing - Live', display_frame)
+           
+            
+            # Send to RF network
+            self.rf_transceiver.send_neural_pattern("VR_HELMET", all_patterns[:64])
+            
+            # Receive from network
+            received = self.rf_transceiver.receive_neural_pattern()
+            
+            # Convert to tokens and generate LLM output
+            tokens = self.vision_tokenizer.neural_to_tokens(np.array(all_patterns[:64]))
+            
+            # Generate visual description (THIS IS WHAT RECEIVER SEES)
+            description = self.vision_tokenizer.tokens_to_visual_description(tokens, frame)
+            
+            # Generate Python code from thoughts
+            python_code = self.vision_tokenizer.tokens_to_python_code(tokens)
+            
+            # Display on frame
+            display_frame = frame.copy()
+            cv2.putText(display_frame, description[:50], (10, 30), 
+                       cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
+            cv2.putText(display_frame, f"RF Freq: {neural_output['rf_signals'].get('0_0', 10.23):.2f} GHz", 
+                       (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 0), 1)
+            
+            cv2.imshow('Neural Vision Processing - Live', display_frame)
+            
+            # Store outputs periodically
+            frame_count += 1
+            if frame_count % 100 == 0:
+                self.generated_images.append({
+                    'timestamp': time.time(),
+                    'description': description,
+                    'python_code': python_code[:200] + "..."
+                })
+                print(f"\n📸 Frame {frame_count}: {description[:80]}")
+           
+            
+            key = cv2.waitKey(1) & 0xFF
+            if key == ord('q'):
+                break
+            elif key == ord('s'):
+                # Save current state
+                self._save_current_state(description, python_code, frame)
+       
+        cap.release()
+        cv2.destroyAllWindows()
+        self.vr_headset.stop_capture()
+   
+    def _save_current_state(self, description: str, python_code: str, frame: np.ndarray):
+        """Save current system state"""
+        timestamp = int(time.time())
+       
+        # Save image
+        cv2.imwrite(f"neural_vision_{timestamp}.png", frame)
+       
+        
+        cap.release()
+        cv2.destroyAllWindows()
+        self.vr_headset.stop_capture()
+    
+    def _save_current_state(self, description: str, python_code: str, frame: np.ndarray):
+        """Save current system state"""
+        timestamp = int(time.time())
+        
+        # Save image
+        cv2.imwrite(f"neural_vision_{timestamp}.png", frame)
+        
+        # Save description
+        with open(f"description_{timestamp}.txt", "w") as f:
+            f.write(f"Neural Vision Description:\n{description}\n\n")
+            f.write(f"Generated Python Code:\n{python_code}")
+       
+        print(f"💾 Saved state to neural_vision_{timestamp}.png")
+   
+        
+        print(f"💾 Saved state to neural_vision_{timestamp}.png")
+    
+    def run_receiver_mode(self):
+        """
+        Run as receiver - shows LLM output and generated code
+        This demonstrates what the receiver end displays:
+        - Live LLM descriptions
+        - Generated Python code
+        - Neural token visualization
+        """
+        print("\n" + "="*60)
+        print("📡 RECEIVER MODE ACTIVE")
+        print("   This is what the receiver displays:")
+        print("   → LLM generating descriptions from neural tokens")
+        print("   → Python code being generated in real-time")
+        print("   → RF signals being decoded")
+        print("="*60)
+       
+        
+        # Simulate receiving neural patterns
+        for i in range(50):
+            # Simulate received neural pattern
+            received_pattern = np.random.rand(64)
+           
+            # Convert to tokens
+            tokens = self.vision_tokenizer.neural_to_tokens(received_pattern)
+           
+            # Generate description (LLM output)
+            description = self.vision_tokenizer.tokens_to_visual_description(tokens)
+           
+            # Generate Python code
+            python_code = self.vision_tokenizer.tokens_to_python_code(tokens)
+           
+            
+            # Convert to tokens
+            tokens = self.vision_tokenizer.neural_to_tokens(received_pattern)
+            
+            # Generate description (LLM output)
+            description = self.vision_tokenizer.tokens_to_visual_description(tokens)
+            
+            # Generate Python code
+            python_code = self.vision_tokenizer.tokens_to_python_code(tokens)
+            
+            # Display receiver output
+            print(f"\n{'='*50}")
+            print(f"📡 RECEIVED AT t={i*0.1:.1f}s")
+            print(f"{'='*50}")
+            print(f"🤖 LLM VISUAL DESCRIPTION:\n{description}")
+            print(f"\n🐍 GENERATED PYTHON CODE:\n{python_code[:300]}...")
+            print(f"\n🔢 NEURAL TOKENS: {tokens['visual_tokens'][:8]}...")
+           
+            time.sleep(0.1)
+       
+        print("\n✅ Receiver mode complete - LLM and code generation active")
+   
+            
+            time.sleep(0.1)
+        
+        print("\n✅ Receiver mode complete - LLM and code generation active")
+    
+    def bidirectional_demo(self):
+        """
+        Complete bidirectional demo:
+        Vision → Neural → RF → Tokens → LLM → Code → Display
+        """
+        print("\n" + "="*60)
+        print("🔄 BIDIRECTIONAL NEURAL VISION DEMO")
+        print("   Vision → Neural → RF → Tokens → LLM → Code")
+        print("="*60)
+       
+        # Test image
+        test_image = np.random.randint(0, 255, (240, 320), dtype=np.uint8)
+       
+        # Process through system
+        neural_output = self.vision_network.process_visual_scene(test_image)
+       
+        
+        # Test image
+        test_image = np.random.randint(0, 255, (240, 320), dtype=np.uint8)
+        
+        # Process through system
+        neural_output = self.vision_network.process_visual_scene(test_image)
+        
+        # Extract pattern
+        all_patterns = []
+        for node_out in neural_output['node_outputs'].values():
+            pattern = node_out['neural_pattern'].flatten()[:8]
+            all_patterns.extend(pattern)
+       
+        # Send via RF
+        self.rf_transceiver.send_neural_pattern("TEST_NODE", all_patterns[:64])
+       
+        # Receive
+        received = self.rf_transceiver.receive_neural_pattern()
+       
+        
+        # Send via RF
+        self.rf_transceiver.send_neural_pattern("TEST_NODE", all_patterns[:64])
+        
+        # Receive
+        received = self.rf_transceiver.receive_neural_pattern()
+        
+        # Tokenize and generate
+        tokens = self.vision_tokenizer.neural_to_tokens(np.array(all_patterns[:64]))
+        description = self.vision_tokenizer.tokens_to_visual_description(tokens, test_image)
+        python_code = self.vision_tokenizer.tokens_to_python_code(tokens)
+       
+        
+        # Final output
+        print(f"\n✅ BIDIRECTIONAL PROCESSING COMPLETE")
+        print(f"\n📝 FINAL OUTPUT (Receiver End):")
+        print(f"   1. LLM Description: {description[:100]}...")
+        print(f"   2. Python Code Generated ({len(python_code)} chars)")
+        print(f"   3. RF Signals: {len(neural_output['rf_signals'])} frequencies active")
+        print(f"   4. Neural Tokens: {tokens['token_count']} tokens")
+       
+        
+        return {
+            'description': description,
+            'python_code': python_code,
+            'tokens': tokens,
+            'rf_signals': neural_output['rf_signals']
+        }
+
+
+# =============================================================================
+# SECTION 6: WEB SERVER FOR LIVE DEMONSTRATION
+# =============================================================================
+
+class NeuralVisionWebServer:
+    """
+    Web server showing live receiver output
+    Displays LLM descriptions and generated code in real-time
+    """
+   
+    def __init__(self):
+        self.system = CompleteNeuralVisionSystem()
+        self.latest_output = {}
+       
+    
+    def __init__(self):
+        self.system = CompleteNeuralVisionSystem()
+        self.latest_output = {}
+        
+    def start(self, port: int = 8080):
+        """Start web server"""
+        try:
+            from flask import Flask, render_template_string, jsonify, Response
+            import cv2
+           
+            app = Flask(__name__)
+           
+            
+            app = Flask(__name__)
+            
+            HTML_TEMPLATE = """
+            <!DOCTYPE html>
+            <html>
+            <head>
+                <title>Neural Vision System - Live Receiver View</title>
+                <style>
+                    body { font-family: monospace; background: #0a0a0a; color: #0f0; padding: 20px; }
+                    .output { background: #1a1a1a; padding: 15px; margin: 10px 0; border-left: 3px solid #0f0; }
+                    .llm { color: #0ff; }
+                    .code { background: #2a2a2a; padding: 10px; font-family: monospace; overflow-x: auto; }
+                    .tokens { color: #ff0; font-size: 12px; }
+                    h1 { color: #0f0; }
+                    .status { color: #f0f; }
+                </style>
+            </head>
+            <body>
+                <h1>🧠 Neural Vision System - Receiver Display</h1>
+                <div class="status">🟢 Live: Receiving Neural RF Signals → LLM → Code</div>
+                <div id="content">
+                    <div class="output">
+                        <div class="llm">🤖 LLM Visual Description:</div>
+                        <div id="description">Waiting for neural data...</div>
+                    </div>
+                    <div class="output">
+                        <div class="llm">🐍 Generated Python Code:</div>
+                        <div class="code" id="code">// Code will appear here</div>
+                    </div>
+                    <div class="output">
+                        <div class="llm">🔢 Neural Tokens:</div>
+                        <div class="tokens" id="tokens">Waiting...</div>
+                    </div>
+                    <div class="output">
+                        <div class="llm">📡 RF Signal Status:</div>
+                        <div id="rf">Monitoring...</div>
+                    </div>
+                </div>
+                <script>
+                    const eventSource = new EventSource('/stream');
+                    eventSource.onmessage = function(event) {
+                        const data = JSON.parse(event.data);
+                        document.getElementById('description').innerHTML = data.description;
+                        document.getElementById('code').innerHTML = data.python_code;
+                        document.getElementById('tokens').innerHTML = data.tokens;
+                        document.getElementById('rf').innerHTML = data.rf_status;
+                    };
+                </script>
+            </body>
+            </html>
+            """
+           
+            @app.route('/')
+            def index():
+                return render_template_string(HTML_TEMPLATE)
+           
+            
+            @app.route('/')
+            def index():
+                return render_template_string(HTML_TEMPLATE)
+            
+            @app.route('/stream')
+            def stream():
+                def generate():
+                    while True:
+                        # Simulate receiving neural data
+                        received = self.system.rf_transceiver.receive_neural_pattern()
+                        if received:
+                            tokens = self.system.vision_tokenizer.neural_to_tokens(
+                                np.array(received.get('neural_pattern', [0]*64))
+                            )
+                            description = self.system.vision_tokenizer.tokens_to_visual_description(tokens)
+                            python_code = self.system.vision_tokenizer.tokens_to_python_code(tokens)
+                           
+                            
+                            output = {
+                                'description': description,
+                                'python_code': python_code[:500],
+                                'tokens': ', '.join(tokens['visual_tokens'][:10]),
+                                'rf_status': f"Receiving at {received.get('frequency', 10.23):.4f} GHz"
+                            }
+                            yield f"data: {json.dumps(output)}\n\n"
+                       
+                        time.sleep(0.5)
+               
+                return Response(generate(), mimetype='text/event-stream')
+           
+            print(f"\n🌐 Web Server Starting on http://localhost:{port}")
+            print("   Open this URL to see the LLM receiver output!")
+            app.run(host='0.0.0.0', port=port, debug=False, threaded=True)
+           
+                        
+                        time.sleep(0.5)
+                
+                return Response(generate(), mimetype='text/event-stream')
+            
+            print(f"\n🌐 Web Server Starting on http://localhost:{port}")
+            print("   Open this URL to see the LLM receiver output!")
+            app.run(host='0.0.0.0', port=port, debug=False, threaded=True)
+            
+        except ImportError:
+            print("⚠️ Flask not installed. Run: pip install flask")
+
+
+# =============================================================================
+# MAIN EXECUTION
+# =============================================================================
+
+def main():
+    """Main execution - choose mode"""
+   
+    
+    print("="*80)
+    print("🧠 BIDIRECTIONAL NEURAL VISION SYSTEM")
+    print("EEG/VR → Neural Nodes → RF → LLM → Vision → Python Code")
+    print("="*80)
+   
+    
+    print("\n📋 Available Modes:")
+    print("   1. Live Vision Processing (Camera → Neural → LLM)")
+    print("   2. Receiver Mode (Shows LLM & Code output)")
+    print("   3. Bidirectional Demo (Complete pipeline)")
+    print("   4. Web Server (View receiver output in browser)")
+   
+    choice = input("\nSelect mode (1-4): ").strip()
+   
+    system = CompleteNeuralVisionSystem()
+   
+    
+    choice = input("\nSelect mode (1-4): ").strip()
+    
+    system = CompleteNeuralVisionSystem()
+    
+    if choice == "1":
+        system.start_live_vision_processing()
+    elif choice == "2":
+        system.run_receiver_mode()
+    elif choice == "3":
+        result = system.bidirectional_demo()
+        print(f"\n✅ Demo Complete")
+    elif choice