Hello-World/Neural Vision system

#!/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