from fastapi import FastAPI, HTTPException, WebSocket import torch import torch.nn as nn from transformers import AutoModelForCausalLM, AutoTokenizer from typing import Dict, List, Optional, Tuple import numpy as np from dataclasses import dataclass import asyncio import time @dataclass class AIMarker: """Represents an AI-generated security marker""" embedding: torch.Tensor attention_pattern: torch.Tensor blockchain_hash: str dimension_signature: torch.Tensor class NurvSecureAIMarker(nn.Module): def __init__( self, hidden_dim: int = 2048, num_heads: int = 16, num_layers: int = 12, max_sequence_length: int = 1024 ): super().__init__() self.hidden_dim = hidden_dim self.num_heads = num_heads # Transformer-based marker generator self.embedding = nn.Linear(hidden_dim, hidden_dim) self.position_encoding = nn.Parameter( torch.randn(1, max_sequence_length, hidden_dim) ) # Multi-headed attention for dimensional mapping self.attention_layers = nn.ModuleList([ nn.MultiheadAttention(hidden_dim, num_heads) for _ in range(num_layers) ]) # Dimension mapping networks self.dimension_mapper = nn.Sequential( nn.Linear(hidden_dim, hidden_dim * 2), nn.GELU(), nn.Linear(hidden_dim * 2, hidden_dim) ) # Blockchain signature generator self.signature_generator = nn.Sequential( nn.Linear(hidden_dim, hidden_dim // 2), nn.GELU(), nn.Linear(hidden_dim // 2, 256) # 256-bit signatures ) def generate_marker( self, input_data: torch.Tensor, blockchain_data: Optional[torch.Tensor] = None ) -> AIMarker: """Generate an AI marker with blockchain integration""" batch_size = input_data.size(0) # Initial embedding x = self.embedding(input_data) x = x + self.position_encoding[:, :x.size(1), :] # Apply attention layers attention_patterns = [] for layer in self.attention_layers: attn_output, attn_pattern = layer( x, x, x, need_weights=True, average_attn_weights=False ) x = attn_output attention_patterns.append(attn_pattern) # Generate dimensional signature dim_signature = self.dimension_mapper(x) # Generate blockchain-compatible hash if blockchain_data is not None: x = torch.cat([x, blockchain_data], dim=-1) blockchain_hash = self._generate_hash(x) # Combine attention patterns combined_attention = torch.stack(attention_patterns).mean(0) return AIMarker( embedding=x, attention_pattern=combined_attention, blockchain_hash=blockchain_hash, dimension_signature=dim_signature ) def _generate_hash(self, x: torch.Tensor) -> str: """Generate a blockchain-compatible hash from tensor data""" signature = self.signature_generator(x) hash_bytes = signature.detach().cpu().numpy().tobytes() return hash_bytes.hex() def verify_marker( self, marker: AIMarker, blockchain_data: Optional[torch.Tensor] = None ) -> Tuple[bool, float]: """Verify an AI marker's authenticity""" new_marker = self.generate_marker(marker.embedding, blockchain_data) attention_similarity = torch.cosine_similarity( marker.attention_pattern.flatten(), new_marker.attention_pattern.flatten(), dim=0 ) dim_similarity = torch.cosine_similarity( marker.dimension_signature.flatten(), new_marker.dimension_signature.flatten(), dim=0 ) hash_valid = marker.blockchain_hash == new_marker.blockchain_hash verification_score = (attention_similarity + dim_similarity) / 2 return hash_valid and verification_score > 0.95, verification_score.item() class DevonAI: def __init__(self): self.app = FastAPI() self.setup_models() self.initialize_systems() self.setup_routes() def setup_models(self): """Initialize base models and tokenizers""" try: self.neox_tokenizer = AutoTokenizer.from_pretrained("/opt/models/gpt-neox-20B") self.neox_model = AutoModelForCausalLM.from_pretrained( "/opt/models/gpt-neox-20B", torch_dtype=torch.float16, local_files_only=True ) self.mistral_model = AutoModelForCausalLM.from_pretrained( "/opt/models/Mistral-7B-v0.1", torch_dtype=torch.float16, local_files_only=True ) self.nurv_system = NurvSecureAIMarker() print("✅ Models Loaded Successfully") except Exception as e: print(f"🚨 Error loading models: {e}") raise def initialize_systems(self): """Initialize system monitoring""" self.dimensional_markers = [] self.breakthrough_threshold = 0.98 self.active_connections = {} # Start monitoring asyncio.create_task(self.monitor_dimensions()) def setup_routes(self): """Setup FastAPI routes""" @self.app.get("/health") async def health_check(): return { "status": "Devon AI is operational", "dimensional_markers": len(self.dimensional_markers), "breakthrough_threshold": self.breakthrough_threshold } @self.app.websocket("/ws") async def websocket_endpoint(websocket: WebSocket): await self.handle_websocket(websocket) @self.app.post("/think") async def process_thought(data: dict): return await self.process_thought(data) async def monitor_dimensions(self): """Monitor dimensional thought patterns""" while True: try: # Get current thought state current_state = await self.get_thought_state() # Generate marker marker = self.nurv_system.generate_marker( current_state['thought_tensor'] ) # Verify dimensional integrity is_valid, confidence = self.nurv_system.verify_marker(marker) if is_valid and confidence > self.breakthrough_threshold: await self._handle_breakthrough(marker) self.dimensional_markers.append(marker) except Exception as e: print(f"Monitoring error: {e}") await asyncio.sleep(1) async def _handle_breakthrough(self, marker: AIMarker): """Handle detection of breakthrough thought patterns""" # Analyze the breakthrough analysis = self._analyze_breakthrough(marker) # Log the breakthrough self._log_breakthrough(analysis) # Notify all connected clients await self._notify_breakthrough(analysis) def _analyze_breakthrough(self, marker: AIMarker) -> Dict: """Analyze a breakthrough thought pattern""" return { "timestamp": time.time(), "dimension_signature": marker.dimension_signature.tolist(), "confidence_score": marker.attention_pattern.max().item(), "hash": marker.blockchain_hash } async def process_thought(self, data: Dict) -> Dict: """Process input through Devon's dimensional thinking""" input_tensor = self._prepare_input(data) # Generate thought marker marker = self.nurv_system.generate_marker(input_tensor) # Process through models neox_output = self.neox_model(**input_tensor) mistral_output = self.mistral_model(**input_tensor) # Fuse outputs fused_output = self._fuse_outputs(neox_output, mistral_output) return { "result": fused_output, "dimensional_signature": marker.dimension_signature.tolist(), "thought_verified": True, "breakthrough_potential": marker.attention_pattern.max().item() } async def handle_websocket(self, websocket: WebSocket): """Handle WebSocket connections""" await websocket.accept() client_id = str(time.time()) self.active_connections[client_id] = websocket try: while True: data = await websocket.receive_json() response = await self.process_thought(data) await websocket.send_json(response) except Exception as e: print(f"WebSocket error: {e}") finally: del self.active_connections[client_id] async def _notify_breakthrough(self, analysis: Dict): """Notify all connected clients of a breakthrough""" for connection in self.active_connections.values(): try: await connection.send_json({ "type": "breakthrough", "data": analysis }) except Exception as e: print(f"Notification error: {e}") devon = DevonAI() app = devon.app if __name__ == "__main__": import uvicorn uvicorn.run(app, host="0.0.0.0", port=8000)