Full System Code

1. System Configuration

// config.json
{
  "version": "2.5.0",
  "database": {
    "type": "distributed",
    "shards": 16,
    "replication_factor": 3
  },
  "ai_models": [
    "gpt-4-turbo",
    "stable-diffusion-xl",
    "dall-e-3",
    "anthropic-claude-2"
  ],
  "max_concurrent_simulations": 10000,
  "default_memory_per_sim": "4GB"
}

2. Core Simulation Engine

// simulation_engine.rs
use std::sync::Arc;
use tokio::sync::Mutex;
use crate::ai_models::AIModel;
use crate::virtual_dom::VirtualDOM;

pub struct SimulationEngine {
    ai_model: Arc<dyn AIModel>,
    virtual_dom: Arc<Mutex<VirtualDOM>>,
    memory: Arc<Mutex<SimMemory>>,
}

impl SimulationEngine {
    pub async fn process_user_input(&self, input: &str) -> Result<String, SimError> {
        let context = self.memory.lock().await.get_context();
        let ai_response = self.ai_model.generate(input, &context).await?;
        let mut dom = self.virtual_dom.lock().await;
        dom.update_from_ai_response(&ai_response)?;
        Ok(dom.render_to_html())
    }
    
    // ... other methods ...
}

3. AI Integration Layer

// ai_integration.py
import asyncio
from typing import List, Dict
from transformers import AutoModelForCausalLM, AutoTokenizer

class AIIntegration:
    def __init__(self, model_name: str):
        self.model = AutoModelForCausalLM.from_pretrained(model_name)
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
    
    async def generate_response(self, prompt: str, context: Dict[str, any]) -> str:
        inputs = self.tokenizer.encode(prompt + str(context), return_tensors="pt")
        outputs = await asyncio.to_thread(self.model.generate, inputs, max_length=150)
        return self.tokenizer.decode(outputs[0])

# ... other AI-related functions ...

4. Virtual DOM Implementation

// virtual_dom.ts
interface VNode {
  tag: string;
  props: Record<string, any>;
  children: (VNode | string)[];
}

class VirtualDOM {
  private root: VNode;

  constructor() {
    this.root = { tag: 'div', props: {}, children: [] };
  }

  updateFromAIResponse(response: string) {
    // Parse AI response and update virtual DOM
    // ...
  }

  renderToHTML(): string {
    return this.renderNode(this.root);
  }

  private renderNode(node: VNode): string {
    const childrenHTML = node.children
      .map(child => typeof child === 'string' ? child : this.renderNode(child))
      .join('');
    const propsString = Object.entries(node.props)
      .map(([key, value]) => `${key}="${value}"`)
      .join(' ');
    return `<${node.tag} ${propsString}>${childrenHTML}</${node.tag}>`;
  }
}

// ... additional virtual DOM methods ...

5. Database Schema

-- schema.sql
CREATE TABLE users (
  id UUID PRIMARY KEY,
  username VARCHAR(50) UNIQUE NOT NULL,
  email VARCHAR(100) UNIQUE NOT NULL,
  password_hash VARCHAR(255) NOT NULL,
  created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP
);

CREATE TABLE simulations (
  id UUID PRIMARY KEY,
  user_id UUID REFERENCES users(id),
  title VARCHAR(100) NOT NULL,
  description TEXT,
  created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
  last_accessed TIMESTAMP WITH TIME ZONE
);

CREATE TABLE simulation_snapshots (
  id UUID PRIMARY KEY,
  simulation_id UUID REFERENCES simulations(id),
  data JSONB NOT NULL,
  created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP
);

-- ... additional tables and indices ...

6. API Endpoints

// api_routes.js
const express = require('express');
const router = express.Router();
const SimulationController = require('./controllers/SimulationController');

router.post('/simulations', SimulationController.create);
router.get('/simulations/:id', SimulationController.get);
router.put('/simulations/:id', SimulationController.update);
router.delete('/simulations/:id', SimulationController.delete);

router.post('/simulations/:id/interact', SimulationController.interact);
router.get('/simulations/:id/history', SimulationController.getHistory);

// ... other routes ...

module.exports = router;

7. Websocket Handler

// websocket_handler.go
package main

import (
	"github.com/gorilla/websocket"
	"log"
	"net/http"
)

var upgrader = websocket.Upgrader{
	ReadBufferSize:  1024,
	WriteBufferSize: 1024,
}

func handleWebSocket(w http.ResponseWriter, r *http.Request) {
	conn, err := upgrader.Upgrade(w, r, nil)
	if err != nil {
		log.Println(err)
		return
	}
	defer conn.Close()

	for {
		messageType, p, err := conn.ReadMessage()
		if err != nil {
			log.Println(err)
			return
		}
		
		// Process the message and generate a response
		response := processWebSocketMessage(string(p))
		
		if err := conn.WriteMessage(messageType, []byte(response)); err != nil {
			log.Println(err)
			return
		}
	}
}

func processWebSocketMessage(message string) string {
	// Implement message processing logic
	// ...
	return "Processed: " + message
}

8. Client-Side Application

// app.js
import React, { useState, useEffect } from 'react';
import axios from 'axios';

function App() {
  const [simulations, setSimulations] = useState([]);
  const [currentSim, setCurrentSim] = useState(null);

  useEffect(() => {
    fetchSimulations();
  }, []);

  const fetchSimulations = async () => {
    const response = await axios.get('/api/simulations');
    setSimulations(response.data);
  };

  const createSimulation = async (title, description) => {
    await axios.post('/api/simulations', { title, description });
    fetchSimulations();
  };

  const interactWithSimulation = async (input) => {
    const response = await axios.post(`/api/simulations/${currentSim.id}/interact`, { input });
    setCurrentSim(response.data);
  };

  // Render components
  return (
    <div>
      // ... React components for UI ...
    </div>
  );
}

export default App;

9. Machine Learning Model (Example)

# ml_model.py
import torch
import torch.nn as nn

class WebsimAIModel(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(WebsimAIModel, self).__init__()
        self.lstm = nn.LSTM(input_size, hidden_size, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size)
    
    def forward(self, x):
        out, _ = self.lstm(x)
        out = self.fc(out[:, -1, :])
        return out

# Initialize and train the model
model = WebsimAIModel(input_size=100, hidden_size=256, output_size=50)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# Training loop (simplified)
for epoch in range(num_epochs):
    for batch in dataloader:
        inputs, targets = batch
        outputs = model(inputs)
        loss = criterion(outputs, targets)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

# Save the trained model
torch.save(model.state_dict(), 'websim_ai_model.pth')

10. Deployment Configuration

# docker-compose.yml
version: '3.8'

services:
  webapp:
    build: .
    ports:
      - "8080:8080"
    environment:
      - DATABASE_URL=postgres://user:password@db:5432/websim
      - REDIS_URL=redis://cache:6379
    depends_on:
      - db
      - cache

  db:
    image: postgres:13
    volumes:
      - postgres_data:/var/lib/postgresql/data
    environment:
      - POSTGRES_DB=websim
      - POSTGRES_USER=user
      - POSTGRES_PASSWORD=password

  cache:
    image: redis:6
    volumes:
      - redis_data:/data

volumes:
  postgres_data:
  redis_data: