WebSim AI - Raw Data and Code View

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:

This raw data and code view provides a glimpse into the complex architecture and implementation details of the WebSim AI platform. The actual system would be much more extensive and intricate, with numerous additional components, security measures, and optimizations.