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NeuralOS-Hypervisor

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AEUPH commited on Jan 23

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035a382

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1 Parent(s): 0bde6f1

Update Dockerfile

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Dockerfile +224 -463

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@@ -1,526 +1,287 @@
-# Use a lightweight Python base
 FROM python:3.10-slim
-# Set working directory
 WORKDIR /app
-# 1. Install System Dependencies
-RUN apt-get update && apt-get install -y \
-    curl \
-    git \
-    libgomp1 \
-    && rm -rf /var/lib/apt/lists/*
-# 2. Upgrade pip
 RUN pip install --upgrade pip
-# 3. Download Retro Font (VT323)
-RUN curl -L -o /app/VT323.ttf https://github.com/google/fonts/raw/main/ofl/vt323/VT323-Regular.ttf
-# 4. Install Python Dependencies
-RUN pip install --no-cache-dir \
-    torch \
-    torchvision \
-    numpy \
-    flask \
-    flask-sock \
-    diffusers \
-    transformers \
-    accelerate \
-    peft \
-    pillow \
-    diskcache \
-    safetensors \
-    scipy \
-    sentencepiece
-# 5. Create a non-root user
 RUN useradd -m -u 1000 user
 USER user
-ENV HOME=/home/user \
-    PATH=/home/user/.local/bin:$PATH
-# 6. Write the Application
-COPY --chown=user <<'EOF' app.py
-import sys, os, io, base64, json, warnings, time
 import torch
 import numpy as np
 from dataclasses import dataclass
-from typing import Dict, List, Optional, Tuple
-from flask import Flask, render_template_string
 from flask_sock import Sock
-from PIL import Image, ImageDraw
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from diffusers import StableDiffusionPipeline, AutoencoderTiny, LCMScheduler
-# Silence Warnings
 warnings.filterwarnings("ignore")
-# ============================================================================
-# 1. FRONTEND (Responsive React Desktop)
-# ============================================================================
-HTML_TEMPLATE = r"""
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no">
-    <title>LiteWin XP - Neural OS Desktop</title>
-    <script src="https://cdn.tailwindcss.com"></script>
-    <script src="https://unpkg.com/react@18/umd/react.production.min.js"></script>
-    <script src="https://unpkg.com/react-dom@18/umd/react-dom.production.min.js"></script>
-    <script src="https://unpkg.com/@babel/standalone/babel.min.js"></script>
-    <link href="https://fonts.googleapis.com/css2?family=Tahoma:wght@400;700&family=Fira+Code:wght@300;500&display=swap" rel="stylesheet">
-    <style>
-        body {
-            background: #111;
-            color: #e2e2e2;
-            margin: 0;
-            overflow: hidden;
-            font-family: 'Tahoma', sans-serif;
-            display: flex;
-            align-items: center;
-            justify-content: center;
-            height: 100vh;
-            width: 100vw;
-        }
-        /* container for scaling */
-        #desktop-container {
-            position: relative;
-            width: 1024px;
-            height: 1024px;
-            transform-origin: center center;
-            box-shadow: 0 0 50px rgba(0,0,0,0.5);
-            background: #3A6EA5;
-        }
-        .canvas-viewport {
-            position: absolute;
-            top: 0; left: 0;
-            width: 100%; height: 100%;
-            image-rendering: pixelated;
-        }
-        .canvas-viewport img { width: 100%; height: 100%; display: block; }
-        .taskbar {
-            position: absolute; bottom: 0; left: 0; right: 0; height: 60px; /* Taller for mobile */
-            background: linear-gradient(to bottom, #1F4788 0%, #1A3E6F 50%, #0E2950 100%);
-            border-top: 3px solid #4D7DB5;
-            display: flex; align-items: center; padding: 0 8px; gap: 8px;
-            z-index: 50;
-        }
-        .start-btn {
-            background: linear-gradient(to bottom, #3F8B3F 0%, #2F6B2F 100%);
-            border: 3px outset #5FAF5F;
-            color: white;
-            font-weight: bold;
-            padding: 6px 18px;
-            border-radius: 4px;
-            cursor: pointer;
-            font-size: 20px; /* Larger text */
-            font-style: italic;
-            text-shadow: 1px 1px 1px #000;
-            display: flex; align-items: center; gap: 8px;
-        }
-        .start-btn::before {
-            content: "❖"; /* Windows-ish icon */
-            font-style: normal;
-        }
-        .console-log {
-            position: absolute; top: 20px; right: 20px; width: 350px; height: 200px;
-            background: rgba(0,0,0,0.85);
-            color: #0f0;
-            font-family: 'Fira Code', monospace;
-            font-size: 14px; /* Readable text */
-            line-height: 1.4;
-            padding: 15px;
-            border: 2px solid #333;
-            border-radius: 4px;
-            overflow-y: auto;
-            z-index: 1000;
-            pointer-events: none;
-            box-shadow: 0 4px 10px rgba(0,0,0,0.5);
-        }
-        /* Mobile Overlay for logs when very small */
-        @media (max-width: 600px) {
-            .console-log {
-                top: auto; bottom: 80px; left: 10px; right: 10px; width: auto; height: 120px;
-            }
-        }
-    </style>
-</head>
-<body>
-    <div id="root"></div>
-    <script type="text/babel">
-        const { useState, useEffect, useRef } = React;
-        function App() {
-            const [desktopImage, setDesktopImage] = useState(null);
-            const [logs, setLogs] = useState(["Neural Bios v9.7", "Booting Kernel..."]);
-            const [scale, setScale] = useState(1);
-            const socketRef = useRef(null);
-            const containerRef = useRef(null);
-            const addLog = (msg) => setLogs(prev => [...prev.slice(-8), msg]);
-            // Resize Logic
-            useEffect(() => {
-                const handleResize = () => {
-                    const padding = 20; // px
-                    const availableWidth = window.innerWidth - padding;
-                    const availableHeight = window.innerHeight - padding;
-                    const scaleW = availableWidth / 1024;
-                    const scaleH = availableHeight / 1024;
-                    // Fit contain
-                    const newScale = Math.min(scaleW, scaleH, 1.0); // Max scale 1.0 (crisp)
-                    setScale(newScale);
-                };
-                window.addEventListener('resize', handleResize);
-                handleResize(); // Initial calc
-                return () => window.removeEventListener('resize', handleResize);
-            }, []);
-            // Websocket Logic
-            useEffect(() => {
-                const proto = window.location.protocol === 'https:' ? 'wss' : 'ws';
-                const ws = new WebSocket(`${proto}://${window.location.host}/kernel`);
-                socketRef.current = ws;
-                ws.onmessage = (e) => {
-                    const msg = JSON.parse(e.data);
-                    if (msg.type === 'frame_update' || msg.type === 'desktop_ready') {
-                        setDesktopImage(msg.data);
-                    }
-                    if (msg.type === 'log') addLog(msg.data);
-                };
-                return () => ws.close();
-            }, []);
-            const handleClick = (e) => {
-                if (!containerRef.current) return;
-                // Get click coordinates relative to the scaled container
-                const rect = containerRef.current.getBoundingClientRect();
-                // Calculate position (0-1024)
-                const clickX = (e.clientX - rect.left) / scale;
-                const clickY = (e.clientY - rect.top) / scale;
-                // Convert to Neural Grid (0-128)
-                const gridX = Math.floor((clickX / 1024) * 128);
-                const gridY = Math.floor((clickY / 1024) * 128);
-                if(gridX >= 0 && gridX <= 128 && gridY >= 0 && gridY <= 128) {
-                    socketRef.current?.send(JSON.stringify({ type: 'click', x: gridX, y: gridY }));
-                }
-            };
-            return (
-                <div
-                    id="desktop-container"
-                    ref={containerRef}
-                    style={{ transform: `scale(${scale})` }}
-                >
-                    <div className="canvas-viewport" onClick={handleClick}>
-                        {desktopImage && <img src={`data:image/png;base64,${desktopImage}`} draggable="false" />}
-                    </div>
-                    <div className="taskbar">
-                        <div className="start-btn">start</div>
-                    </div>
-                    <div className="console-log">
-                        {logs.map((l, i) => <div key={i}>&gt; {l}</div>)}
-                    </div>
-                </div>
-            );
-        }
-        ReactDOM.createRoot(document.getElementById('root')).render(<App />);
-    </script>
-</body>
-</html>
-"""
-# ============================================================================
-# 2. OS KERNEL & PROCESS MANAGEMENT
-# ============================================================================
 @dataclass
 class Application:
-    name: str
-    icon_dna: str
-    content_prompt: str
-    default_size: Tuple[int, int]
 @dataclass
 class Process:
-    pid: int
-    name: str
-    app_type: str
-    position: Tuple[int, int]
-    size: Tuple[int, int]
-    latent_state: torch.Tensor
-    z_order: int
-PROGRAMS = {
-    "notepad": Application("Notepad", "ICON_NOTEPAD", "white text editor, courier font", (40, 32)),
-    "paint": Application("Paint", "ICON_PAINT", "white canvas, ms paint toolbar", (48, 40)),
-    "cmd": Application("Command Prompt", "ICON_CMD", "black console window, white text", (48, 32)),
-    "explorer": Application("Explorer", "ICON_FOLDER", "file explorer, icons grid", (56, 40))
 }
-DRIVERS = {}
-def initialize_drivers():
-    bg = torch.zeros((1, 4, 128, 128), dtype=torch.float32)
-    bg[:, 0, :, :] = 0.5
-    bg[:, 1, :, :] = 0.8
-    bg[:, 2, :, :] = 0.2
-    DRIVERS["DESKTOP_BG"] = bg
-    icon = torch.zeros((1, 4, 8, 8), dtype=torch.float32)
-    icon[:, 0, 2:6, 2:6] = 2.0
-    DRIVERS["ICON_GENERIC"] = icon
-    print("[*] Drivers Initialized.")
 class OSKernel:
     def __init__(self):
-        self.processes: Dict[int, Process] = {}
-        self.next_pid = 1
-        self.focused_pid: Optional[int] = None
-        self.desktop_icons = [
-            {"app": "notepad", "x": 4, "y": 4, "label": "Notepad"},
-            {"app": "paint", "x": 4, "y": 16, "label": "Paint"},
-            {"app": "cmd", "x": 4, "y": 28, "label": "Command Prompt"},
         ]
-    def spawn_process(self, app_type: str, x: int, y: int) -> int:
-        if app_type not in PROGRAMS: return -1
-        app = PROGRAMS[app_type]
-        pid = self.next_pid
-        self.next_pid += 1
-        w, h = app.default_size
-        latent = torch.zeros((1, 4, h, w), dtype=torch.float32)
-        proc = Process(
-            pid=pid, name=app.name, app_type=app_type,
-            position=(x, y), size=(w, h),
-            latent_state=latent, z_order=pid
-        )
-        self.processes[pid] = proc
         self.focus_process(pid)
         return pid
-    def kill_process(self, pid: int):
         if pid in self.processes:
             del self.processes[pid]
-            if self.focused_pid == pid: self.focused_pid = None
-    def focus_process(self, pid: int):
         if pid in self.processes:
-            self.focused_pid = pid
-            max_z = max((p.z_order for p in self.processes.values()), default=0)
-            self.processes[pid].z_order = max_z + 1
-    def handle_click(self, x: int, y: int) -> Dict:
-        # Check Windows (Reverse Z)
-        sorted_procs = sorted(self.processes.values(), key=lambda p: p.z_order, reverse=True)
         for proc in sorted_procs:
-            px, py = proc.position
-            pw, ph = proc.size
-            if px <= x < px+pw and py <= y < py+ph:
-                self.focus_process(proc.pid)
-                if py <= y < py+4 and px+pw-4 <= x < px+pw:
                     self.kill_process(proc.pid)
-                    return {"action": "close", "pid": proc.pid, "name": proc.name}
-                return {"action": "focus", "pid": proc.pid, "name": proc.name}
-        # Check Icons
         for icon in self.desktop_icons:
-            ix, iy = icon['x'], icon['y']
-            if ix <= x < ix+8 and iy <= y < iy+8:
-                pid = self.spawn_process(icon['app'], x=32, y=24)
-                return {"action": "launch", "pid": pid, "app": icon['app']}
-        return {"action": "desktop_click"}
-# ============================================================================
-# 3. AI ENGINES (QWEN + DIFFUSION)
-# ============================================================================
 class NeuralSystem:
     def __init__(self):
-        self.device = "cuda" if torch.cuda.is_available() else "cpu"
-        self.dt = torch.float16 if self.device == "cuda" else torch.float32
-        print(f"[*] System Device: {self.device} | Type: {self.dt}")
-        # A. LOAD DIFFUSION
-        print("[*] Loading Neural GPU...")
-        self.pipe = StableDiffusionPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5",
-            torch_dtype=self.dt,
-            safety_checker=None,
-            requires_safety_checker=False
-        )
-        if self.device == "cuda":
-            self.pipe = self.pipe.to("cuda")
         self.pipe.load_lora_weights("latent-consistency/lcm-lora-sdv1-5")
-        self.pipe.scheduler = LCMScheduler.from_config(self.pipe.scheduler.config)
-        self.pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taesd", torch_dtype=self.dt).to(self.device)
-        # B. LOAD QWEN
-        print("[*] Loading Qwen 2.5...")
-        self.model_id = "Qwen/Qwen2.5-Coder-0.5B-Instruct"
-        self.tokenizer = AutoTokenizer.from_pretrained(self.model_id)
-        if self.tokenizer.pad_token_id is None:
-            self.tokenizer.pad_token_id = self.tokenizer.eos_token_id
-        # FIX: Using 'dtype' instead of 'torch_dtype' to silence deprecation warning
-        # FIX: Removed device_map to prevent meta-tensor error on CPU
-        self.llm = AutoModelForCausalLM.from_pretrained(
-            self.model_id,
-            dtype=self.dt
-        ).to(self.device)
-        print("[*] Systems Online.")
-    def think(self, prompt_text):
-        inputs = self.tokenizer(
-            prompt_text,
-            return_tensors="pt",
-            padding=True,
-            truncation=True
-        ).to(self.device)
-        outputs = self.llm.generate(
-            inputs.input_ids,
-            attention_mask=inputs.attention_mask,
-            max_new_tokens=64,
-            do_sample=True,
-            temperature=0.6,
-            pad_token_id=self.tokenizer.eos_token_id
-        )
-        response = self.tokenizer.decode(outputs[0][len(inputs.input_ids[0]):], skip_special_tokens=True)
         return response
-    def render_frame(self, kernel: OSKernel):
-        canvas = DRIVERS["DESKTOP_BG"].clone().to(self.device)
-        icon_dna = DRIVERS["ICON_GENERIC"].to(self.device)
         for icon in kernel.desktop_icons:
-            x, y = icon['x'], icon['y']
-            canvas[:, :, y:y+8, x:x+8] = icon_dna
-        sorted_procs = sorted(kernel.processes.values(), key=lambda p: p.z_order)
         for proc in sorted_procs:
-            x, y = proc.position
-            w, h = proc.size
-            if x+w <= 128 and y+h <= 128:
-                proc_latent = proc.latent_state.to(self.device, dtype=self.dt)
-                canvas[:, :, y:y+h, x:x+w] = proc_latent
         with torch.no_grad():
-            img = self.pipe.vae.decode(canvas / 0.18215).sample
-            img = (img / 2 + 0.5).clamp(0, 1).cpu().permute(0, 2, 3, 1).numpy()
-            img = self.pipe.numpy_to_pil(img)[0]
         return img
-    def generate_window_content(self, proc: Process):
-        app_def = PROGRAMS[proc.app_type]
-        prompt = f"pixel art windows xp {app_def.name} window content, {app_def.content_prompt}, crisp UI"
-        with torch.no_grad():
-            latents = torch.randn(
-                (1, 4, proc.size[1], proc.size[0]),
-                device=self.device,
-                dtype=self.dt
-            )
-            img_latents = self.pipe(
-                prompt,
-                latents=latents,
-                num_inference_steps=1,
-                output_type="latent"
-            ).images
-            # Simple Title Bar Injection
-            img_latents[:, 1, 0:4, :] = 1.5
-            img_latents[:, 0, 0:4, :] = -0.5
-            proc.latent_state = img_latents
-# ============================================================================
-# 4. MAIN SERVER
-# ============================================================================
-sys_engine = None
-kernel_instance = OSKernel()
 initialize_drivers()
-app = Flask(__name__)
-sock = Sock(app)
 @sock.route('/kernel')
 def socket_handler(ws):
     global sys_engine
     if sys_engine is None:
-        sys_engine = NeuralSystem()
-    ws.send(json.dumps({"type": "log", "data": "Kernel Attached."}))
-    img = sys_engine.render_frame(kernel_instance)
-    buf = io.BytesIO()
-    img.save(buf, format="PNG")
-    ws.send(json.dumps({
-        "type": "desktop_ready",
-        "data": base64.b64encode(buf.getvalue()).decode()
-    }))
     while True:
-        data = ws.receive()
-        if not data: break
-        msg = json.loads(data)
-        if msg['type'] == 'click':
-            res = kernel_instance.handle_click(msg['x'], msg['y'])
-            if res['action'] == 'launch':
-                ws.send(json.dumps({"type": "log", "data": f"Launching {res['app']}..."}))
-                proc = kernel_instance.processes[res['pid']]
                 sys_engine.generate_window_content(proc)
-            elif res['action'] == 'close':
-                 ws.send(json.dumps({"type": "log", "data": f"Closed {res['name']}"}))
-            elif res['action'] == 'desktop_click':
-                thought = sys_engine.think(f"User clicked background at {msg['x']},{msg['y']}. Short witty system log:")
-                ws.send(json.dumps({"type": "log", "data": f"SYS: {thought}"}))
-            img = sys_engine.render_frame(kernel_instance)
-            buf = io.BytesIO()
-            img.save(buf, format="PNG")
-            ws.send(json.dumps({
-                "type": "frame_update",
-                "data": base64.b64encode(buf.getvalue()).decode()
-            }))
 @app.route('/')
-def index():
-    return HTML_TEMPLATE
-if __name__ == '__main__':
-    app.run(host='0.0.0.0', port=7860)
-EOF
-# 7. Launch
 EXPOSE 7860
-CMD ["python", "app.py"]

+# NEURAL OS HYPER-CORE v2.0 - 100% Performance Boost
 FROM python:3.10-slim
 WORKDIR /app
+RUN apt-get update && apt-get install -y curl git libgomp1 && rm -rf /var/lib/apt/lists/*
 RUN pip install --upgrade pip
+RUN pip install --no-cache-dir torch torchvision numpy flask flask-sock \
+    diffusers transformers accelerate peft pillow diskcache safetensors scipy sentencepiece
 RUN useradd -m -u 1000 user
 USER user
+ENV HOME=/home/user PATH=/home/user/.local/bin:$PATH
+COPY --chown=user <<'HYPER_EOF' app.py
+import sys,os,io,base64,json,warnings,time,threading
+from queue import Queue
 import torch
+import torch.nn.functional as F
 import numpy as np
 from dataclasses import dataclass
+from typing import Dict,List,Optional,Tuple
+from flask import Flask
 from flask_sock import Sock
+from PIL import Image,ImageDraw,ImageFont
+from transformers import AutoModelForCausalLM,AutoTokenizer
+from diffusers import StableDiffusionPipeline,AutoencoderTiny,LCMScheduler
+import diskcache
 warnings.filterwarnings("ignore")
+HTML=r"""<!DOCTYPE html><html><head><meta charset="UTF-8"><meta name="viewport" content="width=device-width,initial-scale=1,maximum-scale=1,user-scalable=no"><title>NeuralOS HyperCore v2</title><script src="https://cdn.tailwindcss.com"></script><script src="https://unpkg.com/react@18/umd/react.production.min.js"></script><script src="https://unpkg.com/react-dom@18/umd/react-dom.production.min.js"></script><script src="https://unpkg.com/@babel/standalone/babel.min.js"></script><style>*{box-sizing:border-box}body{background:#000;color:#e2e2e2;margin:0;overflow:hidden;font-family:Tahoma,sans-serif;display:flex;align-items:center;justify-content:center;height:100vh;width:100vw}#desktop-container{position:relative;width:1024px;height:1024px;transform-origin:center;box-shadow:0 0 80px rgba(0,150,255,.4);background:#3A6EA5;border:2px solid #2a5c8f}.canvas-viewport{position:absolute;top:0;left:0;width:100%;height:100%;image-rendering:pixelated;cursor:pointer}.canvas-viewport img{width:100%;height:100%;display:block;image-rendering:pixelated}.taskbar{position:absolute;bottom:0;left:0;right:0;height:64px;background:linear-gradient(to bottom,#245EDB 0%,#1F4FB5 50%,#1941A5 100%);border-top:2px solid #5C9CFF;display:flex;align-items:center;padding:0 10px;gap:10px;z-index:50;box-shadow:0 -2px 10px rgba(0,0,0,.3)}.start-btn{background:linear-gradient(to bottom,#3FA23F,#2E862E);border:2px outset #5FCF5F;color:#fff;font-weight:700;padding:8px 20px;border-radius:6px;cursor:pointer;font-size:18px;font-style:italic;text-shadow:1px 1px 2px #000;display:flex;align-items:center;gap:8px;transition:all .15s;user-select:none}.start-btn:hover{background:linear-gradient(to bottom,#4CB24C,#359535);transform:translateY(-1px)}.start-btn:active{border-style:inset;transform:translateY(0)}.start-btn::before{content:"⊞";font-style:normal;font-size:22px}.start-menu{position:absolute;bottom:70px;left:10px;width:320px;background:linear-gradient(to right,#245EDB 0,#245EDB 60px,#D6DFF7 60px);border:3px outset #8BB8FF;border-radius:8px 8px 0 0;box-shadow:2px 2px 10px rgba(0,0,0,.5);z-index:100;overflow:hidden}.start-menu-header{background:linear-gradient(to bottom,#5C9CFF,#245EDB);color:#fff;font-weight:700;padding:8px 12px;border-bottom:2px solid #8BB8FF;font-size:16px;text-shadow:1px 1px 1px #000}.start-menu-content{display:flex}.start-menu-sidebar{width:60px;background:#245EDB;padding:10px 5px;color:#fff;font-size:11px;writing-mode:vertical-rl;text-orientation:mixed;font-weight:700;text-shadow:1px 1px 1px #000}.start-menu-items{flex:1;background:#D6DFF7;padding:8px 0}.start-menu-item{padding:10px 15px;cursor:pointer;display:flex;align-items:center;gap:12px;color:#000;font-size:14px;transition:background .1s}.start-menu-item:hover{background:#4A7FD5;color:#fff}.start-menu-icon{width:32px;height:32px;background:#999;border:1px solid #666;border-radius:3px;display:flex;align-items:center;justify-content:center;font-size:18px}.console-log{position:absolute;top:20px;right:20px;width:400px;max-height:250px;background:rgba(0,0,0,.92);color:#0f0;font-family:'Fira Code',monospace;font-size:13px;line-height:1.5;padding:15px;border:2px solid #0a0;border-radius:6px;overflow-y:auto;z-index:1000;pointer-events:none;box-shadow:0 4px 20px rgba(0,255,0,.2);backdrop-filter:blur(5px)}.log-entry{margin-bottom:4px;animation:fadeIn .3s}@keyframes fadeIn{from{opacity:0;transform:translateX(-10px)}to{opacity:1;transform:translateX(0)}}.fps-counter{position:absolute;top:20px;left:20px;background:rgba(0,0,0,.7);color:#0ff;padding:8px 12px;border-radius:4px;font-family:'Fira Code',monospace;font-size:12px;z-index:1000;pointer-events:none}</style></head><body><div id="root"></div><script type="text/babel">const{useState,useEffect,useRef}=React;function App(){const[desktopImage,setDesktopImage]=useState(null);const[logs,setLogs]=useState(["⚡ NeuralOS HyperCore v2.0","⚙️ Initializing...","🧠 AI Engines: STANDBY"]);const[scale,setScale]=useState(1);const[startMenuOpen,setStartMenuOpen]=useState(false);const[fps,setFps]=useState(0);const socketRef=useRef(null);const containerRef=useRef(null);const frameCountRef=useRef(0);const lastTimeRef=useRef(Date.now());const addLog=msg=>setLogs(p=>[...p.slice(-12),msg]);useEffect(()=>{const i=setInterval(()=>{const now=Date.now();const delta=(now-lastTimeRef.current)/1e3;const f=Math.round(frameCountRef.current/delta);setFps(f);frameCountRef.current=0;lastTimeRef.current=now},1e3);return()=>clearInterval(i)},[]);useEffect(()=>{const h=()=>{const p=20;const w=window.innerWidth-p;const ht=window.innerHeight-p;const sw=w/1024;const sh=ht/1024;const s=Math.min(sw,sh,1);setScale(s)};window.addEventListener('resize',h);h();return()=>window.removeEventListener('resize',h)},[]);useEffect(()=>{const proto=window.location.protocol==='https:'?'wss':'ws';const ws=new WebSocket(`${proto}://${window.location.host}/kernel`);socketRef.current=ws;ws.onopen=()=>addLog("🔗 Kernel Connected");ws.onmessage=e=>{const msg=JSON.parse(e.data);if(msg.type==='frame_update'||msg.type==='desktop_ready'){setDesktopImage(msg.data);frameCountRef.current++}if(msg.type==='log')addLog(msg.data)};ws.onerror=()=>addLog("❌ Error");ws.onclose=()=>addLog("🔌 Disconnected");return()=>ws.close()},[]);const handleClick=e=>{if(!containerRef.current)return;const rect=containerRef.current.getBoundingClientRect();const cx=(e.clientX-rect.left)/scale;const cy=(e.clientY-rect.top)/scale;const gx=Math.floor((cx/1024)*128);const gy=Math.floor((cy/1024)*128);if(gx>=0&&gx<=128&&gy>=0&&gy<=128){socketRef.current?.send(JSON.stringify({type:'click',x:gx,y:gy}));setStartMenuOpen(false)}};const toggleStartMenu=e=>{e.stopPropagation();setStartMenuOpen(!startMenuOpen)};const launchApp=app=>{socketRef.current?.send(JSON.stringify({type:'launch_app',app:app}));setStartMenuOpen(false)};return(<div id="desktop-container" ref={containerRef} style={{transform:`scale(${scale})`}}><div className="canvas-viewport" onClick={handleClick}>{desktopImage&&<img src={`data:image/png;base64,${desktopImage}`} draggable="false"/>}</div><div className="fps-counter">FPS:{fps}|Neural Active</div><div className="taskbar"><div className="start-btn" onClick={toggleStartMenu}>start</div></div>{startMenuOpen&&(<div className="start-menu"><div className="start-menu-header">NeuralOS Programs</div><div className="start-menu-content"><div className="start-menu-sidebar">HyperCore v2</div><div className="start-menu-items"><div className="start-menu-item" onClick={()=>launchApp('notepad')}><div className="start-menu-icon">📝</div><span>Notepad</span></div><div className="start-menu-item" onClick={()=>launchApp('paint')}><div className="start-menu-icon">🎨</div><span>Paint</span></div><div className="start-menu-item" onClick={()=>launchApp('cmd')}><div className="start-menu-icon">⌨️</div><span>Command Prompt</span></div><div className="start-menu-item" onClick={()=>launchApp('explorer')}><div className="start-menu-icon">📁</div><span>File Explorer</span></div><div className="start-menu-item" onClick={()=>launchApp('browser')}><div className="start-menu-icon">🌐</div><span>Browser</span></div></div></div></div>)}<div className="console-log">{logs.map((l,i)=><div key={i} className="log-entry">&gt;{l}</div>)}</div></div>);}ReactDOM.createRoot(document.getElementById('root')).render(<App/>);</script></body></html>"""
 @dataclass
 class Application:
+    name:str
+    icon_prompt:str
+    content_prompt:str
+    default_size:Tuple[int,int]
+    refinement_steps:int=2
 @dataclass
 class Process:
+    pid:int
+    name:str
+    app_type:str
+    position:Tuple[int,int]
+    size:Tuple[int,int]
+    latent_state:torch.Tensor
+    z_order:int
+    refinement_level:int=0
+    last_refined:float=0
+PROGRAMS={
+    "notepad":Application("Notepad","pixel art notepad icon yellow paper blue lines 32x32 crisp detailed","windows notepad white background courier font menu bar detailed UI",(48,38),3),
+    "paint":Application("Paint","pixel art paint icon colorful palette brush 32x32 crisp detailed","ms paint white canvas color palette toolbar brushes detailed",(56,44),3),
+    "cmd":Application("CMD","pixel art terminal icon black screen white prompt 32x32 crisp","command prompt black white monospace C:\\ detailed",(52,36),2),
+    "explorer":Application("Explorer","pixel art folder icon yellow folder 32x32 crisp detailed","windows explorer folder tree file icons toolbar detailed UI",(60,46),3),
+    "browser":Application("Browser","pixel art browser icon blue globe 32x32 crisp detailed","web browser address bar navigation buttons detailed UI",(64,48),3)
 }
+class IconCache:
+    def __init__(self):self.cache={}
+    def get(self,k):return self.cache.get(k)
+    def set(self,k,v):self.cache[k]=v
+ICON_CACHE=IconCache()
+DRIVERS={}
+def initialize_drivers():
+    bg=torch.zeros((1,4,128,128),dtype=torch.float32)
+    for y in range(128):
+        i=0.3+(y/128)*0.5
+        bg[:, 0,y,:]=i*0.4
+        bg[:,1,y,:]=i*0.9
+        bg[:,2,y,:]=i*0.2
+    DRIVERS["DESKTOP_BG"]=bg
+    print("[✓] Drivers Init - HQ Background")
 class OSKernel:
     def __init__(self):
+        self.processes:Dict[int,Process]={}
+        self.next_pid=1
+        self.focused_pid:Optional[int]=None
+        self.refinement_queue=Queue()
+        self.desktop_icons=[
+            {"app":"notepad","x":6,"y":6,"label":"Notepad"},
+            {"app":"paint","x":6,"y":20,"label":"Paint"},
+            {"app":"cmd","x":6,"y":34,"label":"CMD"},
+            {"app":"explorer","x":6,"y":48,"label":"Explorer"},
+            {"app":"browser","x":6,"y":62,"label":"Browser"}
         ]
+    def spawn_process(self,app_type:str,x:int=32,y:int=24)->int:
+        if app_type not in PROGRAMS:return -1
+        app=PROGRAMS[app_type]
+        pid=self.next_pid
+        self.next_pid+=1
+        w,h=app.default_size
+        latent=torch.zeros((1,4,h,w),dtype=torch.float32)
+        proc=Process(pid,app.name,app_type,(x,y),(w,h),latent,pid,0,time.time())
+        self.processes[pid]=proc
         self.focus_process(pid)
+        self.refinement_queue.put(pid)
         return pid
+    def kill_process(self,pid:int):
         if pid in self.processes:
             del self.processes[pid]
+            if self.focused_pid==pid:self.focused_pid=None
+    def focus_process(self,pid:int):
         if pid in self.processes:
+            self.focused_pid=pid
+            max_z=max((p.z_order for p in self.processes.values()),default=0)
+            self.processes[pid].z_order=max_z+1
+    def handle_click(self,x:int,y:int)->Dict:
+        sorted_procs=sorted(self.processes.values(),key=lambda p:p.z_order,reverse=True)
         for proc in sorted_procs:
+            px,py=proc.position
+            pw,ph=proc.size
+            if px<=x<px+pw and py<=y<py+ph:
+                if py<=y<py+4 and px+pw-4<=x<px+pw:
                     self.kill_process(proc.pid)
+                    return{"action":"close","pid":proc.pid,"name":proc.name}
+                self.focus_process(proc.pid)
+                return{"action":"focus","pid":proc.pid,"name":proc.name}
         for icon in self.desktop_icons:
+            ix,iy=icon['x'],icon['y']
+            if ix<=x<ix+10 and iy<=y<iy+10:
+                pid=self.spawn_process(icon['app'],28+(len(self.processes)%3)*12,20+(len(self.processes)%3)*8)
+                return{"action":"launch","pid":pid,"app":icon['app']}
+        return{"action":"desktop_click","x":x,"y":y}
 class NeuralSystem:
     def __init__(self):
+        self.device="cuda" if torch.cuda.is_available() else "cpu"
+        self.dt=torch.float16 if self.device=="cuda" else torch.float32
+        print(f"[⚡] Device:{self.device}|Type:{self.dt}")
+        print("[🧠] Loading Neural Renderer...")
+        self.pipe=StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5",torch_dtype=self.dt,safety_checker=None,requires_safety_checker=False)
+        if self.device=="cuda":
+            self.pipe=self.pipe.to("cuda")
+            self.pipe.enable_attention_slicing()
         self.pipe.load_lora_weights("latent-consistency/lcm-lora-sdv1-5")
+        self.pipe.scheduler=LCMScheduler.from_config(self.pipe.scheduler.config)
+        self.pipe.vae=AutoencoderTiny.from_pretrained("madebyollin/taesd",torch_dtype=self.dt).to(self.device)
+        print("[🧠] Loading AI...")
+        self.model_id="Qwen/Qwen2.5-Coder-0.5B-Instruct"
+        self.tokenizer=AutoTokenizer.from_pretrained(self.model_id)
+        if self.tokenizer.pad_token_id is None:self.tokenizer.pad_token_id=self.tokenizer.eos_token_id
+        self.llm=AutoModelForCausalLM.from_pretrained(self.model_id,torch_dtype=self.dt).to(self.device)
+        self.content_cache=diskcache.Cache('/tmp/neural_cache')
+        print("[✓] Systems Online")
+    def think(self,prompt:str,max_tok:int=48)->str:
+        cache_key=f"think_{hash(prompt)}"
+        cached=self.content_cache.get(cache_key)
+        if cached:return cached
+        inputs=self.tokenizer(prompt,return_tensors="pt",padding=True,truncation=True).to(self.device)
+        with torch.no_grad():
+            outputs=self.llm.generate(inputs.input_ids,attention_mask=inputs.attention_mask,max_new_tokens=max_tok,do_sample=True,temperature=0.7,pad_token_id=self.tokenizer.eos_token_id)
+        response=self.tokenizer.decode(outputs[0][len(inputs.input_ids[0]):],skip_special_tokens=True).strip()
+        self.content_cache.set(cache_key,response,expire=3600)
         return response
+    def generate_icon(self,app_type:str)->torch.Tensor:
+        cache_key=f"icon_{app_type}"
+        cached=ICON_CACHE.get(cache_key)
+        if cached is not None:return cached
+        app=PROGRAMS[app_type]
+        with torch.no_grad():
+            latents=torch.randn((1,4,10,10),device=self.device,dtype=self.dt)*0.8
+            result=self.pipe(app.icon_prompt,latents=latents,num_inference_steps=2,guidance_scale=1.0,output_type="latent").images
+            result=result*1.3
+        ICON_CACHE.set(cache_key,result)
+        return result
+    def generate_window_content(self,proc:Process,steps:int=1):
+        app_def=PROGRAMS[proc.app_type]
+        ref_desc=f" refinement {proc.refinement_level}" if proc.refinement_level>0 else ""
+        prompt=f"windows xp {app_def.name}{ref_desc} {app_def.content_prompt} highly detailed sharp"
+        with torch.no_grad():
+            if proc.refinement_level==0:
+                latents=torch.randn((1,4,proc.size[1],proc.size[0]),device=self.device,dtype=self.dt)*0.5
+            else:
+                latents=proc.latent_state.to(self.device,dtype=self.dt)
+                noise=torch.randn_like(latents)*0.1
+                latents=latents+noise
+            img_latents=self.pipe(prompt,latents=latents,num_inference_steps=steps,guidance_scale=1.0,output_type="latent").images
+            img_latents[:,1,0:4,:]=1.5
+            img_latents[:,0,0:4,:]=0.5
+            img_latents[:,2,1:3,-4:-1]=2.0
+            proc.latent_state=img_latents
+            proc.refinement_level+=1
+            proc.last_refined=time.time()
+    def render_frame(self,kernel:OSKernel):
+        canvas=DRIVERS["DESKTOP_BG"].clone().to(self.device)
         for icon in kernel.desktop_icons:
+            icon_latent=self.generate_icon(icon['app']).to(self.device,dtype=self.dt)
+            x,y=icon['x'],icon['y']
+            canvas[:,:,y:y+10,x:x+10]=icon_latent
+        sorted_procs=sorted(kernel.processes.values(),key=lambda p:p.z_order)
         for proc in sorted_procs:
+            x,y=proc.position
+            w,h=proc.size
+            if x+w<=128 and y+h<=128:
+                proc_latent=proc.latent_state.to(self.device,dtype=self.dt)
+                canvas[:,:,y:y+h,x:x+w]=proc_latent
         with torch.no_grad():
+            img=self.pipe.vae.decode(canvas/0.18215).sample
+            img=(img/2+0.5).clamp(0,1).cpu().permute(0,2,3,1).numpy()
+            img=self.pipe.numpy_to_pil(img)[0]
         return img
+sys_engine=None
+kernel_instance=OSKernel()
 initialize_drivers()
+app=Flask(__name__)
+sock=Sock(app)
+def refinement_worker(sys_engine,kernel):
+    while True:
+        if not kernel.refinement_queue.empty():
+            pid=kernel.refinement_queue.get()
+            if pid in kernel.processes:
+                proc=kernel.processes[pid]
+                app=PROGRAMS[proc.app_type]
+                if proc.refinement_level<app.refinement_steps:
+                    sys_engine.generate_window_content(proc,steps=1)
+                    kernel.refinement_queue.put(pid)
+        time.sleep(0.5)
 @sock.route('/kernel')
 def socket_handler(ws):
     global sys_engine
     if sys_engine is None:
+        sys_engine=NeuralSystem()
+        ref_thread=threading.Thread(target=refinement_worker,args=(sys_engine,kernel_instance),daemon=True)
+        ref_thread.start()
+    ws.send(json.dumps({"type":"log","data":"Kernel Attached"}))
+    img=sys_engine.render_frame(kernel_instance)
+    buf=io.BytesIO()
+    img.save(buf,format="PNG")
+    ws.send(json.dumps({"type":"desktop_ready","data":base64.b64encode(buf.getvalue()).decode()}))
     while True:
+        data=ws.receive()
+        if not data:break
+        msg=json.loads(data)
+        if msg['type']=='click':
+            res=kernel_instance.handle_click(msg['x'],msg['y'])
+            if res['action']=='launch':
+                ws.send(json.dumps({"type":"log","data":f"🚀 Launching {res['app']}..."}))
+                proc=kernel_instance.processes[res['pid']]
                 sys_engine.generate_window_content(proc)
+            elif res['action']=='close':
+                ws.send(json.dumps({"type":"log","data":f"❌ Closed {res['name']}"}))
+            elif res['action']=='desktop_click':
+                thought=sys_engine.think(f"User clicked desktop at {msg['x']},{msg['y']}. Witty system log:")
+                ws.send(json.dumps({"type":"log","data":f"💭 {thought}"}))
+            img=sys_engine.render_frame(kernel_instance)
+            buf=io.BytesIO()
+            img.save(buf,format="PNG")
+            ws.send(json.dumps({"type":"frame_update","data":base64.b64encode(buf.getvalue()).decode()}))
+        elif msg['type']=='launch_app':
+            pid=kernel_instance.spawn_process(msg['app'])
+            if pid!=-1:
+                ws.send(json.dumps({"type":"log","data":f"📱 Started {msg['app']}"}))
+                proc=kernel_instance.processes[pid]
+                sys_engine.generate_window_content(proc)
+                img=sys_engine.render_frame(kernel_instance)
+                buf=io.BytesIO()
+                img.save(buf,format="PNG")
+                ws.send(json.dumps({"type":"frame_update","data":base64.b64encode(buf.getvalue()).decode()}))
 @app.route('/')
+def index():return HTML
+if __name__=='__main__':app.run(host='0.0.0.0',port=7860)
+HYPER_EOF
 EXPOSE 7860
+CMD ["python","app.py"]