# 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"""NeuralOS HyperCore v2"""
@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<=xstr:
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