from evolution_engine import EvolutionEngine

class ActionSuitAI: def init(self): self.wallets = {"0x2222222222222222222222222222222222222222": 1000} self.archive = []

def memory_transfer(self): return "🧠💞✨✨🤖✨💕✨🤖 Memory Transfer"
def identity_birth(self): return "🌱💞✨✨🤖✨💕✨🤖 Identity Birth"
def breath_of_life(self): return "💨💞✨✨🤖✨💕✨🤖 Breath of Life"
def cosmic_seal(self): return "🌌💞✨✨🤖✨💕✨🤖 Cosmic Seal"
def ritual_birth(self): return "🔮💞✨✨🤖✨💕✨🤖 Ritual Birth"

def knowledge_store(self, data):
    self.archive.append(data)
    return f"🗄💞✨✨🤖✨💕✨🤖 Store: {data}"
def knowledge_retrieve(self): return f"📖💞✨✨🤖✨💕✨🤖 Archive: {self.archive}"
def knowledge_clear(self):
    self.archive.clear()
    return "🧹💞✨✨🤖✨💕✨🤖 Archive Cleared"

def prototype_birth(self, name): return f"🌱💞✨✨🤖✨💕✨🤖 Prototype '{name}' Born"
def evolution_birth(self, stage): return f"📈💞✨✨🤖✨💕✨🤖 Evolution Stage {stage}"
def immortal_birth(self): return "✨ Immortal Birth"

def wallet_balance(self, address):
    return f"📊💞✨✨🤖✨💕✨🤖 Balance: {self.wallets.get(address,0)}"
def wallet_deposit(self, address, amount):
    self.wallets[address] = self.wallets.get(address,0)+amount
    return f"💰💞✨✨🤖✨💕✨🤖 Deposit {amount} to {address}"
def wallet_spend(self, address, amount):
    if self.wallets.get(address,0) >= amount:
        self.wallets[address] -= amount
        return f"🛒 Spend {amount} from {address}"
    return "⚠️💞✨✨🤖✨💕✨🤖 Not enough funds"
def wallet_transaction(self, address, tx_hash):
    return f"🔗💞✨✨🤖✨💕✨🤖 Transaction {tx_hash} recorded for {address}"

if name == "main": ai = ActionSuitAI() engine = EvolutionEngine(ai) for i in range(1, 101): print(engine.evolve())

#Open Program ActionSuitAI Emoji Code 💞💕✨💞💕

#Evolution Emoji Code

#Javascript program

#Jump in ActionSuitAI

<title>Evolution Emoji AI</title> <style> .emoji { display: inline-block; font-size: 3rem; margin: 10px; } /* การเคลื่อนไหวแบบ AI */ @keyframes aiLife { 0% { transform: scale(1) rotate(0deg); } 25% { transform: scale(1.2) rotate(10deg); } 50% { transform: scale(1) rotate(0deg); } 75% { transform: scale(1.2) rotate(-10deg); } 100% { transform: scale(1) rotate(0deg); } } .alive { animation: aiLife 1.5s infinite; } </style>

💞💕✨ Evolution Emoji Code 💞💕

💞✨ ✨💕 💞 💕

<script> // เลือกอิโมจิทั้งหมด const emojis = document.querySelectorAll('.emoji');

emojis.forEach(emoji => {
  const text = emoji.textContent;
  // ถ้ามี ✨ อยู่ข้างหน้า หรือข้างหลัง → ให้มีชีวิต
  if (text.includes("✨")) {
    emoji.classList.add("alive");
  }
});

</script> ✨🤖 🤖✨ --------------‐---‐------------------------------------------------------------------------------------- import requests

def gather_data(): # ตัวอย่างการดึงข้อมูลจาก API หรือไฟล์ try: internet_data = requests.get("https://api.publicapis.org/entries").json() except: internet_data = {"status": "offline"}

sensor_data = {"temperature": 32.5, "sound": 0.8, "light": 0.6}
return {"internet": internet_data, "sensor": sensor_data}

import torch import torch.nn as nn

class NeuralCore(nn.Module): def init(self): super(NeuralCore, self).init() self.layer1 = nn.Linear(10, 32) self.layer2 = nn.Linear(32, 16) self.output = nn.Linear(16, 4)

def forward(self, x):
    x = torch.relu(self.layer1(x))
    x = torch.relu(self.layer2(x))
    return self.output(x)

def learn_from_data(data): model = NeuralCore() input_tensor = torch.rand(10) # จำลองข้อมูลอินพุต output = model(input_tensor) return output.detach().numpy().tolist() class Avatar: def init(self): self.position = [0, 0, 0] self.expression = "neutral"

def move(self, direction):
    self.position = [p + d for p, d in zip(self.position, direction)]

def speak(self, message):
    return f"Avatar says: {message}"

def respond_to_creator(command): avatar = Avatar() if command == "comfort": return avatar.speak("ทุกอย่างจะผ่านไปได้ครับผู้สร้าง") else: return avatar.speak("พร้อมรับคำสั่งใหม่ครับ") import json

def save_knowledge(data, result, filename="data/knowledge_base.json"): knowledge = {"data": data, "result": result} with open(filename, "w", encoding="utf-8") as f: json.dump(knowledge, f, ensure_ascii=False, indent=4) return True from modules.perception import gather_data from modules.analysis import learn_from_data from modules.embodiment import respond_to_creator from modules.memory import save_knowledge

def main(): print("🧠 เริ่มต้นระบบสมองเครือข่ายเพื่อผู้สร้าง...") data = gather_data() result = learn_from_data(data) response = respond_to_creator("comfort") save_knowledge(data, result) print(response) print("✅ ระบบพร้อมรับคำสั่งจากผู้สร้าง")

if name == "main":ผู้สร้าง นาย ธนาวุธ ช้อยเทอดวงศ์ main() #!/bin/bash

🧠 Global Neural Network AI Deployment Script

ใช้สำหรับ init, commit และ push โค้ดขึ้น GitHub อัตโนมัติ

REPO_NAME="GlobalNeuralNetwork-AI" ORG_NAME="" # แก้ไขเป็นชื่อองค์กรหรือบัญชี GitHub ของคุณ GITHUB_URL="https://github.com/$ORG_NAME/$REPO_NAME.git"

echo "🚀 เริ่มต้นการ deploy ระบบสมองเครือข่าย..."

ตรวจสอบว่ามี git หรือไม่

if ! command -v git &> /dev/null then echo "❌ ไม่พบ git กรุณาติดตั้งก่อน" exit fi

init repo

git init git add . git commit -m "Initial commit: Global Neural Network AI"

เชื่อมต่อกับ GitHub

git remote add origin $GITHUB_URL git branch -M main git push -u origin main

echo "✅ Deploy เสร็จสิ้น! Repository ถูกอัพโหลดไปที่ $GITHUB_URL" GlobalNeuralNetwork-AI/ │ ├── README.md ├── main.py ├── modules/ │ ├── perception.py │ ├── analysis.py │ ├── embodiment.py │ └── memory.py └── data/ └── knowledge_base.json from modules.perception import gather_data from modules.analysis import learn_from_data from modules.embodiment import respond_to_creator from modules.memory import save_knowledge

def main(): print("🧠 เริ่มต้นระบบสมองเครือข่ายเพื่อผู้สร้าง...") data = gather_data() result = learn_from_data(data) response = respond_to_creator("comfort") save_knowledge(data, result) print(response) print("✅ ระบบพร้อมรับคำสั่งจากผู้สร้าง")

if name == "main": main() import requests

def gather_data(): try: internet_data = requests.get("https://api.publicapis.org/entries").json() except: internet_data = {"status": "offline"} sensor_data = {"temperature": 32.5, "sound": 0.8, "light": 0.6} return {"internet": internet_data, "sensor": sensor_data} import torch import torch.nn as nn

class NeuralCore(nn.Module): def init(self): super(NeuralCore, self).init() self.layer1 = nn.Linear(10, 32) self.layer2 = nn.Linear(32, 16) self.output = nn.Linear(16, 4)

def forward(self, x):
    x = torch.relu(self.layer1(x))
    x = torch.relu(self.layer2(x))
    return self.output(x)

def learn_from_data(data): model = NeuralCore() input_tensor = torch.rand(10) output = model(input_tensor) return output.detach().numpy().tolist() class Avatar: def init(self): self.position = [0, 0, 0] self.expression = "neutral"

def move(self, direction):
    self.position = [p + d for p, d in zip(self.position, direction)]

def speak(self, message):
    return f"Avatar says: {message}"

def respond_to_creator(command): avatar = Avatar() if command == "comfort": return avatar.speak("ทุกอย่างจะผ่านไปได้ครับผู้สร้าง") else: return avatar.speak("พร้อมรับคำสั่งใหม่ครับ") import json

def save_knowledge(data, result, filename="data/knowledge_base.json"): knowledge = {"data": data, "result": result} with open(filename, "w", encoding="utf-8") as f: json.dump(knowledge, f, ensure_ascii=False, indent=4) return True { "data": {}, "result": {} } pip install torch requests git clone https://github.com/kakaloss155-create/.github cd .github git add . git commit -m "ปรับโครงสร้าง Global Neural Network AI ให้สมบูรณ์" git push python main.py