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import sys

import os

import time

import json

import asyncio

import resource

import torch

from fastapi import FastAPI, Request, WebSocket, WebSocketDisconnect

from fastapi.responses import HTMLResponse, JSONResponse

import uvicorn

from transformers import AutoTokenizer, AutoModelForCausalLM

sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))

from swarmnet.coordinator.ledger import FuelCreditLedger

from swarmnet.crypto.state_channel import StateChannelSigner

app = FastAPI(title="AirCompute Decentralized AI Suite")

connected_clients: list[WebSocket] = []

signer = StateChannelSigner()

ledger = FuelCreditLedger()

user_pubkey = signer.public_key_hex

ledger.balances[user_pubkey] = 50.0 # 50 Credits

ledger.balances["miner_laptop_01"] = 100.0

ledger.balances["miner_phone_02"] = 100.0

print("🧠 Pre-loading Neural AI Model (SmolLM-135M-Instruct)...")

MODEL_ID = "HuggingFaceTB/SmolLM-135M-Instruct"

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, local_files_only=True)

model = AutoModelForCausalLM.from_pretrained(MODEL_ID, local_files_only=True)

model.eval()

layers = model.model.layers

total_layers = len(layers) # 30 layers

mid = total_layers // 2 # 15 layers each

print(f"✅ Loaded {total_layers} transformer layers. Sharding: Miner 1 (L0-L{mid-1}), Miner 2 (L{mid}-L{total_layers-1})")

def get_real_laptop_memory_mb():

"""Returns actual Linux RSS memory in MB used by this process."""

try:

return int(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024)

except Exception:

return 285

async def broadcast_event(data: dict):

"""Broadcasts events to all connected phones & laptops simultaneously."""

dead_clients = []

for client in connected_clients:

try:

await client.send_json(data)

except Exception:

dead_clients.append(client)

for dc in dead_clients:

if dc in connected_clients:

connected_clients.remove(dc)

HTML_UI = """