from threading import Thread import gradio as gr import inspect from gradio import routes from typing import List, Type import requests, os, re, asyncio, json import math import time import datetime import hashlib from Blockchain import Blockchain loop = asyncio.get_event_loop() # init code def get_types(cls_set: List[Type], component: str): docset = [] types = [] if component == "input": for cls in cls_set: doc = inspect.getdoc(cls) doc_lines = doc.split("\n") docset.append(doc_lines[1].split(":")[-1]) types.append(doc_lines[1].split(")")[0].split("(")[-1]) else: for cls in cls_set: doc = inspect.getdoc(cls) doc_lines = doc.split("\n") docset.append(doc_lines[-1].split(":")[-1]) types.append(doc_lines[-1].split(")")[0].split("(")[-1]) return docset, types routes.get_types = get_types # App code account_list = dict() account_list['id'] = "pass" name_list = dict() name_list['id'] = 'name' p2p_list = dict() p2p_list['id'] = '11111111' gpu_add_list = [] def register(id, pw): if id in account_list: return "exist" else: account_list[id] = pw return "ok" def login(id, pw): if id in account_list: if account_list[id] == pw: return "ok" else: return "password error" else: return "no id" def add_name(id, name): name_list[id] = name return "ok" def get_name(id): if id in name_list: return name_list[id] else: return "no id" def get_id(name): reverse_dict= dict(map(reversed,name_list.items())) if name in reverse_dict: return reverse_dict[name] else: return "no name" def add_p(id, p_id): p2p_list[id] = p_id return "ok" def get_p(id): if id in p2p_list: return p2p_list[id] else: return "no id" def get_id_from_p2p(i): reverse_dict= dict(map(reversed,p2p_list.items())) if i in reverse_dict: return reverse_dict[i] else: return "no id" # Blockchain code model_name = "petals-team/StableBeluga2" def get_peers(model_name): data = requests.get("https://health.petals.dev/api/v1/state").json() out = [] for d in data['model_reports']: if d['name'] == model_name: for r in d['server_rows']: out.append(r['peer_id']) return out blockchain = Blockchain() def add_transaction(id, kind, data): if kind == "add" or kind == "inference" or kind == "out": blockchain.new_transaction(id, kind, data) return "ok" else: return "fail" def proof(model_name): peers = get_peers(model_name) for p in gpu_add_list: if not p in peers: add_transaction(get_id_from_p2p(peer), "out", 0) def get_coin(id): c = blockchain.get_user_balance(id) return c def get_gpus(): output = [] for id, mem in blockchain.user_gpus.items(): output.append({"name":get_name(id),"gpu":mem}) return output def get_data(): output = [] output.append({"gpus":get_gpus(), "total":{"total" : blockchain.get_total_gpu_mem(), "used":38}, "chain":blockchain.chain}) return output def chat(id, npc, prompt): if get_coin(id) == 0: return "no coin" # model inference output = "AI 응답입니다." add_transaction(id, "inference", {"prompt":prompt, "output":output}) if len(blockchain.current_transactions)>=10: proof(model_name) new_block = blockchain.new_block() return output with gr.Blocks() as demo: rr = gr.Interface( fn=register, inputs=["text", "text"], outputs="text", description="register, 회원가입(성공시:ok, 중복시:exist 반환)\n /run/predict", ) ll = gr.Interface( fn=login, inputs=["text", "text"], outputs="text", description="login, 로그인(성공시: ok, 실패시: password error, 아이디가 없으면: no id) \n /run/predict_", ) ad = gr.Interface( fn=add_name, inputs=["text", "text"], outputs="text", description="add_name, id로 닉네임 추가. ok 반환.\n /run/predict_2", ) nn = gr.Interface( fn=get_name, inputs=["text"], outputs="text", description="get_name, id로 닉네임 반환(없으면 no id)\n /run/predict_3", ) adp = gr.Interface( fn=add_p, inputs=["text", "text"], outputs="text", description="add_p, id로 p2p id 추가. ok 반환. \n /run/predict_4", ) addp = gr.Interface( fn=add_transaction, inputs=["text", "text", "text"], outputs="text", description="add_transaction \n /run/predict_5", ) gc = gr.Interface( fn=get_coin, inputs=["text"], outputs="text", description="get_coin, id로 잔여 코인(행동력) 반환. \n /run/predict_6", ) gd = gr.Interface( fn=get_data, inputs=[], outputs="text", description="get_data, 시각화용 모든 데이터 반환. gpu기여 목록, total/used, chain \n /run/predict_7", ) demo.queue(max_size=32).launch(enable_queue=True)