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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 ์๋ต์
๋๋ค."
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, ํ์๊ฐ์
(์ฑ๊ณต์:ok, ์ค๋ณต์:exist ๋ฐํ)\n /run/predict",
)
ll = gr.Interface(
fn=login,
inputs=["text", "text"],
outputs="text",
description="login, ๋ก๊ทธ์ธ(์ฑ๊ณต์: ok, ์คํจ์: password error, ์์ด๋๊ฐ ์์ผ๋ฉด: no id) \n /run/predict_",
)
ad = gr.Interface(
fn=add_name,
inputs=["text", "text"],
outputs="text",
description="add_name, id๋ก ๋๋ค์ ์ถ๊ฐ. ok ๋ฐํ.\n /run/predict_2",
)
nn = gr.Interface(
fn=get_name,
inputs=["text"],
outputs="text",
description="get_name, id๋ก ๋๋ค์ ๋ฐํ(์์ผ๋ฉด no id)\n /run/predict_3",
)
adp = gr.Interface(
fn=add_p,
inputs=["text", "text"],
outputs="text",
description="add_p, id๋ก p2p id ์ถ๊ฐ. ok ๋ฐํ. \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, id๋ก ์์ฌ ์ฝ์ธ(ํ๋๋ ฅ) ๋ฐํ. \n /run/predict_6",
)
gd = gr.Interface(
fn=get_data,
inputs=[],
outputs="text",
description="get_data, ์๊ฐํ์ฉ ๋ชจ๋ ๋ฐ์ดํฐ ๋ฐํ. gpu๊ธฐ์ฌ ๋ชฉ๋ก, total/used, chain \n /run/predict_7",
)
demo.queue(max_size=32).launch(enable_queue=True) |