api_for_unity / app.py
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import gradio as gr
from gradio_client import Client as GrClient
import inspect
from gradio import routes
from typing import List, Type
from aiogoogletrans import Translator
import requests, os, re, asyncio
loop = asyncio.get_event_loop()
gradio_client = GrClient(os.environ.get('GrClient_url'))
translator = Translator()
# Monkey patch
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
def mbti(x):
t = loop.run_until_complete(translator.translate(x, src='ko', dest='en'))
str_trans = re.sub('[-=+,#/\?:^.@*\"β€»~ㆍ!γ€β€˜|\(\)\[\]`\'…》\”\β€œ\’·]', '', t.text)
result = gradio_client.predict(
str_trans, # str representing input in 'User input' Textbox component
fn_index=2
)
r = sorted(eval(result), key=lambda x : x['score'], reverse=True)
return r
def chat(x):
x = f"{x}"
result = gradio_client.predict(
x,# str representing input in 'User input' Textbox component
0.92, # float, representing input in 'Top-p (nucleus sampling)' Slider component
30, # int, representing input in 'Top-k (nucleus sampling)' Slider component
0.82, # float, representing input in 'Temperature' Slider component
18, # int, representing input in 'Max New Tokens' Slider component
1.08, # float, representing input in 'repetition_penalty' Slider component
fn_index=0
)
result = str(result)
output = result[len(x)-3:]
outout = re.sub("[ν•˜ν•˜]", "γ…Žγ…Ž", output)
output = output.split('<|endoftext|>')[0]
output = output.split('\n')[0]
output = re.sub('[=+#/\:@*\"β€»γ†γ€β€˜|\\\<\>\(\)\[\]`\'…》\”\β€œ\’·]', ' ', output)
output = re.sub('[a-zA-Z]',' ',output)
return output
def yn(x):
result = gradio_client.predict(
x, # str representing input in 'User input' Textbox component
fn_index=1
)
return result
with gr.Blocks() as demo:
aa = gr.Interface(
fn=chat,
inputs="text",
outputs="text",
description="chat",
examples= [[f"\nfriend: λ„ˆλŠ” 꿈이 뭐야? \n\n### \nyou: "],[f"\nyou: λ„ˆλŠ” 무슨 색을 κ°€μž₯ μ’‹μ•„ν•΄? \nfriend: κΈ€μŽ„ λ„ˆλŠ”? \n\n### \nyou: "]]
)
bb = gr.Interface(
fn=mbti,
inputs="text",
outputs="text",
description="mbti"
)
cc = gr.Interface(
fn=yn,
inputs="text",
outputs="text",
description="yn"
)
demo.queue(max_size=32).launch(enable_queue=True)