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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('[-=+,#/\?:^.@*\"※~ㆍ!』‘|\(\)\[\]`\'…》\”\“\’·]', '', t.text)
result = gradio_client.predict(
str_trans, # str representing input in 'User input' Textbox component
fn_index=2
)
return result
def chat(x):
result = gradio_client.predict(
x,# str representing input in 'User input' Textbox component
0.9, # float, representing input in 'Top-p (nucleus sampling)' Slider component
50, # int, representing input in 'Top-k (nucleus sampling)' Slider component
0.9, # float, representing input in 'Temperature' Slider component
25, # int, representing input in 'Max New Tokens' Slider component
1.1, # float, representing input in 'repetition_penalty' Slider component
fn_index=0
)
return result
def yn(x):
result = gradio_client.predict(
x, # str representing input in 'User input' Textbox component
fn_index=1
)
return result
aa = gradio.Interface(
fn=yn,
inputs="text",
outputs="text",
examples=[
["Jill"],
["Sam"]
],
title="REST API with Gradio and Huggingface Spaces",
description="This is a demo of how to build an AI powered REST API with Gradio and Huggingface Spaces – for free! Based on [this article](https://www.tomsoderlund.com/ai/building-ai-powered-rest-api). See the **Use via API** link at the bottom of this page.",
article="© Tom Söderlund 2022"
)
aa.launch()
bb = gradio.Interface(
fn=chat,
inputs="text",
outputs="text",
examples=[
["Jill"],
["Sam"]
],
title="REST API with Gradio and Huggingface Spaces",
description="This is a demo of how to build an AI powered REST API with Gradio and Huggingface Spaces – for free! Based on [this article](https://www.tomsoderlund.com/ai/building-ai-powered-rest-api). See the **Use via API** link at the bottom of this page.",
article="© Tom Söderlund 2022"
)
bb.launch()
cc = gradio.Interface(
fn=mbti,
inputs="text",
outputs="text",
examples=[
["Jill"],
["Sam"]
],
title="REST API with Gradio and Huggingface Spaces",
description="This is a demo of how to build an AI powered REST API with Gradio and Huggingface Spaces – for free! Based on [this article](https://www.tomsoderlund.com/ai/building-ai-powered-rest-api). See the **Use via API** link at the bottom of this page.",
article="© Tom Söderlund 2022"
)
cc.launch()
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