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()