#from transformers import pipeline
import gradio as gr

#from transformers import AutoTokenizer, AutoModelForCausalLM
##from os import path

##MODEL_DIRECTORY = "/models/mrm8488-t5-base-finetuned-emotion"
#tokenizer = AutoTokenizer.from_pretrained("tuner007/pegasus_paraphrase", use_fast=False)
##if not path.exists(MODEL_DIRECTORY):
#model = AutoModelForCausalLM.from_pretrained("tuner007/pegasus_paraphrase")
##    model.save_pretrained(MODEL_DIRECTORY)
##else:
##    model = AutoModelWithLMHead.from_pretrained(MODEL_DIRECTORY)
#

def get_emotion(text):
#    input_ids = tokenizer.encode(text + '</s>', return_tensors='pt')
 #   output = model.generate(input_ids=input_ids, max_length=2)
#
#    # print(output)
#    dec = [tokenizer.decode(ids) for ids in output]
#    print(dec)
#    label = dec[0]
    return text
 



def parph(name= "paraphrase: This is something which I cannt understand at all."):
  #text2text = pipeline("text2text-generation")
  ##model_name = 'tuner007/pegasus_paraphrase'
  #text2text = pipeline('text2text-generation', model = "Vamsi/T5_Paraphrase_Paws")
  ##text2text(name)
  test = get_emotion(name)
  return test # text2text(name)

 
iface = gr.Interface(fn=parph, inputs="text", outputs="text")
iface.launch()