import transformers
from huggingface_hub import notebook_login
from datasets import load_dataset
from transformers import AutoModelForSequenceClassification
from transformers import AutoTokenizer
model_checkpoint = "distilgpt2"
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True)
from transformers import pipeline
import gradio as gr
from transformers import pipeline

generator = pipeline('text-generation', model='DioLiu/GPT2_Suggestion',tokenizer=tokenizer)
def get_suggestion(input_text):
  answer=generator(input_text, max_length=200, num_return_sequences=1)[0]['generated_text']
  if answer.find('\n\nGiven')!=-1:
   output=answer[0:answer.find('\n\nGiven')]
  else:
   output=answer[0:answer.rfind('.')+1]
  return output
iface = gr.Interface(fn= get_suggestion, inputs="text", outputs = ["text"], title="Suggestions")
iface.launch()