from newspaper import Article

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("Vamsi/T5_Paraphrase_Paws")
model = AutoModelForSeq2SeqLM.from_pretrained("Vamsi/T5_Paraphrase_Paws")

import nltk
nltk.download('punkt')
from nltk.tokenize import sent_tokenize

def my_paraphrase(sentence):

  sentence = "paraphrase: " + sentence + " </s>"
  encoding = tokenizer.encode_plus(sentence,padding=True, return_tensors="pt")
  input_ids, attention_masks = encoding["input_ids"], encoding["attention_mask"]
  
  outputs = model.generate(
    input_ids=input_ids, attention_mask=attention_masks,
    max_length=256,
    do_sample=True,
    top_k=120,
    top_p=0.95,
    early_stopping=True,
    num_return_sequences=1)
  output = tokenizer.decode(outputs[0], skip_special_tokens=True,clean_up_tokenization_spaces=True)

  return(output)

def text(input_text):
 
  output = " ".join([my_paraphrase(sent) for sent in sent_tokenize(input_text)])

  return output

import gradio as gr
def summarize(Input_Text):

  outputtext = text(Input_Text)
  return outputtext
gr.Interface(fn=summarize, inputs=gr.inputs.Textbox(lines=7, placeholder="Enter text here"), css="""span.svelte-1l2rj76{color: #591fc9;font-size: 18px;
    font-weight: 600;}.secondary.svelte-1ma3u5b{background: #591fc9; color: #fff;}.secondary.svelte-1ma3u5b:hover{background:#8a59e8;color:#000;}
    .svelte-2xzfnp textarea {border: 1px solid #591fc9}.primary.svelte-1ma3u5b{background: #f8d605;color: #000;}.primary.svelte-1ma3u5b:hover{background: #ffe751;color: #591fc9;}.svelte-2xzfnp{height: 168px !important;} label.svelte-2xzfnp{display: contents !important;}  """, outputs=[gr.outputs.Textbox(label="Paraphrased Text")],examples=[["developed by python team"
]]).launch(inline=False)