app.py

import torch
from transformers import (T5ForConditionalGeneration,T5Tokenizer)
import gradio as gr

def set_seed(seed):
  torch.manual_seed(seed)
set_seed(42)

best_model_path = "aditi2222/t5-paraphrase"
model = T5ForConditionalGeneration.from_pretrained(best_model_path)
tokenizer = T5Tokenizer.from_pretrained("aditi2222/t5-paraphrase")

def tokenize_data(text):
    # Tokenize the review body
    #input_ = "paraphrase: "+ str(text) + ' </s>'

    max_len = 64
    # tokenize inputs
    tokenized_inputs = tokenizer(input_, padding='max_length', truncation=True, max_length=max_len, return_attention_mask=True, return_tensors='pt')

    inputs={"input_ids": tokenized_inputs['input_ids'],
        "attention_mask": tokenized_inputs['attention_mask']
    }
    
    return inputs

def generate_answers(text):
    inputs = tokenize_data(text)
    results= model.generate(input_ids= inputs['input_ids'].to(device), attention_mask=inputs['attention_mask'].to(device), do_sample=True,
                            max_length=64,
                            top_k=120,
                            top_p=0.98,
                            early_stopping=True,
                            num_return_sequences=1)
    answer = tokenizer.decode(results[0], skip_special_tokens=True)
    return answer

iface = gr.Interface(fn=generate_answers, inputs=['text'],results=["text"])
iface.launch(share=True)