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import torch
from transformers import (T5ForConditionalGeneration,T5Tokenizer)
import gradio as gr
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'], attention_mask=inputs['attention_mask'], 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'], outputs=["text"])
#iface.launch(inline=False, share=True)
iface = gr.Interface(fn=generate_answers, inputs=[gr.inputs.Textbox(lines=30)],outputs=["text"])
#iface = gr.Interface(fn=generate_answers, inputs=[gr.inputs.Textbox(lines=30)],outputs=#[gr.outputs.Textbox(lines=15)])
iface.launch(inline=False, share=True) |