import gradio as gr
import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel


tokenizer = GPT2Tokenizer.from_pretrained('NlpHUST/gpt2-vietnamese')
model = GPT2LMHeadModel.from_pretrained('NlpHUST/gpt2-vietnamese')
# max_length = 100


def run(text, intensity):
    res=""
    max_length=intensity
    input_ids = tokenizer.encode(text, return_tensors='pt')
    sample_outputs = model.generate(input_ids,pad_token_id=tokenizer.eos_token_id,
                                   do_sample=True,
                                   max_length=max_length,
                                   min_length=max_length,
                                   top_k=40,
                                   num_beams=5,
                                   early_stopping=True,
                                   no_repeat_ngram_size=2,
                                   num_return_sequences=3)
    
    for i, sample_output in enumerate(sample_outputs):
        res +="Generated text {}\n\n{}".format(i+1, tokenizer.decode(sample_output.tolist()))
        res +='\n---'
    return res

demo = gr.Interface(
    fn=run,
    inputs=["text", "slider"],
    outputs=["text"],
)

demo.launch()