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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="Tham khảo NlpHUST model \n \n \n" | |
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=5, | |
top_k=40, | |
num_beams=5, | |
early_stopping=True, | |
no_repeat_ngram_size=2, | |
num_return_sequences=2) | |
for i, sample_output in enumerate(sample_outputs): | |
res +="Mẫu số {}\n \n{}".format(i+1, tokenizer.decode(sample_output.tolist())) | |
res +='\n \n \n \n' | |
return res | |
# demo = gr.Interface( | |
# fn=run, | |
# inputs=["text", "slider"], | |
# outputs=["text"], | |
# ) | |
demo = gr.Interface(fn=run, | |
inputs=[gr.Textbox(label="Nhập vào nội dung input",value="Con đường xưa em đi"),gr.Slider(label="Độ dài output muốn tạo ra", value=20, minimum=10, maximum=100, step=2)], | |
outputs=gr.Textbox(label="Output"), # <-- Number of output components: 1 | |
) | |
demo.launch() | |