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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()
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