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| import numpy as np | |
| #import itertools | |
| import gradio as gr | |
| import pandas as pd | |
| # make function using import pip to install torch | |
| import pip | |
| #pip.main(['install', 'torch']) | |
| #pip.main(['install', 'transformers']) | |
| import torch | |
| import transformers | |
| import random | |
| # saved_model | |
| def load_model(): | |
| pretrained_model_name = "skt/kogpt2-base-v2" | |
| tokenizer = transformers.PreTrainedTokenizerFast.from_pretrained( | |
| pretrained_model_name, # kogpt2 | |
| # kogpt๋ ์ฌ์ ์ ํ ํฐ์ ์ง์ ํด์ฃผ์ง ์์ผ๋ฉด, None ๊ฐ์ผ๋ก ๋ฐ์๋์ด์์ | |
| # ๋ฐ๋์ ์ง์ ํด์ฃผ์ด์ผ ํจ | |
| bos_token='</s>', eos_token='</s>', unk_token='<unk>', | |
| pad_token='<pad>', mask_token='<mask>' | |
| ) | |
| model = transformers.GPT2LMHeadModel.from_pretrained( | |
| pretrained_model_name # kogpt2 | |
| ) | |
| model.resize_token_embeddings( len(tokenizer) ) | |
| return model, tokenizer | |
| # main | |
| def inference(prompt): | |
| model, tokenizer = load_model() | |
| input_ids = tokenizer.encode(prompt, return_tensors="pt") | |
| gen_ids = model.generate(input_ids, | |
| max_length=128, | |
| repetition_penalty=2.0, | |
| pad_token_id=tokenizer.pad_token_id, | |
| eos_token_id=tokenizer.eos_token_id, | |
| bos_token_id=tokenizer.bos_token_id, | |
| use_cache=True, | |
| do_sample=True, | |
| top_k=50, | |
| top_p=0.92, | |
| num_return_sequences=3 | |
| ) | |
| outputs = [] | |
| for gen_id in gen_ids: | |
| output = tokenizer.decode(gen_id.tolist(), skip_special_tokens=True) | |
| if prompt in output: | |
| output = output.replace(prompt, '') | |
| output = output.split('.')[0] | |
| outputs.append(output) | |
| return outputs | |
| def restore(inputs, outputs): | |
| result = inputs + outputs | |
| return result | |
| demo = gr.Blocks() | |
| with demo: | |
| gr.Markdown("## Kogpt2 Generation for Writing Education") | |
| with gr.Row(): | |
| text_input = gr.Textbox(lines=20, label="Input") | |
| with gr.Column(): | |
| with gr.Box(): | |
| text_output1 = gr.Textbox(lines=1, label="Output1") | |
| output1_btn = gr.Button("Select ouput1") | |
| with gr.Box(): | |
| text_output2 = gr.Textbox(lines=1, label="Output2") | |
| output2_btn = gr.Button("Select ouput2") | |
| with gr.Box(): | |
| text_output3 = gr.Textbox(lines=1, label="Output3") | |
| output3_btn = gr.Button("Select ouput3") | |
| text_button = gr.Button("Generate") | |
| text_button.click( | |
| inference, | |
| inputs=[text_input], | |
| outputs=[text_output1, text_output2, text_output3] | |
| ) | |
| output1_btn.click( | |
| restore, | |
| inputs=[text_input, text_output1], | |
| outputs=[text_input] | |
| ) | |
| output2_btn.click( | |
| restore, | |
| inputs=[text_input, text_output2], | |
| outputs=[text_input] | |
| ) | |
| output3_btn.click( | |
| restore, | |
| inputs=[text_input, text_output3], | |
| outputs=[text_input] | |
| ) | |
| demo.launch() # launch(share=True)๋ฅผ ์ค์ ํ๋ฉด ์ธ๋ถ์์ ์ ์ ๊ฐ๋ฅํ ๋งํฌ๊ฐ ์์ฑ๋จ |