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import gradio as gr
# make function using import pip to install torch
import pip
pip.main(['install', 'torch'])
pip.main(['install', 'transformers'])
import torch
import transformers
# saved_model
def load_model(model_path):
saved_data = torch.load(
model_path,
map_location="cpu"
)
bart_best = saved_data["model"]
train_config = saved_data["config"]
tokenizer = transformers.PreTrainedTokenizerFast.from_pretrained('gogamza/kobart-base-v1')
## Load weights.
model = transformers.BartForConditionalGeneration.from_pretrained('gogamza/kobart-base-v1')
model.load_state_dict(bart_best)
return model, tokenizer
# main
def inference(prompt):
model_path = "./kobart-model-logical.pth"
model, tokenizer = load_model(
model_path=model_path
)
input_ids = tokenizer.encode(prompt)
input_ids = torch.tensor(input_ids)
input_ids = input_ids.unsqueeze(0)
output = model.generate(input_ids)
output = tokenizer.decode(output[0], skip_special_tokens=True)
return output
demo = gr.Interface(
fn=inference,
inputs="text",
outputs="text", #return ๊ฐ’
examples=[
"๊ฐ•์›๋„ ์–‘์–‘๊ตฐ์—์„œ ์‹ ์ฒญํ•œ ์„ค์•…์‚ฐ๊ตญ๋ฆฝ๊ณต์› ์ผ€์ด๋ธ”์นด ์‚ฌ์—…์ด 7๊ฐ€์ง€ ๋ถ€๋Œ€์กฐ๊ฑด์„ ์ง€ํ‚ค๋Š” ์กฐ๊ฑด์œผ๋กœ 2015๋…„ 8์›” 28์ผ ํ™˜๊ฒฝ๋ถ€ ์‚ฐํ•˜ ๊ตญ๋ฆฝ๊ณต์›์œ„์›ํšŒ์˜ ์‹ฌ์˜๋ฅผ ํ†ต๊ณผํ•˜์˜€๋‹ค. ์ œ113์ฐจ ๊ตญ๋ฆฝ๊ณต์›์œ„์›ํšŒ์—์„œ ๋‚ด๋ ค์ง„ ์ด ๊ฒฐ์ •์œผ๋กœ ์„ค์•… ์˜ค์ƒ‰-๋์ฒญ ํ•˜๋‹จ์„ ์—ฐ๊ฒฐํ•˜๋Š” 3,492km์˜ ์ผ€์ด๋ธ”์นด๊ณผ ๊ฑด๋ฆฝ๋  ์˜ˆ์ •์ด๋ฉฐ, ์„ค์•…์‚ฐ ๋Œ€์ฒญ๋ด‰๊ณผ 1.4km ๋–จ์–ด์ง„ ์ง€์ ๊นŒ์ง€ ์ผ€์ด๋ธ”์นด๋ฅผ ์ด์šฉํ•˜์—ฌ ์ ‘๊ทผํ•  ์ˆ˜ ์žˆ๊ฒŒ ๋˜์—ˆ๋‹ค. ์‚ฌ์—…๋น„๋Š” ์ด 460์–ต์ด๋ฉฐ, ๊ตญ๋น„ 50%, ๊ตฐ๋น„ 35%, ๋„๋น„ 15%๋กœ ๊ฒฐ์ •๋˜์—ˆ๋‹ค. ์ด ์‚ฌ์—…์€ ์ด ์„ธ์ฐจ๋ก€์— ๊ฑธ์นœ ๋ฐ˜๋ ค์™€ ๋ถ€๊ฒฐ ๋“ฑ์„ ํ†ตํ•ด ๊ธฐํš์— ์ˆ˜์ •๋˜์–ด๊ฐ€๋‹ค ๊ฒฐ๊ตญ ์‚ฌ์—… ํƒ€๋‹น์„ฑ๊ณผ ์ ์ •์„ฑ์„ ํ–ฅ์ƒ์‹œ์ผฐ๋‹ค๋Š” ํ‰๊ฐ€๋ฅผ ๋ฐ›์œผ๋ฉฐ ํ™•์ •๋˜์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋งŽ์€ ์‹œ๋ฏผ๋‹จ์ฒด์™€ ํ™˜๊ฒฝ๋‹จ์ฒด๋Š” ์ด์™€ ๊ฐ™์€ ๊ฒฐ์ •์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ๊ตญ๋ฆฝ๊ณต์›์˜ ํ™˜๊ฒฝํŒŒ๊ดด๋ฅผ ์ด์œ ๋กœ ์—ฌ์ „ํžˆ ์ด๋ฅผ ๋ฐ˜๋Œ€ํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, โ€˜์‚ฐ์œผ๋กœ ๊ฐ„ 4๋Œ€๊ฐ• ์‚ฌ์—…โ€™์ด๋ผ๋Š” ๋น„๋‚œ๊ณผ ํ•จ๊ป˜ ๊ตญ๋ฆฝ๊ณต์›์ด ์œ ์›์ง€ํ™” ๋˜๋Š” ๋น„๊ทน์„ ๋ง‰์ž๊ณ  ์ฃผ์žฅํ•œ๋‹ค. ํŽธ์˜์„ฑ๊ณผ ๊ฒฝ์ œ์„ฑ์˜ ๋…ผ๋ฆฌ์™€ ํ•จ๊ป˜ ํ™˜๊ฒฝ๋ณด์กด์˜ ๋…ผ๋ฆฌ๊ฐ€ ๋ถ€๋”ชํžˆ๋Š” ์„ค์•…์‚ฐ๊ตญ๋ฆฝ๊ณต์›์˜ ์ผ€์ด๋ธ”์นด๋Š” ๊ฑด์„ค๋˜์–ด์•ผ ํ•˜๋Š”๊ฐ€."
]
).launch() # launch(share=True)๋ฅผ ์„ค์ •ํ•˜๋ฉด ์™ธ๋ถ€์—์„œ ์ ‘์† ๊ฐ€๋Šฅํ•œ ๋งํฌ๊ฐ€ ์ƒ์„ฑ๋จ
demo.launch()