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app.py
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import gradio as gr
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# make function using import pip to install torch
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import pip
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pip.main(['install', 'torch'])
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pip.main(['install', 'transformers'])
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import torch
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import transformers
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# saved_model
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def load_model(model_path):
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saved_data = torch.load(
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model_path,
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map_location="cpu"
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)
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bart_best = saved_data["model"]
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train_config = saved_data["config"]
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tokenizer = transformers.PreTrainedTokenizerFast.from_pretrained('gogamza/kobart-base-v1')
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## Load weights.
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model = transformers.BartForConditionalGeneration.from_pretrained('gogamza/kobart-base-v1')
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model.load_state_dict(bart_best)
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return model, tokenizer
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# main
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def inference(prompt):
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model_path = "./kobart-model-logical.pth"
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model, tokenizer = load_model(
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model_path=model_path
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)
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input_ids = tokenizer.encode(prompt)
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input_ids = torch.tensor(input_ids)
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input_ids = input_ids.unsqueeze(0)
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output = model.generate(input_ids)
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output = tokenizer.decode(output[0], skip_special_tokens=True)
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return output
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demo = gr.Interface(
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fn=inference,
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inputs="text",
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outputs="text" #return ๊ฐ
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).launch() # launch(share=True)๋ฅผ ์ค์ ํ๋ฉด ์ธ๋ถ์์ ์ ์ ๊ฐ๋ฅํ ๋งํฌ๊ฐ ์์ฑ๋จ
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demo.launch()
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