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53 lines
2.4 KiB
Markdown
53 lines
2.4 KiB
Markdown
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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# 머신러닝 앱 [[machine-learning-apps]]
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머신러닝 앱을 빠르고 쉽게 구축하고 공유할 수 있는 라이브러리인 [Gradio](https://www.gradio.app/)는 [`Pipeline`]과 통합되어 추론을 위한 간단한 인터페이스를 빠르게 생성할 수 있습니다.
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시작하기 전에 Gradio가 설치되어 있는지 확인하세요.
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```py
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!pip install gradio
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```
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원하는 작업에 맞는 pipeline을 생성한 다음, Gradio의 [Interface.from_pipeline](https://www.gradio.app/docs/gradio/interface#interface-from_pipeline) 함수에 전달하여 인터페이스를 만드세요. Gradio는 [`Pipeline`]에 맞는 입력 및 출력 컴포넌트를 자동으로 결정합니다.
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[launch](https://www.gradio.app/main/docs/gradio/blocks#blocks-launch)를 추가하여 웹 서버를 생성하고 앱을 시작하세요.
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```py
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from transformers import pipeline
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import gradio as gr
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pipeline = pipeline("image-classification", model="google/vit-base-patch16-224")
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gr.Interface.from_pipeline(pipeline).launch()
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```
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웹 앱은 기본적으로 로컬 서버에서 실행됩니다. 다른 사용자와 앱을 공유하려면 [launch](https://www.gradio.app/main/docs/gradio/blocks#blocks-launch)에서 `share=True`로 설정하여 임시 공개 링크를 생성하세요. 더 지속적인 솔루션을 원한다면 Hugging Face [Spaces](https://hf.co/spaces)에서 앱을 호스팅하세요.
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```py
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gr.Interface.from_pipeline(pipeline).launch(share=True)
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```
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아래 Space는 위 코드를 사용하여 생성되었으며, Spaces에서 호스팅됩니다.
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<iframe
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src="https://stevhliu-gradio-pipeline-demo.hf.space"
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frameborder="0"
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width="850"
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height="850"
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></iframe>
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