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README.md
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---
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tags:
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- document-
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- endpoints-template
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library_name: generic
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---
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# Deploy a Space as inference Endpoint
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_This is a fork of the [naver-clova-ix/donut-base-finetuned-cord-v2](https://huggingface.co/spaces/naver-clova-ix/donut-base-finetuned-cord-v2)
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This repository implements a custom container for 🤗 Inference Endpoints using a
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To
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* CPU image: `philschmi/gradio-api:cpu`
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* GPU image: `philschmi/gradio-api:gpu`
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Also make sure to add `server_name="0.0.0.0"` in your `launch()` call to make sure the request is correct proxied.
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If you want to use the UI with the inference Endpoint, you have to select as endpoint type `public` and add [auth through gradio](https://gradio.app/docs/#launch-header)
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### Example API Request Payload
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```bash
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!wget https://datasets-server.huggingface.co/assets/naver-clova-ix/cord-v2/--/naver-clova-ix--cord-v2/train/0/image/image.jpg
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prediction = predict(path_to_image="image.jpg")
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```
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---
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tags:
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- document-understanding
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- endpoints-template
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library_name: generic
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---
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# Deploy a Space as inference Endpoint
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_This is a fork of the [naver-clova-ix/donut-base-finetuned-cord-v2](https://huggingface.co/spaces/naver-clova-ix/donut-base-finetuned-cord-v2) Space.
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This repository implements a custom container for 🤗 Inference Endpoints using a Gradio space.
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To deploy this model as an Inference Endpoint, you have to select Custom as task and a custom image.
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* CPU image: `philschmi/gradio-api:cpu`
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* GPU image: `philschmi/gradio-api:gpu`
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Also make sure to add `server_name="0.0.0.0"` in your `launch()` call to make sure the request is correct proxied.
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If you want to use the UI with the Inference Endpoint, you have to select as endpoint type `public` and add [auth through gradio](https://gradio.app/docs/#launch-header)
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### Example API Request Payload
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Get an image you want to use, e.g.
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```bash
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!wget https://datasets-server.huggingface.co/assets/naver-clova-ix/cord-v2/--/naver-clova-ix--cord-v2/train/0/image/image.jpg
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prediction = predict(path_to_image="image.jpg")
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```
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