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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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* PORT: `7860` |
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* ~Health Route: `/`~-> is default |
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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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``` |
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run inference |
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```python |
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import requests as r |
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import base64 |
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ENDPOINT_URL = "" |
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HF_TOKEN = "" |
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def predict(path_to_image: str = None): |
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ext = path_to_image.split('.')[-1] |
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prefix = f'data:image/{ext};base64,' |
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with open(path_to_image, 'rb') as f: |
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img = f.read() |
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payload = {"data": [prefix + base64.b64encode(img).decode('utf-8')]} |
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response = r.post( |
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f"{ENDPOINT_URL}/api/predict", headers={"Authorization": f"Bearer {HF_TOKEN}"}, json=payload |
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) |
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if response.status_code == 200: |
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return response.json() |
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else: |
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raise Exception(f"Error: {response.status_code}") |
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prediction = predict(path_to_image="image.jpg") |
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``` |
