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---
tags:
- document-understanding
- endpoints-template
library_name: generic
---
# Deploy a Space as inference Endpoint
_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.
This repository implements a custom container for 🤗 Inference Endpoints using a Gradio space.
To deploy this model as an Inference Endpoint, you have to select Custom as task and a custom image.
* CPU image: `philschmi/gradio-api:cpu`
* GPU image: `philschmi/gradio-api:gpu`
* PORT: `7860`
* ~Health Route: `/`~-> is default
Also make sure to add `server_name="0.0.0.0"` in your `launch()` call to make sure the request is correct proxied.
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)
### Example API Request Payload
Get an image you want to use, e.g.
```bash
!wget https://datasets-server.huggingface.co/assets/naver-clova-ix/cord-v2/--/naver-clova-ix--cord-v2/train/0/image/image.jpg
```
run inference
```python
import requests as r
import base64
ENDPOINT_URL = ""
HF_TOKEN = ""
def predict(path_to_image: str = None):
ext = path_to_image.split('.')[-1]
prefix = f'data:image/{ext};base64,'
with open(path_to_image, 'rb') as f:
img = f.read()
payload = {"data": [prefix + base64.b64encode(img).decode('utf-8')]}
response = r.post(
f"{ENDPOINT_URL}/api/predict", headers={"Authorization": f"Bearer {HF_TOKEN}"}, json=payload
)
if response.status_code == 200:
return response.json()
else:
raise Exception(f"Error: {response.status_code}")
prediction = predict(path_to_image="image.jpg")
``` |