
# 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")

```
{"library_name": "generic", "tags": ["document-understanding", "endpoints-template"]}
generic document-understanding endpoints-template region:us
TAGS
#generic #document-understanding #endpoints-template #region-us 


# Deploy a Space as inference Endpoint

_This is a fork of the 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
 
### Example API Request Payload

Get an image you want to use, e.g.



run inference


# Deploy a Space as inference Endpoint

_This is a fork of the 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 ### Example API Request Payload

Get an image you want to use, e.g.



run inference
TAGS
#generic #document-understanding #endpoints-template #region-us 
 # Deploy a Space as inference Endpoint

_This is a fork of the 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 ### Example API Request Payload

Get an image you want to use, e.g.



run inference
passage: TAGS
#generic #document-understanding #endpoints-template #region-us 
# Deploy a Space as inference Endpoint

_This is a fork of the 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### Example API Request Payload

Get an image you want to use, e.g.



run inference