readme: ---
  license: mit
  tags:
  - donut
  - image-to-text
  - vision
  - endpoints-template
  ---
  
  # Fork of [naver-clova-ix/donut-base-finetuned-cord-v2](https://huggingface.co/naver-clova-ix/donut-base-finetuned-cord-v2)
  
  > This is fork of [naver-clova-ix/donut-base-finetuned-cord-v2](https://huggingface.co/naver-clova-ix/donut-base-finetuned-cord-v2) implementing a custom `handler.py` as an example for how to use `donut` models with [inference-endpoints](https://hf.co/inference-endpoints)
  
  --- 
  
  # Donut (base-sized model, fine-tuned on CORD) 
  
  Donut model fine-tuned on CORD. It was introduced in the paper [OCR-free Document Understanding Transformer](https://arxiv.org/abs/2111.15664) by Geewok et al. and first released in [this repository](https://github.com/clovaai/donut).
  
  Donut consists of a vision encoder (Swin Transformer) and a text decoder (BART). Given an image, the encoder first encodes the image into a tensor of embeddings (of shape batch_size, seq_len, hidden_size), after which the decoder autoregressively generates text, conditioned on the encoding of the encoder. 
  
  # Use with Inference Endpoints 
  
  Hugging Face Inference endpoints can directly work with binary data, this means that we can directly send our image from our document to the endpoint. We are going to use requests to send our requests. (make your you have it installed `pip install requests`)
  
  ![result](res.png)
  
  ## Send requests with Pyton
  
  load sample image
  
  ```bash
  wget https://huggingface.co/philschmid/donut-base-finetuned-cord-v2/resolve/main/sample.png
  ```
  
  send request to endpoint
  
  ```python
  import json
  import requests as r
  import mimetypes
  
  ENDPOINT_URL="" # url of your endpoint
  HF_TOKEN="" # organization token where you deployed your endpoint
  
  def predict(path_to_image:str=None):
      with open(path_to_image, "rb") as i:
        b = i.read()
      headers= {
          "Authorization": f"Bearer {HF_TOKEN}",
          "Content-Type": mimetypes.guess_type(path_to_image)[0]
      }
      response = r.post(ENDPOINT_URL, headers=headers, data=b)
      return response.json()
  
  prediction = predict(path_to_image="sample.png")
  
  print(prediction)
  # {'menu': [{'nm': '0571-1854 BLUS WANITA',
  #   'unitprice': '@120.000',
  #   'cnt': '1',
  #   'price': '120,000'},
  #  {'nm': '1002-0060 SHOPPING BAG', 'cnt': '1', 'price': '0'}],
  # 'total': {'total_price': '120,000',
  #  'changeprice': '0',
  #  'creditcardprice': '120,000',
  #  'menuqty_cnt': '1'}}
  ```
  
  
  
  **curl example**
  
  ```bash
  curl https://ak7gduay2ypyr9vp.us-east-1.aws.endpoints.huggingface.cloud \
  -X POST \
  --data-binary 'sample.png' \
  -H "Authorization: Bearer XXX" \
  -H "Content-Type: null"
  ```