File size: 2,127 Bytes
719f4cb
 
4d05c0d
c9d23d9
719f4cb
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
cdfa5ca
3cbc22b
 
719f4cb
3334517
719f4cb
 
c5abf80
4d05c0d
c5abf80
 
 
 
1391eac
 
c5abf80
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
import re
import gradio as gr
import os
import json
import torch
from transformers import DonutProcessor, VisionEncoderDecoderModel

processor = DonutProcessor.from_pretrained("debu-das/donut_receipt_v2.29")
model = VisionEncoderDecoderModel.from_pretrained("debu-das/donut_receipt_v2.29")

device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

def process_document(image):
    # prepare encoder inputs
    pixel_values = processor(image, return_tensors="pt").pixel_values
    
    # prepare decoder inputs
    task_prompt = "<s_cord-v2>"
    decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids
          
    # generate answer
    outputs = model.generate(
        pixel_values.to(device),
        decoder_input_ids=decoder_input_ids.to(device),
        max_length=model.decoder.config.max_position_embeddings,
        early_stopping=True,
        pad_token_id=processor.tokenizer.pad_token_id,
        eos_token_id=processor.tokenizer.eos_token_id,
        use_cache=True,
        num_beams=1,
        bad_words_ids=[[processor.tokenizer.unk_token_id]],
        return_dict_in_generate=True,
    )
    
    # postprocess
    sequence = processor.batch_decode(outputs.sequences)[0]
    sequence = sequence.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
    sequence = re.sub(r"<.*?>", "", sequence, count=1).strip()  # remove first task start token
    
    return processor.token2json(sequence)


demo = gr.Interface(
    fn=process_document,
    inputs="image",
    outputs="json",
    title="Demo: bentobytes for Receipt Parsing",
    # description=description,
    # article=article,
    enable_queue=True,
    examples=[["example.png"], ["example_1.png"],["example_2.png"], ["example_3.png"],["example_4.png"],["example_5.png"]],
    cache_examples=False)

credentials_json = os.environ.get("CREDENTIALS")

if credentials_json is None:
    print("Error: Please set the CREDENTIALS")
else:
    credentials = json.loads(credentials_json)
    
if __name__ == "__main__":   
    demo.launch(auth=credentials)