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---
widget:
- text: >-
def add ( severity , progname , & block ) return true if io . nil? ||
severity < level message = format_message ( severity , progname , yield )
MUTEX . synchronize { io . write ( message ) } true end
license: mit
language:
- id
- en
datasets:
- jonathanjordan21/drugs-composition-indonesian-donut
library_name: transformers
tags:
- medical
- chemistry
---
## Model description
This model is based on the `jonathanjordan21/donut_fine_tuning_food_composition_id` model. The training dataset is created by manually scrapping images across the internet, available in `jonathanjordan21/drugs-composition-indonesian-donut`
## Usage & limitations
The model could be used to detect the text of drug compositions from images of drug packages. It is capable to create a json format of the components described in the image. However, due to lack of data, the texts in the image must be concisely upright.
### Output Example
Model Output :
```python
'<s_kmpsi><s_komposisi><s_obat>Vitamin E</s_obat><s_takaran>30 I.U.</s_takaran><sep/><s_obat>Tiamin HCl (B1)</s_obat><s_takaran>100 mg</s_takaran><sep/><s_obat>Piridoksin HCl (B6)</s_obat><s_takaran>50 mg</s_takaran><sep/><s_obat>Sianokobalamin (B12)</s_obat><s_takaran>100 mcg</s_takaran><sep/><s_obat>K-l-aspartat</s_obat><s_takaran>100 mg</s_takaran><sep/><s_obat>Mg-l-aspartat</s_obat><s_takaran>100 mg</s_takaran></s_komposisi><s_desc></s_desc></s_kmpsi>'
```
Json Parsed Output :
```python
{'komposisi': [{'obat': 'Vitamin E', 'takaran': '30 I.U.'}, {'obat': 'Tiamin HCl (B1)', 'takaran': '100 mg'}, {'obat': 'Piridoksin HCl (B6)', 'takaran': '50 mg'}, {'obat': 'Sianokobalamin (B12)', 'takaran': '100 mcg'}, {'obat': 'K-l-aspartat', 'takaran': '100 mg'}, {'obat': 'Mg-l-aspartat', 'takaran': '100 mg'}], 'desc': ''}
```
### How to use
Load Donut Processor and Model
```python
from transformers import DonutProcessor, VisionEncoderDecoderModel
# Load processor
processor = DonutProcessor.from_pretrained("jonathanjordan21/donut-finetuned-drugs-composition-indonesian")
# Load model
model = VisionEncoderDecoderModel.from_pretrained("jonathanjordan21/donut-finetuned-drugs-composition-indonesian")
```
Create JSON parser
```python
from PIL import Image
from io import BytesIO
import re
import torch
def get_komposisi(image_path, image=None):
device = "cuda" if torch.cuda.is_available() else "cpu"
image = Image.open(image_path).convert('RGB') if image== None else image.convert('RGB')
task_prompt = "<s_kmpsi>"
decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids
pixel_values = processor(image, return_tensors="pt").pixel_values
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,
bad_words_ids=[[processor.tokenizer.unk_token_id]],
return_dict_in_generate=True,
)
sequence1 = processor.batch_decode(outputs.sequences)[0]
sequence2 = sequence1.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
sequence3 = re.sub(r"<.*?>", "", sequence2, count=1).strip() # remove first task start token
return processor.token2json(sequence3)
```
Get JSON output from an image
```python
import requests
image = requests.get('https://down-id.img.susercontent.com/file/b6812557ba97d24354970cebeac04d48').content
print(get_komposisi("", Image.open(BytesIO(image))))
``` |