Create README.md

README.md

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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))))
+```