Create README.md

README.md

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+---
+tags:
+- summarization
+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
+pipeline_tag: document-question-answering
+---
+
+## Model description
+
+This model is based on the `naver-clova-ix/donut-base` model. The training dataset is created by manually scrapping images across the internet
+
+## Usage & limitations
+
+The model could be used to detect the nutritional facts or compositions from images of food or 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
+```python
+'<s_kmpsi><s_komposisi><s_obat>Phyllantus niruri herba extract (Meniran)</s_obat><s_takaran>150mg</s_takaran><sep/><s_obat>Blumea balsamifera folium extract (Sembung)</s_obat><s_takaran>150mg</s_takaran><sep/><s_obat>Zingiber officinale var. Rubra rhizoma extract (Jahe Merah)</s_obat><s_takaran>150mg</s_takaran><sep/><s_obat>Andrographis paniculata herba extract (Sambiloto)</s_obat><s_takaran>100mg</s_takaran></s_komposisi><s_desc>Tiap kapsul mengandung :</s_desc></s_kmpsi>'
+```
+
+### How to use
+
+Load Donut Processor and Model
+```python
+import re
+from transformers import DonutProcessor, VisionEncoderDecoderModel
+
+# Load processor
+processor = DonutProcessor.from_pretrained("jonathanjordan21/donut_fine_tuning_food_composition_id")
+
+# Load model
+model = VisionEncoderDecoderModel.from_pretrained("jonathanjordan21/donut_fine_tuning_food_composition_id")
+```
+
+Create JSON parser
+```python
+from PIL import Image
+from io import BytesIO
+import torch
+
+def get_komposisi(image_path, image=None):
+    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
+import io
+
+image = requests.get('https://pintarjualan.id/wp-content/uploads/sites/2/2022/04/label-nustrisi-fact-1.png').content
+print(get_komposisi("", Image.open(io.BytesIO(image))))
+```