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import gradio as gr | |
import re | |
from PIL import Image | |
from io import BytesIO | |
import torch | |
from transformers import DonutProcessor, VisionEncoderDecoderModel | |
def predict(inp): | |
# Check GPU | |
device = "cuda" if torch.cuda.is_available() else "cpu" | |
# 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") | |
# Define Json Parser | |
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) | |
#Generate Output | |
out = get_komposisi("", inp) | |
return out | |
def upload_file(files): | |
file_paths = [file.name for file in files] | |
return file_paths | |
gr.Interface(fn=predict, | |
inputs=gr.Image(type="pil"), | |
outputs="json").launch() | |