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import gradio as gr | |
import torch | |
from transformers import DonutProcessor, VisionEncoderDecoderModel | |
import re | |
import json | |
from huggingface_hub import HfApi | |
import os | |
p1=os.environ.get("PATH_MODEL") | |
p2=os.environ.get("PATH_MODEL_v2") | |
print(p1,p2) | |
PATH_MODEL = "fruk19/donut_nfact_v4" | |
processor = DonutProcessor.from_pretrained(PATH_MODEL) | |
model = VisionEncoderDecoderModel.from_pretrained(PATH_MODEL) | |
device = "cuda" if torch.cuda.is_available() else "cpu" | |
model.eval() | |
model.to(device) | |
def predict(test_image): | |
pixel_values = processor(test_image, return_tensors="pt").pixel_values | |
pixel_values = pixel_values.to(device) | |
task_prompt = "<s_nfact>" | |
decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids | |
decoder_input_ids = decoder_input_ids.to(device) | |
# autoregressively generate sequence | |
outputs = model.generate( | |
pixel_values, | |
decoder_input_ids=decoder_input_ids, | |
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, | |
) | |
# turn into JSON | |
seq = processor.batch_decode(outputs.sequences)[0] | |
seq = seq.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "") | |
seq = re.sub(r"<.*?>", "", seq, count=1).strip() # remove first task start token | |
pred = processor.token2json(seq) | |
return pred | |
demo = gr.Interface(fn=predict, | |
inputs=gr.inputs.Image(type="pil"), | |
outputs="text", | |
examples=["image_0.png","image_1.png","image_2.png","image_3.png"], | |
) | |
demo.launch() |