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import torch
import re
import gradio as gr
from PIL import Image
from transformers import DonutProcessor, VisionEncoderDecoderModel
def demo_process(input_img):
# input_img = Image.fromarray(input_img)
processor = DonutProcessor.from_pretrained("thinkersloop/donut-demo")
pretrained_model = VisionEncoderDecoderModel.from_pretrained("thinkersloop/donut-demo")
device = "cuda" if torch.cuda.is_available() else "cpu"
pretrained_model.to(device)
pixel_values = processor(input_img, return_tensors="pt").pixel_values
task_prompt = "<s_cord-v2>"
decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt")["input_ids"]
outputs = pretrained_model.generate(pixel_values.to(device),
decoder_input_ids=decoder_input_ids.to(device),
max_length=pretrained_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,
output_scores=True,)
sequence = processor.batch_decode(outputs.sequences)[0]
sequence = sequence.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
sequence = re.sub(r"<.*?>", "", sequence, count=1).strip()
return processor.token2json(sequence)
# task_prompt = f"<s_cord-v2>"
image = Image.open("./sample_1.jpg")
image.save("cord_sample_1.png")
image = Image.open("./sample_2.jpg")
image.save("cord_sample_2.png")
image = Image.open("./sample_3.jpg")
image.save("cord_sample_3.png")
demo = gr.Interface(
fn=demo_process,
inputs= gr.inputs.Image(type="pil"),
outputs="json",
title=f"Transformers demo for `cord-v2` task",
description="""This model is trained with 66 driver's license images of CORD dataset. <br>""",
# examples=[["cord_sample_1.png"], ["cord_sample_2.png"], ["cord_sample_3.png"]],
cache_examples=False,
)
demo.launch() |