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import os
import gradio as gr
import re
import torch
import cv2
import numpy as np
from PIL import Image
from transformers import DonutProcessor, VisionEncoderDecoderModel
title = "OCR using Donut"
description = """
This demo application uses `naver-clova-ix/donut-base` model to extract text from images.
"""
article = "Check out [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) documentation that this demo is based off of."
checkpoint = "naver-clova-ix/donut-base"
processor = DonutProcessor.from_pretrained(checkpoint)
model = VisionEncoderDecoderModel.from_pretrained(checkpoint)
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
# prepare decoder inputs
task_prompt = "<s_synthdog>"
decoder_input_ids = processor.tokenizer(
task_prompt, add_special_tokens=False, return_tensors="pt"
).input_ids
def convert_image_GRAY2BGR(image):
if len(np.asarray(image).shape) != 3:
image = cv2.cvtColor(np.array(image), cv2.COLOR_GRAY2BGR)
image = Image.fromarray(np.uint8(image))
return image
def predict(image):
image = convert_image_GRAY2BGR(image)
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,
num_beams=1,
bad_words_ids=[[processor.tokenizer.unk_token_id]],
return_dict_in_generate=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() # remove first task start token
return processor.token2json(sequence)["text_sequence"]
# We instantiate the Textbox class
input_textbox = gr.Textbox(
label="Type your prompt here:", placeholder="John Doe", lines=2
)
gr.Interface(
fn=predict,
inputs="image",
outputs="text",
title=title,
description=description,
article=article,
examples=[
os.path.join(os.path.dirname(__file__), "./images/newyorkercartoons.png"),
os.path.join(os.path.dirname(__file__), "./images/lorem_ipsum.png"),
],
).launch()
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