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import gradio as gr | |
from transformers import TrOCRProcessor, VisionEncoderDecoderModel | |
import requests | |
from PIL import Image | |
processor = TrOCRProcessor.from_pretrained("microsoft/trocr-small-printed") | |
model = VisionEncoderDecoderModel.from_pretrained("pENrknSoysneed/8kun-captcha-ocr-meme") | |
# load image examples | |
urls = [ | |
'https://storage.googleapis.com/trocr-captcha.appspot.com/captcha_images_v2/nfcb5.png', | |
'https://storage.googleapis.com/trocr-captcha.appspot.com/captcha_images_v2/p57fn.png', | |
'https://storage.googleapis.com/trocr-captcha.appspot.com/captcha_images_v2/w2yp7.png', | |
'https://storage.googleapis.com/trocr-captcha.appspot.com/captcha_images_v2/pme86.png', | |
'https://storage.googleapis.com/trocr-captcha.appspot.com/captcha_images_v2/w4nfx.png', | |
'https://storage.googleapis.com/trocr-captcha.appspot.com/captcha_images_v2/nf8b8.png' | |
] | |
for idx, url in enumerate(urls): | |
image = Image.open(requests.get(url, stream=True).raw) | |
image.save(f"image_{idx}.png") | |
def process_image(image): | |
# prepare image | |
pixel_values = processor(image, return_tensors="pt").pixel_values | |
# generate (no beam search) | |
generated_ids = model.generate(pixel_values) | |
# decode | |
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] | |
return generated_text | |
title = "Who is that? Its a racoon ??? 2022 20021 and the year is 5400bc" | |
description = "What the???? a weird graygreen racconn on my board REEEEEEEEEEEEEEEEEEEEEEEE PEEEPEEE PEPEPEEE REEEE." | |
# article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2109.10282'>TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models</a> | <a href='https://github.com/microsoft/unilm/tree/master/trocr'>Github Repo</a></p>" | |
# examples =[["image_0.png"], ["image_1.png"], ["image_2.png"], ["image_3.png"], ["image_4.png"], ["image_5.png"]] | |
#css = """.output_image, .input_image {height: 600px !important}""" | |
iface = gr.Interface(fn=process_image, | |
inputs=gr.inputs.Image(type="pil"), | |
outputs=gr.outputs.Textbox(), | |
title=title, | |
description=description, | |
# article=article, | |
# examples=examples | |
) | |
iface.launch(debug=True) |