app.py

import os
import gdown
gdown.download(id='1mYM_26qHUom_5NU7iutHneB_KHlLjL5y', output='workdir.zip')
os.system('unzip workdir.zip')

import glob
import gradio as gr
from demo import get_model, preprocess, postprocess, load
from utils import Config, Logger, CharsetMapper

def process_image(image):
  config = Config('configs/train_abinet.yaml')
  config.model_vision_checkpoint = None
  model = get_model(config)
  model = load(model, 'workdir/train-abinet/best-train-abinet.pth')
  charset = CharsetMapper(filename=config.dataset_charset_path, max_length=config.dataset_max_length + 1)

  img = image.convert('RGB')
  img = preprocess(img, config.dataset_image_width, config.dataset_image_height)
  res = model(img)
  return postprocess(res, charset, 'alignment')[0][0]

title = "Interactive demo: ABINet"
description = "Demo for ABINet, ABINet uses a vision model and an explicit language model to recognize text in the wild, which are trained in end-to-end way. The language model (BCN) achieves bidirectional language representation in simulating cloze test, additionally utilizing iterative correction strategy. To use it, simply upload a (single-text line) image or use one of the example images below and click 'submit'. Results will show up in a few seconds."
article = "<p style='text-align: center'><a href='https://arxiv.org/pdf/2103.06495.pdf'>Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition</a> | <a href='https://github.com/FangShancheng/ABINet'>Github Repo</a></p>"

iface = gr.Interface(fn=process_image, 
                     inputs=gr.inputs.Image(type="pil"), 
                     outputs=gr.outputs.Textbox(),
                     title=title,
                     description=description,
                     article=article,
                     examples=glob.glob('figs/test/*.png'))
iface.launch(debug=True)