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| import tensorflow as tf | |
| import os | |
| import CALTextModel | |
| import gradio as gr | |
| import data | |
| os.environ['CUDA_VISIBLE_DEVICES'] = '-1' | |
| # Create an instance of the model | |
| CALText = CALTextModel.CALText_Model(training=False) | |
| CALText.load_weights('final_caltextModel/cp-0037.ckpt') | |
| test_loss = tf.keras.metrics.Mean(name='test_loss') | |
| def recognize_text(input_image): | |
| x, x_mask=data.preprocess_img(input_image) | |
| output_str, gifImage=CALTextModel.predict(CALText, x, x_mask) | |
| return output_str,gifImage | |
| examples = [['sample_test_images/91-34.png'], | |
| ['sample_test_images/97-58.png'], | |
| ['sample_test_images/99-18.png'], | |
| ['sample_test_images/98-37.png'], | |
| ['sample_test_images/99-17.png'], | |
| ['sample_test_images/98-56.png'], | |
| ['sample_test_images/59-11.png'], | |
| ['sample_test_images/59-14.png'], | |
| ] | |
| title = "CALText Demo" | |
| description = "<p style='text-align: center'>Gradio demo for CALText model architecture <a href='https://github.com/nazar-khan/CALText'>[GitHub Code]</a> trained on the <a href='http://faculty.pucit.edu.pk/nazarkhan/work/urdu_ohtr/pucit_ohul_dataset.html'>PUCIT-OHUL</a> dataset. To use it, simply add your image, or click one of the examples to load them. This demo is running on CPU that's why it can take a bit more time. </p>" | |
| article="<p style='text-align: center'><a href='https://link.springer.com/article/10.1007/s11063-023-11258-5'>CALText: Contextual Attention Localization for Offline Handwritten Text</a></p>" | |
| inputs = gr.Image(label="Input Image") | |
| demo = gr.Interface(fn=recognize_text, | |
| inputs=inputs, | |
| outputs=[gr.Textbox(label="Output"), gr.Image(label="Attended Regions")], | |
| examples=examples, | |
| title=title, | |
| description=description, | |
| article=article, | |
| allow_flagging='never') | |
| demo.launch() |