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Create app.py
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app.py
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from layers import BilinearUpSampling2D
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from tensorflow.keras.models import load_model
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from utils import load_images, predict
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import matplotlib.pyplot as plt
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import numpy as np
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import gradio as gr
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from huggingface_hub import from_pretrained_keras
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import os
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import sys
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print('Loading model...')
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model = from_pretrained_keras("mostafapasha/ribs-segmentation-model", compile=False)
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print('Successfully loaded model...')
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examples = ['examples/VinDr_RibCXR_val_008.png', 'examples/VinDr_RibCXR_val_013.png']
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def infer(image):
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inputs = load_images([image])[..., 1:2]
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logits = predict(model, inputs)
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plasma = plt.get_cmap('plasma')
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threshold = 0.5
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prob = tf.sigmoid(logits)
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pred = tf.cast(prob > threshold, dtype=tf.float32)
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pred = pred.numpy())[0,:,:,0]
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image_out = plasma(rescaled)[:, :, :3]
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return image_out
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iface = gr.Interface(
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fn=infer,
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title="ribs segmentation",
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description = "Keras Implementation of Unet++ architecture for ribs segmentation π",
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inputs=[gr.inputs.Image(label="image", type="numpy", shape=(640, 480))],
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outputs="image",
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examples=examples).launch(debug=True, cache_examples=True)
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