Create app.py

app.py

@@ -0,0 +1,34 @@
+from layers import BilinearUpSampling2D
+from tensorflow.keras.models import load_model
+from utils import load_images, predict
+import matplotlib.pyplot as plt
+import numpy as np
+import gradio as gr
+from huggingface_hub import from_pretrained_keras
+import os
+import sys
+
+print('Loading model...')
+model = from_pretrained_keras("mostafapasha/ribs-segmentation-model", compile=False)
+print('Successfully loaded model...')
+examples = ['examples/VinDr_RibCXR_val_008.png', 'examples/VinDr_RibCXR_val_013.png']
+
+
+def infer(image):
+    inputs = load_images([image])[..., 1:2]
+    logits = predict(model, inputs)
+    plasma = plt.get_cmap('plasma')
+    threshold = 0.5
+    prob = tf.sigmoid(logits)
+    pred = tf.cast(prob > threshold, dtype=tf.float32)
+    pred = pred.numpy())[0,:,:,0]
+    image_out = plasma(rescaled)[:, :, :3]
+    return image_out
+
+iface = gr.Interface(
+    fn=infer,
+    title="ribs segmentation",
+    description = "Keras Implementation of Unet++ architecture for ribs segmentation 📏",
+    inputs=[gr.inputs.Image(label="image", type="numpy", shape=(640, 480))],
+    outputs="image",
+    examples=examples).launch(debug=True, cache_examples=True)