Create app.py

app.py

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+import gradio as gr
+from PIL import Image
+
+size=128
+model = build_model(input_shape=(size, size, 1))
+model.load_weights('BreastCancerSegmentation.h5')
+
+def preprocess_image(image, size: int=128):
+    image = cv2.resize(image, (size,size))
+    image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)  
+    image = image/255.
+    return image
+  
+def segment(image):
+    image = preprocess_image(image, size=size)
+    image = np.expand_dims(image, 0)
+    output = model.predict(image, verbose=0)
+    mask_image = output[0]
+    mask_image = np.squeeze(mask_image, -1)
+    mask_image *= 255
+    mask_image = mask_image.astype(np.uint8)
+    mask_image = Image.fromarray(mask_image).convert("L")
+    return mask_image
+
+if __name__ == "__main__":
+  gr.Interface(
+      fn=segment,
+      inputs="image",
+      outputs=gr.Image(type="pil", label="Breast Cancer Mask"),
+      examples = [["/content/benign(10).png"], ["/content/benign(109).png"]],
+      title = "Breast Cancer Ultrasound Image Segmentation",
+      description = "Check out this exciting development in the field of breast cancer diagnosis and treatment! A demo of Breast Cancer Ultrasound Image Segmentation has been developed. Upload image file, or try out one of the examples below!"
+      ).launch(share=True, debug=True)