Delete app.py

app.py

@@ -1,33 +0,0 @@
-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)