Upload app.py

app.py

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+import tensorflow as tf
+from simple_unet_model import simple_unet_model
+from tensorflow.keras.utils import normalize
+import os
+from PIL import Image, ImageOps
+import numpy as np
+import gradio as gr
+
+#Loading Model
+def get_model():
+    return simple_unet_model(256, 256, 1)
+
+model = get_model()
+model.load_weights('mitochondria.hdf5')
+
+def predict(input_image):
+  img = Image.fromarray(input_image)
+  gray_img = ImageOps.grayscale(img)
+  resized_img = gray_img.resize((256,256))
+  img = np.array(resized_img)
+  img = np.expand_dims(img, axis = (0,3))
+  img = normalize(img, axis=1)
+  mask = model.predict(img)[0,:,:,0]
+  return mask
+
+def load_examples():
+  files = os.listdir('examples/')
+  img_list = []
+  for file in files:
+    if '.jpg' in file:
+      img_list.append(str('examples/' + file))
+  return img_list
+
+examples = load_examples()
+
+demo = gr.Interface(fn=predict,
+                    inputs="image",
+                    outputs=gr.Image(shape=(256, 256)),
+                    title = "Mitochondria Detection",
+                    examples =examples )
+demo.launch()