update app.py

app.py

@@ -17,6 +17,7 @@ model = None
 model = load_model("MelaNet.h5", compile=False)
 model.summary()
 
+# Path to examples and class label list
 examples = ["benign.png", "malignant.png"]
 labels = ["Benign", "Malignant"]
 
@@ -35,6 +36,7 @@ def preprocess_image(img_array):
     return img_array
 
 
+# Main inference function
 def inference(img):
     img = preprocess_image(img)
     img = np.expand_dims(img, 0)
@@ -44,13 +46,14 @@ def inference(img):
     labels_probs = {labels[i]: float(preds[i]) for i, _ in enumerate(labels)}
     return labels_probs
 
-
+title = "Melanoma Detection Demo"
+description = "This model predicts if the given image has benign or malignant symptoms. To use it, simply upload a skin lesion image, or click one of the examples to load them. Read more at the links below"
 article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2004.06824' target='_blank'>Melanoma Detection using Adversarial Training and Deep Transfer Learning</a> | <a href='https://github.com/hasibzunair/adversarial-lesions' target='_blank'>Github</a></p>"
 
 gr.Interface(
     fn=inference,
-    title="Melanoma Detection Demo",
-    description = "This model predicts if the given image has benign or malignant symptoms. To use it, simply upload a skin lesion image, or click one of the examples to load them. Read more at the links below",
+    title=title,
+    description = description,
     article=article,
     inputs="image",
     outputs="label",