add files

app.py

@@ -17,7 +17,7 @@ model = None
 model = load_model("MelaNet.h5", compile=False)
 model.summary()
 
-examples = ["benign.jpg", "malignant.jpg"]
+examples = ["benign.png", "malignant.png"]
 labels = ["Benign", "Malignant"]
 
 # Helpers
@@ -44,10 +44,14 @@ def inference(img):
     labels_probs = {labels[i]: float(preds[i]) for i, _ in enumerate(labels)}
     return labels_probs
 
+
+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="Benign or Malignant",
-    description = "Predict if this image has benign or malignant symptoms",
+    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=article,
     inputs="image",
     outputs="label",
     examples=examples,