Update app.py

app.py

@@ -11,24 +11,30 @@ def predict(image):
     image = image / 255.0
     image = np.expand_dims(image, axis=0)
     
-    # Make prediction using the model
+    # Make a prediction using the model
     prediction = model.predict(image)
     
     # Get the predicted class label
     if prediction[0][0] < 0.5:
-        label = 'benign'
+        label = 'Benign'
     else:
-        label = 'malignant'
+        label = 'Malignant'
     
     return label
 
-examples=[["benign.jpg"], ["malignant.jpg"]]
+examples = [["benign.jpg"], ["malignant.jpg"]]
 
 # Define a Gradio interface for user interaction
 image_input = gr.inputs.Image(shape=(150, 150))
 label_output = gr.outputs.Label()
 
-iface= gr.Interface(fn=predict, inputs=image_input, outputs=label_output, examples=examples,
-             title="Identifying Skin Cancer", description="Predicts whether an image of skin is cancerous or not")
+iface = gr.Interface(
+    fn=predict,
+    inputs=image_input,
+    outputs=label_output,
+    examples=examples,
+    title="Skin Cancer Classification",
+    description="Predicts whether a skin image is cancerous or not."
+)
 
-iface.launch()
+iface.launch()