Updated app.py

app.py

@@ -23,6 +23,12 @@ def classify_question_tags(description):
   
   return dict(zip(tags, map(float, probs))) 
 
+examples = [
+    "Image recognition uses deep learning, and in particular CNNs to train on and recognise faces. Usually, this entails training on lots of data. However, recently, we have seen face recognition being deployed everywhere, and being used for passport control, for example, and some airlines have deployed in lieu of boarding pass scanners. How is this accomplished? How can they achieve such accuracy without having hundreds of pictures of everyone? How can, for example, governments, with a quick scan of my face, recognise me and identify my name and identity without having any picture of me besides the ones available from my official ids? There is a link here which, however, does not really explain it technically. Is it done using neural nets? What is their architecture. Are there papers describing it (technically) anywhere?",
+    "I want to develop a machine learning model that predicts the correct medicine dosage required to keep a specific lab value within the target range of 5 to 7. I also have several other predictor variables available. I am unsure which machine learning algorithm would be most suitable for deployment and use with future patients. Additionally, should I define the outcome as binary (1 if the value is between 5 and 7, and 0 otherwise), or is there a better approach?",
+    "What is the best way to evaluate performance of Generative Adverserial Network (GAN)? Perhaps measuring the distance between two distributions or maybe something else?"
+]
+
 label = gr.Label(num_top_classes=5)
-iface = gr.Interface(fn=classify_question_tags, inputs="text", outputs=label)
+iface = gr.Interface(fn=classify_question_tags, inputs="text", outputs=label, examples=examples)
 iface.launch(inline=False)