adding title, descritpio, and examples to the app.

app.py

@@ -40,16 +40,37 @@ def recommend(txt):
     return recs_output
 
 
-def inputs():
-    pass
+title = "Interactive demo: paper-rec"
+description = """What paper in ML/AI should I read next? It is difficult to choose from all great research publications 
+published daily. This demo gives you a personalized selection of papers from the latest scientific contributions 
+available in arXiv – https://arxiv.org/.
 
+You just input the title or abstract (or both) of paper(s) you liked in the past or you can also use keywords of topics 
+of interest and get the top-10 article recommendations tailored to your taste.
 
-title = "Interactive demo: paper-rec"
-description = "Demo that recommends you what recent papers in AI/ML to read next based on what you like."
+Enjoy!"""
+
+examples = ["""Attention Is All You Need – The dominant sequence transduction models are based on complex recurrent or 
+convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder 
+and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely 
+on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation 
+tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time 
+to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing 
+best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model 
+establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small 
+fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to 
+other tasks by applying it successfully to English constituency parsing both with large and limited training data.""",
+            "GANs, Diffusion Models, Art"]
 
 iface = gr.Interface(fn=recommend,
-                     inputs=[Textbox(lines=10, placeholder="Titles and abstracts from papers you like", default="", label="Sample of what I like <3")],
+                     inputs=[Textbox(lines=10, placeholder="Titles and abstracts from papers you like", default="",
+                                     label="""Sample of what I like: title(s) or abstract(s) of papers you love or a set 
+                                     of keywords about your interests (e.g., Transformers, GANs, Recommender Systems):
+                                     """)],
                      outputs="json",
-                     layout='vertical'
+                     layout='vertical',
+                     title=title,
+                     description=description,
+                     examples=examples
                      )
 iface.launch()