Changes for latest gradio

app.py

@@ -63,12 +63,6 @@ def downvote_last_response(state, request: gr.Request):
     return ""
 
 
-# example_abstract = """We explore the zero-shot abilities of recent large language models (LLMs) for the task of writing the literature review of a scientific research paper conditioned on its abstract and the content of related papers.
-# We propose and examine a novel strategy for literature review generation with an LLM in which we first generate a plan for the review, and then use it to generate the actual text. While modern LLMs can easily be trained or prompted to 
-# condition on all abstracts of papers to be cited to generate a literature review without such intermediate plans, our empirical study shows that these intermediate plans improve the quality of generated literature reviews over vanilla 
-# zero-shot generation. Furthermore, we also create a new test corpus consisting of recent arXiv papers (with full content) posted after both open-sourced and closed-sourced LLMs that were used in our study were released. This allows us 
-# to ensure that our zero-shot experiments do not suffer from test set contamination. 
-# """
 
 example_abstract = """We explore the zero-shot abilities of recent large language models (LLMs) for the task of writing the literature review of a scientific research paper conditioned on its abstract and the content of related papers."""
 
@@ -102,6 +96,16 @@ The service is a research preview intended for non-commercial use only, subject
 
 block_css = """
 
+h1 {
+    text-align: center;
+    display:block;
+}
+
+h2 {
+    text-align: center;
+    display:block;
+}
+
 #buttons button {
     min-width: min(120px,100%);
 }
@@ -192,7 +196,7 @@ def format_results_into_markdown(recommendations):
     for index, r in enumerate(recommendations):
         # hub_paper_url = f"https://huggingface.co/papers/{r['externalIds']['ArXiv']}"
         # comment += f"* [{r['title']}]({hub_paper_url}) ({r['year']})\n"
-        comment += f"[{index+1}] [{r['title']}]({r['url']}) ({r['year']}) Cited by {r['citationCount']}\n"
+        comment += f"[{index+1}] [{r['title']}]({r['url']}) ({r['year']}) Cited by {r['citationCount']} <br>"
     return comment
 
 def find_basis_paper(query, num_papers_api=20):
@@ -487,7 +491,7 @@ class GradioChatApp:
                         llm_rerank = gr.Radio(choices=["True", "False"], value="True", interactive=True, label="LLM Re-rank (May override sorting)")
                         with gr.Row():
                             temperature = gr.Slider(minimum=0.0, maximum=1.0, value=0.2, step=0.1, interactive=True, label="Temperature", scale=1)
-                            max_tokens = gr.Slider(minimum=0, maximum=3000, value=800, step=64, interactive=True, label="Max output tokens", scale=2)
+                            max_tokens = gr.Slider(minimum=0, maximum=3000, value=500, step=64, interactive=True, label="Max output tokens", scale=2)
                     display_1 = gr.Markdown(value=f"Retrieved papers", label="Retrieved papers!", elem_id="display_mrkdwn") #, visible=True)
                     # with gr.Accordion("Generation Parameters", open=False) as parameter_row:
                         # top_p = gr.Slider(minimum=0.0, maximum=1.0, value=0.7, step=0.1, interactive=True, label="Top P")