Update app.py

app.py

@@ -25,7 +25,7 @@ def make_leaderboard_md(elo_results):
 - [MT-Bench](https://arxiv.org/abs/2306.05685) - a set of challenging multi-turn questions. We use GPT-4 to grade the model responses.
 - [MMLU](https://arxiv.org/abs/2009.03300) (5-shot) - a test to measure a model's multitask accuracy on 57 tasks.
 
-💻 Code: The Arena Elo ratings are computed by this [notebook]({notebook_url}). The MT-bench scores (single-answer grading on a scale of 10) are computed by [fastchat.llm_judge](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge). The MMLU scores are computed by [InstructEval](https://github.com/declare-lab/instruct-eval) and [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub). Higher values are better for all benchmarks. Empty cells mean not available.
+💻 Code: The Arena Elo ratings are computed by this [notebook]({notebook_url}). The MT-bench scores (single-answer grading on a scale of 10) are computed by [fastchat.llm_judge](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge). The MMLU scores are computed by [InstructEval](https://github.com/declare-lab/instruct-eval) and [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub). Higher values are better for all benchmarks. Empty cells mean not available. Last updated: Sept, 2023.
 """
     return leaderboard_md
 
@@ -233,6 +233,9 @@ Please note that you may see different orders from different ranking methods. Th
                 "#### Figure 4: Average Win Rate Against All Other Models (Assuming Uniform Sampling and No Ties)"
             )
             plot_4 = gr.Plot(p4, show_label=False)
+
+    gr.Markdown(acknowledgment_md)
+
     return [md_1, plot_1, plot_2, plot_3, plot_4]
 
 block_css = """
@@ -294,7 +297,6 @@ def build_demo(elo_results_file, leaderboard_table_file):
         leader_components = build_leaderboard_tab(
             elo_results_file, leaderboard_table_file
         )
-        gr.Markdown(acknowledgment_md)
 
     return demo